From ae708e474a8342b032a5d10fd890f02bab9ab9b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Wed, 14 Sep 2022 08:11:12 -0300 Subject: [PATCH 01/16] last changes --- components/black_scholes_options_pricing.py | 2 +- files/aave_results.csv | 1018 +- files/dydx_results.csv | 1018 +- files/stgy.historical_data.csv | 31000 +----------------- hedge_scripts/aave.py | 233 - hedge_scripts/binance_client_.py | 89 - hedge_scripts/checking_var.py | 179 + hedge_scripts/data_dumper.py | 302 - hedge_scripts/dydx.py | 189 - hedge_scripts/dydx_client.py | 85 +- hedge_scripts/interval.py | 7 +- hedge_scripts/metrics_calculator.py | 76 + hedge_scripts/parameter_manager.py | 360 - hedge_scripts/sm_interactor.py | 49 +- hedge_scripts/stgyapp.py | 265 - hedge_scripts/volatility_calculator.py | 291 +- jupyter-lab/Simulations_lab.ipynb | 1863 ++ jupyter-lab/strategy_dydx_aave.ipynb | 1358 - requirements.txt | 108 +- services/dydx_p_client.py | 54 + 20 files changed, 5471 insertions(+), 33075 deletions(-) delete mode 100644 hedge_scripts/aave.py delete mode 100644 hedge_scripts/binance_client_.py create mode 100644 hedge_scripts/checking_var.py delete mode 100644 hedge_scripts/data_dumper.py delete mode 100644 hedge_scripts/dydx.py create mode 100644 hedge_scripts/metrics_calculator.py delete mode 100644 hedge_scripts/parameter_manager.py delete mode 100644 hedge_scripts/stgyapp.py create mode 100644 jupyter-lab/Simulations_lab.ipynb delete mode 100644 jupyter-lab/strategy_dydx_aave.ipynb create mode 100644 services/dydx_p_client.py diff --git a/components/black_scholes_options_pricing.py b/components/black_scholes_options_pricing.py index 6e19aca..a3c1fe1 100644 --- a/components/black_scholes_options_pricing.py +++ b/components/black_scholes_options_pricing.py @@ -22,7 +22,7 @@ def __init__( def get_put_option_price(self): d1 = ( np.log(self.current_asset_price / self.strike_price) - + (self.risk_free_rate + self.sigma ** 2 / 2) * self.option_expiration + + (self.risk_free_rate + self.sigma**2 / 2) * self.option_expiration ) / (self.sigma * np.sqrt(self.option_expiration)) d2 = d1 - self.sigma * np.sqrt(self.option_expiration) return self.strike_price * np.exp( diff --git a/files/aave_results.csv b/files/aave_results.csv index 08fd57a..f5ca08d 100644 --- a/files/aave_results.csv +++ b/files/aave_results.csv @@ -1,45 +1,973 @@ -market_price,I_current,I_old,entry_price,collateral_eth,usdc_status,debt,ltv,lending_rate,interest_on_lending_usd,borrowing_rate,interest_on_borrowing,lend_minus_borrow_interest,costs,gas_fees,total_costs,index_of_mkt_price -1592.05,minus_infty,infty,1681.1618964201652,0.9000000085616439,True,378.26144468642394,0.26399327288579466,0.005,1.363056506849315e-05,0.025,1.799188673394869e-05,-4.3613216654555405e-06,0,10,0,10081 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19:37:00,1723.56,,infty -2022-07-28 19:38:00,1724.05,,infty -2022-07-28 19:39:00,1725.28,,infty -2022-07-28 19:40:00,1724.65,,infty -2022-07-28 19:41:00,1724.5,,infty -2022-07-28 19:42:00,1725.74,,infty -2022-07-28 19:43:00,1727.0,,infty -2022-07-28 19:44:00,1726.0,,infty -2022-07-28 19:45:00,1725.65,,infty -2022-07-28 19:46:00,1728.34,,infty -2022-07-28 19:47:00,1728.77,,infty -2022-07-28 19:48:00,1727.34,,infty -2022-07-28 19:49:00,1729.73,,infty -2022-07-28 19:50:00,1724.82,,infty -2022-07-28 19:51:00,1725.3,,infty -2022-07-28 19:52:00,1721.86,,infty -2022-07-28 19:53:00,1717.27,,infty -2022-07-28 19:54:00,1721.95,,infty -2022-07-28 19:55:00,1720.43,,infty -2022-07-28 19:56:00,1721.61,,infty -2022-07-28 19:57:00,1721.59,,infty -2022-07-28 19:58:00,1724.02,,infty -2022-07-28 19:59:00,1725.61,,infty -2022-07-28 20:00:00,1727.05,,infty -2022-07-28 20:01:00,1730.74,,infty -2022-07-28 20:02:00,1728.48,,infty -2022-07-28 20:03:00,1732.62,,infty -2022-07-28 20:04:00,1733.72,,infty 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20:32:00,1748.44,,infty -2022-07-28 20:33:00,1746.55,,infty -2022-07-28 20:34:00,1753.35,,infty -2022-07-28 20:35:00,1764.7,,infty -2022-07-28 20:36:00,1773.7,,infty -2022-07-28 20:37:00,1783.24,,infty -2022-07-28 20:38:00,1780.49,,infty -2022-07-28 20:39:00,1775.15,,infty -2022-07-28 20:40:00,1777.76,,infty -2022-07-28 20:41:00,1779.8,,infty -2022-07-28 20:42:00,1773.61,,infty -2022-07-28 20:43:00,1758.17,,infty -2022-07-28 20:44:00,1748.57,,infty -2022-07-28 20:45:00,1741.34,,infty -2022-07-28 20:46:00,1734.32,,infty -2022-07-28 20:47:00,1735.59,,infty -2022-07-28 20:48:00,1739.55,,infty -2022-07-28 20:49:00,1745.61,,infty -2022-07-28 20:50:00,1760.49,,infty -2022-07-28 20:51:00,1756.42,,infty -2022-07-28 20:52:00,1758.79,,infty -2022-07-28 20:53:00,1761.11,,infty -2022-07-28 20:54:00,1758.23,,infty -2022-07-28 20:55:00,1754.75,,infty -2022-07-28 20:56:00,1757.04,,infty -2022-07-28 20:57:00,1761.13,,infty -2022-07-28 20:58:00,1757.59,,infty -2022-07-28 20:59:00,1757.44,,infty 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21:27:00,1739.94,,infty -2022-07-28 21:28:00,1744.94,,infty -2022-07-28 21:29:00,1745.9,,infty -2022-07-28 21:30:00,1745.66,,infty -2022-07-28 21:31:00,1743.12,,infty -2022-07-28 21:32:00,1741.04,,infty -2022-07-28 21:33:00,1740.38,,infty -2022-07-28 21:34:00,1740.1,,infty -2022-07-28 21:35:00,1741.15,,infty -2022-07-28 21:36:00,1740.65,,infty -2022-07-28 21:37:00,1740.14,,infty -2022-07-28 21:38:00,1740.21,,infty -2022-07-28 21:39:00,1739.14,,infty -2022-07-28 21:40:00,1736.37,,infty -2022-07-28 21:41:00,1739.45,,infty -2022-07-28 21:42:00,1742.58,,infty -2022-07-28 21:43:00,1743.64,,infty -2022-07-28 21:44:00,1742.06,,infty -2022-07-28 21:45:00,1744.93,,infty -2022-07-28 21:46:00,1743.02,,infty -2022-07-28 21:47:00,1739.5,,infty -2022-07-28 21:48:00,1738.49,,infty -2022-07-28 21:49:00,1739.98,,infty -2022-07-28 21:50:00,1737.92,,infty -2022-07-28 21:51:00,1739.27,,infty -2022-07-28 21:52:00,1740.15,,infty -2022-07-28 21:53:00,1743.77,,infty -2022-07-28 21:54:00,1743.25,,infty 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22:22:00,1747.73,,infty -2022-07-28 22:23:00,1748.41,,infty -2022-07-28 22:24:00,1748.96,,infty -2022-07-28 22:25:00,1747.79,,infty -2022-07-28 22:26:00,1748.66,,infty -2022-07-28 22:27:00,1748.77,,infty -2022-07-28 22:28:00,1749.16,,infty -2022-07-28 22:29:00,1747.64,,infty -2022-07-28 22:30:00,1748.6,,infty -2022-07-28 22:31:00,1748.83,,infty -2022-07-28 22:32:00,1747.24,,infty -2022-07-28 22:33:00,1745.0,,infty -2022-07-28 22:34:00,1747.98,,infty -2022-07-28 22:35:00,1745.0,,infty -2022-07-28 22:36:00,1742.32,,infty -2022-07-28 22:37:00,1739.95,,infty -2022-07-28 22:38:00,1740.12,,infty -2022-07-28 22:39:00,1740.48,,infty -2022-07-28 22:40:00,1739.9,,infty -2022-07-28 22:41:00,1742.75,,infty -2022-07-28 22:42:00,1748.51,,infty -2022-07-28 22:43:00,1746.37,,infty -2022-07-28 22:44:00,1745.87,,infty -2022-07-28 22:45:00,1745.15,,infty -2022-07-28 22:46:00,1745.42,,infty -2022-07-28 22:47:00,1744.65,,infty -2022-07-28 22:48:00,1746.22,,infty -2022-07-28 22:49:00,1743.93,,infty 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23:17:00,1729.72,,infty -2022-07-28 23:18:00,1730.8,,infty -2022-07-28 23:19:00,1731.4,,infty -2022-07-28 23:20:00,1732.16,,infty -2022-07-28 23:21:00,1730.86,,infty -2022-07-28 23:22:00,1729.51,,infty -2022-07-28 23:23:00,1725.0,,infty -2022-07-28 23:24:00,1721.25,,infty -2022-07-28 23:25:00,1722.21,,infty -2022-07-28 23:26:00,1720.64,,infty -2022-07-28 23:27:00,1722.8,,infty -2022-07-28 23:28:00,1720.64,,infty -2022-07-28 23:29:00,1722.48,,infty -2022-07-28 23:30:00,1723.34,,infty -2022-07-28 23:31:00,1719.56,,infty -2022-07-28 23:32:00,1715.53,,infty -2022-07-28 23:33:00,1717.72,,infty -2022-07-28 23:34:00,1715.21,,infty -2022-07-28 23:35:00,1716.38,,infty -2022-07-28 23:36:00,1717.64,,infty -2022-07-28 23:37:00,1719.89,,infty -2022-07-28 23:38:00,1723.04,,infty -2022-07-28 23:39:00,1721.11,,infty -2022-07-28 23:40:00,1720.52,,infty -2022-07-28 23:41:00,1721.33,,infty -2022-07-28 23:42:00,1720.81,,infty -2022-07-28 23:43:00,1723.25,,infty -2022-07-28 23:44:00,1719.35,,infty 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00:12:00,1718.65,,infty -2022-07-29 00:13:00,1720.57,,infty -2022-07-29 00:14:00,1720.39,,infty -2022-07-29 00:15:00,1718.99,,infty -2022-07-29 00:16:00,1716.67,,infty -2022-07-29 00:17:00,1707.31,,infty -2022-07-29 00:18:00,1705.64,,infty -2022-07-29 00:19:00,1699.13,,infty -2022-07-29 00:20:00,1698.81,,infty -2022-07-29 00:21:00,1697.28,,infty -2022-07-29 00:22:00,1703.61,,infty -2022-07-29 00:23:00,1700.75,,infty -2022-07-29 00:24:00,1703.53,,infty -2022-07-29 00:25:00,1700.93,,infty -2022-07-29 00:26:00,1703.07,,infty -2022-07-29 00:27:00,1704.65,,infty -2022-07-29 00:28:00,1702.78,,infty -2022-07-29 00:29:00,1700.32,,infty -2022-07-29 00:30:00,1699.89,,infty -2022-07-29 00:31:00,1702.69,,infty -2022-07-29 00:32:00,1703.75,,infty -2022-07-29 00:33:00,1713.84,,infty -2022-07-29 00:34:00,1715.86,,infty -2022-07-29 00:35:00,1720.04,,infty -2022-07-29 00:36:00,1719.76,,infty -2022-07-29 00:37:00,1720.01,,infty -2022-07-29 00:38:00,1720.45,,infty -2022-07-29 00:39:00,1723.06,,infty -2022-07-29 00:40:00,1724.88,,infty -2022-07-29 00:41:00,1721.97,,infty -2022-07-29 00:42:00,1720.94,,infty -2022-07-29 00:43:00,1718.48,,infty -2022-07-29 00:44:00,1723.6,,infty -2022-07-29 00:45:00,1722.07,,infty -2022-07-29 00:46:00,1726.26,,infty -2022-07-29 00:47:00,1731.82,,infty -2022-07-29 00:48:00,1731.15,,infty -2022-07-29 00:49:00,1727.67,,infty -2022-07-29 00:50:00,1727.82,,infty -2022-07-29 00:51:00,1727.28,,infty -2022-07-29 00:52:00,1728.49,,infty -2022-07-29 00:53:00,1729.69,,infty -2022-07-29 00:54:00,1726.32,,infty -2022-07-29 00:55:00,1725.14,,infty -2022-07-29 00:56:00,1723.72,,infty -2022-07-29 00:57:00,1729.24,,infty -2022-07-29 00:58:00,1726.39,,infty -2022-07-29 00:59:00,1726.23,,infty -2022-07-29 01:00:00,1726.14,,infty -2022-07-29 01:01:00,1726.46,,infty -2022-07-29 01:02:00,1725.65,,infty -2022-07-29 01:03:00,1726.8,,infty -2022-07-29 01:04:00,1727.84,,infty -2022-07-29 01:05:00,1727.83,,infty -2022-07-29 01:06:00,1725.56,,infty -2022-07-29 01:07:00,1725.11,,infty -2022-07-29 01:08:00,1723.76,,infty -2022-07-29 01:09:00,1721.96,,infty -2022-07-29 01:10:00,1722.6,,infty -2022-07-29 01:11:00,1722.31,,infty -2022-07-29 01:12:00,1722.55,,infty -2022-07-29 01:13:00,1721.15,,infty -2022-07-29 01:14:00,1721.38,,infty -2022-07-29 01:15:00,1723.31,,infty -2022-07-29 01:16:00,1725.7,,infty -2022-07-29 01:17:00,1722.14,,infty -2022-07-29 01:18:00,1721.7,,infty -2022-07-29 01:19:00,1723.42,,infty -2022-07-29 01:20:00,1721.66,,infty -2022-07-29 01:21:00,1721.93,,infty -2022-07-29 01:22:00,1721.25,,infty -2022-07-29 01:23:00,1722.56,,infty -2022-07-29 01:24:00,1721.75,,infty -2022-07-29 01:25:00,1721.69,,infty -2022-07-29 01:26:00,1716.06,,infty -2022-07-29 01:27:00,1714.43,,infty -2022-07-29 01:28:00,1714.59,,infty -2022-07-29 01:29:00,1715.22,,infty -2022-07-29 01:30:00,1712.43,,infty -2022-07-29 01:31:00,1712.35,,infty -2022-07-29 01:32:00,1710.46,,infty -2022-07-29 01:33:00,1709.42,,infty -2022-07-29 01:34:00,1707.47,,infty 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02:02:00,1716.06,,infty -2022-07-29 02:03:00,1713.94,,infty -2022-07-29 02:04:00,1709.58,,infty -2022-07-29 02:05:00,1712.93,,infty -2022-07-29 02:06:00,1711.7,,infty -2022-07-29 02:07:00,1712.41,,infty -2022-07-29 02:08:00,1712.39,,infty -2022-07-29 02:09:00,1715.72,,infty -2022-07-29 02:10:00,1714.61,,infty -2022-07-29 02:11:00,1716.33,,infty -2022-07-29 02:12:00,1717.65,,infty -2022-07-29 02:13:00,1719.6,,infty -2022-07-29 02:14:00,1718.47,,infty -2022-07-29 02:15:00,1719.55,,infty -2022-07-29 02:16:00,1721.51,,infty -2022-07-29 02:17:00,1724.1,,infty -2022-07-29 02:18:00,1723.11,,infty -2022-07-29 02:19:00,1724.96,,infty -2022-07-29 02:20:00,1722.86,,infty -2022-07-29 02:21:00,1721.1,,infty -2022-07-29 02:22:00,1717.14,,infty -2022-07-29 02:23:00,1710.0,,infty -2022-07-29 02:24:00,1704.5,,infty -2022-07-29 02:25:00,1709.92,,infty -2022-07-29 02:26:00,1714.4,,infty -2022-07-29 02:27:00,1711.93,,infty -2022-07-29 02:28:00,1710.79,,infty -2022-07-29 02:29:00,1712.71,,infty -2022-07-29 02:30:00,1712.46,,infty -2022-07-29 02:31:00,1709.5,,infty -2022-07-29 02:32:00,1712.18,,infty -2022-07-29 02:33:00,1709.99,,infty -2022-07-29 02:34:00,1711.08,,infty -2022-07-29 02:35:00,1710.33,,infty -2022-07-29 02:36:00,1713.3,,infty -2022-07-29 02:37:00,1711.05,,infty -2022-07-29 02:38:00,1711.99,,infty -2022-07-29 02:39:00,1711.75,,infty -2022-07-29 02:40:00,1710.99,,infty -2022-07-29 02:41:00,1714.16,,infty -2022-07-29 02:42:00,1716.08,,infty -2022-07-29 02:43:00,1713.63,,infty -2022-07-29 02:44:00,1716.59,,infty -2022-07-29 02:45:00,1716.46,,infty -2022-07-29 02:46:00,1713.95,,infty -2022-07-29 02:47:00,1712.29,,infty -2022-07-29 02:48:00,1711.09,,infty -2022-07-29 02:49:00,1709.64,,infty -2022-07-29 02:50:00,1709.47,,infty -2022-07-29 02:51:00,1710.5,,infty -2022-07-29 02:52:00,1707.96,,infty -2022-07-29 02:53:00,1710.96,,infty -2022-07-29 02:54:00,1708.0,,infty -2022-07-29 02:55:00,1709.57,,infty -2022-07-29 02:56:00,1709.68,,infty -2022-07-29 02:57:00,1711.17,,infty 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03:25:00,1719.16,,infty -2022-07-29 03:26:00,1719.02,,infty -2022-07-29 03:27:00,1718.51,,infty -2022-07-29 03:28:00,1718.68,,infty -2022-07-29 03:29:00,1716.8,,infty -2022-07-29 03:30:00,1719.46,,infty -2022-07-29 03:31:00,1719.38,,infty -2022-07-29 03:32:00,1718.78,,infty -2022-07-29 03:33:00,1719.7,,infty -2022-07-29 03:34:00,1721.85,,infty -2022-07-29 03:35:00,1721.02,,infty -2022-07-29 03:36:00,1720.65,,infty -2022-07-29 03:37:00,1721.37,,infty -2022-07-29 03:38:00,1723.82,,infty -2022-07-29 03:39:00,1723.51,,infty -2022-07-29 03:40:00,1723.62,,infty -2022-07-29 03:41:00,1723.55,,infty -2022-07-29 03:42:00,1724.99,,infty -2022-07-29 03:43:00,1726.65,,infty -2022-07-29 03:44:00,1724.93,,infty -2022-07-29 03:45:00,1726.7,,infty -2022-07-29 03:46:00,1725.79,,infty -2022-07-29 03:47:00,1725.32,,infty -2022-07-29 03:48:00,1725.84,,infty -2022-07-29 03:49:00,1726.41,,infty -2022-07-29 03:50:00,1726.46,,infty -2022-07-29 03:51:00,1728.12,,infty -2022-07-29 03:52:00,1729.33,,infty -2022-07-29 03:53:00,1728.36,,infty -2022-07-29 03:54:00,1727.56,,infty -2022-07-29 03:55:00,1728.88,,infty -2022-07-29 03:56:00,1726.41,,infty -2022-07-29 03:57:00,1727.21,,infty -2022-07-29 03:58:00,1728.84,,infty -2022-07-29 03:59:00,1727.67,,infty -2022-07-29 04:00:00,1727.37,,infty -2022-07-29 04:01:00,1725.44,,infty -2022-07-29 04:02:00,1725.86,,infty -2022-07-29 04:03:00,1728.04,,infty -2022-07-29 04:04:00,1726.59,,infty -2022-07-29 04:05:00,1726.8,,infty -2022-07-29 04:06:00,1726.42,,infty -2022-07-29 04:07:00,1723.53,,infty -2022-07-29 04:08:00,1728.9,,infty -2022-07-29 04:09:00,1729.84,,infty -2022-07-29 04:10:00,1728.6,,infty -2022-07-29 04:11:00,1728.45,,infty -2022-07-29 04:12:00,1727.93,,infty -2022-07-29 04:13:00,1727.82,,infty -2022-07-29 04:14:00,1726.88,,infty -2022-07-29 04:15:00,1726.38,,infty -2022-07-29 04:16:00,1726.0,,infty -2022-07-29 04:17:00,1726.38,,infty -2022-07-29 04:18:00,1726.44,,infty -2022-07-29 04:19:00,1727.76,,infty -2022-07-29 04:20:00,1728.54,,infty -2022-07-29 04:21:00,1729.68,,infty -2022-07-29 04:22:00,1729.63,,infty -2022-07-29 04:23:00,1730.92,,infty -2022-07-29 04:24:00,1731.72,,infty -2022-07-29 04:25:00,1730.95,,infty -2022-07-29 04:26:00,1730.63,,infty -2022-07-29 04:27:00,1730.75,,infty -2022-07-29 04:28:00,1730.56,,infty -2022-07-29 04:29:00,1731.12,,infty -2022-07-29 04:30:00,1728.69,,infty -2022-07-29 04:31:00,1729.44,,infty -2022-07-29 04:32:00,1732.91,,infty -2022-07-29 04:33:00,1739.65,,infty -2022-07-29 04:34:00,1736.98,,infty -2022-07-29 04:35:00,1736.22,,infty -2022-07-29 04:36:00,1734.66,,infty -2022-07-29 04:37:00,1731.83,,infty -2022-07-29 04:38:00,1729.55,,infty -2022-07-29 04:39:00,1729.64,,infty -2022-07-29 04:40:00,1729.75,,infty -2022-07-29 04:41:00,1730.16,,infty -2022-07-29 04:42:00,1731.85,,infty -2022-07-29 04:43:00,1734.61,,infty -2022-07-29 04:44:00,1735.68,,infty -2022-07-29 04:45:00,1734.98,,infty -2022-07-29 04:46:00,1735.42,,infty -2022-07-29 04:47:00,1738.17,,infty -2022-07-29 04:48:00,1738.76,,infty -2022-07-29 04:49:00,1736.72,,infty -2022-07-29 04:50:00,1736.51,,infty -2022-07-29 04:51:00,1735.55,,infty -2022-07-29 04:52:00,1743.72,,infty -2022-07-29 04:53:00,1739.43,,infty -2022-07-29 04:54:00,1740.13,,infty -2022-07-29 04:55:00,1739.16,,infty -2022-07-29 04:56:00,1739.09,,infty -2022-07-29 04:57:00,1739.48,,infty -2022-07-29 04:58:00,1740.8,,infty -2022-07-29 04:59:00,1740.43,,infty -2022-07-29 05:00:00,1740.09,,infty -2022-07-29 05:01:00,1739.67,,infty -2022-07-29 05:02:00,1737.98,,infty -2022-07-29 05:03:00,1737.36,,infty -2022-07-29 05:04:00,1738.19,,infty -2022-07-29 05:05:00,1737.23,,infty -2022-07-29 05:06:00,1739.41,,infty -2022-07-29 05:07:00,1738.49,,infty -2022-07-29 05:08:00,1737.35,,infty -2022-07-29 05:09:00,1732.79,,infty -2022-07-29 05:10:00,1733.72,,infty -2022-07-29 05:11:00,1733.82,,infty -2022-07-29 05:12:00,1732.63,,infty -2022-07-29 05:13:00,1721.3,,infty -2022-07-29 05:14:00,1724.06,,infty -2022-07-29 05:15:00,1724.27,,infty -2022-07-29 05:16:00,1726.08,,infty -2022-07-29 05:17:00,1726.46,,infty -2022-07-29 05:18:00,1728.01,,infty -2022-07-29 05:19:00,1727.98,,infty -2022-07-29 05:20:00,1727.77,,infty -2022-07-29 05:21:00,1727.32,,infty -2022-07-29 05:22:00,1724.8,,infty -2022-07-29 05:23:00,1722.94,,infty -2022-07-29 05:24:00,1725.23,,infty -2022-07-29 05:25:00,1725.93,,infty -2022-07-29 05:26:00,1725.0,,infty -2022-07-29 05:27:00,1726.04,,infty -2022-07-29 05:28:00,1725.79,,infty -2022-07-29 05:29:00,1725.7,,infty -2022-07-29 05:30:00,1725.85,,infty -2022-07-29 05:31:00,1726.7,,infty -2022-07-29 05:32:00,1726.64,,infty -2022-07-29 05:33:00,1727.31,,infty -2022-07-29 05:34:00,1731.25,,infty -2022-07-29 05:35:00,1732.83,,infty -2022-07-29 05:36:00,1735.69,,infty -2022-07-29 05:37:00,1740.34,,infty -2022-07-29 05:38:00,1739.14,,infty -2022-07-29 05:39:00,1737.42,,infty -2022-07-29 05:40:00,1737.89,,infty -2022-07-29 05:41:00,1737.32,,infty -2022-07-29 05:42:00,1735.67,,infty -2022-07-29 05:43:00,1733.09,,infty -2022-07-29 05:44:00,1734.48,,infty -2022-07-29 05:45:00,1735.51,,infty -2022-07-29 05:46:00,1738.56,,infty -2022-07-29 05:47:00,1739.36,,infty -2022-07-29 05:48:00,1738.42,,infty -2022-07-29 05:49:00,1736.4,,infty -2022-07-29 05:50:00,1734.09,,infty -2022-07-29 05:51:00,1726.99,,infty -2022-07-29 05:52:00,1727.86,,infty -2022-07-29 05:53:00,1727.79,,infty -2022-07-29 05:54:00,1728.51,,infty -2022-07-29 05:55:00,1730.65,,infty -2022-07-29 05:56:00,1730.77,,infty -2022-07-29 05:57:00,1728.55,,infty -2022-07-29 05:58:00,1730.09,,infty -2022-07-29 05:59:00,1729.28,,infty -2022-07-29 06:00:00,1729.37,,infty -2022-07-29 06:01:00,1727.49,,infty -2022-07-29 06:02:00,1726.27,,infty -2022-07-29 06:03:00,1726.03,,infty -2022-07-29 06:04:00,1728.73,,infty -2022-07-29 06:05:00,1728.3,,infty -2022-07-29 06:06:00,1726.52,,infty -2022-07-29 06:07:00,1727.04,,infty -2022-07-29 06:08:00,1726.28,,infty -2022-07-29 06:09:00,1725.88,,infty -2022-07-29 06:10:00,1726.23,,infty -2022-07-29 06:11:00,1727.28,,infty -2022-07-29 06:12:00,1728.19,,infty -2022-07-29 06:13:00,1726.01,,infty -2022-07-29 06:14:00,1725.09,,infty -2022-07-29 06:15:00,1725.0,,infty -2022-07-29 06:16:00,1722.72,,infty -2022-07-29 06:17:00,1725.2,,infty -2022-07-29 06:18:00,1726.14,,infty -2022-07-29 06:19:00,1726.32,,infty -2022-07-29 06:20:00,1725.4,,infty -2022-07-29 06:21:00,1725.8,,infty -2022-07-29 06:22:00,1725.23,,infty -2022-07-29 06:23:00,1728.36,,infty -2022-07-29 06:24:00,1736.47,,infty -2022-07-29 06:25:00,1734.53,,infty -2022-07-29 06:26:00,1733.4,,infty -2022-07-29 06:27:00,1735.24,,infty -2022-07-29 06:28:00,1735.06,,infty -2022-07-29 06:29:00,1732.25,,infty -2022-07-29 06:30:00,1732.79,,infty -2022-07-29 06:31:00,1735.51,,infty -2022-07-29 06:32:00,1733.67,,infty -2022-07-29 06:33:00,1736.9,,infty -2022-07-29 06:34:00,1736.96,,infty -2022-07-29 06:35:00,1737.64,,infty -2022-07-29 06:36:00,1747.75,,infty -2022-07-29 06:37:00,1738.91,,infty -2022-07-29 06:38:00,1743.96,,infty -2022-07-29 06:39:00,1743.04,,infty -2022-07-29 06:40:00,1746.85,,infty -2022-07-29 06:41:00,1744.46,,infty -2022-07-29 06:42:00,1741.94,,infty -2022-07-29 06:43:00,1737.65,,infty -2022-07-29 06:44:00,1739.61,,infty -2022-07-29 06:45:00,1733.7,,infty -2022-07-29 06:46:00,1728.14,,infty -2022-07-29 06:47:00,1725.78,,infty -2022-07-29 06:48:00,1727.16,,infty -2022-07-29 06:49:00,1727.92,,infty -2022-07-29 06:50:00,1726.47,,infty -2022-07-29 06:51:00,1719.91,,infty -2022-07-29 06:52:00,1721.16,,infty -2022-07-29 06:53:00,1722.82,,infty -2022-07-29 06:54:00,1724.79,,infty -2022-07-29 06:55:00,1724.69,,infty -2022-07-29 06:56:00,1723.69,,infty -2022-07-29 06:57:00,1719.61,,infty -2022-07-29 06:58:00,1715.91,,infty -2022-07-29 06:59:00,1717.18,,infty -2022-07-29 07:00:00,1718.76,,infty -2022-07-29 07:01:00,1713.63,,infty -2022-07-29 07:02:00,1714.36,,infty -2022-07-29 07:03:00,1717.92,,infty -2022-07-29 07:04:00,1719.51,,infty -2022-07-29 07:05:00,1717.96,,infty -2022-07-29 07:06:00,1718.12,,infty -2022-07-29 07:07:00,1720.89,,infty -2022-07-29 07:08:00,1718.61,,infty -2022-07-29 07:09:00,1719.9,,infty -2022-07-29 07:10:00,1718.94,,infty -2022-07-29 07:11:00,1714.64,,infty -2022-07-29 07:12:00,1713.0,,infty -2022-07-29 07:13:00,1711.87,,infty -2022-07-29 07:14:00,1714.42,,infty -2022-07-29 07:15:00,1716.43,,infty -2022-07-29 07:16:00,1715.4,,infty -2022-07-29 07:17:00,1715.71,,infty -2022-07-29 07:18:00,1711.23,,infty -2022-07-29 07:19:00,1711.41,,infty -2022-07-29 07:20:00,1707.55,,infty -2022-07-29 07:21:00,1709.13,,infty -2022-07-29 07:22:00,1709.31,,infty -2022-07-29 07:23:00,1708.5,,infty -2022-07-29 07:24:00,1714.32,,infty -2022-07-29 07:25:00,1712.99,,infty -2022-07-29 07:26:00,1710.53,,infty -2022-07-29 07:27:00,1709.22,,infty -2022-07-29 07:28:00,1710.49,,infty -2022-07-29 07:29:00,1711.1,,infty -2022-07-29 07:30:00,1712.86,,infty -2022-07-29 07:31:00,1714.82,,infty -2022-07-29 07:32:00,1715.72,,infty -2022-07-29 07:33:00,1714.09,,infty -2022-07-29 07:34:00,1716.68,,infty -2022-07-29 07:35:00,1716.82,,infty -2022-07-29 07:36:00,1716.62,,infty -2022-07-29 07:37:00,1719.74,,infty -2022-07-29 07:38:00,1719.71,,infty -2022-07-29 07:39:00,1719.26,,infty -2022-07-29 07:40:00,1718.47,,infty -2022-07-29 07:41:00,1715.22,,infty -2022-07-29 07:42:00,1717.47,,infty -2022-07-29 07:43:00,1717.55,,infty -2022-07-29 07:44:00,1715.41,,infty -2022-07-29 07:45:00,1715.66,,infty -2022-07-29 07:46:00,1718.12,,infty -2022-07-29 07:47:00,1717.2,,infty -2022-07-29 07:48:00,1717.7,,infty -2022-07-29 07:49:00,1715.26,,infty -2022-07-29 07:50:00,1716.86,,infty -2022-07-29 07:51:00,1718.34,,infty -2022-07-29 07:52:00,1718.2,,infty -2022-07-29 07:53:00,1717.12,,infty -2022-07-29 07:54:00,1718.03,,infty -2022-07-29 07:55:00,1717.62,,infty -2022-07-29 07:56:00,1716.56,,infty -2022-07-29 07:57:00,1715.83,,infty -2022-07-29 07:58:00,1716.55,,infty -2022-07-29 07:59:00,1715.59,,infty -2022-07-29 08:00:00,1715.66,,infty -2022-07-29 08:01:00,1713.48,,infty -2022-07-29 08:02:00,1714.36,,infty -2022-07-29 08:03:00,1712.39,,infty -2022-07-29 08:04:00,1710.41,,infty -2022-07-29 08:05:00,1708.08,,infty -2022-07-29 08:06:00,1707.94,,infty -2022-07-29 08:07:00,1706.86,,infty -2022-07-29 08:08:00,1704.56,,infty -2022-07-29 08:09:00,1701.84,,infty -2022-07-29 08:10:00,1695.91,,infty -2022-07-29 08:11:00,1698.43,,infty -2022-07-29 08:12:00,1705.1,,infty -2022-07-29 08:13:00,1704.63,,infty -2022-07-29 08:14:00,1711.76,,infty -2022-07-29 08:15:00,1710.74,,infty -2022-07-29 08:16:00,1709.06,,infty -2022-07-29 08:17:00,1711.87,,infty -2022-07-29 08:18:00,1716.83,,infty -2022-07-29 08:19:00,1717.26,,infty -2022-07-29 08:20:00,1718.39,,infty -2022-07-29 08:21:00,1717.7,,infty -2022-07-29 08:22:00,1717.67,,infty -2022-07-29 08:23:00,1715.16,,infty -2022-07-29 08:24:00,1715.39,,infty -2022-07-29 08:25:00,1712.83,,infty -2022-07-29 08:26:00,1714.37,,infty -2022-07-29 08:27:00,1716.87,,infty -2022-07-29 08:28:00,1714.98,,infty -2022-07-29 08:29:00,1716.01,,infty -2022-07-29 08:30:00,1716.4,,infty -2022-07-29 08:31:00,1716.95,,infty -2022-07-29 08:32:00,1717.74,,infty -2022-07-29 08:33:00,1716.07,,infty -2022-07-29 08:34:00,1715.44,,infty -2022-07-29 08:35:00,1714.51,,infty -2022-07-29 08:36:00,1714.88,,infty -2022-07-29 08:37:00,1715.01,,infty -2022-07-29 08:38:00,1714.2,,infty -2022-07-29 08:39:00,1711.04,,infty -2022-07-29 08:40:00,1714.11,,infty -2022-07-29 08:41:00,1715.75,,infty -2022-07-29 08:42:00,1719.14,,infty -2022-07-29 08:43:00,1718.19,,infty -2022-07-29 08:44:00,1721.58,,infty -2022-07-29 08:45:00,1725.24,,infty -2022-07-29 08:46:00,1723.76,,infty -2022-07-29 08:47:00,1722.0,,infty -2022-07-29 08:48:00,1717.96,,infty -2022-07-29 08:49:00,1717.73,,infty -2022-07-29 08:50:00,1717.27,,infty -2022-07-29 08:51:00,1717.32,,infty -2022-07-29 08:52:00,1718.47,,infty -2022-07-29 08:53:00,1720.41,,infty -2022-07-29 08:54:00,1720.2,,infty -2022-07-29 08:55:00,1722.76,,infty -2022-07-29 08:56:00,1721.5,,infty -2022-07-29 08:57:00,1719.57,,infty -2022-07-29 08:58:00,1719.04,,infty -2022-07-29 08:59:00,1719.86,,infty -2022-07-29 09:00:00,1719.2,,infty -2022-07-29 09:01:00,1718.19,,infty -2022-07-29 09:02:00,1719.59,,infty -2022-07-29 09:03:00,1716.74,,infty -2022-07-29 09:04:00,1717.83,,infty -2022-07-29 09:05:00,1715.67,,infty -2022-07-29 09:06:00,1713.99,,infty -2022-07-29 09:07:00,1716.09,,infty -2022-07-29 09:08:00,1716.07,,infty -2022-07-29 09:09:00,1718.87,,infty -2022-07-29 09:10:00,1719.62,,infty -2022-07-29 09:11:00,1720.58,,infty -2022-07-29 09:12:00,1719.85,,infty -2022-07-29 09:13:00,1720.09,,infty -2022-07-29 09:14:00,1721.08,,infty -2022-07-29 09:15:00,1722.76,,infty -2022-07-29 09:16:00,1723.37,,infty -2022-07-29 09:17:00,1723.85,,infty -2022-07-29 09:18:00,1723.13,,infty -2022-07-29 09:19:00,1722.63,,infty -2022-07-29 09:20:00,1718.47,,infty -2022-07-29 09:21:00,1718.99,,infty -2022-07-29 09:22:00,1718.93,,infty -2022-07-29 09:23:00,1719.7,,infty -2022-07-29 09:24:00,1718.81,,infty -2022-07-29 09:25:00,1719.73,,infty -2022-07-29 09:26:00,1719.8,,infty -2022-07-29 09:27:00,1721.4,,infty -2022-07-29 09:28:00,1721.59,,infty -2022-07-29 09:29:00,1722.33,,infty -2022-07-29 09:30:00,1723.15,,infty -2022-07-29 09:31:00,1719.93,,infty -2022-07-29 09:32:00,1720.43,,infty -2022-07-29 09:33:00,1723.19,,infty -2022-07-29 09:34:00,1724.05,,infty -2022-07-29 09:35:00,1723.58,,infty -2022-07-29 09:36:00,1724.14,,infty -2022-07-29 09:37:00,1727.55,,infty -2022-07-29 09:38:00,1727.1,,infty -2022-07-29 09:39:00,1723.69,,infty -2022-07-29 09:40:00,1724.58,,infty -2022-07-29 09:41:00,1724.46,,infty -2022-07-29 09:42:00,1724.95,,infty -2022-07-29 09:43:00,1723.47,,infty -2022-07-29 09:44:00,1724.21,,infty -2022-07-29 09:45:00,1724.31,,infty -2022-07-29 09:46:00,1724.22,,infty -2022-07-29 09:47:00,1722.4,,infty -2022-07-29 09:48:00,1723.28,,infty -2022-07-29 09:49:00,1723.22,,infty -2022-07-29 09:50:00,1723.43,,infty -2022-07-29 09:51:00,1724.33,,infty -2022-07-29 09:52:00,1725.34,,infty -2022-07-29 09:53:00,1723.17,,infty -2022-07-29 09:54:00,1723.78,,infty -2022-07-29 09:55:00,1723.38,,infty -2022-07-29 09:56:00,1725.31,,infty -2022-07-29 09:57:00,1727.37,,infty -2022-07-29 09:58:00,1729.52,,infty -2022-07-29 09:59:00,1729.02,,infty -2022-07-29 10:00:00,1729.07,,infty -2022-07-29 10:01:00,1729.91,,infty -2022-07-29 10:02:00,1726.83,,infty -2022-07-29 10:03:00,1728.42,,infty -2022-07-29 10:04:00,1727.55,,infty -2022-07-29 10:05:00,1728.59,,infty -2022-07-29 10:06:00,1727.36,,infty -2022-07-29 10:07:00,1729.17,,infty -2022-07-29 10:08:00,1730.6,,infty -2022-07-29 10:09:00,1729.7,,infty -2022-07-29 10:10:00,1730.26,,infty -2022-07-29 10:11:00,1730.52,,infty -2022-07-29 10:12:00,1732.47,,infty -2022-07-29 10:13:00,1731.36,,infty -2022-07-29 10:14:00,1730.48,,infty -2022-07-29 10:15:00,1731.24,,infty -2022-07-29 10:16:00,1730.68,,infty -2022-07-29 10:17:00,1728.04,,infty -2022-07-29 10:18:00,1728.75,,infty -2022-07-29 10:19:00,1723.19,,infty -2022-07-29 10:20:00,1726.01,,infty -2022-07-29 10:21:00,1725.34,,infty -2022-07-29 10:22:00,1723.83,,infty -2022-07-29 10:23:00,1722.97,,infty -2022-07-29 10:24:00,1722.77,,infty -2022-07-29 10:25:00,1721.8,,infty -2022-07-29 10:26:00,1718.03,,infty -2022-07-29 10:27:00,1718.82,,infty -2022-07-29 10:28:00,1720.39,,infty -2022-07-29 10:29:00,1720.32,,infty -2022-07-29 10:30:00,1721.88,,infty -2022-07-29 10:31:00,1719.44,,infty -2022-07-29 10:32:00,1719.74,,infty -2022-07-29 10:33:00,1719.61,,infty -2022-07-29 10:34:00,1718.56,,infty -2022-07-29 10:35:00,1721.67,,infty -2022-07-29 10:36:00,1723.21,,infty -2022-07-29 10:37:00,1723.79,,infty -2022-07-29 10:38:00,1723.95,,infty -2022-07-29 10:39:00,1724.76,,infty -2022-07-29 10:40:00,1724.68,,infty -2022-07-29 10:41:00,1723.32,,infty -2022-07-29 10:42:00,1722.2,,infty -2022-07-29 10:43:00,1721.05,,infty -2022-07-29 10:44:00,1721.12,,infty -2022-07-29 10:45:00,1721.81,,infty -2022-07-29 10:46:00,1724.09,,infty -2022-07-29 10:47:00,1723.55,,infty -2022-07-29 10:48:00,1725.31,,infty -2022-07-29 10:49:00,1725.77,,infty -2022-07-29 10:50:00,1725.98,,infty -2022-07-29 10:51:00,1725.59,,infty -2022-07-29 10:52:00,1724.83,,infty -2022-07-29 10:53:00,1725.61,,infty -2022-07-29 10:54:00,1726.55,,infty -2022-07-29 10:55:00,1724.07,,infty -2022-07-29 10:56:00,1722.97,,infty -2022-07-29 10:57:00,1724.04,,infty -2022-07-29 10:58:00,1721.42,,infty -2022-07-29 10:59:00,1720.93,,infty -2022-07-29 11:00:00,1719.21,,infty -2022-07-29 11:01:00,1719.81,,infty -2022-07-29 11:02:00,1720.98,,infty -2022-07-29 11:03:00,1720.98,,infty -2022-07-29 11:04:00,1719.77,,infty -2022-07-29 11:05:00,1718.98,,infty -2022-07-29 11:06:00,1716.99,,infty -2022-07-29 11:07:00,1715.69,,infty -2022-07-29 11:08:00,1716.03,,infty -2022-07-29 11:09:00,1714.2,,infty -2022-07-29 11:10:00,1712.89,,infty -2022-07-29 11:11:00,1714.11,,infty -2022-07-29 11:12:00,1713.05,,infty -2022-07-29 11:13:00,1713.01,,infty -2022-07-29 11:14:00,1712.0,,infty -2022-07-29 11:15:00,1710.54,,infty -2022-07-29 11:16:00,1713.98,,infty -2022-07-29 11:17:00,1712.63,,infty -2022-07-29 11:18:00,1713.92,,infty -2022-07-29 11:19:00,1712.11,,infty -2022-07-29 11:20:00,1709.96,,infty -2022-07-29 11:21:00,1708.91,,infty -2022-07-29 11:22:00,1704.53,,infty -2022-07-29 11:23:00,1705.86,,infty -2022-07-29 11:24:00,1706.81,,infty -2022-07-29 11:25:00,1703.14,,infty -2022-07-29 11:26:00,1698.53,,infty -2022-07-29 11:27:00,1699.53,,infty -2022-07-29 11:28:00,1696.71,,infty -2022-07-29 11:29:00,1696.69,,infty -2022-07-29 11:30:00,1695.95,,infty -2022-07-29 11:31:00,1697.65,,infty -2022-07-29 11:32:00,1694.66,,infty -2022-07-29 11:33:00,1685.77,,infty -2022-07-29 11:34:00,1684.67,,infty -2022-07-29 11:35:00,1686.46,,infty -2022-07-29 11:36:00,1683.56,,infty -2022-07-29 11:37:00,1686.63,,infty -2022-07-29 11:38:00,1686.81,,infty -2022-07-29 11:39:00,1684.58,,infty -2022-07-29 11:40:00,1685.37,,infty -2022-07-29 11:41:00,1680.37,,open_close -2022-07-29 11:42:00,1679.39,,open_close -2022-07-29 11:43:00,1670.34,,open_close -2022-07-29 11:44:00,1675.04,,open_close -2022-07-29 11:45:00,1679.47,,open_close -2022-07-29 11:46:00,1677.19,,open_close -2022-07-29 11:47:00,1679.37,,open_close -2022-07-29 11:48:00,1678.92,,open_close -2022-07-29 11:49:00,1676.22,,open_close -2022-07-29 11:50:00,1681.9,,infty -2022-07-29 11:51:00,1684.59,,infty -2022-07-29 11:52:00,1682.23,,infty -2022-07-29 11:53:00,1681.52,,infty -2022-07-29 11:54:00,1681.65,,infty -2022-07-29 11:55:00,1682.18,,infty -2022-07-29 11:56:00,1685.39,,infty -2022-07-29 11:57:00,1683.41,,infty -2022-07-29 11:58:00,1682.81,,infty -2022-07-29 11:59:00,1681.95,,infty -2022-07-29 12:00:00,1682.39,,infty -2022-07-29 12:01:00,1684.1,,infty -2022-07-29 12:02:00,1682.57,,infty -2022-07-29 12:03:00,1680.94,,open_close -2022-07-29 12:04:00,1674.42,,open_close -2022-07-29 12:05:00,1681.45,,infty -2022-07-29 12:06:00,1681.74,,infty -2022-07-29 12:07:00,1678.41,,open_close -2022-07-29 12:08:00,1681.48,,infty -2022-07-29 12:09:00,1681.34,,infty -2022-07-29 12:10:00,1679.07,,open_close -2022-07-29 12:11:00,1678.04,,open_close -2022-07-29 12:12:00,1676.73,,open_close -2022-07-29 12:13:00,1675.0,,open_close -2022-07-29 12:14:00,1675.11,,open_close -2022-07-29 12:15:00,1670.41,,open_close -2022-07-29 12:16:00,1672.41,,open_close -2022-07-29 12:17:00,1680.08,,open_close -2022-07-29 12:18:00,1676.85,,open_close -2022-07-29 12:19:00,1683.94,,infty -2022-07-29 12:20:00,1681.59,,infty -2022-07-29 12:21:00,1681.39,,infty -2022-07-29 12:22:00,1678.82,,open_close -2022-07-29 12:23:00,1680.38,,open_close -2022-07-29 12:24:00,1678.97,,open_close -2022-07-29 12:25:00,1681.18,,infty -2022-07-29 12:26:00,1680.46,,open_close -2022-07-29 12:27:00,1681.03,,open_close -2022-07-29 12:28:00,1681.49,,infty -2022-07-29 12:29:00,1679.2,,open_close -2022-07-29 12:30:00,1675.8,,open_close -2022-07-29 12:31:00,1676.19,,open_close -2022-07-29 12:32:00,1679.52,,open_close -2022-07-29 12:33:00,1676.99,,open_close -2022-07-29 12:34:00,1671.06,,open_close -2022-07-29 12:35:00,1665.0,,open_close -2022-07-29 12:36:00,1672.12,,open_close -2022-07-29 12:37:00,1671.9,,open_close -2022-07-29 12:38:00,1670.62,,open_close -2022-07-29 12:39:00,1669.55,,open_close -2022-07-29 12:40:00,1670.26,,open_close -2022-07-29 12:41:00,1665.61,,open_close -2022-07-29 12:42:00,1664.53,,open_close -2022-07-29 12:43:00,1666.37,,open_close -2022-07-29 12:44:00,1659.53,,open_close -2022-07-29 12:45:00,1660.05,,open_close -2022-07-29 12:46:00,1660.54,,open_close -2022-07-29 12:47:00,1664.16,,open_close -2022-07-29 12:48:00,1664.52,,open_close -2022-07-29 12:49:00,1658.79,,open_close -2022-07-29 12:50:00,1661.87,,open_close -2022-07-29 12:51:00,1663.77,,open_close -2022-07-29 12:52:00,1668.19,,open_close -2022-07-29 12:53:00,1669.63,,open_close -2022-07-29 12:54:00,1669.61,,open_close -2022-07-29 12:55:00,1668.72,,open_close -2022-07-29 12:56:00,1666.05,,open_close -2022-07-29 12:57:00,1666.59,,open_close -2022-07-29 12:58:00,1668.29,,open_close -2022-07-29 12:59:00,1666.81,,open_close -2022-07-29 13:00:00,1665.79,,open_close -2022-07-29 13:01:00,1666.94,,open_close -2022-07-29 13:02:00,1664.97,,open_close -2022-07-29 13:03:00,1661.08,,open_close -2022-07-29 13:04:00,1662.49,,open_close -2022-07-29 13:05:00,1661.08,,open_close -2022-07-29 13:06:00,1665.99,,open_close -2022-07-29 13:07:00,1667.74,,open_close -2022-07-29 13:08:00,1666.99,,open_close -2022-07-29 13:09:00,1669.11,,open_close -2022-07-29 13:10:00,1665.69,,open_close -2022-07-29 13:11:00,1665.72,,open_close -2022-07-29 13:12:00,1666.56,,open_close -2022-07-29 13:13:00,1666.08,,open_close -2022-07-29 13:14:00,1665.65,,open_close -2022-07-29 13:15:00,1664.05,,open_close -2022-07-29 13:16:00,1665.67,,open_close -2022-07-29 13:17:00,1669.66,,open_close -2022-07-29 13:18:00,1669.02,,open_close -2022-07-29 13:19:00,1668.71,,open_close -2022-07-29 13:20:00,1673.11,,open_close -2022-07-29 13:21:00,1674.36,,open_close -2022-07-29 13:22:00,1674.61,,open_close -2022-07-29 13:23:00,1675.94,,open_close -2022-07-29 13:24:00,1674.84,,open_close -2022-07-29 13:25:00,1675.11,,open_close -2022-07-29 13:26:00,1678.38,,open_close -2022-07-29 13:27:00,1679.91,,open_close -2022-07-29 13:28:00,1677.18,,open_close -2022-07-29 13:29:00,1676.46,,open_close -2022-07-29 13:30:00,1675.23,,open_close -2022-07-29 13:31:00,1678.4,,open_close -2022-07-29 13:32:00,1673.39,,open_close -2022-07-29 13:33:00,1674.29,,open_close -2022-07-29 13:34:00,1672.65,,open_close -2022-07-29 13:35:00,1665.67,,open_close -2022-07-29 13:36:00,1665.39,,open_close -2022-07-29 13:37:00,1665.2,,open_close -2022-07-29 13:38:00,1667.36,,open_close -2022-07-29 13:39:00,1674.07,,open_close -2022-07-29 13:40:00,1684.55,,infty -2022-07-29 13:41:00,1693.75,,infty -2022-07-29 13:42:00,1693.65,,infty -2022-07-29 13:43:00,1687.1,,infty -2022-07-29 13:44:00,1687.97,,infty -2022-07-29 13:45:00,1701.02,,infty -2022-07-29 13:46:00,1695.88,,infty -2022-07-29 13:47:00,1698.04,,infty -2022-07-29 13:48:00,1702.61,,infty -2022-07-29 13:49:00,1700.74,,infty -2022-07-29 13:50:00,1705.13,,infty -2022-07-29 13:51:00,1702.53,,infty -2022-07-29 13:52:00,1701.69,,infty -2022-07-29 13:53:00,1700.32,,infty -2022-07-29 13:54:00,1700.69,,infty -2022-07-29 13:55:00,1702.8,,infty -2022-07-29 13:56:00,1703.71,,infty -2022-07-29 13:57:00,1700.67,,infty -2022-07-29 13:58:00,1699.45,,infty -2022-07-29 13:59:00,1699.88,,infty -2022-07-29 14:00:00,1703.83,,infty -2022-07-29 14:01:00,1705.37,,infty -2022-07-29 14:02:00,1708.96,,infty -2022-07-29 14:03:00,1706.06,,infty -2022-07-29 14:04:00,1707.17,,infty -2022-07-29 14:05:00,1711.14,,infty -2022-07-29 14:06:00,1718.98,,infty -2022-07-29 14:07:00,1718.69,,infty -2022-07-29 14:08:00,1713.74,,infty -2022-07-29 14:09:00,1715.71,,infty -2022-07-29 14:10:00,1719.18,,infty -2022-07-29 14:11:00,1729.84,,infty -2022-07-29 14:12:00,1734.28,,infty -2022-07-29 14:13:00,1734.35,,infty -2022-07-29 14:14:00,1733.03,,infty -2022-07-29 14:15:00,1731.07,,infty -2022-07-29 14:16:00,1735.59,,infty -2022-07-29 14:17:00,1734.49,,infty -2022-07-29 14:18:00,1734.66,,infty -2022-07-29 14:19:00,1735.0,,infty -2022-07-29 14:20:00,1735.33,,infty -2022-07-29 14:21:00,1732.94,,infty -2022-07-29 14:22:00,1736.64,,infty -2022-07-29 14:23:00,1737.09,,infty -2022-07-29 14:24:00,1736.78,,infty -2022-07-29 14:25:00,1736.43,,infty -2022-07-29 14:26:00,1742.95,,infty -2022-07-29 14:27:00,1744.29,,infty -2022-07-29 14:28:00,1742.44,,infty -2022-07-29 14:29:00,1735.34,,infty -2022-07-29 14:30:00,1734.5,,infty -2022-07-29 14:31:00,1733.23,,infty -2022-07-29 14:32:00,1730.67,,infty -2022-07-29 14:33:00,1730.21,,infty -2022-07-29 14:34:00,1728.78,,infty -2022-07-29 14:35:00,1733.16,,infty -2022-07-29 14:36:00,1731.96,,infty -2022-07-29 14:37:00,1736.05,,infty -2022-07-29 14:38:00,1737.0,,infty -2022-07-29 14:39:00,1739.68,,infty -2022-07-29 14:40:00,1740.44,,infty -2022-07-29 14:41:00,1739.8,,infty -2022-07-29 14:42:00,1740.61,,infty -2022-07-29 14:43:00,1739.14,,infty -2022-07-29 14:44:00,1735.3,,infty -2022-07-29 14:45:00,1733.22,,infty -2022-07-29 14:46:00,1735.55,,infty -2022-07-29 14:47:00,1731.46,,infty -2022-07-29 14:48:00,1734.93,,infty -2022-07-29 14:49:00,1732.15,,infty -2022-07-29 14:50:00,1731.53,,infty -2022-07-29 14:51:00,1732.51,,infty -2022-07-29 14:52:00,1732.58,,infty -2022-07-29 14:53:00,1734.79,,infty -2022-07-29 14:54:00,1733.1,,infty -2022-07-29 14:55:00,1732.93,,infty -2022-07-29 14:56:00,1732.76,,infty -2022-07-29 14:57:00,1730.93,,infty -2022-07-29 14:58:00,1728.98,,infty -2022-07-29 14:59:00,1724.12,,infty -2022-07-29 15:00:00,1722.81,,infty -2022-07-29 15:01:00,1719.37,,infty -2022-07-29 15:02:00,1717.81,,infty -2022-07-29 15:03:00,1718.11,,infty -2022-07-29 15:04:00,1718.04,,infty -2022-07-29 15:05:00,1723.83,,infty -2022-07-29 15:06:00,1726.21,,infty -2022-07-29 15:07:00,1720.2,,infty -2022-07-29 15:08:00,1722.54,,infty -2022-07-29 15:09:00,1726.15,,infty -2022-07-29 15:10:00,1723.35,,infty -2022-07-29 15:11:00,1722.82,,infty -2022-07-29 15:12:00,1727.04,,infty -2022-07-29 15:13:00,1726.0,,infty -2022-07-29 15:14:00,1724.0,,infty -2022-07-29 15:15:00,1721.13,,infty -2022-07-29 15:16:00,1717.91,,infty -2022-07-29 15:17:00,1714.91,,infty -2022-07-29 15:18:00,1719.5,,infty -2022-07-29 15:19:00,1725.12,,infty -2022-07-29 15:20:00,1726.85,,infty -2022-07-29 15:21:00,1724.88,,infty -2022-07-29 15:22:00,1724.73,,infty -2022-07-29 15:23:00,1721.24,,infty -2022-07-29 15:24:00,1719.56,,infty -2022-07-29 15:25:00,1720.06,,infty -2022-07-29 15:26:00,1718.52,,infty -2022-07-29 15:27:00,1713.82,,infty -2022-07-29 15:28:00,1716.26,,infty -2022-07-29 15:29:00,1712.12,,infty -2022-07-29 15:30:00,1713.93,,infty -2022-07-29 15:31:00,1718.21,,infty -2022-07-29 15:32:00,1715.34,,infty -2022-07-29 15:33:00,1715.75,,infty -2022-07-29 15:34:00,1716.82,,infty -2022-07-29 15:35:00,1717.91,,infty -2022-07-29 15:36:00,1719.12,,infty -2022-07-29 15:37:00,1721.94,,infty -2022-07-29 15:38:00,1721.81,,infty -2022-07-29 15:39:00,1722.43,,infty -2022-07-29 15:40:00,1724.01,,infty -2022-07-29 15:41:00,1725.0,,infty -2022-07-29 15:42:00,1723.0,,infty -2022-07-29 15:43:00,1724.16,,infty -2022-07-29 15:44:00,1724.38,,infty -2022-07-29 15:45:00,1724.9,,infty -2022-07-29 15:46:00,1722.0,,infty -2022-07-29 15:47:00,1723.49,,infty -2022-07-29 15:48:00,1723.95,,infty -2022-07-29 15:49:00,1722.01,,infty -2022-07-29 15:50:00,1721.42,,infty -2022-07-29 15:51:00,1723.73,,infty -2022-07-29 15:52:00,1723.12,,infty -2022-07-29 15:53:00,1722.28,,infty -2022-07-29 15:54:00,1721.75,,infty -2022-07-29 15:55:00,1721.28,,infty -2022-07-29 15:56:00,1719.38,,infty -2022-07-29 15:57:00,1720.35,,infty -2022-07-29 15:58:00,1723.49,,infty -2022-07-29 15:59:00,1721.99,,infty -2022-07-29 16:00:00,1721.09,,infty -2022-07-29 16:01:00,1709.85,,infty -2022-07-29 16:02:00,1703.68,,infty -2022-07-29 16:03:00,1706.63,,infty -2022-07-29 16:04:00,1700.74,,infty -2022-07-29 16:05:00,1700.16,,infty -2022-07-29 16:06:00,1695.53,,infty -2022-07-29 16:07:00,1689.3,,infty -2022-07-29 16:08:00,1687.96,,infty -2022-07-29 16:09:00,1689.54,,infty -2022-07-29 16:10:00,1682.81,,infty -2022-07-29 16:11:00,1683.01,,infty -2022-07-29 16:12:00,1685.62,,infty -2022-07-29 16:13:00,1681.8,,infty -2022-07-29 16:14:00,1676.77,,open_close -2022-07-29 16:15:00,1680.78,,open_close -2022-07-29 16:16:00,1682.03,,infty -2022-07-29 16:17:00,1684.73,,infty -2022-07-29 16:18:00,1684.94,,infty -2022-07-29 16:19:00,1682.94,,infty -2022-07-29 16:20:00,1684.01,,infty -2022-07-29 16:21:00,1682.16,,infty -2022-07-29 16:22:00,1680.51,,open_close -2022-07-29 16:23:00,1674.82,,open_close -2022-07-29 16:24:00,1678.05,,open_close -2022-07-29 16:25:00,1675.41,,open_close -2022-07-29 16:26:00,1671.6,,open_close -2022-07-29 16:27:00,1671.53,,open_close -2022-07-29 16:28:00,1670.84,,open_close -2022-07-29 16:29:00,1670.08,,open_close -2022-07-29 16:30:00,1674.05,,open_close -2022-07-29 16:31:00,1677.57,,open_close -2022-07-29 16:32:00,1679.99,,open_close -2022-07-29 16:33:00,1678.05,,open_close -2022-07-29 16:34:00,1677.74,,open_close -2022-07-29 16:35:00,1675.67,,open_close -2022-07-29 16:36:00,1674.19,,open_close -2022-07-29 16:37:00,1676.46,,open_close -2022-07-29 16:38:00,1675.97,,open_close -2022-07-29 16:39:00,1674.78,,open_close -2022-07-29 16:40:00,1671.75,,open_close -2022-07-29 16:41:00,1675.76,,open_close -2022-07-29 16:42:00,1673.33,,open_close -2022-07-29 16:43:00,1674.51,,open_close -2022-07-29 16:44:00,1676.34,,open_close -2022-07-29 16:45:00,1678.75,,open_close -2022-07-29 16:46:00,1675.68,,open_close -2022-07-29 16:47:00,1676.87,,open_close -2022-07-29 16:48:00,1680.15,,open_close -2022-07-29 16:49:00,1683.13,,infty -2022-07-29 16:50:00,1685.66,,infty -2022-07-29 16:51:00,1695.06,,infty -2022-07-29 16:52:00,1692.8,,infty -2022-07-29 16:53:00,1696.15,,infty -2022-07-29 16:54:00,1694.19,,infty -2022-07-29 16:55:00,1691.49,,infty -2022-07-29 16:56:00,1691.5,,infty -2022-07-29 16:57:00,1692.17,,infty -2022-07-29 16:58:00,1692.36,,infty -2022-07-29 16:59:00,1691.39,,infty -2022-07-29 17:00:00,1686.55,,infty -2022-07-29 17:01:00,1683.0,,infty -2022-07-29 17:02:00,1684.08,,infty -2022-07-29 17:03:00,1683.13,,infty -2022-07-29 17:04:00,1685.34,,infty -2022-07-29 17:05:00,1686.46,,infty -2022-07-29 17:06:00,1686.83,,infty -2022-07-29 17:07:00,1685.9,,infty -2022-07-29 17:08:00,1685.25,,infty -2022-07-29 17:09:00,1686.18,,infty -2022-07-29 17:10:00,1687.25,,infty -2022-07-29 17:11:00,1686.07,,infty -2022-07-29 17:12:00,1687.05,,infty -2022-07-29 17:13:00,1682.55,,infty -2022-07-29 17:14:00,1682.01,,infty -2022-07-29 17:15:00,1683.27,,infty -2022-07-29 17:16:00,1684.98,,infty -2022-07-29 17:17:00,1684.91,,infty -2022-07-29 17:18:00,1686.43,,infty -2022-07-29 17:19:00,1684.54,,infty -2022-07-29 17:20:00,1684.25,,infty -2022-07-29 17:21:00,1683.41,,infty -2022-07-29 17:22:00,1680.9,,open_close -2022-07-29 17:23:00,1678.29,,open_close -2022-07-29 17:24:00,1677.67,,open_close -2022-07-29 17:25:00,1678.89,,open_close -2022-07-29 17:26:00,1676.42,,open_close -2022-07-29 17:27:00,1675.18,,open_close -2022-07-29 17:28:00,1674.45,,open_close -2022-07-29 17:29:00,1678.39,,open_close -2022-07-29 17:30:00,1679.61,,open_close -2022-07-29 17:31:00,1681.7,,infty -2022-07-29 17:32:00,1685.19,,infty -2022-07-29 17:33:00,1683.45,,infty -2022-07-29 17:34:00,1684.2,,infty -2022-07-29 17:35:00,1685.09,,infty -2022-07-29 17:36:00,1690.24,,infty -2022-07-29 17:37:00,1688.84,,infty -2022-07-29 17:38:00,1692.5,,infty -2022-07-29 17:39:00,1690.6,,infty -2022-07-29 17:40:00,1689.58,,infty -2022-07-29 17:41:00,1692.03,,infty -2022-07-29 17:42:00,1690.65,,infty -2022-07-29 17:43:00,1695.78,,infty -2022-07-29 17:44:00,1693.8,,infty -2022-07-29 17:45:00,1695.74,,infty -2022-07-29 17:46:00,1703.97,,infty -2022-07-29 17:47:00,1701.87,,infty -2022-07-29 17:48:00,1703.93,,infty -2022-07-29 17:49:00,1702.5,,infty -2022-07-29 17:50:00,1702.39,,infty -2022-07-29 17:51:00,1699.82,,infty -2022-07-29 17:52:00,1699.81,,infty -2022-07-29 17:53:00,1697.25,,infty -2022-07-29 17:54:00,1697.04,,infty -2022-07-29 17:55:00,1699.06,,infty -2022-07-29 17:56:00,1698.37,,infty -2022-07-29 17:57:00,1694.91,,infty -2022-07-29 17:58:00,1695.28,,infty -2022-07-29 17:59:00,1695.27,,infty -2022-07-29 18:00:00,1692.83,,infty -2022-07-29 18:01:00,1697.92,,infty -2022-07-29 18:02:00,1699.7,,infty -2022-07-29 18:03:00,1699.2,,infty -2022-07-29 18:04:00,1701.29,,infty -2022-07-29 18:05:00,1704.54,,infty -2022-07-29 18:06:00,1706.72,,infty -2022-07-29 18:07:00,1704.25,,infty -2022-07-29 18:08:00,1707.66,,infty -2022-07-29 18:09:00,1708.77,,infty -2022-07-29 18:10:00,1711.77,,infty -2022-07-29 18:11:00,1718.31,,infty -2022-07-29 18:12:00,1728.5,,infty -2022-07-29 18:13:00,1728.72,,infty -2022-07-29 18:14:00,1732.75,,infty -2022-07-29 18:15:00,1730.72,,infty -2022-07-29 18:16:00,1734.64,,infty -2022-07-29 18:17:00,1729.28,,infty -2022-07-29 18:18:00,1733.55,,infty -2022-07-29 18:19:00,1734.65,,infty -2022-07-29 18:20:00,1731.78,,infty -2022-07-29 18:21:00,1730.05,,infty -2022-07-29 18:22:00,1727.37,,infty -2022-07-29 18:23:00,1728.55,,infty -2022-07-29 18:24:00,1730.72,,infty -2022-07-29 18:25:00,1733.57,,infty -2022-07-29 18:26:00,1730.0,,infty -2022-07-29 18:27:00,1729.84,,infty -2022-07-29 18:28:00,1724.13,,infty -2022-07-29 18:29:00,1720.0,,infty -2022-07-29 18:30:00,1718.25,,infty -2022-07-29 18:31:00,1721.62,,infty -2022-07-29 18:32:00,1721.44,,infty -2022-07-29 18:33:00,1722.38,,infty -2022-07-29 18:34:00,1718.74,,infty -2022-07-29 18:35:00,1719.9,,infty -2022-07-29 18:36:00,1726.95,,infty -2022-07-29 18:37:00,1728.0,,infty -2022-07-29 18:38:00,1726.81,,infty -2022-07-29 18:39:00,1726.48,,infty -2022-07-29 18:40:00,1726.01,,infty -2022-07-29 18:41:00,1716.65,,infty -2022-07-29 18:42:00,1719.83,,infty -2022-07-29 18:43:00,1719.64,,infty -2022-07-29 18:44:00,1723.58,,infty -2022-07-29 18:45:00,1723.16,,infty -2022-07-29 18:46:00,1727.48,,infty -2022-07-29 18:47:00,1728.3,,infty -2022-07-29 18:48:00,1727.37,,infty -2022-07-29 18:49:00,1725.49,,infty -2022-07-29 18:50:00,1725.87,,infty -2022-07-29 18:51:00,1722.75,,infty -2022-07-29 18:52:00,1725.17,,infty -2022-07-29 18:53:00,1725.34,,infty -2022-07-29 18:54:00,1727.09,,infty -2022-07-29 18:55:00,1728.24,,infty -2022-07-29 18:56:00,1725.29,,infty -2022-07-29 18:57:00,1725.36,,infty -2022-07-29 18:58:00,1724.35,,infty -2022-07-29 18:59:00,1723.73,,infty -2022-07-29 19:00:00,1726.23,,infty -2022-07-29 19:01:00,1720.96,,infty -2022-07-29 19:02:00,1723.23,,infty -2022-07-29 19:03:00,1729.25,,infty -2022-07-29 19:04:00,1731.18,,infty -2022-07-29 19:05:00,1724.17,,infty -2022-07-29 19:06:00,1725.46,,infty -2022-07-29 19:07:00,1729.32,,infty -2022-07-29 19:08:00,1725.44,,infty 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19:36:00,1718.59,,infty -2022-07-29 19:37:00,1717.06,,infty -2022-07-29 19:38:00,1718.72,,infty -2022-07-29 19:39:00,1718.09,,infty -2022-07-29 19:40:00,1717.7,,infty -2022-07-29 19:41:00,1716.77,,infty -2022-07-29 19:42:00,1718.0,,infty -2022-07-29 19:43:00,1720.19,,infty -2022-07-29 19:44:00,1721.58,,infty -2022-07-29 19:45:00,1724.8,,infty -2022-07-29 19:46:00,1724.46,,infty -2022-07-29 19:47:00,1725.93,,infty -2022-07-29 19:48:00,1727.94,,infty -2022-07-29 19:49:00,1727.35,,infty -2022-07-29 19:50:00,1728.87,,infty -2022-07-29 19:51:00,1729.83,,infty -2022-07-29 19:52:00,1725.47,,infty -2022-07-29 19:53:00,1721.58,,infty -2022-07-29 19:54:00,1723.72,,infty -2022-07-29 19:55:00,1722.74,,infty -2022-07-29 19:56:00,1715.26,,infty -2022-07-29 19:57:00,1718.02,,infty -2022-07-29 19:58:00,1722.43,,infty -2022-07-29 19:59:00,1723.86,,infty -2022-07-29 20:00:00,1724.27,,infty -2022-07-29 20:01:00,1721.16,,infty -2022-07-29 20:02:00,1718.57,,infty -2022-07-29 20:03:00,1718.77,,infty 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20:31:00,1725.18,,infty -2022-07-29 20:32:00,1727.6,,infty -2022-07-29 20:33:00,1726.84,,infty -2022-07-29 20:34:00,1724.82,,infty -2022-07-29 20:35:00,1726.38,,infty -2022-07-29 20:36:00,1729.39,,infty -2022-07-29 20:37:00,1729.79,,infty -2022-07-29 20:38:00,1732.57,,infty -2022-07-29 20:39:00,1727.29,,infty -2022-07-29 20:40:00,1725.51,,infty -2022-07-29 20:41:00,1723.46,,infty -2022-07-29 20:42:00,1724.66,,infty -2022-07-29 20:43:00,1722.16,,infty -2022-07-29 20:44:00,1725.0,,infty -2022-07-29 20:45:00,1726.28,,infty -2022-07-29 20:46:00,1735.68,,infty -2022-07-29 20:47:00,1734.96,,infty -2022-07-29 20:48:00,1737.42,,infty -2022-07-29 20:49:00,1734.53,,infty -2022-07-29 20:50:00,1733.02,,infty -2022-07-29 20:51:00,1729.89,,infty -2022-07-29 20:52:00,1726.34,,infty -2022-07-29 20:53:00,1728.54,,infty -2022-07-29 20:54:00,1730.61,,infty -2022-07-29 20:55:00,1731.96,,infty -2022-07-29 20:56:00,1728.6,,infty -2022-07-29 20:57:00,1732.19,,infty -2022-07-29 20:58:00,1733.24,,infty 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21:26:00,1725.78,,infty -2022-07-29 21:27:00,1726.31,,infty -2022-07-29 21:28:00,1725.5,,infty -2022-07-29 21:29:00,1725.51,,infty -2022-07-29 21:30:00,1722.64,,infty -2022-07-29 21:31:00,1719.18,,infty -2022-07-29 21:32:00,1717.87,,infty -2022-07-29 21:33:00,1716.01,,infty -2022-07-29 21:34:00,1711.64,,infty -2022-07-29 21:35:00,1713.36,,infty -2022-07-29 21:36:00,1716.75,,infty -2022-07-29 21:37:00,1709.58,,infty -2022-07-29 21:38:00,1710.52,,infty -2022-07-29 21:39:00,1705.94,,infty -2022-07-29 21:40:00,1706.33,,infty -2022-07-29 21:41:00,1706.96,,infty -2022-07-29 21:42:00,1710.18,,infty -2022-07-29 21:43:00,1708.63,,infty -2022-07-29 21:44:00,1709.59,,infty -2022-07-29 21:45:00,1712.08,,infty -2022-07-29 21:46:00,1703.65,,infty -2022-07-29 21:47:00,1706.38,,infty -2022-07-29 21:48:00,1708.32,,infty -2022-07-29 21:49:00,1711.57,,infty -2022-07-29 21:50:00,1712.99,,infty -2022-07-29 21:51:00,1715.07,,infty -2022-07-29 21:52:00,1716.27,,infty -2022-07-29 21:53:00,1714.78,,infty 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22:21:00,1718.84,,infty -2022-07-29 22:22:00,1720.93,,infty -2022-07-29 22:23:00,1719.3,,infty -2022-07-29 22:24:00,1721.55,,infty -2022-07-29 22:25:00,1725.67,,infty -2022-07-29 22:26:00,1731.63,,infty -2022-07-29 22:27:00,1730.96,,infty -2022-07-29 22:28:00,1730.04,,infty -2022-07-29 22:29:00,1730.82,,infty -2022-07-29 22:30:00,1731.55,,infty -2022-07-29 22:31:00,1731.0,,infty -2022-07-29 22:32:00,1729.0,,infty -2022-07-29 22:33:00,1729.98,,infty -2022-07-29 22:34:00,1733.05,,infty -2022-07-29 22:35:00,1734.35,,infty -2022-07-29 22:36:00,1732.88,,infty -2022-07-29 22:37:00,1730.87,,infty -2022-07-29 22:38:00,1730.97,,infty -2022-07-29 22:39:00,1735.38,,infty -2022-07-29 22:40:00,1738.21,,infty -2022-07-29 22:41:00,1740.81,,infty -2022-07-29 22:42:00,1740.74,,infty -2022-07-29 22:43:00,1738.65,,infty -2022-07-29 22:44:00,1739.72,,infty -2022-07-29 22:45:00,1741.76,,infty -2022-07-29 22:46:00,1739.36,,infty -2022-07-29 22:47:00,1739.18,,infty -2022-07-29 22:48:00,1738.04,,infty 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23:16:00,1757.92,,infty -2022-07-29 23:17:00,1757.9,,infty -2022-07-29 23:18:00,1759.97,,infty -2022-07-29 23:19:00,1759.13,,infty -2022-07-29 23:20:00,1759.35,,infty -2022-07-29 23:21:00,1764.17,,infty -2022-07-29 23:22:00,1760.38,,infty -2022-07-29 23:23:00,1760.22,,infty -2022-07-29 23:24:00,1759.49,,infty -2022-07-29 23:25:00,1758.9,,infty -2022-07-29 23:26:00,1759.77,,infty -2022-07-29 23:27:00,1760.39,,infty -2022-07-29 23:28:00,1761.58,,infty -2022-07-29 23:29:00,1760.96,,infty -2022-07-29 23:30:00,1765.0,,infty -2022-07-29 23:31:00,1763.45,,infty -2022-07-29 23:32:00,1760.14,,infty -2022-07-29 23:33:00,1760.2,,infty -2022-07-29 23:34:00,1760.49,,infty -2022-07-29 23:35:00,1753.56,,infty -2022-07-29 23:36:00,1751.97,,infty -2022-07-29 23:37:00,1753.09,,infty -2022-07-29 23:38:00,1752.02,,infty -2022-07-29 23:39:00,1753.16,,infty -2022-07-29 23:40:00,1753.21,,infty -2022-07-29 23:41:00,1752.62,,infty -2022-07-29 23:42:00,1750.89,,infty -2022-07-29 23:43:00,1754.55,,infty 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00:11:00,1729.78,,infty -2022-07-30 00:12:00,1727.13,,infty -2022-07-30 00:13:00,1726.64,,infty -2022-07-30 00:14:00,1726.34,,infty -2022-07-30 00:15:00,1728.29,,infty -2022-07-30 00:16:00,1732.31,,infty -2022-07-30 00:17:00,1732.08,,infty -2022-07-30 00:18:00,1734.02,,infty -2022-07-30 00:19:00,1735.24,,infty -2022-07-30 00:20:00,1733.01,,infty -2022-07-30 00:21:00,1732.47,,infty -2022-07-30 00:22:00,1730.88,,infty -2022-07-30 00:23:00,1729.76,,infty -2022-07-30 00:24:00,1731.32,,infty -2022-07-30 00:25:00,1729.02,,infty -2022-07-30 00:26:00,1728.8,,infty -2022-07-30 00:27:00,1729.48,,infty -2022-07-30 00:28:00,1729.04,,infty -2022-07-30 00:29:00,1729.74,,infty -2022-07-30 00:30:00,1729.72,,infty -2022-07-30 00:31:00,1725.8,,infty -2022-07-30 00:32:00,1721.92,,infty -2022-07-30 00:33:00,1720.56,,infty -2022-07-30 00:34:00,1719.48,,infty -2022-07-30 00:35:00,1721.04,,infty -2022-07-30 00:36:00,1720.76,,infty -2022-07-30 00:37:00,1719.49,,infty -2022-07-30 00:38:00,1717.5,,infty 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01:06:00,1732.82,,infty -2022-07-30 01:07:00,1731.4,,infty -2022-07-30 01:08:00,1728.36,,infty -2022-07-30 01:09:00,1725.82,,infty -2022-07-30 01:10:00,1723.79,,infty -2022-07-30 01:11:00,1720.31,,infty -2022-07-30 01:12:00,1720.91,,infty -2022-07-30 01:13:00,1719.21,,infty -2022-07-30 01:14:00,1718.73,,infty -2022-07-30 01:15:00,1720.78,,infty -2022-07-30 01:16:00,1717.68,,infty -2022-07-30 01:17:00,1718.72,,infty -2022-07-30 01:18:00,1719.11,,infty -2022-07-30 01:19:00,1722.12,,infty -2022-07-30 01:20:00,1722.17,,infty -2022-07-30 01:21:00,1720.19,,infty -2022-07-30 01:22:00,1720.98,,infty -2022-07-30 01:23:00,1722.61,,infty -2022-07-30 01:24:00,1722.94,,infty -2022-07-30 01:25:00,1722.5,,infty -2022-07-30 01:26:00,1726.62,,infty -2022-07-30 01:27:00,1727.53,,infty -2022-07-30 01:28:00,1726.73,,infty -2022-07-30 01:29:00,1726.46,,infty -2022-07-30 01:30:00,1725.04,,infty -2022-07-30 01:31:00,1724.87,,infty -2022-07-30 01:32:00,1723.82,,infty -2022-07-30 01:33:00,1728.17,,infty 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02:01:00,1719.13,,infty -2022-07-30 02:02:00,1719.13,,infty -2022-07-30 02:03:00,1719.34,,infty -2022-07-30 02:04:00,1719.7,,infty -2022-07-30 02:05:00,1719.46,,infty -2022-07-30 02:06:00,1716.53,,infty -2022-07-30 02:07:00,1709.3,,infty -2022-07-30 02:08:00,1710.39,,infty -2022-07-30 02:09:00,1707.35,,infty -2022-07-30 02:10:00,1708.47,,infty -2022-07-30 02:11:00,1707.36,,infty -2022-07-30 02:12:00,1708.13,,infty -2022-07-30 02:13:00,1711.04,,infty -2022-07-30 02:14:00,1710.97,,infty -2022-07-30 02:15:00,1709.71,,infty -2022-07-30 02:16:00,1708.5,,infty -2022-07-30 02:17:00,1710.4,,infty -2022-07-30 02:18:00,1714.19,,infty -2022-07-30 02:19:00,1713.17,,infty -2022-07-30 02:20:00,1713.76,,infty -2022-07-30 02:21:00,1714.58,,infty -2022-07-30 02:22:00,1714.55,,infty -2022-07-30 02:23:00,1708.48,,infty -2022-07-30 02:24:00,1708.48,,infty -2022-07-30 02:25:00,1709.67,,infty -2022-07-30 02:26:00,1708.59,,infty -2022-07-30 02:27:00,1712.73,,infty -2022-07-30 02:28:00,1712.75,,infty 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02:56:00,1707.38,,infty -2022-07-30 02:57:00,1713.09,,infty -2022-07-30 02:58:00,1713.42,,infty -2022-07-30 02:59:00,1714.48,,infty -2022-07-30 03:00:00,1715.43,,infty -2022-07-30 03:01:00,1707.68,,infty -2022-07-30 03:02:00,1708.1,,infty -2022-07-30 03:03:00,1708.36,,infty -2022-07-30 03:04:00,1708.74,,infty -2022-07-30 03:05:00,1708.76,,infty -2022-07-30 03:06:00,1703.18,,infty -2022-07-30 03:07:00,1699.38,,infty -2022-07-30 03:08:00,1701.26,,infty -2022-07-30 03:09:00,1703.53,,infty -2022-07-30 03:10:00,1703.13,,infty -2022-07-30 03:11:00,1702.48,,infty -2022-07-30 03:12:00,1703.59,,infty -2022-07-30 03:13:00,1701.91,,infty -2022-07-30 03:14:00,1699.38,,infty -2022-07-30 03:15:00,1698.03,,infty -2022-07-30 03:16:00,1700.53,,infty -2022-07-30 03:17:00,1702.86,,infty -2022-07-30 03:18:00,1706.65,,infty -2022-07-30 03:19:00,1705.96,,infty -2022-07-30 03:20:00,1706.0,,infty -2022-07-30 03:21:00,1706.06,,infty -2022-07-30 03:22:00,1704.94,,infty -2022-07-30 03:23:00,1705.86,,infty 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03:51:00,1707.62,,infty -2022-07-30 03:52:00,1708.11,,infty -2022-07-30 03:53:00,1707.68,,infty -2022-07-30 03:54:00,1708.4,,infty -2022-07-30 03:55:00,1706.73,,infty -2022-07-30 03:56:00,1709.18,,infty -2022-07-30 03:57:00,1708.22,,infty -2022-07-30 03:58:00,1708.05,,infty -2022-07-30 03:59:00,1707.79,,infty -2022-07-30 04:00:00,1708.77,,infty -2022-07-30 04:01:00,1708.56,,infty -2022-07-30 04:02:00,1710.25,,infty -2022-07-30 04:03:00,1705.65,,infty -2022-07-30 04:04:00,1701.26,,infty -2022-07-30 04:05:00,1699.09,,infty -2022-07-30 04:06:00,1700.66,,infty -2022-07-30 04:07:00,1703.55,,infty -2022-07-30 04:08:00,1705.48,,infty -2022-07-30 04:09:00,1702.12,,infty -2022-07-30 04:10:00,1701.16,,infty -2022-07-30 04:11:00,1700.7,,infty -2022-07-30 04:12:00,1707.14,,infty -2022-07-30 04:13:00,1707.12,,infty -2022-07-30 04:14:00,1706.3,,infty -2022-07-30 04:15:00,1703.88,,infty -2022-07-30 04:16:00,1701.12,,infty -2022-07-30 04:17:00,1701.01,,infty -2022-07-30 04:18:00,1700.94,,infty 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04:46:00,1704.76,,infty -2022-07-30 04:47:00,1704.79,,infty -2022-07-30 04:48:00,1703.52,,infty -2022-07-30 04:49:00,1705.83,,infty -2022-07-30 04:50:00,1706.76,,infty -2022-07-30 04:51:00,1703.42,,infty -2022-07-30 04:52:00,1703.28,,infty -2022-07-30 04:53:00,1702.76,,infty -2022-07-30 04:54:00,1704.13,,infty -2022-07-30 04:55:00,1703.82,,infty -2022-07-30 04:56:00,1704.17,,infty -2022-07-30 04:57:00,1701.34,,infty -2022-07-30 04:58:00,1701.62,,infty -2022-07-30 04:59:00,1702.58,,infty -2022-07-30 05:00:00,1703.36,,infty -2022-07-30 05:01:00,1704.05,,infty -2022-07-30 05:02:00,1703.36,,infty -2022-07-30 05:03:00,1706.4,,infty -2022-07-30 05:04:00,1705.9,,infty -2022-07-30 05:05:00,1705.4,,infty -2022-07-30 05:06:00,1705.32,,infty -2022-07-30 05:07:00,1703.51,,infty -2022-07-30 05:08:00,1702.6,,infty -2022-07-30 05:09:00,1704.72,,infty -2022-07-30 05:10:00,1705.04,,infty -2022-07-30 05:11:00,1705.35,,infty -2022-07-30 05:12:00,1705.41,,infty -2022-07-30 05:13:00,1705.21,,infty 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05:41:00,1705.95,,infty -2022-07-30 05:42:00,1706.99,,infty -2022-07-30 05:43:00,1707.27,,infty -2022-07-30 05:44:00,1707.57,,infty -2022-07-30 05:45:00,1707.7,,infty -2022-07-30 05:46:00,1706.93,,infty -2022-07-30 05:47:00,1706.9,,infty -2022-07-30 05:48:00,1707.76,,infty -2022-07-30 05:49:00,1707.7,,infty -2022-07-30 05:50:00,1707.36,,infty -2022-07-30 05:51:00,1705.09,,infty -2022-07-30 05:52:00,1703.67,,infty -2022-07-30 05:53:00,1705.1,,infty -2022-07-30 05:54:00,1705.14,,infty -2022-07-30 05:55:00,1704.1,,infty -2022-07-30 05:56:00,1705.18,,infty -2022-07-30 05:57:00,1703.78,,infty -2022-07-30 05:58:00,1705.1,,infty -2022-07-30 05:59:00,1707.7,,infty -2022-07-30 06:00:00,1706.49,,infty -2022-07-30 06:01:00,1707.7,,infty -2022-07-30 06:02:00,1707.38,,infty -2022-07-30 06:03:00,1707.53,,infty -2022-07-30 06:04:00,1705.97,,infty -2022-07-30 06:05:00,1706.5,,infty -2022-07-30 06:06:00,1706.38,,infty -2022-07-30 06:07:00,1704.8,,infty -2022-07-30 06:08:00,1704.5,,infty -2022-07-30 06:09:00,1703.81,,infty -2022-07-30 06:10:00,1708.42,,infty -2022-07-30 06:11:00,1708.03,,infty -2022-07-30 06:12:00,1708.94,,infty -2022-07-30 06:13:00,1708.49,,infty -2022-07-30 06:14:00,1707.5,,infty -2022-07-30 06:15:00,1706.92,,infty -2022-07-30 06:16:00,1706.76,,infty -2022-07-30 06:17:00,1706.54,,infty -2022-07-30 06:18:00,1706.05,,infty -2022-07-30 06:19:00,1706.16,,infty -2022-07-30 06:20:00,1705.09,,infty -2022-07-30 06:21:00,1705.16,,infty -2022-07-30 06:22:00,1706.18,,infty -2022-07-30 06:23:00,1707.37,,infty -2022-07-30 06:24:00,1710.46,,infty -2022-07-30 06:25:00,1712.45,,infty -2022-07-30 06:26:00,1718.42,,infty -2022-07-30 06:27:00,1715.61,,infty -2022-07-30 06:28:00,1715.69,,infty -2022-07-30 06:29:00,1716.41,,infty -2022-07-30 06:30:00,1714.18,,infty -2022-07-30 06:31:00,1714.25,,infty -2022-07-30 06:32:00,1713.7,,infty -2022-07-30 06:33:00,1715.94,,infty -2022-07-30 06:34:00,1715.9,,infty -2022-07-30 06:35:00,1716.53,,infty -2022-07-30 06:36:00,1715.2,,infty 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07:04:00,1718.02,,infty -2022-07-30 07:05:00,1717.94,,infty -2022-07-30 07:06:00,1718.43,,infty -2022-07-30 07:07:00,1720.08,,infty -2022-07-30 07:08:00,1718.4,,infty -2022-07-30 07:09:00,1717.54,,infty -2022-07-30 07:10:00,1715.88,,infty -2022-07-30 07:11:00,1717.78,,infty -2022-07-30 07:12:00,1716.56,,infty -2022-07-30 07:13:00,1719.04,,infty -2022-07-30 07:14:00,1717.3,,infty -2022-07-30 07:15:00,1716.56,,infty -2022-07-30 07:16:00,1713.99,,infty -2022-07-30 07:17:00,1715.16,,infty -2022-07-30 07:18:00,1715.27,,infty -2022-07-30 07:19:00,1717.91,,infty -2022-07-30 07:20:00,1717.93,,infty -2022-07-30 07:21:00,1717.33,,infty -2022-07-30 07:22:00,1717.55,,infty -2022-07-30 07:23:00,1717.41,,infty -2022-07-30 07:24:00,1717.1,,infty -2022-07-30 07:25:00,1715.81,,infty -2022-07-30 07:26:00,1714.92,,infty -2022-07-30 07:27:00,1715.85,,infty -2022-07-30 07:28:00,1715.75,,infty -2022-07-30 07:29:00,1716.72,,infty -2022-07-30 07:30:00,1716.08,,infty -2022-07-30 07:31:00,1714.48,,infty 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07:59:00,1715.75,,infty -2022-07-30 08:00:00,1716.29,,infty -2022-07-30 08:01:00,1715.25,,infty -2022-07-30 08:02:00,1714.43,,infty -2022-07-30 08:03:00,1712.7,,infty -2022-07-30 08:04:00,1713.84,,infty -2022-07-30 08:05:00,1714.54,,infty -2022-07-30 08:06:00,1713.82,,infty -2022-07-30 08:07:00,1714.14,,infty -2022-07-30 08:08:00,1714.75,,infty -2022-07-30 08:09:00,1714.74,,infty -2022-07-30 08:10:00,1712.65,,infty -2022-07-30 08:11:00,1712.55,,infty -2022-07-30 08:12:00,1712.89,,infty -2022-07-30 08:13:00,1713.35,,infty -2022-07-30 08:14:00,1713.24,,infty -2022-07-30 08:15:00,1711.82,,infty -2022-07-30 08:16:00,1711.99,,infty -2022-07-30 08:17:00,1715.52,,infty -2022-07-30 08:18:00,1717.03,,infty -2022-07-30 08:19:00,1720.2,,infty -2022-07-30 08:20:00,1722.72,,infty -2022-07-30 08:21:00,1720.28,,infty -2022-07-30 08:22:00,1721.11,,infty -2022-07-30 08:23:00,1719.35,,infty -2022-07-30 08:24:00,1717.5,,infty -2022-07-30 08:25:00,1719.79,,infty -2022-07-30 08:26:00,1719.47,,infty 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08:54:00,1714.46,,infty -2022-07-30 08:55:00,1714.98,,infty -2022-07-30 08:56:00,1714.98,,infty -2022-07-30 08:57:00,1712.45,,infty -2022-07-30 08:58:00,1713.62,,infty -2022-07-30 08:59:00,1713.18,,infty -2022-07-30 09:00:00,1713.14,,infty -2022-07-30 09:01:00,1712.0,,infty -2022-07-30 09:02:00,1711.54,,infty -2022-07-30 09:03:00,1711.89,,infty -2022-07-30 09:04:00,1714.51,,infty -2022-07-30 09:05:00,1714.49,,infty -2022-07-30 09:06:00,1714.66,,infty -2022-07-30 09:07:00,1714.8,,infty -2022-07-30 09:08:00,1714.18,,infty -2022-07-30 09:09:00,1713.49,,infty -2022-07-30 09:10:00,1713.65,,infty -2022-07-30 09:11:00,1716.4,,infty -2022-07-30 09:12:00,1716.88,,infty -2022-07-30 09:13:00,1717.71,,infty -2022-07-30 09:14:00,1716.91,,infty -2022-07-30 09:15:00,1717.15,,infty -2022-07-30 09:16:00,1716.98,,infty -2022-07-30 09:17:00,1716.34,,infty -2022-07-30 09:18:00,1716.34,,infty -2022-07-30 09:19:00,1716.44,,infty -2022-07-30 09:20:00,1716.97,,infty -2022-07-30 09:21:00,1715.93,,infty 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09:49:00,1698.38,,infty -2022-07-30 09:50:00,1700.71,,infty -2022-07-30 09:51:00,1701.35,,infty -2022-07-30 09:52:00,1702.01,,infty -2022-07-30 09:53:00,1702.54,,infty -2022-07-30 09:54:00,1704.59,,infty -2022-07-30 09:55:00,1702.94,,infty -2022-07-30 09:56:00,1701.49,,infty -2022-07-30 09:57:00,1702.45,,infty -2022-07-30 09:58:00,1701.43,,infty -2022-07-30 09:59:00,1701.62,,infty -2022-07-30 10:00:00,1700.01,,infty -2022-07-30 10:01:00,1696.88,,infty -2022-07-30 10:02:00,1690.89,,infty -2022-07-30 10:03:00,1690.76,,infty -2022-07-30 10:04:00,1686.83,,infty -2022-07-30 10:05:00,1686.92,,infty -2022-07-30 10:06:00,1686.05,,infty -2022-07-30 10:07:00,1685.9,,infty -2022-07-30 10:08:00,1685.96,,infty -2022-07-30 10:09:00,1686.03,,infty -2022-07-30 10:10:00,1686.43,,infty -2022-07-30 10:11:00,1685.6,,infty -2022-07-30 10:12:00,1684.77,,infty -2022-07-30 10:13:00,1682.12,,infty -2022-07-30 10:14:00,1680.89,,open_close -2022-07-30 10:15:00,1681.17,,infty -2022-07-30 10:16:00,1681.47,,infty 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10:44:00,1682.2,,infty -2022-07-30 10:45:00,1684.51,,infty -2022-07-30 10:46:00,1683.84,,infty -2022-07-30 10:47:00,1685.01,,infty -2022-07-30 10:48:00,1685.6,,infty -2022-07-30 10:49:00,1685.98,,infty -2022-07-30 10:50:00,1687.59,,infty -2022-07-30 10:51:00,1686.81,,infty -2022-07-30 10:52:00,1688.08,,infty -2022-07-30 10:53:00,1689.09,,infty -2022-07-30 10:54:00,1685.62,,infty -2022-07-30 10:55:00,1688.92,,infty -2022-07-30 10:56:00,1689.19,,infty -2022-07-30 10:57:00,1688.49,,infty -2022-07-30 10:58:00,1688.17,,infty -2022-07-30 10:59:00,1685.84,,infty -2022-07-30 11:00:00,1685.06,,infty -2022-07-30 11:01:00,1686.47,,infty -2022-07-30 11:02:00,1687.05,,infty -2022-07-30 11:03:00,1687.07,,infty -2022-07-30 11:04:00,1686.19,,infty -2022-07-30 11:05:00,1686.55,,infty -2022-07-30 11:06:00,1687.04,,infty -2022-07-30 11:07:00,1687.59,,infty -2022-07-30 11:08:00,1689.45,,infty -2022-07-30 11:09:00,1691.04,,infty -2022-07-30 11:10:00,1689.03,,infty -2022-07-30 11:11:00,1689.11,,infty 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12:07:00,1704.87,,infty -2022-07-30 12:08:00,1706.34,,infty -2022-07-30 12:09:00,1705.8,,infty -2022-07-30 12:10:00,1706.78,,infty -2022-07-30 12:11:00,1707.02,,infty -2022-07-30 12:12:00,1707.07,,infty -2022-07-30 12:13:00,1705.17,,infty -2022-07-30 12:14:00,1706.33,,infty -2022-07-30 12:15:00,1705.49,,infty -2022-07-30 12:16:00,1705.03,,infty -2022-07-30 12:17:00,1698.97,,infty -2022-07-30 12:18:00,1699.31,,infty -2022-07-30 12:19:00,1701.58,,infty -2022-07-30 12:20:00,1709.48,,infty -2022-07-30 12:21:00,1708.01,,infty -2022-07-30 12:22:00,1707.57,,infty -2022-07-30 12:23:00,1708.67,,infty -2022-07-30 12:24:00,1706.06,,infty -2022-07-30 12:25:00,1705.43,,infty -2022-07-30 12:26:00,1706.9,,infty -2022-07-30 12:27:00,1709.02,,infty -2022-07-30 12:28:00,1709.56,,infty -2022-07-30 12:29:00,1713.29,,infty -2022-07-30 12:30:00,1715.45,,infty -2022-07-30 12:31:00,1708.61,,infty -2022-07-30 12:32:00,1708.11,,infty -2022-07-30 12:33:00,1710.95,,infty -2022-07-30 12:34:00,1715.3,,infty 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13:02:00,1720.89,,infty -2022-07-30 13:03:00,1722.93,,infty -2022-07-30 13:04:00,1728.92,,infty -2022-07-30 13:05:00,1730.56,,infty -2022-07-30 13:06:00,1729.46,,infty -2022-07-30 13:07:00,1732.82,,infty -2022-07-30 13:08:00,1730.82,,infty -2022-07-30 13:09:00,1732.1,,infty -2022-07-30 13:10:00,1726.96,,infty -2022-07-30 13:11:00,1728.35,,infty -2022-07-30 13:12:00,1724.46,,infty -2022-07-30 13:13:00,1726.79,,infty -2022-07-30 13:14:00,1724.9,,infty -2022-07-30 13:15:00,1727.35,,infty -2022-07-30 13:16:00,1728.2,,infty -2022-07-30 13:17:00,1727.12,,infty -2022-07-30 13:18:00,1729.97,,infty -2022-07-30 13:19:00,1734.94,,infty -2022-07-30 13:20:00,1734.43,,infty -2022-07-30 13:21:00,1732.4,,infty -2022-07-30 13:22:00,1729.55,,infty -2022-07-30 13:23:00,1729.74,,infty -2022-07-30 13:24:00,1730.9,,infty -2022-07-30 13:25:00,1730.24,,infty -2022-07-30 13:26:00,1726.82,,infty -2022-07-30 13:27:00,1726.98,,infty -2022-07-30 13:28:00,1728.7,,infty -2022-07-30 13:29:00,1727.87,,infty -2022-07-30 13:30:00,1727.89,,infty -2022-07-30 13:31:00,1729.74,,infty -2022-07-30 13:32:00,1730.59,,infty -2022-07-30 13:33:00,1731.35,,infty -2022-07-30 13:34:00,1729.41,,infty -2022-07-30 13:35:00,1726.69,,infty -2022-07-30 13:36:00,1726.2,,infty -2022-07-30 13:37:00,1718.52,,infty -2022-07-30 13:38:00,1724.7,,infty -2022-07-30 13:39:00,1721.3,,infty -2022-07-30 13:40:00,1723.46,,infty -2022-07-30 13:41:00,1723.71,,infty -2022-07-30 13:42:00,1725.13,,infty -2022-07-30 13:43:00,1727.58,,infty -2022-07-30 13:44:00,1729.56,,infty -2022-07-30 13:45:00,1728.59,,infty -2022-07-30 13:46:00,1729.9,,infty -2022-07-30 13:47:00,1734.35,,infty -2022-07-30 13:48:00,1732.19,,infty -2022-07-30 13:49:00,1730.42,,infty -2022-07-30 13:50:00,1730.0,,infty -2022-07-30 13:51:00,1730.15,,infty -2022-07-30 13:52:00,1729.17,,infty -2022-07-30 13:53:00,1726.73,,infty -2022-07-30 13:54:00,1727.09,,infty -2022-07-30 13:55:00,1724.36,,infty -2022-07-30 13:56:00,1726.52,,infty -2022-07-30 13:57:00,1725.7,,infty -2022-07-30 13:58:00,1725.71,,infty -2022-07-30 13:59:00,1726.55,,infty -2022-07-30 14:00:00,1724.47,,infty -2022-07-30 14:01:00,1726.95,,infty -2022-07-30 14:02:00,1725.81,,infty -2022-07-30 14:03:00,1724.54,,infty -2022-07-30 14:04:00,1721.21,,infty -2022-07-30 14:05:00,1722.12,,infty -2022-07-30 14:06:00,1723.95,,infty -2022-07-30 14:07:00,1725.17,,infty -2022-07-30 14:08:00,1728.56,,infty -2022-07-30 14:09:00,1728.15,,infty -2022-07-30 14:10:00,1727.63,,infty -2022-07-30 14:11:00,1727.61,,infty -2022-07-30 14:12:00,1726.15,,infty -2022-07-30 14:13:00,1726.76,,infty -2022-07-30 14:14:00,1727.56,,infty -2022-07-30 14:15:00,1727.42,,infty -2022-07-30 14:16:00,1729.77,,infty -2022-07-30 14:17:00,1728.79,,infty -2022-07-30 14:18:00,1728.03,,infty -2022-07-30 14:19:00,1727.68,,infty -2022-07-30 14:20:00,1727.05,,infty -2022-07-30 14:21:00,1728.61,,infty -2022-07-30 14:22:00,1728.25,,infty -2022-07-30 14:23:00,1726.89,,infty -2022-07-30 14:24:00,1727.68,,infty -2022-07-30 14:25:00,1726.69,,infty -2022-07-30 14:26:00,1726.87,,infty -2022-07-30 14:27:00,1726.05,,infty -2022-07-30 14:28:00,1724.34,,infty -2022-07-30 14:29:00,1725.59,,infty -2022-07-30 14:30:00,1725.11,,infty -2022-07-30 14:31:00,1724.91,,infty -2022-07-30 14:32:00,1719.57,,infty -2022-07-30 14:33:00,1721.01,,infty -2022-07-30 14:34:00,1720.44,,infty -2022-07-30 14:35:00,1714.64,,infty -2022-07-30 14:36:00,1718.17,,infty -2022-07-30 14:37:00,1718.09,,infty -2022-07-30 14:38:00,1718.21,,infty -2022-07-30 14:39:00,1719.72,,infty -2022-07-30 14:40:00,1719.76,,infty -2022-07-30 14:41:00,1718.93,,infty -2022-07-30 14:42:00,1719.21,,infty -2022-07-30 14:43:00,1720.47,,infty -2022-07-30 14:44:00,1720.51,,infty -2022-07-30 14:45:00,1722.44,,infty -2022-07-30 14:46:00,1722.21,,infty -2022-07-30 14:47:00,1723.1,,infty -2022-07-30 14:48:00,1723.35,,infty -2022-07-30 14:49:00,1723.97,,infty -2022-07-30 14:50:00,1722.69,,infty -2022-07-30 14:51:00,1723.17,,infty -2022-07-30 14:52:00,1723.37,,infty -2022-07-30 14:53:00,1719.8,,infty -2022-07-30 14:54:00,1716.94,,infty -2022-07-30 14:55:00,1715.4,,infty -2022-07-30 14:56:00,1717.68,,infty -2022-07-30 14:57:00,1716.97,,infty -2022-07-30 14:58:00,1716.63,,infty -2022-07-30 14:59:00,1716.45,,infty -2022-07-30 15:00:00,1717.5,,infty -2022-07-30 15:01:00,1720.27,,infty -2022-07-30 15:02:00,1719.57,,infty -2022-07-30 15:03:00,1719.59,,infty -2022-07-30 15:04:00,1719.79,,infty -2022-07-30 15:05:00,1719.7,,infty -2022-07-30 15:06:00,1721.49,,infty -2022-07-30 15:07:00,1719.46,,infty -2022-07-30 15:08:00,1719.69,,infty -2022-07-30 15:09:00,1718.46,,infty -2022-07-30 15:10:00,1720.1,,infty -2022-07-30 15:11:00,1719.84,,infty -2022-07-30 15:12:00,1720.08,,infty -2022-07-30 15:13:00,1720.29,,infty -2022-07-30 15:14:00,1721.12,,infty -2022-07-30 15:15:00,1720.64,,infty -2022-07-30 15:16:00,1720.75,,infty -2022-07-30 15:17:00,1722.87,,infty -2022-07-30 15:18:00,1726.36,,infty -2022-07-30 15:19:00,1727.67,,infty -2022-07-30 15:20:00,1726.29,,infty -2022-07-30 15:21:00,1726.68,,infty -2022-07-30 15:22:00,1726.49,,infty -2022-07-30 15:23:00,1727.72,,infty -2022-07-30 15:24:00,1728.13,,infty -2022-07-30 15:25:00,1732.75,,infty -2022-07-30 15:26:00,1734.48,,infty -2022-07-30 15:27:00,1735.43,,infty -2022-07-30 15:28:00,1733.83,,infty -2022-07-30 15:29:00,1733.01,,infty -2022-07-30 15:30:00,1733.35,,infty -2022-07-30 15:31:00,1735.92,,infty -2022-07-30 15:32:00,1738.53,,infty -2022-07-30 15:33:00,1736.7,,infty -2022-07-30 15:34:00,1736.93,,infty -2022-07-30 15:35:00,1738.69,,infty -2022-07-30 15:36:00,1739.0,,infty -2022-07-30 15:37:00,1738.86,,infty -2022-07-30 15:38:00,1738.96,,infty -2022-07-30 15:39:00,1738.84,,infty -2022-07-30 15:40:00,1738.47,,infty -2022-07-30 15:41:00,1738.76,,infty -2022-07-30 15:42:00,1737.36,,infty -2022-07-30 15:43:00,1736.45,,infty -2022-07-30 15:44:00,1736.39,,infty -2022-07-30 15:45:00,1736.22,,infty -2022-07-30 15:46:00,1735.64,,infty -2022-07-30 15:47:00,1743.02,,infty -2022-07-30 15:48:00,1739.74,,infty -2022-07-30 15:49:00,1738.67,,infty -2022-07-30 15:50:00,1734.48,,infty -2022-07-30 15:51:00,1736.47,,infty -2022-07-30 15:52:00,1740.44,,infty -2022-07-30 15:53:00,1741.65,,infty -2022-07-30 15:54:00,1738.04,,infty -2022-07-30 15:55:00,1739.75,,infty -2022-07-30 15:56:00,1740.37,,infty -2022-07-30 15:57:00,1739.05,,infty -2022-07-30 15:58:00,1737.52,,infty -2022-07-30 15:59:00,1736.62,,infty -2022-07-30 16:00:00,1735.8,,infty -2022-07-30 16:01:00,1733.89,,infty -2022-07-30 16:02:00,1733.98,,infty -2022-07-30 16:03:00,1735.19,,infty -2022-07-30 16:04:00,1736.64,,infty -2022-07-30 16:05:00,1735.94,,infty -2022-07-30 16:06:00,1734.85,,infty -2022-07-30 16:07:00,1733.22,,infty -2022-07-30 16:08:00,1729.73,,infty -2022-07-30 16:09:00,1730.15,,infty -2022-07-30 16:10:00,1729.59,,infty -2022-07-30 16:11:00,1730.18,,infty -2022-07-30 16:12:00,1728.4,,infty -2022-07-30 16:13:00,1729.07,,infty -2022-07-30 16:14:00,1729.13,,infty -2022-07-30 16:15:00,1729.02,,infty -2022-07-30 16:16:00,1732.79,,infty -2022-07-30 16:17:00,1731.02,,infty -2022-07-30 16:18:00,1731.26,,infty -2022-07-30 16:19:00,1734.86,,infty -2022-07-30 16:20:00,1733.36,,infty -2022-07-30 16:21:00,1731.14,,infty -2022-07-30 16:22:00,1731.29,,infty -2022-07-30 16:23:00,1731.14,,infty -2022-07-30 16:24:00,1731.42,,infty -2022-07-30 16:25:00,1730.56,,infty -2022-07-30 16:26:00,1731.95,,infty -2022-07-30 16:27:00,1734.05,,infty -2022-07-30 16:28:00,1732.62,,infty -2022-07-30 16:29:00,1733.12,,infty -2022-07-30 16:30:00,1732.04,,infty -2022-07-30 16:31:00,1732.06,,infty -2022-07-30 16:32:00,1731.09,,infty -2022-07-30 16:33:00,1732.21,,infty -2022-07-30 16:34:00,1733.17,,infty -2022-07-30 16:35:00,1732.56,,infty -2022-07-30 16:36:00,1732.42,,infty -2022-07-30 16:37:00,1732.3,,infty -2022-07-30 16:38:00,1732.61,,infty -2022-07-30 16:39:00,1732.02,,infty -2022-07-30 16:40:00,1732.18,,infty -2022-07-30 16:41:00,1735.94,,infty -2022-07-30 16:42:00,1734.62,,infty -2022-07-30 16:43:00,1729.93,,infty -2022-07-30 16:44:00,1730.54,,infty -2022-07-30 16:45:00,1731.8,,infty -2022-07-30 16:46:00,1734.28,,infty -2022-07-30 16:47:00,1733.81,,infty -2022-07-30 16:48:00,1734.48,,infty -2022-07-30 16:49:00,1733.69,,infty -2022-07-30 16:50:00,1733.73,,infty -2022-07-30 16:51:00,1735.37,,infty -2022-07-30 16:52:00,1734.79,,infty -2022-07-30 16:53:00,1734.46,,infty -2022-07-30 16:54:00,1734.61,,infty -2022-07-30 16:55:00,1734.72,,infty -2022-07-30 16:56:00,1735.61,,infty -2022-07-30 16:57:00,1735.35,,infty -2022-07-30 16:58:00,1734.36,,infty -2022-07-30 16:59:00,1735.44,,infty -2022-07-30 17:00:00,1733.66,,infty -2022-07-30 17:01:00,1732.9,,infty -2022-07-30 17:02:00,1734.27,,infty -2022-07-30 17:03:00,1735.24,,infty -2022-07-30 17:04:00,1734.38,,infty -2022-07-30 17:05:00,1734.21,,infty -2022-07-30 17:06:00,1732.99,,infty -2022-07-30 17:07:00,1733.29,,infty -2022-07-30 17:08:00,1729.67,,infty -2022-07-30 17:09:00,1727.7,,infty -2022-07-30 17:10:00,1728.24,,infty -2022-07-30 17:11:00,1730.61,,infty -2022-07-30 17:12:00,1730.67,,infty -2022-07-30 17:13:00,1731.05,,infty -2022-07-30 17:14:00,1729.88,,infty -2022-07-30 17:15:00,1728.65,,infty -2022-07-30 17:16:00,1727.45,,infty -2022-07-30 17:17:00,1727.5,,infty -2022-07-30 17:18:00,1730.55,,infty -2022-07-30 17:19:00,1730.61,,infty -2022-07-30 17:20:00,1728.63,,infty -2022-07-30 17:21:00,1724.95,,infty -2022-07-30 17:22:00,1725.34,,infty -2022-07-30 17:23:00,1724.97,,infty -2022-07-30 17:24:00,1724.61,,infty -2022-07-30 17:25:00,1724.91,,infty -2022-07-30 17:26:00,1725.06,,infty -2022-07-30 17:27:00,1724.25,,infty -2022-07-30 17:28:00,1724.26,,infty -2022-07-30 17:29:00,1724.58,,infty -2022-07-30 17:30:00,1725.01,,infty -2022-07-30 17:31:00,1725.72,,infty -2022-07-30 17:32:00,1725.7,,infty -2022-07-30 17:33:00,1725.32,,infty -2022-07-30 17:34:00,1725.16,,infty -2022-07-30 17:35:00,1725.82,,infty -2022-07-30 17:36:00,1724.14,,infty -2022-07-30 17:37:00,1724.32,,infty -2022-07-30 17:38:00,1724.16,,infty -2022-07-30 17:39:00,1720.3,,infty -2022-07-30 17:40:00,1720.38,,infty -2022-07-30 17:41:00,1720.28,,infty -2022-07-30 17:42:00,1720.91,,infty -2022-07-30 17:43:00,1723.48,,infty -2022-07-30 17:44:00,1722.56,,infty -2022-07-30 17:45:00,1722.56,,infty -2022-07-30 17:46:00,1723.12,,infty -2022-07-30 17:47:00,1722.63,,infty -2022-07-30 17:48:00,1724.06,,infty -2022-07-30 17:49:00,1724.81,,infty -2022-07-30 17:50:00,1725.94,,infty -2022-07-30 17:51:00,1727.92,,infty -2022-07-30 17:52:00,1728.65,,infty -2022-07-30 17:53:00,1727.48,,infty -2022-07-30 17:54:00,1726.51,,infty -2022-07-30 17:55:00,1730.26,,infty -2022-07-30 17:56:00,1726.85,,infty -2022-07-30 17:57:00,1727.81,,infty -2022-07-30 17:58:00,1727.17,,infty -2022-07-30 17:59:00,1727.77,,infty -2022-07-30 18:00:00,1727.06,,infty -2022-07-30 18:01:00,1727.7,,infty -2022-07-30 18:02:00,1726.69,,infty -2022-07-30 18:03:00,1725.41,,infty -2022-07-30 18:04:00,1725.99,,infty -2022-07-30 18:05:00,1727.85,,infty -2022-07-30 18:06:00,1729.34,,infty -2022-07-30 18:07:00,1728.23,,infty -2022-07-30 18:08:00,1727.9,,infty -2022-07-30 18:09:00,1728.07,,infty -2022-07-30 18:10:00,1725.64,,infty -2022-07-30 18:11:00,1725.57,,infty -2022-07-30 18:12:00,1727.25,,infty -2022-07-30 18:13:00,1727.58,,infty -2022-07-30 18:14:00,1726.88,,infty -2022-07-30 18:15:00,1727.26,,infty -2022-07-30 18:16:00,1727.62,,infty -2022-07-30 18:17:00,1723.89,,infty -2022-07-30 18:18:00,1724.41,,infty -2022-07-30 18:19:00,1724.44,,infty -2022-07-30 18:20:00,1724.38,,infty -2022-07-30 18:21:00,1725.42,,infty -2022-07-30 18:22:00,1725.51,,infty -2022-07-30 18:23:00,1725.59,,infty -2022-07-30 18:24:00,1726.29,,infty -2022-07-30 18:25:00,1727.52,,infty -2022-07-30 18:26:00,1730.53,,infty -2022-07-30 18:27:00,1728.82,,infty -2022-07-30 18:28:00,1729.67,,infty -2022-07-30 18:29:00,1730.75,,infty -2022-07-30 18:30:00,1730.45,,infty -2022-07-30 18:31:00,1730.93,,infty -2022-07-30 18:32:00,1730.74,,infty -2022-07-30 18:33:00,1729.39,,infty -2022-07-30 18:34:00,1730.77,,infty -2022-07-30 18:35:00,1730.78,,infty -2022-07-30 18:36:00,1729.1,,infty -2022-07-30 18:37:00,1727.01,,infty -2022-07-30 18:38:00,1728.18,,infty -2022-07-30 18:39:00,1728.4,,infty -2022-07-30 18:40:00,1730.12,,infty -2022-07-30 18:41:00,1732.18,,infty -2022-07-30 18:42:00,1731.95,,infty -2022-07-30 18:43:00,1731.01,,infty -2022-07-30 18:44:00,1730.38,,infty -2022-07-30 18:45:00,1730.29,,infty -2022-07-30 18:46:00,1729.4,,infty -2022-07-30 18:47:00,1727.52,,infty -2022-07-30 18:48:00,1729.9,,infty -2022-07-30 18:49:00,1728.93,,infty -2022-07-30 18:50:00,1728.64,,infty -2022-07-30 18:51:00,1725.67,,infty -2022-07-30 18:52:00,1725.34,,infty -2022-07-30 18:53:00,1726.05,,infty -2022-07-30 18:54:00,1729.07,,infty -2022-07-30 18:55:00,1729.58,,infty -2022-07-30 18:56:00,1730.7,,infty -2022-07-30 18:57:00,1729.97,,infty -2022-07-30 18:58:00,1729.22,,infty -2022-07-30 18:59:00,1727.64,,infty -2022-07-30 19:00:00,1726.29,,infty -2022-07-30 19:01:00,1724.93,,infty -2022-07-30 19:02:00,1725.67,,infty -2022-07-30 19:03:00,1725.09,,infty -2022-07-30 19:04:00,1723.64,,infty -2022-07-30 19:05:00,1719.66,,infty -2022-07-30 19:06:00,1717.58,,infty -2022-07-30 19:07:00,1718.59,,infty -2022-07-30 19:08:00,1719.0,,infty -2022-07-30 19:09:00,1719.47,,infty -2022-07-30 19:10:00,1723.19,,infty -2022-07-30 19:11:00,1721.11,,infty -2022-07-30 19:12:00,1721.75,,infty -2022-07-30 19:13:00,1719.6,,infty -2022-07-30 19:14:00,1718.61,,infty -2022-07-30 19:15:00,1719.58,,infty -2022-07-30 19:16:00,1721.08,,infty -2022-07-30 19:17:00,1720.78,,infty -2022-07-30 19:18:00,1721.81,,infty -2022-07-30 19:19:00,1721.07,,infty -2022-07-30 19:20:00,1719.27,,infty -2022-07-30 19:21:00,1715.36,,infty -2022-07-30 19:22:00,1716.96,,infty -2022-07-30 19:23:00,1715.04,,infty -2022-07-30 19:24:00,1709.36,,infty -2022-07-30 19:25:00,1705.79,,infty -2022-07-30 19:26:00,1705.21,,infty -2022-07-30 19:27:00,1709.24,,infty -2022-07-30 19:28:00,1713.64,,infty -2022-07-30 19:29:00,1713.24,,infty -2022-07-30 19:30:00,1710.15,,infty -2022-07-30 19:31:00,1710.4,,infty -2022-07-30 19:32:00,1709.66,,infty -2022-07-30 19:33:00,1711.2,,infty -2022-07-30 19:34:00,1711.63,,infty -2022-07-30 19:35:00,1713.18,,infty -2022-07-30 19:36:00,1711.27,,infty -2022-07-30 19:37:00,1711.61,,infty -2022-07-30 19:38:00,1710.06,,infty -2022-07-30 19:39:00,1709.29,,infty -2022-07-30 19:40:00,1710.04,,infty -2022-07-30 19:41:00,1706.37,,infty -2022-07-30 19:42:00,1700.64,,infty -2022-07-30 19:43:00,1700.06,,infty -2022-07-30 19:44:00,1698.05,,infty -2022-07-30 19:45:00,1693.28,,infty -2022-07-30 19:46:00,1691.86,,infty -2022-07-30 19:47:00,1699.57,,infty -2022-07-30 19:48:00,1696.1,,infty -2022-07-30 19:49:00,1695.5,,infty -2022-07-30 19:50:00,1694.89,,infty -2022-07-30 19:51:00,1696.68,,infty -2022-07-30 19:52:00,1691.84,,infty -2022-07-30 19:53:00,1691.66,,infty -2022-07-30 19:54:00,1693.08,,infty -2022-07-30 19:55:00,1693.5,,infty -2022-07-30 19:56:00,1693.81,,infty -2022-07-30 19:57:00,1695.98,,infty -2022-07-30 19:58:00,1692.94,,infty -2022-07-30 19:59:00,1692.31,,infty -2022-07-30 20:00:00,1692.67,,infty -2022-07-30 20:01:00,1695.02,,infty -2022-07-30 20:02:00,1694.03,,infty -2022-07-30 20:03:00,1696.75,,infty -2022-07-30 20:04:00,1695.06,,infty -2022-07-30 20:05:00,1696.29,,infty -2022-07-30 20:06:00,1694.12,,infty -2022-07-30 20:07:00,1694.67,,infty -2022-07-30 20:08:00,1691.76,,infty -2022-07-30 20:09:00,1690.21,,infty -2022-07-30 20:10:00,1691.98,,infty -2022-07-30 20:11:00,1691.27,,infty -2022-07-30 20:12:00,1691.52,,infty -2022-07-30 20:13:00,1691.65,,infty -2022-07-30 20:14:00,1690.86,,infty -2022-07-30 20:15:00,1690.75,,infty -2022-07-30 20:16:00,1688.77,,infty -2022-07-30 20:17:00,1688.76,,infty -2022-07-30 20:18:00,1693.19,,infty -2022-07-30 20:19:00,1694.41,,infty -2022-07-30 20:20:00,1697.23,,infty -2022-07-30 20:21:00,1696.79,,infty -2022-07-30 20:22:00,1698.01,,infty -2022-07-30 20:23:00,1702.91,,infty -2022-07-30 20:24:00,1699.78,,infty -2022-07-30 20:25:00,1701.82,,infty -2022-07-30 20:26:00,1700.0,,infty -2022-07-30 20:27:00,1700.85,,infty -2022-07-30 20:28:00,1700.58,,infty -2022-07-30 20:29:00,1701.72,,infty -2022-07-30 20:30:00,1700.53,,infty -2022-07-30 20:31:00,1698.04,,infty -2022-07-30 20:32:00,1700.48,,infty -2022-07-30 20:33:00,1699.17,,infty -2022-07-30 20:34:00,1699.8,,infty -2022-07-30 20:35:00,1699.37,,infty -2022-07-30 20:36:00,1700.08,,infty -2022-07-30 20:37:00,1701.17,,infty -2022-07-30 20:38:00,1702.77,,infty -2022-07-30 20:39:00,1706.21,,infty -2022-07-30 20:40:00,1708.71,,infty -2022-07-30 20:41:00,1709.75,,infty -2022-07-30 20:42:00,1708.2,,infty -2022-07-30 20:43:00,1707.19,,infty -2022-07-30 20:44:00,1707.39,,infty -2022-07-30 20:45:00,1698.99,,infty -2022-07-30 20:46:00,1702.57,,infty -2022-07-30 20:47:00,1700.57,,infty -2022-07-30 20:48:00,1700.85,,infty -2022-07-30 20:49:00,1701.46,,infty -2022-07-30 20:50:00,1700.74,,infty -2022-07-30 20:51:00,1700.9,,infty -2022-07-30 20:52:00,1700.76,,infty -2022-07-30 20:53:00,1702.6,,infty -2022-07-30 20:54:00,1702.41,,infty -2022-07-30 20:55:00,1702.38,,infty -2022-07-30 20:56:00,1701.07,,infty -2022-07-30 20:57:00,1703.19,,infty -2022-07-30 20:58:00,1702.31,,infty -2022-07-30 20:59:00,1702.61,,infty -2022-07-30 21:00:00,1703.75,,infty -2022-07-30 21:01:00,1705.26,,infty -2022-07-30 21:02:00,1706.84,,infty -2022-07-30 21:03:00,1705.52,,infty -2022-07-30 21:04:00,1705.28,,infty -2022-07-30 21:05:00,1706.3,,infty -2022-07-30 21:06:00,1700.75,,infty -2022-07-30 21:07:00,1699.98,,infty -2022-07-30 21:08:00,1701.3,,infty -2022-07-30 21:09:00,1701.65,,infty -2022-07-30 21:10:00,1701.66,,infty -2022-07-30 21:11:00,1701.24,,infty -2022-07-30 21:12:00,1700.63,,infty -2022-07-30 21:13:00,1700.82,,infty -2022-07-30 21:14:00,1701.31,,infty -2022-07-30 21:15:00,1700.99,,infty -2022-07-30 21:16:00,1700.13,,infty -2022-07-30 21:17:00,1699.48,,infty -2022-07-30 21:18:00,1699.28,,infty -2022-07-30 21:19:00,1699.77,,infty -2022-07-30 21:20:00,1697.76,,infty -2022-07-30 21:21:00,1697.68,,infty -2022-07-30 21:22:00,1699.65,,infty -2022-07-30 21:23:00,1701.76,,infty -2022-07-30 21:24:00,1702.81,,infty -2022-07-30 21:25:00,1701.04,,infty -2022-07-30 21:26:00,1700.56,,infty -2022-07-30 21:27:00,1701.25,,infty -2022-07-30 21:28:00,1702.66,,infty -2022-07-30 21:29:00,1700.64,,infty -2022-07-30 21:30:00,1700.42,,infty -2022-07-30 21:31:00,1699.92,,infty -2022-07-30 21:32:00,1701.06,,infty -2022-07-30 21:33:00,1700.22,,infty -2022-07-30 21:34:00,1701.03,,infty -2022-07-30 21:35:00,1700.62,,infty -2022-07-30 21:36:00,1701.77,,infty -2022-07-30 21:37:00,1701.45,,infty -2022-07-30 21:38:00,1703.0,,infty -2022-07-30 21:39:00,1704.7,,infty -2022-07-30 21:40:00,1705.77,,infty -2022-07-30 21:41:00,1706.4,,infty -2022-07-30 21:42:00,1707.04,,infty -2022-07-30 21:43:00,1706.0,,infty -2022-07-30 21:44:00,1706.6,,infty -2022-07-30 21:45:00,1704.74,,infty -2022-07-30 21:46:00,1706.4,,infty -2022-07-30 21:47:00,1704.99,,infty -2022-07-30 21:48:00,1703.07,,infty -2022-07-30 21:49:00,1703.03,,infty -2022-07-30 21:50:00,1700.01,,infty -2022-07-30 21:51:00,1701.43,,infty -2022-07-30 21:52:00,1700.59,,infty -2022-07-30 21:53:00,1700.84,,infty -2022-07-30 21:54:00,1700.62,,infty -2022-07-30 21:55:00,1700.16,,infty -2022-07-30 21:56:00,1698.21,,infty -2022-07-30 21:57:00,1690.0,,infty -2022-07-30 21:58:00,1694.95,,infty -2022-07-30 21:59:00,1692.95,,infty -2022-07-30 22:00:00,1683.59,,infty -2022-07-30 22:01:00,1683.39,,infty -2022-07-30 22:02:00,1684.93,,infty -2022-07-30 22:03:00,1684.67,,infty -2022-07-30 22:04:00,1684.15,,infty -2022-07-30 22:05:00,1686.67,,infty -2022-07-30 22:06:00,1684.82,,infty -2022-07-30 22:07:00,1684.62,,infty -2022-07-30 22:08:00,1683.35,,infty -2022-07-30 22:09:00,1681.11,,open_close -2022-07-30 22:10:00,1679.01,,open_close -2022-07-30 22:11:00,1678.62,,open_close -2022-07-30 22:12:00,1678.19,,open_close -2022-07-30 22:13:00,1677.5,,open_close -2022-07-30 22:14:00,1678.58,,open_close -2022-07-30 22:15:00,1677.6,,open_close -2022-07-30 22:16:00,1676.62,,open_close -2022-07-30 22:17:00,1677.19,,open_close -2022-07-30 22:18:00,1679.13,,open_close -2022-07-30 22:19:00,1678.56,,open_close -2022-07-30 22:20:00,1680.93,,open_close -2022-07-30 22:21:00,1684.93,,infty -2022-07-30 22:22:00,1684.26,,infty -2022-07-30 22:23:00,1684.26,,infty -2022-07-30 22:24:00,1685.15,,infty -2022-07-30 22:25:00,1686.84,,infty -2022-07-30 22:26:00,1688.71,,infty -2022-07-30 22:27:00,1690.28,,infty -2022-07-30 22:28:00,1687.58,,infty -2022-07-30 22:29:00,1688.32,,infty -2022-07-30 22:30:00,1690.95,,infty -2022-07-30 22:31:00,1691.53,,infty -2022-07-30 22:32:00,1691.15,,infty -2022-07-30 22:33:00,1693.08,,infty -2022-07-30 22:34:00,1691.79,,infty -2022-07-30 22:35:00,1691.62,,infty -2022-07-30 22:36:00,1692.55,,infty -2022-07-30 22:37:00,1692.2,,infty -2022-07-30 22:38:00,1692.92,,infty -2022-07-30 22:39:00,1694.26,,infty -2022-07-30 22:40:00,1694.23,,infty -2022-07-30 22:41:00,1693.67,,infty -2022-07-30 22:42:00,1698.8,,infty -2022-07-30 22:43:00,1697.66,,infty -2022-07-30 22:44:00,1698.72,,infty -2022-07-30 22:45:00,1697.78,,infty -2022-07-30 22:46:00,1696.45,,infty -2022-07-30 22:47:00,1697.29,,infty -2022-07-30 22:48:00,1697.38,,infty -2022-07-30 22:49:00,1696.45,,infty -2022-07-30 22:50:00,1696.49,,infty -2022-07-30 22:51:00,1697.81,,infty -2022-07-30 22:52:00,1696.83,,infty -2022-07-30 22:53:00,1700.11,,infty -2022-07-30 22:54:00,1697.35,,infty -2022-07-30 22:55:00,1699.65,,infty -2022-07-30 22:56:00,1699.85,,infty -2022-07-30 22:57:00,1698.92,,infty -2022-07-30 22:58:00,1699.93,,infty -2022-07-30 22:59:00,1699.4,,infty -2022-07-30 23:00:00,1700.0,,infty -2022-07-30 23:01:00,1698.01,,infty -2022-07-30 23:02:00,1697.03,,infty -2022-07-30 23:03:00,1699.72,,infty -2022-07-30 23:04:00,1705.19,,infty -2022-07-30 23:05:00,1703.84,,infty -2022-07-30 23:06:00,1703.47,,infty -2022-07-30 23:07:00,1702.92,,infty -2022-07-30 23:08:00,1703.67,,infty -2022-07-30 23:09:00,1704.04,,infty -2022-07-30 23:10:00,1702.98,,infty -2022-07-30 23:11:00,1697.79,,infty -2022-07-30 23:12:00,1695.6,,infty -2022-07-30 23:13:00,1694.15,,infty -2022-07-30 23:14:00,1693.05,,infty -2022-07-30 23:15:00,1690.53,,infty -2022-07-30 23:16:00,1690.48,,infty -2022-07-30 23:17:00,1690.26,,infty -2022-07-30 23:18:00,1689.17,,infty -2022-07-30 23:19:00,1686.98,,infty -2022-07-30 23:20:00,1685.01,,infty -2022-07-30 23:21:00,1682.1,,infty -2022-07-30 23:22:00,1685.4,,infty -2022-07-30 23:23:00,1689.44,,infty -2022-07-30 23:24:00,1688.38,,infty -2022-07-30 23:25:00,1685.79,,infty -2022-07-30 23:26:00,1685.62,,infty -2022-07-30 23:27:00,1687.16,,infty -2022-07-30 23:28:00,1686.44,,infty -2022-07-30 23:29:00,1683.48,,infty -2022-07-30 23:30:00,1686.69,,infty -2022-07-30 23:31:00,1689.09,,infty -2022-07-30 23:32:00,1687.8,,infty -2022-07-30 23:33:00,1687.17,,infty -2022-07-30 23:34:00,1687.5,,infty -2022-07-30 23:35:00,1691.5,,infty -2022-07-30 23:36:00,1691.67,,infty -2022-07-30 23:37:00,1693.13,,infty -2022-07-30 23:38:00,1694.9,,infty -2022-07-30 23:39:00,1695.25,,infty -2022-07-30 23:40:00,1690.65,,infty -2022-07-30 23:41:00,1689.39,,infty -2022-07-30 23:42:00,1689.88,,infty -2022-07-30 23:43:00,1693.04,,infty -2022-07-30 23:44:00,1693.17,,infty -2022-07-30 23:45:00,1693.63,,infty -2022-07-30 23:46:00,1692.94,,infty -2022-07-30 23:47:00,1694.9,,infty -2022-07-30 23:48:00,1693.3,,infty -2022-07-30 23:49:00,1692.71,,infty -2022-07-30 23:50:00,1693.55,,infty -2022-07-30 23:51:00,1695.18,,infty -2022-07-30 23:52:00,1698.31,,infty -2022-07-30 23:53:00,1697.93,,infty -2022-07-30 23:54:00,1697.3,,infty -2022-07-30 23:55:00,1695.4,,infty -2022-07-30 23:56:00,1696.68,,infty -2022-07-30 23:57:00,1696.98,,infty -2022-07-30 23:58:00,1695.88,,infty -2022-07-30 23:59:00,1697.3,,infty -2022-07-31 00:00:00,1696.91,,infty -2022-07-31 00:01:00,1696.92,,infty -2022-07-31 00:02:00,1693.35,,infty -2022-07-31 00:03:00,1691.45,,infty -2022-07-31 00:04:00,1687.63,,infty -2022-07-31 00:05:00,1690.78,,infty -2022-07-31 00:06:00,1696.94,,infty -2022-07-31 00:07:00,1698.5,,infty -2022-07-31 00:08:00,1702.88,,infty -2022-07-31 00:09:00,1700.73,,infty -2022-07-31 00:10:00,1696.48,,infty -2022-07-31 00:11:00,1696.6,,infty -2022-07-31 00:12:00,1696.62,,infty -2022-07-31 00:13:00,1694.21,,infty -2022-07-31 00:14:00,1695.62,,infty -2022-07-31 00:15:00,1696.47,,infty -2022-07-31 00:16:00,1694.31,,infty -2022-07-31 00:17:00,1695.65,,infty -2022-07-31 00:18:00,1698.34,,infty -2022-07-31 00:19:00,1700.7,,infty -2022-07-31 00:20:00,1700.44,,infty -2022-07-31 00:21:00,1701.44,,infty -2022-07-31 00:22:00,1701.2,,infty -2022-07-31 00:23:00,1700.26,,infty -2022-07-31 00:24:00,1698.43,,infty -2022-07-31 00:25:00,1699.86,,infty -2022-07-31 00:26:00,1699.63,,infty -2022-07-31 00:27:00,1697.09,,infty -2022-07-31 00:28:00,1697.2,,infty 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00:56:00,1706.56,,infty -2022-07-31 00:57:00,1708.39,,infty -2022-07-31 00:58:00,1711.05,,infty -2022-07-31 00:59:00,1709.73,,infty -2022-07-31 01:00:00,1709.56,,infty -2022-07-31 01:01:00,1707.97,,infty -2022-07-31 01:02:00,1708.67,,infty -2022-07-31 01:03:00,1709.54,,infty -2022-07-31 01:04:00,1710.05,,infty -2022-07-31 01:05:00,1709.72,,infty -2022-07-31 01:06:00,1706.72,,infty -2022-07-31 01:07:00,1707.11,,infty -2022-07-31 01:08:00,1706.93,,infty -2022-07-31 01:09:00,1705.95,,infty -2022-07-31 01:10:00,1704.75,,infty -2022-07-31 01:11:00,1704.18,,infty -2022-07-31 01:12:00,1705.12,,infty -2022-07-31 01:13:00,1705.99,,infty -2022-07-31 01:14:00,1705.01,,infty -2022-07-31 01:15:00,1706.38,,infty -2022-07-31 01:16:00,1704.59,,infty -2022-07-31 01:17:00,1704.73,,infty -2022-07-31 01:18:00,1705.3,,infty -2022-07-31 01:19:00,1703.97,,infty -2022-07-31 01:20:00,1703.91,,infty -2022-07-31 01:21:00,1705.03,,infty -2022-07-31 01:22:00,1705.2,,infty -2022-07-31 01:23:00,1705.54,,infty 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01:51:00,1701.36,,infty -2022-07-31 01:52:00,1699.72,,infty -2022-07-31 01:53:00,1699.44,,infty -2022-07-31 01:54:00,1697.64,,infty -2022-07-31 01:55:00,1698.99,,infty -2022-07-31 01:56:00,1697.95,,infty -2022-07-31 01:57:00,1697.52,,infty -2022-07-31 01:58:00,1698.11,,infty -2022-07-31 01:59:00,1697.4,,infty -2022-07-31 02:00:00,1699.29,,infty -2022-07-31 02:01:00,1699.44,,infty -2022-07-31 02:02:00,1703.84,,infty -2022-07-31 02:03:00,1706.05,,infty -2022-07-31 02:04:00,1704.8,,infty -2022-07-31 02:05:00,1704.18,,infty -2022-07-31 02:06:00,1708.11,,infty -2022-07-31 02:07:00,1708.78,,infty -2022-07-31 02:08:00,1706.18,,infty -2022-07-31 02:09:00,1704.01,,infty -2022-07-31 02:10:00,1705.88,,infty -2022-07-31 02:11:00,1705.18,,infty -2022-07-31 02:12:00,1703.49,,infty -2022-07-31 02:13:00,1703.18,,infty -2022-07-31 02:14:00,1702.31,,infty -2022-07-31 02:15:00,1703.54,,infty -2022-07-31 02:16:00,1705.09,,infty -2022-07-31 02:17:00,1704.94,,infty -2022-07-31 02:18:00,1705.18,,infty 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03:14:00,1697.51,,infty -2022-07-31 03:15:00,1693.6,,infty -2022-07-31 03:16:00,1695.2,,infty -2022-07-31 03:17:00,1696.59,,infty -2022-07-31 03:18:00,1697.29,,infty -2022-07-31 03:19:00,1696.71,,infty -2022-07-31 03:20:00,1696.71,,infty -2022-07-31 03:21:00,1693.76,,infty -2022-07-31 03:22:00,1693.6,,infty -2022-07-31 03:23:00,1693.55,,infty -2022-07-31 03:24:00,1694.32,,infty -2022-07-31 03:25:00,1693.0,,infty -2022-07-31 03:26:00,1692.19,,infty -2022-07-31 03:27:00,1692.98,,infty -2022-07-31 03:28:00,1692.77,,infty -2022-07-31 03:29:00,1692.01,,infty -2022-07-31 03:30:00,1691.78,,infty -2022-07-31 03:31:00,1689.83,,infty -2022-07-31 03:32:00,1692.87,,infty -2022-07-31 03:33:00,1693.97,,infty -2022-07-31 03:34:00,1693.13,,infty -2022-07-31 03:35:00,1691.77,,infty -2022-07-31 03:36:00,1695.03,,infty -2022-07-31 03:37:00,1694.81,,infty -2022-07-31 03:38:00,1695.15,,infty -2022-07-31 03:39:00,1693.2,,infty -2022-07-31 03:40:00,1691.68,,infty -2022-07-31 03:41:00,1690.81,,infty 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04:09:00,1698.02,,infty -2022-07-31 04:10:00,1697.18,,infty -2022-07-31 04:11:00,1696.98,,infty -2022-07-31 04:12:00,1696.58,,infty -2022-07-31 04:13:00,1694.94,,infty -2022-07-31 04:14:00,1695.42,,infty -2022-07-31 04:15:00,1695.93,,infty -2022-07-31 04:16:00,1698.14,,infty -2022-07-31 04:17:00,1698.71,,infty -2022-07-31 04:18:00,1697.87,,infty -2022-07-31 04:19:00,1697.87,,infty -2022-07-31 04:20:00,1698.72,,infty -2022-07-31 04:21:00,1698.95,,infty -2022-07-31 04:22:00,1697.48,,infty -2022-07-31 04:23:00,1696.85,,infty -2022-07-31 04:24:00,1699.33,,infty -2022-07-31 04:25:00,1699.11,,infty -2022-07-31 04:26:00,1699.57,,infty -2022-07-31 04:27:00,1700.37,,infty -2022-07-31 04:28:00,1700.54,,infty -2022-07-31 04:29:00,1703.14,,infty -2022-07-31 04:30:00,1703.97,,infty -2022-07-31 04:31:00,1702.65,,infty -2022-07-31 04:32:00,1705.01,,infty -2022-07-31 04:33:00,1704.04,,infty -2022-07-31 04:34:00,1704.35,,infty -2022-07-31 04:35:00,1702.93,,infty -2022-07-31 04:36:00,1701.83,,infty 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05:04:00,1704.3,,infty -2022-07-31 05:05:00,1705.08,,infty -2022-07-31 05:06:00,1701.0,,infty -2022-07-31 05:07:00,1702.47,,infty -2022-07-31 05:08:00,1703.36,,infty -2022-07-31 05:09:00,1705.92,,infty -2022-07-31 05:10:00,1705.11,,infty -2022-07-31 05:11:00,1704.91,,infty -2022-07-31 05:12:00,1705.4,,infty -2022-07-31 05:13:00,1703.15,,infty -2022-07-31 05:14:00,1701.83,,infty -2022-07-31 05:15:00,1702.58,,infty -2022-07-31 05:16:00,1704.71,,infty -2022-07-31 05:17:00,1704.21,,infty -2022-07-31 05:18:00,1703.75,,infty -2022-07-31 05:19:00,1702.42,,infty -2022-07-31 05:20:00,1700.39,,infty -2022-07-31 05:21:00,1699.07,,infty -2022-07-31 05:22:00,1699.68,,infty -2022-07-31 05:23:00,1697.24,,infty -2022-07-31 05:24:00,1696.73,,infty -2022-07-31 05:25:00,1697.38,,infty -2022-07-31 05:26:00,1696.22,,infty -2022-07-31 05:27:00,1695.27,,infty -2022-07-31 05:28:00,1697.05,,infty -2022-07-31 05:29:00,1693.85,,infty -2022-07-31 05:30:00,1695.35,,infty -2022-07-31 05:31:00,1695.39,,infty 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05:59:00,1691.43,,infty -2022-07-31 06:00:00,1692.97,,infty -2022-07-31 06:01:00,1693.94,,infty -2022-07-31 06:02:00,1694.78,,infty -2022-07-31 06:03:00,1696.5,,infty -2022-07-31 06:04:00,1694.73,,infty -2022-07-31 06:05:00,1695.04,,infty -2022-07-31 06:06:00,1692.89,,infty -2022-07-31 06:07:00,1693.92,,infty -2022-07-31 06:08:00,1694.37,,infty -2022-07-31 06:09:00,1693.96,,infty -2022-07-31 06:10:00,1693.02,,infty -2022-07-31 06:11:00,1691.99,,infty -2022-07-31 06:12:00,1694.22,,infty -2022-07-31 06:13:00,1694.39,,infty -2022-07-31 06:14:00,1693.7,,infty -2022-07-31 06:15:00,1694.2,,infty -2022-07-31 06:16:00,1694.88,,infty -2022-07-31 06:17:00,1695.32,,infty -2022-07-31 06:18:00,1697.28,,infty -2022-07-31 06:19:00,1697.25,,infty -2022-07-31 06:20:00,1696.57,,infty -2022-07-31 06:21:00,1695.72,,infty -2022-07-31 06:22:00,1696.19,,infty -2022-07-31 06:23:00,1697.56,,infty -2022-07-31 06:24:00,1698.64,,infty -2022-07-31 06:25:00,1699.09,,infty -2022-07-31 06:26:00,1699.41,,infty 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06:54:00,1700.97,,infty -2022-07-31 06:55:00,1701.52,,infty -2022-07-31 06:56:00,1701.93,,infty -2022-07-31 06:57:00,1702.58,,infty -2022-07-31 06:58:00,1702.36,,infty -2022-07-31 06:59:00,1702.04,,infty -2022-07-31 07:00:00,1702.46,,infty -2022-07-31 07:01:00,1702.89,,infty -2022-07-31 07:02:00,1703.07,,infty -2022-07-31 07:03:00,1702.94,,infty -2022-07-31 07:04:00,1703.28,,infty -2022-07-31 07:05:00,1705.47,,infty -2022-07-31 07:06:00,1708.11,,infty -2022-07-31 07:07:00,1706.68,,infty -2022-07-31 07:08:00,1705.36,,infty -2022-07-31 07:09:00,1699.84,,infty -2022-07-31 07:10:00,1700.53,,infty -2022-07-31 07:11:00,1700.52,,infty -2022-07-31 07:12:00,1700.9,,infty -2022-07-31 07:13:00,1703.33,,infty -2022-07-31 07:14:00,1708.08,,infty -2022-07-31 07:15:00,1706.87,,infty -2022-07-31 07:16:00,1706.6,,infty -2022-07-31 07:17:00,1706.19,,infty -2022-07-31 07:18:00,1705.25,,infty -2022-07-31 07:19:00,1703.82,,infty -2022-07-31 07:20:00,1703.54,,infty -2022-07-31 07:21:00,1704.96,,infty 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07:49:00,1698.23,,infty -2022-07-31 07:50:00,1698.4,,infty -2022-07-31 07:51:00,1698.38,,infty -2022-07-31 07:52:00,1700.54,,infty -2022-07-31 07:53:00,1698.57,,infty -2022-07-31 07:54:00,1698.81,,infty -2022-07-31 07:55:00,1698.74,,infty -2022-07-31 07:56:00,1699.13,,infty -2022-07-31 07:57:00,1698.61,,infty -2022-07-31 07:58:00,1698.01,,infty -2022-07-31 07:59:00,1698.23,,infty -2022-07-31 08:00:00,1696.46,,infty -2022-07-31 08:01:00,1697.71,,infty -2022-07-31 08:02:00,1699.11,,infty -2022-07-31 08:03:00,1700.32,,infty -2022-07-31 08:04:00,1700.7,,infty -2022-07-31 08:05:00,1701.72,,infty -2022-07-31 08:06:00,1701.62,,infty -2022-07-31 08:07:00,1701.97,,infty -2022-07-31 08:08:00,1701.06,,infty -2022-07-31 08:09:00,1701.48,,infty -2022-07-31 08:10:00,1701.3,,infty -2022-07-31 08:11:00,1700.39,,infty -2022-07-31 08:12:00,1695.89,,infty -2022-07-31 08:13:00,1690.57,,infty -2022-07-31 08:14:00,1693.68,,infty -2022-07-31 08:15:00,1690.94,,infty -2022-07-31 08:16:00,1692.61,,infty 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08:44:00,1695.84,,infty -2022-07-31 08:45:00,1693.24,,infty -2022-07-31 08:46:00,1692.78,,infty -2022-07-31 08:47:00,1691.16,,infty -2022-07-31 08:48:00,1690.67,,infty -2022-07-31 08:49:00,1691.91,,infty -2022-07-31 08:50:00,1690.81,,infty -2022-07-31 08:51:00,1691.39,,infty -2022-07-31 08:52:00,1691.93,,infty -2022-07-31 08:53:00,1694.55,,infty -2022-07-31 08:54:00,1694.34,,infty -2022-07-31 08:55:00,1694.54,,infty -2022-07-31 08:56:00,1694.54,,infty -2022-07-31 08:57:00,1694.57,,infty -2022-07-31 08:58:00,1692.23,,infty -2022-07-31 08:59:00,1693.43,,infty -2022-07-31 09:00:00,1692.98,,infty -2022-07-31 09:01:00,1686.86,,infty -2022-07-31 09:02:00,1691.98,,infty -2022-07-31 09:03:00,1695.58,,infty -2022-07-31 09:04:00,1695.1,,infty -2022-07-31 09:05:00,1694.31,,infty -2022-07-31 09:06:00,1694.57,,infty -2022-07-31 09:07:00,1693.77,,infty -2022-07-31 09:08:00,1693.88,,infty -2022-07-31 09:09:00,1695.34,,infty -2022-07-31 09:10:00,1694.32,,infty -2022-07-31 09:11:00,1694.85,,infty 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09:39:00,1697.71,,infty -2022-07-31 09:40:00,1697.73,,infty -2022-07-31 09:41:00,1697.66,,infty -2022-07-31 09:42:00,1697.37,,infty -2022-07-31 09:43:00,1697.69,,infty -2022-07-31 09:44:00,1697.64,,infty -2022-07-31 09:45:00,1698.17,,infty -2022-07-31 09:46:00,1698.25,,infty -2022-07-31 09:47:00,1699.05,,infty -2022-07-31 09:48:00,1699.0,,infty -2022-07-31 09:49:00,1698.75,,infty -2022-07-31 09:50:00,1699.69,,infty -2022-07-31 09:51:00,1699.47,,infty -2022-07-31 09:52:00,1698.91,,infty -2022-07-31 09:53:00,1698.93,,infty -2022-07-31 09:54:00,1699.86,,infty -2022-07-31 09:55:00,1700.01,,infty -2022-07-31 09:56:00,1700.35,,infty -2022-07-31 09:57:00,1701.51,,infty -2022-07-31 09:58:00,1702.46,,infty -2022-07-31 09:59:00,1704.53,,infty -2022-07-31 10:00:00,1702.86,,infty -2022-07-31 10:01:00,1702.89,,infty -2022-07-31 10:02:00,1702.0,,infty -2022-07-31 10:03:00,1702.11,,infty -2022-07-31 10:04:00,1701.67,,infty -2022-07-31 10:05:00,1702.71,,infty -2022-07-31 10:06:00,1700.38,,infty 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10:34:00,1701.45,,infty -2022-07-31 10:35:00,1703.27,,infty -2022-07-31 10:36:00,1702.91,,infty -2022-07-31 10:37:00,1704.57,,infty -2022-07-31 10:38:00,1704.4,,infty -2022-07-31 10:39:00,1704.04,,infty -2022-07-31 10:40:00,1704.47,,infty -2022-07-31 10:41:00,1703.98,,infty -2022-07-31 10:42:00,1703.52,,infty -2022-07-31 10:43:00,1703.22,,infty -2022-07-31 10:44:00,1702.68,,infty -2022-07-31 10:45:00,1702.88,,infty -2022-07-31 10:46:00,1703.6,,infty -2022-07-31 10:47:00,1704.01,,infty -2022-07-31 10:48:00,1705.39,,infty -2022-07-31 10:49:00,1704.8,,infty -2022-07-31 10:50:00,1705.11,,infty -2022-07-31 10:51:00,1703.59,,infty -2022-07-31 10:52:00,1700.78,,infty -2022-07-31 10:53:00,1702.75,,infty -2022-07-31 10:54:00,1702.67,,infty -2022-07-31 10:55:00,1706.04,,infty -2022-07-31 10:56:00,1707.96,,infty -2022-07-31 10:57:00,1708.67,,infty -2022-07-31 10:58:00,1715.92,,infty -2022-07-31 10:59:00,1716.73,,infty -2022-07-31 11:00:00,1718.15,,infty -2022-07-31 11:01:00,1715.26,,infty 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11:29:00,1714.16,,infty -2022-07-31 11:30:00,1714.36,,infty -2022-07-31 11:31:00,1714.31,,infty -2022-07-31 11:32:00,1714.29,,infty -2022-07-31 11:33:00,1712.7,,infty -2022-07-31 11:34:00,1713.67,,infty -2022-07-31 11:35:00,1712.08,,infty -2022-07-31 11:36:00,1713.22,,infty -2022-07-31 11:37:00,1711.99,,infty -2022-07-31 11:38:00,1712.52,,infty -2022-07-31 11:39:00,1712.96,,infty -2022-07-31 11:40:00,1711.9,,infty -2022-07-31 11:41:00,1710.27,,infty -2022-07-31 11:42:00,1710.43,,infty -2022-07-31 11:43:00,1712.23,,infty -2022-07-31 11:44:00,1713.23,,infty -2022-07-31 11:45:00,1715.4,,infty -2022-07-31 11:46:00,1716.12,,infty -2022-07-31 11:47:00,1715.0,,infty -2022-07-31 11:48:00,1712.89,,infty -2022-07-31 11:49:00,1714.42,,infty -2022-07-31 11:50:00,1714.47,,infty -2022-07-31 11:51:00,1713.61,,infty -2022-07-31 11:52:00,1713.09,,infty -2022-07-31 11:53:00,1713.55,,infty -2022-07-31 11:54:00,1716.21,,infty -2022-07-31 11:55:00,1716.12,,infty -2022-07-31 11:56:00,1722.47,,infty 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12:24:00,1713.71,,infty -2022-07-31 12:25:00,1714.08,,infty -2022-07-31 12:26:00,1713.28,,infty -2022-07-31 12:27:00,1714.85,,infty -2022-07-31 12:28:00,1712.78,,infty -2022-07-31 12:29:00,1713.75,,infty -2022-07-31 12:30:00,1712.32,,infty -2022-07-31 12:31:00,1711.26,,infty -2022-07-31 12:32:00,1713.24,,infty -2022-07-31 12:33:00,1713.78,,infty -2022-07-31 12:34:00,1713.04,,infty -2022-07-31 12:35:00,1713.89,,infty -2022-07-31 12:36:00,1713.18,,infty -2022-07-31 12:37:00,1713.75,,infty -2022-07-31 12:38:00,1713.49,,infty -2022-07-31 12:39:00,1713.82,,infty -2022-07-31 12:40:00,1714.68,,infty -2022-07-31 12:41:00,1715.48,,infty -2022-07-31 12:42:00,1715.66,,infty -2022-07-31 12:43:00,1715.68,,infty -2022-07-31 12:44:00,1713.56,,infty -2022-07-31 12:45:00,1714.09,,infty -2022-07-31 12:46:00,1714.92,,infty -2022-07-31 12:47:00,1716.73,,infty -2022-07-31 12:48:00,1717.56,,infty -2022-07-31 12:49:00,1717.36,,infty -2022-07-31 12:50:00,1716.92,,infty -2022-07-31 12:51:00,1716.59,,infty 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13:19:00,1712.88,,infty -2022-07-31 13:20:00,1710.39,,infty -2022-07-31 13:21:00,1708.05,,infty -2022-07-31 13:22:00,1704.86,,infty -2022-07-31 13:23:00,1706.1,,infty -2022-07-31 13:24:00,1705.77,,infty -2022-07-31 13:25:00,1705.79,,infty -2022-07-31 13:26:00,1706.31,,infty -2022-07-31 13:27:00,1708.84,,infty -2022-07-31 13:28:00,1709.88,,infty -2022-07-31 13:29:00,1707.45,,infty -2022-07-31 13:30:00,1706.16,,infty -2022-07-31 13:31:00,1705.99,,infty -2022-07-31 13:32:00,1706.62,,infty -2022-07-31 13:33:00,1707.07,,infty -2022-07-31 13:34:00,1708.42,,infty -2022-07-31 13:35:00,1708.7,,infty -2022-07-31 13:36:00,1709.79,,infty -2022-07-31 13:37:00,1708.94,,infty -2022-07-31 13:38:00,1709.42,,infty -2022-07-31 13:39:00,1708.62,,infty -2022-07-31 13:40:00,1706.63,,infty -2022-07-31 13:41:00,1707.02,,infty -2022-07-31 13:42:00,1708.93,,infty -2022-07-31 13:43:00,1710.26,,infty -2022-07-31 13:44:00,1713.32,,infty -2022-07-31 13:45:00,1712.56,,infty -2022-07-31 13:46:00,1711.45,,infty 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14:14:00,1705.61,,infty -2022-07-31 14:15:00,1705.39,,infty -2022-07-31 14:16:00,1706.92,,infty -2022-07-31 14:17:00,1708.25,,infty -2022-07-31 14:18:00,1707.84,,infty -2022-07-31 14:19:00,1708.41,,infty -2022-07-31 14:20:00,1710.01,,infty -2022-07-31 14:21:00,1708.45,,infty -2022-07-31 14:22:00,1709.23,,infty -2022-07-31 14:23:00,1709.38,,infty -2022-07-31 14:24:00,1708.67,,infty -2022-07-31 14:25:00,1708.11,,infty -2022-07-31 14:26:00,1710.05,,infty -2022-07-31 14:27:00,1709.9,,infty -2022-07-31 14:28:00,1710.69,,infty -2022-07-31 14:29:00,1714.87,,infty -2022-07-31 14:30:00,1716.07,,infty -2022-07-31 14:31:00,1717.83,,infty -2022-07-31 14:32:00,1718.84,,infty -2022-07-31 14:33:00,1716.33,,infty -2022-07-31 14:34:00,1715.72,,infty -2022-07-31 14:35:00,1717.07,,infty -2022-07-31 14:36:00,1717.61,,infty -2022-07-31 14:37:00,1715.43,,infty -2022-07-31 14:38:00,1717.27,,infty -2022-07-31 14:39:00,1715.86,,infty -2022-07-31 14:40:00,1713.81,,infty -2022-07-31 14:41:00,1711.88,,infty 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15:09:00,1709.16,,infty -2022-07-31 15:10:00,1708.86,,infty -2022-07-31 15:11:00,1706.5,,infty -2022-07-31 15:12:00,1706.33,,infty -2022-07-31 15:13:00,1703.26,,infty -2022-07-31 15:14:00,1702.42,,infty -2022-07-31 15:15:00,1703.44,,infty -2022-07-31 15:16:00,1704.2,,infty -2022-07-31 15:17:00,1704.19,,infty -2022-07-31 15:18:00,1705.96,,infty -2022-07-31 15:19:00,1706.4,,infty -2022-07-31 15:20:00,1708.78,,infty -2022-07-31 15:21:00,1710.85,,infty -2022-07-31 15:22:00,1710.02,,infty -2022-07-31 15:23:00,1709.31,,infty -2022-07-31 15:24:00,1709.3,,infty -2022-07-31 15:25:00,1708.26,,infty -2022-07-31 15:26:00,1709.93,,infty -2022-07-31 15:27:00,1709.03,,infty -2022-07-31 15:28:00,1708.81,,infty -2022-07-31 15:29:00,1708.98,,infty -2022-07-31 15:30:00,1710.58,,infty -2022-07-31 15:31:00,1708.78,,infty -2022-07-31 15:32:00,1709.63,,infty -2022-07-31 15:33:00,1709.82,,infty -2022-07-31 15:34:00,1710.32,,infty -2022-07-31 15:35:00,1711.13,,infty -2022-07-31 15:36:00,1712.26,,infty 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16:04:00,1712.11,,infty -2022-07-31 16:05:00,1709.35,,infty -2022-07-31 16:06:00,1709.99,,infty -2022-07-31 16:07:00,1712.64,,infty -2022-07-31 16:08:00,1714.67,,infty -2022-07-31 16:09:00,1714.28,,infty -2022-07-31 16:10:00,1713.75,,infty -2022-07-31 16:11:00,1714.03,,infty -2022-07-31 16:12:00,1713.59,,infty -2022-07-31 16:13:00,1713.32,,infty -2022-07-31 16:14:00,1712.54,,infty -2022-07-31 16:15:00,1713.26,,infty -2022-07-31 16:16:00,1710.36,,infty -2022-07-31 16:17:00,1706.78,,infty -2022-07-31 16:18:00,1708.21,,infty -2022-07-31 16:19:00,1705.45,,infty -2022-07-31 16:20:00,1706.46,,infty -2022-07-31 16:21:00,1704.63,,infty -2022-07-31 16:22:00,1707.66,,infty -2022-07-31 16:23:00,1705.55,,infty -2022-07-31 16:24:00,1707.63,,infty -2022-07-31 16:25:00,1707.19,,infty -2022-07-31 16:26:00,1706.17,,infty -2022-07-31 16:27:00,1706.19,,infty -2022-07-31 16:28:00,1707.31,,infty -2022-07-31 16:29:00,1707.22,,infty -2022-07-31 16:30:00,1707.0,,infty -2022-07-31 16:31:00,1706.65,,infty 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16:59:00,1709.35,,infty -2022-07-31 17:00:00,1709.67,,infty -2022-07-31 17:01:00,1707.35,,infty -2022-07-31 17:02:00,1708.28,,infty -2022-07-31 17:03:00,1708.46,,infty -2022-07-31 17:04:00,1708.82,,infty -2022-07-31 17:05:00,1707.65,,infty -2022-07-31 17:06:00,1708.34,,infty -2022-07-31 17:07:00,1711.22,,infty -2022-07-31 17:08:00,1710.57,,infty -2022-07-31 17:09:00,1710.3,,infty -2022-07-31 17:10:00,1710.81,,infty -2022-07-31 17:11:00,1711.46,,infty -2022-07-31 17:12:00,1711.4,,infty -2022-07-31 17:13:00,1710.47,,infty -2022-07-31 17:14:00,1710.14,,infty -2022-07-31 17:15:00,1710.41,,infty -2022-07-31 17:16:00,1710.04,,infty -2022-07-31 17:17:00,1710.89,,infty -2022-07-31 17:18:00,1711.11,,infty -2022-07-31 17:19:00,1710.83,,infty -2022-07-31 17:20:00,1709.65,,infty -2022-07-31 17:21:00,1709.03,,infty -2022-07-31 17:22:00,1709.29,,infty -2022-07-31 17:23:00,1707.73,,infty -2022-07-31 17:24:00,1708.18,,infty -2022-07-31 17:25:00,1707.96,,infty -2022-07-31 17:26:00,1706.51,,infty 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17:54:00,1705.93,,infty -2022-07-31 17:55:00,1706.38,,infty -2022-07-31 17:56:00,1708.58,,infty -2022-07-31 17:57:00,1708.84,,infty -2022-07-31 17:58:00,1707.35,,infty -2022-07-31 17:59:00,1708.36,,infty -2022-07-31 18:00:00,1708.19,,infty -2022-07-31 18:01:00,1708.97,,infty -2022-07-31 18:02:00,1711.85,,infty -2022-07-31 18:03:00,1713.13,,infty -2022-07-31 18:04:00,1713.8,,infty -2022-07-31 18:05:00,1714.95,,infty -2022-07-31 18:06:00,1717.15,,infty -2022-07-31 18:07:00,1722.76,,infty -2022-07-31 18:08:00,1725.53,,infty -2022-07-31 18:09:00,1726.23,,infty -2022-07-31 18:10:00,1735.17,,infty -2022-07-31 18:11:00,1734.09,,infty -2022-07-31 18:12:00,1738.7,,infty -2022-07-31 18:13:00,1745.18,,infty -2022-07-31 18:14:00,1743.75,,infty -2022-07-31 18:15:00,1742.81,,infty -2022-07-31 18:16:00,1740.84,,infty -2022-07-31 18:17:00,1742.83,,infty -2022-07-31 18:18:00,1741.29,,infty -2022-07-31 18:19:00,1740.45,,infty -2022-07-31 18:20:00,1744.52,,infty -2022-07-31 18:21:00,1745.0,,infty -2022-07-31 18:22:00,1744.71,,infty -2022-07-31 18:23:00,1744.48,,infty -2022-07-31 18:24:00,1747.92,,infty -2022-07-31 18:25:00,1748.39,,infty -2022-07-31 18:26:00,1750.78,,infty -2022-07-31 18:27:00,1747.7,,infty -2022-07-31 18:28:00,1725.02,,infty -2022-07-31 18:29:00,1716.46,,infty -2022-07-31 18:30:00,1717.34,,infty -2022-07-31 18:31:00,1701.84,,infty -2022-07-31 18:32:00,1701.87,,infty -2022-07-31 18:33:00,1701.43,,infty -2022-07-31 18:34:00,1695.62,,infty -2022-07-31 18:35:00,1691.8,,infty -2022-07-31 18:36:00,1690.45,,infty -2022-07-31 18:37:00,1688.76,,infty -2022-07-31 18:38:00,1692.91,,infty -2022-07-31 18:39:00,1704.08,,infty -2022-07-31 18:40:00,1701.33,,infty -2022-07-31 18:41:00,1695.95,,infty -2022-07-31 18:42:00,1700.49,,infty -2022-07-31 18:43:00,1700.92,,infty -2022-07-31 18:44:00,1704.12,,infty -2022-07-31 18:45:00,1699.44,,infty -2022-07-31 18:46:00,1698.74,,infty -2022-07-31 18:47:00,1699.77,,infty -2022-07-31 18:48:00,1698.73,,infty -2022-07-31 18:49:00,1699.7,,infty -2022-07-31 18:50:00,1698.85,,infty -2022-07-31 18:51:00,1696.22,,infty -2022-07-31 18:52:00,1697.75,,infty -2022-07-31 18:53:00,1698.2,,infty -2022-07-31 18:54:00,1703.38,,infty -2022-07-31 18:55:00,1704.58,,infty -2022-07-31 18:56:00,1703.44,,infty -2022-07-31 18:57:00,1704.64,,infty -2022-07-31 18:58:00,1706.89,,infty -2022-07-31 18:59:00,1707.74,,infty -2022-07-31 19:00:00,1707.0,,infty -2022-07-31 19:01:00,1707.42,,infty -2022-07-31 19:02:00,1711.11,,infty -2022-07-31 19:03:00,1713.54,,infty -2022-07-31 19:04:00,1718.82,,infty -2022-07-31 19:05:00,1719.09,,infty -2022-07-31 19:06:00,1717.67,,infty -2022-07-31 19:07:00,1719.56,,infty -2022-07-31 19:08:00,1718.91,,infty -2022-07-31 19:09:00,1719.08,,infty -2022-07-31 19:10:00,1719.48,,infty -2022-07-31 19:11:00,1719.8,,infty -2022-07-31 19:12:00,1719.88,,infty -2022-07-31 19:13:00,1718.29,,infty -2022-07-31 19:14:00,1719.83,,infty -2022-07-31 19:15:00,1717.99,,infty -2022-07-31 19:16:00,1720.32,,infty -2022-07-31 19:17:00,1722.16,,infty -2022-07-31 19:18:00,1721.66,,infty -2022-07-31 19:19:00,1719.32,,infty -2022-07-31 19:20:00,1721.15,,infty -2022-07-31 19:21:00,1720.4,,infty -2022-07-31 19:22:00,1721.53,,infty -2022-07-31 19:23:00,1720.46,,infty -2022-07-31 19:24:00,1722.26,,infty -2022-07-31 19:25:00,1720.31,,infty -2022-07-31 19:26:00,1720.08,,infty -2022-07-31 19:27:00,1718.84,,infty -2022-07-31 19:28:00,1719.22,,infty -2022-07-31 19:29:00,1718.08,,infty -2022-07-31 19:30:00,1718.53,,infty -2022-07-31 19:31:00,1717.56,,infty -2022-07-31 19:32:00,1718.42,,infty -2022-07-31 19:33:00,1718.42,,infty -2022-07-31 19:34:00,1718.85,,infty -2022-07-31 19:35:00,1718.76,,infty -2022-07-31 19:36:00,1719.43,,infty -2022-07-31 19:37:00,1723.2,,infty -2022-07-31 19:38:00,1724.99,,infty -2022-07-31 19:39:00,1728.47,,infty -2022-07-31 19:40:00,1726.62,,infty -2022-07-31 19:41:00,1726.96,,infty -2022-07-31 19:42:00,1723.95,,infty -2022-07-31 19:43:00,1724.31,,infty -2022-07-31 19:44:00,1724.98,,infty -2022-07-31 19:45:00,1724.57,,infty -2022-07-31 19:46:00,1725.12,,infty -2022-07-31 19:47:00,1722.8,,infty -2022-07-31 19:48:00,1723.06,,infty -2022-07-31 19:49:00,1723.23,,infty -2022-07-31 19:50:00,1723.11,,infty -2022-07-31 19:51:00,1722.89,,infty -2022-07-31 19:52:00,1722.14,,infty -2022-07-31 19:53:00,1722.09,,infty -2022-07-31 19:54:00,1719.95,,infty -2022-07-31 19:55:00,1720.01,,infty -2022-07-31 19:56:00,1721.56,,infty -2022-07-31 19:57:00,1719.95,,infty -2022-07-31 19:58:00,1717.87,,infty -2022-07-31 19:59:00,1717.91,,infty -2022-07-31 20:00:00,1718.62,,infty -2022-07-31 20:01:00,1717.48,,infty -2022-07-31 20:02:00,1721.5,,infty -2022-07-31 20:03:00,1720.12,,infty -2022-07-31 20:04:00,1719.78,,infty -2022-07-31 20:05:00,1720.48,,infty -2022-07-31 20:06:00,1720.99,,infty -2022-07-31 20:07:00,1719.3,,infty -2022-07-31 20:08:00,1719.31,,infty -2022-07-31 20:09:00,1719.5,,infty -2022-07-31 20:10:00,1721.44,,infty -2022-07-31 20:11:00,1721.06,,infty 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20:39:00,1715.95,,infty -2022-07-31 20:40:00,1716.54,,infty -2022-07-31 20:41:00,1716.83,,infty -2022-07-31 20:42:00,1717.92,,infty -2022-07-31 20:43:00,1718.7,,infty -2022-07-31 20:44:00,1718.73,,infty -2022-07-31 20:45:00,1718.65,,infty -2022-07-31 20:46:00,1719.17,,infty -2022-07-31 20:47:00,1719.98,,infty -2022-07-31 20:48:00,1720.2,,infty -2022-07-31 20:49:00,1719.53,,infty -2022-07-31 20:50:00,1721.54,,infty -2022-07-31 20:51:00,1719.89,,infty -2022-07-31 20:52:00,1721.0,,infty -2022-07-31 20:53:00,1721.57,,infty -2022-07-31 20:54:00,1722.12,,infty -2022-07-31 20:55:00,1720.7,,infty -2022-07-31 20:56:00,1720.65,,infty -2022-07-31 20:57:00,1720.42,,infty -2022-07-31 20:58:00,1721.45,,infty -2022-07-31 20:59:00,1721.37,,infty -2022-07-31 21:00:00,1721.34,,infty -2022-07-31 21:01:00,1721.75,,infty -2022-07-31 21:02:00,1723.52,,infty -2022-07-31 21:03:00,1723.28,,infty -2022-07-31 21:04:00,1722.28,,infty -2022-07-31 21:05:00,1720.51,,infty -2022-07-31 21:06:00,1721.79,,infty -2022-07-31 21:07:00,1721.17,,infty -2022-07-31 21:08:00,1721.09,,infty -2022-07-31 21:09:00,1721.37,,infty -2022-07-31 21:10:00,1721.48,,infty -2022-07-31 21:11:00,1722.2,,infty -2022-07-31 21:12:00,1723.42,,infty -2022-07-31 21:13:00,1722.82,,infty -2022-07-31 21:14:00,1723.12,,infty -2022-07-31 21:15:00,1724.02,,infty -2022-07-31 21:16:00,1725.11,,infty -2022-07-31 21:17:00,1725.27,,infty -2022-07-31 21:18:00,1724.18,,infty -2022-07-31 21:19:00,1723.81,,infty -2022-07-31 21:20:00,1723.98,,infty -2022-07-31 21:21:00,1721.25,,infty -2022-07-31 21:22:00,1723.29,,infty -2022-07-31 21:23:00,1723.36,,infty -2022-07-31 21:24:00,1722.5,,infty -2022-07-31 21:25:00,1722.04,,infty -2022-07-31 21:26:00,1722.72,,infty -2022-07-31 21:27:00,1721.77,,infty -2022-07-31 21:28:00,1720.01,,infty -2022-07-31 21:29:00,1719.1,,infty -2022-07-31 21:30:00,1719.94,,infty -2022-07-31 21:31:00,1719.41,,infty -2022-07-31 21:32:00,1718.32,,infty -2022-07-31 21:33:00,1718.18,,infty -2022-07-31 21:34:00,1714.66,,infty -2022-07-31 21:35:00,1716.42,,infty -2022-07-31 21:36:00,1713.72,,infty -2022-07-31 21:37:00,1714.46,,infty -2022-07-31 21:38:00,1713.5,,infty -2022-07-31 21:39:00,1713.68,,infty -2022-07-31 21:40:00,1712.49,,infty -2022-07-31 21:41:00,1707.28,,infty -2022-07-31 21:42:00,1707.75,,infty -2022-07-31 21:43:00,1706.76,,infty -2022-07-31 21:44:00,1706.74,,infty -2022-07-31 21:45:00,1703.2,,infty -2022-07-31 21:46:00,1703.66,,infty -2022-07-31 21:47:00,1700.92,,infty -2022-07-31 21:48:00,1704.23,,infty -2022-07-31 21:49:00,1704.77,,infty -2022-07-31 21:50:00,1705.44,,infty -2022-07-31 21:51:00,1705.34,,infty -2022-07-31 21:52:00,1704.26,,infty -2022-07-31 21:53:00,1704.37,,infty -2022-07-31 21:54:00,1702.67,,infty -2022-07-31 21:55:00,1705.81,,infty -2022-07-31 21:56:00,1703.17,,infty -2022-07-31 21:57:00,1703.27,,infty -2022-07-31 21:58:00,1704.8,,infty -2022-07-31 21:59:00,1702.74,,infty -2022-07-31 22:00:00,1704.94,,infty -2022-07-31 22:01:00,1702.3,,infty -2022-07-31 22:02:00,1699.47,,infty -2022-07-31 22:03:00,1700.34,,infty -2022-07-31 22:04:00,1699.16,,infty -2022-07-31 22:05:00,1699.68,,infty -2022-07-31 22:06:00,1699.26,,infty -2022-07-31 22:07:00,1699.15,,infty -2022-07-31 22:08:00,1697.28,,infty -2022-07-31 22:09:00,1701.44,,infty -2022-07-31 22:10:00,1701.52,,infty -2022-07-31 22:11:00,1700.99,,infty -2022-07-31 22:12:00,1699.13,,infty -2022-07-31 22:13:00,1698.08,,infty -2022-07-31 22:14:00,1697.5,,infty -2022-07-31 22:15:00,1700.35,,infty -2022-07-31 22:16:00,1698.51,,infty -2022-07-31 22:17:00,1700.11,,infty -2022-07-31 22:18:00,1700.11,,infty -2022-07-31 22:19:00,1698.71,,infty -2022-07-31 22:20:00,1696.63,,infty -2022-07-31 22:21:00,1690.76,,infty -2022-07-31 22:22:00,1695.46,,infty -2022-07-31 22:23:00,1690.3,,infty -2022-07-31 22:24:00,1689.83,,infty -2022-07-31 22:25:00,1690.21,,infty -2022-07-31 22:26:00,1684.35,,infty -2022-07-31 22:27:00,1683.31,,infty -2022-07-31 22:28:00,1683.78,,infty -2022-07-31 22:29:00,1685.09,,infty -2022-07-31 22:30:00,1683.18,,infty -2022-07-31 22:31:00,1679.13,,open_close -2022-07-31 22:32:00,1676.77,,open_close -2022-07-31 22:33:00,1679.56,,open_close -2022-07-31 22:34:00,1679.08,,open_close -2022-07-31 22:35:00,1678.15,,open_close -2022-07-31 22:36:00,1677.19,,open_close -2022-07-31 22:37:00,1677.57,,open_close -2022-07-31 22:38:00,1673.41,,open_close -2022-07-31 22:39:00,1670.38,,open_close -2022-07-31 22:40:00,1671.82,,open_close -2022-07-31 22:41:00,1675.68,,open_close -2022-07-31 22:42:00,1671.99,,open_close -2022-07-31 22:43:00,1670.04,,open_close -2022-07-31 22:44:00,1674.55,,open_close -2022-07-31 22:45:00,1674.24,,open_close -2022-07-31 22:46:00,1671.74,,open_close -2022-07-31 22:47:00,1671.43,,open_close -2022-07-31 22:48:00,1671.28,,open_close -2022-07-31 22:49:00,1668.02,,open_close -2022-07-31 22:50:00,1670.93,,open_close -2022-07-31 22:51:00,1675.63,,open_close -2022-07-31 22:52:00,1675.07,,open_close -2022-07-31 22:53:00,1674.23,,open_close -2022-07-31 22:54:00,1674.16,,open_close -2022-07-31 22:55:00,1674.25,,open_close -2022-07-31 22:56:00,1677.89,,open_close -2022-07-31 22:57:00,1680.34,,open_close -2022-07-31 22:58:00,1681.97,,infty -2022-07-31 22:59:00,1685.18,,infty -2022-07-31 23:00:00,1682.53,,infty -2022-07-31 23:01:00,1680.06,,open_close -2022-07-31 23:02:00,1680.42,,open_close -2022-07-31 23:03:00,1682.67,,infty -2022-07-31 23:04:00,1682.41,,infty -2022-07-31 23:05:00,1679.53,,open_close -2022-07-31 23:06:00,1680.32,,open_close -2022-07-31 23:07:00,1679.47,,open_close -2022-07-31 23:08:00,1678.99,,open_close -2022-07-31 23:09:00,1678.37,,open_close -2022-07-31 23:10:00,1677.96,,open_close -2022-07-31 23:11:00,1677.87,,open_close -2022-07-31 23:12:00,1679.61,,open_close -2022-07-31 23:13:00,1678.55,,open_close -2022-07-31 23:14:00,1679.0,,open_close -2022-07-31 23:15:00,1680.49,,open_close -2022-07-31 23:16:00,1681.13,,open_close -2022-07-31 23:17:00,1680.08,,open_close -2022-07-31 23:18:00,1681.53,,infty -2022-07-31 23:19:00,1682.27,,infty -2022-07-31 23:20:00,1683.54,,infty -2022-07-31 23:21:00,1683.01,,infty -2022-07-31 23:22:00,1683.09,,infty -2022-07-31 23:23:00,1679.93,,open_close -2022-07-31 23:24:00,1681.69,,infty -2022-07-31 23:25:00,1679.0,,open_close -2022-07-31 23:26:00,1680.16,,open_close -2022-07-31 23:27:00,1679.45,,open_close -2022-07-31 23:28:00,1678.59,,open_close -2022-07-31 23:29:00,1680.0,,open_close -2022-07-31 23:30:00,1681.68,,infty -2022-07-31 23:31:00,1680.07,,open_close -2022-07-31 23:32:00,1679.97,,open_close -2022-07-31 23:33:00,1680.0,,open_close -2022-07-31 23:34:00,1681.92,,infty -2022-07-31 23:35:00,1681.87,,infty -2022-07-31 23:36:00,1683.83,,infty -2022-07-31 23:37:00,1683.3,,infty -2022-07-31 23:38:00,1684.86,,infty -2022-07-31 23:39:00,1685.31,,infty -2022-07-31 23:40:00,1690.76,,infty -2022-07-31 23:41:00,1688.21,,infty -2022-07-31 23:42:00,1688.85,,infty -2022-07-31 23:43:00,1687.71,,infty -2022-07-31 23:44:00,1687.15,,infty -2022-07-31 23:45:00,1683.88,,infty -2022-07-31 23:46:00,1684.46,,infty -2022-07-31 23:47:00,1683.42,,infty -2022-07-31 23:48:00,1683.17,,infty -2022-07-31 23:49:00,1684.3,,infty -2022-07-31 23:50:00,1684.54,,infty -2022-07-31 23:51:00,1680.06,,open_close -2022-07-31 23:52:00,1681.97,,infty -2022-07-31 23:53:00,1680.87,,open_close -2022-07-31 23:54:00,1680.06,,open_close -2022-07-31 23:55:00,1681.43,,infty -2022-07-31 23:56:00,1681.2,,infty -2022-07-31 23:57:00,1681.54,,infty -2022-07-31 23:58:00,1681.14,,open_close -2022-07-31 23:59:00,1678.95,,open_close -2022-08-01 00:00:00,1676.65,,open_close -2022-08-01 00:01:00,1681.27,,infty -2022-08-01 00:02:00,1677.88,,open_close -2022-08-01 00:03:00,1678.09,,open_close -2022-08-01 00:04:00,1677.75,,open_close -2022-08-01 00:05:00,1677.24,,open_close -2022-08-01 00:06:00,1677.03,,open_close -2022-08-01 00:07:00,1676.16,,open_close -2022-08-01 00:08:00,1675.1,,open_close -2022-08-01 00:09:00,1676.95,,open_close -2022-08-01 00:10:00,1678.14,,open_close -2022-08-01 00:11:00,1679.91,,open_close -2022-08-01 00:12:00,1677.24,,open_close -2022-08-01 00:13:00,1677.35,,open_close -2022-08-01 00:14:00,1676.23,,open_close -2022-08-01 00:15:00,1679.19,,open_close -2022-08-01 00:16:00,1678.39,,open_close -2022-08-01 00:17:00,1678.31,,open_close -2022-08-01 00:18:00,1677.24,,open_close -2022-08-01 00:19:00,1676.44,,open_close -2022-08-01 00:20:00,1676.44,,open_close -2022-08-01 00:21:00,1676.08,,open_close -2022-08-01 00:22:00,1675.73,,open_close -2022-08-01 00:23:00,1676.29,,open_close -2022-08-01 00:24:00,1678.91,,open_close -2022-08-01 00:25:00,1682.41,,infty -2022-08-01 00:26:00,1682.77,,infty -2022-08-01 00:27:00,1684.18,,infty -2022-08-01 00:28:00,1686.73,,infty -2022-08-01 00:29:00,1686.97,,infty -2022-08-01 00:30:00,1687.78,,infty -2022-08-01 00:31:00,1691.12,,infty -2022-08-01 00:32:00,1688.73,,infty -2022-08-01 00:33:00,1686.45,,infty -2022-08-01 00:34:00,1685.08,,infty -2022-08-01 00:35:00,1683.78,,infty -2022-08-01 00:36:00,1685.53,,infty -2022-08-01 00:37:00,1685.49,,infty -2022-08-01 00:38:00,1686.65,,infty -2022-08-01 00:39:00,1687.33,,infty -2022-08-01 00:40:00,1687.54,,infty -2022-08-01 00:41:00,1688.93,,infty -2022-08-01 00:42:00,1688.39,,infty -2022-08-01 00:43:00,1688.53,,infty -2022-08-01 00:44:00,1686.32,,infty -2022-08-01 00:45:00,1687.56,,infty -2022-08-01 00:46:00,1687.74,,infty -2022-08-01 00:47:00,1688.94,,infty -2022-08-01 00:48:00,1688.91,,infty -2022-08-01 00:49:00,1688.18,,infty -2022-08-01 00:50:00,1688.54,,infty -2022-08-01 00:51:00,1688.96,,infty -2022-08-01 00:52:00,1689.07,,infty -2022-08-01 00:53:00,1689.77,,infty -2022-08-01 00:54:00,1689.87,,infty -2022-08-01 00:55:00,1690.67,,infty -2022-08-01 00:56:00,1690.55,,infty -2022-08-01 00:57:00,1689.6,,infty -2022-08-01 00:58:00,1687.71,,infty -2022-08-01 00:59:00,1689.09,,infty -2022-08-01 01:00:00,1688.16,,infty -2022-08-01 01:01:00,1688.33,,infty -2022-08-01 01:02:00,1685.16,,infty -2022-08-01 01:03:00,1686.78,,infty -2022-08-01 01:04:00,1686.27,,infty -2022-08-01 01:05:00,1686.77,,infty -2022-08-01 01:06:00,1685.86,,infty -2022-08-01 01:07:00,1685.01,,infty -2022-08-01 01:08:00,1687.93,,infty -2022-08-01 01:09:00,1687.56,,infty -2022-08-01 01:10:00,1687.44,,infty -2022-08-01 01:11:00,1688.93,,infty -2022-08-01 01:12:00,1689.94,,infty -2022-08-01 01:13:00,1688.86,,infty -2022-08-01 01:14:00,1689.87,,infty -2022-08-01 01:15:00,1688.58,,infty -2022-08-01 01:16:00,1688.49,,infty -2022-08-01 01:17:00,1689.2,,infty -2022-08-01 01:18:00,1691.47,,infty -2022-08-01 01:19:00,1694.27,,infty -2022-08-01 01:20:00,1693.71,,infty -2022-08-01 01:21:00,1692.44,,infty -2022-08-01 01:22:00,1691.7,,infty -2022-08-01 01:23:00,1691.5,,infty -2022-08-01 01:24:00,1693.44,,infty -2022-08-01 01:25:00,1693.33,,infty -2022-08-01 01:26:00,1695.21,,infty -2022-08-01 01:27:00,1695.15,,infty -2022-08-01 01:28:00,1696.87,,infty -2022-08-01 01:29:00,1697.55,,infty -2022-08-01 01:30:00,1696.72,,infty -2022-08-01 01:31:00,1696.65,,infty -2022-08-01 01:32:00,1696.01,,infty -2022-08-01 01:33:00,1696.86,,infty -2022-08-01 01:34:00,1695.69,,infty -2022-08-01 01:35:00,1695.31,,infty -2022-08-01 01:36:00,1697.93,,infty -2022-08-01 01:37:00,1696.51,,infty -2022-08-01 01:38:00,1697.39,,infty -2022-08-01 01:39:00,1698.34,,infty -2022-08-01 01:40:00,1696.33,,infty -2022-08-01 01:41:00,1697.94,,infty -2022-08-01 01:42:00,1697.66,,infty -2022-08-01 01:43:00,1696.28,,infty -2022-08-01 01:44:00,1697.28,,infty -2022-08-01 01:45:00,1696.93,,infty -2022-08-01 01:46:00,1696.7,,infty -2022-08-01 01:47:00,1693.77,,infty -2022-08-01 01:48:00,1695.25,,infty -2022-08-01 01:49:00,1694.24,,infty -2022-08-01 01:50:00,1693.3,,infty -2022-08-01 01:51:00,1694.64,,infty -2022-08-01 01:52:00,1695.37,,infty -2022-08-01 01:53:00,1694.3,,infty -2022-08-01 01:54:00,1694.2,,infty -2022-08-01 01:55:00,1695.68,,infty -2022-08-01 01:56:00,1695.42,,infty -2022-08-01 01:57:00,1696.37,,infty -2022-08-01 01:58:00,1696.11,,infty -2022-08-01 01:59:00,1697.49,,infty -2022-08-01 02:00:00,1696.95,,infty -2022-08-01 02:01:00,1701.0,,infty -2022-08-01 02:02:00,1704.11,,infty -2022-08-01 02:03:00,1701.19,,infty -2022-08-01 02:04:00,1701.9,,infty -2022-08-01 02:05:00,1701.42,,infty -2022-08-01 02:06:00,1696.73,,infty -2022-08-01 02:07:00,1698.08,,infty -2022-08-01 02:08:00,1697.55,,infty -2022-08-01 02:09:00,1696.16,,infty -2022-08-01 02:10:00,1694.93,,infty -2022-08-01 02:11:00,1694.2,,infty -2022-08-01 02:12:00,1696.76,,infty -2022-08-01 02:13:00,1698.77,,infty -2022-08-01 02:14:00,1699.28,,infty -2022-08-01 02:15:00,1698.49,,infty -2022-08-01 02:16:00,1700.09,,infty -2022-08-01 02:17:00,1697.6,,infty -2022-08-01 02:18:00,1698.15,,infty -2022-08-01 02:19:00,1697.25,,infty -2022-08-01 02:20:00,1697.1,,infty -2022-08-01 02:21:00,1696.24,,infty -2022-08-01 02:22:00,1696.56,,infty -2022-08-01 02:23:00,1696.69,,infty -2022-08-01 02:24:00,1695.94,,infty -2022-08-01 02:25:00,1696.9,,infty -2022-08-01 02:26:00,1698.02,,infty -2022-08-01 02:27:00,1696.85,,infty -2022-08-01 02:28:00,1697.12,,infty -2022-08-01 02:29:00,1697.92,,infty -2022-08-01 02:30:00,1697.42,,infty -2022-08-01 02:31:00,1697.3,,infty -2022-08-01 02:32:00,1697.85,,infty -2022-08-01 02:33:00,1697.76,,infty -2022-08-01 02:34:00,1697.28,,infty -2022-08-01 02:35:00,1693.7,,infty -2022-08-01 02:36:00,1695.67,,infty -2022-08-01 02:37:00,1694.6,,infty -2022-08-01 02:38:00,1695.18,,infty -2022-08-01 02:39:00,1693.13,,infty -2022-08-01 02:40:00,1692.65,,infty -2022-08-01 02:41:00,1693.57,,infty -2022-08-01 02:42:00,1692.63,,infty -2022-08-01 02:43:00,1692.46,,infty -2022-08-01 02:44:00,1693.24,,infty -2022-08-01 02:45:00,1692.72,,infty -2022-08-01 02:46:00,1692.72,,infty -2022-08-01 02:47:00,1693.12,,infty -2022-08-01 02:48:00,1693.89,,infty -2022-08-01 02:49:00,1691.46,,infty -2022-08-01 02:50:00,1694.69,,infty -2022-08-01 02:51:00,1694.64,,infty -2022-08-01 02:52:00,1692.89,,infty -2022-08-01 02:53:00,1693.59,,infty -2022-08-01 02:54:00,1695.08,,infty -2022-08-01 02:55:00,1694.75,,infty -2022-08-01 02:56:00,1696.41,,infty -2022-08-01 02:57:00,1696.34,,infty -2022-08-01 02:58:00,1695.21,,infty -2022-08-01 02:59:00,1695.53,,infty -2022-08-01 03:00:00,1695.29,,infty -2022-08-01 03:01:00,1696.16,,infty -2022-08-01 03:02:00,1694.76,,infty -2022-08-01 03:03:00,1696.29,,infty -2022-08-01 03:04:00,1694.43,,infty -2022-08-01 03:05:00,1695.52,,infty -2022-08-01 03:06:00,1693.9,,infty -2022-08-01 03:07:00,1693.12,,infty -2022-08-01 03:08:00,1692.95,,infty -2022-08-01 03:09:00,1693.39,,infty -2022-08-01 03:10:00,1692.7,,infty -2022-08-01 03:11:00,1691.63,,infty -2022-08-01 03:12:00,1692.8,,infty -2022-08-01 03:13:00,1692.83,,infty -2022-08-01 03:14:00,1692.43,,infty -2022-08-01 03:15:00,1689.21,,infty -2022-08-01 03:16:00,1688.26,,infty -2022-08-01 03:17:00,1688.71,,infty -2022-08-01 03:18:00,1686.33,,infty -2022-08-01 03:19:00,1686.36,,infty -2022-08-01 03:20:00,1687.69,,infty -2022-08-01 03:21:00,1687.37,,infty -2022-08-01 03:22:00,1688.42,,infty -2022-08-01 03:23:00,1687.93,,infty -2022-08-01 03:24:00,1687.32,,infty -2022-08-01 03:25:00,1687.54,,infty -2022-08-01 03:26:00,1688.27,,infty -2022-08-01 03:27:00,1686.63,,infty -2022-08-01 03:28:00,1684.94,,infty -2022-08-01 03:29:00,1685.64,,infty -2022-08-01 03:30:00,1686.29,,infty -2022-08-01 03:31:00,1685.79,,infty -2022-08-01 03:32:00,1685.81,,infty -2022-08-01 03:33:00,1687.14,,infty -2022-08-01 03:34:00,1686.6,,infty -2022-08-01 03:35:00,1686.32,,infty -2022-08-01 03:36:00,1687.66,,infty -2022-08-01 03:37:00,1687.75,,infty -2022-08-01 03:38:00,1687.57,,infty -2022-08-01 03:39:00,1687.19,,infty -2022-08-01 03:40:00,1687.5,,infty -2022-08-01 03:41:00,1686.97,,infty -2022-08-01 03:42:00,1687.4,,infty -2022-08-01 03:43:00,1687.08,,infty -2022-08-01 03:44:00,1687.11,,infty -2022-08-01 03:45:00,1686.46,,infty -2022-08-01 03:46:00,1689.8,,infty -2022-08-01 03:47:00,1689.81,,infty -2022-08-01 03:48:00,1689.46,,infty -2022-08-01 03:49:00,1690.12,,infty -2022-08-01 03:50:00,1690.64,,infty -2022-08-01 03:51:00,1692.43,,infty -2022-08-01 03:52:00,1692.28,,infty -2022-08-01 03:53:00,1691.69,,infty -2022-08-01 03:54:00,1692.1,,infty -2022-08-01 03:55:00,1692.49,,infty -2022-08-01 03:56:00,1692.78,,infty -2022-08-01 03:57:00,1692.6,,infty -2022-08-01 03:58:00,1690.91,,infty -2022-08-01 03:59:00,1692.61,,infty -2022-08-01 04:00:00,1694.5,,infty -2022-08-01 04:01:00,1693.38,,infty -2022-08-01 04:02:00,1692.79,,infty -2022-08-01 04:03:00,1692.84,,infty -2022-08-01 04:04:00,1693.65,,infty -2022-08-01 04:05:00,1691.15,,infty -2022-08-01 04:06:00,1690.41,,infty -2022-08-01 04:07:00,1689.15,,infty -2022-08-01 04:08:00,1690.98,,infty -2022-08-01 04:09:00,1689.2,,infty -2022-08-01 04:10:00,1689.88,,infty -2022-08-01 04:11:00,1691.54,,infty -2022-08-01 04:12:00,1692.58,,infty -2022-08-01 04:13:00,1691.92,,infty -2022-08-01 04:14:00,1691.91,,infty -2022-08-01 04:15:00,1690.09,,infty -2022-08-01 04:16:00,1691.26,,infty -2022-08-01 04:17:00,1691.11,,infty -2022-08-01 04:18:00,1690.79,,infty -2022-08-01 04:19:00,1690.46,,infty -2022-08-01 04:20:00,1690.16,,infty -2022-08-01 04:21:00,1692.38,,infty -2022-08-01 04:22:00,1692.71,,infty -2022-08-01 04:23:00,1695.0,,infty -2022-08-01 04:24:00,1694.55,,infty -2022-08-01 04:25:00,1693.89,,infty -2022-08-01 04:26:00,1693.53,,infty -2022-08-01 04:27:00,1693.45,,infty -2022-08-01 04:28:00,1693.25,,infty -2022-08-01 04:29:00,1693.79,,infty -2022-08-01 04:30:00,1693.79,,infty -2022-08-01 04:31:00,1695.23,,infty -2022-08-01 04:32:00,1695.12,,infty -2022-08-01 04:33:00,1694.25,,infty -2022-08-01 04:34:00,1695.0,,infty -2022-08-01 04:35:00,1695.57,,infty -2022-08-01 04:36:00,1695.79,,infty -2022-08-01 04:37:00,1694.64,,infty -2022-08-01 04:38:00,1694.64,,infty -2022-08-01 04:39:00,1693.33,,infty -2022-08-01 04:40:00,1693.58,,infty -2022-08-01 04:41:00,1693.28,,infty -2022-08-01 04:42:00,1693.88,,infty -2022-08-01 04:43:00,1695.86,,infty -2022-08-01 04:44:00,1695.45,,infty -2022-08-01 04:45:00,1692.68,,infty -2022-08-01 04:46:00,1695.35,,infty -2022-08-01 04:47:00,1694.86,,infty -2022-08-01 04:48:00,1694.81,,infty -2022-08-01 04:49:00,1694.98,,infty -2022-08-01 04:50:00,1695.91,,infty -2022-08-01 04:51:00,1695.58,,infty -2022-08-01 04:52:00,1695.4,,infty -2022-08-01 04:53:00,1695.61,,infty -2022-08-01 04:54:00,1695.77,,infty -2022-08-01 04:55:00,1695.43,,infty -2022-08-01 04:56:00,1695.25,,infty -2022-08-01 04:57:00,1696.0,,infty -2022-08-01 04:58:00,1697.47,,infty -2022-08-01 04:59:00,1695.34,,infty -2022-08-01 05:00:00,1695.61,,infty -2022-08-01 05:01:00,1696.4,,infty -2022-08-01 05:02:00,1697.2,,infty -2022-08-01 05:03:00,1696.19,,infty -2022-08-01 05:04:00,1695.73,,infty -2022-08-01 05:05:00,1695.99,,infty -2022-08-01 05:06:00,1694.92,,infty -2022-08-01 05:07:00,1693.94,,infty -2022-08-01 05:08:00,1693.52,,infty -2022-08-01 05:09:00,1693.23,,infty -2022-08-01 05:10:00,1690.96,,infty 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05:38:00,1690.39,,infty -2022-08-01 05:39:00,1691.85,,infty -2022-08-01 05:40:00,1691.61,,infty -2022-08-01 05:41:00,1690.16,,infty -2022-08-01 05:42:00,1688.3,,infty -2022-08-01 05:43:00,1687.31,,infty -2022-08-01 05:44:00,1686.6,,infty -2022-08-01 05:45:00,1685.71,,infty -2022-08-01 05:46:00,1686.51,,infty -2022-08-01 05:47:00,1688.15,,infty -2022-08-01 05:48:00,1687.97,,infty -2022-08-01 05:49:00,1688.51,,infty -2022-08-01 05:50:00,1687.72,,infty -2022-08-01 05:51:00,1688.25,,infty -2022-08-01 05:52:00,1687.64,,infty -2022-08-01 05:53:00,1688.19,,infty -2022-08-01 05:54:00,1686.82,,infty -2022-08-01 05:55:00,1688.02,,infty -2022-08-01 05:56:00,1692.99,,infty -2022-08-01 05:57:00,1690.82,,infty -2022-08-01 05:58:00,1690.8,,infty -2022-08-01 05:59:00,1691.96,,infty -2022-08-01 06:00:00,1691.89,,infty -2022-08-01 06:01:00,1691.17,,infty -2022-08-01 06:02:00,1691.64,,infty -2022-08-01 06:03:00,1691.06,,infty -2022-08-01 06:04:00,1691.04,,infty -2022-08-01 06:05:00,1690.77,,infty -2022-08-01 06:06:00,1689.32,,infty -2022-08-01 06:07:00,1687.41,,infty -2022-08-01 06:08:00,1688.91,,infty -2022-08-01 06:09:00,1689.28,,infty -2022-08-01 06:10:00,1690.95,,infty -2022-08-01 06:11:00,1688.58,,infty -2022-08-01 06:12:00,1688.44,,infty -2022-08-01 06:13:00,1687.73,,infty -2022-08-01 06:14:00,1686.63,,infty -2022-08-01 06:15:00,1686.09,,infty -2022-08-01 06:16:00,1684.01,,infty -2022-08-01 06:17:00,1684.97,,infty -2022-08-01 06:18:00,1685.11,,infty -2022-08-01 06:19:00,1684.63,,infty -2022-08-01 06:20:00,1686.01,,infty -2022-08-01 06:21:00,1685.28,,infty -2022-08-01 06:22:00,1685.24,,infty -2022-08-01 06:23:00,1685.08,,infty -2022-08-01 06:24:00,1685.76,,infty -2022-08-01 06:25:00,1683.5,,infty -2022-08-01 06:26:00,1679.72,,open_close -2022-08-01 06:27:00,1680.18,,open_close -2022-08-01 06:28:00,1677.0,,open_close -2022-08-01 06:29:00,1676.65,,open_close -2022-08-01 06:30:00,1678.13,,open_close -2022-08-01 06:31:00,1677.5,,open_close -2022-08-01 06:32:00,1677.55,,open_close -2022-08-01 06:33:00,1679.17,,open_close -2022-08-01 06:34:00,1679.72,,open_close -2022-08-01 06:35:00,1680.65,,open_close -2022-08-01 06:36:00,1680.75,,open_close -2022-08-01 06:37:00,1680.19,,open_close -2022-08-01 06:38:00,1679.12,,open_close -2022-08-01 06:39:00,1678.44,,open_close -2022-08-01 06:40:00,1679.58,,open_close -2022-08-01 06:41:00,1678.55,,open_close -2022-08-01 06:42:00,1681.35,,infty -2022-08-01 06:43:00,1681.27,,infty -2022-08-01 06:44:00,1679.32,,open_close -2022-08-01 06:45:00,1684.95,,infty -2022-08-01 06:46:00,1684.23,,infty -2022-08-01 06:47:00,1684.24,,infty -2022-08-01 06:48:00,1684.07,,infty -2022-08-01 06:49:00,1683.62,,infty -2022-08-01 06:50:00,1686.29,,infty -2022-08-01 06:51:00,1687.12,,infty -2022-08-01 06:52:00,1685.52,,infty -2022-08-01 06:53:00,1685.35,,infty -2022-08-01 06:54:00,1685.9,,infty -2022-08-01 06:55:00,1684.67,,infty -2022-08-01 06:56:00,1685.46,,infty -2022-08-01 06:57:00,1686.53,,infty -2022-08-01 06:58:00,1685.69,,infty -2022-08-01 06:59:00,1686.3,,infty -2022-08-01 07:00:00,1684.86,,infty -2022-08-01 07:01:00,1684.54,,infty -2022-08-01 07:02:00,1684.45,,infty -2022-08-01 07:03:00,1683.83,,infty -2022-08-01 07:04:00,1684.29,,infty -2022-08-01 07:05:00,1683.8,,infty -2022-08-01 07:06:00,1684.75,,infty -2022-08-01 07:07:00,1685.75,,infty -2022-08-01 07:08:00,1686.28,,infty -2022-08-01 07:09:00,1684.66,,infty -2022-08-01 07:10:00,1684.84,,infty -2022-08-01 07:11:00,1685.55,,infty -2022-08-01 07:12:00,1684.66,,infty -2022-08-01 07:13:00,1683.74,,infty -2022-08-01 07:14:00,1682.45,,infty -2022-08-01 07:15:00,1680.95,,open_close -2022-08-01 07:16:00,1676.42,,open_close -2022-08-01 07:17:00,1678.21,,open_close -2022-08-01 07:18:00,1677.54,,open_close -2022-08-01 07:19:00,1675.64,,open_close -2022-08-01 07:20:00,1676.54,,open_close -2022-08-01 07:21:00,1676.65,,open_close -2022-08-01 07:22:00,1677.07,,open_close -2022-08-01 07:23:00,1679.21,,open_close -2022-08-01 07:24:00,1678.75,,open_close -2022-08-01 07:25:00,1677.88,,open_close -2022-08-01 07:26:00,1676.88,,open_close -2022-08-01 07:27:00,1676.64,,open_close -2022-08-01 07:28:00,1676.85,,open_close -2022-08-01 07:29:00,1676.63,,open_close -2022-08-01 07:30:00,1676.91,,open_close -2022-08-01 07:31:00,1677.83,,open_close -2022-08-01 07:32:00,1679.94,,open_close -2022-08-01 07:33:00,1679.04,,open_close -2022-08-01 07:34:00,1677.48,,open_close -2022-08-01 07:35:00,1679.03,,open_close -2022-08-01 07:36:00,1679.75,,open_close -2022-08-01 07:37:00,1680.0,,open_close -2022-08-01 07:38:00,1679.2,,open_close -2022-08-01 07:39:00,1681.91,,infty -2022-08-01 07:40:00,1681.5,,infty -2022-08-01 07:41:00,1681.36,,infty -2022-08-01 07:42:00,1683.0,,infty -2022-08-01 07:43:00,1685.13,,infty -2022-08-01 07:44:00,1686.14,,infty -2022-08-01 07:45:00,1686.45,,infty -2022-08-01 07:46:00,1689.84,,infty -2022-08-01 07:47:00,1689.19,,infty -2022-08-01 07:48:00,1688.49,,infty -2022-08-01 07:49:00,1688.91,,infty -2022-08-01 07:50:00,1690.98,,infty -2022-08-01 07:51:00,1690.8,,infty -2022-08-01 07:52:00,1690.3,,infty -2022-08-01 07:53:00,1689.81,,infty -2022-08-01 07:54:00,1689.77,,infty -2022-08-01 07:55:00,1690.68,,infty -2022-08-01 07:56:00,1691.4,,infty -2022-08-01 07:57:00,1694.28,,infty -2022-08-01 07:58:00,1694.19,,infty -2022-08-01 07:59:00,1693.31,,infty -2022-08-01 08:00:00,1694.22,,infty -2022-08-01 08:01:00,1696.12,,infty -2022-08-01 08:02:00,1692.17,,infty -2022-08-01 08:03:00,1692.4,,infty -2022-08-01 08:04:00,1693.92,,infty -2022-08-01 08:05:00,1693.21,,infty -2022-08-01 08:06:00,1691.63,,infty -2022-08-01 08:07:00,1693.39,,infty -2022-08-01 08:08:00,1693.41,,infty -2022-08-01 08:09:00,1694.87,,infty -2022-08-01 08:10:00,1694.35,,infty -2022-08-01 08:11:00,1693.01,,infty -2022-08-01 08:12:00,1692.74,,infty -2022-08-01 08:13:00,1692.14,,infty -2022-08-01 08:14:00,1691.09,,infty -2022-08-01 08:15:00,1691.45,,infty -2022-08-01 08:16:00,1689.52,,infty -2022-08-01 08:17:00,1691.35,,infty -2022-08-01 08:18:00,1692.91,,infty -2022-08-01 08:19:00,1692.24,,infty -2022-08-01 08:20:00,1691.5,,infty -2022-08-01 08:21:00,1691.63,,infty -2022-08-01 08:22:00,1690.99,,infty -2022-08-01 08:23:00,1690.3,,infty -2022-08-01 08:24:00,1690.25,,infty -2022-08-01 08:25:00,1690.45,,infty -2022-08-01 08:26:00,1689.4,,infty -2022-08-01 08:27:00,1689.31,,infty -2022-08-01 08:28:00,1689.24,,infty -2022-08-01 08:29:00,1686.99,,infty -2022-08-01 08:30:00,1688.01,,infty -2022-08-01 08:31:00,1686.83,,infty -2022-08-01 08:32:00,1689.17,,infty -2022-08-01 08:33:00,1689.88,,infty -2022-08-01 08:34:00,1690.52,,infty -2022-08-01 08:35:00,1688.76,,infty -2022-08-01 08:36:00,1688.6,,infty -2022-08-01 08:37:00,1688.7,,infty -2022-08-01 08:38:00,1689.78,,infty -2022-08-01 08:39:00,1689.62,,infty -2022-08-01 08:40:00,1688.26,,infty -2022-08-01 08:41:00,1688.31,,infty -2022-08-01 08:42:00,1688.21,,infty -2022-08-01 08:43:00,1683.65,,infty -2022-08-01 08:44:00,1684.17,,infty -2022-08-01 08:45:00,1682.57,,infty -2022-08-01 08:46:00,1683.15,,infty -2022-08-01 08:47:00,1684.99,,infty -2022-08-01 08:48:00,1685.49,,infty -2022-08-01 08:49:00,1685.93,,infty -2022-08-01 08:50:00,1685.04,,infty -2022-08-01 08:51:00,1684.14,,infty -2022-08-01 08:52:00,1683.95,,infty -2022-08-01 08:53:00,1683.71,,infty -2022-08-01 08:54:00,1684.13,,infty -2022-08-01 08:55:00,1683.83,,infty -2022-08-01 08:56:00,1684.14,,infty -2022-08-01 08:57:00,1684.99,,infty -2022-08-01 08:58:00,1683.79,,infty -2022-08-01 08:59:00,1683.85,,infty -2022-08-01 09:00:00,1683.61,,infty -2022-08-01 09:01:00,1684.13,,infty -2022-08-01 09:02:00,1683.15,,infty -2022-08-01 09:03:00,1684.94,,infty -2022-08-01 09:04:00,1686.19,,infty -2022-08-01 09:05:00,1685.91,,infty -2022-08-01 09:06:00,1687.28,,infty -2022-08-01 09:07:00,1685.8,,infty -2022-08-01 09:08:00,1686.47,,infty -2022-08-01 09:09:00,1686.84,,infty -2022-08-01 09:10:00,1688.78,,infty -2022-08-01 09:11:00,1687.51,,infty -2022-08-01 09:12:00,1689.4,,infty -2022-08-01 09:13:00,1688.61,,infty -2022-08-01 09:14:00,1690.17,,infty -2022-08-01 09:15:00,1689.24,,infty -2022-08-01 09:16:00,1689.41,,infty -2022-08-01 09:17:00,1688.57,,infty -2022-08-01 09:18:00,1688.82,,infty -2022-08-01 09:19:00,1688.69,,infty -2022-08-01 09:20:00,1691.5,,infty -2022-08-01 09:21:00,1692.48,,infty -2022-08-01 09:22:00,1692.29,,infty -2022-08-01 09:23:00,1691.4,,infty -2022-08-01 09:24:00,1691.61,,infty -2022-08-01 09:25:00,1690.82,,infty -2022-08-01 09:26:00,1690.68,,infty -2022-08-01 09:27:00,1691.52,,infty -2022-08-01 09:28:00,1690.67,,infty -2022-08-01 09:29:00,1690.05,,infty -2022-08-01 09:30:00,1691.59,,infty -2022-08-01 09:31:00,1692.67,,infty -2022-08-01 09:32:00,1691.56,,infty -2022-08-01 09:33:00,1691.75,,infty -2022-08-01 09:34:00,1692.34,,infty -2022-08-01 09:35:00,1689.99,,infty -2022-08-01 09:36:00,1689.57,,infty -2022-08-01 09:37:00,1689.92,,infty -2022-08-01 09:38:00,1689.79,,infty -2022-08-01 09:39:00,1690.27,,infty -2022-08-01 09:40:00,1689.55,,infty -2022-08-01 09:41:00,1690.47,,infty -2022-08-01 09:42:00,1691.06,,infty -2022-08-01 09:43:00,1690.53,,infty -2022-08-01 09:44:00,1689.35,,infty -2022-08-01 09:45:00,1689.84,,infty -2022-08-01 09:46:00,1689.01,,infty -2022-08-01 09:47:00,1687.68,,infty -2022-08-01 09:48:00,1688.86,,infty -2022-08-01 09:49:00,1689.79,,infty -2022-08-01 09:50:00,1688.92,,infty -2022-08-01 09:51:00,1689.51,,infty -2022-08-01 09:52:00,1689.64,,infty -2022-08-01 09:53:00,1687.61,,infty -2022-08-01 09:54:00,1685.93,,infty -2022-08-01 09:55:00,1685.07,,infty -2022-08-01 09:56:00,1686.24,,infty -2022-08-01 09:57:00,1687.0,,infty -2022-08-01 09:58:00,1686.85,,infty -2022-08-01 09:59:00,1687.07,,infty -2022-08-01 10:00:00,1687.32,,infty -2022-08-01 10:01:00,1687.53,,infty -2022-08-01 10:02:00,1687.94,,infty -2022-08-01 10:03:00,1688.03,,infty -2022-08-01 10:04:00,1688.47,,infty -2022-08-01 10:05:00,1686.88,,infty -2022-08-01 10:06:00,1688.49,,infty -2022-08-01 10:07:00,1688.09,,infty -2022-08-01 10:08:00,1688.6,,infty -2022-08-01 10:09:00,1686.22,,infty -2022-08-01 10:10:00,1684.2,,infty -2022-08-01 10:11:00,1685.49,,infty -2022-08-01 10:12:00,1685.61,,infty -2022-08-01 10:13:00,1685.63,,infty -2022-08-01 10:14:00,1681.41,,infty -2022-08-01 10:15:00,1682.93,,infty -2022-08-01 10:16:00,1683.16,,infty -2022-08-01 10:17:00,1683.21,,infty -2022-08-01 10:18:00,1682.64,,infty -2022-08-01 10:19:00,1682.38,,infty -2022-08-01 10:20:00,1680.2,,open_close -2022-08-01 10:21:00,1681.23,,infty -2022-08-01 10:22:00,1681.82,,infty -2022-08-01 10:23:00,1682.08,,infty -2022-08-01 10:24:00,1682.56,,infty -2022-08-01 10:25:00,1682.11,,infty -2022-08-01 10:26:00,1681.45,,infty -2022-08-01 10:27:00,1681.36,,infty -2022-08-01 10:28:00,1682.28,,infty -2022-08-01 10:29:00,1682.81,,infty -2022-08-01 10:30:00,1682.06,,infty -2022-08-01 10:31:00,1678.55,,open_close -2022-08-01 10:32:00,1680.03,,open_close -2022-08-01 10:33:00,1682.82,,infty -2022-08-01 10:34:00,1681.77,,infty -2022-08-01 10:35:00,1679.61,,open_close -2022-08-01 10:36:00,1677.96,,open_close -2022-08-01 10:37:00,1676.43,,open_close -2022-08-01 10:38:00,1679.88,,open_close -2022-08-01 10:39:00,1679.87,,open_close -2022-08-01 10:40:00,1679.18,,open_close -2022-08-01 10:41:00,1677.32,,open_close -2022-08-01 10:42:00,1673.4,,open_close -2022-08-01 10:43:00,1672.39,,open_close -2022-08-01 10:44:00,1671.28,,open_close -2022-08-01 10:45:00,1673.54,,open_close -2022-08-01 10:46:00,1670.81,,open_close -2022-08-01 10:47:00,1666.41,,open_close -2022-08-01 10:48:00,1660.8,,open_close -2022-08-01 10:49:00,1659.86,,open_close -2022-08-01 10:50:00,1658.04,,open_close -2022-08-01 10:51:00,1653.22,,open_close -2022-08-01 10:52:00,1655.82,,open_close -2022-08-01 10:53:00,1655.06,,open_close -2022-08-01 10:54:00,1655.76,,open_close -2022-08-01 10:55:00,1657.37,,open_close -2022-08-01 10:56:00,1658.03,,open_close -2022-08-01 10:57:00,1657.42,,open_close -2022-08-01 10:58:00,1658.37,,open_close -2022-08-01 10:59:00,1658.06,,open_close -2022-08-01 11:00:00,1657.19,,open_close -2022-08-01 11:01:00,1659.22,,open_close -2022-08-01 11:02:00,1658.85,,open_close -2022-08-01 11:03:00,1657.44,,open_close -2022-08-01 11:04:00,1655.69,,open_close -2022-08-01 11:05:00,1655.87,,open_close -2022-08-01 11:06:00,1655.38,,open_close -2022-08-01 11:07:00,1652.6,,open_close -2022-08-01 11:08:00,1645.85,,open_close -2022-08-01 11:09:00,1646.21,,open_close -2022-08-01 11:10:00,1648.72,,open_close -2022-08-01 11:11:00,1649.02,,open_close -2022-08-01 11:12:00,1650.43,,open_close -2022-08-01 11:13:00,1648.59,,open_close -2022-08-01 11:14:00,1649.47,,open_close -2022-08-01 11:15:00,1649.16,,open_close -2022-08-01 11:16:00,1649.02,,open_close -2022-08-01 11:17:00,1652.95,,open_close -2022-08-01 11:18:00,1654.63,,open_close -2022-08-01 11:19:00,1657.77,,open_close -2022-08-01 11:20:00,1657.4,,open_close -2022-08-01 11:21:00,1655.65,,open_close -2022-08-01 11:22:00,1654.16,,open_close -2022-08-01 11:23:00,1654.79,,open_close -2022-08-01 11:24:00,1655.21,,open_close -2022-08-01 11:25:00,1656.73,,open_close -2022-08-01 11:26:00,1655.2,,open_close -2022-08-01 11:27:00,1656.06,,open_close -2022-08-01 11:28:00,1657.13,,open_close -2022-08-01 11:29:00,1656.26,,open_close -2022-08-01 11:30:00,1658.41,,open_close -2022-08-01 11:31:00,1658.09,,open_close -2022-08-01 11:32:00,1657.4,,open_close -2022-08-01 11:33:00,1657.32,,open_close -2022-08-01 11:34:00,1656.01,,open_close -2022-08-01 11:35:00,1656.67,,open_close -2022-08-01 11:36:00,1657.03,,open_close -2022-08-01 11:37:00,1655.31,,open_close -2022-08-01 11:38:00,1656.38,,open_close -2022-08-01 11:39:00,1657.59,,open_close -2022-08-01 11:40:00,1660.16,,open_close -2022-08-01 11:41:00,1662.07,,open_close -2022-08-01 11:42:00,1664.69,,open_close -2022-08-01 11:43:00,1663.31,,open_close -2022-08-01 11:44:00,1662.4,,open_close -2022-08-01 11:45:00,1665.71,,open_close -2022-08-01 11:46:00,1667.09,,open_close -2022-08-01 11:47:00,1666.12,,open_close -2022-08-01 11:48:00,1666.19,,open_close -2022-08-01 11:49:00,1666.71,,open_close -2022-08-01 11:50:00,1665.32,,open_close -2022-08-01 11:51:00,1665.41,,open_close -2022-08-01 11:52:00,1667.36,,open_close -2022-08-01 11:53:00,1668.6,,open_close -2022-08-01 11:54:00,1668.76,,open_close -2022-08-01 11:55:00,1667.71,,open_close -2022-08-01 11:56:00,1668.93,,open_close -2022-08-01 11:57:00,1669.97,,open_close -2022-08-01 11:58:00,1669.18,,open_close -2022-08-01 11:59:00,1668.52,,open_close -2022-08-01 12:00:00,1668.89,,open_close -2022-08-01 12:01:00,1670.67,,open_close -2022-08-01 12:02:00,1670.36,,open_close -2022-08-01 12:03:00,1670.45,,open_close -2022-08-01 12:04:00,1667.49,,open_close -2022-08-01 12:05:00,1665.14,,open_close -2022-08-01 12:06:00,1665.13,,open_close -2022-08-01 12:07:00,1666.33,,open_close -2022-08-01 12:08:00,1666.15,,open_close -2022-08-01 12:09:00,1666.96,,open_close -2022-08-01 12:10:00,1666.61,,open_close -2022-08-01 12:11:00,1667.51,,open_close -2022-08-01 12:12:00,1664.81,,open_close -2022-08-01 12:13:00,1666.34,,open_close -2022-08-01 12:14:00,1664.88,,open_close -2022-08-01 12:15:00,1664.54,,open_close -2022-08-01 12:16:00,1664.53,,open_close -2022-08-01 12:17:00,1664.25,,open_close -2022-08-01 12:18:00,1663.13,,open_close -2022-08-01 12:19:00,1662.68,,open_close -2022-08-01 12:20:00,1660.11,,open_close -2022-08-01 12:21:00,1661.45,,open_close -2022-08-01 12:22:00,1662.17,,open_close -2022-08-01 12:23:00,1661.87,,open_close -2022-08-01 12:24:00,1662.48,,open_close -2022-08-01 12:25:00,1660.98,,open_close -2022-08-01 12:26:00,1660.47,,open_close -2022-08-01 12:27:00,1661.46,,open_close -2022-08-01 12:28:00,1663.24,,open_close -2022-08-01 12:29:00,1666.28,,open_close -2022-08-01 12:30:00,1673.76,,open_close -2022-08-01 12:31:00,1673.17,,open_close -2022-08-01 12:32:00,1669.73,,open_close -2022-08-01 12:33:00,1668.51,,open_close -2022-08-01 12:34:00,1672.58,,open_close -2022-08-01 12:35:00,1670.58,,open_close -2022-08-01 12:36:00,1670.92,,open_close -2022-08-01 12:37:00,1671.67,,open_close -2022-08-01 12:38:00,1670.23,,open_close -2022-08-01 12:39:00,1670.38,,open_close -2022-08-01 12:40:00,1666.36,,open_close -2022-08-01 12:41:00,1665.75,,open_close -2022-08-01 12:42:00,1664.08,,open_close -2022-08-01 12:43:00,1662.32,,open_close -2022-08-01 12:44:00,1663.74,,open_close -2022-08-01 12:45:00,1661.88,,open_close -2022-08-01 12:46:00,1661.08,,open_close -2022-08-01 12:47:00,1661.77,,open_close -2022-08-01 12:48:00,1659.67,,open_close -2022-08-01 12:49:00,1660.08,,open_close -2022-08-01 12:50:00,1661.64,,open_close -2022-08-01 12:51:00,1660.95,,open_close -2022-08-01 12:52:00,1660.64,,open_close -2022-08-01 12:53:00,1659.11,,open_close -2022-08-01 12:54:00,1656.52,,open_close -2022-08-01 12:55:00,1659.08,,open_close -2022-08-01 12:56:00,1659.8,,open_close -2022-08-01 12:57:00,1659.31,,open_close -2022-08-01 12:58:00,1658.27,,open_close -2022-08-01 12:59:00,1656.49,,open_close -2022-08-01 13:00:00,1654.89,,open_close -2022-08-01 13:01:00,1653.68,,open_close -2022-08-01 13:02:00,1656.32,,open_close -2022-08-01 13:03:00,1653.72,,open_close -2022-08-01 13:04:00,1651.71,,open_close -2022-08-01 13:05:00,1654.4,,open_close -2022-08-01 13:06:00,1653.13,,open_close -2022-08-01 13:07:00,1653.4,,open_close -2022-08-01 13:08:00,1652.88,,open_close -2022-08-01 13:09:00,1653.93,,open_close -2022-08-01 13:10:00,1652.12,,open_close -2022-08-01 13:11:00,1650.95,,open_close -2022-08-01 13:12:00,1650.16,,open_close -2022-08-01 13:13:00,1648.63,,open_close -2022-08-01 13:14:00,1645.94,,open_close -2022-08-01 13:15:00,1648.45,,open_close -2022-08-01 13:16:00,1647.72,,open_close -2022-08-01 13:17:00,1647.79,,open_close -2022-08-01 13:18:00,1647.61,,open_close -2022-08-01 13:19:00,1647.58,,open_close -2022-08-01 13:20:00,1650.48,,open_close -2022-08-01 13:21:00,1653.87,,open_close -2022-08-01 13:22:00,1650.42,,open_close -2022-08-01 13:23:00,1653.17,,open_close -2022-08-01 13:24:00,1651.48,,open_close -2022-08-01 13:25:00,1651.44,,open_close -2022-08-01 13:26:00,1649.74,,open_close -2022-08-01 13:27:00,1651.2,,open_close -2022-08-01 13:28:00,1648.79,,open_close -2022-08-01 13:29:00,1649.26,,open_close -2022-08-01 13:30:00,1650.73,,open_close -2022-08-01 13:31:00,1643.13,,open_close -2022-08-01 13:32:00,1639.99,,open_close -2022-08-01 13:33:00,1643.11,,open_close -2022-08-01 13:34:00,1645.29,,open_close -2022-08-01 13:35:00,1644.3,,open_close -2022-08-01 13:36:00,1645.24,,open_close -2022-08-01 13:37:00,1640.94,,open_close -2022-08-01 13:38:00,1639.26,,open_close -2022-08-01 13:39:00,1645.45,,open_close -2022-08-01 13:40:00,1647.36,,open_close -2022-08-01 13:41:00,1654.01,,open_close -2022-08-01 13:42:00,1651.89,,open_close -2022-08-01 13:43:00,1653.14,,open_close -2022-08-01 13:44:00,1655.55,,open_close -2022-08-01 13:45:00,1656.68,,open_close -2022-08-01 13:46:00,1655.04,,open_close -2022-08-01 13:47:00,1656.97,,open_close -2022-08-01 13:48:00,1662.38,,open_close -2022-08-01 13:49:00,1659.3,,open_close -2022-08-01 13:50:00,1660.65,,open_close -2022-08-01 13:51:00,1661.31,,open_close -2022-08-01 13:52:00,1665.17,,open_close -2022-08-01 13:53:00,1666.01,,open_close -2022-08-01 13:54:00,1667.29,,open_close -2022-08-01 13:55:00,1666.17,,open_close -2022-08-01 13:56:00,1672.66,,open_close -2022-08-01 13:57:00,1669.31,,open_close -2022-08-01 13:58:00,1667.09,,open_close -2022-08-01 13:59:00,1666.35,,open_close -2022-08-01 14:00:00,1670.26,,open_close -2022-08-01 14:01:00,1672.33,,open_close -2022-08-01 14:02:00,1674.53,,open_close -2022-08-01 14:03:00,1673.14,,open_close -2022-08-01 14:04:00,1672.98,,open_close -2022-08-01 14:05:00,1677.7,,open_close -2022-08-01 14:06:00,1682.57,,infty -2022-08-01 14:07:00,1683.38,,infty -2022-08-01 14:08:00,1683.2,,infty -2022-08-01 14:09:00,1684.93,,infty -2022-08-01 14:10:00,1686.27,,infty -2022-08-01 14:11:00,1689.09,,infty -2022-08-01 14:12:00,1689.48,,infty -2022-08-01 14:13:00,1686.18,,infty -2022-08-01 14:14:00,1683.21,,infty -2022-08-01 14:15:00,1684.83,,infty -2022-08-01 14:16:00,1683.33,,infty -2022-08-01 14:17:00,1677.81,,open_close -2022-08-01 14:18:00,1679.05,,open_close -2022-08-01 14:19:00,1673.87,,open_close -2022-08-01 14:20:00,1675.06,,open_close -2022-08-01 14:21:00,1674.34,,open_close -2022-08-01 14:22:00,1676.8,,open_close -2022-08-01 14:23:00,1678.86,,open_close -2022-08-01 14:24:00,1677.39,,open_close -2022-08-01 14:25:00,1671.56,,open_close -2022-08-01 14:26:00,1673.7,,open_close -2022-08-01 14:27:00,1669.81,,open_close -2022-08-01 14:28:00,1669.7,,open_close -2022-08-01 14:29:00,1670.62,,open_close -2022-08-01 14:30:00,1673.97,,open_close -2022-08-01 14:31:00,1674.6,,open_close -2022-08-01 14:32:00,1671.72,,open_close -2022-08-01 14:33:00,1670.21,,open_close -2022-08-01 14:34:00,1671.5,,open_close -2022-08-01 14:35:00,1673.99,,open_close -2022-08-01 14:36:00,1673.39,,open_close -2022-08-01 14:37:00,1673.52,,open_close -2022-08-01 14:38:00,1673.9,,open_close -2022-08-01 14:39:00,1675.92,,open_close -2022-08-01 14:40:00,1680.14,,open_close -2022-08-01 14:41:00,1677.5,,open_close -2022-08-01 14:42:00,1679.23,,open_close -2022-08-01 14:43:00,1680.67,,open_close -2022-08-01 14:44:00,1678.98,,open_close -2022-08-01 14:45:00,1682.29,,infty -2022-08-01 14:46:00,1685.97,,infty -2022-08-01 14:47:00,1682.82,,infty -2022-08-01 14:48:00,1683.81,,infty -2022-08-01 14:49:00,1685.75,,infty -2022-08-01 14:50:00,1686.66,,infty -2022-08-01 14:51:00,1687.31,,infty -2022-08-01 14:52:00,1688.1,,infty -2022-08-01 14:53:00,1687.57,,infty -2022-08-01 14:54:00,1688.31,,infty -2022-08-01 14:55:00,1688.21,,infty -2022-08-01 14:56:00,1686.15,,infty -2022-08-01 14:57:00,1686.68,,infty -2022-08-01 14:58:00,1685.01,,infty -2022-08-01 14:59:00,1685.62,,infty -2022-08-01 15:00:00,1685.73,,infty -2022-08-01 15:01:00,1682.65,,infty -2022-08-01 15:02:00,1682.78,,infty -2022-08-01 15:03:00,1680.98,,open_close -2022-08-01 15:04:00,1680.68,,open_close -2022-08-01 15:05:00,1677.48,,open_close -2022-08-01 15:06:00,1677.75,,open_close -2022-08-01 15:07:00,1679.05,,open_close -2022-08-01 15:08:00,1680.14,,open_close -2022-08-01 15:09:00,1680.87,,open_close -2022-08-01 15:10:00,1682.93,,infty -2022-08-01 15:11:00,1683.38,,infty -2022-08-01 15:12:00,1684.01,,infty -2022-08-01 15:13:00,1683.69,,infty -2022-08-01 15:14:00,1684.42,,infty -2022-08-01 15:15:00,1678.15,,open_close -2022-08-01 15:16:00,1674.31,,open_close -2022-08-01 15:17:00,1675.47,,open_close -2022-08-01 15:18:00,1674.24,,open_close -2022-08-01 15:19:00,1673.69,,open_close -2022-08-01 15:20:00,1674.18,,open_close -2022-08-01 15:21:00,1675.02,,open_close -2022-08-01 15:22:00,1673.1,,open_close -2022-08-01 15:23:00,1675.71,,open_close -2022-08-01 15:24:00,1674.0,,open_close -2022-08-01 15:25:00,1674.41,,open_close -2022-08-01 15:26:00,1674.78,,open_close -2022-08-01 15:27:00,1676.2,,open_close -2022-08-01 15:28:00,1675.17,,open_close -2022-08-01 15:29:00,1675.5,,open_close -2022-08-01 15:30:00,1672.64,,open_close -2022-08-01 15:31:00,1671.32,,open_close -2022-08-01 15:32:00,1671.24,,open_close -2022-08-01 15:33:00,1670.64,,open_close -2022-08-01 15:34:00,1670.52,,open_close -2022-08-01 15:35:00,1668.5,,open_close -2022-08-01 15:36:00,1668.91,,open_close -2022-08-01 15:37:00,1668.86,,open_close -2022-08-01 15:38:00,1669.82,,open_close -2022-08-01 15:39:00,1671.74,,open_close -2022-08-01 15:40:00,1670.16,,open_close -2022-08-01 15:41:00,1670.17,,open_close -2022-08-01 15:42:00,1670.76,,open_close -2022-08-01 15:43:00,1671.69,,open_close -2022-08-01 15:44:00,1672.87,,open_close -2022-08-01 15:45:00,1672.41,,open_close -2022-08-01 15:46:00,1674.39,,open_close -2022-08-01 15:47:00,1676.98,,open_close -2022-08-01 15:48:00,1678.49,,open_close -2022-08-01 15:49:00,1678.96,,open_close -2022-08-01 15:50:00,1675.34,,open_close -2022-08-01 15:51:00,1675.77,,open_close -2022-08-01 15:52:00,1675.0,,open_close -2022-08-01 15:53:00,1673.58,,open_close -2022-08-01 15:54:00,1673.75,,open_close -2022-08-01 15:55:00,1671.76,,open_close -2022-08-01 15:56:00,1671.78,,open_close -2022-08-01 15:57:00,1673.2,,open_close -2022-08-01 15:58:00,1674.05,,open_close -2022-08-01 15:59:00,1673.09,,open_close -2022-08-01 16:00:00,1673.38,,open_close -2022-08-01 16:01:00,1671.93,,open_close -2022-08-01 16:02:00,1671.64,,open_close -2022-08-01 16:03:00,1668.0,,open_close -2022-08-01 16:04:00,1664.47,,open_close -2022-08-01 16:05:00,1665.92,,open_close -2022-08-01 16:06:00,1664.35,,open_close -2022-08-01 16:07:00,1664.13,,open_close -2022-08-01 16:08:00,1664.63,,open_close -2022-08-01 16:09:00,1663.78,,open_close -2022-08-01 16:10:00,1668.3,,open_close -2022-08-01 16:11:00,1666.35,,open_close -2022-08-01 16:12:00,1663.26,,open_close -2022-08-01 16:13:00,1660.27,,open_close -2022-08-01 16:14:00,1659.25,,open_close -2022-08-01 16:15:00,1658.11,,open_close -2022-08-01 16:16:00,1658.84,,open_close -2022-08-01 16:17:00,1657.28,,open_close -2022-08-01 16:18:00,1657.13,,open_close -2022-08-01 16:19:00,1654.01,,open_close -2022-08-01 16:20:00,1656.32,,open_close -2022-08-01 16:21:00,1657.47,,open_close -2022-08-01 16:22:00,1657.94,,open_close -2022-08-01 16:23:00,1659.58,,open_close -2022-08-01 16:24:00,1658.0,,open_close -2022-08-01 16:25:00,1661.32,,open_close -2022-08-01 16:26:00,1662.5,,open_close -2022-08-01 16:27:00,1662.97,,open_close -2022-08-01 16:28:00,1661.8,,open_close -2022-08-01 16:29:00,1660.62,,open_close -2022-08-01 16:30:00,1659.59,,open_close -2022-08-01 16:31:00,1659.38,,open_close -2022-08-01 16:32:00,1657.95,,open_close -2022-08-01 16:33:00,1656.16,,open_close -2022-08-01 16:34:00,1661.32,,open_close -2022-08-01 16:35:00,1657.95,,open_close -2022-08-01 16:36:00,1656.36,,open_close -2022-08-01 16:37:00,1655.07,,open_close -2022-08-01 16:38:00,1656.01,,open_close -2022-08-01 16:39:00,1655.41,,open_close -2022-08-01 16:40:00,1655.54,,open_close -2022-08-01 16:41:00,1656.23,,open_close -2022-08-01 16:42:00,1653.72,,open_close -2022-08-01 16:43:00,1656.6,,open_close -2022-08-01 16:44:00,1659.27,,open_close -2022-08-01 16:45:00,1661.58,,open_close -2022-08-01 16:46:00,1660.15,,open_close -2022-08-01 16:47:00,1659.27,,open_close -2022-08-01 16:48:00,1657.68,,open_close -2022-08-01 16:49:00,1657.75,,open_close -2022-08-01 16:50:00,1656.42,,open_close -2022-08-01 16:51:00,1654.46,,open_close -2022-08-01 16:52:00,1657.38,,open_close -2022-08-01 16:53:00,1659.1,,open_close -2022-08-01 16:54:00,1658.38,,open_close -2022-08-01 16:55:00,1657.1,,open_close -2022-08-01 16:56:00,1654.72,,open_close -2022-08-01 16:57:00,1656.69,,open_close -2022-08-01 16:58:00,1654.95,,open_close -2022-08-01 16:59:00,1655.74,,open_close -2022-08-01 17:00:00,1654.26,,open_close -2022-08-01 17:01:00,1650.62,,open_close -2022-08-01 17:02:00,1650.07,,open_close -2022-08-01 17:03:00,1646.93,,open_close -2022-08-01 17:04:00,1647.43,,open_close -2022-08-01 17:05:00,1648.27,,open_close -2022-08-01 17:06:00,1651.14,,open_close -2022-08-01 17:07:00,1652.88,,open_close -2022-08-01 17:08:00,1651.53,,open_close -2022-08-01 17:09:00,1649.7,,open_close -2022-08-01 17:10:00,1648.52,,open_close -2022-08-01 17:11:00,1648.4,,open_close -2022-08-01 17:12:00,1647.64,,open_close -2022-08-01 17:13:00,1650.02,,open_close -2022-08-01 17:14:00,1650.87,,open_close -2022-08-01 17:15:00,1650.29,,open_close -2022-08-01 17:16:00,1648.23,,open_close -2022-08-01 17:17:00,1645.93,,open_close -2022-08-01 17:18:00,1641.93,,open_close -2022-08-01 17:19:00,1638.81,,open_close -2022-08-01 17:20:00,1638.64,,open_close -2022-08-01 17:21:00,1632.69,,open_close -2022-08-01 17:22:00,1636.77,,open_close -2022-08-01 17:23:00,1635.92,,open_close -2022-08-01 17:24:00,1636.09,,open_close -2022-08-01 17:25:00,1634.96,,open_close -2022-08-01 17:26:00,1634.35,,open_close -2022-08-01 17:27:00,1635.04,,open_close -2022-08-01 17:28:00,1633.83,,open_close -2022-08-01 17:29:00,1633.58,,open_close -2022-08-01 17:30:00,1628.11,,open_close -2022-08-01 17:31:00,1635.41,,open_close -2022-08-01 17:32:00,1632.85,,open_close -2022-08-01 17:33:00,1633.76,,open_close -2022-08-01 17:34:00,1632.67,,open_close -2022-08-01 17:35:00,1635.19,,open_close -2022-08-01 17:36:00,1635.43,,open_close -2022-08-01 17:37:00,1635.68,,open_close -2022-08-01 17:38:00,1636.04,,open_close -2022-08-01 17:39:00,1635.05,,open_close -2022-08-01 17:40:00,1630.91,,open_close -2022-08-01 17:41:00,1634.28,,open_close -2022-08-01 17:42:00,1633.11,,open_close -2022-08-01 17:43:00,1631.45,,open_close -2022-08-01 17:44:00,1630.81,,open_close -2022-08-01 17:45:00,1630.05,,open_close -2022-08-01 17:46:00,1626.56,,open_close -2022-08-01 17:47:00,1628.66,,open_close -2022-08-01 17:48:00,1629.57,,open_close -2022-08-01 17:49:00,1631.36,,open_close -2022-08-01 17:50:00,1628.13,,open_close -2022-08-01 17:51:00,1626.44,,open_close -2022-08-01 17:52:00,1629.05,,open_close -2022-08-01 17:53:00,1626.16,,open_close -2022-08-01 17:54:00,1629.55,,open_close -2022-08-01 17:55:00,1628.64,,open_close -2022-08-01 17:56:00,1630.65,,open_close -2022-08-01 17:57:00,1630.63,,open_close -2022-08-01 17:58:00,1632.7,,open_close -2022-08-01 17:59:00,1633.65,,open_close -2022-08-01 18:00:00,1631.94,,open_close -2022-08-01 18:01:00,1630.2,,open_close -2022-08-01 18:02:00,1631.53,,open_close -2022-08-01 18:03:00,1631.51,,open_close -2022-08-01 18:04:00,1631.13,,open_close -2022-08-01 18:05:00,1628.87,,open_close -2022-08-01 18:06:00,1630.71,,open_close -2022-08-01 18:07:00,1628.22,,open_close -2022-08-01 18:08:00,1630.17,,open_close -2022-08-01 18:09:00,1630.5,,open_close -2022-08-01 18:10:00,1629.82,,open_close -2022-08-01 18:11:00,1629.05,,open_close -2022-08-01 18:12:00,1629.23,,open_close -2022-08-01 18:13:00,1631.33,,open_close -2022-08-01 18:14:00,1636.24,,open_close -2022-08-01 18:15:00,1634.79,,open_close -2022-08-01 18:16:00,1634.04,,open_close -2022-08-01 18:17:00,1632.32,,open_close -2022-08-01 18:18:00,1625.73,,open_close -2022-08-01 18:19:00,1627.86,,open_close -2022-08-01 18:20:00,1626.4,,open_close -2022-08-01 18:21:00,1628.03,,open_close -2022-08-01 18:22:00,1626.83,,open_close -2022-08-01 18:23:00,1627.58,,open_close -2022-08-01 18:24:00,1628.91,,open_close -2022-08-01 18:25:00,1627.52,,open_close -2022-08-01 18:26:00,1628.18,,open_close -2022-08-01 18:27:00,1626.1,,open_close -2022-08-01 18:28:00,1626.05,,open_close -2022-08-01 18:29:00,1625.84,,open_close -2022-08-01 18:30:00,1627.53,,open_close -2022-08-01 18:31:00,1627.95,,open_close -2022-08-01 18:32:00,1627.41,,open_close -2022-08-01 18:33:00,1626.55,,open_close -2022-08-01 18:34:00,1626.74,,open_close -2022-08-01 18:35:00,1627.48,,open_close -2022-08-01 18:36:00,1629.24,,open_close -2022-08-01 18:37:00,1629.97,,open_close -2022-08-01 18:38:00,1630.04,,open_close -2022-08-01 18:39:00,1630.25,,open_close -2022-08-01 18:40:00,1629.83,,open_close -2022-08-01 18:41:00,1626.04,,open_close -2022-08-01 18:42:00,1626.83,,open_close -2022-08-01 18:43:00,1621.46,,open_close -2022-08-01 18:44:00,1617.92,,minus_infty -2022-08-01 18:45:00,1621.48,,open_close -2022-08-01 18:46:00,1620.01,,minus_infty -2022-08-01 18:47:00,1617.33,,minus_infty -2022-08-01 18:48:00,1617.14,,minus_infty -2022-08-01 18:49:00,1620.03,,minus_infty -2022-08-01 18:50:00,1617.7,,minus_infty -2022-08-01 18:51:00,1617.8,,minus_infty -2022-08-01 18:52:00,1620.08,,minus_infty -2022-08-01 18:53:00,1622.91,,open_close -2022-08-01 18:54:00,1623.98,,open_close -2022-08-01 18:55:00,1625.34,,open_close -2022-08-01 18:56:00,1627.01,,open_close -2022-08-01 18:57:00,1628.76,,open_close -2022-08-01 18:58:00,1630.16,,open_close -2022-08-01 18:59:00,1627.46,,open_close -2022-08-01 19:00:00,1625.04,,open_close -2022-08-01 19:01:00,1626.25,,open_close -2022-08-01 19:02:00,1625.71,,open_close -2022-08-01 19:03:00,1624.74,,open_close -2022-08-01 19:04:00,1623.03,,open_close -2022-08-01 19:05:00,1623.39,,open_close -2022-08-01 19:06:00,1623.8,,open_close -2022-08-01 19:07:00,1625.75,,open_close -2022-08-01 19:08:00,1627.22,,open_close -2022-08-01 19:09:00,1630.09,,open_close -2022-08-01 19:10:00,1629.38,,open_close -2022-08-01 19:11:00,1627.49,,open_close -2022-08-01 19:12:00,1627.74,,open_close -2022-08-01 19:13:00,1628.88,,open_close -2022-08-01 19:14:00,1630.21,,open_close -2022-08-01 19:15:00,1630.16,,open_close -2022-08-01 19:16:00,1629.51,,open_close -2022-08-01 19:17:00,1628.72,,open_close -2022-08-01 19:18:00,1627.91,,open_close -2022-08-01 19:19:00,1626.18,,open_close -2022-08-01 19:20:00,1625.98,,open_close -2022-08-01 19:21:00,1626.04,,open_close -2022-08-01 19:22:00,1624.51,,open_close -2022-08-01 19:23:00,1625.91,,open_close -2022-08-01 19:24:00,1623.58,,open_close -2022-08-01 19:25:00,1623.9,,open_close -2022-08-01 19:26:00,1624.94,,open_close -2022-08-01 19:27:00,1623.33,,open_close -2022-08-01 19:28:00,1621.79,,open_close -2022-08-01 19:29:00,1622.51,,open_close -2022-08-01 19:30:00,1624.81,,open_close -2022-08-01 19:31:00,1625.05,,open_close -2022-08-01 19:32:00,1624.1,,open_close -2022-08-01 19:33:00,1623.94,,open_close -2022-08-01 19:34:00,1625.97,,open_close -2022-08-01 19:35:00,1625.66,,open_close -2022-08-01 19:36:00,1625.78,,open_close -2022-08-01 19:37:00,1626.7,,open_close -2022-08-01 19:38:00,1626.36,,open_close -2022-08-01 19:39:00,1626.25,,open_close -2022-08-01 19:40:00,1627.67,,open_close -2022-08-01 19:41:00,1627.61,,open_close -2022-08-01 19:42:00,1624.44,,open_close -2022-08-01 19:43:00,1620.78,,minus_infty -2022-08-01 19:44:00,1620.24,,minus_infty -2022-08-01 19:45:00,1620.29,,minus_infty -2022-08-01 19:46:00,1622.37,,open_close -2022-08-01 19:47:00,1622.46,,open_close -2022-08-01 19:48:00,1623.49,,open_close -2022-08-01 19:49:00,1623.03,,open_close -2022-08-01 19:50:00,1622.16,,open_close -2022-08-01 19:51:00,1621.71,,open_close -2022-08-01 19:52:00,1620.71,,minus_infty -2022-08-01 19:53:00,1620.77,,minus_infty -2022-08-01 19:54:00,1621.72,,open_close -2022-08-01 19:55:00,1622.67,,open_close -2022-08-01 19:56:00,1622.99,,open_close -2022-08-01 19:57:00,1622.3,,open_close -2022-08-01 19:58:00,1623.0,,open_close -2022-08-01 19:59:00,1622.96,,open_close -2022-08-01 20:00:00,1621.44,,open_close -2022-08-01 20:01:00,1625.35,,open_close -2022-08-01 20:02:00,1625.08,,open_close -2022-08-01 20:03:00,1619.88,,minus_infty -2022-08-01 20:04:00,1618.06,,minus_infty -2022-08-01 20:05:00,1620.41,,minus_infty -2022-08-01 20:06:00,1621.02,,open_close -2022-08-01 20:07:00,1621.78,,open_close -2022-08-01 20:08:00,1621.84,,open_close -2022-08-01 20:09:00,1618.27,,minus_infty -2022-08-01 20:10:00,1618.13,,minus_infty -2022-08-01 20:11:00,1617.48,,minus_infty -2022-08-01 20:12:00,1614.7,,minus_infty -2022-08-01 20:13:00,1619.39,,minus_infty -2022-08-01 20:14:00,1621.63,,open_close -2022-08-01 20:15:00,1621.18,,open_close -2022-08-01 20:16:00,1619.29,,minus_infty -2022-08-01 20:17:00,1619.12,,minus_infty -2022-08-01 20:18:00,1621.32,,open_close -2022-08-01 20:19:00,1623.34,,open_close -2022-08-01 20:20:00,1625.18,,open_close -2022-08-01 20:21:00,1627.9,,open_close -2022-08-01 20:22:00,1627.15,,open_close -2022-08-01 20:23:00,1626.47,,open_close -2022-08-01 20:24:00,1626.26,,open_close -2022-08-01 20:25:00,1629.11,,open_close -2022-08-01 20:26:00,1630.41,,open_close -2022-08-01 20:27:00,1627.31,,open_close -2022-08-01 20:28:00,1628.06,,open_close -2022-08-01 20:29:00,1625.61,,open_close -2022-08-01 20:30:00,1625.87,,open_close -2022-08-01 20:31:00,1627.35,,open_close -2022-08-01 20:32:00,1627.87,,open_close -2022-08-01 20:33:00,1626.62,,open_close -2022-08-01 20:34:00,1624.31,,open_close -2022-08-01 20:35:00,1623.46,,open_close -2022-08-01 20:36:00,1625.42,,open_close -2022-08-01 20:37:00,1623.87,,open_close -2022-08-01 20:38:00,1625.4,,open_close -2022-08-01 20:39:00,1625.69,,open_close -2022-08-01 20:40:00,1625.13,,open_close -2022-08-01 20:41:00,1626.59,,open_close -2022-08-01 20:42:00,1627.22,,open_close -2022-08-01 20:43:00,1626.81,,open_close -2022-08-01 20:44:00,1624.96,,open_close -2022-08-01 20:45:00,1625.83,,open_close -2022-08-01 20:46:00,1625.8,,open_close -2022-08-01 20:47:00,1625.47,,open_close -2022-08-01 20:48:00,1624.21,,open_close -2022-08-01 20:49:00,1621.36,,open_close -2022-08-01 20:50:00,1618.54,,minus_infty -2022-08-01 20:51:00,1618.99,,minus_infty -2022-08-01 20:52:00,1617.87,,minus_infty -2022-08-01 20:53:00,1619.19,,minus_infty -2022-08-01 20:54:00,1618.54,,minus_infty -2022-08-01 20:55:00,1617.5,,minus_infty -2022-08-01 20:56:00,1620.02,,minus_infty -2022-08-01 20:57:00,1622.4,,open_close -2022-08-01 20:58:00,1622.94,,open_close -2022-08-01 20:59:00,1628.43,,open_close -2022-08-01 21:00:00,1628.32,,open_close -2022-08-01 21:01:00,1627.86,,open_close -2022-08-01 21:02:00,1627.17,,open_close -2022-08-01 21:03:00,1628.55,,open_close -2022-08-01 21:04:00,1627.94,,open_close -2022-08-01 21:05:00,1628.35,,open_close -2022-08-01 21:06:00,1629.06,,open_close -2022-08-01 21:07:00,1628.38,,open_close -2022-08-01 21:08:00,1630.44,,open_close -2022-08-01 21:09:00,1629.58,,open_close -2022-08-01 21:10:00,1628.61,,open_close -2022-08-01 21:11:00,1628.78,,open_close -2022-08-01 21:12:00,1629.67,,open_close -2022-08-01 21:13:00,1631.03,,open_close -2022-08-01 21:14:00,1633.54,,open_close -2022-08-01 21:15:00,1632.55,,open_close -2022-08-01 21:16:00,1628.48,,open_close -2022-08-01 21:17:00,1627.95,,open_close -2022-08-01 21:18:00,1628.61,,open_close -2022-08-01 21:19:00,1626.66,,open_close -2022-08-01 21:20:00,1623.01,,open_close -2022-08-01 21:21:00,1621.54,,open_close -2022-08-01 21:22:00,1622.01,,open_close -2022-08-01 21:23:00,1623.17,,open_close -2022-08-01 21:24:00,1624.61,,open_close -2022-08-01 21:25:00,1624.37,,open_close -2022-08-01 21:26:00,1625.07,,open_close -2022-08-01 21:27:00,1626.22,,open_close -2022-08-01 21:28:00,1626.42,,open_close -2022-08-01 21:29:00,1627.43,,open_close -2022-08-01 21:30:00,1624.85,,open_close -2022-08-01 21:31:00,1623.7,,open_close -2022-08-01 21:32:00,1623.19,,open_close -2022-08-01 21:33:00,1624.82,,open_close -2022-08-01 21:34:00,1625.13,,open_close -2022-08-01 21:35:00,1625.51,,open_close -2022-08-01 21:36:00,1625.01,,open_close -2022-08-01 21:37:00,1625.73,,open_close -2022-08-01 21:38:00,1624.75,,open_close -2022-08-01 21:39:00,1622.83,,open_close -2022-08-01 21:40:00,1625.64,,open_close -2022-08-01 21:41:00,1624.7,,open_close -2022-08-01 21:42:00,1621.66,,open_close -2022-08-01 21:43:00,1622.14,,open_close -2022-08-01 21:44:00,1621.9,,open_close -2022-08-01 21:45:00,1622.8,,open_close -2022-08-01 21:46:00,1621.4,,open_close -2022-08-01 21:47:00,1621.43,,open_close -2022-08-01 21:48:00,1620.76,,minus_infty -2022-08-01 21:49:00,1620.09,,minus_infty -2022-08-01 21:50:00,1620.12,,minus_infty -2022-08-01 21:51:00,1620.11,,minus_infty -2022-08-01 21:52:00,1623.36,,open_close -2022-08-01 21:53:00,1623.7,,open_close -2022-08-01 21:54:00,1623.57,,open_close -2022-08-01 21:55:00,1623.52,,open_close -2022-08-01 21:56:00,1621.41,,open_close -2022-08-01 21:57:00,1622.51,,open_close -2022-08-01 21:58:00,1624.01,,open_close -2022-08-01 21:59:00,1624.61,,open_close -2022-08-01 22:00:00,1624.27,,open_close -2022-08-01 22:01:00,1627.4,,open_close -2022-08-01 22:02:00,1624.7,,open_close -2022-08-01 22:03:00,1619.49,,minus_infty -2022-08-01 22:04:00,1616.27,,minus_infty -2022-08-01 22:05:00,1619.18,,minus_infty -2022-08-01 22:06:00,1619.3,,minus_infty -2022-08-01 22:07:00,1618.66,,minus_infty -2022-08-01 22:08:00,1616.41,,minus_infty -2022-08-01 22:09:00,1613.87,,minus_infty -2022-08-01 22:10:00,1614.65,,minus_infty -2022-08-01 22:11:00,1610.87,,minus_infty -2022-08-01 22:12:00,1607.52,,minus_infty -2022-08-01 22:13:00,1608.45,,minus_infty -2022-08-01 22:14:00,1613.62,,minus_infty -2022-08-01 22:15:00,1612.43,,minus_infty -2022-08-01 22:16:00,1614.5,,minus_infty -2022-08-01 22:17:00,1619.2,,minus_infty -2022-08-01 22:18:00,1617.65,,minus_infty -2022-08-01 22:19:00,1617.05,,minus_infty -2022-08-01 22:20:00,1614.88,,minus_infty -2022-08-01 22:21:00,1614.86,,minus_infty -2022-08-01 22:22:00,1616.96,,minus_infty -2022-08-01 22:23:00,1613.55,,minus_infty -2022-08-01 22:24:00,1618.39,,minus_infty -2022-08-01 22:25:00,1617.53,,minus_infty -2022-08-01 22:26:00,1616.47,,minus_infty -2022-08-01 22:27:00,1618.1,,minus_infty -2022-08-01 22:28:00,1614.27,,minus_infty -2022-08-01 22:29:00,1615.32,,minus_infty -2022-08-01 22:30:00,1613.48,,minus_infty -2022-08-01 22:31:00,1618.33,,minus_infty -2022-08-01 22:32:00,1619.25,,minus_infty -2022-08-01 22:33:00,1620.15,,minus_infty -2022-08-01 22:34:00,1622.05,,open_close -2022-08-01 22:35:00,1620.57,,minus_infty -2022-08-01 22:36:00,1621.77,,open_close -2022-08-01 22:37:00,1621.66,,open_close -2022-08-01 22:38:00,1624.26,,open_close -2022-08-01 22:39:00,1624.02,,open_close -2022-08-01 22:40:00,1624.8,,open_close -2022-08-01 22:41:00,1625.38,,open_close -2022-08-01 22:42:00,1625.6,,open_close -2022-08-01 22:43:00,1624.9,,open_close -2022-08-01 22:44:00,1624.56,,open_close -2022-08-01 22:45:00,1623.18,,open_close -2022-08-01 22:46:00,1622.4,,open_close -2022-08-01 22:47:00,1625.63,,open_close -2022-08-01 22:48:00,1626.35,,open_close -2022-08-01 22:49:00,1627.04,,open_close -2022-08-01 22:50:00,1627.32,,open_close -2022-08-01 22:51:00,1628.81,,open_close -2022-08-01 22:52:00,1631.38,,open_close -2022-08-01 22:53:00,1629.46,,open_close -2022-08-01 22:54:00,1628.9,,open_close -2022-08-01 22:55:00,1630.0,,open_close -2022-08-01 22:56:00,1629.38,,open_close -2022-08-01 22:57:00,1629.53,,open_close -2022-08-01 22:58:00,1629.64,,open_close -2022-08-01 22:59:00,1629.48,,open_close -2022-08-01 23:00:00,1628.24,,open_close -2022-08-01 23:01:00,1631.03,,open_close -2022-08-01 23:02:00,1634.61,,open_close -2022-08-01 23:03:00,1632.02,,open_close -2022-08-01 23:04:00,1631.78,,open_close -2022-08-01 23:05:00,1632.01,,open_close -2022-08-01 23:06:00,1630.92,,open_close -2022-08-01 23:07:00,1634.09,,open_close -2022-08-01 23:08:00,1637.43,,open_close -2022-08-01 23:09:00,1638.47,,open_close -2022-08-01 23:10:00,1637.19,,open_close -2022-08-01 23:11:00,1641.08,,open_close -2022-08-01 23:12:00,1641.19,,open_close -2022-08-01 23:13:00,1643.17,,open_close -2022-08-01 23:14:00,1642.19,,open_close -2022-08-01 23:15:00,1640.01,,open_close -2022-08-01 23:16:00,1640.16,,open_close -2022-08-01 23:17:00,1638.88,,open_close -2022-08-01 23:18:00,1637.08,,open_close -2022-08-01 23:19:00,1638.1,,open_close -2022-08-01 23:20:00,1635.01,,open_close -2022-08-01 23:21:00,1636.64,,open_close -2022-08-01 23:22:00,1636.89,,open_close -2022-08-01 23:23:00,1636.82,,open_close -2022-08-01 23:24:00,1638.44,,open_close -2022-08-01 23:25:00,1639.0,,open_close -2022-08-01 23:26:00,1640.06,,open_close -2022-08-01 23:27:00,1639.94,,open_close -2022-08-01 23:28:00,1640.54,,open_close -2022-08-01 23:29:00,1639.66,,open_close -2022-08-01 23:30:00,1640.01,,open_close -2022-08-01 23:31:00,1638.33,,open_close -2022-08-01 23:32:00,1639.17,,open_close -2022-08-01 23:33:00,1640.01,,open_close -2022-08-01 23:34:00,1638.52,,open_close -2022-08-01 23:35:00,1638.17,,open_close -2022-08-01 23:36:00,1638.98,,open_close -2022-08-01 23:37:00,1638.76,,open_close -2022-08-01 23:38:00,1640.26,,open_close -2022-08-01 23:39:00,1639.67,,open_close -2022-08-01 23:40:00,1640.79,,open_close -2022-08-01 23:41:00,1639.86,,open_close -2022-08-01 23:42:00,1637.06,,open_close -2022-08-01 23:43:00,1635.96,,open_close -2022-08-01 23:44:00,1636.63,,open_close -2022-08-01 23:45:00,1632.59,,open_close -2022-08-01 23:46:00,1630.47,,open_close -2022-08-01 23:47:00,1630.54,,open_close -2022-08-01 23:48:00,1626.01,,open_close -2022-08-01 23:49:00,1628.66,,open_close -2022-08-01 23:50:00,1628.66,,open_close -2022-08-01 23:51:00,1630.38,,open_close -2022-08-01 23:52:00,1632.36,,open_close -2022-08-01 23:53:00,1634.04,,open_close -2022-08-01 23:54:00,1634.82,,open_close -2022-08-01 23:55:00,1635.07,,open_close -2022-08-01 23:56:00,1636.53,,open_close -2022-08-01 23:57:00,1635.2,,open_close -2022-08-01 23:58:00,1633.14,,open_close -2022-08-01 23:59:00,1630.73,,open_close -2022-08-02 00:00:00,1629.75,,open_close -2022-08-02 00:01:00,1629.9,,open_close -2022-08-02 00:02:00,1631.44,,open_close -2022-08-02 00:03:00,1632.57,,open_close -2022-08-02 00:04:00,1633.83,,open_close -2022-08-02 00:05:00,1635.01,,open_close -2022-08-02 00:06:00,1636.97,,open_close -2022-08-02 00:07:00,1635.37,,open_close -2022-08-02 00:08:00,1635.56,,open_close -2022-08-02 00:09:00,1636.01,,open_close -2022-08-02 00:10:00,1637.15,,open_close -2022-08-02 00:11:00,1641.64,,open_close -2022-08-02 00:12:00,1644.77,,open_close -2022-08-02 00:13:00,1649.98,,open_close -2022-08-02 00:14:00,1651.42,,open_close -2022-08-02 00:15:00,1651.03,,open_close -2022-08-02 00:16:00,1656.23,,open_close -2022-08-02 00:17:00,1652.99,,open_close -2022-08-02 00:18:00,1646.59,,open_close -2022-08-02 00:19:00,1643.17,,open_close -2022-08-02 00:20:00,1647.68,,open_close -2022-08-02 00:21:00,1645.82,,open_close -2022-08-02 00:22:00,1643.35,,open_close -2022-08-02 00:23:00,1645.25,,open_close -2022-08-02 00:24:00,1644.87,,open_close -2022-08-02 00:25:00,1644.4,,open_close -2022-08-02 00:26:00,1642.31,,open_close -2022-08-02 00:27:00,1643.51,,open_close -2022-08-02 00:28:00,1644.47,,open_close -2022-08-02 00:29:00,1644.19,,open_close -2022-08-02 00:30:00,1642.55,,open_close -2022-08-02 00:31:00,1640.9,,open_close -2022-08-02 00:32:00,1640.66,,open_close -2022-08-02 00:33:00,1636.16,,open_close -2022-08-02 00:34:00,1635.47,,open_close -2022-08-02 00:35:00,1635.8,,open_close -2022-08-02 00:36:00,1636.53,,open_close -2022-08-02 00:37:00,1637.02,,open_close -2022-08-02 00:38:00,1638.09,,open_close -2022-08-02 00:39:00,1638.06,,open_close -2022-08-02 00:40:00,1636.42,,open_close -2022-08-02 00:41:00,1636.34,,open_close -2022-08-02 00:42:00,1636.34,,open_close -2022-08-02 00:43:00,1637.12,,open_close -2022-08-02 00:44:00,1637.69,,open_close -2022-08-02 00:45:00,1636.78,,open_close -2022-08-02 00:46:00,1638.17,,open_close -2022-08-02 00:47:00,1633.33,,open_close -2022-08-02 00:48:00,1632.21,,open_close -2022-08-02 00:49:00,1631.96,,open_close -2022-08-02 00:50:00,1631.39,,open_close -2022-08-02 00:51:00,1630.84,,open_close -2022-08-02 00:52:00,1631.78,,open_close -2022-08-02 00:53:00,1632.46,,open_close -2022-08-02 00:54:00,1634.02,,open_close -2022-08-02 00:55:00,1634.44,,open_close -2022-08-02 00:56:00,1634.19,,open_close -2022-08-02 00:57:00,1632.89,,open_close -2022-08-02 00:58:00,1633.42,,open_close -2022-08-02 00:59:00,1632.63,,open_close -2022-08-02 01:00:00,1633.41,,open_close -2022-08-02 01:01:00,1634.61,,open_close -2022-08-02 01:02:00,1635.7,,open_close -2022-08-02 01:03:00,1632.96,,open_close -2022-08-02 01:04:00,1633.24,,open_close -2022-08-02 01:05:00,1634.17,,open_close -2022-08-02 01:06:00,1634.61,,open_close -2022-08-02 01:07:00,1634.37,,open_close -2022-08-02 01:08:00,1635.49,,open_close -2022-08-02 01:09:00,1634.53,,open_close -2022-08-02 01:10:00,1635.56,,open_close -2022-08-02 01:11:00,1635.5,,open_close -2022-08-02 01:12:00,1638.96,,open_close -2022-08-02 01:13:00,1640.81,,open_close -2022-08-02 01:14:00,1640.07,,open_close -2022-08-02 01:15:00,1639.33,,open_close -2022-08-02 01:16:00,1637.9,,open_close -2022-08-02 01:17:00,1633.97,,open_close -2022-08-02 01:18:00,1630.28,,open_close -2022-08-02 01:19:00,1629.07,,open_close -2022-08-02 01:20:00,1629.46,,open_close -2022-08-02 01:21:00,1632.59,,open_close -2022-08-02 01:22:00,1633.01,,open_close -2022-08-02 01:23:00,1630.6,,open_close -2022-08-02 01:24:00,1631.02,,open_close -2022-08-02 01:25:00,1632.05,,open_close -2022-08-02 01:26:00,1631.15,,open_close -2022-08-02 01:27:00,1630.84,,open_close -2022-08-02 01:28:00,1629.06,,open_close -2022-08-02 01:29:00,1630.31,,open_close -2022-08-02 01:30:00,1628.96,,open_close -2022-08-02 01:31:00,1625.13,,open_close -2022-08-02 01:32:00,1624.84,,open_close -2022-08-02 01:33:00,1626.85,,open_close -2022-08-02 01:34:00,1623.87,,open_close -2022-08-02 01:35:00,1625.02,,open_close -2022-08-02 01:36:00,1625.46,,open_close -2022-08-02 01:37:00,1622.23,,open_close -2022-08-02 01:38:00,1618.69,,minus_infty -2022-08-02 01:39:00,1616.69,,minus_infty -2022-08-02 01:40:00,1614.23,,minus_infty -2022-08-02 01:41:00,1612.24,,minus_infty -2022-08-02 01:42:00,1609.79,,minus_infty -2022-08-02 01:43:00,1608.05,,minus_infty -2022-08-02 01:44:00,1610.44,,minus_infty -2022-08-02 01:45:00,1612.09,,minus_infty -2022-08-02 01:46:00,1607.28,,minus_infty -2022-08-02 01:47:00,1606.51,,minus_infty -2022-08-02 01:48:00,1604.47,,minus_infty -2022-08-02 01:49:00,1605.4,,minus_infty -2022-08-02 01:50:00,1604.27,,minus_infty -2022-08-02 01:51:00,1606.78,,minus_infty -2022-08-02 01:52:00,1604.83,,minus_infty -2022-08-02 01:53:00,1601.0,,minus_infty -2022-08-02 01:54:00,1603.31,,minus_infty -2022-08-02 01:55:00,1605.41,,minus_infty -2022-08-02 01:56:00,1607.05,,minus_infty -2022-08-02 01:57:00,1608.02,,minus_infty -2022-08-02 01:58:00,1605.71,,minus_infty -2022-08-02 01:59:00,1605.98,,minus_infty -2022-08-02 02:00:00,1605.34,,minus_infty -2022-08-02 02:01:00,1604.92,,minus_infty -2022-08-02 02:02:00,1607.17,,minus_infty -2022-08-02 02:03:00,1608.37,,minus_infty -2022-08-02 02:04:00,1607.35,,minus_infty -2022-08-02 02:05:00,1608.61,,minus_infty -2022-08-02 02:06:00,1609.68,,minus_infty -2022-08-02 02:07:00,1612.21,,minus_infty -2022-08-02 02:08:00,1608.86,,minus_infty -2022-08-02 02:09:00,1609.37,,minus_infty -2022-08-02 02:10:00,1610.19,,minus_infty -2022-08-02 02:11:00,1609.72,,minus_infty -2022-08-02 02:12:00,1607.06,,minus_infty -2022-08-02 02:13:00,1607.91,,minus_infty -2022-08-02 02:14:00,1608.25,,minus_infty -2022-08-02 02:15:00,1609.37,,minus_infty -2022-08-02 02:16:00,1608.57,,minus_infty -2022-08-02 02:17:00,1610.63,,minus_infty -2022-08-02 02:18:00,1612.88,,minus_infty -2022-08-02 02:19:00,1611.84,,minus_infty -2022-08-02 02:20:00,1610.02,,minus_infty -2022-08-02 02:21:00,1609.27,,minus_infty -2022-08-02 02:22:00,1610.09,,minus_infty -2022-08-02 02:23:00,1611.92,,minus_infty -2022-08-02 02:24:00,1610.74,,minus_infty -2022-08-02 02:25:00,1607.9,,minus_infty -2022-08-02 02:26:00,1606.17,,minus_infty -2022-08-02 02:27:00,1605.43,,minus_infty -2022-08-02 02:28:00,1606.19,,minus_infty -2022-08-02 02:29:00,1605.19,,minus_infty -2022-08-02 02:30:00,1604.72,,minus_infty -2022-08-02 02:31:00,1595.33,,minus_infty -2022-08-02 02:32:00,1594.91,,minus_infty -2022-08-02 02:33:00,1585.65,,minus_infty -2022-08-02 02:34:00,1588.43,,minus_infty -2022-08-02 02:35:00,1590.64,,minus_infty -2022-08-02 02:36:00,1593.13,,minus_infty -2022-08-02 02:37:00,1597.47,,minus_infty -2022-08-02 02:38:00,1592.06,,minus_infty -2022-08-02 02:39:00,1592.21,,minus_infty -2022-08-02 02:40:00,1594.37,,minus_infty -2022-08-02 02:41:00,1593.95,,minus_infty -2022-08-02 02:42:00,1594.04,,minus_infty -2022-08-02 02:43:00,1594.05,,minus_infty -2022-08-02 02:44:00,1592.75,,minus_infty -2022-08-02 02:45:00,1594.43,,minus_infty -2022-08-02 02:46:00,1591.02,,minus_infty -2022-08-02 02:47:00,1586.86,,minus_infty -2022-08-02 02:48:00,1589.35,,minus_infty -2022-08-02 02:49:00,1590.29,,minus_infty -2022-08-02 02:50:00,1590.39,,minus_infty -2022-08-02 02:51:00,1587.8,,minus_infty -2022-08-02 02:52:00,1588.81,,minus_infty -2022-08-02 02:53:00,1589.18,,minus_infty -2022-08-02 02:54:00,1587.76,,minus_infty -2022-08-02 02:55:00,1585.63,,minus_infty -2022-08-02 02:56:00,1583.65,,minus_infty -2022-08-02 02:57:00,1586.38,,minus_infty -2022-08-02 02:58:00,1585.46,,minus_infty -2022-08-02 02:59:00,1589.36,,minus_infty -2022-08-02 03:00:00,1587.91,,minus_infty -2022-08-02 03:01:00,1583.06,,minus_infty -2022-08-02 03:02:00,1584.19,,minus_infty -2022-08-02 03:03:00,1585.16,,minus_infty -2022-08-02 03:04:00,1585.34,,minus_infty -2022-08-02 03:05:00,1582.91,,minus_infty -2022-08-02 03:06:00,1579.49,,minus_infty -2022-08-02 03:07:00,1580.46,,minus_infty -2022-08-02 03:08:00,1583.3,,minus_infty -2022-08-02 03:09:00,1581.12,,minus_infty -2022-08-02 03:10:00,1579.04,,minus_infty -2022-08-02 03:11:00,1576.49,,minus_infty -2022-08-02 03:12:00,1577.29,,minus_infty -2022-08-02 03:13:00,1574.11,,minus_infty -2022-08-02 03:14:00,1574.96,,minus_infty -2022-08-02 03:15:00,1572.8,,minus_infty -2022-08-02 03:16:00,1578.51,,minus_infty -2022-08-02 03:17:00,1579.26,,minus_infty -2022-08-02 03:18:00,1578.66,,minus_infty -2022-08-02 03:19:00,1579.57,,minus_infty -2022-08-02 03:20:00,1579.13,,minus_infty -2022-08-02 03:21:00,1577.17,,minus_infty -2022-08-02 03:22:00,1575.13,,minus_infty -2022-08-02 03:23:00,1576.0,,minus_infty -2022-08-02 03:24:00,1575.77,,minus_infty -2022-08-02 03:25:00,1577.52,,minus_infty -2022-08-02 03:26:00,1574.23,,minus_infty -2022-08-02 03:27:00,1574.54,,minus_infty -2022-08-02 03:28:00,1574.32,,minus_infty -2022-08-02 03:29:00,1574.0,,minus_infty -2022-08-02 03:30:00,1573.4,,minus_infty -2022-08-02 03:31:00,1573.73,,minus_infty -2022-08-02 03:32:00,1575.44,,minus_infty -2022-08-02 03:33:00,1577.25,,minus_infty -2022-08-02 03:34:00,1577.67,,minus_infty -2022-08-02 03:35:00,1576.21,,minus_infty -2022-08-02 03:36:00,1577.25,,minus_infty -2022-08-02 03:37:00,1577.2,,minus_infty -2022-08-02 03:38:00,1577.41,,minus_infty -2022-08-02 03:39:00,1577.98,,minus_infty -2022-08-02 03:40:00,1577.06,,minus_infty -2022-08-02 03:41:00,1577.57,,minus_infty -2022-08-02 03:42:00,1576.13,,minus_infty -2022-08-02 03:43:00,1575.86,,minus_infty -2022-08-02 03:44:00,1577.21,,minus_infty -2022-08-02 03:45:00,1577.21,,minus_infty -2022-08-02 03:46:00,1576.29,,minus_infty -2022-08-02 03:47:00,1580.57,,minus_infty -2022-08-02 03:48:00,1581.0,,minus_infty -2022-08-02 03:49:00,1584.16,,minus_infty -2022-08-02 03:50:00,1583.89,,minus_infty -2022-08-02 03:51:00,1580.51,,minus_infty -2022-08-02 03:52:00,1579.0,,minus_infty -2022-08-02 03:53:00,1579.59,,minus_infty -2022-08-02 03:54:00,1578.66,,minus_infty -2022-08-02 03:55:00,1578.05,,minus_infty -2022-08-02 03:56:00,1575.55,,minus_infty -2022-08-02 03:57:00,1575.6,,minus_infty -2022-08-02 03:58:00,1577.16,,minus_infty -2022-08-02 03:59:00,1577.65,,minus_infty -2022-08-02 04:00:00,1576.57,,minus_infty -2022-08-02 04:01:00,1578.68,,minus_infty -2022-08-02 04:02:00,1578.71,,minus_infty -2022-08-02 04:03:00,1578.86,,minus_infty -2022-08-02 04:04:00,1580.15,,minus_infty -2022-08-02 04:05:00,1581.51,,minus_infty -2022-08-02 04:06:00,1582.28,,minus_infty -2022-08-02 04:07:00,1583.45,,minus_infty -2022-08-02 04:08:00,1584.66,,minus_infty -2022-08-02 04:09:00,1585.98,,minus_infty -2022-08-02 04:10:00,1585.37,,minus_infty -2022-08-02 04:11:00,1583.22,,minus_infty -2022-08-02 04:12:00,1582.51,,minus_infty -2022-08-02 04:13:00,1583.63,,minus_infty -2022-08-02 04:14:00,1582.95,,minus_infty -2022-08-02 04:15:00,1580.67,,minus_infty -2022-08-02 04:16:00,1581.37,,minus_infty -2022-08-02 04:17:00,1578.5,,minus_infty -2022-08-02 04:18:00,1577.65,,minus_infty -2022-08-02 04:19:00,1579.69,,minus_infty -2022-08-02 04:20:00,1579.72,,minus_infty -2022-08-02 04:21:00,1578.0,,minus_infty -2022-08-02 04:22:00,1577.5,,minus_infty -2022-08-02 04:23:00,1577.94,,minus_infty -2022-08-02 04:24:00,1578.39,,minus_infty -2022-08-02 04:25:00,1579.14,,minus_infty -2022-08-02 04:26:00,1579.91,,minus_infty -2022-08-02 04:27:00,1580.22,,minus_infty -2022-08-02 04:28:00,1576.31,,minus_infty -2022-08-02 04:29:00,1575.31,,minus_infty -2022-08-02 04:30:00,1575.33,,minus_infty -2022-08-02 04:31:00,1578.27,,minus_infty -2022-08-02 04:32:00,1577.46,,minus_infty -2022-08-02 04:33:00,1576.69,,minus_infty -2022-08-02 04:34:00,1577.8,,minus_infty -2022-08-02 04:35:00,1577.76,,minus_infty -2022-08-02 04:36:00,1578.12,,minus_infty -2022-08-02 04:37:00,1576.71,,minus_infty -2022-08-02 04:38:00,1577.96,,minus_infty -2022-08-02 04:39:00,1577.38,,minus_infty -2022-08-02 04:40:00,1575.96,,minus_infty -2022-08-02 04:41:00,1574.14,,minus_infty -2022-08-02 04:42:00,1574.43,,minus_infty -2022-08-02 04:43:00,1572.9,,minus_infty -2022-08-02 04:44:00,1566.84,,minus_infty -2022-08-02 04:45:00,1569.62,,minus_infty -2022-08-02 04:46:00,1570.38,,minus_infty -2022-08-02 04:47:00,1570.68,,minus_infty -2022-08-02 04:48:00,1570.39,,minus_infty -2022-08-02 04:49:00,1570.23,,minus_infty -2022-08-02 04:50:00,1570.84,,minus_infty -2022-08-02 04:51:00,1574.93,,minus_infty -2022-08-02 04:52:00,1570.92,,minus_infty -2022-08-02 04:53:00,1572.85,,minus_infty -2022-08-02 04:54:00,1574.3,,minus_infty -2022-08-02 04:55:00,1575.08,,minus_infty -2022-08-02 04:56:00,1576.83,,minus_infty -2022-08-02 04:57:00,1576.62,,minus_infty -2022-08-02 04:58:00,1576.57,,minus_infty -2022-08-02 04:59:00,1576.57,,minus_infty -2022-08-02 05:00:00,1576.33,,minus_infty -2022-08-02 05:01:00,1575.26,,minus_infty -2022-08-02 05:02:00,1577.72,,minus_infty -2022-08-02 05:03:00,1578.31,,minus_infty -2022-08-02 05:04:00,1578.93,,minus_infty -2022-08-02 05:05:00,1580.32,,minus_infty -2022-08-02 05:06:00,1582.13,,minus_infty -2022-08-02 05:07:00,1581.43,,minus_infty -2022-08-02 05:08:00,1581.33,,minus_infty -2022-08-02 05:09:00,1582.3,,minus_infty -2022-08-02 05:10:00,1581.13,,minus_infty -2022-08-02 05:11:00,1580.35,,minus_infty -2022-08-02 05:12:00,1581.95,,minus_infty -2022-08-02 05:13:00,1581.04,,minus_infty -2022-08-02 05:14:00,1582.26,,minus_infty -2022-08-02 05:15:00,1581.5,,minus_infty -2022-08-02 05:16:00,1579.78,,minus_infty -2022-08-02 05:17:00,1578.33,,minus_infty -2022-08-02 05:18:00,1580.35,,minus_infty -2022-08-02 05:19:00,1578.98,,minus_infty -2022-08-02 05:20:00,1579.43,,minus_infty -2022-08-02 05:21:00,1581.2,,minus_infty -2022-08-02 05:22:00,1581.33,,minus_infty -2022-08-02 05:23:00,1581.06,,minus_infty -2022-08-02 05:24:00,1581.99,,minus_infty -2022-08-02 05:25:00,1583.46,,minus_infty -2022-08-02 05:26:00,1585.46,,minus_infty -2022-08-02 05:27:00,1584.5,,minus_infty -2022-08-02 05:28:00,1583.97,,minus_infty -2022-08-02 05:29:00,1583.81,,minus_infty -2022-08-02 05:30:00,1582.36,,minus_infty -2022-08-02 05:31:00,1583.17,,minus_infty -2022-08-02 05:32:00,1582.24,,minus_infty -2022-08-02 05:33:00,1583.49,,minus_infty -2022-08-02 05:34:00,1583.2,,minus_infty -2022-08-02 05:35:00,1583.79,,minus_infty -2022-08-02 05:36:00,1582.39,,minus_infty -2022-08-02 05:37:00,1582.59,,minus_infty -2022-08-02 05:38:00,1582.36,,minus_infty -2022-08-02 05:39:00,1583.43,,minus_infty -2022-08-02 05:40:00,1585.75,,minus_infty -2022-08-02 05:41:00,1585.5,,minus_infty -2022-08-02 05:42:00,1584.35,,minus_infty -2022-08-02 05:43:00,1586.01,,minus_infty -2022-08-02 05:44:00,1585.89,,minus_infty -2022-08-02 05:45:00,1585.46,,minus_infty -2022-08-02 05:46:00,1586.08,,minus_infty -2022-08-02 05:47:00,1585.36,,minus_infty -2022-08-02 05:48:00,1584.52,,minus_infty -2022-08-02 05:49:00,1584.85,,minus_infty -2022-08-02 05:50:00,1583.39,,minus_infty -2022-08-02 05:51:00,1583.68,,minus_infty -2022-08-02 05:52:00,1583.38,,minus_infty -2022-08-02 05:53:00,1584.12,,minus_infty -2022-08-02 05:54:00,1584.43,,minus_infty -2022-08-02 05:55:00,1584.29,,minus_infty -2022-08-02 05:56:00,1580.93,,minus_infty -2022-08-02 05:57:00,1581.23,,minus_infty -2022-08-02 05:58:00,1581.18,,minus_infty -2022-08-02 05:59:00,1578.97,,minus_infty -2022-08-02 06:00:00,1581.28,,minus_infty -2022-08-02 06:01:00,1583.88,,minus_infty -2022-08-02 06:02:00,1584.04,,minus_infty -2022-08-02 06:03:00,1583.44,,minus_infty -2022-08-02 06:04:00,1584.29,,minus_infty -2022-08-02 06:05:00,1589.0,,minus_infty -2022-08-02 06:06:00,1588.19,,minus_infty -2022-08-02 06:07:00,1587.36,,minus_infty -2022-08-02 06:08:00,1587.49,,minus_infty -2022-08-02 06:09:00,1588.21,,minus_infty -2022-08-02 06:10:00,1588.49,,minus_infty -2022-08-02 06:11:00,1587.21,,minus_infty -2022-08-02 06:12:00,1587.35,,minus_infty -2022-08-02 06:13:00,1587.56,,minus_infty -2022-08-02 06:14:00,1586.76,,minus_infty -2022-08-02 06:15:00,1588.13,,minus_infty -2022-08-02 06:16:00,1587.65,,minus_infty -2022-08-02 06:17:00,1588.17,,minus_infty -2022-08-02 06:18:00,1589.92,,minus_infty -2022-08-02 06:19:00,1589.94,,minus_infty -2022-08-02 06:20:00,1587.84,,minus_infty -2022-08-02 06:21:00,1588.2,,minus_infty -2022-08-02 06:22:00,1586.49,,minus_infty -2022-08-02 06:23:00,1586.29,,minus_infty -2022-08-02 06:24:00,1588.11,,minus_infty -2022-08-02 06:25:00,1588.02,,minus_infty -2022-08-02 06:26:00,1587.16,,minus_infty -2022-08-02 06:27:00,1587.69,,minus_infty -2022-08-02 06:28:00,1587.7,,minus_infty -2022-08-02 06:29:00,1587.87,,minus_infty -2022-08-02 06:30:00,1587.8,,minus_infty -2022-08-02 06:31:00,1588.83,,minus_infty -2022-08-02 06:32:00,1590.44,,minus_infty -2022-08-02 06:33:00,1590.52,,minus_infty -2022-08-02 06:34:00,1589.59,,minus_infty -2022-08-02 06:35:00,1589.77,,minus_infty -2022-08-02 06:36:00,1589.96,,minus_infty -2022-08-02 06:37:00,1586.92,,minus_infty -2022-08-02 06:38:00,1589.67,,minus_infty -2022-08-02 06:39:00,1589.42,,minus_infty -2022-08-02 06:40:00,1588.98,,minus_infty -2022-08-02 06:41:00,1590.33,,minus_infty -2022-08-02 06:42:00,1591.4,,minus_infty -2022-08-02 06:43:00,1591.02,,minus_infty -2022-08-02 06:44:00,1590.16,,minus_infty -2022-08-02 06:45:00,1589.15,,minus_infty -2022-08-02 06:46:00,1588.01,,minus_infty -2022-08-02 06:47:00,1587.73,,minus_infty -2022-08-02 06:48:00,1587.08,,minus_infty -2022-08-02 06:49:00,1587.58,,minus_infty -2022-08-02 06:50:00,1587.76,,minus_infty -2022-08-02 06:51:00,1586.24,,minus_infty -2022-08-02 06:52:00,1586.12,,minus_infty -2022-08-02 06:53:00,1585.84,,minus_infty -2022-08-02 06:54:00,1585.95,,minus_infty -2022-08-02 06:55:00,1586.29,,minus_infty -2022-08-02 06:56:00,1586.61,,minus_infty -2022-08-02 06:57:00,1588.51,,minus_infty -2022-08-02 06:58:00,1586.51,,minus_infty -2022-08-02 06:59:00,1586.92,,minus_infty -2022-08-02 07:00:00,1585.48,,minus_infty -2022-08-02 07:01:00,1581.28,,minus_infty -2022-08-02 07:02:00,1580.9,,minus_infty -2022-08-02 07:03:00,1580.51,,minus_infty -2022-08-02 07:04:00,1579.09,,minus_infty -2022-08-02 07:05:00,1579.58,,minus_infty -2022-08-02 07:06:00,1576.15,,minus_infty -2022-08-02 07:07:00,1576.8,,minus_infty -2022-08-02 07:08:00,1575.09,,minus_infty -2022-08-02 07:09:00,1577.19,,minus_infty -2022-08-02 07:10:00,1580.16,,minus_infty -2022-08-02 07:11:00,1581.75,,minus_infty -2022-08-02 07:12:00,1580.42,,minus_infty -2022-08-02 07:13:00,1579.22,,minus_infty -2022-08-02 07:14:00,1579.19,,minus_infty -2022-08-02 07:15:00,1579.07,,minus_infty -2022-08-02 07:16:00,1577.8,,minus_infty -2022-08-02 07:17:00,1576.46,,minus_infty -2022-08-02 07:18:00,1577.31,,minus_infty -2022-08-02 07:19:00,1579.7,,minus_infty -2022-08-02 07:20:00,1579.23,,minus_infty -2022-08-02 07:21:00,1577.97,,minus_infty -2022-08-02 07:22:00,1577.36,,minus_infty -2022-08-02 07:23:00,1576.58,,minus_infty -2022-08-02 07:24:00,1576.89,,minus_infty -2022-08-02 07:25:00,1576.37,,minus_infty -2022-08-02 07:26:00,1575.36,,minus_infty -2022-08-02 07:27:00,1576.7,,minus_infty -2022-08-02 07:28:00,1577.46,,minus_infty -2022-08-02 07:29:00,1578.09,,minus_infty -2022-08-02 07:30:00,1578.14,,minus_infty -2022-08-02 07:31:00,1579.45,,minus_infty -2022-08-02 07:32:00,1577.13,,minus_infty -2022-08-02 07:33:00,1577.56,,minus_infty -2022-08-02 07:34:00,1577.89,,minus_infty -2022-08-02 07:35:00,1578.69,,minus_infty -2022-08-02 07:36:00,1577.53,,minus_infty -2022-08-02 07:37:00,1580.07,,minus_infty -2022-08-02 07:38:00,1580.84,,minus_infty -2022-08-02 07:39:00,1580.32,,minus_infty -2022-08-02 07:40:00,1582.77,,minus_infty -2022-08-02 07:41:00,1586.21,,minus_infty -2022-08-02 07:42:00,1587.85,,minus_infty -2022-08-02 07:43:00,1585.0,,minus_infty -2022-08-02 07:44:00,1584.71,,minus_infty -2022-08-02 07:45:00,1584.95,,minus_infty -2022-08-02 07:46:00,1585.62,,minus_infty -2022-08-02 07:47:00,1585.86,,minus_infty -2022-08-02 07:48:00,1592.61,,minus_infty -2022-08-02 07:49:00,1594.59,,minus_infty -2022-08-02 07:50:00,1594.45,,minus_infty -2022-08-02 07:51:00,1591.2,,minus_infty -2022-08-02 07:52:00,1590.14,,minus_infty -2022-08-02 07:53:00,1590.07,,minus_infty -2022-08-02 07:54:00,1591.47,,minus_infty -2022-08-02 07:55:00,1592.81,,minus_infty -2022-08-02 07:56:00,1593.9,,minus_infty -2022-08-02 07:57:00,1592.95,,minus_infty -2022-08-02 07:58:00,1593.28,,minus_infty -2022-08-02 07:59:00,1593.93,,minus_infty -2022-08-02 08:00:00,1595.53,,minus_infty -2022-08-02 08:01:00,1590.39,,minus_infty -2022-08-02 08:02:00,1588.97,,minus_infty -2022-08-02 08:03:00,1589.87,,minus_infty -2022-08-02 08:04:00,1588.73,,minus_infty -2022-08-02 08:05:00,1588.33,,minus_infty -2022-08-02 08:06:00,1592.38,,minus_infty -2022-08-02 08:07:00,1589.38,,minus_infty -2022-08-02 08:08:00,1590.35,,minus_infty -2022-08-02 08:09:00,1587.94,,minus_infty -2022-08-02 08:10:00,1587.35,,minus_infty -2022-08-02 08:11:00,1587.76,,minus_infty -2022-08-02 08:12:00,1586.44,,minus_infty -2022-08-02 08:13:00,1585.53,,minus_infty -2022-08-02 08:14:00,1585.48,,minus_infty -2022-08-02 08:15:00,1583.34,,minus_infty -2022-08-02 08:16:00,1585.01,,minus_infty -2022-08-02 08:17:00,1583.55,,minus_infty -2022-08-02 08:18:00,1583.72,,minus_infty -2022-08-02 08:19:00,1585.76,,minus_infty -2022-08-02 08:20:00,1585.29,,minus_infty -2022-08-02 08:21:00,1585.2,,minus_infty -2022-08-02 08:22:00,1585.4,,minus_infty -2022-08-02 08:23:00,1585.39,,minus_infty -2022-08-02 08:24:00,1587.46,,minus_infty -2022-08-02 08:25:00,1586.69,,minus_infty -2022-08-02 08:26:00,1585.74,,minus_infty -2022-08-02 08:27:00,1587.44,,minus_infty -2022-08-02 08:28:00,1586.89,,minus_infty -2022-08-02 08:29:00,1585.28,,minus_infty -2022-08-02 08:30:00,1584.17,,minus_infty -2022-08-02 08:31:00,1584.78,,minus_infty -2022-08-02 08:32:00,1586.25,,minus_infty -2022-08-02 08:33:00,1587.5,,minus_infty -2022-08-02 08:34:00,1587.32,,minus_infty -2022-08-02 08:35:00,1585.94,,minus_infty -2022-08-02 08:36:00,1586.07,,minus_infty -2022-08-02 08:37:00,1584.23,,minus_infty -2022-08-02 08:38:00,1584.98,,minus_infty -2022-08-02 08:39:00,1581.58,,minus_infty -2022-08-02 08:40:00,1581.85,,minus_infty -2022-08-02 08:41:00,1586.88,,minus_infty -2022-08-02 08:42:00,1587.72,,minus_infty -2022-08-02 08:43:00,1586.12,,minus_infty -2022-08-02 08:44:00,1586.56,,minus_infty -2022-08-02 08:45:00,1586.44,,minus_infty -2022-08-02 08:46:00,1583.18,,minus_infty -2022-08-02 08:47:00,1583.03,,minus_infty -2022-08-02 08:48:00,1579.7,,minus_infty -2022-08-02 08:49:00,1582.25,,minus_infty -2022-08-02 08:50:00,1583.17,,minus_infty -2022-08-02 08:51:00,1583.01,,minus_infty -2022-08-02 08:52:00,1581.92,,minus_infty -2022-08-02 08:53:00,1580.54,,minus_infty -2022-08-02 08:54:00,1581.19,,minus_infty -2022-08-02 08:55:00,1579.66,,minus_infty -2022-08-02 08:56:00,1579.46,,minus_infty -2022-08-02 08:57:00,1578.3,,minus_infty -2022-08-02 08:58:00,1580.46,,minus_infty -2022-08-02 08:59:00,1580.83,,minus_infty -2022-08-02 09:00:00,1580.18,,minus_infty -2022-08-02 09:01:00,1579.8,,minus_infty -2022-08-02 09:02:00,1579.2,,minus_infty -2022-08-02 09:03:00,1576.58,,minus_infty -2022-08-02 09:04:00,1578.63,,minus_infty -2022-08-02 09:05:00,1579.14,,minus_infty -2022-08-02 09:06:00,1580.58,,minus_infty -2022-08-02 09:07:00,1581.57,,minus_infty -2022-08-02 09:08:00,1581.72,,minus_infty -2022-08-02 09:09:00,1582.09,,minus_infty -2022-08-02 09:10:00,1580.97,,minus_infty -2022-08-02 09:11:00,1582.45,,minus_infty -2022-08-02 09:12:00,1581.18,,minus_infty -2022-08-02 09:13:00,1580.16,,minus_infty -2022-08-02 09:14:00,1582.63,,minus_infty -2022-08-02 09:15:00,1581.87,,minus_infty -2022-08-02 09:16:00,1581.02,,minus_infty -2022-08-02 09:17:00,1581.65,,minus_infty -2022-08-02 09:18:00,1580.94,,minus_infty -2022-08-02 09:19:00,1580.89,,minus_infty -2022-08-02 09:20:00,1579.65,,minus_infty -2022-08-02 09:21:00,1577.88,,minus_infty -2022-08-02 09:22:00,1577.05,,minus_infty -2022-08-02 09:23:00,1575.78,,minus_infty -2022-08-02 09:24:00,1571.47,,minus_infty -2022-08-02 09:25:00,1572.97,,minus_infty -2022-08-02 09:26:00,1573.86,,minus_infty -2022-08-02 09:27:00,1576.11,,minus_infty -2022-08-02 09:28:00,1575.86,,minus_infty -2022-08-02 09:29:00,1575.8,,minus_infty -2022-08-02 09:30:00,1573.64,,minus_infty -2022-08-02 09:31:00,1570.5,,minus_infty -2022-08-02 09:32:00,1570.37,,minus_infty -2022-08-02 09:33:00,1572.33,,minus_infty -2022-08-02 09:34:00,1572.89,,minus_infty -2022-08-02 09:35:00,1571.85,,minus_infty -2022-08-02 09:36:00,1573.08,,minus_infty -2022-08-02 09:37:00,1573.94,,minus_infty -2022-08-02 09:38:00,1573.31,,minus_infty -2022-08-02 09:39:00,1571.34,,minus_infty -2022-08-02 09:40:00,1573.66,,minus_infty -2022-08-02 09:41:00,1572.58,,minus_infty -2022-08-02 09:42:00,1572.11,,minus_infty -2022-08-02 09:43:00,1571.98,,minus_infty -2022-08-02 09:44:00,1569.79,,minus_infty -2022-08-02 09:45:00,1572.33,,minus_infty -2022-08-02 09:46:00,1571.41,,minus_infty -2022-08-02 09:47:00,1571.31,,minus_infty -2022-08-02 09:48:00,1571.19,,minus_infty -2022-08-02 09:49:00,1574.25,,minus_infty -2022-08-02 09:50:00,1574.92,,minus_infty -2022-08-02 09:51:00,1575.37,,minus_infty -2022-08-02 09:52:00,1573.87,,minus_infty -2022-08-02 09:53:00,1571.81,,minus_infty -2022-08-02 09:54:00,1570.53,,minus_infty -2022-08-02 09:55:00,1560.26,,minus_infty -2022-08-02 09:56:00,1569.32,,minus_infty -2022-08-02 09:57:00,1566.36,,minus_infty -2022-08-02 09:58:00,1568.78,,minus_infty -2022-08-02 09:59:00,1570.75,,minus_infty -2022-08-02 10:00:00,1568.82,,minus_infty -2022-08-02 10:01:00,1566.15,,minus_infty -2022-08-02 10:02:00,1565.85,,minus_infty -2022-08-02 10:03:00,1564.47,,minus_infty -2022-08-02 10:04:00,1566.81,,minus_infty -2022-08-02 10:05:00,1567.82,,minus_infty -2022-08-02 10:06:00,1569.97,,minus_infty -2022-08-02 10:07:00,1571.02,,minus_infty -2022-08-02 10:08:00,1575.02,,minus_infty -2022-08-02 10:09:00,1574.32,,minus_infty -2022-08-02 10:10:00,1574.39,,minus_infty -2022-08-02 10:11:00,1576.2,,minus_infty -2022-08-02 10:12:00,1578.15,,minus_infty -2022-08-02 10:13:00,1579.42,,minus_infty -2022-08-02 10:14:00,1581.43,,minus_infty -2022-08-02 10:15:00,1578.51,,minus_infty -2022-08-02 10:16:00,1579.0,,minus_infty -2022-08-02 10:17:00,1575.32,,minus_infty -2022-08-02 10:18:00,1576.85,,minus_infty -2022-08-02 10:19:00,1575.79,,minus_infty -2022-08-02 10:20:00,1576.54,,minus_infty -2022-08-02 10:21:00,1578.49,,minus_infty -2022-08-02 10:22:00,1578.53,,minus_infty -2022-08-02 10:23:00,1579.55,,minus_infty -2022-08-02 10:24:00,1579.1,,minus_infty -2022-08-02 10:25:00,1577.6,,minus_infty -2022-08-02 10:26:00,1578.29,,minus_infty -2022-08-02 10:27:00,1576.05,,minus_infty -2022-08-02 10:28:00,1575.96,,minus_infty -2022-08-02 10:29:00,1576.24,,minus_infty -2022-08-02 10:30:00,1576.1,,minus_infty -2022-08-02 10:31:00,1577.51,,minus_infty -2022-08-02 10:32:00,1577.56,,minus_infty -2022-08-02 10:33:00,1578.02,,minus_infty -2022-08-02 10:34:00,1577.62,,minus_infty -2022-08-02 10:35:00,1578.19,,minus_infty -2022-08-02 10:36:00,1577.45,,minus_infty -2022-08-02 10:37:00,1579.01,,minus_infty -2022-08-02 10:38:00,1577.83,,minus_infty -2022-08-02 10:39:00,1575.73,,minus_infty -2022-08-02 10:40:00,1576.69,,minus_infty -2022-08-02 10:41:00,1576.07,,minus_infty -2022-08-02 10:42:00,1578.19,,minus_infty -2022-08-02 10:43:00,1578.61,,minus_infty -2022-08-02 10:44:00,1580.21,,minus_infty -2022-08-02 10:45:00,1579.8,,minus_infty -2022-08-02 10:46:00,1583.25,,minus_infty -2022-08-02 10:47:00,1585.45,,minus_infty -2022-08-02 10:48:00,1588.31,,minus_infty -2022-08-02 10:49:00,1588.03,,minus_infty -2022-08-02 10:50:00,1588.71,,minus_infty -2022-08-02 10:51:00,1590.06,,minus_infty -2022-08-02 10:52:00,1587.25,,minus_infty -2022-08-02 10:53:00,1585.67,,minus_infty -2022-08-02 10:54:00,1584.96,,minus_infty -2022-08-02 10:55:00,1584.28,,minus_infty -2022-08-02 10:56:00,1584.28,,minus_infty -2022-08-02 10:57:00,1585.5,,minus_infty -2022-08-02 10:58:00,1585.23,,minus_infty -2022-08-02 10:59:00,1587.4,,minus_infty -2022-08-02 11:00:00,1586.13,,minus_infty -2022-08-02 11:01:00,1587.98,,minus_infty -2022-08-02 11:02:00,1589.29,,minus_infty -2022-08-02 11:03:00,1589.59,,minus_infty -2022-08-02 11:04:00,1586.87,,minus_infty -2022-08-02 11:05:00,1586.44,,minus_infty -2022-08-02 11:06:00,1588.36,,minus_infty -2022-08-02 11:07:00,1589.27,,minus_infty -2022-08-02 11:08:00,1589.63,,minus_infty -2022-08-02 11:09:00,1588.66,,minus_infty -2022-08-02 11:10:00,1588.86,,minus_infty -2022-08-02 11:11:00,1590.31,,minus_infty -2022-08-02 11:12:00,1589.28,,minus_infty -2022-08-02 11:13:00,1588.86,,minus_infty -2022-08-02 11:14:00,1586.0,,minus_infty -2022-08-02 11:15:00,1584.73,,minus_infty -2022-08-02 11:16:00,1585.73,,minus_infty -2022-08-02 11:17:00,1587.63,,minus_infty -2022-08-02 11:18:00,1589.99,,minus_infty -2022-08-02 11:19:00,1589.28,,minus_infty -2022-08-02 11:20:00,1588.97,,minus_infty -2022-08-02 11:21:00,1587.72,,minus_infty -2022-08-02 11:22:00,1588.16,,minus_infty -2022-08-02 11:23:00,1587.27,,minus_infty -2022-08-02 11:24:00,1589.44,,minus_infty -2022-08-02 11:25:00,1587.5,,minus_infty -2022-08-02 11:26:00,1588.56,,minus_infty -2022-08-02 11:27:00,1587.79,,minus_infty -2022-08-02 11:28:00,1586.98,,minus_infty -2022-08-02 11:29:00,1586.58,,minus_infty -2022-08-02 11:30:00,1587.13,,minus_infty -2022-08-02 11:31:00,1587.01,,minus_infty -2022-08-02 11:32:00,1588.47,,minus_infty -2022-08-02 11:33:00,1591.12,,minus_infty -2022-08-02 11:34:00,1594.48,,minus_infty -2022-08-02 11:35:00,1592.99,,minus_infty -2022-08-02 11:36:00,1591.67,,minus_infty -2022-08-02 11:37:00,1591.64,,minus_infty -2022-08-02 11:38:00,1591.35,,minus_infty -2022-08-02 11:39:00,1593.05,,minus_infty -2022-08-02 11:40:00,1596.21,,minus_infty -2022-08-02 11:41:00,1596.98,,minus_infty -2022-08-02 11:42:00,1593.62,,minus_infty -2022-08-02 11:43:00,1592.0,,minus_infty -2022-08-02 11:44:00,1590.7,,minus_infty -2022-08-02 11:45:00,1592.29,,minus_infty -2022-08-02 11:46:00,1593.09,,minus_infty -2022-08-02 11:47:00,1595.22,,minus_infty -2022-08-02 11:48:00,1592.01,,minus_infty -2022-08-02 11:49:00,1594.76,,minus_infty -2022-08-02 11:50:00,1595.39,,minus_infty -2022-08-02 11:51:00,1596.31,,minus_infty -2022-08-02 11:52:00,1597.7,,minus_infty -2022-08-02 11:53:00,1595.78,,minus_infty -2022-08-02 11:54:00,1592.69,,minus_infty -2022-08-02 11:55:00,1592.03,,minus_infty -2022-08-02 11:56:00,1589.43,,minus_infty -2022-08-02 11:57:00,1587.47,,minus_infty -2022-08-02 11:58:00,1586.59,,minus_infty -2022-08-02 11:59:00,1588.11,,minus_infty -2022-08-02 12:00:00,1589.47,,minus_infty -2022-08-02 12:01:00,1590.1,,minus_infty -2022-08-02 12:02:00,1590.92,,minus_infty -2022-08-02 12:03:00,1587.57,,minus_infty -2022-08-02 12:04:00,1589.61,,minus_infty -2022-08-02 12:05:00,1589.43,,minus_infty -2022-08-02 12:06:00,1589.46,,minus_infty -2022-08-02 12:07:00,1586.72,,minus_infty -2022-08-02 12:08:00,1583.73,,minus_infty -2022-08-02 12:09:00,1587.1,,minus_infty -2022-08-02 12:10:00,1588.94,,minus_infty -2022-08-02 12:11:00,1587.46,,minus_infty -2022-08-02 12:12:00,1586.72,,minus_infty -2022-08-02 12:13:00,1585.93,,minus_infty -2022-08-02 12:14:00,1585.13,,minus_infty -2022-08-02 12:15:00,1585.13,,minus_infty -2022-08-02 12:16:00,1581.84,,minus_infty -2022-08-02 12:17:00,1582.81,,minus_infty -2022-08-02 12:18:00,1582.12,,minus_infty -2022-08-02 12:19:00,1580.44,,minus_infty -2022-08-02 12:20:00,1578.95,,minus_infty -2022-08-02 12:21:00,1579.12,,minus_infty -2022-08-02 12:22:00,1577.3,,minus_infty -2022-08-02 12:23:00,1579.92,,minus_infty -2022-08-02 12:24:00,1581.69,,minus_infty -2022-08-02 12:25:00,1581.72,,minus_infty -2022-08-02 12:26:00,1583.17,,minus_infty -2022-08-02 12:27:00,1581.41,,minus_infty -2022-08-02 12:28:00,1581.26,,minus_infty -2022-08-02 12:29:00,1581.47,,minus_infty -2022-08-02 12:30:00,1582.81,,minus_infty -2022-08-02 12:31:00,1582.49,,minus_infty -2022-08-02 12:32:00,1582.55,,minus_infty -2022-08-02 12:33:00,1576.5,,minus_infty -2022-08-02 12:34:00,1575.05,,minus_infty -2022-08-02 12:35:00,1577.3,,minus_infty -2022-08-02 12:36:00,1578.94,,minus_infty -2022-08-02 12:37:00,1574.69,,minus_infty -2022-08-02 12:38:00,1574.72,,minus_infty -2022-08-02 12:39:00,1574.55,,minus_infty -2022-08-02 12:40:00,1572.1,,minus_infty -2022-08-02 12:41:00,1574.54,,minus_infty -2022-08-02 12:42:00,1575.02,,minus_infty -2022-08-02 12:43:00,1575.17,,minus_infty -2022-08-02 12:44:00,1575.79,,minus_infty -2022-08-02 12:45:00,1574.39,,minus_infty -2022-08-02 12:46:00,1575.16,,minus_infty -2022-08-02 12:47:00,1573.12,,minus_infty -2022-08-02 12:48:00,1571.85,,minus_infty -2022-08-02 12:49:00,1570.34,,minus_infty -2022-08-02 12:50:00,1571.5,,minus_infty -2022-08-02 12:51:00,1571.14,,minus_infty -2022-08-02 12:52:00,1570.33,,minus_infty -2022-08-02 12:53:00,1569.29,,minus_infty -2022-08-02 12:54:00,1566.77,,minus_infty -2022-08-02 12:55:00,1565.95,,minus_infty -2022-08-02 12:56:00,1568.95,,minus_infty -2022-08-02 12:57:00,1570.01,,minus_infty -2022-08-02 12:58:00,1570.97,,minus_infty -2022-08-02 12:59:00,1571.47,,minus_infty -2022-08-02 13:00:00,1573.85,,minus_infty -2022-08-02 13:01:00,1573.23,,minus_infty -2022-08-02 13:02:00,1573.86,,minus_infty -2022-08-02 13:03:00,1574.16,,minus_infty -2022-08-02 13:04:00,1575.87,,minus_infty -2022-08-02 13:05:00,1578.62,,minus_infty -2022-08-02 13:06:00,1577.68,,minus_infty -2022-08-02 13:07:00,1575.72,,minus_infty -2022-08-02 13:08:00,1574.27,,minus_infty -2022-08-02 13:09:00,1577.09,,minus_infty -2022-08-02 13:10:00,1579.9,,minus_infty -2022-08-02 13:11:00,1579.94,,minus_infty -2022-08-02 13:12:00,1581.72,,minus_infty -2022-08-02 13:13:00,1580.11,,minus_infty -2022-08-02 13:14:00,1580.15,,minus_infty -2022-08-02 13:15:00,1578.99,,minus_infty -2022-08-02 13:16:00,1576.66,,minus_infty -2022-08-02 13:17:00,1577.94,,minus_infty -2022-08-02 13:18:00,1576.92,,minus_infty -2022-08-02 13:19:00,1577.82,,minus_infty -2022-08-02 13:20:00,1576.89,,minus_infty -2022-08-02 13:21:00,1572.86,,minus_infty -2022-08-02 13:22:00,1574.48,,minus_infty -2022-08-02 13:23:00,1576.12,,minus_infty -2022-08-02 13:24:00,1576.27,,minus_infty -2022-08-02 13:25:00,1575.71,,minus_infty -2022-08-02 13:26:00,1578.36,,minus_infty -2022-08-02 13:27:00,1576.92,,minus_infty -2022-08-02 13:28:00,1574.1,,minus_infty -2022-08-02 13:29:00,1575.54,,minus_infty -2022-08-02 13:30:00,1572.14,,minus_infty -2022-08-02 13:31:00,1581.29,,minus_infty -2022-08-02 13:32:00,1581.06,,minus_infty -2022-08-02 13:33:00,1581.47,,minus_infty -2022-08-02 13:34:00,1580.77,,minus_infty -2022-08-02 13:35:00,1575.83,,minus_infty -2022-08-02 13:36:00,1579.31,,minus_infty -2022-08-02 13:37:00,1577.37,,minus_infty -2022-08-02 13:38:00,1576.59,,minus_infty -2022-08-02 13:39:00,1580.76,,minus_infty -2022-08-02 13:40:00,1580.31,,minus_infty -2022-08-02 13:41:00,1581.88,,minus_infty -2022-08-02 13:42:00,1582.04,,minus_infty -2022-08-02 13:43:00,1576.28,,minus_infty -2022-08-02 13:44:00,1576.8,,minus_infty -2022-08-02 13:45:00,1584.43,,minus_infty -2022-08-02 13:46:00,1584.0,,minus_infty -2022-08-02 13:47:00,1582.99,,minus_infty -2022-08-02 13:48:00,1581.8,,minus_infty -2022-08-02 13:49:00,1578.83,,minus_infty -2022-08-02 13:50:00,1577.86,,minus_infty -2022-08-02 13:51:00,1581.21,,minus_infty -2022-08-02 13:52:00,1578.58,,minus_infty -2022-08-02 13:53:00,1574.69,,minus_infty -2022-08-02 13:54:00,1575.87,,minus_infty -2022-08-02 13:55:00,1572.62,,minus_infty -2022-08-02 13:56:00,1573.29,,minus_infty -2022-08-02 13:57:00,1577.64,,minus_infty -2022-08-02 13:58:00,1583.22,,minus_infty -2022-08-02 13:59:00,1581.23,,minus_infty -2022-08-02 14:00:00,1578.98,,minus_infty -2022-08-02 14:01:00,1581.41,,minus_infty -2022-08-02 14:02:00,1578.55,,minus_infty -2022-08-02 14:03:00,1578.51,,minus_infty -2022-08-02 14:04:00,1575.58,,minus_infty -2022-08-02 14:05:00,1577.05,,minus_infty -2022-08-02 14:06:00,1578.85,,minus_infty -2022-08-02 14:07:00,1576.45,,minus_infty -2022-08-02 14:08:00,1575.66,,minus_infty -2022-08-02 14:09:00,1572.2,,minus_infty -2022-08-02 14:10:00,1569.59,,minus_infty -2022-08-02 14:11:00,1574.82,,minus_infty -2022-08-02 14:12:00,1577.02,,minus_infty -2022-08-02 14:13:00,1574.87,,minus_infty -2022-08-02 14:14:00,1577.93,,minus_infty -2022-08-02 14:15:00,1581.11,,minus_infty -2022-08-02 14:16:00,1580.59,,minus_infty -2022-08-02 14:17:00,1579.66,,minus_infty -2022-08-02 14:18:00,1579.82,,minus_infty -2022-08-02 14:19:00,1581.0,,minus_infty -2022-08-02 14:20:00,1583.29,,minus_infty -2022-08-02 14:21:00,1580.63,,minus_infty -2022-08-02 14:22:00,1578.83,,minus_infty -2022-08-02 14:23:00,1577.71,,minus_infty -2022-08-02 14:24:00,1577.08,,minus_infty -2022-08-02 14:25:00,1578.81,,minus_infty -2022-08-02 14:26:00,1580.47,,minus_infty -2022-08-02 14:27:00,1581.39,,minus_infty -2022-08-02 14:28:00,1591.26,,minus_infty -2022-08-02 14:29:00,1611.94,,minus_infty -2022-08-02 14:30:00,1596.72,,minus_infty -2022-08-02 14:31:00,1588.55,,minus_infty -2022-08-02 14:32:00,1590.06,,minus_infty -2022-08-02 14:33:00,1581.09,,minus_infty -2022-08-02 14:34:00,1577.91,,minus_infty -2022-08-02 14:35:00,1577.78,,minus_infty -2022-08-02 14:36:00,1582.28,,minus_infty -2022-08-02 14:37:00,1584.11,,minus_infty -2022-08-02 14:38:00,1587.72,,minus_infty -2022-08-02 14:39:00,1591.47,,minus_infty -2022-08-02 14:40:00,1586.93,,minus_infty -2022-08-02 14:41:00,1590.64,,minus_infty -2022-08-02 14:42:00,1597.45,,minus_infty -2022-08-02 14:43:00,1599.64,,minus_infty -2022-08-02 14:44:00,1605.49,,minus_infty -2022-08-02 14:45:00,1600.12,,minus_infty -2022-08-02 14:46:00,1605.58,,minus_infty -2022-08-02 14:47:00,1610.03,,minus_infty -2022-08-02 14:48:00,1616.19,,minus_infty -2022-08-02 14:49:00,1627.2,,open_close -2022-08-02 14:50:00,1635.2,,open_close -2022-08-02 14:51:00,1630.81,,open_close -2022-08-02 14:52:00,1631.32,,open_close -2022-08-02 14:53:00,1631.73,,open_close -2022-08-02 14:54:00,1627.2,,open_close -2022-08-02 14:55:00,1620.06,,minus_infty -2022-08-02 14:56:00,1623.87,,open_close -2022-08-02 14:57:00,1624.07,,open_close -2022-08-02 14:58:00,1624.27,,open_close -2022-08-02 14:59:00,1624.48,,open_close -2022-08-02 15:00:00,1624.14,,open_close -2022-08-02 15:01:00,1628.26,,open_close -2022-08-02 15:02:00,1630.73,,open_close -2022-08-02 15:03:00,1630.92,,open_close -2022-08-02 15:04:00,1636.34,,open_close -2022-08-02 15:05:00,1632.31,,open_close -2022-08-02 15:06:00,1630.84,,open_close -2022-08-02 15:07:00,1628.43,,open_close -2022-08-02 15:08:00,1626.31,,open_close -2022-08-02 15:09:00,1630.8,,open_close -2022-08-02 15:10:00,1626.4,,open_close -2022-08-02 15:11:00,1624.59,,open_close -2022-08-02 15:12:00,1622.86,,open_close -2022-08-02 15:13:00,1615.4,,minus_infty -2022-08-02 15:14:00,1615.96,,minus_infty -2022-08-02 15:15:00,1613.09,,minus_infty -2022-08-02 15:16:00,1607.33,,minus_infty -2022-08-02 15:17:00,1614.4,,minus_infty -2022-08-02 15:18:00,1619.46,,minus_infty -2022-08-02 15:19:00,1615.0,,minus_infty -2022-08-02 15:20:00,1611.73,,minus_infty -2022-08-02 15:21:00,1611.75,,minus_infty -2022-08-02 15:22:00,1609.07,,minus_infty -2022-08-02 15:23:00,1610.16,,minus_infty -2022-08-02 15:24:00,1611.58,,minus_infty -2022-08-02 15:25:00,1612.88,,minus_infty -2022-08-02 15:26:00,1617.37,,minus_infty -2022-08-02 15:27:00,1615.6,,minus_infty -2022-08-02 15:28:00,1616.73,,minus_infty -2022-08-02 15:29:00,1617.3,,minus_infty -2022-08-02 15:30:00,1612.72,,minus_infty -2022-08-02 15:31:00,1610.69,,minus_infty -2022-08-02 15:32:00,1612.79,,minus_infty -2022-08-02 15:33:00,1616.61,,minus_infty -2022-08-02 15:34:00,1619.93,,minus_infty -2022-08-02 15:35:00,1625.24,,open_close -2022-08-02 15:36:00,1624.79,,open_close -2022-08-02 15:37:00,1626.33,,open_close -2022-08-02 15:38:00,1626.36,,open_close -2022-08-02 15:39:00,1627.18,,open_close -2022-08-02 15:40:00,1626.46,,open_close -2022-08-02 15:41:00,1626.38,,open_close -2022-08-02 15:42:00,1625.6,,open_close -2022-08-02 15:43:00,1629.07,,open_close -2022-08-02 15:44:00,1627.42,,open_close -2022-08-02 15:45:00,1629.44,,open_close -2022-08-02 15:46:00,1628.28,,open_close -2022-08-02 15:47:00,1625.63,,open_close -2022-08-02 15:48:00,1625.0,,open_close -2022-08-02 15:49:00,1628.1,,open_close -2022-08-02 15:50:00,1626.44,,open_close -2022-08-02 15:51:00,1626.25,,open_close -2022-08-02 15:52:00,1627.22,,open_close -2022-08-02 15:53:00,1631.1,,open_close -2022-08-02 15:54:00,1632.05,,open_close -2022-08-02 15:55:00,1630.84,,open_close -2022-08-02 15:56:00,1631.28,,open_close -2022-08-02 15:57:00,1632.02,,open_close -2022-08-02 15:58:00,1629.93,,open_close -2022-08-02 15:59:00,1629.94,,open_close -2022-08-02 16:00:00,1627.03,,open_close -2022-08-02 16:01:00,1635.79,,open_close -2022-08-02 16:02:00,1644.4,,open_close -2022-08-02 16:03:00,1636.69,,open_close -2022-08-02 16:04:00,1639.0,,open_close -2022-08-02 16:05:00,1644.24,,open_close -2022-08-02 16:06:00,1645.78,,open_close -2022-08-02 16:07:00,1646.64,,open_close -2022-08-02 16:08:00,1643.13,,open_close -2022-08-02 16:09:00,1642.15,,open_close -2022-08-02 16:10:00,1643.55,,open_close -2022-08-02 16:11:00,1641.74,,open_close -2022-08-02 16:12:00,1642.66,,open_close -2022-08-02 16:13:00,1646.46,,open_close -2022-08-02 16:14:00,1646.92,,open_close -2022-08-02 16:15:00,1645.28,,open_close -2022-08-02 16:16:00,1647.31,,open_close -2022-08-02 16:17:00,1650.12,,open_close -2022-08-02 16:18:00,1651.1,,open_close -2022-08-02 16:19:00,1650.1,,open_close -2022-08-02 16:20:00,1655.55,,open_close -2022-08-02 16:21:00,1660.0,,open_close -2022-08-02 16:22:00,1660.51,,open_close -2022-08-02 16:23:00,1656.43,,open_close -2022-08-02 16:24:00,1657.76,,open_close -2022-08-02 16:25:00,1658.53,,open_close -2022-08-02 16:26:00,1660.77,,open_close -2022-08-02 16:27:00,1672.52,,open_close -2022-08-02 16:28:00,1669.14,,open_close -2022-08-02 16:29:00,1671.17,,open_close -2022-08-02 16:30:00,1669.19,,open_close -2022-08-02 16:31:00,1665.41,,open_close -2022-08-02 16:32:00,1667.07,,open_close -2022-08-02 16:33:00,1666.71,,open_close -2022-08-02 16:34:00,1666.84,,open_close -2022-08-02 16:35:00,1665.46,,open_close -2022-08-02 16:36:00,1664.56,,open_close -2022-08-02 16:37:00,1666.78,,open_close -2022-08-02 16:38:00,1666.28,,open_close -2022-08-02 16:39:00,1671.7,,open_close -2022-08-02 16:40:00,1671.08,,open_close -2022-08-02 16:41:00,1671.24,,open_close -2022-08-02 16:42:00,1671.06,,open_close -2022-08-02 16:43:00,1668.45,,open_close -2022-08-02 16:44:00,1670.08,,open_close -2022-08-02 16:45:00,1670.42,,open_close -2022-08-02 16:46:00,1668.47,,open_close -2022-08-02 16:47:00,1668.34,,open_close -2022-08-02 16:48:00,1668.77,,open_close -2022-08-02 16:49:00,1669.19,,open_close -2022-08-02 16:50:00,1667.71,,open_close -2022-08-02 16:51:00,1666.28,,open_close -2022-08-02 16:52:00,1663.79,,open_close -2022-08-02 16:53:00,1665.16,,open_close -2022-08-02 16:54:00,1667.98,,open_close -2022-08-02 16:55:00,1667.29,,open_close -2022-08-02 16:56:00,1668.1,,open_close -2022-08-02 16:57:00,1667.81,,open_close -2022-08-02 16:58:00,1665.93,,open_close -2022-08-02 16:59:00,1668.76,,open_close -2022-08-02 17:00:00,1670.24,,open_close -2022-08-02 17:01:00,1672.2,,open_close -2022-08-02 17:02:00,1671.13,,open_close -2022-08-02 17:03:00,1674.56,,open_close -2022-08-02 17:04:00,1676.33,,open_close -2022-08-02 17:05:00,1675.22,,open_close -2022-08-02 17:06:00,1673.46,,open_close -2022-08-02 17:07:00,1671.91,,open_close -2022-08-02 17:08:00,1670.28,,open_close -2022-08-02 17:09:00,1668.19,,open_close -2022-08-02 17:10:00,1669.64,,open_close -2022-08-02 17:11:00,1670.36,,open_close -2022-08-02 17:12:00,1665.68,,open_close -2022-08-02 17:13:00,1666.96,,open_close -2022-08-02 17:14:00,1669.37,,open_close -2022-08-02 17:15:00,1669.72,,open_close -2022-08-02 17:16:00,1673.75,,open_close -2022-08-02 17:17:00,1673.49,,open_close -2022-08-02 17:18:00,1672.39,,open_close -2022-08-02 17:19:00,1674.2,,open_close -2022-08-02 17:20:00,1672.72,,open_close -2022-08-02 17:21:00,1673.52,,open_close -2022-08-02 17:22:00,1673.63,,open_close -2022-08-02 17:23:00,1670.62,,open_close -2022-08-02 17:24:00,1667.53,,open_close -2022-08-02 17:25:00,1667.36,,open_close -2022-08-02 17:26:00,1667.84,,open_close -2022-08-02 17:27:00,1666.35,,open_close -2022-08-02 17:28:00,1662.68,,open_close -2022-08-02 17:29:00,1661.8,,open_close -2022-08-02 17:30:00,1659.43,,open_close -2022-08-02 17:31:00,1660.11,,open_close -2022-08-02 17:32:00,1658.91,,open_close -2022-08-02 17:33:00,1659.19,,open_close -2022-08-02 17:34:00,1654.64,,open_close -2022-08-02 17:35:00,1654.35,,open_close -2022-08-02 17:36:00,1652.54,,open_close -2022-08-02 17:37:00,1654.52,,open_close -2022-08-02 17:38:00,1656.06,,open_close -2022-08-02 17:39:00,1655.94,,open_close -2022-08-02 17:40:00,1654.82,,open_close -2022-08-02 17:41:00,1654.23,,open_close -2022-08-02 17:42:00,1654.18,,open_close -2022-08-02 17:43:00,1652.68,,open_close -2022-08-02 17:44:00,1650.3,,open_close -2022-08-02 17:45:00,1649.7,,open_close -2022-08-02 17:46:00,1646.91,,open_close -2022-08-02 17:47:00,1647.33,,open_close -2022-08-02 17:48:00,1647.14,,open_close -2022-08-02 17:49:00,1646.9,,open_close -2022-08-02 17:50:00,1645.44,,open_close -2022-08-02 17:51:00,1645.81,,open_close -2022-08-02 17:52:00,1648.7,,open_close -2022-08-02 17:53:00,1650.06,,open_close -2022-08-02 17:54:00,1650.46,,open_close -2022-08-02 17:55:00,1650.13,,open_close -2022-08-02 17:56:00,1649.69,,open_close -2022-08-02 17:57:00,1649.67,,open_close -2022-08-02 17:58:00,1650.68,,open_close -2022-08-02 17:59:00,1651.32,,open_close -2022-08-02 18:00:00,1648.75,,open_close -2022-08-02 18:01:00,1652.48,,open_close -2022-08-02 18:02:00,1651.91,,open_close -2022-08-02 18:03:00,1648.76,,open_close -2022-08-02 18:04:00,1650.95,,open_close -2022-08-02 18:05:00,1652.0,,open_close -2022-08-02 18:06:00,1656.54,,open_close -2022-08-02 18:07:00,1658.21,,open_close -2022-08-02 18:08:00,1658.97,,open_close -2022-08-02 18:09:00,1661.33,,open_close -2022-08-02 18:10:00,1661.38,,open_close -2022-08-02 18:11:00,1661.25,,open_close -2022-08-02 18:12:00,1664.22,,open_close -2022-08-02 18:13:00,1663.58,,open_close -2022-08-02 18:14:00,1664.64,,open_close -2022-08-02 18:15:00,1661.58,,open_close -2022-08-02 18:16:00,1658.0,,open_close -2022-08-02 18:17:00,1658.37,,open_close -2022-08-02 18:18:00,1655.94,,open_close -2022-08-02 18:19:00,1655.19,,open_close -2022-08-02 18:20:00,1655.84,,open_close -2022-08-02 18:21:00,1658.11,,open_close -2022-08-02 18:22:00,1657.53,,open_close -2022-08-02 18:23:00,1659.56,,open_close -2022-08-02 18:24:00,1658.13,,open_close -2022-08-02 18:25:00,1658.8,,open_close -2022-08-02 18:26:00,1659.31,,open_close -2022-08-02 18:27:00,1658.13,,open_close -2022-08-02 18:28:00,1655.7,,open_close -2022-08-02 18:29:00,1655.03,,open_close -2022-08-02 18:30:00,1655.08,,open_close -2022-08-02 18:31:00,1656.64,,open_close -2022-08-02 18:32:00,1659.57,,open_close -2022-08-02 18:33:00,1660.5,,open_close -2022-08-02 18:34:00,1659.62,,open_close -2022-08-02 18:35:00,1659.69,,open_close -2022-08-02 18:36:00,1658.51,,open_close -2022-08-02 18:37:00,1652.44,,open_close -2022-08-02 18:38:00,1649.22,,open_close -2022-08-02 18:39:00,1651.48,,open_close -2022-08-02 18:40:00,1652.65,,open_close -2022-08-02 18:41:00,1652.53,,open_close -2022-08-02 18:42:00,1654.47,,open_close -2022-08-02 18:43:00,1654.39,,open_close -2022-08-02 18:44:00,1654.57,,open_close -2022-08-02 18:45:00,1654.6,,open_close -2022-08-02 18:46:00,1655.39,,open_close -2022-08-02 18:47:00,1650.8,,open_close -2022-08-02 18:48:00,1651.14,,open_close -2022-08-02 18:49:00,1652.64,,open_close -2022-08-02 18:50:00,1651.9,,open_close -2022-08-02 18:51:00,1646.0,,open_close -2022-08-02 18:52:00,1645.52,,open_close -2022-08-02 18:53:00,1642.86,,open_close -2022-08-02 18:54:00,1641.92,,open_close -2022-08-02 18:55:00,1642.3,,open_close -2022-08-02 18:56:00,1641.09,,open_close -2022-08-02 18:57:00,1639.22,,open_close -2022-08-02 18:58:00,1635.41,,open_close -2022-08-02 18:59:00,1635.85,,open_close -2022-08-02 19:00:00,1636.25,,open_close -2022-08-02 19:01:00,1631.56,,open_close -2022-08-02 19:02:00,1632.77,,open_close -2022-08-02 19:03:00,1628.03,,open_close -2022-08-02 19:04:00,1630.09,,open_close -2022-08-02 19:05:00,1624.26,,open_close -2022-08-02 19:06:00,1624.86,,open_close -2022-08-02 19:07:00,1625.75,,open_close -2022-08-02 19:08:00,1627.06,,open_close -2022-08-02 19:09:00,1627.9,,open_close -2022-08-02 19:10:00,1626.47,,open_close -2022-08-02 19:11:00,1623.41,,open_close -2022-08-02 19:12:00,1624.97,,open_close -2022-08-02 19:13:00,1625.21,,open_close -2022-08-02 19:14:00,1626.58,,open_close -2022-08-02 19:15:00,1627.6,,open_close -2022-08-02 19:16:00,1628.15,,open_close -2022-08-02 19:17:00,1628.72,,open_close -2022-08-02 19:18:00,1624.54,,open_close -2022-08-02 19:19:00,1628.73,,open_close -2022-08-02 19:20:00,1629.16,,open_close -2022-08-02 19:21:00,1634.37,,open_close -2022-08-02 19:22:00,1636.93,,open_close -2022-08-02 19:23:00,1636.64,,open_close -2022-08-02 19:24:00,1640.38,,open_close -2022-08-02 19:25:00,1644.52,,open_close -2022-08-02 19:26:00,1646.06,,open_close -2022-08-02 19:27:00,1646.34,,open_close -2022-08-02 19:28:00,1649.57,,open_close -2022-08-02 19:29:00,1652.71,,open_close -2022-08-02 19:30:00,1650.26,,open_close -2022-08-02 19:31:00,1644.71,,open_close -2022-08-02 19:32:00,1643.23,,open_close -2022-08-02 19:33:00,1646.81,,open_close -2022-08-02 19:34:00,1648.5,,open_close -2022-08-02 19:35:00,1647.88,,open_close -2022-08-02 19:36:00,1651.3,,open_close -2022-08-02 19:37:00,1651.15,,open_close -2022-08-02 19:38:00,1648.73,,open_close -2022-08-02 19:39:00,1648.92,,open_close -2022-08-02 19:40:00,1646.71,,open_close -2022-08-02 19:41:00,1646.18,,open_close -2022-08-02 19:42:00,1645.71,,open_close -2022-08-02 19:43:00,1646.21,,open_close -2022-08-02 19:44:00,1649.23,,open_close -2022-08-02 19:45:00,1648.31,,open_close -2022-08-02 19:46:00,1645.87,,open_close -2022-08-02 19:47:00,1645.72,,open_close -2022-08-02 19:48:00,1643.26,,open_close -2022-08-02 19:49:00,1643.28,,open_close -2022-08-02 19:50:00,1644.15,,open_close -2022-08-02 19:51:00,1646.09,,open_close -2022-08-02 19:52:00,1645.19,,open_close -2022-08-02 19:53:00,1645.19,,open_close -2022-08-02 19:54:00,1643.6,,open_close -2022-08-02 19:55:00,1643.44,,open_close -2022-08-02 19:56:00,1642.55,,open_close -2022-08-02 19:57:00,1638.47,,open_close -2022-08-02 19:58:00,1638.11,,open_close -2022-08-02 19:59:00,1636.44,,open_close -2022-08-02 20:00:00,1638.21,,open_close -2022-08-02 20:01:00,1640.5,,open_close -2022-08-02 20:02:00,1634.37,,open_close -2022-08-02 20:03:00,1637.05,,open_close -2022-08-02 20:04:00,1640.81,,open_close -2022-08-02 20:05:00,1647.27,,open_close -2022-08-02 20:06:00,1645.75,,open_close -2022-08-02 20:07:00,1640.75,,open_close -2022-08-02 20:08:00,1642.19,,open_close -2022-08-02 20:09:00,1642.55,,open_close -2022-08-02 20:10:00,1643.41,,open_close -2022-08-02 20:11:00,1642.62,,open_close -2022-08-02 20:12:00,1642.91,,open_close -2022-08-02 20:13:00,1641.31,,open_close -2022-08-02 20:14:00,1645.87,,open_close -2022-08-02 20:15:00,1649.15,,open_close -2022-08-02 20:16:00,1650.78,,open_close -2022-08-02 20:17:00,1650.41,,open_close -2022-08-02 20:18:00,1650.62,,open_close -2022-08-02 20:19:00,1646.41,,open_close -2022-08-02 20:20:00,1644.83,,open_close -2022-08-02 20:21:00,1645.39,,open_close -2022-08-02 20:22:00,1648.12,,open_close -2022-08-02 20:23:00,1646.98,,open_close -2022-08-02 20:24:00,1650.43,,open_close -2022-08-02 20:25:00,1650.31,,open_close -2022-08-02 20:26:00,1652.75,,open_close -2022-08-02 20:27:00,1655.17,,open_close -2022-08-02 20:28:00,1653.85,,open_close -2022-08-02 20:29:00,1652.55,,open_close -2022-08-02 20:30:00,1650.97,,open_close -2022-08-02 20:31:00,1650.0,,open_close -2022-08-02 20:32:00,1651.59,,open_close -2022-08-02 20:33:00,1651.67,,open_close -2022-08-02 20:34:00,1650.47,,open_close -2022-08-02 20:35:00,1650.4,,open_close -2022-08-02 20:36:00,1647.0,,open_close -2022-08-02 20:37:00,1647.14,,open_close -2022-08-02 20:38:00,1648.17,,open_close -2022-08-02 20:39:00,1649.72,,open_close -2022-08-02 20:40:00,1647.48,,open_close -2022-08-02 20:41:00,1649.38,,open_close -2022-08-02 20:42:00,1653.65,,open_close -2022-08-02 20:43:00,1654.7,,open_close -2022-08-02 20:44:00,1653.7,,open_close -2022-08-02 20:45:00,1651.82,,open_close -2022-08-02 20:46:00,1651.44,,open_close -2022-08-02 20:47:00,1648.71,,open_close -2022-08-02 20:48:00,1647.2,,open_close -2022-08-02 20:49:00,1646.91,,open_close -2022-08-02 20:50:00,1646.76,,open_close -2022-08-02 20:51:00,1648.9,,open_close -2022-08-02 20:52:00,1650.79,,open_close -2022-08-02 20:53:00,1651.94,,open_close -2022-08-02 20:54:00,1649.77,,open_close -2022-08-02 20:55:00,1651.91,,open_close -2022-08-02 20:56:00,1650.69,,open_close -2022-08-02 20:57:00,1650.96,,open_close -2022-08-02 20:58:00,1652.18,,open_close -2022-08-02 20:59:00,1652.05,,open_close -2022-08-02 21:00:00,1650.36,,open_close -2022-08-02 21:01:00,1649.34,,open_close -2022-08-02 21:02:00,1648.22,,open_close -2022-08-02 21:03:00,1650.69,,open_close -2022-08-02 21:04:00,1650.32,,open_close -2022-08-02 21:05:00,1648.62,,open_close -2022-08-02 21:06:00,1647.32,,open_close -2022-08-02 21:07:00,1644.04,,open_close -2022-08-02 21:08:00,1643.47,,open_close -2022-08-02 21:09:00,1642.48,,open_close -2022-08-02 21:10:00,1639.4,,open_close -2022-08-02 21:11:00,1640.5,,open_close -2022-08-02 21:12:00,1642.59,,open_close -2022-08-02 21:13:00,1645.67,,open_close -2022-08-02 21:14:00,1644.69,,open_close -2022-08-02 21:15:00,1640.72,,open_close -2022-08-02 21:16:00,1638.46,,open_close -2022-08-02 21:17:00,1637.63,,open_close -2022-08-02 21:18:00,1639.23,,open_close -2022-08-02 21:19:00,1640.18,,open_close -2022-08-02 21:20:00,1638.44,,open_close -2022-08-02 21:21:00,1637.97,,open_close -2022-08-02 21:22:00,1638.02,,open_close -2022-08-02 21:23:00,1637.34,,open_close -2022-08-02 21:24:00,1637.01,,open_close -2022-08-02 21:25:00,1636.72,,open_close -2022-08-02 21:26:00,1639.41,,open_close -2022-08-02 21:27:00,1638.59,,open_close -2022-08-02 21:28:00,1638.42,,open_close -2022-08-02 21:29:00,1638.78,,open_close -2022-08-02 21:30:00,1638.55,,open_close -2022-08-02 21:31:00,1642.79,,open_close -2022-08-02 21:32:00,1645.1,,open_close -2022-08-02 21:33:00,1642.35,,open_close -2022-08-02 21:34:00,1641.02,,open_close -2022-08-02 21:35:00,1640.88,,open_close -2022-08-02 21:36:00,1642.84,,open_close -2022-08-02 21:37:00,1641.68,,open_close -2022-08-02 21:38:00,1639.15,,open_close -2022-08-02 21:39:00,1638.8,,open_close -2022-08-02 21:40:00,1640.03,,open_close -2022-08-02 21:41:00,1640.13,,open_close -2022-08-02 21:42:00,1640.4,,open_close -2022-08-02 21:43:00,1641.67,,open_close -2022-08-02 21:44:00,1644.74,,open_close -2022-08-02 21:45:00,1649.57,,open_close -2022-08-02 21:46:00,1653.58,,open_close -2022-08-02 21:47:00,1654.69,,open_close -2022-08-02 21:48:00,1651.56,,open_close -2022-08-02 21:49:00,1655.14,,open_close -2022-08-02 21:50:00,1656.26,,open_close -2022-08-02 21:51:00,1658.07,,open_close -2022-08-02 21:52:00,1657.95,,open_close -2022-08-02 21:53:00,1653.01,,open_close -2022-08-02 21:54:00,1648.91,,open_close -2022-08-02 21:55:00,1648.45,,open_close -2022-08-02 21:56:00,1653.14,,open_close -2022-08-02 21:57:00,1653.0,,open_close -2022-08-02 21:58:00,1657.15,,open_close -2022-08-02 21:59:00,1657.07,,open_close -2022-08-02 22:00:00,1657.51,,open_close -2022-08-02 22:01:00,1659.0,,open_close -2022-08-02 22:02:00,1659.32,,open_close -2022-08-02 22:03:00,1656.8,,open_close -2022-08-02 22:04:00,1655.19,,open_close -2022-08-02 22:05:00,1654.52,,open_close -2022-08-02 22:06:00,1655.06,,open_close -2022-08-02 22:07:00,1655.39,,open_close -2022-08-02 22:08:00,1655.3,,open_close -2022-08-02 22:09:00,1657.7,,open_close -2022-08-02 22:10:00,1655.92,,open_close -2022-08-02 22:11:00,1655.73,,open_close -2022-08-02 22:12:00,1655.59,,open_close -2022-08-02 22:13:00,1654.16,,open_close -2022-08-02 22:14:00,1652.51,,open_close -2022-08-02 22:15:00,1650.62,,open_close -2022-08-02 22:16:00,1649.01,,open_close -2022-08-02 22:17:00,1648.13,,open_close -2022-08-02 22:18:00,1651.63,,open_close -2022-08-02 22:19:00,1650.7,,open_close -2022-08-02 22:20:00,1650.69,,open_close -2022-08-02 22:21:00,1650.95,,open_close -2022-08-02 22:22:00,1650.94,,open_close -2022-08-02 22:23:00,1649.59,,open_close -2022-08-02 22:24:00,1646.28,,open_close -2022-08-02 22:25:00,1648.96,,open_close -2022-08-02 22:26:00,1647.3,,open_close -2022-08-02 22:27:00,1646.93,,open_close -2022-08-02 22:28:00,1646.8,,open_close -2022-08-02 22:29:00,1644.66,,open_close -2022-08-02 22:30:00,1645.14,,open_close -2022-08-02 22:31:00,1645.87,,open_close -2022-08-02 22:32:00,1642.55,,open_close -2022-08-02 22:33:00,1644.01,,open_close -2022-08-02 22:34:00,1643.38,,open_close -2022-08-02 22:35:00,1647.69,,open_close -2022-08-02 22:36:00,1650.41,,open_close -2022-08-02 22:37:00,1650.57,,open_close -2022-08-02 22:38:00,1651.18,,open_close -2022-08-02 22:39:00,1651.96,,open_close -2022-08-02 22:40:00,1650.18,,open_close -2022-08-02 22:41:00,1651.44,,open_close -2022-08-02 22:42:00,1651.65,,open_close -2022-08-02 22:43:00,1650.79,,open_close -2022-08-02 22:44:00,1650.19,,open_close -2022-08-02 22:45:00,1651.57,,open_close -2022-08-02 22:46:00,1646.19,,open_close -2022-08-02 22:47:00,1645.84,,open_close -2022-08-02 22:48:00,1647.12,,open_close -2022-08-02 22:49:00,1646.28,,open_close -2022-08-02 22:50:00,1646.83,,open_close -2022-08-02 22:51:00,1649.95,,open_close -2022-08-02 22:52:00,1651.73,,open_close -2022-08-02 22:53:00,1651.05,,open_close -2022-08-02 22:54:00,1649.57,,open_close -2022-08-02 22:55:00,1650.04,,open_close -2022-08-02 22:56:00,1645.83,,open_close -2022-08-02 22:57:00,1645.38,,open_close -2022-08-02 22:58:00,1645.84,,open_close -2022-08-02 22:59:00,1647.82,,open_close -2022-08-02 23:00:00,1651.2,,open_close -2022-08-02 23:01:00,1652.0,,open_close -2022-08-02 23:02:00,1650.3,,open_close -2022-08-02 23:03:00,1646.71,,open_close -2022-08-02 23:04:00,1649.23,,open_close -2022-08-02 23:05:00,1649.54,,open_close -2022-08-02 23:06:00,1647.05,,open_close -2022-08-02 23:07:00,1644.5,,open_close -2022-08-02 23:08:00,1645.18,,open_close -2022-08-02 23:09:00,1645.62,,open_close -2022-08-02 23:10:00,1647.03,,open_close -2022-08-02 23:11:00,1645.96,,open_close -2022-08-02 23:12:00,1646.97,,open_close -2022-08-02 23:13:00,1650.14,,open_close -2022-08-02 23:14:00,1645.96,,open_close -2022-08-02 23:15:00,1646.13,,open_close -2022-08-02 23:16:00,1643.26,,open_close -2022-08-02 23:17:00,1638.84,,open_close -2022-08-02 23:18:00,1639.4,,open_close -2022-08-02 23:19:00,1638.89,,open_close -2022-08-02 23:20:00,1640.54,,open_close -2022-08-02 23:21:00,1641.19,,open_close -2022-08-02 23:22:00,1641.08,,open_close -2022-08-02 23:23:00,1641.22,,open_close -2022-08-02 23:24:00,1640.69,,open_close -2022-08-02 23:25:00,1644.84,,open_close -2022-08-02 23:26:00,1644.22,,open_close -2022-08-02 23:27:00,1643.17,,open_close -2022-08-02 23:28:00,1642.61,,open_close -2022-08-02 23:29:00,1641.48,,open_close -2022-08-02 23:30:00,1642.55,,open_close -2022-08-02 23:31:00,1643.54,,open_close -2022-08-02 23:32:00,1644.09,,open_close -2022-08-02 23:33:00,1646.63,,open_close -2022-08-02 23:34:00,1646.88,,open_close -2022-08-02 23:35:00,1648.44,,open_close -2022-08-02 23:36:00,1647.52,,open_close -2022-08-02 23:37:00,1647.73,,open_close -2022-08-02 23:38:00,1646.31,,open_close -2022-08-02 23:39:00,1644.34,,open_close -2022-08-02 23:40:00,1645.94,,open_close -2022-08-02 23:41:00,1646.23,,open_close -2022-08-02 23:42:00,1646.33,,open_close -2022-08-02 23:43:00,1647.16,,open_close -2022-08-02 23:44:00,1646.85,,open_close -2022-08-02 23:45:00,1647.22,,open_close -2022-08-02 23:46:00,1642.04,,open_close -2022-08-02 23:47:00,1642.5,,open_close -2022-08-02 23:48:00,1643.37,,open_close -2022-08-02 23:49:00,1644.92,,open_close -2022-08-02 23:50:00,1640.98,,open_close -2022-08-02 23:51:00,1638.15,,open_close -2022-08-02 23:52:00,1634.68,,open_close -2022-08-02 23:53:00,1630.99,,open_close -2022-08-02 23:54:00,1633.67,,open_close -2022-08-02 23:55:00,1629.1,,open_close -2022-08-02 23:56:00,1632.39,,open_close -2022-08-02 23:57:00,1632.15,,open_close -2022-08-02 23:58:00,1631.03,,open_close -2022-08-02 23:59:00,1631.03,,open_close -2022-08-03 00:00:00,1633.1,,open_close -2022-08-03 00:01:00,1631.62,,open_close -2022-08-03 00:02:00,1631.03,,open_close -2022-08-03 00:03:00,1628.59,,open_close -2022-08-03 00:04:00,1631.31,,open_close -2022-08-03 00:05:00,1629.64,,open_close -2022-08-03 00:06:00,1634.55,,open_close -2022-08-03 00:07:00,1636.89,,open_close -2022-08-03 00:08:00,1636.37,,open_close -2022-08-03 00:09:00,1636.93,,open_close -2022-08-03 00:10:00,1638.18,,open_close -2022-08-03 00:11:00,1634.33,,open_close -2022-08-03 00:12:00,1628.76,,open_close -2022-08-03 00:13:00,1629.3,,open_close -2022-08-03 00:14:00,1626.16,,open_close -2022-08-03 00:15:00,1623.72,,open_close -2022-08-03 00:16:00,1622.27,,open_close -2022-08-03 00:17:00,1623.42,,open_close -2022-08-03 00:18:00,1620.37,,minus_infty -2022-08-03 00:19:00,1616.42,,minus_infty -2022-08-03 00:20:00,1620.92,,open_close -2022-08-03 00:21:00,1618.04,,minus_infty -2022-08-03 00:22:00,1616.09,,minus_infty -2022-08-03 00:23:00,1614.69,,minus_infty -2022-08-03 00:24:00,1609.78,,minus_infty -2022-08-03 00:25:00,1606.84,,minus_infty -2022-08-03 00:26:00,1603.53,,minus_infty -2022-08-03 00:27:00,1604.47,,minus_infty -2022-08-03 00:28:00,1606.77,,minus_infty -2022-08-03 00:29:00,1605.35,,minus_infty -2022-08-03 00:30:00,1608.14,,minus_infty -2022-08-03 00:31:00,1610.0,,minus_infty -2022-08-03 00:32:00,1612.41,,minus_infty -2022-08-03 00:33:00,1611.37,,minus_infty -2022-08-03 00:34:00,1609.35,,minus_infty -2022-08-03 00:35:00,1609.85,,minus_infty -2022-08-03 00:36:00,1608.89,,minus_infty -2022-08-03 00:37:00,1610.73,,minus_infty -2022-08-03 00:38:00,1612.48,,minus_infty -2022-08-03 00:39:00,1610.34,,minus_infty -2022-08-03 00:40:00,1608.92,,minus_infty -2022-08-03 00:41:00,1606.47,,minus_infty -2022-08-03 00:42:00,1602.09,,minus_infty -2022-08-03 00:43:00,1602.28,,minus_infty -2022-08-03 00:44:00,1603.26,,minus_infty -2022-08-03 00:45:00,1605.37,,minus_infty -2022-08-03 00:46:00,1603.78,,minus_infty -2022-08-03 00:47:00,1601.8,,minus_infty -2022-08-03 00:48:00,1605.45,,minus_infty -2022-08-03 00:49:00,1602.03,,minus_infty -2022-08-03 00:50:00,1604.69,,minus_infty -2022-08-03 00:51:00,1604.34,,minus_infty -2022-08-03 00:52:00,1603.66,,minus_infty -2022-08-03 00:53:00,1599.84,,minus_infty -2022-08-03 00:54:00,1594.91,,minus_infty -2022-08-03 00:55:00,1596.7,,minus_infty -2022-08-03 00:56:00,1597.62,,minus_infty -2022-08-03 00:57:00,1598.12,,minus_infty -2022-08-03 00:58:00,1599.2,,minus_infty -2022-08-03 00:59:00,1600.23,,minus_infty -2022-08-03 01:00:00,1599.5,,minus_infty -2022-08-03 01:01:00,1598.27,,minus_infty -2022-08-03 01:02:00,1592.54,,minus_infty -2022-08-03 01:03:00,1595.28,,minus_infty -2022-08-03 01:04:00,1594.88,,minus_infty -2022-08-03 01:05:00,1592.38,,minus_infty -2022-08-03 01:06:00,1595.4,,minus_infty -2022-08-03 01:07:00,1596.99,,minus_infty -2022-08-03 01:08:00,1597.75,,minus_infty -2022-08-03 01:09:00,1596.41,,minus_infty -2022-08-03 01:10:00,1597.21,,minus_infty -2022-08-03 01:11:00,1597.57,,minus_infty -2022-08-03 01:12:00,1597.29,,minus_infty -2022-08-03 01:13:00,1596.19,,minus_infty -2022-08-03 01:14:00,1596.47,,minus_infty -2022-08-03 01:15:00,1598.87,,minus_infty -2022-08-03 01:16:00,1599.52,,minus_infty -2022-08-03 01:17:00,1597.96,,minus_infty -2022-08-03 01:18:00,1599.06,,minus_infty -2022-08-03 01:19:00,1598.82,,minus_infty -2022-08-03 01:20:00,1596.25,,minus_infty -2022-08-03 01:21:00,1598.98,,minus_infty -2022-08-03 01:22:00,1599.95,,minus_infty -2022-08-03 01:23:00,1603.25,,minus_infty -2022-08-03 01:24:00,1602.69,,minus_infty -2022-08-03 01:25:00,1605.96,,minus_infty -2022-08-03 01:26:00,1604.01,,minus_infty -2022-08-03 01:27:00,1603.43,,minus_infty -2022-08-03 01:28:00,1604.56,,minus_infty -2022-08-03 01:29:00,1604.2,,minus_infty -2022-08-03 01:30:00,1606.98,,minus_infty -2022-08-03 01:31:00,1611.05,,minus_infty -2022-08-03 01:32:00,1613.48,,minus_infty -2022-08-03 01:33:00,1614.39,,minus_infty -2022-08-03 01:34:00,1612.14,,minus_infty -2022-08-03 01:35:00,1609.9,,minus_infty -2022-08-03 01:36:00,1609.15,,minus_infty -2022-08-03 01:37:00,1609.0,,minus_infty -2022-08-03 01:38:00,1609.48,,minus_infty -2022-08-03 01:39:00,1606.56,,minus_infty -2022-08-03 01:40:00,1608.48,,minus_infty -2022-08-03 01:41:00,1605.36,,minus_infty -2022-08-03 01:42:00,1609.33,,minus_infty -2022-08-03 01:43:00,1608.91,,minus_infty -2022-08-03 01:44:00,1611.23,,minus_infty -2022-08-03 01:45:00,1611.83,,minus_infty -2022-08-03 01:46:00,1610.82,,minus_infty -2022-08-03 01:47:00,1611.25,,minus_infty -2022-08-03 01:48:00,1613.02,,minus_infty -2022-08-03 01:49:00,1614.46,,minus_infty -2022-08-03 01:50:00,1615.14,,minus_infty -2022-08-03 01:51:00,1617.62,,minus_infty -2022-08-03 01:52:00,1615.47,,minus_infty -2022-08-03 01:53:00,1614.67,,minus_infty -2022-08-03 01:54:00,1614.78,,minus_infty -2022-08-03 01:55:00,1612.91,,minus_infty -2022-08-03 01:56:00,1611.77,,minus_infty -2022-08-03 01:57:00,1612.11,,minus_infty -2022-08-03 01:58:00,1612.42,,minus_infty -2022-08-03 01:59:00,1612.68,,minus_infty -2022-08-03 02:00:00,1614.96,,minus_infty -2022-08-03 02:01:00,1613.77,,minus_infty -2022-08-03 02:02:00,1613.39,,minus_infty -2022-08-03 02:03:00,1617.85,,minus_infty -2022-08-03 02:04:00,1616.19,,minus_infty -2022-08-03 02:05:00,1615.3,,minus_infty -2022-08-03 02:06:00,1616.43,,minus_infty -2022-08-03 02:07:00,1615.43,,minus_infty -2022-08-03 02:08:00,1616.91,,minus_infty -2022-08-03 02:09:00,1616.79,,minus_infty -2022-08-03 02:10:00,1615.51,,minus_infty -2022-08-03 02:11:00,1614.67,,minus_infty -2022-08-03 02:12:00,1613.65,,minus_infty -2022-08-03 02:13:00,1615.43,,minus_infty -2022-08-03 02:14:00,1615.35,,minus_infty -2022-08-03 02:15:00,1616.4,,minus_infty -2022-08-03 02:16:00,1616.04,,minus_infty -2022-08-03 02:17:00,1614.42,,minus_infty -2022-08-03 02:18:00,1614.31,,minus_infty -2022-08-03 02:19:00,1610.82,,minus_infty -2022-08-03 02:20:00,1610.31,,minus_infty -2022-08-03 02:21:00,1609.31,,minus_infty -2022-08-03 02:22:00,1608.84,,minus_infty -2022-08-03 02:23:00,1608.93,,minus_infty -2022-08-03 02:24:00,1607.02,,minus_infty -2022-08-03 02:25:00,1607.61,,minus_infty -2022-08-03 02:26:00,1604.41,,minus_infty -2022-08-03 02:27:00,1603.99,,minus_infty -2022-08-03 02:28:00,1604.0,,minus_infty -2022-08-03 02:29:00,1604.05,,minus_infty -2022-08-03 02:30:00,1605.92,,minus_infty -2022-08-03 02:31:00,1607.06,,minus_infty -2022-08-03 02:32:00,1607.57,,minus_infty -2022-08-03 02:33:00,1605.91,,minus_infty -2022-08-03 02:34:00,1608.06,,minus_infty -2022-08-03 02:35:00,1606.72,,minus_infty -2022-08-03 02:36:00,1611.16,,minus_infty -2022-08-03 02:37:00,1613.94,,minus_infty -2022-08-03 02:38:00,1612.85,,minus_infty -2022-08-03 02:39:00,1616.84,,minus_infty -2022-08-03 02:40:00,1616.44,,minus_infty -2022-08-03 02:41:00,1614.56,,minus_infty -2022-08-03 02:42:00,1614.65,,minus_infty -2022-08-03 02:43:00,1612.81,,minus_infty -2022-08-03 02:44:00,1613.67,,minus_infty -2022-08-03 02:45:00,1614.76,,minus_infty -2022-08-03 02:46:00,1613.1,,minus_infty -2022-08-03 02:47:00,1610.56,,minus_infty -2022-08-03 02:48:00,1609.71,,minus_infty -2022-08-03 02:49:00,1606.94,,minus_infty -2022-08-03 02:50:00,1607.18,,minus_infty -2022-08-03 02:51:00,1606.41,,minus_infty -2022-08-03 02:52:00,1606.06,,minus_infty -2022-08-03 02:53:00,1608.18,,minus_infty -2022-08-03 02:54:00,1608.47,,minus_infty -2022-08-03 02:55:00,1608.6,,minus_infty -2022-08-03 02:56:00,1609.47,,minus_infty -2022-08-03 02:57:00,1609.42,,minus_infty -2022-08-03 02:58:00,1607.54,,minus_infty -2022-08-03 02:59:00,1607.65,,minus_infty -2022-08-03 03:00:00,1608.49,,minus_infty -2022-08-03 03:01:00,1609.92,,minus_infty -2022-08-03 03:02:00,1609.77,,minus_infty -2022-08-03 03:03:00,1611.67,,minus_infty -2022-08-03 03:04:00,1613.04,,minus_infty -2022-08-03 03:05:00,1612.49,,minus_infty -2022-08-03 03:06:00,1613.98,,minus_infty -2022-08-03 03:07:00,1612.18,,minus_infty -2022-08-03 03:08:00,1613.06,,minus_infty -2022-08-03 03:09:00,1611.21,,minus_infty -2022-08-03 03:10:00,1612.32,,minus_infty -2022-08-03 03:11:00,1615.77,,minus_infty -2022-08-03 03:12:00,1617.22,,minus_infty -2022-08-03 03:13:00,1622.45,,open_close -2022-08-03 03:14:00,1623.97,,open_close -2022-08-03 03:15:00,1625.36,,open_close -2022-08-03 03:16:00,1621.93,,open_close -2022-08-03 03:17:00,1619.59,,minus_infty -2022-08-03 03:18:00,1619.18,,minus_infty -2022-08-03 03:19:00,1619.52,,minus_infty -2022-08-03 03:20:00,1618.47,,minus_infty -2022-08-03 03:21:00,1622.63,,open_close -2022-08-03 03:22:00,1622.46,,open_close -2022-08-03 03:23:00,1621.99,,open_close -2022-08-03 03:24:00,1621.08,,open_close -2022-08-03 03:25:00,1622.39,,open_close -2022-08-03 03:26:00,1620.46,,minus_infty -2022-08-03 03:27:00,1619.1,,minus_infty -2022-08-03 03:28:00,1618.35,,minus_infty -2022-08-03 03:29:00,1617.39,,minus_infty -2022-08-03 03:30:00,1619.29,,minus_infty -2022-08-03 03:31:00,1619.23,,minus_infty -2022-08-03 03:32:00,1620.4,,minus_infty -2022-08-03 03:33:00,1618.88,,minus_infty -2022-08-03 03:34:00,1618.46,,minus_infty -2022-08-03 03:35:00,1618.11,,minus_infty -2022-08-03 03:36:00,1617.04,,minus_infty -2022-08-03 03:37:00,1616.63,,minus_infty -2022-08-03 03:38:00,1614.81,,minus_infty -2022-08-03 03:39:00,1612.4,,minus_infty -2022-08-03 03:40:00,1613.85,,minus_infty -2022-08-03 03:41:00,1613.41,,minus_infty -2022-08-03 03:42:00,1614.93,,minus_infty -2022-08-03 03:43:00,1615.46,,minus_infty -2022-08-03 03:44:00,1615.46,,minus_infty -2022-08-03 03:45:00,1614.94,,minus_infty -2022-08-03 03:46:00,1617.47,,minus_infty -2022-08-03 03:47:00,1618.33,,minus_infty -2022-08-03 03:48:00,1618.42,,minus_infty -2022-08-03 03:49:00,1620.34,,minus_infty -2022-08-03 03:50:00,1619.1,,minus_infty -2022-08-03 03:51:00,1619.63,,minus_infty -2022-08-03 03:52:00,1616.75,,minus_infty -2022-08-03 03:53:00,1617.88,,minus_infty -2022-08-03 03:54:00,1618.97,,minus_infty -2022-08-03 03:55:00,1618.73,,minus_infty -2022-08-03 03:56:00,1616.69,,minus_infty -2022-08-03 03:57:00,1615.58,,minus_infty -2022-08-03 03:58:00,1619.5,,minus_infty -2022-08-03 03:59:00,1620.11,,minus_infty -2022-08-03 04:00:00,1620.36,,minus_infty -2022-08-03 04:01:00,1619.2,,minus_infty -2022-08-03 04:02:00,1620.47,,minus_infty -2022-08-03 04:03:00,1617.53,,minus_infty -2022-08-03 04:04:00,1618.38,,minus_infty -2022-08-03 04:05:00,1618.81,,minus_infty -2022-08-03 04:06:00,1622.33,,open_close -2022-08-03 04:07:00,1619.44,,minus_infty -2022-08-03 04:08:00,1620.57,,minus_infty -2022-08-03 04:09:00,1618.46,,minus_infty -2022-08-03 04:10:00,1617.03,,minus_infty -2022-08-03 04:11:00,1618.16,,minus_infty -2022-08-03 04:12:00,1618.98,,minus_infty -2022-08-03 04:13:00,1618.75,,minus_infty -2022-08-03 04:14:00,1619.34,,minus_infty -2022-08-03 04:15:00,1618.0,,minus_infty -2022-08-03 04:16:00,1618.27,,minus_infty -2022-08-03 04:17:00,1621.24,,open_close -2022-08-03 04:18:00,1621.99,,open_close -2022-08-03 04:19:00,1623.86,,open_close -2022-08-03 04:20:00,1624.53,,open_close -2022-08-03 04:21:00,1622.91,,open_close -2022-08-03 04:22:00,1623.22,,open_close -2022-08-03 04:23:00,1622.27,,open_close -2022-08-03 04:24:00,1621.0,,open_close -2022-08-03 04:25:00,1620.51,,minus_infty -2022-08-03 04:26:00,1622.1,,open_close -2022-08-03 04:27:00,1621.6,,open_close -2022-08-03 04:28:00,1620.26,,minus_infty -2022-08-03 04:29:00,1620.55,,minus_infty -2022-08-03 04:30:00,1621.99,,open_close -2022-08-03 04:31:00,1621.98,,open_close -2022-08-03 04:32:00,1621.46,,open_close -2022-08-03 04:33:00,1622.12,,open_close -2022-08-03 04:34:00,1620.94,,open_close -2022-08-03 04:35:00,1619.62,,minus_infty -2022-08-03 04:36:00,1616.47,,minus_infty -2022-08-03 04:37:00,1617.28,,minus_infty -2022-08-03 04:38:00,1618.57,,minus_infty -2022-08-03 04:39:00,1618.16,,minus_infty -2022-08-03 04:40:00,1616.27,,minus_infty -2022-08-03 04:41:00,1616.29,,minus_infty -2022-08-03 04:42:00,1617.47,,minus_infty -2022-08-03 04:43:00,1617.79,,minus_infty -2022-08-03 04:44:00,1616.71,,minus_infty -2022-08-03 04:45:00,1617.86,,minus_infty -2022-08-03 04:46:00,1620.06,,minus_infty -2022-08-03 04:47:00,1619.13,,minus_infty -2022-08-03 04:48:00,1619.26,,minus_infty -2022-08-03 04:49:00,1617.03,,minus_infty -2022-08-03 04:50:00,1617.5,,minus_infty -2022-08-03 04:51:00,1616.93,,minus_infty -2022-08-03 04:52:00,1615.27,,minus_infty -2022-08-03 04:53:00,1613.67,,minus_infty -2022-08-03 04:54:00,1615.3,,minus_infty -2022-08-03 04:55:00,1615.9,,minus_infty -2022-08-03 04:56:00,1614.91,,minus_infty -2022-08-03 04:57:00,1615.43,,minus_infty -2022-08-03 04:58:00,1615.44,,minus_infty -2022-08-03 04:59:00,1614.41,,minus_infty -2022-08-03 05:00:00,1614.65,,minus_infty -2022-08-03 05:01:00,1614.38,,minus_infty -2022-08-03 05:02:00,1614.64,,minus_infty -2022-08-03 05:03:00,1615.71,,minus_infty -2022-08-03 05:04:00,1615.86,,minus_infty -2022-08-03 05:05:00,1615.31,,minus_infty -2022-08-03 05:06:00,1615.3,,minus_infty -2022-08-03 05:07:00,1614.64,,minus_infty -2022-08-03 05:08:00,1614.49,,minus_infty -2022-08-03 05:09:00,1615.15,,minus_infty -2022-08-03 05:10:00,1615.42,,minus_infty -2022-08-03 05:11:00,1614.69,,minus_infty -2022-08-03 05:12:00,1615.33,,minus_infty -2022-08-03 05:13:00,1615.31,,minus_infty -2022-08-03 05:14:00,1616.41,,minus_infty -2022-08-03 05:15:00,1617.89,,minus_infty -2022-08-03 05:16:00,1618.53,,minus_infty -2022-08-03 05:17:00,1620.27,,minus_infty -2022-08-03 05:18:00,1618.9,,minus_infty -2022-08-03 05:19:00,1619.53,,minus_infty -2022-08-03 05:20:00,1619.86,,minus_infty -2022-08-03 05:21:00,1621.03,,open_close -2022-08-03 05:22:00,1621.28,,open_close -2022-08-03 05:23:00,1621.2,,open_close -2022-08-03 05:24:00,1622.51,,open_close -2022-08-03 05:25:00,1624.1,,open_close -2022-08-03 05:26:00,1623.26,,open_close -2022-08-03 05:27:00,1621.82,,open_close -2022-08-03 05:28:00,1622.5,,open_close -2022-08-03 05:29:00,1621.9,,open_close -2022-08-03 05:30:00,1622.82,,open_close -2022-08-03 05:31:00,1628.63,,open_close -2022-08-03 05:32:00,1628.22,,open_close -2022-08-03 05:33:00,1627.7,,open_close -2022-08-03 05:34:00,1629.8,,open_close -2022-08-03 05:35:00,1629.18,,open_close -2022-08-03 05:36:00,1628.17,,open_close -2022-08-03 05:37:00,1629.69,,open_close -2022-08-03 05:38:00,1627.82,,open_close -2022-08-03 05:39:00,1628.32,,open_close -2022-08-03 05:40:00,1628.74,,open_close -2022-08-03 05:41:00,1628.69,,open_close -2022-08-03 05:42:00,1628.98,,open_close -2022-08-03 05:43:00,1630.7,,open_close -2022-08-03 05:44:00,1629.04,,open_close -2022-08-03 05:45:00,1629.64,,open_close -2022-08-03 05:46:00,1629.7,,open_close -2022-08-03 05:47:00,1628.8,,open_close -2022-08-03 05:48:00,1627.19,,open_close -2022-08-03 05:49:00,1629.69,,open_close -2022-08-03 05:50:00,1630.28,,open_close -2022-08-03 05:51:00,1630.47,,open_close -2022-08-03 05:52:00,1630.77,,open_close -2022-08-03 05:53:00,1630.5,,open_close -2022-08-03 05:54:00,1630.56,,open_close -2022-08-03 05:55:00,1630.63,,open_close -2022-08-03 05:56:00,1629.96,,open_close -2022-08-03 05:57:00,1629.92,,open_close -2022-08-03 05:58:00,1630.16,,open_close -2022-08-03 05:59:00,1629.05,,open_close -2022-08-03 06:00:00,1630.74,,open_close -2022-08-03 06:01:00,1632.5,,open_close -2022-08-03 06:02:00,1633.22,,open_close -2022-08-03 06:03:00,1632.87,,open_close -2022-08-03 06:04:00,1632.41,,open_close -2022-08-03 06:05:00,1633.68,,open_close -2022-08-03 06:06:00,1636.45,,open_close -2022-08-03 06:07:00,1637.17,,open_close -2022-08-03 06:08:00,1639.44,,open_close -2022-08-03 06:09:00,1639.47,,open_close -2022-08-03 06:10:00,1642.87,,open_close -2022-08-03 06:11:00,1640.52,,open_close -2022-08-03 06:12:00,1640.93,,open_close -2022-08-03 06:13:00,1642.63,,open_close -2022-08-03 06:14:00,1642.56,,open_close -2022-08-03 06:15:00,1642.62,,open_close -2022-08-03 06:16:00,1640.13,,open_close -2022-08-03 06:17:00,1640.12,,open_close -2022-08-03 06:18:00,1639.57,,open_close -2022-08-03 06:19:00,1640.07,,open_close -2022-08-03 06:20:00,1640.06,,open_close -2022-08-03 06:21:00,1639.24,,open_close -2022-08-03 06:22:00,1638.49,,open_close -2022-08-03 06:23:00,1639.11,,open_close -2022-08-03 06:24:00,1640.49,,open_close -2022-08-03 06:25:00,1642.41,,open_close -2022-08-03 06:26:00,1641.93,,open_close -2022-08-03 06:27:00,1639.93,,open_close -2022-08-03 06:28:00,1639.78,,open_close -2022-08-03 06:29:00,1638.2,,open_close -2022-08-03 06:30:00,1637.61,,open_close -2022-08-03 06:31:00,1636.27,,open_close -2022-08-03 06:32:00,1636.91,,open_close -2022-08-03 06:33:00,1636.9,,open_close -2022-08-03 06:34:00,1639.45,,open_close -2022-08-03 06:35:00,1639.51,,open_close -2022-08-03 06:36:00,1638.86,,open_close -2022-08-03 06:37:00,1638.54,,open_close -2022-08-03 06:38:00,1638.69,,open_close -2022-08-03 06:39:00,1638.17,,open_close -2022-08-03 06:40:00,1637.25,,open_close -2022-08-03 06:41:00,1635.52,,open_close -2022-08-03 06:42:00,1635.33,,open_close -2022-08-03 06:43:00,1635.59,,open_close -2022-08-03 06:44:00,1635.07,,open_close -2022-08-03 06:45:00,1636.4,,open_close -2022-08-03 06:46:00,1635.07,,open_close -2022-08-03 06:47:00,1634.71,,open_close -2022-08-03 06:48:00,1632.57,,open_close -2022-08-03 06:49:00,1634.22,,open_close -2022-08-03 06:50:00,1633.81,,open_close -2022-08-03 06:51:00,1635.73,,open_close -2022-08-03 06:52:00,1636.2,,open_close -2022-08-03 06:53:00,1636.0,,open_close -2022-08-03 06:54:00,1635.29,,open_close -2022-08-03 06:55:00,1637.82,,open_close -2022-08-03 06:56:00,1636.68,,open_close -2022-08-03 06:57:00,1635.79,,open_close -2022-08-03 06:58:00,1634.69,,open_close -2022-08-03 06:59:00,1634.55,,open_close -2022-08-03 07:00:00,1634.42,,open_close -2022-08-03 07:01:00,1635.15,,open_close -2022-08-03 07:02:00,1633.41,,open_close -2022-08-03 07:03:00,1631.77,,open_close -2022-08-03 07:04:00,1631.31,,open_close -2022-08-03 07:05:00,1632.96,,open_close -2022-08-03 07:06:00,1634.38,,open_close -2022-08-03 07:07:00,1633.38,,open_close -2022-08-03 07:08:00,1634.56,,open_close -2022-08-03 07:09:00,1635.04,,open_close -2022-08-03 07:10:00,1634.79,,open_close -2022-08-03 07:11:00,1633.48,,open_close -2022-08-03 07:12:00,1633.42,,open_close -2022-08-03 07:13:00,1632.57,,open_close -2022-08-03 07:14:00,1630.6,,open_close -2022-08-03 07:15:00,1633.33,,open_close -2022-08-03 07:16:00,1634.52,,open_close -2022-08-03 07:17:00,1635.21,,open_close -2022-08-03 07:18:00,1635.18,,open_close -2022-08-03 07:19:00,1635.54,,open_close -2022-08-03 07:20:00,1636.99,,open_close -2022-08-03 07:21:00,1634.99,,open_close -2022-08-03 07:22:00,1636.0,,open_close -2022-08-03 07:23:00,1637.37,,open_close -2022-08-03 07:24:00,1636.69,,open_close -2022-08-03 07:25:00,1635.84,,open_close -2022-08-03 07:26:00,1635.54,,open_close -2022-08-03 07:27:00,1634.53,,open_close -2022-08-03 07:28:00,1635.31,,open_close -2022-08-03 07:29:00,1635.33,,open_close -2022-08-03 07:30:00,1635.61,,open_close -2022-08-03 07:31:00,1634.5,,open_close -2022-08-03 07:32:00,1635.61,,open_close -2022-08-03 07:33:00,1634.85,,open_close -2022-08-03 07:34:00,1633.69,,open_close -2022-08-03 07:35:00,1632.99,,open_close -2022-08-03 07:36:00,1633.57,,open_close -2022-08-03 07:37:00,1633.57,,open_close -2022-08-03 07:38:00,1632.91,,open_close -2022-08-03 07:39:00,1633.05,,open_close -2022-08-03 07:40:00,1633.94,,open_close -2022-08-03 07:41:00,1633.9,,open_close -2022-08-03 07:42:00,1633.28,,open_close -2022-08-03 07:43:00,1631.88,,open_close -2022-08-03 07:44:00,1632.78,,open_close -2022-08-03 07:45:00,1632.61,,open_close -2022-08-03 07:46:00,1631.82,,open_close -2022-08-03 07:47:00,1631.68,,open_close -2022-08-03 07:48:00,1628.86,,open_close -2022-08-03 07:49:00,1629.01,,open_close -2022-08-03 07:50:00,1629.47,,open_close -2022-08-03 07:51:00,1627.97,,open_close -2022-08-03 07:52:00,1629.32,,open_close -2022-08-03 07:53:00,1627.64,,open_close -2022-08-03 07:54:00,1627.77,,open_close -2022-08-03 07:55:00,1628.7,,open_close -2022-08-03 07:56:00,1629.19,,open_close -2022-08-03 07:57:00,1627.81,,open_close -2022-08-03 07:58:00,1628.65,,open_close -2022-08-03 07:59:00,1629.19,,open_close -2022-08-03 08:00:00,1629.62,,open_close -2022-08-03 08:01:00,1630.67,,open_close -2022-08-03 08:02:00,1631.14,,open_close -2022-08-03 08:03:00,1635.79,,open_close -2022-08-03 08:04:00,1638.1,,open_close -2022-08-03 08:05:00,1635.46,,open_close -2022-08-03 08:06:00,1634.05,,open_close -2022-08-03 08:07:00,1634.59,,open_close -2022-08-03 08:08:00,1635.07,,open_close -2022-08-03 08:09:00,1636.09,,open_close -2022-08-03 08:10:00,1637.89,,open_close -2022-08-03 08:11:00,1637.9,,open_close -2022-08-03 08:12:00,1638.65,,open_close -2022-08-03 08:13:00,1638.97,,open_close -2022-08-03 08:14:00,1638.3,,open_close -2022-08-03 08:15:00,1638.07,,open_close -2022-08-03 08:16:00,1636.36,,open_close -2022-08-03 08:17:00,1636.01,,open_close -2022-08-03 08:18:00,1636.21,,open_close -2022-08-03 08:19:00,1636.37,,open_close -2022-08-03 08:20:00,1636.79,,open_close -2022-08-03 08:21:00,1635.72,,open_close -2022-08-03 08:22:00,1637.93,,open_close -2022-08-03 08:23:00,1637.42,,open_close -2022-08-03 08:24:00,1638.84,,open_close -2022-08-03 08:25:00,1638.44,,open_close -2022-08-03 08:26:00,1639.73,,open_close -2022-08-03 08:27:00,1637.67,,open_close -2022-08-03 08:28:00,1638.19,,open_close -2022-08-03 08:29:00,1638.18,,open_close -2022-08-03 08:30:00,1637.76,,open_close -2022-08-03 08:31:00,1638.61,,open_close -2022-08-03 08:32:00,1639.28,,open_close -2022-08-03 08:33:00,1639.64,,open_close -2022-08-03 08:34:00,1640.19,,open_close -2022-08-03 08:35:00,1641.41,,open_close -2022-08-03 08:36:00,1641.72,,open_close -2022-08-03 08:37:00,1642.88,,open_close -2022-08-03 08:38:00,1641.72,,open_close -2022-08-03 08:39:00,1642.58,,open_close -2022-08-03 08:40:00,1643.07,,open_close -2022-08-03 08:41:00,1641.86,,open_close -2022-08-03 08:42:00,1642.86,,open_close -2022-08-03 08:43:00,1645.73,,open_close -2022-08-03 08:44:00,1647.9,,open_close -2022-08-03 08:45:00,1649.02,,open_close -2022-08-03 08:46:00,1645.62,,open_close -2022-08-03 08:47:00,1645.7,,open_close -2022-08-03 08:48:00,1645.55,,open_close -2022-08-03 08:49:00,1645.84,,open_close -2022-08-03 08:50:00,1645.92,,open_close -2022-08-03 08:51:00,1644.35,,open_close -2022-08-03 08:52:00,1644.67,,open_close -2022-08-03 08:53:00,1644.77,,open_close -2022-08-03 08:54:00,1647.39,,open_close -2022-08-03 08:55:00,1649.66,,open_close -2022-08-03 08:56:00,1648.67,,open_close -2022-08-03 08:57:00,1648.76,,open_close -2022-08-03 08:58:00,1649.44,,open_close -2022-08-03 08:59:00,1651.35,,open_close -2022-08-03 09:00:00,1650.0,,open_close -2022-08-03 09:01:00,1649.42,,open_close -2022-08-03 09:02:00,1648.86,,open_close -2022-08-03 09:03:00,1648.19,,open_close -2022-08-03 09:04:00,1648.56,,open_close -2022-08-03 09:05:00,1648.49,,open_close -2022-08-03 09:06:00,1649.38,,open_close -2022-08-03 09:07:00,1648.39,,open_close -2022-08-03 09:08:00,1649.92,,open_close -2022-08-03 09:09:00,1650.71,,open_close -2022-08-03 09:10:00,1654.72,,open_close -2022-08-03 09:11:00,1652.77,,open_close -2022-08-03 09:12:00,1657.05,,open_close -2022-08-03 09:13:00,1656.21,,open_close -2022-08-03 09:14:00,1659.27,,open_close -2022-08-03 09:15:00,1657.25,,open_close -2022-08-03 09:16:00,1656.98,,open_close -2022-08-03 09:17:00,1661.59,,open_close -2022-08-03 09:18:00,1662.54,,open_close -2022-08-03 09:19:00,1660.35,,open_close -2022-08-03 09:20:00,1661.58,,open_close -2022-08-03 09:21:00,1661.84,,open_close -2022-08-03 09:22:00,1661.13,,open_close -2022-08-03 09:23:00,1662.22,,open_close -2022-08-03 09:24:00,1658.62,,open_close -2022-08-03 09:25:00,1656.75,,open_close -2022-08-03 09:26:00,1655.21,,open_close -2022-08-03 09:27:00,1655.21,,open_close -2022-08-03 09:28:00,1655.37,,open_close -2022-08-03 09:29:00,1655.71,,open_close -2022-08-03 09:30:00,1655.9,,open_close -2022-08-03 09:31:00,1654.31,,open_close -2022-08-03 09:32:00,1654.67,,open_close -2022-08-03 09:33:00,1654.5,,open_close -2022-08-03 09:34:00,1656.47,,open_close -2022-08-03 09:35:00,1656.72,,open_close -2022-08-03 09:36:00,1657.64,,open_close -2022-08-03 09:37:00,1655.4,,open_close -2022-08-03 09:38:00,1656.73,,open_close -2022-08-03 09:39:00,1654.95,,open_close -2022-08-03 09:40:00,1654.1,,open_close -2022-08-03 09:41:00,1653.42,,open_close -2022-08-03 09:42:00,1653.73,,open_close -2022-08-03 09:43:00,1654.26,,open_close -2022-08-03 09:44:00,1654.28,,open_close -2022-08-03 09:45:00,1654.46,,open_close -2022-08-03 09:46:00,1653.89,,open_close -2022-08-03 09:47:00,1652.36,,open_close -2022-08-03 09:48:00,1655.36,,open_close -2022-08-03 09:49:00,1656.67,,open_close -2022-08-03 09:50:00,1654.57,,open_close -2022-08-03 09:51:00,1654.55,,open_close -2022-08-03 09:52:00,1655.04,,open_close -2022-08-03 09:53:00,1655.03,,open_close -2022-08-03 09:54:00,1654.99,,open_close -2022-08-03 09:55:00,1655.61,,open_close -2022-08-03 09:56:00,1656.25,,open_close -2022-08-03 09:57:00,1654.48,,open_close -2022-08-03 09:58:00,1655.03,,open_close -2022-08-03 09:59:00,1654.71,,open_close -2022-08-03 10:00:00,1656.21,,open_close -2022-08-03 10:01:00,1654.83,,open_close -2022-08-03 10:02:00,1654.39,,open_close -2022-08-03 10:03:00,1654.03,,open_close -2022-08-03 10:04:00,1653.91,,open_close -2022-08-03 10:05:00,1653.63,,open_close -2022-08-03 10:06:00,1654.53,,open_close -2022-08-03 10:07:00,1653.29,,open_close -2022-08-03 10:08:00,1654.69,,open_close -2022-08-03 10:09:00,1654.77,,open_close -2022-08-03 10:10:00,1656.84,,open_close -2022-08-03 10:11:00,1659.31,,open_close -2022-08-03 10:12:00,1657.63,,open_close -2022-08-03 10:13:00,1659.14,,open_close -2022-08-03 10:14:00,1659.23,,open_close -2022-08-03 10:15:00,1658.54,,open_close -2022-08-03 10:16:00,1655.91,,open_close -2022-08-03 10:17:00,1655.5,,open_close -2022-08-03 10:18:00,1655.86,,open_close -2022-08-03 10:19:00,1655.56,,open_close -2022-08-03 10:20:00,1656.19,,open_close -2022-08-03 10:21:00,1656.87,,open_close -2022-08-03 10:22:00,1657.02,,open_close -2022-08-03 10:23:00,1658.04,,open_close -2022-08-03 10:24:00,1656.92,,open_close -2022-08-03 10:25:00,1657.34,,open_close -2022-08-03 10:26:00,1658.17,,open_close -2022-08-03 10:27:00,1658.07,,open_close -2022-08-03 10:28:00,1658.58,,open_close -2022-08-03 10:29:00,1659.01,,open_close -2022-08-03 10:30:00,1659.41,,open_close -2022-08-03 10:31:00,1657.87,,open_close -2022-08-03 10:32:00,1656.0,,open_close -2022-08-03 10:33:00,1653.44,,open_close -2022-08-03 10:34:00,1654.42,,open_close -2022-08-03 10:35:00,1654.59,,open_close -2022-08-03 10:36:00,1653.76,,open_close -2022-08-03 10:37:00,1654.57,,open_close -2022-08-03 10:38:00,1658.48,,open_close -2022-08-03 10:39:00,1660.06,,open_close -2022-08-03 10:40:00,1659.02,,open_close -2022-08-03 10:41:00,1657.89,,open_close -2022-08-03 10:42:00,1656.69,,open_close -2022-08-03 10:43:00,1658.69,,open_close -2022-08-03 10:44:00,1659.51,,open_close -2022-08-03 10:45:00,1659.97,,open_close -2022-08-03 10:46:00,1658.63,,open_close -2022-08-03 10:47:00,1658.1,,open_close -2022-08-03 10:48:00,1659.91,,open_close -2022-08-03 10:49:00,1659.99,,open_close -2022-08-03 10:50:00,1659.35,,open_close -2022-08-03 10:51:00,1659.17,,open_close -2022-08-03 10:52:00,1659.5,,open_close -2022-08-03 10:53:00,1659.08,,open_close -2022-08-03 10:54:00,1658.72,,open_close -2022-08-03 10:55:00,1658.36,,open_close -2022-08-03 10:56:00,1656.45,,open_close -2022-08-03 10:57:00,1656.97,,open_close -2022-08-03 10:58:00,1658.54,,open_close -2022-08-03 10:59:00,1659.29,,open_close -2022-08-03 11:00:00,1659.54,,open_close -2022-08-03 11:01:00,1658.52,,open_close -2022-08-03 11:02:00,1660.74,,open_close -2022-08-03 11:03:00,1659.82,,open_close -2022-08-03 11:04:00,1660.64,,open_close -2022-08-03 11:05:00,1661.77,,open_close -2022-08-03 11:06:00,1664.21,,open_close -2022-08-03 11:07:00,1667.0,,open_close -2022-08-03 11:08:00,1669.6,,open_close -2022-08-03 11:09:00,1668.65,,open_close -2022-08-03 11:10:00,1663.47,,open_close -2022-08-03 11:11:00,1662.13,,open_close -2022-08-03 11:12:00,1661.88,,open_close -2022-08-03 11:13:00,1661.3,,open_close -2022-08-03 11:14:00,1663.27,,open_close -2022-08-03 11:15:00,1665.43,,open_close -2022-08-03 11:16:00,1664.03,,open_close -2022-08-03 11:17:00,1661.16,,open_close -2022-08-03 11:18:00,1661.02,,open_close -2022-08-03 11:19:00,1660.54,,open_close -2022-08-03 11:20:00,1660.99,,open_close -2022-08-03 11:21:00,1659.83,,open_close -2022-08-03 11:22:00,1658.77,,open_close -2022-08-03 11:23:00,1660.24,,open_close -2022-08-03 11:24:00,1659.06,,open_close -2022-08-03 11:25:00,1658.67,,open_close -2022-08-03 11:26:00,1660.31,,open_close -2022-08-03 11:27:00,1663.2,,open_close -2022-08-03 11:28:00,1663.34,,open_close -2022-08-03 11:29:00,1666.81,,open_close -2022-08-03 11:30:00,1666.19,,open_close -2022-08-03 11:31:00,1666.36,,open_close -2022-08-03 11:32:00,1666.23,,open_close -2022-08-03 11:33:00,1665.72,,open_close -2022-08-03 11:34:00,1665.08,,open_close -2022-08-03 11:35:00,1666.9,,open_close -2022-08-03 11:36:00,1666.32,,open_close -2022-08-03 11:37:00,1665.64,,open_close -2022-08-03 11:38:00,1666.29,,open_close -2022-08-03 11:39:00,1666.21,,open_close -2022-08-03 11:40:00,1665.1,,open_close -2022-08-03 11:41:00,1665.37,,open_close -2022-08-03 11:42:00,1665.51,,open_close -2022-08-03 11:43:00,1662.99,,open_close -2022-08-03 11:44:00,1663.58,,open_close -2022-08-03 11:45:00,1663.03,,open_close -2022-08-03 11:46:00,1662.49,,open_close -2022-08-03 11:47:00,1663.41,,open_close -2022-08-03 11:48:00,1662.45,,open_close -2022-08-03 11:49:00,1662.63,,open_close -2022-08-03 11:50:00,1664.02,,open_close -2022-08-03 11:51:00,1663.9,,open_close -2022-08-03 11:52:00,1663.38,,open_close -2022-08-03 11:53:00,1662.86,,open_close -2022-08-03 11:54:00,1663.31,,open_close -2022-08-03 11:55:00,1664.19,,open_close -2022-08-03 11:56:00,1662.96,,open_close -2022-08-03 11:57:00,1664.04,,open_close -2022-08-03 11:58:00,1665.19,,open_close -2022-08-03 11:59:00,1666.0,,open_close -2022-08-03 12:00:00,1667.01,,open_close -2022-08-03 12:01:00,1666.48,,open_close -2022-08-03 12:02:00,1666.05,,open_close -2022-08-03 12:03:00,1663.26,,open_close -2022-08-03 12:04:00,1665.17,,open_close -2022-08-03 12:05:00,1665.65,,open_close -2022-08-03 12:06:00,1663.55,,open_close -2022-08-03 12:07:00,1665.93,,open_close -2022-08-03 12:08:00,1667.52,,open_close -2022-08-03 12:09:00,1670.92,,open_close -2022-08-03 12:10:00,1669.38,,open_close -2022-08-03 12:11:00,1671.0,,open_close -2022-08-03 12:12:00,1672.07,,open_close -2022-08-03 12:13:00,1672.78,,open_close -2022-08-03 12:14:00,1672.87,,open_close -2022-08-03 12:15:00,1683.18,,infty -2022-08-03 12:16:00,1677.91,,open_close -2022-08-03 12:17:00,1679.1,,open_close -2022-08-03 12:18:00,1679.14,,open_close -2022-08-03 12:19:00,1680.06,,open_close -2022-08-03 12:20:00,1680.74,,open_close -2022-08-03 12:21:00,1679.58,,open_close -2022-08-03 12:22:00,1677.46,,open_close -2022-08-03 12:23:00,1677.67,,open_close -2022-08-03 12:24:00,1679.37,,open_close -2022-08-03 12:25:00,1676.63,,open_close -2022-08-03 12:26:00,1674.22,,open_close -2022-08-03 12:27:00,1672.66,,open_close -2022-08-03 12:28:00,1670.19,,open_close -2022-08-03 12:29:00,1668.54,,open_close -2022-08-03 12:30:00,1662.95,,open_close -2022-08-03 12:31:00,1664.41,,open_close -2022-08-03 12:32:00,1666.18,,open_close -2022-08-03 12:33:00,1666.54,,open_close -2022-08-03 12:34:00,1664.31,,open_close -2022-08-03 12:35:00,1667.01,,open_close -2022-08-03 12:36:00,1663.19,,open_close -2022-08-03 12:37:00,1662.38,,open_close -2022-08-03 12:38:00,1661.66,,open_close -2022-08-03 12:39:00,1660.8,,open_close -2022-08-03 12:40:00,1661.74,,open_close -2022-08-03 12:41:00,1660.75,,open_close -2022-08-03 12:42:00,1661.03,,open_close -2022-08-03 12:43:00,1654.27,,open_close -2022-08-03 12:44:00,1656.55,,open_close -2022-08-03 12:45:00,1657.94,,open_close -2022-08-03 12:46:00,1658.86,,open_close -2022-08-03 12:47:00,1657.79,,open_close -2022-08-03 12:48:00,1656.07,,open_close -2022-08-03 12:49:00,1651.97,,open_close -2022-08-03 12:50:00,1654.14,,open_close -2022-08-03 12:51:00,1652.07,,open_close -2022-08-03 12:52:00,1654.15,,open_close -2022-08-03 12:53:00,1654.61,,open_close -2022-08-03 12:54:00,1657.31,,open_close -2022-08-03 12:55:00,1657.97,,open_close -2022-08-03 12:56:00,1658.88,,open_close -2022-08-03 12:57:00,1657.58,,open_close -2022-08-03 12:58:00,1656.52,,open_close -2022-08-03 12:59:00,1654.85,,open_close -2022-08-03 13:00:00,1656.53,,open_close -2022-08-03 13:01:00,1657.39,,open_close -2022-08-03 13:02:00,1656.97,,open_close -2022-08-03 13:03:00,1657.5,,open_close -2022-08-03 13:04:00,1656.54,,open_close -2022-08-03 13:05:00,1657.54,,open_close -2022-08-03 13:06:00,1656.27,,open_close -2022-08-03 13:07:00,1656.75,,open_close -2022-08-03 13:08:00,1657.27,,open_close -2022-08-03 13:09:00,1658.12,,open_close -2022-08-03 13:10:00,1660.06,,open_close -2022-08-03 13:11:00,1660.95,,open_close -2022-08-03 13:12:00,1661.88,,open_close -2022-08-03 13:13:00,1661.43,,open_close -2022-08-03 13:14:00,1661.47,,open_close -2022-08-03 13:15:00,1660.49,,open_close -2022-08-03 13:16:00,1661.55,,open_close -2022-08-03 13:17:00,1663.74,,open_close -2022-08-03 13:18:00,1664.86,,open_close -2022-08-03 13:19:00,1662.43,,open_close -2022-08-03 13:20:00,1660.14,,open_close -2022-08-03 13:21:00,1659.98,,open_close -2022-08-03 13:22:00,1660.27,,open_close -2022-08-03 13:23:00,1660.04,,open_close -2022-08-03 13:24:00,1657.89,,open_close -2022-08-03 13:25:00,1653.52,,open_close -2022-08-03 13:26:00,1656.27,,open_close -2022-08-03 13:27:00,1656.51,,open_close -2022-08-03 13:28:00,1657.0,,open_close -2022-08-03 13:29:00,1657.67,,open_close -2022-08-03 13:30:00,1664.05,,open_close -2022-08-03 13:31:00,1659.14,,open_close -2022-08-03 13:32:00,1661.82,,open_close -2022-08-03 13:33:00,1663.43,,open_close -2022-08-03 13:34:00,1661.49,,open_close -2022-08-03 13:35:00,1660.31,,open_close -2022-08-03 13:36:00,1662.39,,open_close -2022-08-03 13:37:00,1663.82,,open_close -2022-08-03 13:38:00,1666.47,,open_close -2022-08-03 13:39:00,1665.42,,open_close -2022-08-03 13:40:00,1663.99,,open_close -2022-08-03 13:41:00,1662.87,,open_close -2022-08-03 13:42:00,1661.46,,open_close -2022-08-03 13:43:00,1662.16,,open_close -2022-08-03 13:44:00,1659.37,,open_close -2022-08-03 13:45:00,1658.83,,open_close -2022-08-03 13:46:00,1655.72,,open_close -2022-08-03 13:47:00,1652.55,,open_close -2022-08-03 13:48:00,1656.72,,open_close -2022-08-03 13:49:00,1658.4,,open_close -2022-08-03 13:50:00,1662.0,,open_close -2022-08-03 13:51:00,1660.2,,open_close -2022-08-03 13:52:00,1659.47,,open_close -2022-08-03 13:53:00,1659.41,,open_close -2022-08-03 13:54:00,1664.18,,open_close -2022-08-03 13:55:00,1663.37,,open_close -2022-08-03 13:56:00,1663.7,,open_close -2022-08-03 13:57:00,1663.31,,open_close -2022-08-03 13:58:00,1663.76,,open_close -2022-08-03 13:59:00,1663.51,,open_close -2022-08-03 14:00:00,1657.85,,open_close -2022-08-03 14:01:00,1659.23,,open_close -2022-08-03 14:02:00,1661.92,,open_close -2022-08-03 14:03:00,1666.88,,open_close -2022-08-03 14:04:00,1667.35,,open_close -2022-08-03 14:05:00,1669.17,,open_close -2022-08-03 14:06:00,1668.29,,open_close -2022-08-03 14:07:00,1667.31,,open_close -2022-08-03 14:08:00,1666.39,,open_close -2022-08-03 14:09:00,1666.78,,open_close -2022-08-03 14:10:00,1669.39,,open_close -2022-08-03 14:11:00,1668.94,,open_close -2022-08-03 14:12:00,1667.56,,open_close -2022-08-03 14:13:00,1668.41,,open_close -2022-08-03 14:14:00,1668.72,,open_close -2022-08-03 14:15:00,1669.78,,open_close -2022-08-03 14:16:00,1666.92,,open_close -2022-08-03 14:17:00,1662.13,,open_close -2022-08-03 14:18:00,1661.53,,open_close -2022-08-03 14:19:00,1660.51,,open_close -2022-08-03 14:20:00,1659.82,,open_close -2022-08-03 14:21:00,1659.05,,open_close -2022-08-03 14:22:00,1658.78,,open_close -2022-08-03 14:23:00,1657.72,,open_close -2022-08-03 14:24:00,1659.75,,open_close -2022-08-03 14:25:00,1658.79,,open_close -2022-08-03 14:26:00,1658.23,,open_close -2022-08-03 14:27:00,1661.53,,open_close -2022-08-03 14:28:00,1665.01,,open_close -2022-08-03 14:29:00,1662.77,,open_close -2022-08-03 14:30:00,1659.7,,open_close -2022-08-03 14:31:00,1658.52,,open_close -2022-08-03 14:32:00,1655.94,,open_close -2022-08-03 14:33:00,1657.16,,open_close -2022-08-03 14:34:00,1658.47,,open_close -2022-08-03 14:35:00,1653.83,,open_close -2022-08-03 14:36:00,1651.6,,open_close -2022-08-03 14:37:00,1654.46,,open_close -2022-08-03 14:38:00,1656.47,,open_close -2022-08-03 14:39:00,1655.35,,open_close -2022-08-03 14:40:00,1652.45,,open_close -2022-08-03 14:41:00,1647.57,,open_close -2022-08-03 14:42:00,1646.85,,open_close -2022-08-03 14:43:00,1649.89,,open_close -2022-08-03 14:44:00,1651.51,,open_close -2022-08-03 14:45:00,1650.74,,open_close -2022-08-03 14:46:00,1650.63,,open_close -2022-08-03 14:47:00,1648.57,,open_close -2022-08-03 14:48:00,1654.86,,open_close -2022-08-03 14:49:00,1654.41,,open_close -2022-08-03 14:50:00,1654.73,,open_close -2022-08-03 14:51:00,1656.08,,open_close -2022-08-03 14:52:00,1657.58,,open_close -2022-08-03 14:53:00,1655.43,,open_close -2022-08-03 14:54:00,1655.39,,open_close -2022-08-03 14:55:00,1656.0,,open_close -2022-08-03 14:56:00,1655.34,,open_close -2022-08-03 14:57:00,1656.45,,open_close -2022-08-03 14:58:00,1655.65,,open_close -2022-08-03 14:59:00,1654.69,,open_close -2022-08-03 15:00:00,1655.89,,open_close -2022-08-03 15:01:00,1656.08,,open_close -2022-08-03 15:02:00,1656.24,,open_close -2022-08-03 15:03:00,1651.4,,open_close -2022-08-03 15:04:00,1651.98,,open_close -2022-08-03 15:05:00,1654.1,,open_close -2022-08-03 15:06:00,1656.82,,open_close -2022-08-03 15:07:00,1657.12,,open_close -2022-08-03 15:08:00,1660.72,,open_close -2022-08-03 15:09:00,1659.97,,open_close -2022-08-03 15:10:00,1659.94,,open_close -2022-08-03 15:11:00,1661.01,,open_close -2022-08-03 15:12:00,1661.99,,open_close -2022-08-03 15:13:00,1661.27,,open_close -2022-08-03 15:14:00,1659.6,,open_close -2022-08-03 15:15:00,1659.72,,open_close -2022-08-03 15:16:00,1659.31,,open_close -2022-08-03 15:17:00,1658.37,,open_close -2022-08-03 15:18:00,1657.14,,open_close -2022-08-03 15:19:00,1655.01,,open_close -2022-08-03 15:20:00,1655.45,,open_close -2022-08-03 15:21:00,1657.85,,open_close -2022-08-03 15:22:00,1657.68,,open_close -2022-08-03 15:23:00,1657.24,,open_close -2022-08-03 15:24:00,1658.2,,open_close -2022-08-03 15:25:00,1657.35,,open_close -2022-08-03 15:26:00,1658.9,,open_close -2022-08-03 15:27:00,1661.17,,open_close -2022-08-03 15:28:00,1659.48,,open_close -2022-08-03 15:29:00,1659.32,,open_close -2022-08-03 15:30:00,1659.02,,open_close -2022-08-03 15:31:00,1659.26,,open_close -2022-08-03 15:32:00,1659.04,,open_close -2022-08-03 15:33:00,1657.37,,open_close -2022-08-03 15:34:00,1657.54,,open_close -2022-08-03 15:35:00,1660.65,,open_close -2022-08-03 15:36:00,1659.15,,open_close -2022-08-03 15:37:00,1659.65,,open_close -2022-08-03 15:38:00,1659.89,,open_close -2022-08-03 15:39:00,1661.22,,open_close -2022-08-03 15:40:00,1661.97,,open_close -2022-08-03 15:41:00,1662.58,,open_close -2022-08-03 15:42:00,1663.62,,open_close -2022-08-03 15:43:00,1661.84,,open_close -2022-08-03 15:44:00,1660.3,,open_close -2022-08-03 15:45:00,1660.35,,open_close -2022-08-03 15:46:00,1658.49,,open_close -2022-08-03 15:47:00,1659.05,,open_close -2022-08-03 15:48:00,1658.08,,open_close -2022-08-03 15:49:00,1657.54,,open_close -2022-08-03 15:50:00,1654.66,,open_close -2022-08-03 15:51:00,1655.75,,open_close -2022-08-03 15:52:00,1652.48,,open_close -2022-08-03 15:53:00,1654.41,,open_close -2022-08-03 15:54:00,1658.22,,open_close -2022-08-03 15:55:00,1659.34,,open_close -2022-08-03 15:56:00,1660.36,,open_close -2022-08-03 15:57:00,1658.18,,open_close -2022-08-03 15:58:00,1656.45,,open_close -2022-08-03 15:59:00,1656.3,,open_close -2022-08-03 16:00:00,1653.85,,open_close -2022-08-03 16:01:00,1654.15,,open_close -2022-08-03 16:02:00,1655.41,,open_close -2022-08-03 16:03:00,1653.73,,open_close -2022-08-03 16:04:00,1656.02,,open_close -2022-08-03 16:05:00,1655.7,,open_close -2022-08-03 16:06:00,1655.64,,open_close -2022-08-03 16:07:00,1655.31,,open_close -2022-08-03 16:08:00,1652.02,,open_close -2022-08-03 16:09:00,1650.32,,open_close -2022-08-03 16:10:00,1651.16,,open_close -2022-08-03 16:11:00,1649.16,,open_close -2022-08-03 16:12:00,1649.92,,open_close -2022-08-03 16:13:00,1651.03,,open_close -2022-08-03 16:14:00,1652.27,,open_close -2022-08-03 16:15:00,1649.93,,open_close -2022-08-03 16:16:00,1650.48,,open_close -2022-08-03 16:17:00,1649.26,,open_close -2022-08-03 16:18:00,1650.02,,open_close -2022-08-03 16:19:00,1651.45,,open_close -2022-08-03 16:20:00,1654.48,,open_close -2022-08-03 16:21:00,1656.04,,open_close -2022-08-03 16:22:00,1655.0,,open_close -2022-08-03 16:23:00,1655.76,,open_close -2022-08-03 16:24:00,1656.62,,open_close -2022-08-03 16:25:00,1656.98,,open_close -2022-08-03 16:26:00,1658.58,,open_close -2022-08-03 16:27:00,1658.68,,open_close -2022-08-03 16:28:00,1658.09,,open_close -2022-08-03 16:29:00,1656.76,,open_close -2022-08-03 16:30:00,1656.91,,open_close -2022-08-03 16:31:00,1660.03,,open_close -2022-08-03 16:32:00,1659.95,,open_close -2022-08-03 16:33:00,1662.52,,open_close -2022-08-03 16:34:00,1663.01,,open_close -2022-08-03 16:35:00,1661.57,,open_close -2022-08-03 16:36:00,1660.94,,open_close -2022-08-03 16:37:00,1662.23,,open_close -2022-08-03 16:38:00,1661.57,,open_close -2022-08-03 16:39:00,1661.49,,open_close -2022-08-03 16:40:00,1661.57,,open_close -2022-08-03 16:41:00,1662.4,,open_close -2022-08-03 16:42:00,1663.36,,open_close -2022-08-03 16:43:00,1665.1,,open_close -2022-08-03 16:44:00,1665.68,,open_close -2022-08-03 16:45:00,1665.66,,open_close -2022-08-03 16:46:00,1662.86,,open_close -2022-08-03 16:47:00,1663.72,,open_close -2022-08-03 16:48:00,1662.4,,open_close -2022-08-03 16:49:00,1661.07,,open_close -2022-08-03 16:50:00,1661.85,,open_close -2022-08-03 16:51:00,1662.25,,open_close -2022-08-03 16:52:00,1663.24,,open_close -2022-08-03 16:53:00,1665.0,,open_close -2022-08-03 16:54:00,1666.76,,open_close -2022-08-03 16:55:00,1668.24,,open_close -2022-08-03 16:56:00,1668.45,,open_close -2022-08-03 16:57:00,1666.31,,open_close -2022-08-03 16:58:00,1665.37,,open_close -2022-08-03 16:59:00,1663.89,,open_close -2022-08-03 17:00:00,1663.98,,open_close -2022-08-03 17:01:00,1664.79,,open_close -2022-08-03 17:02:00,1667.48,,open_close -2022-08-03 17:03:00,1667.78,,open_close -2022-08-03 17:04:00,1668.71,,open_close -2022-08-03 17:05:00,1666.83,,open_close -2022-08-03 17:06:00,1665.43,,open_close -2022-08-03 17:07:00,1665.51,,open_close -2022-08-03 17:08:00,1663.7,,open_close -2022-08-03 17:09:00,1665.13,,open_close -2022-08-03 17:10:00,1663.1,,open_close -2022-08-03 17:11:00,1659.55,,open_close -2022-08-03 17:12:00,1660.65,,open_close -2022-08-03 17:13:00,1661.38,,open_close -2022-08-03 17:14:00,1660.64,,open_close -2022-08-03 17:15:00,1662.02,,open_close -2022-08-03 17:16:00,1662.14,,open_close -2022-08-03 17:17:00,1661.63,,open_close -2022-08-03 17:18:00,1666.12,,open_close -2022-08-03 17:19:00,1661.23,,open_close -2022-08-03 17:20:00,1662.89,,open_close -2022-08-03 17:21:00,1662.73,,open_close -2022-08-03 17:22:00,1666.14,,open_close -2022-08-03 17:23:00,1667.44,,open_close -2022-08-03 17:24:00,1666.18,,open_close -2022-08-03 17:25:00,1664.11,,open_close -2022-08-03 17:26:00,1666.13,,open_close -2022-08-03 17:27:00,1666.26,,open_close -2022-08-03 17:28:00,1667.34,,open_close -2022-08-03 17:29:00,1666.62,,open_close -2022-08-03 17:30:00,1667.88,,open_close -2022-08-03 17:31:00,1669.43,,open_close -2022-08-03 17:32:00,1670.34,,open_close -2022-08-03 17:33:00,1668.47,,open_close -2022-08-03 17:34:00,1668.52,,open_close -2022-08-03 17:35:00,1667.95,,open_close -2022-08-03 17:36:00,1665.86,,open_close -2022-08-03 17:37:00,1664.52,,open_close -2022-08-03 17:38:00,1662.73,,open_close -2022-08-03 17:39:00,1662.89,,open_close -2022-08-03 17:40:00,1660.82,,open_close -2022-08-03 17:41:00,1662.42,,open_close -2022-08-03 17:42:00,1662.99,,open_close -2022-08-03 17:43:00,1663.32,,open_close -2022-08-03 17:44:00,1664.06,,open_close -2022-08-03 17:45:00,1661.48,,open_close -2022-08-03 17:46:00,1660.91,,open_close -2022-08-03 17:47:00,1661.14,,open_close -2022-08-03 17:48:00,1661.7,,open_close -2022-08-03 17:49:00,1663.08,,open_close -2022-08-03 17:50:00,1663.19,,open_close -2022-08-03 17:51:00,1663.03,,open_close -2022-08-03 17:52:00,1663.17,,open_close -2022-08-03 17:53:00,1662.62,,open_close -2022-08-03 17:54:00,1662.59,,open_close -2022-08-03 17:55:00,1660.73,,open_close -2022-08-03 17:56:00,1660.5,,open_close -2022-08-03 17:57:00,1660.8,,open_close -2022-08-03 17:58:00,1661.25,,open_close -2022-08-03 17:59:00,1660.92,,open_close -2022-08-03 18:00:00,1659.24,,open_close -2022-08-03 18:01:00,1658.45,,open_close -2022-08-03 18:02:00,1658.54,,open_close -2022-08-03 18:03:00,1659.18,,open_close -2022-08-03 18:04:00,1658.03,,open_close -2022-08-03 18:05:00,1658.05,,open_close -2022-08-03 18:06:00,1656.56,,open_close -2022-08-03 18:07:00,1655.73,,open_close -2022-08-03 18:08:00,1656.64,,open_close -2022-08-03 18:09:00,1656.28,,open_close -2022-08-03 18:10:00,1657.53,,open_close -2022-08-03 18:11:00,1659.16,,open_close -2022-08-03 18:12:00,1659.72,,open_close -2022-08-03 18:13:00,1658.74,,open_close -2022-08-03 18:14:00,1658.26,,open_close -2022-08-03 18:15:00,1658.63,,open_close -2022-08-03 18:16:00,1658.52,,open_close -2022-08-03 18:17:00,1658.47,,open_close -2022-08-03 18:18:00,1660.29,,open_close -2022-08-03 18:19:00,1661.7,,open_close -2022-08-03 18:20:00,1665.26,,open_close -2022-08-03 18:21:00,1664.51,,open_close -2022-08-03 18:22:00,1663.62,,open_close -2022-08-03 18:23:00,1668.05,,open_close -2022-08-03 18:24:00,1667.0,,open_close -2022-08-03 18:25:00,1664.1,,open_close -2022-08-03 18:26:00,1665.27,,open_close -2022-08-03 18:27:00,1665.38,,open_close -2022-08-03 18:28:00,1666.02,,open_close -2022-08-03 18:29:00,1666.33,,open_close -2022-08-03 18:30:00,1666.27,,open_close -2022-08-03 18:31:00,1665.29,,open_close -2022-08-03 18:32:00,1667.79,,open_close -2022-08-03 18:33:00,1665.35,,open_close -2022-08-03 18:34:00,1665.74,,open_close -2022-08-03 18:35:00,1666.23,,open_close -2022-08-03 18:36:00,1667.1,,open_close -2022-08-03 18:37:00,1667.24,,open_close -2022-08-03 18:38:00,1666.59,,open_close -2022-08-03 18:39:00,1667.02,,open_close -2022-08-03 18:40:00,1667.55,,open_close -2022-08-03 18:41:00,1667.65,,open_close -2022-08-03 18:42:00,1666.0,,open_close -2022-08-03 18:43:00,1665.29,,open_close -2022-08-03 18:44:00,1666.31,,open_close -2022-08-03 18:45:00,1663.56,,open_close -2022-08-03 18:46:00,1664.69,,open_close -2022-08-03 18:47:00,1667.09,,open_close -2022-08-03 18:48:00,1667.04,,open_close -2022-08-03 18:49:00,1667.44,,open_close -2022-08-03 18:50:00,1666.43,,open_close -2022-08-03 18:51:00,1666.09,,open_close -2022-08-03 18:52:00,1665.99,,open_close -2022-08-03 18:53:00,1665.6,,open_close -2022-08-03 18:54:00,1665.4,,open_close -2022-08-03 18:55:00,1663.37,,open_close -2022-08-03 18:56:00,1663.62,,open_close -2022-08-03 18:57:00,1663.06,,open_close -2022-08-03 18:58:00,1663.1,,open_close -2022-08-03 18:59:00,1663.14,,open_close -2022-08-03 19:00:00,1661.88,,open_close -2022-08-03 19:01:00,1663.64,,open_close -2022-08-03 19:02:00,1665.14,,open_close -2022-08-03 19:03:00,1666.69,,open_close -2022-08-03 19:04:00,1666.66,,open_close -2022-08-03 19:05:00,1665.18,,open_close -2022-08-03 19:06:00,1662.16,,open_close -2022-08-03 19:07:00,1663.35,,open_close -2022-08-03 19:08:00,1661.58,,open_close -2022-08-03 19:09:00,1661.06,,open_close -2022-08-03 19:10:00,1659.63,,open_close -2022-08-03 19:11:00,1659.64,,open_close -2022-08-03 19:12:00,1658.73,,open_close -2022-08-03 19:13:00,1657.68,,open_close -2022-08-03 19:14:00,1658.11,,open_close -2022-08-03 19:15:00,1658.97,,open_close -2022-08-03 19:16:00,1658.96,,open_close -2022-08-03 19:17:00,1656.66,,open_close -2022-08-03 19:18:00,1657.4,,open_close -2022-08-03 19:19:00,1656.84,,open_close -2022-08-03 19:20:00,1658.47,,open_close -2022-08-03 19:21:00,1658.34,,open_close -2022-08-03 19:22:00,1658.9,,open_close -2022-08-03 19:23:00,1658.82,,open_close -2022-08-03 19:24:00,1658.35,,open_close -2022-08-03 19:25:00,1659.08,,open_close -2022-08-03 19:26:00,1658.58,,open_close -2022-08-03 19:27:00,1659.01,,open_close -2022-08-03 19:28:00,1658.01,,open_close -2022-08-03 19:29:00,1656.94,,open_close -2022-08-03 19:30:00,1657.72,,open_close -2022-08-03 19:31:00,1655.33,,open_close -2022-08-03 19:32:00,1657.27,,open_close -2022-08-03 19:33:00,1657.41,,open_close -2022-08-03 19:34:00,1657.35,,open_close -2022-08-03 19:35:00,1657.87,,open_close -2022-08-03 19:36:00,1657.2,,open_close -2022-08-03 19:37:00,1651.08,,open_close -2022-08-03 19:38:00,1651.85,,open_close -2022-08-03 19:39:00,1652.56,,open_close -2022-08-03 19:40:00,1655.53,,open_close -2022-08-03 19:41:00,1659.99,,open_close -2022-08-03 19:42:00,1662.38,,open_close -2022-08-03 19:43:00,1661.47,,open_close -2022-08-03 19:44:00,1662.49,,open_close -2022-08-03 19:45:00,1660.3,,open_close -2022-08-03 19:46:00,1659.33,,open_close -2022-08-03 19:47:00,1656.15,,open_close -2022-08-03 19:48:00,1653.99,,open_close -2022-08-03 19:49:00,1655.12,,open_close -2022-08-03 19:50:00,1654.86,,open_close -2022-08-03 19:51:00,1655.05,,open_close -2022-08-03 19:52:00,1656.17,,open_close -2022-08-03 19:53:00,1657.21,,open_close -2022-08-03 19:54:00,1658.47,,open_close -2022-08-03 19:55:00,1656.93,,open_close -2022-08-03 19:56:00,1655.55,,open_close -2022-08-03 19:57:00,1653.06,,open_close -2022-08-03 19:58:00,1654.01,,open_close -2022-08-03 19:59:00,1654.79,,open_close -2022-08-03 20:00:00,1653.6,,open_close -2022-08-03 20:01:00,1657.71,,open_close -2022-08-03 20:02:00,1655.09,,open_close -2022-08-03 20:03:00,1652.1,,open_close -2022-08-03 20:04:00,1653.13,,open_close -2022-08-03 20:05:00,1650.7,,open_close -2022-08-03 20:06:00,1650.32,,open_close -2022-08-03 20:07:00,1649.91,,open_close -2022-08-03 20:08:00,1650.69,,open_close -2022-08-03 20:09:00,1651.16,,open_close -2022-08-03 20:10:00,1651.24,,open_close -2022-08-03 20:11:00,1654.53,,open_close -2022-08-03 20:12:00,1651.88,,open_close -2022-08-03 20:13:00,1652.11,,open_close -2022-08-03 20:14:00,1651.42,,open_close -2022-08-03 20:15:00,1649.35,,open_close -2022-08-03 20:16:00,1647.4,,open_close -2022-08-03 20:17:00,1648.27,,open_close -2022-08-03 20:18:00,1647.9,,open_close -2022-08-03 20:19:00,1648.41,,open_close -2022-08-03 20:20:00,1649.01,,open_close -2022-08-03 20:21:00,1649.06,,open_close -2022-08-03 20:22:00,1648.03,,open_close -2022-08-03 20:23:00,1643.07,,open_close -2022-08-03 20:24:00,1646.04,,open_close -2022-08-03 20:25:00,1646.89,,open_close -2022-08-03 20:26:00,1645.65,,open_close -2022-08-03 20:27:00,1646.35,,open_close -2022-08-03 20:28:00,1645.55,,open_close -2022-08-03 20:29:00,1644.99,,open_close -2022-08-03 20:30:00,1643.89,,open_close -2022-08-03 20:31:00,1641.64,,open_close -2022-08-03 20:32:00,1641.52,,open_close -2022-08-03 20:33:00,1635.16,,open_close -2022-08-03 20:34:00,1633.13,,open_close -2022-08-03 20:35:00,1636.02,,open_close -2022-08-03 20:36:00,1636.1,,open_close -2022-08-03 20:37:00,1637.04,,open_close -2022-08-03 20:38:00,1637.56,,open_close -2022-08-03 20:39:00,1638.54,,open_close -2022-08-03 20:40:00,1638.45,,open_close -2022-08-03 20:41:00,1641.51,,open_close -2022-08-03 20:42:00,1639.26,,open_close -2022-08-03 20:43:00,1638.59,,open_close -2022-08-03 20:44:00,1639.18,,open_close -2022-08-03 20:45:00,1640.21,,open_close -2022-08-03 20:46:00,1641.29,,open_close -2022-08-03 20:47:00,1640.05,,open_close -2022-08-03 20:48:00,1645.05,,open_close -2022-08-03 20:49:00,1642.42,,open_close -2022-08-03 20:50:00,1642.81,,open_close -2022-08-03 20:51:00,1643.99,,open_close -2022-08-03 20:52:00,1643.08,,open_close -2022-08-03 20:53:00,1642.81,,open_close -2022-08-03 20:54:00,1644.07,,open_close -2022-08-03 20:55:00,1641.07,,open_close -2022-08-03 20:56:00,1642.41,,open_close -2022-08-03 20:57:00,1642.01,,open_close -2022-08-03 20:58:00,1641.35,,open_close -2022-08-03 20:59:00,1643.23,,open_close -2022-08-03 21:00:00,1642.47,,open_close -2022-08-03 21:01:00,1646.58,,open_close -2022-08-03 21:02:00,1646.99,,open_close -2022-08-03 21:03:00,1647.98,,open_close -2022-08-03 21:04:00,1647.98,,open_close -2022-08-03 21:05:00,1647.53,,open_close -2022-08-03 21:06:00,1646.95,,open_close -2022-08-03 21:07:00,1646.52,,open_close -2022-08-03 21:08:00,1645.97,,open_close -2022-08-03 21:09:00,1645.94,,open_close -2022-08-03 21:10:00,1645.42,,open_close -2022-08-03 21:11:00,1642.33,,open_close -2022-08-03 21:12:00,1640.04,,open_close -2022-08-03 21:13:00,1641.67,,open_close -2022-08-03 21:14:00,1640.07,,open_close -2022-08-03 21:15:00,1639.8,,open_close -2022-08-03 21:16:00,1639.96,,open_close -2022-08-03 21:17:00,1640.71,,open_close -2022-08-03 21:18:00,1639.89,,open_close -2022-08-03 21:19:00,1643.22,,open_close -2022-08-03 21:20:00,1641.9,,open_close -2022-08-03 21:21:00,1642.48,,open_close -2022-08-03 21:22:00,1642.97,,open_close -2022-08-03 21:23:00,1642.88,,open_close -2022-08-03 21:24:00,1643.0,,open_close -2022-08-03 21:25:00,1645.19,,open_close -2022-08-03 21:26:00,1642.86,,open_close -2022-08-03 21:27:00,1642.75,,open_close -2022-08-03 21:28:00,1643.54,,open_close -2022-08-03 21:29:00,1643.55,,open_close -2022-08-03 21:30:00,1643.57,,open_close -2022-08-03 21:31:00,1644.52,,open_close -2022-08-03 21:32:00,1644.83,,open_close -2022-08-03 21:33:00,1646.17,,open_close -2022-08-03 21:34:00,1646.19,,open_close -2022-08-03 21:35:00,1645.94,,open_close -2022-08-03 21:36:00,1646.11,,open_close -2022-08-03 21:37:00,1645.95,,open_close -2022-08-03 21:38:00,1646.48,,open_close -2022-08-03 21:39:00,1646.48,,open_close -2022-08-03 21:40:00,1645.38,,open_close -2022-08-03 21:41:00,1646.84,,open_close -2022-08-03 21:42:00,1647.36,,open_close -2022-08-03 21:43:00,1647.88,,open_close -2022-08-03 21:44:00,1645.8,,open_close -2022-08-03 21:45:00,1645.82,,open_close -2022-08-03 21:46:00,1645.96,,open_close -2022-08-03 21:47:00,1643.82,,open_close -2022-08-03 21:48:00,1644.77,,open_close -2022-08-03 21:49:00,1646.09,,open_close -2022-08-03 21:50:00,1646.1,,open_close -2022-08-03 21:51:00,1644.11,,open_close -2022-08-03 21:52:00,1641.66,,open_close -2022-08-03 21:53:00,1642.28,,open_close -2022-08-03 21:54:00,1641.23,,open_close -2022-08-03 21:55:00,1639.89,,open_close -2022-08-03 21:56:00,1638.5,,open_close -2022-08-03 21:57:00,1636.93,,open_close -2022-08-03 21:58:00,1638.19,,open_close -2022-08-03 21:59:00,1635.84,,open_close -2022-08-03 22:00:00,1635.77,,open_close -2022-08-03 22:01:00,1635.47,,open_close -2022-08-03 22:02:00,1635.83,,open_close -2022-08-03 22:03:00,1638.45,,open_close -2022-08-03 22:04:00,1636.5,,open_close -2022-08-03 22:05:00,1636.32,,open_close -2022-08-03 22:06:00,1634.13,,open_close -2022-08-03 22:07:00,1633.94,,open_close -2022-08-03 22:08:00,1635.0,,open_close -2022-08-03 22:09:00,1635.2,,open_close -2022-08-03 22:10:00,1636.76,,open_close -2022-08-03 22:11:00,1635.03,,open_close -2022-08-03 22:12:00,1635.7,,open_close -2022-08-03 22:13:00,1637.35,,open_close -2022-08-03 22:14:00,1638.61,,open_close -2022-08-03 22:15:00,1637.92,,open_close -2022-08-03 22:16:00,1637.74,,open_close -2022-08-03 22:17:00,1632.18,,open_close -2022-08-03 22:18:00,1633.08,,open_close -2022-08-03 22:19:00,1631.19,,open_close -2022-08-03 22:20:00,1630.67,,open_close -2022-08-03 22:21:00,1630.31,,open_close -2022-08-03 22:22:00,1628.34,,open_close -2022-08-03 22:23:00,1630.7,,open_close -2022-08-03 22:24:00,1631.56,,open_close -2022-08-03 22:25:00,1628.82,,open_close -2022-08-03 22:26:00,1630.2,,open_close -2022-08-03 22:27:00,1629.2,,open_close -2022-08-03 22:28:00,1631.14,,open_close -2022-08-03 22:29:00,1630.68,,open_close -2022-08-03 22:30:00,1631.87,,open_close -2022-08-03 22:31:00,1630.9,,open_close -2022-08-03 22:32:00,1629.17,,open_close -2022-08-03 22:33:00,1628.11,,open_close -2022-08-03 22:34:00,1623.68,,open_close -2022-08-03 22:35:00,1625.19,,open_close -2022-08-03 22:36:00,1627.19,,open_close -2022-08-03 22:37:00,1626.16,,open_close -2022-08-03 22:38:00,1626.35,,open_close -2022-08-03 22:39:00,1626.01,,open_close -2022-08-03 22:40:00,1626.58,,open_close -2022-08-03 22:41:00,1625.14,,open_close -2022-08-03 22:42:00,1624.0,,open_close -2022-08-03 22:43:00,1621.11,,open_close -2022-08-03 22:44:00,1622.55,,open_close -2022-08-03 22:45:00,1624.58,,open_close -2022-08-03 22:46:00,1625.62,,open_close -2022-08-03 22:47:00,1622.92,,open_close -2022-08-03 22:48:00,1618.42,,minus_infty -2022-08-03 22:49:00,1621.12,,open_close -2022-08-03 22:50:00,1622.18,,open_close -2022-08-03 22:51:00,1620.64,,minus_infty -2022-08-03 22:52:00,1621.51,,open_close -2022-08-03 22:53:00,1620.57,,minus_infty -2022-08-03 22:54:00,1619.22,,minus_infty -2022-08-03 22:55:00,1617.52,,minus_infty -2022-08-03 22:56:00,1619.03,,minus_infty -2022-08-03 22:57:00,1621.8,,open_close -2022-08-03 22:58:00,1621.23,,open_close -2022-08-03 22:59:00,1622.19,,open_close -2022-08-03 23:00:00,1617.79,,minus_infty -2022-08-03 23:01:00,1618.37,,minus_infty -2022-08-03 23:02:00,1619.22,,minus_infty -2022-08-03 23:03:00,1615.43,,minus_infty -2022-08-03 23:04:00,1613.01,,minus_infty -2022-08-03 23:05:00,1613.78,,minus_infty -2022-08-03 23:06:00,1614.53,,minus_infty -2022-08-03 23:07:00,1614.45,,minus_infty -2022-08-03 23:08:00,1614.26,,minus_infty -2022-08-03 23:09:00,1613.0,,minus_infty -2022-08-03 23:10:00,1613.39,,minus_infty -2022-08-03 23:11:00,1613.31,,minus_infty -2022-08-03 23:12:00,1610.08,,minus_infty -2022-08-03 23:13:00,1612.28,,minus_infty -2022-08-03 23:14:00,1609.8,,minus_infty -2022-08-03 23:15:00,1611.53,,minus_infty -2022-08-03 23:16:00,1613.79,,minus_infty -2022-08-03 23:17:00,1612.09,,minus_infty -2022-08-03 23:18:00,1611.14,,minus_infty -2022-08-03 23:19:00,1612.48,,minus_infty -2022-08-03 23:20:00,1610.29,,minus_infty -2022-08-03 23:21:00,1614.19,,minus_infty -2022-08-03 23:22:00,1614.35,,minus_infty -2022-08-03 23:23:00,1612.98,,minus_infty -2022-08-03 23:24:00,1613.1,,minus_infty -2022-08-03 23:25:00,1610.72,,minus_infty -2022-08-03 23:26:00,1612.6,,minus_infty -2022-08-03 23:27:00,1614.65,,minus_infty -2022-08-03 23:28:00,1615.25,,minus_infty -2022-08-03 23:29:00,1613.26,,minus_infty -2022-08-03 23:30:00,1613.02,,minus_infty -2022-08-03 23:31:00,1612.16,,minus_infty -2022-08-03 23:32:00,1614.68,,minus_infty -2022-08-03 23:33:00,1616.33,,minus_infty -2022-08-03 23:34:00,1614.26,,minus_infty -2022-08-03 23:35:00,1615.66,,minus_infty -2022-08-03 23:36:00,1614.4,,minus_infty -2022-08-03 23:37:00,1614.8,,minus_infty -2022-08-03 23:38:00,1613.93,,minus_infty -2022-08-03 23:39:00,1613.04,,minus_infty -2022-08-03 23:40:00,1613.84,,minus_infty -2022-08-03 23:41:00,1613.56,,minus_infty -2022-08-03 23:42:00,1613.78,,minus_infty -2022-08-03 23:43:00,1616.47,,minus_infty -2022-08-03 23:44:00,1615.37,,minus_infty -2022-08-03 23:45:00,1615.58,,minus_infty -2022-08-03 23:46:00,1616.29,,minus_infty -2022-08-03 23:47:00,1617.33,,minus_infty -2022-08-03 23:48:00,1619.08,,minus_infty -2022-08-03 23:49:00,1619.41,,minus_infty -2022-08-03 23:50:00,1618.9,,minus_infty -2022-08-03 23:51:00,1619.63,,minus_infty -2022-08-03 23:52:00,1619.25,,minus_infty -2022-08-03 23:53:00,1620.94,,open_close -2022-08-03 23:54:00,1620.16,,minus_infty -2022-08-03 23:55:00,1617.83,,minus_infty -2022-08-03 23:56:00,1619.69,,minus_infty -2022-08-03 23:57:00,1618.59,,minus_infty -2022-08-03 23:58:00,1620.17,,minus_infty -2022-08-03 23:59:00,1618.62,,minus_infty -2022-08-04 00:00:00,1618.06,,minus_infty -2022-08-04 00:01:00,1617.92,,minus_infty -2022-08-04 00:02:00,1620.18,,minus_infty -2022-08-04 00:03:00,1620.13,,minus_infty -2022-08-04 00:04:00,1620.37,,minus_infty -2022-08-04 00:05:00,1621.41,,open_close -2022-08-04 00:06:00,1620.89,,open_close -2022-08-04 00:07:00,1620.05,,minus_infty -2022-08-04 00:08:00,1617.16,,minus_infty -2022-08-04 00:09:00,1621.0,,open_close -2022-08-04 00:10:00,1620.82,,minus_infty -2022-08-04 00:11:00,1620.64,,minus_infty -2022-08-04 00:12:00,1620.48,,minus_infty -2022-08-04 00:13:00,1619.2,,minus_infty -2022-08-04 00:14:00,1619.64,,minus_infty -2022-08-04 00:15:00,1619.38,,minus_infty -2022-08-04 00:16:00,1617.19,,minus_infty -2022-08-04 00:17:00,1619.7,,minus_infty -2022-08-04 00:18:00,1620.17,,minus_infty -2022-08-04 00:19:00,1623.09,,open_close -2022-08-04 00:20:00,1625.0,,open_close -2022-08-04 00:21:00,1625.11,,open_close -2022-08-04 00:22:00,1626.03,,open_close -2022-08-04 00:23:00,1627.66,,open_close -2022-08-04 00:24:00,1631.54,,open_close -2022-08-04 00:25:00,1630.67,,open_close -2022-08-04 00:26:00,1631.44,,open_close -2022-08-04 00:27:00,1631.12,,open_close -2022-08-04 00:28:00,1632.65,,open_close -2022-08-04 00:29:00,1635.79,,open_close -2022-08-04 00:30:00,1635.96,,open_close -2022-08-04 00:31:00,1640.73,,open_close -2022-08-04 00:32:00,1639.1,,open_close -2022-08-04 00:33:00,1637.22,,open_close -2022-08-04 00:34:00,1634.18,,open_close -2022-08-04 00:35:00,1634.27,,open_close -2022-08-04 00:36:00,1631.75,,open_close -2022-08-04 00:37:00,1633.11,,open_close -2022-08-04 00:38:00,1630.95,,open_close -2022-08-04 00:39:00,1630.99,,open_close -2022-08-04 00:40:00,1633.42,,open_close -2022-08-04 00:41:00,1637.94,,open_close -2022-08-04 00:42:00,1637.78,,open_close -2022-08-04 00:43:00,1637.77,,open_close -2022-08-04 00:44:00,1635.34,,open_close -2022-08-04 00:45:00,1634.5,,open_close -2022-08-04 00:46:00,1635.52,,open_close -2022-08-04 00:47:00,1636.4,,open_close -2022-08-04 00:48:00,1637.41,,open_close -2022-08-04 00:49:00,1637.54,,open_close -2022-08-04 00:50:00,1638.62,,open_close -2022-08-04 00:51:00,1639.71,,open_close -2022-08-04 00:52:00,1638.76,,open_close -2022-08-04 00:53:00,1638.48,,open_close -2022-08-04 00:54:00,1642.85,,open_close -2022-08-04 00:55:00,1641.3,,open_close -2022-08-04 00:56:00,1641.35,,open_close -2022-08-04 00:57:00,1642.79,,open_close -2022-08-04 00:58:00,1642.29,,open_close -2022-08-04 00:59:00,1641.06,,open_close -2022-08-04 01:00:00,1641.85,,open_close -2022-08-04 01:01:00,1645.54,,open_close -2022-08-04 01:02:00,1645.77,,open_close -2022-08-04 01:03:00,1649.01,,open_close -2022-08-04 01:04:00,1650.89,,open_close -2022-08-04 01:05:00,1653.67,,open_close -2022-08-04 01:06:00,1650.85,,open_close -2022-08-04 01:07:00,1652.38,,open_close -2022-08-04 01:08:00,1650.94,,open_close -2022-08-04 01:09:00,1651.39,,open_close -2022-08-04 01:10:00,1647.99,,open_close -2022-08-04 01:11:00,1647.63,,open_close -2022-08-04 01:12:00,1648.85,,open_close -2022-08-04 01:13:00,1648.4,,open_close -2022-08-04 01:14:00,1648.49,,open_close -2022-08-04 01:15:00,1646.9,,open_close -2022-08-04 01:16:00,1647.3,,open_close -2022-08-04 01:17:00,1648.79,,open_close -2022-08-04 01:18:00,1649.14,,open_close -2022-08-04 01:19:00,1649.29,,open_close -2022-08-04 01:20:00,1650.96,,open_close -2022-08-04 01:21:00,1651.43,,open_close -2022-08-04 01:22:00,1648.81,,open_close -2022-08-04 01:23:00,1647.43,,open_close -2022-08-04 01:24:00,1647.88,,open_close -2022-08-04 01:25:00,1647.45,,open_close -2022-08-04 01:26:00,1644.59,,open_close -2022-08-04 01:27:00,1645.4,,open_close -2022-08-04 01:28:00,1649.89,,open_close -2022-08-04 01:29:00,1648.96,,open_close -2022-08-04 01:30:00,1650.5,,open_close -2022-08-04 01:31:00,1650.35,,open_close -2022-08-04 01:32:00,1652.47,,open_close -2022-08-04 01:33:00,1651.66,,open_close -2022-08-04 01:34:00,1649.35,,open_close -2022-08-04 01:35:00,1650.69,,open_close -2022-08-04 01:36:00,1648.16,,open_close -2022-08-04 01:37:00,1650.97,,open_close -2022-08-04 01:38:00,1656.86,,open_close -2022-08-04 01:39:00,1656.03,,open_close -2022-08-04 01:40:00,1654.02,,open_close -2022-08-04 01:41:00,1655.44,,open_close -2022-08-04 01:42:00,1656.01,,open_close -2022-08-04 01:43:00,1658.31,,open_close -2022-08-04 01:44:00,1658.67,,open_close -2022-08-04 01:45:00,1657.8,,open_close -2022-08-04 01:46:00,1653.6,,open_close -2022-08-04 01:47:00,1653.93,,open_close -2022-08-04 01:48:00,1652.17,,open_close -2022-08-04 01:49:00,1652.79,,open_close -2022-08-04 01:50:00,1649.62,,open_close -2022-08-04 01:51:00,1650.39,,open_close -2022-08-04 01:52:00,1649.48,,open_close -2022-08-04 01:53:00,1650.46,,open_close -2022-08-04 01:54:00,1648.37,,open_close -2022-08-04 01:55:00,1649.65,,open_close -2022-08-04 01:56:00,1649.81,,open_close -2022-08-04 01:57:00,1650.01,,open_close -2022-08-04 01:58:00,1649.42,,open_close -2022-08-04 01:59:00,1649.47,,open_close -2022-08-04 02:00:00,1650.13,,open_close -2022-08-04 02:01:00,1653.74,,open_close -2022-08-04 02:02:00,1651.22,,open_close -2022-08-04 02:03:00,1650.5,,open_close -2022-08-04 02:04:00,1648.34,,open_close -2022-08-04 02:05:00,1649.6,,open_close -2022-08-04 02:06:00,1650.5,,open_close -2022-08-04 02:07:00,1651.04,,open_close -2022-08-04 02:08:00,1650.7,,open_close -2022-08-04 02:09:00,1645.98,,open_close -2022-08-04 02:10:00,1645.95,,open_close -2022-08-04 02:11:00,1647.34,,open_close -2022-08-04 02:12:00,1646.53,,open_close -2022-08-04 02:13:00,1646.23,,open_close -2022-08-04 02:14:00,1646.08,,open_close -2022-08-04 02:15:00,1647.76,,open_close -2022-08-04 02:16:00,1647.37,,open_close -2022-08-04 02:17:00,1652.01,,open_close -2022-08-04 02:18:00,1650.79,,open_close -2022-08-04 02:19:00,1649.91,,open_close -2022-08-04 02:20:00,1650.51,,open_close -2022-08-04 02:21:00,1652.09,,open_close -2022-08-04 02:22:00,1654.67,,open_close -2022-08-04 02:23:00,1653.34,,open_close -2022-08-04 02:24:00,1654.22,,open_close -2022-08-04 02:25:00,1652.72,,open_close -2022-08-04 02:26:00,1652.96,,open_close -2022-08-04 02:27:00,1659.08,,open_close -2022-08-04 02:28:00,1656.89,,open_close -2022-08-04 02:29:00,1658.08,,open_close -2022-08-04 02:30:00,1656.94,,open_close -2022-08-04 02:31:00,1654.84,,open_close -2022-08-04 02:32:00,1658.12,,open_close -2022-08-04 02:33:00,1655.44,,open_close -2022-08-04 02:34:00,1656.99,,open_close -2022-08-04 02:35:00,1657.03,,open_close -2022-08-04 02:36:00,1654.59,,open_close -2022-08-04 02:37:00,1654.22,,open_close -2022-08-04 02:38:00,1653.24,,open_close -2022-08-04 02:39:00,1652.51,,open_close -2022-08-04 02:40:00,1652.5,,open_close -2022-08-04 02:41:00,1651.68,,open_close -2022-08-04 02:42:00,1653.43,,open_close -2022-08-04 02:43:00,1650.41,,open_close -2022-08-04 02:44:00,1652.4,,open_close -2022-08-04 02:45:00,1651.92,,open_close -2022-08-04 02:46:00,1651.76,,open_close -2022-08-04 02:47:00,1651.45,,open_close -2022-08-04 02:48:00,1651.49,,open_close -2022-08-04 02:49:00,1652.28,,open_close -2022-08-04 02:50:00,1653.62,,open_close -2022-08-04 02:51:00,1652.54,,open_close -2022-08-04 02:52:00,1652.77,,open_close -2022-08-04 02:53:00,1653.7,,open_close -2022-08-04 02:54:00,1653.53,,open_close -2022-08-04 02:55:00,1653.85,,open_close -2022-08-04 02:56:00,1654.71,,open_close -2022-08-04 02:57:00,1654.33,,open_close -2022-08-04 02:58:00,1654.69,,open_close -2022-08-04 02:59:00,1654.74,,open_close -2022-08-04 03:00:00,1655.59,,open_close -2022-08-04 03:01:00,1654.79,,open_close -2022-08-04 03:02:00,1654.78,,open_close -2022-08-04 03:03:00,1653.06,,open_close -2022-08-04 03:04:00,1650.43,,open_close -2022-08-04 03:05:00,1650.09,,open_close -2022-08-04 03:06:00,1650.1,,open_close -2022-08-04 03:07:00,1649.4,,open_close -2022-08-04 03:08:00,1649.5,,open_close -2022-08-04 03:09:00,1649.92,,open_close -2022-08-04 03:10:00,1649.87,,open_close -2022-08-04 03:11:00,1649.23,,open_close -2022-08-04 03:12:00,1648.79,,open_close -2022-08-04 03:13:00,1649.34,,open_close -2022-08-04 03:14:00,1648.7,,open_close -2022-08-04 03:15:00,1648.64,,open_close -2022-08-04 03:16:00,1649.74,,open_close -2022-08-04 03:17:00,1650.47,,open_close -2022-08-04 03:18:00,1648.18,,open_close -2022-08-04 03:19:00,1650.67,,open_close -2022-08-04 03:20:00,1649.58,,open_close -2022-08-04 03:21:00,1649.59,,open_close -2022-08-04 03:22:00,1649.8,,open_close -2022-08-04 03:23:00,1649.87,,open_close -2022-08-04 03:24:00,1649.28,,open_close -2022-08-04 03:25:00,1648.72,,open_close -2022-08-04 03:26:00,1649.3,,open_close -2022-08-04 03:27:00,1648.29,,open_close -2022-08-04 03:28:00,1648.47,,open_close -2022-08-04 03:29:00,1647.32,,open_close -2022-08-04 03:30:00,1648.94,,open_close -2022-08-04 03:31:00,1648.94,,open_close -2022-08-04 03:32:00,1647.56,,open_close -2022-08-04 03:33:00,1649.16,,open_close -2022-08-04 03:34:00,1649.98,,open_close -2022-08-04 03:35:00,1650.88,,open_close -2022-08-04 03:36:00,1654.02,,open_close -2022-08-04 03:37:00,1653.37,,open_close -2022-08-04 03:38:00,1655.12,,open_close -2022-08-04 03:39:00,1654.55,,open_close -2022-08-04 03:40:00,1654.46,,open_close -2022-08-04 03:41:00,1652.42,,open_close -2022-08-04 03:42:00,1650.81,,open_close -2022-08-04 03:43:00,1651.51,,open_close -2022-08-04 03:44:00,1650.84,,open_close -2022-08-04 03:45:00,1651.59,,open_close -2022-08-04 03:46:00,1651.54,,open_close -2022-08-04 03:47:00,1650.69,,open_close -2022-08-04 03:48:00,1650.98,,open_close -2022-08-04 03:49:00,1649.39,,open_close -2022-08-04 03:50:00,1650.06,,open_close -2022-08-04 03:51:00,1650.77,,open_close -2022-08-04 03:52:00,1650.48,,open_close -2022-08-04 03:53:00,1650.18,,open_close -2022-08-04 03:54:00,1649.42,,open_close -2022-08-04 03:55:00,1647.26,,open_close -2022-08-04 03:56:00,1648.12,,open_close -2022-08-04 03:57:00,1647.95,,open_close -2022-08-04 03:58:00,1649.21,,open_close -2022-08-04 03:59:00,1651.48,,open_close -2022-08-04 04:00:00,1650.86,,open_close -2022-08-04 04:01:00,1649.8,,open_close -2022-08-04 04:02:00,1651.64,,open_close -2022-08-04 04:03:00,1653.66,,open_close -2022-08-04 04:04:00,1652.31,,open_close -2022-08-04 04:05:00,1653.44,,open_close -2022-08-04 04:06:00,1653.9,,open_close -2022-08-04 04:07:00,1654.03,,open_close -2022-08-04 04:08:00,1655.24,,open_close -2022-08-04 04:09:00,1654.52,,open_close -2022-08-04 04:10:00,1654.87,,open_close -2022-08-04 04:11:00,1656.68,,open_close -2022-08-04 04:12:00,1657.27,,open_close -2022-08-04 04:13:00,1656.44,,open_close -2022-08-04 04:14:00,1655.94,,open_close -2022-08-04 04:15:00,1655.82,,open_close -2022-08-04 04:16:00,1655.08,,open_close -2022-08-04 04:17:00,1653.87,,open_close -2022-08-04 04:18:00,1653.47,,open_close -2022-08-04 04:19:00,1653.45,,open_close -2022-08-04 04:20:00,1652.4,,open_close -2022-08-04 04:21:00,1651.69,,open_close -2022-08-04 04:22:00,1651.01,,open_close -2022-08-04 04:23:00,1650.8,,open_close -2022-08-04 04:24:00,1651.34,,open_close -2022-08-04 04:25:00,1651.12,,open_close -2022-08-04 04:26:00,1652.9,,open_close -2022-08-04 04:27:00,1653.24,,open_close -2022-08-04 04:28:00,1653.98,,open_close -2022-08-04 04:29:00,1653.92,,open_close -2022-08-04 04:30:00,1652.49,,open_close -2022-08-04 04:31:00,1653.09,,open_close -2022-08-04 04:32:00,1654.07,,open_close -2022-08-04 04:33:00,1653.39,,open_close -2022-08-04 04:34:00,1654.37,,open_close -2022-08-04 04:35:00,1655.09,,open_close -2022-08-04 04:36:00,1655.05,,open_close -2022-08-04 04:37:00,1655.12,,open_close -2022-08-04 04:38:00,1656.99,,open_close -2022-08-04 04:39:00,1658.5,,open_close -2022-08-04 04:40:00,1658.71,,open_close -2022-08-04 04:41:00,1655.65,,open_close -2022-08-04 04:42:00,1655.9,,open_close -2022-08-04 04:43:00,1656.55,,open_close -2022-08-04 04:44:00,1656.27,,open_close -2022-08-04 04:45:00,1657.16,,open_close -2022-08-04 04:46:00,1655.66,,open_close -2022-08-04 04:47:00,1654.71,,open_close -2022-08-04 04:48:00,1655.64,,open_close -2022-08-04 04:49:00,1654.86,,open_close -2022-08-04 04:50:00,1654.89,,open_close -2022-08-04 04:51:00,1654.69,,open_close -2022-08-04 04:52:00,1653.87,,open_close -2022-08-04 04:53:00,1655.07,,open_close -2022-08-04 04:54:00,1654.16,,open_close -2022-08-04 04:55:00,1653.23,,open_close -2022-08-04 04:56:00,1655.06,,open_close -2022-08-04 04:57:00,1654.8,,open_close -2022-08-04 04:58:00,1655.44,,open_close -2022-08-04 04:59:00,1655.32,,open_close -2022-08-04 05:00:00,1656.06,,open_close -2022-08-04 05:01:00,1656.25,,open_close -2022-08-04 05:02:00,1662.37,,open_close -2022-08-04 05:03:00,1660.05,,open_close -2022-08-04 05:04:00,1658.74,,open_close -2022-08-04 05:05:00,1656.33,,open_close -2022-08-04 05:06:00,1654.16,,open_close -2022-08-04 05:07:00,1655.26,,open_close -2022-08-04 05:08:00,1654.46,,open_close -2022-08-04 05:09:00,1654.87,,open_close -2022-08-04 05:10:00,1655.25,,open_close -2022-08-04 05:11:00,1655.85,,open_close -2022-08-04 05:12:00,1654.4,,open_close -2022-08-04 05:13:00,1653.08,,open_close -2022-08-04 05:14:00,1653.66,,open_close -2022-08-04 05:15:00,1652.23,,open_close -2022-08-04 05:16:00,1653.29,,open_close -2022-08-04 05:17:00,1652.41,,open_close -2022-08-04 05:18:00,1652.87,,open_close -2022-08-04 05:19:00,1653.99,,open_close -2022-08-04 05:20:00,1652.9,,open_close -2022-08-04 05:21:00,1652.03,,open_close -2022-08-04 05:22:00,1653.13,,open_close -2022-08-04 05:23:00,1650.93,,open_close -2022-08-04 05:24:00,1650.93,,open_close -2022-08-04 05:25:00,1649.45,,open_close -2022-08-04 05:26:00,1650.54,,open_close -2022-08-04 05:27:00,1649.94,,open_close -2022-08-04 05:28:00,1650.61,,open_close -2022-08-04 05:29:00,1649.64,,open_close -2022-08-04 05:30:00,1649.37,,open_close -2022-08-04 05:31:00,1649.07,,open_close -2022-08-04 05:32:00,1650.32,,open_close -2022-08-04 05:33:00,1649.99,,open_close -2022-08-04 05:34:00,1649.97,,open_close -2022-08-04 05:35:00,1650.38,,open_close -2022-08-04 05:36:00,1649.95,,open_close -2022-08-04 05:37:00,1650.49,,open_close -2022-08-04 05:38:00,1650.77,,open_close -2022-08-04 05:39:00,1649.59,,open_close -2022-08-04 05:40:00,1650.3,,open_close -2022-08-04 05:41:00,1650.53,,open_close -2022-08-04 05:42:00,1650.58,,open_close -2022-08-04 05:43:00,1650.06,,open_close -2022-08-04 05:44:00,1648.84,,open_close -2022-08-04 05:45:00,1649.37,,open_close -2022-08-04 05:46:00,1650.21,,open_close -2022-08-04 05:47:00,1650.9,,open_close -2022-08-04 05:48:00,1651.03,,open_close -2022-08-04 05:49:00,1652.36,,open_close -2022-08-04 05:50:00,1652.41,,open_close -2022-08-04 05:51:00,1652.89,,open_close -2022-08-04 05:52:00,1651.77,,open_close -2022-08-04 05:53:00,1651.85,,open_close -2022-08-04 05:54:00,1649.74,,open_close -2022-08-04 05:55:00,1651.6,,open_close -2022-08-04 05:56:00,1649.9,,open_close -2022-08-04 05:57:00,1649.58,,open_close -2022-08-04 05:58:00,1649.89,,open_close -2022-08-04 05:59:00,1649.83,,open_close -2022-08-04 06:00:00,1649.71,,open_close -2022-08-04 06:01:00,1649.67,,open_close -2022-08-04 06:02:00,1648.44,,open_close -2022-08-04 06:03:00,1648.84,,open_close -2022-08-04 06:04:00,1651.33,,open_close -2022-08-04 06:05:00,1649.6,,open_close -2022-08-04 06:06:00,1651.36,,open_close -2022-08-04 06:07:00,1652.01,,open_close -2022-08-04 06:08:00,1646.6,,open_close -2022-08-04 06:09:00,1647.61,,open_close -2022-08-04 06:10:00,1646.51,,open_close -2022-08-04 06:11:00,1648.57,,open_close -2022-08-04 06:12:00,1647.32,,open_close -2022-08-04 06:13:00,1647.26,,open_close -2022-08-04 06:14:00,1645.47,,open_close -2022-08-04 06:15:00,1634.96,,open_close -2022-08-04 06:16:00,1631.83,,open_close -2022-08-04 06:17:00,1634.34,,open_close -2022-08-04 06:18:00,1629.97,,open_close -2022-08-04 06:19:00,1630.11,,open_close -2022-08-04 06:20:00,1625.16,,open_close -2022-08-04 06:21:00,1622.17,,open_close -2022-08-04 06:22:00,1622.5,,open_close -2022-08-04 06:23:00,1623.75,,open_close -2022-08-04 06:24:00,1621.47,,open_close -2022-08-04 06:25:00,1619.36,,minus_infty -2022-08-04 06:26:00,1618.46,,minus_infty -2022-08-04 06:27:00,1619.87,,minus_infty -2022-08-04 06:28:00,1620.92,,open_close -2022-08-04 06:29:00,1619.9,,minus_infty -2022-08-04 06:30:00,1617.62,,minus_infty -2022-08-04 06:31:00,1618.05,,minus_infty -2022-08-04 06:32:00,1620.87,,open_close -2022-08-04 06:33:00,1624.0,,open_close -2022-08-04 06:34:00,1627.24,,open_close -2022-08-04 06:35:00,1626.47,,open_close -2022-08-04 06:36:00,1628.29,,open_close -2022-08-04 06:37:00,1626.27,,open_close -2022-08-04 06:38:00,1628.36,,open_close -2022-08-04 06:39:00,1626.88,,open_close -2022-08-04 06:40:00,1624.11,,open_close -2022-08-04 06:41:00,1625.35,,open_close -2022-08-04 06:42:00,1624.71,,open_close -2022-08-04 06:43:00,1625.58,,open_close -2022-08-04 06:44:00,1627.73,,open_close -2022-08-04 06:45:00,1629.67,,open_close -2022-08-04 06:46:00,1629.57,,open_close -2022-08-04 06:47:00,1626.25,,open_close -2022-08-04 06:48:00,1626.87,,open_close -2022-08-04 06:49:00,1626.24,,open_close -2022-08-04 06:50:00,1627.35,,open_close -2022-08-04 06:51:00,1624.84,,open_close -2022-08-04 06:52:00,1624.81,,open_close -2022-08-04 06:53:00,1623.89,,open_close -2022-08-04 06:54:00,1622.73,,open_close -2022-08-04 06:55:00,1624.63,,open_close -2022-08-04 06:56:00,1623.25,,open_close -2022-08-04 06:57:00,1625.15,,open_close -2022-08-04 06:58:00,1624.54,,open_close -2022-08-04 06:59:00,1627.05,,open_close -2022-08-04 07:00:00,1625.88,,open_close -2022-08-04 07:01:00,1623.92,,open_close -2022-08-04 07:02:00,1626.33,,open_close -2022-08-04 07:03:00,1626.92,,open_close -2022-08-04 07:04:00,1627.5,,open_close -2022-08-04 07:05:00,1628.15,,open_close -2022-08-04 07:06:00,1629.66,,open_close -2022-08-04 07:07:00,1629.92,,open_close -2022-08-04 07:08:00,1631.84,,open_close -2022-08-04 07:09:00,1631.84,,open_close -2022-08-04 07:10:00,1631.76,,open_close -2022-08-04 07:11:00,1631.81,,open_close -2022-08-04 07:12:00,1632.22,,open_close -2022-08-04 07:13:00,1632.76,,open_close -2022-08-04 07:14:00,1630.73,,open_close -2022-08-04 07:15:00,1630.78,,open_close -2022-08-04 07:16:00,1629.29,,open_close -2022-08-04 07:17:00,1627.56,,open_close -2022-08-04 07:18:00,1630.65,,open_close -2022-08-04 07:19:00,1630.52,,open_close -2022-08-04 07:20:00,1629.63,,open_close -2022-08-04 07:21:00,1630.45,,open_close -2022-08-04 07:22:00,1631.26,,open_close -2022-08-04 07:23:00,1632.94,,open_close -2022-08-04 07:24:00,1633.28,,open_close -2022-08-04 07:25:00,1633.37,,open_close -2022-08-04 07:26:00,1633.67,,open_close -2022-08-04 07:27:00,1633.56,,open_close -2022-08-04 07:28:00,1632.33,,open_close -2022-08-04 07:29:00,1632.54,,open_close -2022-08-04 07:30:00,1634.32,,open_close -2022-08-04 07:31:00,1632.42,,open_close -2022-08-04 07:32:00,1632.06,,open_close -2022-08-04 07:33:00,1631.05,,open_close -2022-08-04 07:34:00,1631.01,,open_close -2022-08-04 07:35:00,1629.66,,open_close -2022-08-04 07:36:00,1630.88,,open_close -2022-08-04 07:37:00,1630.65,,open_close -2022-08-04 07:38:00,1629.65,,open_close -2022-08-04 07:39:00,1630.01,,open_close -2022-08-04 07:40:00,1629.1,,open_close -2022-08-04 07:41:00,1631.25,,open_close -2022-08-04 07:42:00,1629.14,,open_close -2022-08-04 07:43:00,1629.66,,open_close -2022-08-04 07:44:00,1629.79,,open_close -2022-08-04 07:45:00,1628.69,,open_close -2022-08-04 07:46:00,1630.18,,open_close -2022-08-04 07:47:00,1628.66,,open_close -2022-08-04 07:48:00,1630.2,,open_close -2022-08-04 07:49:00,1631.73,,open_close -2022-08-04 07:50:00,1629.71,,open_close -2022-08-04 07:51:00,1629.15,,open_close -2022-08-04 07:52:00,1629.42,,open_close -2022-08-04 07:53:00,1629.29,,open_close -2022-08-04 07:54:00,1627.71,,open_close -2022-08-04 07:55:00,1625.57,,open_close -2022-08-04 07:56:00,1626.11,,open_close -2022-08-04 07:57:00,1626.51,,open_close -2022-08-04 07:58:00,1626.28,,open_close -2022-08-04 07:59:00,1626.27,,open_close -2022-08-04 08:00:00,1624.58,,open_close -2022-08-04 08:01:00,1624.38,,open_close -2022-08-04 08:02:00,1625.85,,open_close -2022-08-04 08:03:00,1624.91,,open_close -2022-08-04 08:04:00,1624.4,,open_close -2022-08-04 08:05:00,1622.56,,open_close -2022-08-04 08:06:00,1623.68,,open_close -2022-08-04 08:07:00,1622.21,,open_close -2022-08-04 08:08:00,1620.8,,minus_infty -2022-08-04 08:09:00,1619.14,,minus_infty -2022-08-04 08:10:00,1619.08,,minus_infty -2022-08-04 08:11:00,1620.94,,open_close -2022-08-04 08:12:00,1619.76,,minus_infty -2022-08-04 08:13:00,1619.21,,minus_infty -2022-08-04 08:14:00,1619.79,,minus_infty -2022-08-04 08:15:00,1622.98,,open_close -2022-08-04 08:16:00,1624.91,,open_close -2022-08-04 08:17:00,1621.4,,open_close -2022-08-04 08:18:00,1621.01,,open_close -2022-08-04 08:19:00,1620.46,,minus_infty -2022-08-04 08:20:00,1621.71,,open_close -2022-08-04 08:21:00,1620.24,,minus_infty -2022-08-04 08:22:00,1621.07,,open_close -2022-08-04 08:23:00,1623.7,,open_close -2022-08-04 08:24:00,1623.08,,open_close -2022-08-04 08:25:00,1626.73,,open_close -2022-08-04 08:26:00,1627.24,,open_close -2022-08-04 08:27:00,1626.72,,open_close -2022-08-04 08:28:00,1627.45,,open_close -2022-08-04 08:29:00,1626.82,,open_close -2022-08-04 08:30:00,1628.87,,open_close -2022-08-04 08:31:00,1630.48,,open_close -2022-08-04 08:32:00,1630.11,,open_close -2022-08-04 08:33:00,1628.85,,open_close -2022-08-04 08:34:00,1627.74,,open_close -2022-08-04 08:35:00,1626.53,,open_close -2022-08-04 08:36:00,1623.08,,open_close -2022-08-04 08:37:00,1624.0,,open_close -2022-08-04 08:38:00,1623.2,,open_close -2022-08-04 08:39:00,1623.43,,open_close -2022-08-04 08:40:00,1624.75,,open_close -2022-08-04 08:41:00,1623.53,,open_close -2022-08-04 08:42:00,1623.14,,open_close -2022-08-04 08:43:00,1622.25,,open_close -2022-08-04 08:44:00,1622.49,,open_close -2022-08-04 08:45:00,1623.37,,open_close -2022-08-04 08:46:00,1622.04,,open_close -2022-08-04 08:47:00,1621.2,,open_close -2022-08-04 08:48:00,1625.01,,open_close -2022-08-04 08:49:00,1623.15,,open_close -2022-08-04 08:50:00,1621.44,,open_close -2022-08-04 08:51:00,1618.08,,minus_infty -2022-08-04 08:52:00,1619.32,,minus_infty -2022-08-04 08:53:00,1620.24,,minus_infty -2022-08-04 08:54:00,1620.85,,open_close -2022-08-04 08:55:00,1618.68,,minus_infty -2022-08-04 08:56:00,1620.31,,minus_infty -2022-08-04 08:57:00,1619.7,,minus_infty -2022-08-04 08:58:00,1613.95,,minus_infty -2022-08-04 08:59:00,1615.77,,minus_infty -2022-08-04 09:00:00,1616.24,,minus_infty -2022-08-04 09:01:00,1617.38,,minus_infty -2022-08-04 09:02:00,1616.54,,minus_infty -2022-08-04 09:03:00,1617.52,,minus_infty -2022-08-04 09:04:00,1617.88,,minus_infty -2022-08-04 09:05:00,1617.57,,minus_infty -2022-08-04 09:06:00,1619.41,,minus_infty -2022-08-04 09:07:00,1619.03,,minus_infty -2022-08-04 09:08:00,1619.48,,minus_infty -2022-08-04 09:09:00,1620.8,,minus_infty -2022-08-04 09:10:00,1620.98,,open_close -2022-08-04 09:11:00,1621.01,,open_close -2022-08-04 09:12:00,1621.19,,open_close -2022-08-04 09:13:00,1620.68,,minus_infty -2022-08-04 09:14:00,1621.48,,open_close -2022-08-04 09:15:00,1621.83,,open_close -2022-08-04 09:16:00,1621.53,,open_close -2022-08-04 09:17:00,1622.56,,open_close -2022-08-04 09:18:00,1622.68,,open_close -2022-08-04 09:19:00,1622.85,,open_close -2022-08-04 09:20:00,1623.76,,open_close -2022-08-04 09:21:00,1623.71,,open_close -2022-08-04 09:22:00,1621.56,,open_close -2022-08-04 09:23:00,1622.16,,open_close -2022-08-04 09:24:00,1621.9,,open_close -2022-08-04 09:25:00,1622.26,,open_close -2022-08-04 09:26:00,1622.24,,open_close -2022-08-04 09:27:00,1624.51,,open_close -2022-08-04 09:28:00,1623.8,,open_close -2022-08-04 09:29:00,1623.13,,open_close -2022-08-04 09:30:00,1624.25,,open_close -2022-08-04 09:31:00,1622.99,,open_close -2022-08-04 09:32:00,1623.68,,open_close -2022-08-04 09:33:00,1622.86,,open_close -2022-08-04 09:34:00,1620.47,,minus_infty -2022-08-04 09:35:00,1618.46,,minus_infty -2022-08-04 09:36:00,1615.32,,minus_infty -2022-08-04 09:37:00,1616.21,,minus_infty -2022-08-04 09:38:00,1616.46,,minus_infty -2022-08-04 09:39:00,1617.04,,minus_infty -2022-08-04 09:40:00,1618.42,,minus_infty -2022-08-04 09:41:00,1620.47,,minus_infty -2022-08-04 09:42:00,1621.22,,open_close -2022-08-04 09:43:00,1622.0,,open_close -2022-08-04 09:44:00,1620.69,,minus_infty -2022-08-04 09:45:00,1620.0,,minus_infty -2022-08-04 09:46:00,1621.46,,open_close -2022-08-04 09:47:00,1621.57,,open_close -2022-08-04 09:48:00,1622.12,,open_close -2022-08-04 09:49:00,1624.12,,open_close -2022-08-04 09:50:00,1624.2,,open_close -2022-08-04 09:51:00,1622.35,,open_close -2022-08-04 09:52:00,1623.57,,open_close -2022-08-04 09:53:00,1623.0,,open_close -2022-08-04 09:54:00,1622.95,,open_close -2022-08-04 09:55:00,1622.87,,open_close -2022-08-04 09:56:00,1623.48,,open_close -2022-08-04 09:57:00,1623.3,,open_close -2022-08-04 09:58:00,1622.81,,open_close -2022-08-04 09:59:00,1623.25,,open_close -2022-08-04 10:00:00,1623.48,,open_close -2022-08-04 10:01:00,1623.8,,open_close -2022-08-04 10:02:00,1624.63,,open_close -2022-08-04 10:03:00,1625.01,,open_close -2022-08-04 10:04:00,1626.69,,open_close -2022-08-04 10:05:00,1627.45,,open_close -2022-08-04 10:06:00,1628.05,,open_close -2022-08-04 10:07:00,1626.87,,open_close -2022-08-04 10:08:00,1627.57,,open_close -2022-08-04 10:09:00,1627.14,,open_close -2022-08-04 10:10:00,1625.11,,open_close -2022-08-04 10:11:00,1627.73,,open_close -2022-08-04 10:12:00,1627.42,,open_close -2022-08-04 10:13:00,1627.01,,open_close -2022-08-04 10:14:00,1628.11,,open_close -2022-08-04 10:15:00,1628.44,,open_close -2022-08-04 10:16:00,1627.18,,open_close -2022-08-04 10:17:00,1628.1,,open_close -2022-08-04 10:18:00,1627.38,,open_close -2022-08-04 10:19:00,1628.45,,open_close -2022-08-04 10:20:00,1628.96,,open_close -2022-08-04 10:21:00,1628.68,,open_close -2022-08-04 10:22:00,1629.15,,open_close -2022-08-04 10:23:00,1628.77,,open_close -2022-08-04 10:24:00,1628.6,,open_close -2022-08-04 10:25:00,1628.14,,open_close -2022-08-04 10:26:00,1627.9,,open_close -2022-08-04 10:27:00,1627.7,,open_close -2022-08-04 10:28:00,1628.42,,open_close -2022-08-04 10:29:00,1627.55,,open_close -2022-08-04 10:30:00,1627.27,,open_close -2022-08-04 10:31:00,1626.98,,open_close -2022-08-04 10:32:00,1626.33,,open_close -2022-08-04 10:33:00,1629.13,,open_close -2022-08-04 10:34:00,1630.27,,open_close -2022-08-04 10:35:00,1628.47,,open_close -2022-08-04 10:36:00,1626.12,,open_close -2022-08-04 10:37:00,1625.48,,open_close -2022-08-04 10:38:00,1625.53,,open_close -2022-08-04 10:39:00,1624.16,,open_close -2022-08-04 10:40:00,1624.0,,open_close -2022-08-04 10:41:00,1620.86,,open_close -2022-08-04 10:42:00,1621.38,,open_close -2022-08-04 10:43:00,1622.5,,open_close -2022-08-04 10:44:00,1621.11,,open_close -2022-08-04 10:45:00,1619.03,,minus_infty -2022-08-04 10:46:00,1618.0,,minus_infty -2022-08-04 10:47:00,1616.39,,minus_infty -2022-08-04 10:48:00,1616.99,,minus_infty -2022-08-04 10:49:00,1615.99,,minus_infty -2022-08-04 10:50:00,1616.0,,minus_infty -2022-08-04 10:51:00,1616.34,,minus_infty -2022-08-04 10:52:00,1617.86,,minus_infty -2022-08-04 10:53:00,1618.11,,minus_infty -2022-08-04 10:54:00,1617.95,,minus_infty -2022-08-04 10:55:00,1615.37,,minus_infty -2022-08-04 10:56:00,1617.32,,minus_infty -2022-08-04 10:57:00,1618.5,,minus_infty -2022-08-04 10:58:00,1618.8,,minus_infty -2022-08-04 10:59:00,1617.67,,minus_infty -2022-08-04 11:00:00,1617.04,,minus_infty -2022-08-04 11:01:00,1617.22,,minus_infty -2022-08-04 11:02:00,1616.94,,minus_infty -2022-08-04 11:03:00,1615.68,,minus_infty -2022-08-04 11:04:00,1617.52,,minus_infty -2022-08-04 11:05:00,1617.49,,minus_infty -2022-08-04 11:06:00,1617.72,,minus_infty -2022-08-04 11:07:00,1617.82,,minus_infty -2022-08-04 11:08:00,1618.24,,minus_infty -2022-08-04 11:09:00,1617.77,,minus_infty -2022-08-04 11:10:00,1615.76,,minus_infty -2022-08-04 11:11:00,1614.64,,minus_infty -2022-08-04 11:12:00,1615.47,,minus_infty -2022-08-04 11:13:00,1615.02,,minus_infty -2022-08-04 11:14:00,1614.54,,minus_infty -2022-08-04 11:15:00,1615.88,,minus_infty -2022-08-04 11:16:00,1613.19,,minus_infty -2022-08-04 11:17:00,1612.32,,minus_infty -2022-08-04 11:18:00,1614.82,,minus_infty -2022-08-04 11:19:00,1615.52,,minus_infty -2022-08-04 11:20:00,1613.69,,minus_infty -2022-08-04 11:21:00,1609.03,,minus_infty -2022-08-04 11:22:00,1611.13,,minus_infty -2022-08-04 11:23:00,1615.9,,minus_infty -2022-08-04 11:24:00,1618.58,,minus_infty -2022-08-04 11:25:00,1619.15,,minus_infty -2022-08-04 11:26:00,1618.84,,minus_infty -2022-08-04 11:27:00,1618.0,,minus_infty -2022-08-04 11:28:00,1617.3,,minus_infty -2022-08-04 11:29:00,1618.48,,minus_infty -2022-08-04 11:30:00,1620.23,,minus_infty -2022-08-04 11:31:00,1618.66,,minus_infty -2022-08-04 11:32:00,1618.59,,minus_infty -2022-08-04 11:33:00,1619.61,,minus_infty -2022-08-04 11:34:00,1619.7,,minus_infty -2022-08-04 11:35:00,1618.86,,minus_infty -2022-08-04 11:36:00,1617.56,,minus_infty -2022-08-04 11:37:00,1617.79,,minus_infty -2022-08-04 11:38:00,1619.05,,minus_infty -2022-08-04 11:39:00,1618.07,,minus_infty -2022-08-04 11:40:00,1621.01,,open_close -2022-08-04 11:41:00,1620.63,,minus_infty -2022-08-04 11:42:00,1620.28,,minus_infty -2022-08-04 11:43:00,1620.02,,minus_infty -2022-08-04 11:44:00,1619.41,,minus_infty -2022-08-04 11:45:00,1617.87,,minus_infty -2022-08-04 11:46:00,1618.72,,minus_infty -2022-08-04 11:47:00,1619.21,,minus_infty -2022-08-04 11:48:00,1619.17,,minus_infty -2022-08-04 11:49:00,1620.09,,minus_infty -2022-08-04 11:50:00,1618.0,,minus_infty -2022-08-04 11:51:00,1618.03,,minus_infty -2022-08-04 11:52:00,1618.14,,minus_infty -2022-08-04 11:53:00,1617.95,,minus_infty -2022-08-04 11:54:00,1617.25,,minus_infty -2022-08-04 11:55:00,1620.61,,minus_infty -2022-08-04 11:56:00,1619.65,,minus_infty -2022-08-04 11:57:00,1619.09,,minus_infty -2022-08-04 11:58:00,1618.75,,minus_infty -2022-08-04 11:59:00,1616.45,,minus_infty -2022-08-04 12:00:00,1617.79,,minus_infty -2022-08-04 12:01:00,1619.96,,minus_infty -2022-08-04 12:02:00,1623.17,,open_close -2022-08-04 12:03:00,1624.51,,open_close -2022-08-04 12:04:00,1622.63,,open_close -2022-08-04 12:05:00,1621.99,,open_close -2022-08-04 12:06:00,1622.34,,open_close -2022-08-04 12:07:00,1621.25,,open_close -2022-08-04 12:08:00,1620.61,,minus_infty -2022-08-04 12:09:00,1615.87,,minus_infty -2022-08-04 12:10:00,1613.4,,minus_infty -2022-08-04 12:11:00,1616.17,,minus_infty -2022-08-04 12:12:00,1617.08,,minus_infty -2022-08-04 12:13:00,1615.84,,minus_infty -2022-08-04 12:14:00,1611.86,,minus_infty -2022-08-04 12:15:00,1612.53,,minus_infty -2022-08-04 12:16:00,1612.06,,minus_infty -2022-08-04 12:17:00,1611.48,,minus_infty -2022-08-04 12:18:00,1611.92,,minus_infty -2022-08-04 12:19:00,1614.76,,minus_infty -2022-08-04 12:20:00,1613.9,,minus_infty -2022-08-04 12:21:00,1612.37,,minus_infty -2022-08-04 12:22:00,1611.14,,minus_infty -2022-08-04 12:23:00,1608.45,,minus_infty -2022-08-04 12:24:00,1611.12,,minus_infty -2022-08-04 12:25:00,1610.09,,minus_infty -2022-08-04 12:26:00,1609.58,,minus_infty -2022-08-04 12:27:00,1613.96,,minus_infty -2022-08-04 12:28:00,1615.71,,minus_infty -2022-08-04 12:29:00,1614.61,,minus_infty -2022-08-04 12:30:00,1617.35,,minus_infty -2022-08-04 12:31:00,1616.27,,minus_infty -2022-08-04 12:32:00,1613.47,,minus_infty -2022-08-04 12:33:00,1613.88,,minus_infty -2022-08-04 12:34:00,1617.4,,minus_infty -2022-08-04 12:35:00,1616.07,,minus_infty -2022-08-04 12:36:00,1616.81,,minus_infty -2022-08-04 12:37:00,1617.39,,minus_infty -2022-08-04 12:38:00,1618.52,,minus_infty -2022-08-04 12:39:00,1617.52,,minus_infty -2022-08-04 12:40:00,1616.43,,minus_infty -2022-08-04 12:41:00,1616.62,,minus_infty -2022-08-04 12:42:00,1615.7,,minus_infty -2022-08-04 12:43:00,1616.68,,minus_infty -2022-08-04 12:44:00,1615.89,,minus_infty -2022-08-04 12:45:00,1615.82,,minus_infty -2022-08-04 12:46:00,1615.04,,minus_infty -2022-08-04 12:47:00,1616.37,,minus_infty -2022-08-04 12:48:00,1617.78,,minus_infty -2022-08-04 12:49:00,1618.25,,minus_infty -2022-08-04 12:50:00,1617.93,,minus_infty -2022-08-04 12:51:00,1616.28,,minus_infty -2022-08-04 12:52:00,1618.32,,minus_infty -2022-08-04 12:53:00,1619.21,,minus_infty -2022-08-04 12:54:00,1618.95,,minus_infty -2022-08-04 12:55:00,1617.32,,minus_infty -2022-08-04 12:56:00,1618.42,,minus_infty -2022-08-04 12:57:00,1617.78,,minus_infty -2022-08-04 12:58:00,1617.1,,minus_infty -2022-08-04 12:59:00,1617.47,,minus_infty -2022-08-04 13:00:00,1617.47,,minus_infty -2022-08-04 13:01:00,1616.22,,minus_infty -2022-08-04 13:02:00,1614.24,,minus_infty -2022-08-04 13:03:00,1614.79,,minus_infty -2022-08-04 13:04:00,1613.44,,minus_infty -2022-08-04 13:05:00,1613.97,,minus_infty -2022-08-04 13:06:00,1613.73,,minus_infty -2022-08-04 13:07:00,1613.63,,minus_infty -2022-08-04 13:08:00,1614.78,,minus_infty -2022-08-04 13:09:00,1615.05,,minus_infty -2022-08-04 13:10:00,1619.4,,minus_infty -2022-08-04 13:11:00,1625.38,,open_close -2022-08-04 13:12:00,1623.36,,open_close -2022-08-04 13:13:00,1623.47,,open_close -2022-08-04 13:14:00,1622.17,,open_close -2022-08-04 13:15:00,1622.34,,open_close -2022-08-04 13:16:00,1619.23,,minus_infty -2022-08-04 13:17:00,1618.09,,minus_infty -2022-08-04 13:18:00,1618.47,,minus_infty -2022-08-04 13:19:00,1620.53,,minus_infty -2022-08-04 13:20:00,1619.39,,minus_infty -2022-08-04 13:21:00,1620.97,,open_close -2022-08-04 13:22:00,1621.02,,open_close -2022-08-04 13:23:00,1619.88,,minus_infty -2022-08-04 13:24:00,1622.35,,open_close -2022-08-04 13:25:00,1619.86,,minus_infty -2022-08-04 13:26:00,1619.6,,minus_infty -2022-08-04 13:27:00,1620.64,,minus_infty -2022-08-04 13:28:00,1621.37,,open_close -2022-08-04 13:29:00,1619.57,,minus_infty -2022-08-04 13:30:00,1618.06,,minus_infty -2022-08-04 13:31:00,1616.19,,minus_infty -2022-08-04 13:32:00,1623.23,,open_close -2022-08-04 13:33:00,1617.33,,minus_infty -2022-08-04 13:34:00,1622.25,,open_close -2022-08-04 13:35:00,1623.53,,open_close -2022-08-04 13:36:00,1627.05,,open_close -2022-08-04 13:37:00,1624.35,,open_close -2022-08-04 13:38:00,1620.1,,minus_infty -2022-08-04 13:39:00,1619.18,,minus_infty -2022-08-04 13:40:00,1621.85,,open_close -2022-08-04 13:41:00,1627.05,,open_close -2022-08-04 13:42:00,1629.32,,open_close -2022-08-04 13:43:00,1632.8,,open_close -2022-08-04 13:44:00,1637.24,,open_close -2022-08-04 13:45:00,1635.64,,open_close -2022-08-04 13:46:00,1638.05,,open_close -2022-08-04 13:47:00,1637.58,,open_close -2022-08-04 13:48:00,1638.55,,open_close -2022-08-04 13:49:00,1641.83,,open_close -2022-08-04 13:50:00,1642.8,,open_close -2022-08-04 13:51:00,1641.57,,open_close -2022-08-04 13:52:00,1637.4,,open_close -2022-08-04 13:53:00,1635.66,,open_close -2022-08-04 13:54:00,1635.39,,open_close -2022-08-04 13:55:00,1634.76,,open_close -2022-08-04 13:56:00,1636.0,,open_close -2022-08-04 13:57:00,1637.0,,open_close -2022-08-04 13:58:00,1636.9,,open_close -2022-08-04 13:59:00,1633.76,,open_close -2022-08-04 14:00:00,1632.56,,open_close -2022-08-04 14:01:00,1634.1,,open_close -2022-08-04 14:02:00,1636.75,,open_close -2022-08-04 14:03:00,1636.52,,open_close -2022-08-04 14:04:00,1636.28,,open_close -2022-08-04 14:05:00,1639.32,,open_close -2022-08-04 14:06:00,1634.14,,open_close -2022-08-04 14:07:00,1636.95,,open_close -2022-08-04 14:08:00,1634.27,,open_close -2022-08-04 14:09:00,1633.47,,open_close -2022-08-04 14:10:00,1629.83,,open_close -2022-08-04 14:11:00,1628.07,,open_close -2022-08-04 14:12:00,1628.53,,open_close -2022-08-04 14:13:00,1627.85,,open_close -2022-08-04 14:14:00,1622.92,,open_close -2022-08-04 14:15:00,1615.47,,minus_infty -2022-08-04 14:16:00,1615.17,,minus_infty -2022-08-04 14:17:00,1613.66,,minus_infty -2022-08-04 14:18:00,1613.66,,minus_infty -2022-08-04 14:19:00,1614.21,,minus_infty -2022-08-04 14:20:00,1610.89,,minus_infty -2022-08-04 14:21:00,1610.0,,minus_infty -2022-08-04 14:22:00,1613.4,,minus_infty -2022-08-04 14:23:00,1613.97,,minus_infty -2022-08-04 14:24:00,1614.19,,minus_infty -2022-08-04 14:25:00,1612.76,,minus_infty -2022-08-04 14:26:00,1615.32,,minus_infty -2022-08-04 14:27:00,1613.75,,minus_infty -2022-08-04 14:28:00,1612.17,,minus_infty -2022-08-04 14:29:00,1612.92,,minus_infty -2022-08-04 14:30:00,1607.28,,minus_infty -2022-08-04 14:31:00,1606.92,,minus_infty -2022-08-04 14:32:00,1607.63,,minus_infty -2022-08-04 14:33:00,1605.52,,minus_infty -2022-08-04 14:34:00,1599.47,,minus_infty -2022-08-04 14:35:00,1600.36,,minus_infty -2022-08-04 14:36:00,1601.16,,minus_infty -2022-08-04 14:37:00,1603.27,,minus_infty -2022-08-04 14:38:00,1602.72,,minus_infty -2022-08-04 14:39:00,1600.52,,minus_infty -2022-08-04 14:40:00,1600.65,,minus_infty -2022-08-04 14:41:00,1603.23,,minus_infty -2022-08-04 14:42:00,1602.54,,minus_infty -2022-08-04 14:43:00,1605.21,,minus_infty -2022-08-04 14:44:00,1606.04,,minus_infty -2022-08-04 14:45:00,1607.93,,minus_infty -2022-08-04 14:46:00,1607.65,,minus_infty -2022-08-04 14:47:00,1609.18,,minus_infty -2022-08-04 14:48:00,1612.05,,minus_infty -2022-08-04 14:49:00,1611.08,,minus_infty -2022-08-04 14:50:00,1611.56,,minus_infty -2022-08-04 14:51:00,1610.57,,minus_infty -2022-08-04 14:52:00,1607.75,,minus_infty -2022-08-04 14:53:00,1609.19,,minus_infty -2022-08-04 14:54:00,1608.8,,minus_infty -2022-08-04 14:55:00,1609.8,,minus_infty -2022-08-04 14:56:00,1611.46,,minus_infty -2022-08-04 14:57:00,1612.09,,minus_infty -2022-08-04 14:58:00,1614.63,,minus_infty -2022-08-04 14:59:00,1613.5,,minus_infty -2022-08-04 15:00:00,1613.28,,minus_infty -2022-08-04 15:01:00,1610.97,,minus_infty -2022-08-04 15:02:00,1609.98,,minus_infty -2022-08-04 15:03:00,1610.62,,minus_infty -2022-08-04 15:04:00,1612.15,,minus_infty -2022-08-04 15:05:00,1612.36,,minus_infty -2022-08-04 15:06:00,1610.48,,minus_infty -2022-08-04 15:07:00,1611.31,,minus_infty -2022-08-04 15:08:00,1614.03,,minus_infty -2022-08-04 15:09:00,1609.93,,minus_infty -2022-08-04 15:10:00,1611.61,,minus_infty -2022-08-04 15:11:00,1608.69,,minus_infty -2022-08-04 15:12:00,1608.06,,minus_infty -2022-08-04 15:13:00,1608.48,,minus_infty -2022-08-04 15:14:00,1609.56,,minus_infty -2022-08-04 15:15:00,1608.25,,minus_infty -2022-08-04 15:16:00,1608.24,,minus_infty -2022-08-04 15:17:00,1611.83,,minus_infty -2022-08-04 15:18:00,1611.21,,minus_infty -2022-08-04 15:19:00,1611.35,,minus_infty -2022-08-04 15:20:00,1614.44,,minus_infty -2022-08-04 15:21:00,1614.59,,minus_infty -2022-08-04 15:22:00,1613.21,,minus_infty -2022-08-04 15:23:00,1612.11,,minus_infty -2022-08-04 15:24:00,1608.64,,minus_infty -2022-08-04 15:25:00,1611.05,,minus_infty -2022-08-04 15:26:00,1610.36,,minus_infty -2022-08-04 15:27:00,1609.64,,minus_infty -2022-08-04 15:28:00,1609.26,,minus_infty -2022-08-04 15:29:00,1610.37,,minus_infty -2022-08-04 15:30:00,1610.16,,minus_infty -2022-08-04 15:31:00,1609.7,,minus_infty -2022-08-04 15:32:00,1611.94,,minus_infty -2022-08-04 15:33:00,1611.26,,minus_infty -2022-08-04 15:34:00,1611.54,,minus_infty -2022-08-04 15:35:00,1612.43,,minus_infty -2022-08-04 15:36:00,1610.27,,minus_infty -2022-08-04 15:37:00,1609.7,,minus_infty -2022-08-04 15:38:00,1607.37,,minus_infty -2022-08-04 15:39:00,1605.0,,minus_infty -2022-08-04 15:40:00,1607.99,,minus_infty -2022-08-04 15:41:00,1605.63,,minus_infty -2022-08-04 15:42:00,1605.08,,minus_infty -2022-08-04 15:43:00,1606.29,,minus_infty -2022-08-04 15:44:00,1607.32,,minus_infty -2022-08-04 15:45:00,1607.89,,minus_infty -2022-08-04 15:46:00,1611.3,,minus_infty -2022-08-04 15:47:00,1611.25,,minus_infty -2022-08-04 15:48:00,1610.41,,minus_infty -2022-08-04 15:49:00,1611.66,,minus_infty -2022-08-04 15:50:00,1611.8,,minus_infty -2022-08-04 15:51:00,1616.26,,minus_infty -2022-08-04 15:52:00,1616.19,,minus_infty -2022-08-04 15:53:00,1615.43,,minus_infty -2022-08-04 15:54:00,1614.48,,minus_infty -2022-08-04 15:55:00,1614.26,,minus_infty -2022-08-04 15:56:00,1614.61,,minus_infty -2022-08-04 15:57:00,1616.48,,minus_infty -2022-08-04 15:58:00,1615.02,,minus_infty -2022-08-04 15:59:00,1615.93,,minus_infty -2022-08-04 16:00:00,1617.74,,minus_infty -2022-08-04 16:01:00,1617.38,,minus_infty -2022-08-04 16:02:00,1616.06,,minus_infty -2022-08-04 16:03:00,1615.32,,minus_infty -2022-08-04 16:04:00,1615.31,,minus_infty -2022-08-04 16:05:00,1613.75,,minus_infty -2022-08-04 16:06:00,1614.76,,minus_infty -2022-08-04 16:07:00,1616.94,,minus_infty -2022-08-04 16:08:00,1620.91,,open_close -2022-08-04 16:09:00,1619.64,,minus_infty -2022-08-04 16:10:00,1621.12,,open_close -2022-08-04 16:11:00,1620.62,,minus_infty -2022-08-04 16:12:00,1621.65,,open_close -2022-08-04 16:13:00,1622.64,,open_close -2022-08-04 16:14:00,1622.02,,open_close -2022-08-04 16:15:00,1620.93,,open_close -2022-08-04 16:16:00,1621.17,,open_close -2022-08-04 16:17:00,1622.27,,open_close -2022-08-04 16:18:00,1621.29,,open_close -2022-08-04 16:19:00,1621.07,,open_close -2022-08-04 16:20:00,1620.34,,minus_infty -2022-08-04 16:21:00,1620.45,,minus_infty -2022-08-04 16:22:00,1621.05,,open_close -2022-08-04 16:23:00,1620.67,,minus_infty -2022-08-04 16:24:00,1621.72,,open_close -2022-08-04 16:25:00,1620.18,,minus_infty -2022-08-04 16:26:00,1620.21,,minus_infty -2022-08-04 16:27:00,1620.18,,minus_infty -2022-08-04 16:28:00,1616.52,,minus_infty -2022-08-04 16:29:00,1618.73,,minus_infty -2022-08-04 16:30:00,1615.1,,minus_infty -2022-08-04 16:31:00,1614.3,,minus_infty -2022-08-04 16:32:00,1615.36,,minus_infty -2022-08-04 16:33:00,1613.27,,minus_infty -2022-08-04 16:34:00,1614.2,,minus_infty -2022-08-04 16:35:00,1613.52,,minus_infty -2022-08-04 16:36:00,1612.45,,minus_infty -2022-08-04 16:37:00,1613.48,,minus_infty -2022-08-04 16:38:00,1612.63,,minus_infty -2022-08-04 16:39:00,1611.86,,minus_infty -2022-08-04 16:40:00,1611.18,,minus_infty -2022-08-04 16:41:00,1608.83,,minus_infty -2022-08-04 16:42:00,1610.29,,minus_infty -2022-08-04 16:43:00,1609.85,,minus_infty -2022-08-04 16:44:00,1610.31,,minus_infty -2022-08-04 16:45:00,1608.32,,minus_infty -2022-08-04 16:46:00,1604.36,,minus_infty -2022-08-04 16:47:00,1596.96,,minus_infty -2022-08-04 16:48:00,1594.13,,minus_infty -2022-08-04 16:49:00,1591.48,,minus_infty -2022-08-04 16:50:00,1592.05,,minus_infty -2022-08-04 16:51:00,1590.67,,minus_infty -2022-08-04 16:52:00,1586.6,,minus_infty -2022-08-04 16:53:00,1583.92,,minus_infty -2022-08-04 16:54:00,1585.09,,minus_infty -2022-08-04 16:55:00,1585.0,,minus_infty -2022-08-04 16:56:00,1581.4,,minus_infty -2022-08-04 16:57:00,1584.99,,minus_infty -2022-08-04 16:58:00,1587.24,,minus_infty -2022-08-04 16:59:00,1587.8,,minus_infty -2022-08-04 17:00:00,1589.01,,minus_infty -2022-08-04 17:01:00,1591.9,,minus_infty -2022-08-04 17:02:00,1591.84,,minus_infty -2022-08-04 17:03:00,1590.02,,minus_infty -2022-08-04 17:04:00,1588.37,,minus_infty -2022-08-04 17:05:00,1588.9,,minus_infty -2022-08-04 17:06:00,1590.89,,minus_infty -2022-08-04 17:07:00,1592.55,,minus_infty -2022-08-04 17:08:00,1593.39,,minus_infty -2022-08-04 17:09:00,1590.9,,minus_infty -2022-08-04 17:10:00,1591.9,,minus_infty -2022-08-04 17:11:00,1590.33,,minus_infty -2022-08-04 17:12:00,1591.92,,minus_infty -2022-08-04 17:13:00,1592.27,,minus_infty -2022-08-04 17:14:00,1590.83,,minus_infty -2022-08-04 17:15:00,1592.72,,minus_infty -2022-08-04 17:16:00,1593.89,,minus_infty -2022-08-04 17:17:00,1590.93,,minus_infty -2022-08-04 17:18:00,1592.08,,minus_infty -2022-08-04 17:19:00,1592.25,,minus_infty -2022-08-04 17:20:00,1590.65,,minus_infty -2022-08-04 17:21:00,1589.74,,minus_infty -2022-08-04 17:22:00,1590.02,,minus_infty -2022-08-04 17:23:00,1593.43,,minus_infty -2022-08-04 17:24:00,1591.46,,minus_infty -2022-08-04 17:25:00,1591.99,,minus_infty -2022-08-04 17:26:00,1592.87,,minus_infty -2022-08-04 17:27:00,1593.49,,minus_infty -2022-08-04 17:28:00,1595.25,,minus_infty -2022-08-04 17:29:00,1595.83,,minus_infty -2022-08-04 17:30:00,1593.59,,minus_infty -2022-08-04 17:31:00,1593.28,,minus_infty -2022-08-04 17:32:00,1592.72,,minus_infty -2022-08-04 17:33:00,1593.93,,minus_infty -2022-08-04 17:34:00,1594.34,,minus_infty -2022-08-04 17:35:00,1592.55,,minus_infty -2022-08-04 17:36:00,1593.6,,minus_infty -2022-08-04 17:37:00,1592.28,,minus_infty -2022-08-04 17:38:00,1590.28,,minus_infty -2022-08-04 17:39:00,1593.41,,minus_infty -2022-08-04 17:40:00,1595.84,,minus_infty -2022-08-04 17:41:00,1594.55,,minus_infty -2022-08-04 17:42:00,1593.74,,minus_infty -2022-08-04 17:43:00,1592.91,,minus_infty -2022-08-04 17:44:00,1593.24,,minus_infty -2022-08-04 17:45:00,1593.48,,minus_infty -2022-08-04 17:46:00,1593.96,,minus_infty -2022-08-04 17:47:00,1594.36,,minus_infty -2022-08-04 17:48:00,1594.03,,minus_infty -2022-08-04 17:49:00,1595.82,,minus_infty -2022-08-04 17:50:00,1596.58,,minus_infty -2022-08-04 17:51:00,1595.36,,minus_infty -2022-08-04 17:52:00,1594.76,,minus_infty -2022-08-04 17:53:00,1596.33,,minus_infty -2022-08-04 17:54:00,1596.3,,minus_infty -2022-08-04 17:55:00,1595.76,,minus_infty -2022-08-04 17:56:00,1595.66,,minus_infty -2022-08-04 17:57:00,1596.2,,minus_infty -2022-08-04 17:58:00,1595.76,,minus_infty -2022-08-04 17:59:00,1596.72,,minus_infty -2022-08-04 18:00:00,1596.95,,minus_infty -2022-08-04 18:01:00,1595.48,,minus_infty -2022-08-04 18:02:00,1596.36,,minus_infty -2022-08-04 18:03:00,1596.62,,minus_infty -2022-08-04 18:04:00,1595.64,,minus_infty -2022-08-04 18:05:00,1595.97,,minus_infty -2022-08-04 18:06:00,1595.53,,minus_infty -2022-08-04 18:07:00,1594.17,,minus_infty -2022-08-04 18:08:00,1592.9,,minus_infty -2022-08-04 18:09:00,1590.84,,minus_infty -2022-08-04 18:10:00,1588.38,,minus_infty -2022-08-04 18:11:00,1588.29,,minus_infty -2022-08-04 18:12:00,1589.86,,minus_infty -2022-08-04 18:13:00,1587.1,,minus_infty -2022-08-04 18:14:00,1588.91,,minus_infty -2022-08-04 18:15:00,1590.33,,minus_infty -2022-08-04 18:16:00,1589.95,,minus_infty -2022-08-04 18:17:00,1590.68,,minus_infty -2022-08-04 18:18:00,1592.93,,minus_infty -2022-08-04 18:19:00,1595.05,,minus_infty -2022-08-04 18:20:00,1594.21,,minus_infty -2022-08-04 18:21:00,1592.78,,minus_infty -2022-08-04 18:22:00,1594.16,,minus_infty -2022-08-04 18:23:00,1596.82,,minus_infty -2022-08-04 18:24:00,1598.81,,minus_infty -2022-08-04 18:25:00,1598.88,,minus_infty -2022-08-04 18:26:00,1600.82,,minus_infty -2022-08-04 18:27:00,1602.32,,minus_infty -2022-08-04 18:28:00,1602.02,,minus_infty -2022-08-04 18:29:00,1602.26,,minus_infty -2022-08-04 18:30:00,1607.21,,minus_infty -2022-08-04 18:31:00,1611.51,,minus_infty -2022-08-04 18:32:00,1614.6,,minus_infty -2022-08-04 18:33:00,1612.17,,minus_infty -2022-08-04 18:34:00,1613.16,,minus_infty -2022-08-04 18:35:00,1609.68,,minus_infty -2022-08-04 18:36:00,1607.49,,minus_infty -2022-08-04 18:37:00,1607.43,,minus_infty -2022-08-04 18:38:00,1608.17,,minus_infty -2022-08-04 18:39:00,1608.06,,minus_infty -2022-08-04 18:40:00,1602.29,,minus_infty -2022-08-04 18:41:00,1600.13,,minus_infty -2022-08-04 18:42:00,1600.14,,minus_infty -2022-08-04 18:43:00,1597.36,,minus_infty -2022-08-04 18:44:00,1597.57,,minus_infty -2022-08-04 18:45:00,1595.75,,minus_infty -2022-08-04 18:46:00,1595.31,,minus_infty -2022-08-04 18:47:00,1596.37,,minus_infty -2022-08-04 18:48:00,1597.12,,minus_infty -2022-08-04 18:49:00,1598.15,,minus_infty -2022-08-04 18:50:00,1597.32,,minus_infty -2022-08-04 18:51:00,1598.62,,minus_infty -2022-08-04 18:52:00,1599.53,,minus_infty -2022-08-04 18:53:00,1599.77,,minus_infty -2022-08-04 18:54:00,1599.54,,minus_infty -2022-08-04 18:55:00,1598.42,,minus_infty -2022-08-04 18:56:00,1598.39,,minus_infty -2022-08-04 18:57:00,1599.3,,minus_infty -2022-08-04 18:58:00,1599.23,,minus_infty -2022-08-04 18:59:00,1598.63,,minus_infty -2022-08-04 19:00:00,1598.76,,minus_infty -2022-08-04 19:01:00,1599.01,,minus_infty -2022-08-04 19:02:00,1598.81,,minus_infty -2022-08-04 19:03:00,1597.86,,minus_infty -2022-08-04 19:04:00,1600.86,,minus_infty -2022-08-04 19:05:00,1599.11,,minus_infty -2022-08-04 19:06:00,1599.15,,minus_infty -2022-08-04 19:07:00,1598.78,,minus_infty -2022-08-04 19:08:00,1599.21,,minus_infty -2022-08-04 19:09:00,1601.52,,minus_infty -2022-08-04 19:10:00,1603.85,,minus_infty -2022-08-04 19:11:00,1604.59,,minus_infty -2022-08-04 19:12:00,1605.41,,minus_infty -2022-08-04 19:13:00,1606.94,,minus_infty -2022-08-04 19:14:00,1608.1,,minus_infty -2022-08-04 19:15:00,1608.14,,minus_infty -2022-08-04 19:16:00,1608.21,,minus_infty -2022-08-04 19:17:00,1608.87,,minus_infty -2022-08-04 19:18:00,1607.88,,minus_infty -2022-08-04 19:19:00,1608.49,,minus_infty -2022-08-04 19:20:00,1605.55,,minus_infty -2022-08-04 19:21:00,1606.6,,minus_infty -2022-08-04 19:22:00,1605.18,,minus_infty -2022-08-04 19:23:00,1605.0,,minus_infty -2022-08-04 19:24:00,1605.16,,minus_infty -2022-08-04 19:25:00,1605.13,,minus_infty -2022-08-04 19:26:00,1606.53,,minus_infty -2022-08-04 19:27:00,1605.4,,minus_infty -2022-08-04 19:28:00,1605.96,,minus_infty -2022-08-04 19:29:00,1605.64,,minus_infty -2022-08-04 19:30:00,1604.35,,minus_infty -2022-08-04 19:31:00,1605.13,,minus_infty -2022-08-04 19:32:00,1602.05,,minus_infty -2022-08-04 19:33:00,1598.59,,minus_infty -2022-08-04 19:34:00,1595.82,,minus_infty -2022-08-04 19:35:00,1596.12,,minus_infty -2022-08-04 19:36:00,1592.74,,minus_infty -2022-08-04 19:37:00,1593.75,,minus_infty -2022-08-04 19:38:00,1590.95,,minus_infty -2022-08-04 19:39:00,1589.2,,minus_infty -2022-08-04 19:40:00,1590.44,,minus_infty -2022-08-04 19:41:00,1589.65,,minus_infty -2022-08-04 19:42:00,1589.85,,minus_infty -2022-08-04 19:43:00,1590.07,,minus_infty -2022-08-04 19:44:00,1590.66,,minus_infty -2022-08-04 19:45:00,1591.28,,minus_infty -2022-08-04 19:46:00,1592.32,,minus_infty -2022-08-04 19:47:00,1592.47,,minus_infty -2022-08-04 19:48:00,1589.63,,minus_infty -2022-08-04 19:49:00,1586.25,,minus_infty -2022-08-04 19:50:00,1590.91,,minus_infty -2022-08-04 19:51:00,1590.16,,minus_infty -2022-08-04 19:52:00,1589.47,,minus_infty -2022-08-04 19:53:00,1590.24,,minus_infty -2022-08-04 19:54:00,1592.0,,minus_infty -2022-08-04 19:55:00,1590.18,,minus_infty -2022-08-04 19:56:00,1591.19,,minus_infty -2022-08-04 19:57:00,1591.88,,minus_infty -2022-08-04 19:58:00,1592.09,,minus_infty -2022-08-04 19:59:00,1591.16,,minus_infty -2022-08-04 20:00:00,1590.26,,minus_infty -2022-08-04 20:01:00,1595.4,,minus_infty -2022-08-04 20:02:00,1593.98,,minus_infty -2022-08-04 20:03:00,1594.77,,minus_infty -2022-08-04 20:04:00,1593.89,,minus_infty -2022-08-04 20:05:00,1594.41,,minus_infty -2022-08-04 20:06:00,1595.17,,minus_infty -2022-08-04 20:07:00,1597.47,,minus_infty -2022-08-04 20:08:00,1598.78,,minus_infty -2022-08-04 20:09:00,1598.67,,minus_infty -2022-08-04 20:10:00,1598.93,,minus_infty -2022-08-04 20:11:00,1598.07,,minus_infty -2022-08-04 20:12:00,1598.79,,minus_infty -2022-08-04 20:13:00,1595.67,,minus_infty -2022-08-04 20:14:00,1595.85,,minus_infty -2022-08-04 20:15:00,1595.57,,minus_infty -2022-08-04 20:16:00,1594.48,,minus_infty -2022-08-04 20:17:00,1593.03,,minus_infty -2022-08-04 20:18:00,1595.86,,minus_infty -2022-08-04 20:19:00,1594.35,,minus_infty -2022-08-04 20:20:00,1595.38,,minus_infty -2022-08-04 20:21:00,1594.86,,minus_infty -2022-08-04 20:22:00,1596.02,,minus_infty -2022-08-04 20:23:00,1597.33,,minus_infty -2022-08-04 20:24:00,1596.98,,minus_infty -2022-08-04 20:25:00,1597.32,,minus_infty -2022-08-04 20:26:00,1596.56,,minus_infty -2022-08-04 20:27:00,1596.98,,minus_infty -2022-08-04 20:28:00,1596.52,,minus_infty -2022-08-04 20:29:00,1596.47,,minus_infty -2022-08-04 20:30:00,1595.74,,minus_infty -2022-08-04 20:31:00,1595.47,,minus_infty -2022-08-04 20:32:00,1594.22,,minus_infty -2022-08-04 20:33:00,1595.16,,minus_infty -2022-08-04 20:34:00,1597.05,,minus_infty -2022-08-04 20:35:00,1598.66,,minus_infty -2022-08-04 20:36:00,1599.27,,minus_infty -2022-08-04 20:37:00,1598.94,,minus_infty -2022-08-04 20:38:00,1599.62,,minus_infty -2022-08-04 20:39:00,1597.54,,minus_infty -2022-08-04 20:40:00,1596.69,,minus_infty -2022-08-04 20:41:00,1597.44,,minus_infty -2022-08-04 20:42:00,1597.69,,minus_infty -2022-08-04 20:43:00,1595.86,,minus_infty -2022-08-04 20:44:00,1595.29,,minus_infty -2022-08-04 20:45:00,1595.99,,minus_infty -2022-08-04 20:46:00,1594.68,,minus_infty -2022-08-04 20:47:00,1594.76,,minus_infty -2022-08-04 20:48:00,1593.82,,minus_infty -2022-08-04 20:49:00,1591.34,,minus_infty -2022-08-04 20:50:00,1589.09,,minus_infty -2022-08-04 20:51:00,1589.3,,minus_infty -2022-08-04 20:52:00,1592.84,,minus_infty -2022-08-04 20:53:00,1593.02,,minus_infty -2022-08-04 20:54:00,1595.17,,minus_infty -2022-08-04 20:55:00,1593.15,,minus_infty -2022-08-04 20:56:00,1593.56,,minus_infty -2022-08-04 20:57:00,1592.01,,minus_infty -2022-08-04 20:58:00,1590.29,,minus_infty -2022-08-04 20:59:00,1591.61,,minus_infty -2022-08-04 21:00:00,1591.51,,minus_infty -2022-08-04 21:01:00,1589.76,,minus_infty -2022-08-04 21:02:00,1593.64,,minus_infty -2022-08-04 21:03:00,1594.53,,minus_infty -2022-08-04 21:04:00,1596.51,,minus_infty -2022-08-04 21:05:00,1595.56,,minus_infty -2022-08-04 21:06:00,1596.7,,minus_infty -2022-08-04 21:07:00,1595.04,,minus_infty -2022-08-04 21:08:00,1595.07,,minus_infty -2022-08-04 21:09:00,1594.34,,minus_infty -2022-08-04 21:10:00,1593.33,,minus_infty -2022-08-04 21:11:00,1594.03,,minus_infty -2022-08-04 21:12:00,1593.73,,minus_infty -2022-08-04 21:13:00,1594.45,,minus_infty -2022-08-04 21:14:00,1592.75,,minus_infty -2022-08-04 21:15:00,1591.42,,minus_infty -2022-08-04 21:16:00,1591.49,,minus_infty -2022-08-04 21:17:00,1592.64,,minus_infty -2022-08-04 21:18:00,1593.71,,minus_infty -2022-08-04 21:19:00,1592.91,,minus_infty -2022-08-04 21:20:00,1592.89,,minus_infty -2022-08-04 21:21:00,1593.07,,minus_infty -2022-08-04 21:22:00,1594.83,,minus_infty -2022-08-04 21:23:00,1595.41,,minus_infty -2022-08-04 21:24:00,1593.07,,minus_infty -2022-08-04 21:25:00,1593.66,,minus_infty -2022-08-04 21:26:00,1594.62,,minus_infty -2022-08-04 21:27:00,1594.65,,minus_infty -2022-08-04 21:28:00,1595.12,,minus_infty -2022-08-04 21:29:00,1594.93,,minus_infty -2022-08-04 21:30:00,1595.18,,minus_infty -2022-08-04 21:31:00,1594.77,,minus_infty -2022-08-04 21:32:00,1595.13,,minus_infty -2022-08-04 21:33:00,1594.59,,minus_infty -2022-08-04 21:34:00,1595.75,,minus_infty -2022-08-04 21:35:00,1596.08,,minus_infty -2022-08-04 21:36:00,1596.61,,minus_infty -2022-08-04 21:37:00,1594.31,,minus_infty -2022-08-04 21:38:00,1592.94,,minus_infty -2022-08-04 21:39:00,1592.76,,minus_infty -2022-08-04 21:40:00,1592.53,,minus_infty -2022-08-04 21:41:00,1592.39,,minus_infty -2022-08-04 21:42:00,1591.87,,minus_infty -2022-08-04 21:43:00,1592.05,,minus_infty -2022-08-04 21:44:00,1594.75,,minus_infty -2022-08-04 21:45:00,1594.5,,minus_infty -2022-08-04 21:46:00,1594.92,,minus_infty -2022-08-04 21:47:00,1595.76,,minus_infty -2022-08-04 21:48:00,1592.99,,minus_infty -2022-08-04 21:49:00,1594.38,,minus_infty -2022-08-04 21:50:00,1592.93,,minus_infty -2022-08-04 21:51:00,1594.11,,minus_infty -2022-08-04 21:52:00,1591.13,,minus_infty -2022-08-04 21:53:00,1590.0,,minus_infty -2022-08-04 21:54:00,1590.91,,minus_infty -2022-08-04 21:55:00,1588.77,,minus_infty -2022-08-04 21:56:00,1588.89,,minus_infty -2022-08-04 21:57:00,1588.06,,minus_infty -2022-08-04 21:58:00,1585.97,,minus_infty -2022-08-04 21:59:00,1586.14,,minus_infty -2022-08-04 22:00:00,1585.34,,minus_infty -2022-08-04 22:01:00,1589.32,,minus_infty -2022-08-04 22:02:00,1590.03,,minus_infty -2022-08-04 22:03:00,1587.81,,minus_infty -2022-08-04 22:04:00,1589.1,,minus_infty -2022-08-04 22:05:00,1591.51,,minus_infty -2022-08-04 22:06:00,1590.91,,minus_infty -2022-08-04 22:07:00,1590.27,,minus_infty -2022-08-04 22:08:00,1590.55,,minus_infty -2022-08-04 22:09:00,1589.07,,minus_infty -2022-08-04 22:10:00,1588.75,,minus_infty -2022-08-04 22:11:00,1583.62,,minus_infty -2022-08-04 22:12:00,1581.66,,minus_infty -2022-08-04 22:13:00,1583.38,,minus_infty -2022-08-04 22:14:00,1583.64,,minus_infty -2022-08-04 22:15:00,1589.21,,minus_infty -2022-08-04 22:16:00,1589.46,,minus_infty -2022-08-04 22:17:00,1590.96,,minus_infty -2022-08-04 22:18:00,1589.71,,minus_infty -2022-08-04 22:19:00,1588.84,,minus_infty -2022-08-04 22:20:00,1592.27,,minus_infty -2022-08-04 22:21:00,1593.23,,minus_infty -2022-08-04 22:22:00,1595.81,,minus_infty -2022-08-04 22:23:00,1594.5,,minus_infty -2022-08-04 22:24:00,1593.83,,minus_infty -2022-08-04 22:25:00,1595.65,,minus_infty -2022-08-04 22:26:00,1594.18,,minus_infty -2022-08-04 22:27:00,1595.1,,minus_infty -2022-08-04 22:28:00,1594.61,,minus_infty -2022-08-04 22:29:00,1593.35,,minus_infty -2022-08-04 22:30:00,1595.18,,minus_infty -2022-08-04 22:31:00,1595.45,,minus_infty -2022-08-04 22:32:00,1594.62,,minus_infty -2022-08-04 22:33:00,1595.15,,minus_infty -2022-08-04 22:34:00,1595.34,,minus_infty -2022-08-04 22:35:00,1594.96,,minus_infty -2022-08-04 22:36:00,1594.36,,minus_infty -2022-08-04 22:37:00,1595.17,,minus_infty -2022-08-04 22:38:00,1596.4,,minus_infty -2022-08-04 22:39:00,1599.93,,minus_infty -2022-08-04 22:40:00,1601.37,,minus_infty -2022-08-04 22:41:00,1599.21,,minus_infty -2022-08-04 22:42:00,1599.23,,minus_infty -2022-08-04 22:43:00,1599.67,,minus_infty -2022-08-04 22:44:00,1597.76,,minus_infty -2022-08-04 22:45:00,1597.88,,minus_infty -2022-08-04 22:46:00,1599.36,,minus_infty -2022-08-04 22:47:00,1598.53,,minus_infty -2022-08-04 22:48:00,1597.78,,minus_infty -2022-08-04 22:49:00,1597.98,,minus_infty -2022-08-04 22:50:00,1598.28,,minus_infty -2022-08-04 22:51:00,1597.27,,minus_infty -2022-08-04 22:52:00,1597.71,,minus_infty -2022-08-04 22:53:00,1596.91,,minus_infty -2022-08-04 22:54:00,1598.26,,minus_infty -2022-08-04 22:55:00,1596.01,,minus_infty -2022-08-04 22:56:00,1596.57,,minus_infty -2022-08-04 22:57:00,1596.81,,minus_infty -2022-08-04 22:58:00,1596.31,,minus_infty -2022-08-04 22:59:00,1597.08,,minus_infty -2022-08-04 23:00:00,1597.91,,minus_infty -2022-08-04 23:01:00,1597.17,,minus_infty -2022-08-04 23:02:00,1595.6,,minus_infty -2022-08-04 23:03:00,1597.64,,minus_infty -2022-08-04 23:04:00,1598.95,,minus_infty -2022-08-04 23:05:00,1600.37,,minus_infty -2022-08-04 23:06:00,1600.06,,minus_infty -2022-08-04 23:07:00,1600.42,,minus_infty -2022-08-04 23:08:00,1600.42,,minus_infty -2022-08-04 23:09:00,1598.56,,minus_infty -2022-08-04 23:10:00,1603.0,,minus_infty -2022-08-04 23:11:00,1603.71,,minus_infty -2022-08-04 23:12:00,1603.81,,minus_infty -2022-08-04 23:13:00,1603.53,,minus_infty -2022-08-04 23:14:00,1601.57,,minus_infty -2022-08-04 23:15:00,1602.49,,minus_infty -2022-08-04 23:16:00,1604.25,,minus_infty -2022-08-04 23:17:00,1602.31,,minus_infty -2022-08-04 23:18:00,1603.37,,minus_infty -2022-08-04 23:19:00,1601.73,,minus_infty -2022-08-04 23:20:00,1603.15,,minus_infty -2022-08-04 23:21:00,1603.06,,minus_infty -2022-08-04 23:22:00,1602.15,,minus_infty -2022-08-04 23:23:00,1603.01,,minus_infty -2022-08-04 23:24:00,1603.36,,minus_infty -2022-08-04 23:25:00,1605.95,,minus_infty -2022-08-04 23:26:00,1606.84,,minus_infty -2022-08-04 23:27:00,1602.9,,minus_infty -2022-08-04 23:28:00,1604.62,,minus_infty -2022-08-04 23:29:00,1605.85,,minus_infty -2022-08-04 23:30:00,1605.02,,minus_infty -2022-08-04 23:31:00,1602.55,,minus_infty -2022-08-04 23:32:00,1601.01,,minus_infty -2022-08-04 23:33:00,1602.17,,minus_infty -2022-08-04 23:34:00,1602.19,,minus_infty -2022-08-04 23:35:00,1602.51,,minus_infty -2022-08-04 23:36:00,1602.28,,minus_infty -2022-08-04 23:37:00,1602.41,,minus_infty -2022-08-04 23:38:00,1603.95,,minus_infty -2022-08-04 23:39:00,1605.46,,minus_infty -2022-08-04 23:40:00,1604.45,,minus_infty -2022-08-04 23:41:00,1605.63,,minus_infty -2022-08-04 23:42:00,1607.84,,minus_infty -2022-08-04 23:43:00,1609.1,,minus_infty -2022-08-04 23:44:00,1608.89,,minus_infty -2022-08-04 23:45:00,1610.56,,minus_infty -2022-08-04 23:46:00,1610.68,,minus_infty -2022-08-04 23:47:00,1610.38,,minus_infty -2022-08-04 23:48:00,1611.87,,minus_infty -2022-08-04 23:49:00,1612.52,,minus_infty -2022-08-04 23:50:00,1609.98,,minus_infty -2022-08-04 23:51:00,1609.97,,minus_infty -2022-08-04 23:52:00,1611.49,,minus_infty -2022-08-04 23:53:00,1611.2,,minus_infty -2022-08-04 23:54:00,1609.84,,minus_infty -2022-08-04 23:55:00,1608.83,,minus_infty -2022-08-04 23:56:00,1608.47,,minus_infty -2022-08-04 23:57:00,1609.67,,minus_infty -2022-08-04 23:58:00,1606.6,,minus_infty -2022-08-04 23:59:00,1608.23,,minus_infty -2022-08-05 00:00:00,1607.68,,minus_infty -2022-08-05 00:01:00,1606.66,,minus_infty -2022-08-05 00:02:00,1605.81,,minus_infty -2022-08-05 00:03:00,1609.83,,minus_infty -2022-08-05 00:04:00,1608.79,,minus_infty -2022-08-05 00:05:00,1610.06,,minus_infty -2022-08-05 00:06:00,1613.7,,minus_infty -2022-08-05 00:07:00,1614.55,,minus_infty -2022-08-05 00:08:00,1615.61,,minus_infty -2022-08-05 00:09:00,1615.05,,minus_infty -2022-08-05 00:10:00,1614.23,,minus_infty -2022-08-05 00:11:00,1614.9,,minus_infty -2022-08-05 00:12:00,1611.83,,minus_infty -2022-08-05 00:13:00,1614.48,,minus_infty -2022-08-05 00:14:00,1612.38,,minus_infty -2022-08-05 00:15:00,1614.03,,minus_infty -2022-08-05 00:16:00,1615.05,,minus_infty -2022-08-05 00:17:00,1614.8,,minus_infty -2022-08-05 00:18:00,1613.53,,minus_infty -2022-08-05 00:19:00,1611.96,,minus_infty -2022-08-05 00:20:00,1612.39,,minus_infty -2022-08-05 00:21:00,1610.76,,minus_infty -2022-08-05 00:22:00,1610.07,,minus_infty -2022-08-05 00:23:00,1609.97,,minus_infty -2022-08-05 00:24:00,1608.61,,minus_infty -2022-08-05 00:25:00,1609.76,,minus_infty -2022-08-05 00:26:00,1609.67,,minus_infty -2022-08-05 00:27:00,1610.64,,minus_infty -2022-08-05 00:28:00,1610.41,,minus_infty -2022-08-05 00:29:00,1609.27,,minus_infty -2022-08-05 00:30:00,1609.74,,minus_infty -2022-08-05 00:31:00,1610.09,,minus_infty -2022-08-05 00:32:00,1609.47,,minus_infty -2022-08-05 00:33:00,1611.4,,minus_infty -2022-08-05 00:34:00,1609.88,,minus_infty -2022-08-05 00:35:00,1610.57,,minus_infty -2022-08-05 00:36:00,1609.96,,minus_infty -2022-08-05 00:37:00,1610.15,,minus_infty -2022-08-05 00:38:00,1610.2,,minus_infty -2022-08-05 00:39:00,1612.55,,minus_infty -2022-08-05 00:40:00,1612.54,,minus_infty -2022-08-05 00:41:00,1612.34,,minus_infty -2022-08-05 00:42:00,1612.33,,minus_infty -2022-08-05 00:43:00,1610.86,,minus_infty -2022-08-05 00:44:00,1610.18,,minus_infty -2022-08-05 00:45:00,1612.28,,minus_infty -2022-08-05 00:46:00,1611.42,,minus_infty -2022-08-05 00:47:00,1609.36,,minus_infty -2022-08-05 00:48:00,1606.46,,minus_infty -2022-08-05 00:49:00,1607.49,,minus_infty -2022-08-05 00:50:00,1609.36,,minus_infty -2022-08-05 00:51:00,1611.45,,minus_infty -2022-08-05 00:52:00,1611.63,,minus_infty -2022-08-05 00:53:00,1610.18,,minus_infty -2022-08-05 00:54:00,1610.84,,minus_infty -2022-08-05 00:55:00,1610.49,,minus_infty -2022-08-05 00:56:00,1611.06,,minus_infty -2022-08-05 00:57:00,1610.09,,minus_infty -2022-08-05 00:58:00,1608.98,,minus_infty -2022-08-05 00:59:00,1607.59,,minus_infty -2022-08-05 01:00:00,1608.86,,minus_infty -2022-08-05 01:01:00,1607.65,,minus_infty -2022-08-05 01:02:00,1609.02,,minus_infty -2022-08-05 01:03:00,1608.94,,minus_infty -2022-08-05 01:04:00,1608.93,,minus_infty -2022-08-05 01:05:00,1610.04,,minus_infty -2022-08-05 01:06:00,1611.86,,minus_infty -2022-08-05 01:07:00,1614.3,,minus_infty -2022-08-05 01:08:00,1613.76,,minus_infty -2022-08-05 01:09:00,1613.47,,minus_infty -2022-08-05 01:10:00,1613.48,,minus_infty -2022-08-05 01:11:00,1614.27,,minus_infty -2022-08-05 01:12:00,1613.66,,minus_infty -2022-08-05 01:13:00,1612.07,,minus_infty -2022-08-05 01:14:00,1612.55,,minus_infty -2022-08-05 01:15:00,1612.19,,minus_infty -2022-08-05 01:16:00,1613.07,,minus_infty -2022-08-05 01:17:00,1612.47,,minus_infty -2022-08-05 01:18:00,1612.28,,minus_infty -2022-08-05 01:19:00,1611.73,,minus_infty -2022-08-05 01:20:00,1611.33,,minus_infty -2022-08-05 01:21:00,1610.75,,minus_infty -2022-08-05 01:22:00,1611.77,,minus_infty -2022-08-05 01:23:00,1612.7,,minus_infty -2022-08-05 01:24:00,1611.52,,minus_infty -2022-08-05 01:25:00,1610.98,,minus_infty -2022-08-05 01:26:00,1611.38,,minus_infty -2022-08-05 01:27:00,1611.16,,minus_infty -2022-08-05 01:28:00,1611.56,,minus_infty -2022-08-05 01:29:00,1610.71,,minus_infty -2022-08-05 01:30:00,1610.3,,minus_infty -2022-08-05 01:31:00,1610.21,,minus_infty -2022-08-05 01:32:00,1610.8,,minus_infty -2022-08-05 01:33:00,1611.23,,minus_infty -2022-08-05 01:34:00,1610.66,,minus_infty -2022-08-05 01:35:00,1610.62,,minus_infty -2022-08-05 01:36:00,1613.57,,minus_infty -2022-08-05 01:37:00,1613.05,,minus_infty -2022-08-05 01:38:00,1612.05,,minus_infty -2022-08-05 01:39:00,1611.64,,minus_infty -2022-08-05 01:40:00,1611.55,,minus_infty -2022-08-05 01:41:00,1612.48,,minus_infty -2022-08-05 01:42:00,1616.0,,minus_infty -2022-08-05 01:43:00,1619.47,,minus_infty -2022-08-05 01:44:00,1621.58,,open_close -2022-08-05 01:45:00,1621.41,,open_close -2022-08-05 01:46:00,1624.76,,open_close -2022-08-05 01:47:00,1626.62,,open_close -2022-08-05 01:48:00,1633.69,,open_close -2022-08-05 01:49:00,1637.65,,open_close -2022-08-05 01:50:00,1634.75,,open_close -2022-08-05 01:51:00,1633.77,,open_close -2022-08-05 01:52:00,1632.72,,open_close -2022-08-05 01:53:00,1638.51,,open_close -2022-08-05 01:54:00,1638.04,,open_close -2022-08-05 01:55:00,1636.1,,open_close -2022-08-05 01:56:00,1638.33,,open_close -2022-08-05 01:57:00,1642.74,,open_close -2022-08-05 01:58:00,1649.21,,open_close -2022-08-05 01:59:00,1647.47,,open_close -2022-08-05 02:00:00,1652.84,,open_close -2022-08-05 02:01:00,1652.65,,open_close -2022-08-05 02:02:00,1652.64,,open_close -2022-08-05 02:03:00,1656.42,,open_close -2022-08-05 02:04:00,1655.28,,open_close -2022-08-05 02:05:00,1653.64,,open_close -2022-08-05 02:06:00,1652.72,,open_close -2022-08-05 02:07:00,1651.69,,open_close -2022-08-05 02:08:00,1655.98,,open_close -2022-08-05 02:09:00,1653.37,,open_close -2022-08-05 02:10:00,1652.91,,open_close -2022-08-05 02:11:00,1652.1,,open_close -2022-08-05 02:12:00,1652.79,,open_close -2022-08-05 02:13:00,1653.53,,open_close -2022-08-05 02:14:00,1652.56,,open_close -2022-08-05 02:15:00,1655.29,,open_close -2022-08-05 02:16:00,1655.52,,open_close -2022-08-05 02:17:00,1655.6,,open_close -2022-08-05 02:18:00,1655.64,,open_close -2022-08-05 02:19:00,1653.82,,open_close -2022-08-05 02:20:00,1657.17,,open_close -2022-08-05 02:21:00,1658.43,,open_close -2022-08-05 02:22:00,1656.87,,open_close -2022-08-05 02:23:00,1657.71,,open_close -2022-08-05 02:24:00,1656.35,,open_close -2022-08-05 02:25:00,1655.81,,open_close -2022-08-05 02:26:00,1658.94,,open_close -2022-08-05 02:27:00,1657.98,,open_close -2022-08-05 02:28:00,1659.81,,open_close -2022-08-05 02:29:00,1661.72,,open_close -2022-08-05 02:30:00,1661.8,,open_close -2022-08-05 02:31:00,1663.23,,open_close -2022-08-05 02:32:00,1664.52,,open_close -2022-08-05 02:33:00,1661.46,,open_close -2022-08-05 02:34:00,1660.98,,open_close -2022-08-05 02:35:00,1660.09,,open_close -2022-08-05 02:36:00,1663.43,,open_close -2022-08-05 02:37:00,1663.41,,open_close -2022-08-05 02:38:00,1665.03,,open_close -2022-08-05 02:39:00,1663.92,,open_close -2022-08-05 02:40:00,1663.51,,open_close -2022-08-05 02:41:00,1666.41,,open_close -2022-08-05 02:42:00,1666.14,,open_close -2022-08-05 02:43:00,1666.85,,open_close -2022-08-05 02:44:00,1665.2,,open_close -2022-08-05 02:45:00,1664.06,,open_close -2022-08-05 02:46:00,1663.2,,open_close -2022-08-05 02:47:00,1664.58,,open_close -2022-08-05 02:48:00,1663.59,,open_close -2022-08-05 02:49:00,1661.7,,open_close -2022-08-05 02:50:00,1662.2,,open_close -2022-08-05 02:51:00,1661.81,,open_close -2022-08-05 02:52:00,1664.53,,open_close -2022-08-05 02:53:00,1662.47,,open_close -2022-08-05 02:54:00,1662.16,,open_close -2022-08-05 02:55:00,1662.59,,open_close -2022-08-05 02:56:00,1661.88,,open_close -2022-08-05 02:57:00,1663.52,,open_close -2022-08-05 02:58:00,1662.57,,open_close -2022-08-05 02:59:00,1662.34,,open_close -2022-08-05 03:00:00,1661.57,,open_close -2022-08-05 03:01:00,1660.34,,open_close -2022-08-05 03:02:00,1656.91,,open_close -2022-08-05 03:03:00,1659.53,,open_close -2022-08-05 03:04:00,1658.76,,open_close -2022-08-05 03:05:00,1658.62,,open_close -2022-08-05 03:06:00,1659.73,,open_close -2022-08-05 03:07:00,1658.01,,open_close -2022-08-05 03:08:00,1658.65,,open_close -2022-08-05 03:09:00,1657.25,,open_close -2022-08-05 03:10:00,1656.83,,open_close -2022-08-05 03:11:00,1655.77,,open_close -2022-08-05 03:12:00,1653.36,,open_close -2022-08-05 03:13:00,1654.3,,open_close -2022-08-05 03:14:00,1654.14,,open_close -2022-08-05 03:15:00,1655.51,,open_close -2022-08-05 03:16:00,1657.03,,open_close -2022-08-05 03:17:00,1656.81,,open_close -2022-08-05 03:18:00,1657.6,,open_close -2022-08-05 03:19:00,1659.75,,open_close -2022-08-05 03:20:00,1658.05,,open_close -2022-08-05 03:21:00,1658.49,,open_close -2022-08-05 03:22:00,1658.75,,open_close -2022-08-05 03:23:00,1660.16,,open_close -2022-08-05 03:24:00,1658.62,,open_close -2022-08-05 03:25:00,1659.6,,open_close -2022-08-05 03:26:00,1662.97,,open_close -2022-08-05 03:27:00,1661.17,,open_close -2022-08-05 03:28:00,1662.33,,open_close -2022-08-05 03:29:00,1661.42,,open_close -2022-08-05 03:30:00,1660.22,,open_close -2022-08-05 03:31:00,1660.1,,open_close -2022-08-05 03:32:00,1659.79,,open_close -2022-08-05 03:33:00,1659.27,,open_close -2022-08-05 03:34:00,1660.07,,open_close -2022-08-05 03:35:00,1659.76,,open_close -2022-08-05 03:36:00,1659.35,,open_close -2022-08-05 03:37:00,1658.62,,open_close -2022-08-05 03:38:00,1659.07,,open_close -2022-08-05 03:39:00,1658.75,,open_close -2022-08-05 03:40:00,1659.96,,open_close -2022-08-05 03:41:00,1660.69,,open_close -2022-08-05 03:42:00,1660.69,,open_close -2022-08-05 03:43:00,1659.27,,open_close -2022-08-05 03:44:00,1659.4,,open_close -2022-08-05 03:45:00,1658.32,,open_close -2022-08-05 03:46:00,1658.99,,open_close -2022-08-05 03:47:00,1658.78,,open_close -2022-08-05 03:48:00,1658.91,,open_close -2022-08-05 03:49:00,1658.59,,open_close -2022-08-05 03:50:00,1659.21,,open_close -2022-08-05 03:51:00,1659.62,,open_close -2022-08-05 03:52:00,1659.32,,open_close -2022-08-05 03:53:00,1659.79,,open_close -2022-08-05 03:54:00,1659.38,,open_close -2022-08-05 03:55:00,1659.23,,open_close -2022-08-05 03:56:00,1660.47,,open_close -2022-08-05 03:57:00,1659.57,,open_close -2022-08-05 03:58:00,1660.0,,open_close -2022-08-05 03:59:00,1659.82,,open_close -2022-08-05 04:00:00,1661.67,,open_close -2022-08-05 04:01:00,1663.81,,open_close -2022-08-05 04:02:00,1662.61,,open_close -2022-08-05 04:03:00,1661.01,,open_close -2022-08-05 04:04:00,1659.8,,open_close -2022-08-05 04:05:00,1658.48,,open_close -2022-08-05 04:06:00,1659.36,,open_close -2022-08-05 04:07:00,1659.46,,open_close -2022-08-05 04:08:00,1660.73,,open_close -2022-08-05 04:09:00,1659.52,,open_close -2022-08-05 04:10:00,1660.09,,open_close -2022-08-05 04:11:00,1662.04,,open_close -2022-08-05 04:12:00,1661.38,,open_close -2022-08-05 04:13:00,1660.1,,open_close -2022-08-05 04:14:00,1659.46,,open_close -2022-08-05 04:15:00,1659.48,,open_close -2022-08-05 04:16:00,1659.02,,open_close -2022-08-05 04:17:00,1657.42,,open_close -2022-08-05 04:18:00,1658.94,,open_close -2022-08-05 04:19:00,1658.97,,open_close -2022-08-05 04:20:00,1658.42,,open_close -2022-08-05 04:21:00,1659.64,,open_close -2022-08-05 04:22:00,1658.89,,open_close -2022-08-05 04:23:00,1659.18,,open_close -2022-08-05 04:24:00,1658.42,,open_close -2022-08-05 04:25:00,1658.38,,open_close -2022-08-05 04:26:00,1657.94,,open_close -2022-08-05 04:27:00,1659.23,,open_close -2022-08-05 04:28:00,1658.5,,open_close -2022-08-05 04:29:00,1658.87,,open_close -2022-08-05 04:30:00,1658.72,,open_close -2022-08-05 04:31:00,1658.07,,open_close -2022-08-05 04:32:00,1659.18,,open_close -2022-08-05 04:33:00,1658.63,,open_close -2022-08-05 04:34:00,1658.74,,open_close -2022-08-05 04:35:00,1658.14,,open_close -2022-08-05 04:36:00,1658.61,,open_close -2022-08-05 04:37:00,1658.25,,open_close -2022-08-05 04:38:00,1658.89,,open_close -2022-08-05 04:39:00,1657.25,,open_close -2022-08-05 04:40:00,1657.43,,open_close -2022-08-05 04:41:00,1655.16,,open_close -2022-08-05 04:42:00,1653.51,,open_close -2022-08-05 04:43:00,1655.49,,open_close -2022-08-05 04:44:00,1653.94,,open_close -2022-08-05 04:45:00,1654.11,,open_close -2022-08-05 04:46:00,1656.51,,open_close -2022-08-05 04:47:00,1655.79,,open_close -2022-08-05 04:48:00,1656.51,,open_close -2022-08-05 04:49:00,1657.21,,open_close -2022-08-05 04:50:00,1657.13,,open_close -2022-08-05 04:51:00,1656.65,,open_close -2022-08-05 04:52:00,1656.8,,open_close -2022-08-05 04:53:00,1657.36,,open_close -2022-08-05 04:54:00,1657.27,,open_close -2022-08-05 04:55:00,1657.0,,open_close -2022-08-05 04:56:00,1656.61,,open_close -2022-08-05 04:57:00,1655.66,,open_close -2022-08-05 04:58:00,1656.02,,open_close -2022-08-05 04:59:00,1655.47,,open_close -2022-08-05 05:00:00,1656.79,,open_close -2022-08-05 05:01:00,1656.25,,open_close -2022-08-05 05:02:00,1656.59,,open_close -2022-08-05 05:03:00,1655.56,,open_close -2022-08-05 05:04:00,1655.27,,open_close -2022-08-05 05:05:00,1654.2,,open_close -2022-08-05 05:06:00,1654.23,,open_close -2022-08-05 05:07:00,1654.08,,open_close -2022-08-05 05:08:00,1653.68,,open_close -2022-08-05 05:09:00,1650.6,,open_close -2022-08-05 05:10:00,1648.46,,open_close -2022-08-05 05:11:00,1649.27,,open_close -2022-08-05 05:12:00,1649.16,,open_close -2022-08-05 05:13:00,1649.51,,open_close -2022-08-05 05:14:00,1650.06,,open_close -2022-08-05 05:15:00,1648.87,,open_close -2022-08-05 05:16:00,1649.57,,open_close -2022-08-05 05:17:00,1649.07,,open_close -2022-08-05 05:18:00,1651.74,,open_close -2022-08-05 05:19:00,1652.33,,open_close -2022-08-05 05:20:00,1651.46,,open_close -2022-08-05 05:21:00,1653.9,,open_close -2022-08-05 05:22:00,1653.06,,open_close -2022-08-05 05:23:00,1653.57,,open_close -2022-08-05 05:24:00,1652.61,,open_close -2022-08-05 05:25:00,1651.53,,open_close -2022-08-05 05:26:00,1651.18,,open_close -2022-08-05 05:27:00,1650.94,,open_close -2022-08-05 05:28:00,1652.11,,open_close -2022-08-05 05:29:00,1650.65,,open_close -2022-08-05 05:30:00,1652.01,,open_close -2022-08-05 05:31:00,1651.44,,open_close -2022-08-05 05:32:00,1652.46,,open_close -2022-08-05 05:33:00,1651.79,,open_close -2022-08-05 05:34:00,1651.5,,open_close -2022-08-05 05:35:00,1652.37,,open_close -2022-08-05 05:36:00,1651.23,,open_close -2022-08-05 05:37:00,1650.81,,open_close -2022-08-05 05:38:00,1650.44,,open_close -2022-08-05 05:39:00,1649.64,,open_close -2022-08-05 05:40:00,1650.79,,open_close -2022-08-05 05:41:00,1649.5,,open_close -2022-08-05 05:42:00,1649.53,,open_close -2022-08-05 05:43:00,1650.89,,open_close -2022-08-05 05:44:00,1651.95,,open_close -2022-08-05 05:45:00,1656.01,,open_close -2022-08-05 05:46:00,1656.46,,open_close -2022-08-05 05:47:00,1655.75,,open_close -2022-08-05 05:48:00,1655.31,,open_close -2022-08-05 05:49:00,1657.61,,open_close -2022-08-05 05:50:00,1657.3,,open_close -2022-08-05 05:51:00,1656.32,,open_close -2022-08-05 05:52:00,1655.99,,open_close -2022-08-05 05:53:00,1657.5,,open_close -2022-08-05 05:54:00,1659.63,,open_close -2022-08-05 05:55:00,1657.98,,open_close -2022-08-05 05:56:00,1659.07,,open_close -2022-08-05 05:57:00,1658.99,,open_close -2022-08-05 05:58:00,1659.69,,open_close -2022-08-05 05:59:00,1660.43,,open_close -2022-08-05 06:00:00,1660.59,,open_close -2022-08-05 06:01:00,1660.79,,open_close -2022-08-05 06:02:00,1661.94,,open_close -2022-08-05 06:03:00,1663.28,,open_close -2022-08-05 06:04:00,1676.26,,open_close -2022-08-05 06:05:00,1676.66,,open_close -2022-08-05 06:06:00,1677.07,,open_close -2022-08-05 06:07:00,1673.66,,open_close -2022-08-05 06:08:00,1675.69,,open_close -2022-08-05 06:09:00,1674.45,,open_close -2022-08-05 06:10:00,1673.98,,open_close -2022-08-05 06:11:00,1670.74,,open_close -2022-08-05 06:12:00,1669.04,,open_close -2022-08-05 06:13:00,1666.99,,open_close -2022-08-05 06:14:00,1667.68,,open_close -2022-08-05 06:15:00,1670.58,,open_close -2022-08-05 06:16:00,1671.04,,open_close -2022-08-05 06:17:00,1672.02,,open_close -2022-08-05 06:18:00,1670.97,,open_close -2022-08-05 06:19:00,1668.24,,open_close -2022-08-05 06:20:00,1669.61,,open_close -2022-08-05 06:21:00,1668.47,,open_close -2022-08-05 06:22:00,1670.82,,open_close -2022-08-05 06:23:00,1670.96,,open_close -2022-08-05 06:24:00,1670.11,,open_close -2022-08-05 06:25:00,1668.75,,open_close -2022-08-05 06:26:00,1669.89,,open_close -2022-08-05 06:27:00,1668.72,,open_close -2022-08-05 06:28:00,1667.03,,open_close -2022-08-05 06:29:00,1666.27,,open_close -2022-08-05 06:30:00,1666.29,,open_close -2022-08-05 06:31:00,1666.36,,open_close -2022-08-05 06:32:00,1666.76,,open_close -2022-08-05 06:33:00,1666.58,,open_close -2022-08-05 06:34:00,1666.66,,open_close -2022-08-05 06:35:00,1665.97,,open_close -2022-08-05 06:36:00,1665.26,,open_close -2022-08-05 06:37:00,1665.31,,open_close -2022-08-05 06:38:00,1665.59,,open_close -2022-08-05 06:39:00,1664.43,,open_close -2022-08-05 06:40:00,1663.08,,open_close -2022-08-05 06:41:00,1662.87,,open_close -2022-08-05 06:42:00,1663.72,,open_close -2022-08-05 06:43:00,1663.84,,open_close -2022-08-05 06:44:00,1663.63,,open_close -2022-08-05 06:45:00,1663.78,,open_close -2022-08-05 06:46:00,1666.67,,open_close -2022-08-05 06:47:00,1664.87,,open_close -2022-08-05 06:48:00,1664.77,,open_close -2022-08-05 06:49:00,1663.27,,open_close -2022-08-05 06:50:00,1664.71,,open_close -2022-08-05 06:51:00,1664.32,,open_close -2022-08-05 06:52:00,1663.46,,open_close -2022-08-05 06:53:00,1664.27,,open_close -2022-08-05 06:54:00,1663.87,,open_close -2022-08-05 06:55:00,1662.73,,open_close -2022-08-05 06:56:00,1663.27,,open_close -2022-08-05 06:57:00,1664.31,,open_close -2022-08-05 06:58:00,1663.73,,open_close -2022-08-05 06:59:00,1664.82,,open_close -2022-08-05 07:00:00,1665.59,,open_close -2022-08-05 07:01:00,1665.11,,open_close -2022-08-05 07:02:00,1664.6,,open_close -2022-08-05 07:03:00,1663.67,,open_close -2022-08-05 07:04:00,1663.88,,open_close -2022-08-05 07:05:00,1663.74,,open_close -2022-08-05 07:06:00,1658.93,,open_close -2022-08-05 07:07:00,1661.14,,open_close -2022-08-05 07:08:00,1662.89,,open_close -2022-08-05 07:09:00,1662.63,,open_close -2022-08-05 07:10:00,1662.75,,open_close -2022-08-05 07:11:00,1662.6,,open_close -2022-08-05 07:12:00,1660.58,,open_close -2022-08-05 07:13:00,1661.02,,open_close -2022-08-05 07:14:00,1661.78,,open_close -2022-08-05 07:15:00,1662.45,,open_close -2022-08-05 07:16:00,1662.29,,open_close -2022-08-05 07:17:00,1660.91,,open_close -2022-08-05 07:18:00,1660.4,,open_close -2022-08-05 07:19:00,1661.97,,open_close -2022-08-05 07:20:00,1662.61,,open_close -2022-08-05 07:21:00,1661.39,,open_close -2022-08-05 07:22:00,1661.96,,open_close -2022-08-05 07:23:00,1662.59,,open_close -2022-08-05 07:24:00,1662.48,,open_close -2022-08-05 07:25:00,1662.96,,open_close -2022-08-05 07:26:00,1663.68,,open_close -2022-08-05 07:27:00,1663.68,,open_close -2022-08-05 07:28:00,1663.57,,open_close -2022-08-05 07:29:00,1663.37,,open_close -2022-08-05 07:30:00,1664.36,,open_close -2022-08-05 07:31:00,1668.06,,open_close -2022-08-05 07:32:00,1666.71,,open_close -2022-08-05 07:33:00,1666.17,,open_close -2022-08-05 07:34:00,1664.99,,open_close -2022-08-05 07:35:00,1665.0,,open_close -2022-08-05 07:36:00,1666.29,,open_close -2022-08-05 07:37:00,1666.23,,open_close -2022-08-05 07:38:00,1665.06,,open_close -2022-08-05 07:39:00,1665.08,,open_close -2022-08-05 07:40:00,1665.74,,open_close -2022-08-05 07:41:00,1666.21,,open_close -2022-08-05 07:42:00,1665.85,,open_close -2022-08-05 07:43:00,1665.5,,open_close -2022-08-05 07:44:00,1666.26,,open_close -2022-08-05 07:45:00,1666.28,,open_close -2022-08-05 07:46:00,1666.63,,open_close -2022-08-05 07:47:00,1666.22,,open_close -2022-08-05 07:48:00,1666.07,,open_close -2022-08-05 07:49:00,1665.33,,open_close -2022-08-05 07:50:00,1665.6,,open_close -2022-08-05 07:51:00,1664.56,,open_close -2022-08-05 07:52:00,1662.97,,open_close -2022-08-05 07:53:00,1663.61,,open_close -2022-08-05 07:54:00,1664.2,,open_close -2022-08-05 07:55:00,1663.91,,open_close -2022-08-05 07:56:00,1664.92,,open_close -2022-08-05 07:57:00,1665.07,,open_close -2022-08-05 07:58:00,1664.26,,open_close -2022-08-05 07:59:00,1663.26,,open_close -2022-08-05 08:00:00,1663.02,,open_close -2022-08-05 08:01:00,1664.11,,open_close -2022-08-05 08:02:00,1663.53,,open_close -2022-08-05 08:03:00,1663.49,,open_close -2022-08-05 08:04:00,1663.14,,open_close -2022-08-05 08:05:00,1664.81,,open_close -2022-08-05 08:06:00,1667.25,,open_close -2022-08-05 08:07:00,1666.13,,open_close -2022-08-05 08:08:00,1668.17,,open_close -2022-08-05 08:09:00,1666.49,,open_close -2022-08-05 08:10:00,1666.64,,open_close -2022-08-05 08:11:00,1666.61,,open_close -2022-08-05 08:12:00,1666.73,,open_close -2022-08-05 08:13:00,1666.06,,open_close -2022-08-05 08:14:00,1666.57,,open_close -2022-08-05 08:15:00,1665.02,,open_close -2022-08-05 08:16:00,1661.56,,open_close -2022-08-05 08:17:00,1661.21,,open_close -2022-08-05 08:18:00,1658.12,,open_close -2022-08-05 08:19:00,1657.47,,open_close -2022-08-05 08:20:00,1659.47,,open_close -2022-08-05 08:21:00,1658.58,,open_close -2022-08-05 08:22:00,1658.53,,open_close -2022-08-05 08:23:00,1658.91,,open_close -2022-08-05 08:24:00,1658.77,,open_close -2022-08-05 08:25:00,1659.26,,open_close -2022-08-05 08:26:00,1659.23,,open_close -2022-08-05 08:27:00,1659.45,,open_close -2022-08-05 08:28:00,1657.51,,open_close -2022-08-05 08:29:00,1657.17,,open_close -2022-08-05 08:30:00,1657.98,,open_close -2022-08-05 08:31:00,1657.57,,open_close -2022-08-05 08:32:00,1658.48,,open_close -2022-08-05 08:33:00,1659.1,,open_close -2022-08-05 08:34:00,1658.86,,open_close -2022-08-05 08:35:00,1658.41,,open_close -2022-08-05 08:36:00,1655.83,,open_close -2022-08-05 08:37:00,1655.05,,open_close -2022-08-05 08:38:00,1656.42,,open_close -2022-08-05 08:39:00,1656.62,,open_close -2022-08-05 08:40:00,1657.68,,open_close -2022-08-05 08:41:00,1659.17,,open_close -2022-08-05 08:42:00,1657.35,,open_close -2022-08-05 08:43:00,1657.17,,open_close -2022-08-05 08:44:00,1656.19,,open_close -2022-08-05 08:45:00,1656.62,,open_close -2022-08-05 08:46:00,1655.99,,open_close -2022-08-05 08:47:00,1655.19,,open_close -2022-08-05 08:48:00,1655.19,,open_close -2022-08-05 08:49:00,1654.56,,open_close -2022-08-05 08:50:00,1654.53,,open_close -2022-08-05 08:51:00,1654.46,,open_close -2022-08-05 08:52:00,1653.28,,open_close -2022-08-05 08:53:00,1652.77,,open_close -2022-08-05 08:54:00,1653.1,,open_close -2022-08-05 08:55:00,1652.63,,open_close -2022-08-05 08:56:00,1653.93,,open_close -2022-08-05 08:57:00,1654.96,,open_close -2022-08-05 08:58:00,1656.26,,open_close -2022-08-05 08:59:00,1657.38,,open_close -2022-08-05 09:00:00,1656.84,,open_close -2022-08-05 09:01:00,1655.86,,open_close -2022-08-05 09:02:00,1654.91,,open_close -2022-08-05 09:03:00,1656.14,,open_close -2022-08-05 09:04:00,1656.05,,open_close -2022-08-05 09:05:00,1654.9,,open_close -2022-08-05 09:06:00,1656.2,,open_close -2022-08-05 09:07:00,1657.45,,open_close -2022-08-05 09:08:00,1655.88,,open_close -2022-08-05 09:09:00,1656.77,,open_close -2022-08-05 09:10:00,1656.62,,open_close -2022-08-05 09:11:00,1658.07,,open_close -2022-08-05 09:12:00,1657.87,,open_close -2022-08-05 09:13:00,1656.62,,open_close -2022-08-05 09:14:00,1656.58,,open_close -2022-08-05 09:15:00,1658.55,,open_close -2022-08-05 09:16:00,1664.6,,open_close -2022-08-05 09:17:00,1664.46,,open_close -2022-08-05 09:18:00,1661.75,,open_close -2022-08-05 09:19:00,1661.26,,open_close -2022-08-05 09:20:00,1660.83,,open_close -2022-08-05 09:21:00,1660.95,,open_close -2022-08-05 09:22:00,1660.8,,open_close -2022-08-05 09:23:00,1661.98,,open_close -2022-08-05 09:24:00,1661.11,,open_close -2022-08-05 09:25:00,1660.73,,open_close -2022-08-05 09:26:00,1660.14,,open_close -2022-08-05 09:27:00,1660.67,,open_close -2022-08-05 09:28:00,1661.76,,open_close -2022-08-05 09:29:00,1661.78,,open_close -2022-08-05 09:30:00,1664.48,,open_close -2022-08-05 09:31:00,1662.78,,open_close -2022-08-05 09:32:00,1663.06,,open_close -2022-08-05 09:33:00,1663.26,,open_close -2022-08-05 09:34:00,1663.92,,open_close -2022-08-05 09:35:00,1662.85,,open_close -2022-08-05 09:36:00,1662.3,,open_close -2022-08-05 09:37:00,1662.29,,open_close -2022-08-05 09:38:00,1662.25,,open_close -2022-08-05 09:39:00,1662.44,,open_close -2022-08-05 09:40:00,1661.95,,open_close -2022-08-05 09:41:00,1661.93,,open_close -2022-08-05 09:42:00,1661.59,,open_close -2022-08-05 09:43:00,1664.17,,open_close -2022-08-05 09:44:00,1665.64,,open_close -2022-08-05 09:45:00,1662.7,,open_close -2022-08-05 09:46:00,1662.31,,open_close -2022-08-05 09:47:00,1662.72,,open_close -2022-08-05 09:48:00,1663.2,,open_close -2022-08-05 09:49:00,1662.46,,open_close -2022-08-05 09:50:00,1663.86,,open_close -2022-08-05 09:51:00,1664.12,,open_close -2022-08-05 09:52:00,1664.54,,open_close -2022-08-05 09:53:00,1663.87,,open_close -2022-08-05 09:54:00,1664.09,,open_close -2022-08-05 09:55:00,1664.18,,open_close -2022-08-05 09:56:00,1664.42,,open_close -2022-08-05 09:57:00,1663.21,,open_close -2022-08-05 09:58:00,1663.09,,open_close -2022-08-05 09:59:00,1662.24,,open_close -2022-08-05 10:00:00,1662.39,,open_close -2022-08-05 10:01:00,1662.22,,open_close -2022-08-05 10:02:00,1661.73,,open_close -2022-08-05 10:03:00,1661.51,,open_close -2022-08-05 10:04:00,1661.53,,open_close -2022-08-05 10:05:00,1659.69,,open_close -2022-08-05 10:06:00,1660.49,,open_close -2022-08-05 10:07:00,1662.49,,open_close -2022-08-05 10:08:00,1665.66,,open_close -2022-08-05 10:09:00,1664.36,,open_close -2022-08-05 10:10:00,1664.75,,open_close -2022-08-05 10:11:00,1664.48,,open_close -2022-08-05 10:12:00,1662.77,,open_close -2022-08-05 10:13:00,1662.76,,open_close -2022-08-05 10:14:00,1662.55,,open_close -2022-08-05 10:15:00,1662.43,,open_close -2022-08-05 10:16:00,1663.25,,open_close -2022-08-05 10:17:00,1662.96,,open_close -2022-08-05 10:18:00,1663.68,,open_close -2022-08-05 10:19:00,1663.47,,open_close -2022-08-05 10:20:00,1662.44,,open_close -2022-08-05 10:21:00,1661.99,,open_close -2022-08-05 10:22:00,1661.0,,open_close -2022-08-05 10:23:00,1662.73,,open_close -2022-08-05 10:24:00,1663.42,,open_close -2022-08-05 10:25:00,1665.15,,open_close -2022-08-05 10:26:00,1665.33,,open_close -2022-08-05 10:27:00,1665.59,,open_close -2022-08-05 10:28:00,1666.36,,open_close -2022-08-05 10:29:00,1664.86,,open_close -2022-08-05 10:30:00,1664.69,,open_close -2022-08-05 10:31:00,1665.23,,open_close -2022-08-05 10:32:00,1664.41,,open_close -2022-08-05 10:33:00,1667.75,,open_close -2022-08-05 10:34:00,1667.46,,open_close -2022-08-05 10:35:00,1667.95,,open_close -2022-08-05 10:36:00,1667.86,,open_close -2022-08-05 10:37:00,1666.98,,open_close -2022-08-05 10:38:00,1665.87,,open_close -2022-08-05 10:39:00,1665.02,,open_close -2022-08-05 10:40:00,1665.76,,open_close -2022-08-05 10:41:00,1665.06,,open_close -2022-08-05 10:42:00,1665.83,,open_close -2022-08-05 10:43:00,1666.91,,open_close -2022-08-05 10:44:00,1666.59,,open_close -2022-08-05 10:45:00,1667.89,,open_close -2022-08-05 10:46:00,1667.71,,open_close -2022-08-05 10:47:00,1666.3,,open_close -2022-08-05 10:48:00,1666.21,,open_close -2022-08-05 10:49:00,1666.29,,open_close -2022-08-05 10:50:00,1667.2,,open_close -2022-08-05 10:51:00,1667.17,,open_close -2022-08-05 10:52:00,1673.68,,open_close -2022-08-05 10:53:00,1671.01,,open_close -2022-08-05 10:54:00,1669.94,,open_close -2022-08-05 10:55:00,1667.23,,open_close -2022-08-05 10:56:00,1666.91,,open_close -2022-08-05 10:57:00,1666.64,,open_close -2022-08-05 10:58:00,1668.5,,open_close -2022-08-05 10:59:00,1669.14,,open_close -2022-08-05 11:00:00,1668.82,,open_close -2022-08-05 11:01:00,1667.35,,open_close -2022-08-05 11:02:00,1667.68,,open_close -2022-08-05 11:03:00,1668.16,,open_close -2022-08-05 11:04:00,1668.51,,open_close -2022-08-05 11:05:00,1668.47,,open_close -2022-08-05 11:06:00,1669.35,,open_close -2022-08-05 11:07:00,1669.89,,open_close -2022-08-05 11:08:00,1672.29,,open_close -2022-08-05 11:09:00,1673.96,,open_close -2022-08-05 11:10:00,1671.97,,open_close -2022-08-05 11:11:00,1672.78,,open_close -2022-08-05 11:12:00,1675.52,,open_close -2022-08-05 11:13:00,1678.4,,open_close -2022-08-05 11:14:00,1677.3,,open_close -2022-08-05 11:15:00,1675.12,,open_close -2022-08-05 11:16:00,1674.99,,open_close -2022-08-05 11:17:00,1683.22,,infty -2022-08-05 11:18:00,1692.5,,infty -2022-08-05 11:19:00,1699.0,,infty -2022-08-05 11:20:00,1693.71,,infty -2022-08-05 11:21:00,1692.83,,infty -2022-08-05 11:22:00,1692.35,,infty -2022-08-05 11:23:00,1693.49,,infty -2022-08-05 11:24:00,1690.5,,infty -2022-08-05 11:25:00,1693.78,,infty -2022-08-05 11:26:00,1692.39,,infty -2022-08-05 11:27:00,1692.57,,infty -2022-08-05 11:28:00,1693.14,,infty -2022-08-05 11:29:00,1695.85,,infty -2022-08-05 11:30:00,1696.09,,infty -2022-08-05 11:31:00,1696.48,,infty -2022-08-05 11:32:00,1698.67,,infty -2022-08-05 11:33:00,1697.85,,infty -2022-08-05 11:34:00,1695.17,,infty -2022-08-05 11:35:00,1697.72,,infty -2022-08-05 11:36:00,1695.53,,infty -2022-08-05 11:37:00,1698.31,,infty -2022-08-05 11:38:00,1698.18,,infty -2022-08-05 11:39:00,1698.51,,infty -2022-08-05 11:40:00,1699.41,,infty -2022-08-05 11:41:00,1701.29,,infty -2022-08-05 11:42:00,1705.76,,infty -2022-08-05 11:43:00,1706.62,,infty -2022-08-05 11:44:00,1706.66,,infty -2022-08-05 11:45:00,1708.55,,infty -2022-08-05 11:46:00,1713.67,,infty -2022-08-05 11:47:00,1713.45,,infty -2022-08-05 11:48:00,1711.79,,infty -2022-08-05 11:49:00,1711.1,,infty -2022-08-05 11:50:00,1711.15,,infty -2022-08-05 11:51:00,1712.94,,infty -2022-08-05 11:52:00,1713.54,,infty -2022-08-05 11:53:00,1711.83,,infty -2022-08-05 11:54:00,1713.21,,infty -2022-08-05 11:55:00,1714.95,,infty -2022-08-05 11:56:00,1711.87,,infty -2022-08-05 11:57:00,1713.09,,infty -2022-08-05 11:58:00,1710.93,,infty -2022-08-05 11:59:00,1713.53,,infty -2022-08-05 12:00:00,1717.88,,infty -2022-08-05 12:01:00,1719.65,,infty -2022-08-05 12:02:00,1718.6,,infty -2022-08-05 12:03:00,1719.26,,infty -2022-08-05 12:04:00,1720.04,,infty -2022-08-05 12:05:00,1719.33,,infty -2022-08-05 12:06:00,1720.4,,infty -2022-08-05 12:07:00,1717.26,,infty -2022-08-05 12:08:00,1720.85,,infty -2022-08-05 12:09:00,1722.15,,infty -2022-08-05 12:10:00,1721.1,,infty -2022-08-05 12:11:00,1719.53,,infty -2022-08-05 12:12:00,1716.07,,infty -2022-08-05 12:13:00,1717.62,,infty -2022-08-05 12:14:00,1718.94,,infty -2022-08-05 12:15:00,1718.81,,infty -2022-08-05 12:16:00,1719.83,,infty -2022-08-05 12:17:00,1719.68,,infty -2022-08-05 12:18:00,1716.52,,infty -2022-08-05 12:19:00,1715.3,,infty -2022-08-05 12:20:00,1716.0,,infty -2022-08-05 12:21:00,1715.89,,infty -2022-08-05 12:22:00,1715.62,,infty -2022-08-05 12:23:00,1714.13,,infty -2022-08-05 12:24:00,1715.02,,infty -2022-08-05 12:25:00,1714.42,,infty -2022-08-05 12:26:00,1717.38,,infty -2022-08-05 12:27:00,1718.41,,infty -2022-08-05 12:28:00,1718.18,,infty -2022-08-05 12:29:00,1717.99,,infty -2022-08-05 12:30:00,1699.79,,infty -2022-08-05 12:31:00,1698.81,,infty -2022-08-05 12:32:00,1693.32,,infty -2022-08-05 12:33:00,1697.7,,infty -2022-08-05 12:34:00,1694.01,,infty -2022-08-05 12:35:00,1693.47,,infty -2022-08-05 12:36:00,1693.61,,infty -2022-08-05 12:37:00,1691.89,,infty -2022-08-05 12:38:00,1692.03,,infty -2022-08-05 12:39:00,1688.31,,infty -2022-08-05 12:40:00,1687.5,,infty -2022-08-05 12:41:00,1686.27,,infty -2022-08-05 12:42:00,1688.34,,infty -2022-08-05 12:43:00,1689.39,,infty -2022-08-05 12:44:00,1688.79,,infty -2022-08-05 12:45:00,1684.64,,infty -2022-08-05 12:46:00,1682.47,,infty -2022-08-05 12:47:00,1680.91,,open_close -2022-08-05 12:48:00,1678.14,,open_close -2022-08-05 12:49:00,1674.28,,open_close -2022-08-05 12:50:00,1681.03,,open_close -2022-08-05 12:51:00,1678.22,,open_close -2022-08-05 12:52:00,1679.74,,open_close -2022-08-05 12:53:00,1675.91,,open_close -2022-08-05 12:54:00,1676.78,,open_close -2022-08-05 12:55:00,1679.49,,open_close -2022-08-05 12:56:00,1679.44,,open_close -2022-08-05 12:57:00,1682.55,,infty -2022-08-05 12:58:00,1681.3,,infty -2022-08-05 12:59:00,1680.29,,open_close -2022-08-05 13:00:00,1684.37,,infty -2022-08-05 13:01:00,1684.15,,infty -2022-08-05 13:02:00,1684.76,,infty -2022-08-05 13:03:00,1684.93,,infty -2022-08-05 13:04:00,1682.01,,infty -2022-08-05 13:05:00,1683.37,,infty -2022-08-05 13:06:00,1680.99,,open_close -2022-08-05 13:07:00,1682.6,,infty -2022-08-05 13:08:00,1681.56,,infty -2022-08-05 13:09:00,1676.89,,open_close -2022-08-05 13:10:00,1677.02,,open_close -2022-08-05 13:11:00,1674.31,,open_close -2022-08-05 13:12:00,1668.87,,open_close -2022-08-05 13:13:00,1671.98,,open_close -2022-08-05 13:14:00,1669.53,,open_close -2022-08-05 13:15:00,1674.59,,open_close -2022-08-05 13:16:00,1675.83,,open_close -2022-08-05 13:17:00,1675.41,,open_close -2022-08-05 13:18:00,1674.82,,open_close -2022-08-05 13:19:00,1675.04,,open_close -2022-08-05 13:20:00,1674.98,,open_close -2022-08-05 13:21:00,1675.31,,open_close -2022-08-05 13:22:00,1673.56,,open_close -2022-08-05 13:23:00,1673.28,,open_close -2022-08-05 13:24:00,1674.16,,open_close -2022-08-05 13:25:00,1675.52,,open_close -2022-08-05 13:26:00,1674.39,,open_close -2022-08-05 13:27:00,1674.05,,open_close -2022-08-05 13:28:00,1676.61,,open_close -2022-08-05 13:29:00,1677.5,,open_close -2022-08-05 13:30:00,1679.24,,open_close -2022-08-05 13:31:00,1676.9,,open_close -2022-08-05 13:32:00,1679.54,,open_close -2022-08-05 13:33:00,1680.11,,open_close -2022-08-05 13:34:00,1685.15,,infty -2022-08-05 13:35:00,1684.79,,infty -2022-08-05 13:36:00,1682.83,,infty -2022-08-05 13:37:00,1684.7,,infty -2022-08-05 13:38:00,1687.28,,infty -2022-08-05 13:39:00,1687.09,,infty -2022-08-05 13:40:00,1685.14,,infty -2022-08-05 13:41:00,1681.27,,infty -2022-08-05 13:42:00,1684.42,,infty -2022-08-05 13:43:00,1684.35,,infty -2022-08-05 13:44:00,1684.93,,infty -2022-08-05 13:45:00,1681.95,,infty -2022-08-05 13:46:00,1681.42,,infty -2022-08-05 13:47:00,1680.85,,open_close -2022-08-05 13:48:00,1682.52,,infty -2022-08-05 13:49:00,1682.98,,infty -2022-08-05 13:50:00,1682.66,,infty -2022-08-05 13:51:00,1685.33,,infty -2022-08-05 13:52:00,1685.4,,infty -2022-08-05 13:53:00,1684.77,,infty -2022-08-05 13:54:00,1686.19,,infty -2022-08-05 13:55:00,1685.47,,infty -2022-08-05 13:56:00,1681.9,,infty -2022-08-05 13:57:00,1682.84,,infty -2022-08-05 13:58:00,1683.42,,infty -2022-08-05 13:59:00,1685.53,,infty -2022-08-05 14:00:00,1684.56,,infty -2022-08-05 14:01:00,1686.18,,infty -2022-08-05 14:02:00,1692.79,,infty -2022-08-05 14:03:00,1692.13,,infty -2022-08-05 14:04:00,1694.09,,infty -2022-08-05 14:05:00,1696.73,,infty -2022-08-05 14:06:00,1696.52,,infty -2022-08-05 14:07:00,1701.37,,infty -2022-08-05 14:08:00,1704.07,,infty -2022-08-05 14:09:00,1710.01,,infty -2022-08-05 14:10:00,1706.73,,infty -2022-08-05 14:11:00,1709.2,,infty -2022-08-05 14:12:00,1709.73,,infty -2022-08-05 14:13:00,1713.4,,infty -2022-08-05 14:14:00,1712.9,,infty -2022-08-05 14:15:00,1714.66,,infty -2022-08-05 14:16:00,1716.8,,infty -2022-08-05 14:17:00,1715.61,,infty -2022-08-05 14:18:00,1711.94,,infty -2022-08-05 14:19:00,1710.48,,infty -2022-08-05 14:20:00,1707.71,,infty -2022-08-05 14:21:00,1709.53,,infty -2022-08-05 14:22:00,1709.38,,infty -2022-08-05 14:23:00,1708.12,,infty -2022-08-05 14:24:00,1702.94,,infty -2022-08-05 14:25:00,1697.9,,infty -2022-08-05 14:26:00,1700.66,,infty -2022-08-05 14:27:00,1698.96,,infty -2022-08-05 14:28:00,1698.83,,infty -2022-08-05 14:29:00,1701.91,,infty -2022-08-05 14:30:00,1699.71,,infty -2022-08-05 14:31:00,1699.76,,infty -2022-08-05 14:32:00,1697.18,,infty -2022-08-05 14:33:00,1697.47,,infty -2022-08-05 14:34:00,1695.46,,infty -2022-08-05 14:35:00,1699.62,,infty -2022-08-05 14:36:00,1698.27,,infty -2022-08-05 14:37:00,1697.66,,infty -2022-08-05 14:38:00,1698.38,,infty -2022-08-05 14:39:00,1699.3,,infty -2022-08-05 14:40:00,1702.22,,infty -2022-08-05 14:41:00,1699.51,,infty -2022-08-05 14:42:00,1699.42,,infty -2022-08-05 14:43:00,1698.9,,infty -2022-08-05 14:44:00,1698.33,,infty -2022-08-05 14:45:00,1700.78,,infty -2022-08-05 14:46:00,1700.86,,infty -2022-08-05 14:47:00,1704.12,,infty -2022-08-05 14:48:00,1706.12,,infty -2022-08-05 14:49:00,1704.61,,infty -2022-08-05 14:50:00,1702.3,,infty -2022-08-05 14:51:00,1697.86,,infty -2022-08-05 14:52:00,1695.85,,infty -2022-08-05 14:53:00,1699.71,,infty -2022-08-05 14:54:00,1699.32,,infty -2022-08-05 14:55:00,1697.44,,infty -2022-08-05 14:56:00,1696.55,,infty -2022-08-05 14:57:00,1697.07,,infty -2022-08-05 14:58:00,1694.55,,infty -2022-08-05 14:59:00,1693.94,,infty -2022-08-05 15:00:00,1692.15,,infty -2022-08-05 15:01:00,1691.35,,infty -2022-08-05 15:02:00,1691.86,,infty -2022-08-05 15:03:00,1692.46,,infty -2022-08-05 15:04:00,1691.0,,infty -2022-08-05 15:05:00,1688.89,,infty -2022-08-05 15:06:00,1686.36,,infty -2022-08-05 15:07:00,1686.99,,infty -2022-08-05 15:08:00,1684.17,,infty -2022-08-05 15:09:00,1686.89,,infty -2022-08-05 15:10:00,1685.13,,infty -2022-08-05 15:11:00,1682.81,,infty -2022-08-05 15:12:00,1683.37,,infty -2022-08-05 15:13:00,1682.16,,infty -2022-08-05 15:14:00,1681.09,,open_close -2022-08-05 15:15:00,1683.42,,infty -2022-08-05 15:16:00,1682.82,,infty -2022-08-05 15:17:00,1678.43,,open_close -2022-08-05 15:18:00,1679.25,,open_close -2022-08-05 15:19:00,1680.22,,open_close -2022-08-05 15:20:00,1681.0,,open_close -2022-08-05 15:21:00,1679.52,,open_close -2022-08-05 15:22:00,1677.01,,open_close -2022-08-05 15:23:00,1676.97,,open_close -2022-08-05 15:24:00,1675.3,,open_close -2022-08-05 15:25:00,1671.22,,open_close -2022-08-05 15:26:00,1673.28,,open_close -2022-08-05 15:27:00,1668.84,,open_close -2022-08-05 15:28:00,1672.59,,open_close -2022-08-05 15:29:00,1669.23,,open_close -2022-08-05 15:30:00,1673.24,,open_close -2022-08-05 15:31:00,1676.31,,open_close -2022-08-05 15:32:00,1679.32,,open_close -2022-08-05 15:33:00,1677.58,,open_close -2022-08-05 15:34:00,1677.27,,open_close -2022-08-05 15:35:00,1674.87,,open_close -2022-08-05 15:36:00,1678.32,,open_close -2022-08-05 15:37:00,1676.43,,open_close -2022-08-05 15:38:00,1676.83,,open_close -2022-08-05 15:39:00,1675.78,,open_close -2022-08-05 15:40:00,1675.83,,open_close -2022-08-05 15:41:00,1672.95,,open_close -2022-08-05 15:42:00,1673.59,,open_close -2022-08-05 15:43:00,1674.48,,open_close -2022-08-05 15:44:00,1672.71,,open_close -2022-08-05 15:45:00,1676.53,,open_close -2022-08-05 15:46:00,1672.63,,open_close -2022-08-05 15:47:00,1673.95,,open_close -2022-08-05 15:48:00,1672.99,,open_close -2022-08-05 15:49:00,1674.05,,open_close -2022-08-05 15:50:00,1676.62,,open_close -2022-08-05 15:51:00,1676.38,,open_close -2022-08-05 15:52:00,1676.31,,open_close -2022-08-05 15:53:00,1677.4,,open_close -2022-08-05 15:54:00,1676.64,,open_close -2022-08-05 15:55:00,1676.78,,open_close -2022-08-05 15:56:00,1675.47,,open_close -2022-08-05 15:57:00,1676.17,,open_close -2022-08-05 15:58:00,1673.89,,open_close -2022-08-05 15:59:00,1670.51,,open_close -2022-08-05 16:00:00,1671.19,,open_close -2022-08-05 16:01:00,1672.67,,open_close -2022-08-05 16:02:00,1671.64,,open_close -2022-08-05 16:03:00,1673.1,,open_close -2022-08-05 16:04:00,1672.56,,open_close -2022-08-05 16:05:00,1674.79,,open_close -2022-08-05 16:06:00,1673.68,,open_close -2022-08-05 16:07:00,1671.75,,open_close -2022-08-05 16:08:00,1671.89,,open_close -2022-08-05 16:09:00,1670.98,,open_close -2022-08-05 16:10:00,1669.83,,open_close -2022-08-05 16:11:00,1671.11,,open_close -2022-08-05 16:12:00,1668.58,,open_close -2022-08-05 16:13:00,1665.68,,open_close -2022-08-05 16:14:00,1668.38,,open_close -2022-08-05 16:15:00,1671.6,,open_close -2022-08-05 16:16:00,1674.03,,open_close -2022-08-05 16:17:00,1673.48,,open_close -2022-08-05 16:18:00,1674.95,,open_close -2022-08-05 16:19:00,1673.89,,open_close -2022-08-05 16:20:00,1675.94,,open_close -2022-08-05 16:21:00,1673.91,,open_close -2022-08-05 16:22:00,1670.7,,open_close -2022-08-05 16:23:00,1667.18,,open_close -2022-08-05 16:24:00,1666.84,,open_close -2022-08-05 16:25:00,1669.52,,open_close -2022-08-05 16:26:00,1669.6,,open_close -2022-08-05 16:27:00,1667.15,,open_close -2022-08-05 16:28:00,1668.2,,open_close -2022-08-05 16:29:00,1668.34,,open_close -2022-08-05 16:30:00,1667.43,,open_close -2022-08-05 16:31:00,1660.73,,open_close -2022-08-05 16:32:00,1659.33,,open_close -2022-08-05 16:33:00,1659.15,,open_close -2022-08-05 16:34:00,1659.69,,open_close -2022-08-05 16:35:00,1660.81,,open_close -2022-08-05 16:36:00,1661.05,,open_close -2022-08-05 16:37:00,1662.45,,open_close -2022-08-05 16:38:00,1661.02,,open_close -2022-08-05 16:39:00,1661.12,,open_close -2022-08-05 16:40:00,1662.98,,open_close -2022-08-05 16:41:00,1663.64,,open_close -2022-08-05 16:42:00,1662.99,,open_close -2022-08-05 16:43:00,1665.06,,open_close -2022-08-05 16:44:00,1666.62,,open_close -2022-08-05 16:45:00,1669.59,,open_close -2022-08-05 16:46:00,1668.46,,open_close -2022-08-05 16:47:00,1667.54,,open_close -2022-08-05 16:48:00,1667.2,,open_close -2022-08-05 16:49:00,1668.74,,open_close -2022-08-05 16:50:00,1668.18,,open_close -2022-08-05 16:51:00,1667.36,,open_close -2022-08-05 16:52:00,1668.04,,open_close -2022-08-05 16:53:00,1668.42,,open_close -2022-08-05 16:54:00,1671.23,,open_close -2022-08-05 16:55:00,1671.75,,open_close -2022-08-05 16:56:00,1669.24,,open_close -2022-08-05 16:57:00,1671.06,,open_close -2022-08-05 16:58:00,1672.64,,open_close -2022-08-05 16:59:00,1673.39,,open_close -2022-08-05 17:00:00,1674.35,,open_close -2022-08-05 17:01:00,1670.04,,open_close -2022-08-05 17:02:00,1672.86,,open_close -2022-08-05 17:03:00,1673.71,,open_close -2022-08-05 17:04:00,1673.91,,open_close -2022-08-05 17:05:00,1675.11,,open_close -2022-08-05 17:06:00,1677.1,,open_close -2022-08-05 17:07:00,1678.69,,open_close -2022-08-05 17:08:00,1678.64,,open_close -2022-08-05 17:09:00,1679.47,,open_close -2022-08-05 17:10:00,1679.7,,open_close -2022-08-05 17:11:00,1682.3,,infty -2022-08-05 17:12:00,1683.22,,infty -2022-08-05 17:13:00,1680.12,,open_close -2022-08-05 17:14:00,1681.16,,open_close -2022-08-05 17:15:00,1681.42,,infty -2022-08-05 17:16:00,1678.89,,open_close -2022-08-05 17:17:00,1677.41,,open_close -2022-08-05 17:18:00,1673.0,,open_close -2022-08-05 17:19:00,1673.75,,open_close -2022-08-05 17:20:00,1670.2,,open_close -2022-08-05 17:21:00,1668.53,,open_close -2022-08-05 17:22:00,1671.52,,open_close -2022-08-05 17:23:00,1669.2,,open_close -2022-08-05 17:24:00,1671.81,,open_close -2022-08-05 17:25:00,1672.45,,open_close -2022-08-05 17:26:00,1673.54,,open_close -2022-08-05 17:27:00,1670.09,,open_close -2022-08-05 17:28:00,1670.06,,open_close -2022-08-05 17:29:00,1666.56,,open_close -2022-08-05 17:30:00,1670.9,,open_close -2022-08-05 17:31:00,1670.4,,open_close -2022-08-05 17:32:00,1669.79,,open_close -2022-08-05 17:33:00,1671.75,,open_close -2022-08-05 17:34:00,1673.25,,open_close -2022-08-05 17:35:00,1671.17,,open_close -2022-08-05 17:36:00,1671.62,,open_close -2022-08-05 17:37:00,1670.42,,open_close -2022-08-05 17:38:00,1669.65,,open_close -2022-08-05 17:39:00,1670.22,,open_close -2022-08-05 17:40:00,1669.12,,open_close -2022-08-05 17:41:00,1670.04,,open_close -2022-08-05 17:42:00,1671.41,,open_close -2022-08-05 17:43:00,1671.5,,open_close -2022-08-05 17:44:00,1669.17,,open_close -2022-08-05 17:45:00,1670.46,,open_close -2022-08-05 17:46:00,1671.46,,open_close -2022-08-05 17:47:00,1667.82,,open_close -2022-08-05 17:48:00,1666.86,,open_close -2022-08-05 17:49:00,1662.76,,open_close -2022-08-05 17:50:00,1661.21,,open_close -2022-08-05 17:51:00,1659.66,,open_close -2022-08-05 17:52:00,1663.18,,open_close -2022-08-05 17:53:00,1663.69,,open_close -2022-08-05 17:54:00,1663.41,,open_close -2022-08-05 17:55:00,1661.94,,open_close -2022-08-05 17:56:00,1663.6,,open_close -2022-08-05 17:57:00,1661.77,,open_close -2022-08-05 17:58:00,1662.19,,open_close -2022-08-05 17:59:00,1663.64,,open_close -2022-08-05 18:00:00,1664.21,,open_close -2022-08-05 18:01:00,1664.43,,open_close -2022-08-05 18:02:00,1669.02,,open_close -2022-08-05 18:03:00,1667.85,,open_close -2022-08-05 18:04:00,1668.9,,open_close -2022-08-05 18:05:00,1669.69,,open_close -2022-08-05 18:06:00,1672.27,,open_close -2022-08-05 18:07:00,1672.25,,open_close -2022-08-05 18:08:00,1670.31,,open_close -2022-08-05 18:09:00,1669.98,,open_close -2022-08-05 18:10:00,1668.81,,open_close -2022-08-05 18:11:00,1665.5,,open_close -2022-08-05 18:12:00,1666.77,,open_close -2022-08-05 18:13:00,1667.34,,open_close -2022-08-05 18:14:00,1666.04,,open_close -2022-08-05 18:15:00,1669.02,,open_close -2022-08-05 18:16:00,1669.46,,open_close -2022-08-05 18:17:00,1668.97,,open_close -2022-08-05 18:18:00,1669.55,,open_close -2022-08-05 18:19:00,1671.01,,open_close -2022-08-05 18:20:00,1670.53,,open_close -2022-08-05 18:21:00,1670.02,,open_close -2022-08-05 18:22:00,1670.93,,open_close -2022-08-05 18:23:00,1670.2,,open_close -2022-08-05 18:24:00,1670.27,,open_close -2022-08-05 18:25:00,1669.27,,open_close -2022-08-05 18:26:00,1666.77,,open_close -2022-08-05 18:27:00,1664.9,,open_close -2022-08-05 18:28:00,1666.81,,open_close -2022-08-05 18:29:00,1664.18,,open_close -2022-08-05 18:30:00,1666.05,,open_close -2022-08-05 18:31:00,1667.46,,open_close -2022-08-05 18:32:00,1666.17,,open_close -2022-08-05 18:33:00,1667.18,,open_close -2022-08-05 18:34:00,1668.5,,open_close -2022-08-05 18:35:00,1668.23,,open_close -2022-08-05 18:36:00,1667.96,,open_close -2022-08-05 18:37:00,1667.64,,open_close -2022-08-05 18:38:00,1667.84,,open_close -2022-08-05 18:39:00,1666.74,,open_close -2022-08-05 18:40:00,1665.89,,open_close -2022-08-05 18:41:00,1667.16,,open_close -2022-08-05 18:42:00,1666.41,,open_close -2022-08-05 18:43:00,1667.15,,open_close -2022-08-05 18:44:00,1668.47,,open_close -2022-08-05 18:45:00,1666.49,,open_close -2022-08-05 18:46:00,1668.48,,open_close -2022-08-05 18:47:00,1667.55,,open_close -2022-08-05 18:48:00,1667.29,,open_close -2022-08-05 18:49:00,1666.56,,open_close -2022-08-05 18:50:00,1665.42,,open_close -2022-08-05 18:51:00,1667.14,,open_close -2022-08-05 18:52:00,1666.67,,open_close -2022-08-05 18:53:00,1666.31,,open_close -2022-08-05 18:54:00,1665.66,,open_close -2022-08-05 18:55:00,1662.53,,open_close -2022-08-05 18:56:00,1661.86,,open_close -2022-08-05 18:57:00,1664.68,,open_close -2022-08-05 18:58:00,1666.4,,open_close -2022-08-05 18:59:00,1666.04,,open_close -2022-08-05 19:00:00,1667.78,,open_close -2022-08-05 19:01:00,1668.07,,open_close -2022-08-05 19:02:00,1667.59,,open_close -2022-08-05 19:03:00,1666.65,,open_close -2022-08-05 19:04:00,1666.98,,open_close -2022-08-05 19:05:00,1666.82,,open_close -2022-08-05 19:06:00,1665.95,,open_close -2022-08-05 19:07:00,1667.73,,open_close -2022-08-05 19:08:00,1667.68,,open_close -2022-08-05 19:09:00,1668.04,,open_close -2022-08-05 19:10:00,1670.27,,open_close -2022-08-05 19:11:00,1670.84,,open_close -2022-08-05 19:12:00,1673.25,,open_close -2022-08-05 19:13:00,1674.0,,open_close -2022-08-05 19:14:00,1674.49,,open_close -2022-08-05 19:15:00,1675.94,,open_close -2022-08-05 19:16:00,1674.77,,open_close -2022-08-05 19:17:00,1675.51,,open_close -2022-08-05 19:18:00,1677.53,,open_close -2022-08-05 19:19:00,1675.1,,open_close -2022-08-05 19:20:00,1673.9,,open_close -2022-08-05 19:21:00,1674.4,,open_close -2022-08-05 19:22:00,1674.0,,open_close -2022-08-05 19:23:00,1673.62,,open_close -2022-08-05 19:24:00,1673.2,,open_close -2022-08-05 19:25:00,1675.59,,open_close -2022-08-05 19:26:00,1675.0,,open_close -2022-08-05 19:27:00,1673.85,,open_close -2022-08-05 19:28:00,1675.22,,open_close -2022-08-05 19:29:00,1674.94,,open_close -2022-08-05 19:30:00,1674.38,,open_close -2022-08-05 19:31:00,1674.37,,open_close -2022-08-05 19:32:00,1674.19,,open_close -2022-08-05 19:33:00,1675.77,,open_close -2022-08-05 19:34:00,1677.69,,open_close -2022-08-05 19:35:00,1679.01,,open_close -2022-08-05 19:36:00,1678.43,,open_close -2022-08-05 19:37:00,1680.6,,open_close -2022-08-05 19:38:00,1679.86,,open_close -2022-08-05 19:39:00,1677.94,,open_close -2022-08-05 19:40:00,1678.93,,open_close -2022-08-05 19:41:00,1677.74,,open_close -2022-08-05 19:42:00,1678.46,,open_close -2022-08-05 19:43:00,1678.01,,open_close -2022-08-05 19:44:00,1680.33,,open_close -2022-08-05 19:45:00,1680.85,,open_close -2022-08-05 19:46:00,1682.94,,infty -2022-08-05 19:47:00,1681.04,,open_close -2022-08-05 19:48:00,1680.27,,open_close -2022-08-05 19:49:00,1680.47,,open_close -2022-08-05 19:50:00,1682.76,,infty -2022-08-05 19:51:00,1681.31,,infty -2022-08-05 19:52:00,1681.81,,infty -2022-08-05 19:53:00,1681.23,,infty -2022-08-05 19:54:00,1682.23,,infty -2022-08-05 19:55:00,1682.72,,infty -2022-08-05 19:56:00,1682.95,,infty -2022-08-05 19:57:00,1681.26,,infty -2022-08-05 19:58:00,1681.07,,open_close -2022-08-05 19:59:00,1681.28,,infty -2022-08-05 20:00:00,1680.16,,open_close -2022-08-05 20:01:00,1681.24,,infty -2022-08-05 20:02:00,1680.39,,open_close -2022-08-05 20:03:00,1678.78,,open_close -2022-08-05 20:04:00,1675.4,,open_close -2022-08-05 20:05:00,1676.41,,open_close -2022-08-05 20:06:00,1673.92,,open_close -2022-08-05 20:07:00,1671.33,,open_close -2022-08-05 20:08:00,1669.39,,open_close -2022-08-05 20:09:00,1667.77,,open_close -2022-08-05 20:10:00,1671.94,,open_close -2022-08-05 20:11:00,1672.52,,open_close -2022-08-05 20:12:00,1672.91,,open_close -2022-08-05 20:13:00,1672.01,,open_close -2022-08-05 20:14:00,1673.86,,open_close -2022-08-05 20:15:00,1674.43,,open_close -2022-08-05 20:16:00,1678.95,,open_close -2022-08-05 20:17:00,1679.39,,open_close -2022-08-05 20:18:00,1677.42,,open_close -2022-08-05 20:19:00,1677.71,,open_close -2022-08-05 20:20:00,1676.68,,open_close -2022-08-05 20:21:00,1677.21,,open_close -2022-08-05 20:22:00,1677.14,,open_close -2022-08-05 20:23:00,1677.85,,open_close -2022-08-05 20:24:00,1678.76,,open_close -2022-08-05 20:25:00,1678.66,,open_close -2022-08-05 20:26:00,1677.54,,open_close -2022-08-05 20:27:00,1678.02,,open_close -2022-08-05 20:28:00,1677.57,,open_close -2022-08-05 20:29:00,1677.12,,open_close -2022-08-05 20:30:00,1675.21,,open_close -2022-08-05 20:31:00,1676.28,,open_close -2022-08-05 20:32:00,1677.52,,open_close -2022-08-05 20:33:00,1677.37,,open_close -2022-08-05 20:34:00,1676.08,,open_close -2022-08-05 20:35:00,1676.79,,open_close -2022-08-05 20:36:00,1676.67,,open_close -2022-08-05 20:37:00,1675.78,,open_close -2022-08-05 20:38:00,1677.72,,open_close -2022-08-05 20:39:00,1677.72,,open_close -2022-08-05 20:40:00,1676.46,,open_close -2022-08-05 20:41:00,1677.14,,open_close -2022-08-05 20:42:00,1677.2,,open_close -2022-08-05 20:43:00,1676.77,,open_close -2022-08-05 20:44:00,1678.26,,open_close -2022-08-05 20:45:00,1679.59,,open_close -2022-08-05 20:46:00,1680.42,,open_close -2022-08-05 20:47:00,1678.67,,open_close -2022-08-05 20:48:00,1682.57,,infty -2022-08-05 20:49:00,1680.04,,open_close -2022-08-05 20:50:00,1681.64,,infty -2022-08-05 20:51:00,1680.8,,open_close -2022-08-05 20:52:00,1681.15,,open_close -2022-08-05 20:53:00,1680.95,,open_close -2022-08-05 20:54:00,1679.0,,open_close -2022-08-05 20:55:00,1677.85,,open_close -2022-08-05 20:56:00,1677.18,,open_close -2022-08-05 20:57:00,1678.6,,open_close -2022-08-05 20:58:00,1677.77,,open_close -2022-08-05 20:59:00,1679.69,,open_close -2022-08-05 21:00:00,1682.0,,infty -2022-08-05 21:01:00,1689.25,,infty -2022-08-05 21:02:00,1693.48,,infty -2022-08-05 21:03:00,1691.75,,infty -2022-08-05 21:04:00,1691.02,,infty -2022-08-05 21:05:00,1693.74,,infty -2022-08-05 21:06:00,1694.81,,infty -2022-08-05 21:07:00,1688.7,,infty -2022-08-05 21:08:00,1696.24,,infty -2022-08-05 21:09:00,1694.69,,infty -2022-08-05 21:10:00,1691.72,,infty -2022-08-05 21:11:00,1691.0,,infty -2022-08-05 21:12:00,1691.3,,infty -2022-08-05 21:13:00,1689.74,,infty -2022-08-05 21:14:00,1700.0,,infty -2022-08-05 21:15:00,1707.31,,infty -2022-08-05 21:16:00,1707.92,,infty -2022-08-05 21:17:00,1708.09,,infty -2022-08-05 21:18:00,1705.94,,infty -2022-08-05 21:19:00,1695.23,,infty -2022-08-05 21:20:00,1696.33,,infty -2022-08-05 21:21:00,1698.38,,infty -2022-08-05 21:22:00,1700.73,,infty -2022-08-05 21:23:00,1700.22,,infty -2022-08-05 21:24:00,1699.26,,infty -2022-08-05 21:25:00,1701.5,,infty -2022-08-05 21:26:00,1706.52,,infty -2022-08-05 21:27:00,1710.17,,infty -2022-08-05 21:28:00,1709.02,,infty -2022-08-05 21:29:00,1706.06,,infty -2022-08-05 21:30:00,1706.68,,infty -2022-08-05 21:31:00,1705.12,,infty -2022-08-05 21:32:00,1703.72,,infty -2022-08-05 21:33:00,1697.87,,infty -2022-08-05 21:34:00,1700.83,,infty -2022-08-05 21:35:00,1698.96,,infty -2022-08-05 21:36:00,1700.0,,infty -2022-08-05 21:37:00,1700.23,,infty -2022-08-05 21:38:00,1695.81,,infty -2022-08-05 21:39:00,1694.55,,infty -2022-08-05 21:40:00,1692.81,,infty -2022-08-05 21:41:00,1694.6,,infty -2022-08-05 21:42:00,1695.89,,infty -2022-08-05 21:43:00,1696.9,,infty -2022-08-05 21:44:00,1696.89,,infty -2022-08-05 21:45:00,1700.01,,infty -2022-08-05 21:46:00,1697.65,,infty -2022-08-05 21:47:00,1699.0,,infty -2022-08-05 21:48:00,1700.07,,infty -2022-08-05 21:49:00,1699.5,,infty -2022-08-05 21:50:00,1704.07,,infty 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23:13:00,1719.16,,infty -2022-08-05 23:14:00,1726.34,,infty -2022-08-05 23:15:00,1722.05,,infty -2022-08-05 23:16:00,1719.99,,infty -2022-08-05 23:17:00,1724.23,,infty -2022-08-05 23:18:00,1723.12,,infty -2022-08-05 23:19:00,1723.61,,infty -2022-08-05 23:20:00,1724.53,,infty -2022-08-05 23:21:00,1724.23,,infty -2022-08-05 23:22:00,1723.38,,infty -2022-08-05 23:23:00,1721.61,,infty -2022-08-05 23:24:00,1720.89,,infty -2022-08-05 23:25:00,1722.49,,infty -2022-08-05 23:26:00,1721.62,,infty -2022-08-05 23:27:00,1719.9,,infty -2022-08-05 23:28:00,1722.72,,infty -2022-08-05 23:29:00,1721.73,,infty -2022-08-05 23:30:00,1720.58,,infty -2022-08-05 23:31:00,1720.41,,infty -2022-08-05 23:32:00,1720.82,,infty -2022-08-05 23:33:00,1724.44,,infty -2022-08-05 23:34:00,1725.94,,infty -2022-08-05 23:35:00,1724.87,,infty -2022-08-05 23:36:00,1723.64,,infty -2022-08-05 23:37:00,1723.13,,infty -2022-08-05 23:38:00,1725.85,,infty -2022-08-05 23:39:00,1726.28,,infty -2022-08-05 23:40:00,1723.96,,infty 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00:08:00,1735.39,,infty -2022-08-06 00:09:00,1737.76,,infty -2022-08-06 00:10:00,1737.75,,infty -2022-08-06 00:11:00,1734.73,,infty -2022-08-06 00:12:00,1735.11,,infty -2022-08-06 00:13:00,1736.35,,infty -2022-08-06 00:14:00,1735.64,,infty -2022-08-06 00:15:00,1734.02,,infty -2022-08-06 00:16:00,1737.27,,infty -2022-08-06 00:17:00,1741.23,,infty -2022-08-06 00:18:00,1744.35,,infty -2022-08-06 00:19:00,1739.89,,infty -2022-08-06 00:20:00,1738.0,,infty -2022-08-06 00:21:00,1736.48,,infty -2022-08-06 00:22:00,1738.85,,infty -2022-08-06 00:23:00,1738.91,,infty -2022-08-06 00:24:00,1738.3,,infty -2022-08-06 00:25:00,1737.24,,infty -2022-08-06 00:26:00,1734.56,,infty -2022-08-06 00:27:00,1737.6,,infty -2022-08-06 00:28:00,1745.37,,infty -2022-08-06 00:29:00,1738.89,,infty -2022-08-06 00:30:00,1743.41,,infty -2022-08-06 00:31:00,1741.84,,infty -2022-08-06 00:32:00,1747.31,,infty -2022-08-06 00:33:00,1744.72,,infty -2022-08-06 00:34:00,1746.65,,infty -2022-08-06 00:35:00,1745.62,,infty 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03:48:00,1736.53,,infty -2022-08-06 03:49:00,1737.48,,infty -2022-08-06 03:50:00,1737.72,,infty -2022-08-06 03:51:00,1735.58,,infty -2022-08-06 03:52:00,1735.85,,infty -2022-08-06 03:53:00,1736.65,,infty -2022-08-06 03:54:00,1736.26,,infty -2022-08-06 03:55:00,1736.27,,infty -2022-08-06 03:56:00,1736.83,,infty -2022-08-06 03:57:00,1736.42,,infty -2022-08-06 03:58:00,1734.42,,infty -2022-08-06 03:59:00,1735.0,,infty -2022-08-06 04:00:00,1734.73,,infty -2022-08-06 04:01:00,1735.33,,infty -2022-08-06 04:02:00,1734.88,,infty -2022-08-06 04:03:00,1736.43,,infty -2022-08-06 04:04:00,1736.08,,infty -2022-08-06 04:05:00,1736.56,,infty -2022-08-06 04:06:00,1734.86,,infty -2022-08-06 04:07:00,1734.76,,infty -2022-08-06 04:08:00,1736.14,,infty -2022-08-06 04:09:00,1735.82,,infty -2022-08-06 04:10:00,1734.96,,infty -2022-08-06 04:11:00,1734.91,,infty -2022-08-06 04:12:00,1732.9,,infty -2022-08-06 04:13:00,1734.27,,infty -2022-08-06 04:14:00,1734.71,,infty -2022-08-06 04:15:00,1735.95,,infty 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04:43:00,1732.57,,infty -2022-08-06 04:44:00,1732.43,,infty -2022-08-06 04:45:00,1731.54,,infty -2022-08-06 04:46:00,1731.68,,infty -2022-08-06 04:47:00,1731.96,,infty -2022-08-06 04:48:00,1732.68,,infty -2022-08-06 04:49:00,1732.08,,infty -2022-08-06 04:50:00,1732.2,,infty -2022-08-06 04:51:00,1733.2,,infty -2022-08-06 04:52:00,1732.15,,infty -2022-08-06 04:53:00,1732.2,,infty -2022-08-06 04:54:00,1732.19,,infty -2022-08-06 04:55:00,1731.63,,infty -2022-08-06 04:56:00,1732.75,,infty -2022-08-06 04:57:00,1732.65,,infty -2022-08-06 04:58:00,1733.45,,infty -2022-08-06 04:59:00,1732.6,,infty -2022-08-06 05:00:00,1732.61,,infty -2022-08-06 05:01:00,1732.02,,infty -2022-08-06 05:02:00,1733.94,,infty -2022-08-06 05:03:00,1735.67,,infty -2022-08-06 05:04:00,1735.19,,infty -2022-08-06 05:05:00,1735.59,,infty -2022-08-06 05:06:00,1735.52,,infty -2022-08-06 05:07:00,1734.92,,infty -2022-08-06 05:08:00,1735.01,,infty -2022-08-06 05:09:00,1733.94,,infty -2022-08-06 05:10:00,1733.1,,infty 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06:06:00,1723.62,,infty -2022-08-06 06:07:00,1721.73,,infty -2022-08-06 06:08:00,1721.61,,infty -2022-08-06 06:09:00,1722.97,,infty -2022-08-06 06:10:00,1721.52,,infty -2022-08-06 06:11:00,1721.53,,infty -2022-08-06 06:12:00,1721.21,,infty -2022-08-06 06:13:00,1720.17,,infty -2022-08-06 06:14:00,1720.85,,infty -2022-08-06 06:15:00,1721.85,,infty -2022-08-06 06:16:00,1721.74,,infty -2022-08-06 06:17:00,1721.09,,infty -2022-08-06 06:18:00,1719.6,,infty -2022-08-06 06:19:00,1719.87,,infty -2022-08-06 06:20:00,1717.37,,infty -2022-08-06 06:21:00,1718.42,,infty -2022-08-06 06:22:00,1719.38,,infty -2022-08-06 06:23:00,1718.96,,infty -2022-08-06 06:24:00,1719.3,,infty -2022-08-06 06:25:00,1719.18,,infty -2022-08-06 06:26:00,1717.65,,infty -2022-08-06 06:27:00,1718.16,,infty -2022-08-06 06:28:00,1717.57,,infty -2022-08-06 06:29:00,1716.92,,infty -2022-08-06 06:30:00,1717.94,,infty -2022-08-06 06:31:00,1719.45,,infty -2022-08-06 06:32:00,1720.76,,infty -2022-08-06 06:33:00,1722.18,,infty 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07:01:00,1724.57,,infty -2022-08-06 07:02:00,1724.43,,infty -2022-08-06 07:03:00,1724.8,,infty -2022-08-06 07:04:00,1723.98,,infty -2022-08-06 07:05:00,1723.96,,infty -2022-08-06 07:06:00,1723.87,,infty -2022-08-06 07:07:00,1725.29,,infty -2022-08-06 07:08:00,1723.27,,infty -2022-08-06 07:09:00,1723.95,,infty -2022-08-06 07:10:00,1723.59,,infty -2022-08-06 07:11:00,1723.54,,infty -2022-08-06 07:12:00,1721.12,,infty -2022-08-06 07:13:00,1717.87,,infty -2022-08-06 07:14:00,1718.27,,infty -2022-08-06 07:15:00,1719.97,,infty -2022-08-06 07:16:00,1721.22,,infty -2022-08-06 07:17:00,1721.26,,infty -2022-08-06 07:18:00,1721.63,,infty -2022-08-06 07:19:00,1721.96,,infty -2022-08-06 07:20:00,1722.11,,infty -2022-08-06 07:21:00,1721.5,,infty -2022-08-06 07:22:00,1722.33,,infty -2022-08-06 07:23:00,1722.4,,infty -2022-08-06 07:24:00,1722.53,,infty -2022-08-06 07:25:00,1722.58,,infty -2022-08-06 07:26:00,1721.96,,infty -2022-08-06 07:27:00,1721.83,,infty -2022-08-06 07:28:00,1721.39,,infty 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07:56:00,1721.06,,infty -2022-08-06 07:57:00,1720.25,,infty -2022-08-06 07:58:00,1720.69,,infty -2022-08-06 07:59:00,1720.93,,infty -2022-08-06 08:00:00,1722.85,,infty -2022-08-06 08:01:00,1723.54,,infty -2022-08-06 08:02:00,1721.55,,infty -2022-08-06 08:03:00,1720.36,,infty -2022-08-06 08:04:00,1720.19,,infty -2022-08-06 08:05:00,1719.34,,infty -2022-08-06 08:06:00,1718.93,,infty -2022-08-06 08:07:00,1721.4,,infty -2022-08-06 08:08:00,1722.51,,infty -2022-08-06 08:09:00,1721.98,,infty -2022-08-06 08:10:00,1722.47,,infty -2022-08-06 08:11:00,1722.15,,infty -2022-08-06 08:12:00,1722.76,,infty -2022-08-06 08:13:00,1721.95,,infty -2022-08-06 08:14:00,1721.0,,infty -2022-08-06 08:15:00,1720.99,,infty -2022-08-06 08:16:00,1721.59,,infty -2022-08-06 08:17:00,1723.93,,infty -2022-08-06 08:18:00,1724.47,,infty -2022-08-06 08:19:00,1723.27,,infty -2022-08-06 08:20:00,1723.76,,infty -2022-08-06 08:21:00,1723.57,,infty -2022-08-06 08:22:00,1723.04,,infty -2022-08-06 08:23:00,1723.1,,infty 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08:51:00,1710.43,,infty -2022-08-06 08:52:00,1712.62,,infty -2022-08-06 08:53:00,1711.34,,infty -2022-08-06 08:54:00,1711.34,,infty -2022-08-06 08:55:00,1710.47,,infty -2022-08-06 08:56:00,1708.47,,infty -2022-08-06 08:57:00,1708.59,,infty -2022-08-06 08:58:00,1707.96,,infty -2022-08-06 08:59:00,1709.0,,infty -2022-08-06 09:00:00,1708.05,,infty -2022-08-06 09:01:00,1710.39,,infty -2022-08-06 09:02:00,1709.62,,infty -2022-08-06 09:03:00,1710.4,,infty -2022-08-06 09:04:00,1711.16,,infty -2022-08-06 09:05:00,1711.16,,infty -2022-08-06 09:06:00,1711.72,,infty -2022-08-06 09:07:00,1711.15,,infty -2022-08-06 09:08:00,1710.86,,infty -2022-08-06 09:09:00,1709.29,,infty -2022-08-06 09:10:00,1707.05,,infty -2022-08-06 09:11:00,1707.7,,infty -2022-08-06 09:12:00,1707.73,,infty -2022-08-06 09:13:00,1708.58,,infty -2022-08-06 09:14:00,1709.48,,infty -2022-08-06 09:15:00,1710.1,,infty -2022-08-06 09:16:00,1711.55,,infty -2022-08-06 09:17:00,1712.42,,infty -2022-08-06 09:18:00,1712.93,,infty 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09:46:00,1712.36,,infty -2022-08-06 09:47:00,1712.76,,infty -2022-08-06 09:48:00,1713.48,,infty -2022-08-06 09:49:00,1713.37,,infty -2022-08-06 09:50:00,1712.6,,infty -2022-08-06 09:51:00,1713.48,,infty -2022-08-06 09:52:00,1713.77,,infty -2022-08-06 09:53:00,1714.17,,infty -2022-08-06 09:54:00,1713.43,,infty -2022-08-06 09:55:00,1713.55,,infty -2022-08-06 09:56:00,1713.46,,infty -2022-08-06 09:57:00,1714.2,,infty -2022-08-06 09:58:00,1713.71,,infty -2022-08-06 09:59:00,1712.69,,infty -2022-08-06 10:00:00,1713.21,,infty -2022-08-06 10:01:00,1713.98,,infty -2022-08-06 10:02:00,1713.35,,infty -2022-08-06 10:03:00,1713.65,,infty -2022-08-06 10:04:00,1714.31,,infty -2022-08-06 10:05:00,1714.99,,infty -2022-08-06 10:06:00,1714.82,,infty -2022-08-06 10:07:00,1714.49,,infty -2022-08-06 10:08:00,1714.03,,infty -2022-08-06 10:09:00,1713.02,,infty -2022-08-06 10:10:00,1713.83,,infty -2022-08-06 10:11:00,1713.68,,infty -2022-08-06 10:12:00,1714.86,,infty -2022-08-06 10:13:00,1714.18,,infty 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10:41:00,1720.79,,infty -2022-08-06 10:42:00,1720.37,,infty -2022-08-06 10:43:00,1721.43,,infty -2022-08-06 10:44:00,1721.95,,infty -2022-08-06 10:45:00,1719.78,,infty -2022-08-06 10:46:00,1719.04,,infty -2022-08-06 10:47:00,1718.78,,infty -2022-08-06 10:48:00,1719.0,,infty -2022-08-06 10:49:00,1719.35,,infty -2022-08-06 10:50:00,1719.97,,infty -2022-08-06 10:51:00,1719.49,,infty -2022-08-06 10:52:00,1719.82,,infty -2022-08-06 10:53:00,1719.51,,infty -2022-08-06 10:54:00,1719.04,,infty -2022-08-06 10:55:00,1719.36,,infty -2022-08-06 10:56:00,1719.01,,infty -2022-08-06 10:57:00,1718.64,,infty -2022-08-06 10:58:00,1718.65,,infty -2022-08-06 10:59:00,1716.19,,infty -2022-08-06 11:00:00,1717.16,,infty -2022-08-06 11:01:00,1718.3,,infty -2022-08-06 11:02:00,1717.95,,infty -2022-08-06 11:03:00,1719.59,,infty -2022-08-06 11:04:00,1720.51,,infty -2022-08-06 11:05:00,1719.69,,infty -2022-08-06 11:06:00,1718.91,,infty -2022-08-06 11:07:00,1718.85,,infty -2022-08-06 11:08:00,1719.32,,infty 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11:36:00,1719.75,,infty -2022-08-06 11:37:00,1720.17,,infty -2022-08-06 11:38:00,1719.44,,infty -2022-08-06 11:39:00,1719.49,,infty -2022-08-06 11:40:00,1719.59,,infty -2022-08-06 11:41:00,1720.65,,infty -2022-08-06 11:42:00,1718.66,,infty -2022-08-06 11:43:00,1718.33,,infty -2022-08-06 11:44:00,1718.85,,infty -2022-08-06 11:45:00,1717.6,,infty -2022-08-06 11:46:00,1717.23,,infty -2022-08-06 11:47:00,1715.08,,infty -2022-08-06 11:48:00,1714.9,,infty -2022-08-06 11:49:00,1715.14,,infty -2022-08-06 11:50:00,1715.54,,infty -2022-08-06 11:51:00,1715.5,,infty -2022-08-06 11:52:00,1715.53,,infty -2022-08-06 11:53:00,1713.57,,infty -2022-08-06 11:54:00,1713.03,,infty -2022-08-06 11:55:00,1713.17,,infty -2022-08-06 11:56:00,1713.3,,infty -2022-08-06 11:57:00,1713.84,,infty -2022-08-06 11:58:00,1713.35,,infty -2022-08-06 11:59:00,1712.37,,infty -2022-08-06 12:00:00,1712.82,,infty -2022-08-06 12:01:00,1713.61,,infty -2022-08-06 12:02:00,1714.99,,infty -2022-08-06 12:03:00,1715.07,,infty 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12:31:00,1714.6,,infty -2022-08-06 12:32:00,1713.83,,infty -2022-08-06 12:33:00,1713.98,,infty -2022-08-06 12:34:00,1713.37,,infty -2022-08-06 12:35:00,1713.27,,infty -2022-08-06 12:36:00,1714.04,,infty -2022-08-06 12:37:00,1714.22,,infty -2022-08-06 12:38:00,1714.66,,infty -2022-08-06 12:39:00,1713.97,,infty -2022-08-06 12:40:00,1712.66,,infty -2022-08-06 12:41:00,1712.64,,infty -2022-08-06 12:42:00,1712.01,,infty -2022-08-06 12:43:00,1711.11,,infty -2022-08-06 12:44:00,1710.29,,infty -2022-08-06 12:45:00,1709.57,,infty -2022-08-06 12:46:00,1710.99,,infty -2022-08-06 12:47:00,1711.36,,infty -2022-08-06 12:48:00,1712.99,,infty -2022-08-06 12:49:00,1712.37,,infty -2022-08-06 12:50:00,1712.06,,infty -2022-08-06 12:51:00,1711.02,,infty -2022-08-06 12:52:00,1711.03,,infty -2022-08-06 12:53:00,1711.01,,infty -2022-08-06 12:54:00,1711.45,,infty -2022-08-06 12:55:00,1712.51,,infty -2022-08-06 12:56:00,1713.49,,infty -2022-08-06 12:57:00,1713.04,,infty -2022-08-06 12:58:00,1711.96,,infty 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13:26:00,1717.12,,infty -2022-08-06 13:27:00,1717.18,,infty -2022-08-06 13:28:00,1717.31,,infty -2022-08-06 13:29:00,1716.37,,infty -2022-08-06 13:30:00,1713.26,,infty -2022-08-06 13:31:00,1712.57,,infty -2022-08-06 13:32:00,1712.49,,infty -2022-08-06 13:33:00,1712.46,,infty -2022-08-06 13:34:00,1714.37,,infty -2022-08-06 13:35:00,1714.47,,infty -2022-08-06 13:36:00,1715.0,,infty -2022-08-06 13:37:00,1714.13,,infty -2022-08-06 13:38:00,1713.03,,infty -2022-08-06 13:39:00,1713.32,,infty -2022-08-06 13:40:00,1713.21,,infty -2022-08-06 13:41:00,1713.72,,infty -2022-08-06 13:42:00,1712.81,,infty -2022-08-06 13:43:00,1713.37,,infty -2022-08-06 13:44:00,1714.53,,infty -2022-08-06 13:45:00,1713.74,,infty -2022-08-06 13:46:00,1714.3,,infty -2022-08-06 13:47:00,1713.06,,infty -2022-08-06 13:48:00,1712.05,,infty -2022-08-06 13:49:00,1712.5,,infty -2022-08-06 13:50:00,1713.01,,infty -2022-08-06 13:51:00,1711.9,,infty -2022-08-06 13:52:00,1712.44,,infty -2022-08-06 13:53:00,1711.62,,infty 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14:21:00,1714.06,,infty -2022-08-06 14:22:00,1713.75,,infty -2022-08-06 14:23:00,1711.84,,infty -2022-08-06 14:24:00,1713.35,,infty -2022-08-06 14:25:00,1712.81,,infty -2022-08-06 14:26:00,1714.33,,infty -2022-08-06 14:27:00,1713.32,,infty -2022-08-06 14:28:00,1713.76,,infty -2022-08-06 14:29:00,1713.72,,infty -2022-08-06 14:30:00,1711.58,,infty -2022-08-06 14:31:00,1712.28,,infty -2022-08-06 14:32:00,1712.86,,infty -2022-08-06 14:33:00,1712.57,,infty -2022-08-06 14:34:00,1713.55,,infty -2022-08-06 14:35:00,1712.33,,infty -2022-08-06 14:36:00,1712.86,,infty -2022-08-06 14:37:00,1712.79,,infty -2022-08-06 14:38:00,1712.23,,infty -2022-08-06 14:39:00,1713.2,,infty -2022-08-06 14:40:00,1713.61,,infty -2022-08-06 14:41:00,1713.69,,infty -2022-08-06 14:42:00,1713.22,,infty -2022-08-06 14:43:00,1713.66,,infty -2022-08-06 14:44:00,1714.31,,infty -2022-08-06 14:45:00,1714.89,,infty -2022-08-06 14:46:00,1715.04,,infty -2022-08-06 14:47:00,1713.99,,infty -2022-08-06 14:48:00,1714.35,,infty 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15:16:00,1714.69,,infty -2022-08-06 15:17:00,1713.41,,infty -2022-08-06 15:18:00,1713.4,,infty -2022-08-06 15:19:00,1713.38,,infty -2022-08-06 15:20:00,1714.12,,infty -2022-08-06 15:21:00,1714.35,,infty -2022-08-06 15:22:00,1714.72,,infty -2022-08-06 15:23:00,1715.09,,infty -2022-08-06 15:24:00,1714.98,,infty -2022-08-06 15:25:00,1715.19,,infty -2022-08-06 15:26:00,1715.35,,infty -2022-08-06 15:27:00,1714.99,,infty -2022-08-06 15:28:00,1713.32,,infty -2022-08-06 15:29:00,1712.36,,infty -2022-08-06 15:30:00,1711.34,,infty -2022-08-06 15:31:00,1710.44,,infty -2022-08-06 15:32:00,1710.63,,infty -2022-08-06 15:33:00,1711.71,,infty -2022-08-06 15:34:00,1711.51,,infty -2022-08-06 15:35:00,1711.77,,infty -2022-08-06 15:36:00,1710.96,,infty -2022-08-06 15:37:00,1714.01,,infty -2022-08-06 15:38:00,1713.84,,infty -2022-08-06 15:39:00,1715.17,,infty -2022-08-06 15:40:00,1714.55,,infty -2022-08-06 15:41:00,1714.51,,infty -2022-08-06 15:42:00,1714.58,,infty -2022-08-06 15:43:00,1715.06,,infty 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16:11:00,1718.99,,infty -2022-08-06 16:12:00,1718.36,,infty -2022-08-06 16:13:00,1717.2,,infty -2022-08-06 16:14:00,1715.62,,infty -2022-08-06 16:15:00,1714.46,,infty -2022-08-06 16:16:00,1716.03,,infty -2022-08-06 16:17:00,1717.91,,infty -2022-08-06 16:18:00,1718.01,,infty -2022-08-06 16:19:00,1718.97,,infty -2022-08-06 16:20:00,1717.75,,infty -2022-08-06 16:21:00,1717.19,,infty -2022-08-06 16:22:00,1716.13,,infty -2022-08-06 16:23:00,1716.48,,infty -2022-08-06 16:24:00,1716.14,,infty -2022-08-06 16:25:00,1717.11,,infty -2022-08-06 16:26:00,1717.08,,infty -2022-08-06 16:27:00,1716.9,,infty -2022-08-06 16:28:00,1716.52,,infty -2022-08-06 16:29:00,1717.3,,infty -2022-08-06 16:30:00,1716.3,,infty -2022-08-06 16:31:00,1717.1,,infty -2022-08-06 16:32:00,1716.82,,infty -2022-08-06 16:33:00,1716.83,,infty -2022-08-06 16:34:00,1715.13,,infty -2022-08-06 16:35:00,1714.22,,infty -2022-08-06 16:36:00,1714.95,,infty -2022-08-06 16:37:00,1715.16,,infty -2022-08-06 16:38:00,1713.96,,infty 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17:06:00,1693.1,,infty -2022-08-06 17:07:00,1694.99,,infty -2022-08-06 17:08:00,1694.99,,infty -2022-08-06 17:09:00,1694.87,,infty -2022-08-06 17:10:00,1694.97,,infty -2022-08-06 17:11:00,1696.61,,infty -2022-08-06 17:12:00,1697.15,,infty -2022-08-06 17:13:00,1697.18,,infty -2022-08-06 17:14:00,1697.52,,infty -2022-08-06 17:15:00,1696.74,,infty -2022-08-06 17:16:00,1700.0,,infty -2022-08-06 17:17:00,1704.65,,infty -2022-08-06 17:18:00,1704.75,,infty -2022-08-06 17:19:00,1710.8,,infty -2022-08-06 17:20:00,1710.08,,infty -2022-08-06 17:21:00,1709.41,,infty -2022-08-06 17:22:00,1708.17,,infty -2022-08-06 17:23:00,1707.14,,infty -2022-08-06 17:24:00,1707.72,,infty -2022-08-06 17:25:00,1708.7,,infty -2022-08-06 17:26:00,1709.06,,infty -2022-08-06 17:27:00,1709.04,,infty -2022-08-06 17:28:00,1709.79,,infty -2022-08-06 17:29:00,1710.19,,infty -2022-08-06 17:30:00,1712.14,,infty -2022-08-06 17:31:00,1712.25,,infty -2022-08-06 17:32:00,1710.54,,infty -2022-08-06 17:33:00,1713.29,,infty 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18:01:00,1703.11,,infty -2022-08-06 18:02:00,1703.78,,infty -2022-08-06 18:03:00,1705.13,,infty -2022-08-06 18:04:00,1704.13,,infty -2022-08-06 18:05:00,1703.79,,infty -2022-08-06 18:06:00,1703.66,,infty -2022-08-06 18:07:00,1705.22,,infty -2022-08-06 18:08:00,1705.9,,infty -2022-08-06 18:09:00,1705.6,,infty -2022-08-06 18:10:00,1705.1,,infty -2022-08-06 18:11:00,1705.86,,infty -2022-08-06 18:12:00,1706.57,,infty -2022-08-06 18:13:00,1707.29,,infty -2022-08-06 18:14:00,1707.3,,infty -2022-08-06 18:15:00,1705.86,,infty -2022-08-06 18:16:00,1705.74,,infty -2022-08-06 18:17:00,1707.69,,infty -2022-08-06 18:18:00,1708.95,,infty -2022-08-06 18:19:00,1709.61,,infty -2022-08-06 18:20:00,1709.65,,infty -2022-08-06 18:21:00,1709.44,,infty -2022-08-06 18:22:00,1710.59,,infty -2022-08-06 18:23:00,1712.02,,infty -2022-08-06 18:24:00,1711.64,,infty -2022-08-06 18:25:00,1712.34,,infty -2022-08-06 18:26:00,1712.03,,infty -2022-08-06 18:27:00,1711.02,,infty -2022-08-06 18:28:00,1709.95,,infty 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18:56:00,1713.84,,infty -2022-08-06 18:57:00,1715.38,,infty -2022-08-06 18:58:00,1714.9,,infty -2022-08-06 18:59:00,1715.62,,infty -2022-08-06 19:00:00,1716.77,,infty -2022-08-06 19:01:00,1715.65,,infty -2022-08-06 19:02:00,1716.46,,infty -2022-08-06 19:03:00,1715.45,,infty -2022-08-06 19:04:00,1715.51,,infty -2022-08-06 19:05:00,1716.27,,infty -2022-08-06 19:06:00,1716.36,,infty -2022-08-06 19:07:00,1717.22,,infty -2022-08-06 19:08:00,1716.02,,infty -2022-08-06 19:09:00,1716.26,,infty -2022-08-06 19:10:00,1714.65,,infty -2022-08-06 19:11:00,1712.24,,infty -2022-08-06 19:12:00,1714.88,,infty -2022-08-06 19:13:00,1714.61,,infty -2022-08-06 19:14:00,1715.0,,infty -2022-08-06 19:15:00,1714.03,,infty -2022-08-06 19:16:00,1713.96,,infty -2022-08-06 19:17:00,1713.02,,infty -2022-08-06 19:18:00,1712.87,,infty -2022-08-06 19:19:00,1713.2,,infty -2022-08-06 19:20:00,1710.16,,infty -2022-08-06 19:21:00,1711.97,,infty -2022-08-06 19:22:00,1711.95,,infty -2022-08-06 19:23:00,1714.35,,infty 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19:51:00,1716.54,,infty -2022-08-06 19:52:00,1716.94,,infty -2022-08-06 19:53:00,1715.07,,infty -2022-08-06 19:54:00,1715.31,,infty -2022-08-06 19:55:00,1715.21,,infty -2022-08-06 19:56:00,1714.89,,infty -2022-08-06 19:57:00,1714.89,,infty -2022-08-06 19:58:00,1716.4,,infty -2022-08-06 19:59:00,1714.95,,infty -2022-08-06 20:00:00,1716.65,,infty -2022-08-06 20:01:00,1716.25,,infty -2022-08-06 20:02:00,1714.2,,infty -2022-08-06 20:03:00,1713.26,,infty -2022-08-06 20:04:00,1712.82,,infty -2022-08-06 20:05:00,1712.85,,infty -2022-08-06 20:06:00,1712.68,,infty -2022-08-06 20:07:00,1712.71,,infty -2022-08-06 20:08:00,1712.47,,infty -2022-08-06 20:09:00,1712.01,,infty -2022-08-06 20:10:00,1712.66,,infty -2022-08-06 20:11:00,1712.29,,infty -2022-08-06 20:12:00,1712.78,,infty -2022-08-06 20:13:00,1712.24,,infty -2022-08-06 20:14:00,1712.11,,infty -2022-08-06 20:15:00,1710.05,,infty -2022-08-06 20:16:00,1709.11,,infty -2022-08-06 20:17:00,1709.6,,infty -2022-08-06 20:18:00,1709.16,,infty 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20:46:00,1709.31,,infty -2022-08-06 20:47:00,1709.35,,infty -2022-08-06 20:48:00,1710.33,,infty -2022-08-06 20:49:00,1710.79,,infty -2022-08-06 20:50:00,1710.24,,infty -2022-08-06 20:51:00,1710.83,,infty -2022-08-06 20:52:00,1711.69,,infty -2022-08-06 20:53:00,1714.31,,infty -2022-08-06 20:54:00,1714.39,,infty -2022-08-06 20:55:00,1714.07,,infty -2022-08-06 20:56:00,1715.35,,infty -2022-08-06 20:57:00,1714.8,,infty -2022-08-06 20:58:00,1714.58,,infty -2022-08-06 20:59:00,1715.42,,infty -2022-08-06 21:00:00,1715.91,,infty -2022-08-06 21:01:00,1715.62,,infty -2022-08-06 21:02:00,1715.42,,infty -2022-08-06 21:03:00,1714.67,,infty -2022-08-06 21:04:00,1715.22,,infty -2022-08-06 21:05:00,1717.42,,infty -2022-08-06 21:06:00,1717.2,,infty -2022-08-06 21:07:00,1718.02,,infty -2022-08-06 21:08:00,1717.0,,infty -2022-08-06 21:09:00,1717.82,,infty -2022-08-06 21:10:00,1717.53,,infty -2022-08-06 21:11:00,1719.12,,infty -2022-08-06 21:12:00,1718.06,,infty -2022-08-06 21:13:00,1717.5,,infty -2022-08-06 21:14:00,1717.66,,infty -2022-08-06 21:15:00,1717.55,,infty -2022-08-06 21:16:00,1717.01,,infty -2022-08-06 21:17:00,1716.72,,infty -2022-08-06 21:18:00,1716.79,,infty -2022-08-06 21:19:00,1716.94,,infty -2022-08-06 21:20:00,1717.3,,infty -2022-08-06 21:21:00,1716.68,,infty -2022-08-06 21:22:00,1717.2,,infty -2022-08-06 21:23:00,1715.86,,infty -2022-08-06 21:24:00,1715.02,,infty -2022-08-06 21:25:00,1714.6,,infty -2022-08-06 21:26:00,1715.4,,infty -2022-08-06 21:27:00,1715.22,,infty -2022-08-06 21:28:00,1716.07,,infty -2022-08-06 21:29:00,1715.79,,infty -2022-08-06 21:30:00,1716.28,,infty -2022-08-06 21:31:00,1715.57,,infty -2022-08-06 21:32:00,1715.4,,infty -2022-08-06 21:33:00,1716.22,,infty -2022-08-06 21:34:00,1715.8,,infty -2022-08-06 21:35:00,1715.55,,infty -2022-08-06 21:36:00,1715.49,,infty -2022-08-06 21:37:00,1715.2,,infty -2022-08-06 21:38:00,1715.15,,infty -2022-08-06 21:39:00,1714.9,,infty -2022-08-06 21:40:00,1715.08,,infty -2022-08-06 21:41:00,1715.09,,infty -2022-08-06 21:42:00,1715.55,,infty -2022-08-06 21:43:00,1715.38,,infty -2022-08-06 21:44:00,1715.58,,infty -2022-08-06 21:45:00,1715.44,,infty -2022-08-06 21:46:00,1715.58,,infty -2022-08-06 21:47:00,1716.02,,infty -2022-08-06 21:48:00,1716.71,,infty -2022-08-06 21:49:00,1718.46,,infty -2022-08-06 21:50:00,1717.46,,infty -2022-08-06 21:51:00,1717.18,,infty -2022-08-06 21:52:00,1717.77,,infty -2022-08-06 21:53:00,1717.58,,infty -2022-08-06 21:54:00,1717.68,,infty -2022-08-06 21:55:00,1717.18,,infty -2022-08-06 21:56:00,1717.86,,infty -2022-08-06 21:57:00,1717.59,,infty -2022-08-06 21:58:00,1717.47,,infty -2022-08-06 21:59:00,1717.92,,infty -2022-08-06 22:00:00,1716.04,,infty -2022-08-06 22:01:00,1716.96,,infty -2022-08-06 22:02:00,1716.88,,infty -2022-08-06 22:03:00,1719.22,,infty -2022-08-06 22:04:00,1721.65,,infty -2022-08-06 22:05:00,1717.65,,infty -2022-08-06 22:06:00,1715.83,,infty -2022-08-06 22:07:00,1715.14,,infty -2022-08-06 22:08:00,1715.99,,infty -2022-08-06 22:09:00,1716.34,,infty -2022-08-06 22:10:00,1717.61,,infty -2022-08-06 22:11:00,1718.63,,infty -2022-08-06 22:12:00,1718.47,,infty -2022-08-06 22:13:00,1718.21,,infty -2022-08-06 22:14:00,1716.61,,infty -2022-08-06 22:15:00,1714.51,,infty -2022-08-06 22:16:00,1714.04,,infty -2022-08-06 22:17:00,1714.12,,infty -2022-08-06 22:18:00,1714.12,,infty -2022-08-06 22:19:00,1711.33,,infty -2022-08-06 22:20:00,1711.4,,infty -2022-08-06 22:21:00,1712.22,,infty -2022-08-06 22:22:00,1713.27,,infty -2022-08-06 22:23:00,1712.73,,infty -2022-08-06 22:24:00,1712.68,,infty -2022-08-06 22:25:00,1712.15,,infty -2022-08-06 22:26:00,1713.63,,infty -2022-08-06 22:27:00,1713.47,,infty -2022-08-06 22:28:00,1712.27,,infty -2022-08-06 22:29:00,1713.21,,infty -2022-08-06 22:30:00,1712.57,,infty -2022-08-06 22:31:00,1711.85,,infty -2022-08-06 22:32:00,1712.54,,infty -2022-08-06 22:33:00,1711.96,,infty -2022-08-06 22:34:00,1712.96,,infty -2022-08-06 22:35:00,1712.17,,infty -2022-08-06 22:36:00,1714.92,,infty -2022-08-06 22:37:00,1714.78,,infty -2022-08-06 22:38:00,1714.88,,infty -2022-08-06 22:39:00,1714.2,,infty -2022-08-06 22:40:00,1714.17,,infty -2022-08-06 22:41:00,1713.69,,infty -2022-08-06 22:42:00,1713.08,,infty -2022-08-06 22:43:00,1713.42,,infty -2022-08-06 22:44:00,1713.25,,infty -2022-08-06 22:45:00,1712.73,,infty -2022-08-06 22:46:00,1713.35,,infty -2022-08-06 22:47:00,1712.93,,infty -2022-08-06 22:48:00,1714.51,,infty -2022-08-06 22:49:00,1714.29,,infty -2022-08-06 22:50:00,1712.95,,infty -2022-08-06 22:51:00,1712.96,,infty -2022-08-06 22:52:00,1712.81,,infty -2022-08-06 22:53:00,1712.9,,infty -2022-08-06 22:54:00,1713.19,,infty -2022-08-06 22:55:00,1713.79,,infty -2022-08-06 22:56:00,1714.71,,infty -2022-08-06 22:57:00,1714.93,,infty -2022-08-06 22:58:00,1712.77,,infty -2022-08-06 22:59:00,1713.47,,infty -2022-08-06 23:00:00,1712.2,,infty -2022-08-06 23:01:00,1711.49,,infty -2022-08-06 23:02:00,1710.12,,infty -2022-08-06 23:03:00,1710.3,,infty -2022-08-06 23:04:00,1710.49,,infty -2022-08-06 23:05:00,1710.4,,infty -2022-08-06 23:06:00,1710.89,,infty -2022-08-06 23:07:00,1710.98,,infty -2022-08-06 23:08:00,1712.05,,infty -2022-08-06 23:09:00,1712.24,,infty -2022-08-06 23:10:00,1712.39,,infty -2022-08-06 23:11:00,1712.71,,infty -2022-08-06 23:12:00,1713.83,,infty -2022-08-06 23:13:00,1711.66,,infty -2022-08-06 23:14:00,1711.46,,infty -2022-08-06 23:15:00,1710.8,,infty -2022-08-06 23:16:00,1708.84,,infty -2022-08-06 23:17:00,1709.39,,infty -2022-08-06 23:18:00,1707.01,,infty -2022-08-06 23:19:00,1700.56,,infty -2022-08-06 23:20:00,1698.35,,infty -2022-08-06 23:21:00,1692.53,,infty -2022-08-06 23:22:00,1693.17,,infty -2022-08-06 23:23:00,1692.0,,infty -2022-08-06 23:24:00,1692.4,,infty -2022-08-06 23:25:00,1692.61,,infty -2022-08-06 23:26:00,1690.31,,infty -2022-08-06 23:27:00,1693.01,,infty -2022-08-06 23:28:00,1694.73,,infty -2022-08-06 23:29:00,1693.98,,infty -2022-08-06 23:30:00,1694.75,,infty -2022-08-06 23:31:00,1696.01,,infty -2022-08-06 23:32:00,1694.98,,infty -2022-08-06 23:33:00,1694.86,,infty -2022-08-06 23:34:00,1692.53,,infty -2022-08-06 23:35:00,1696.17,,infty -2022-08-06 23:36:00,1695.02,,infty -2022-08-06 23:37:00,1695.77,,infty -2022-08-06 23:38:00,1697.98,,infty -2022-08-06 23:39:00,1699.64,,infty -2022-08-06 23:40:00,1700.38,,infty -2022-08-06 23:41:00,1700.35,,infty -2022-08-06 23:42:00,1698.3,,infty -2022-08-06 23:43:00,1697.57,,infty -2022-08-06 23:44:00,1695.46,,infty -2022-08-06 23:45:00,1694.71,,infty -2022-08-06 23:46:00,1695.62,,infty -2022-08-06 23:47:00,1695.34,,infty -2022-08-06 23:48:00,1695.61,,infty -2022-08-06 23:49:00,1694.78,,infty -2022-08-06 23:50:00,1693.85,,infty -2022-08-06 23:51:00,1693.16,,infty -2022-08-06 23:52:00,1690.35,,infty -2022-08-06 23:53:00,1691.61,,infty -2022-08-06 23:54:00,1689.86,,infty -2022-08-06 23:55:00,1689.58,,infty -2022-08-06 23:56:00,1691.06,,infty -2022-08-06 23:57:00,1690.43,,infty -2022-08-06 23:58:00,1689.1,,infty -2022-08-06 23:59:00,1690.33,,infty -2022-08-07 00:00:00,1690.66,,infty -2022-08-07 00:01:00,1690.08,,infty -2022-08-07 00:02:00,1689.38,,infty -2022-08-07 00:03:00,1689.52,,infty -2022-08-07 00:04:00,1689.69,,infty -2022-08-07 00:05:00,1690.56,,infty -2022-08-07 00:06:00,1691.68,,infty -2022-08-07 00:07:00,1692.82,,infty -2022-08-07 00:08:00,1692.6,,infty -2022-08-07 00:09:00,1693.06,,infty -2022-08-07 00:10:00,1696.27,,infty -2022-08-07 00:11:00,1696.27,,infty -2022-08-07 00:12:00,1694.45,,infty -2022-08-07 00:13:00,1692.28,,infty -2022-08-07 00:14:00,1692.96,,infty -2022-08-07 00:15:00,1693.57,,infty -2022-08-07 00:16:00,1694.82,,infty -2022-08-07 00:17:00,1694.0,,infty -2022-08-07 00:18:00,1693.58,,infty -2022-08-07 00:19:00,1691.28,,infty -2022-08-07 00:20:00,1691.67,,infty -2022-08-07 00:21:00,1691.99,,infty -2022-08-07 00:22:00,1691.54,,infty -2022-08-07 00:23:00,1692.34,,infty -2022-08-07 00:24:00,1690.16,,infty -2022-08-07 00:25:00,1690.47,,infty -2022-08-07 00:26:00,1691.55,,infty -2022-08-07 00:27:00,1693.0,,infty -2022-08-07 00:28:00,1692.97,,infty -2022-08-07 00:29:00,1694.43,,infty -2022-08-07 00:30:00,1694.4,,infty -2022-08-07 00:31:00,1696.19,,infty -2022-08-07 00:32:00,1696.3,,infty -2022-08-07 00:33:00,1697.05,,infty -2022-08-07 00:34:00,1697.02,,infty -2022-08-07 00:35:00,1696.81,,infty -2022-08-07 00:36:00,1697.55,,infty -2022-08-07 00:37:00,1697.77,,infty -2022-08-07 00:38:00,1696.41,,infty -2022-08-07 00:39:00,1697.1,,infty -2022-08-07 00:40:00,1696.05,,infty -2022-08-07 00:41:00,1696.46,,infty -2022-08-07 00:42:00,1697.61,,infty -2022-08-07 00:43:00,1696.59,,infty -2022-08-07 00:44:00,1697.52,,infty -2022-08-07 00:45:00,1695.58,,infty -2022-08-07 00:46:00,1693.46,,infty -2022-08-07 00:47:00,1691.45,,infty -2022-08-07 00:48:00,1691.6,,infty -2022-08-07 00:49:00,1691.1,,infty -2022-08-07 00:50:00,1692.71,,infty -2022-08-07 00:51:00,1691.59,,infty -2022-08-07 00:52:00,1691.48,,infty -2022-08-07 00:53:00,1681.45,,infty -2022-08-07 00:54:00,1678.82,,open_close -2022-08-07 00:55:00,1677.63,,open_close -2022-08-07 00:56:00,1675.65,,open_close -2022-08-07 00:57:00,1670.61,,open_close -2022-08-07 00:58:00,1671.73,,open_close -2022-08-07 00:59:00,1672.52,,open_close -2022-08-07 01:00:00,1670.0,,open_close -2022-08-07 01:01:00,1671.91,,open_close -2022-08-07 01:02:00,1671.21,,open_close -2022-08-07 01:03:00,1672.0,,open_close -2022-08-07 01:04:00,1671.25,,open_close -2022-08-07 01:05:00,1671.81,,open_close -2022-08-07 01:06:00,1673.25,,open_close -2022-08-07 01:07:00,1675.14,,open_close -2022-08-07 01:08:00,1675.53,,open_close -2022-08-07 01:09:00,1671.92,,open_close -2022-08-07 01:10:00,1671.7,,open_close -2022-08-07 01:11:00,1672.37,,open_close -2022-08-07 01:12:00,1671.11,,open_close -2022-08-07 01:13:00,1673.07,,open_close -2022-08-07 01:14:00,1673.25,,open_close -2022-08-07 01:15:00,1672.87,,open_close -2022-08-07 01:16:00,1674.13,,open_close -2022-08-07 01:17:00,1672.63,,open_close -2022-08-07 01:18:00,1672.55,,open_close -2022-08-07 01:19:00,1673.69,,open_close -2022-08-07 01:20:00,1672.81,,open_close -2022-08-07 01:21:00,1672.99,,open_close -2022-08-07 01:22:00,1671.65,,open_close -2022-08-07 01:23:00,1672.02,,open_close -2022-08-07 01:24:00,1671.3,,open_close -2022-08-07 01:25:00,1671.7,,open_close -2022-08-07 01:26:00,1672.35,,open_close -2022-08-07 01:27:00,1673.17,,open_close -2022-08-07 01:28:00,1671.95,,open_close -2022-08-07 01:29:00,1672.91,,open_close -2022-08-07 01:30:00,1674.23,,open_close -2022-08-07 01:31:00,1675.41,,open_close -2022-08-07 01:32:00,1675.12,,open_close -2022-08-07 01:33:00,1676.17,,open_close -2022-08-07 01:34:00,1675.79,,open_close -2022-08-07 01:35:00,1674.42,,open_close -2022-08-07 01:36:00,1676.05,,open_close -2022-08-07 01:37:00,1676.44,,open_close -2022-08-07 01:38:00,1677.44,,open_close -2022-08-07 01:39:00,1673.72,,open_close -2022-08-07 01:40:00,1675.67,,open_close -2022-08-07 01:41:00,1676.42,,open_close -2022-08-07 01:42:00,1676.95,,open_close -2022-08-07 01:43:00,1678.19,,open_close -2022-08-07 01:44:00,1676.01,,open_close -2022-08-07 01:45:00,1676.06,,open_close -2022-08-07 01:46:00,1676.59,,open_close -2022-08-07 01:47:00,1677.57,,open_close -2022-08-07 01:48:00,1678.73,,open_close -2022-08-07 01:49:00,1678.11,,open_close -2022-08-07 01:50:00,1677.88,,open_close -2022-08-07 01:51:00,1677.9,,open_close -2022-08-07 01:52:00,1677.02,,open_close -2022-08-07 01:53:00,1676.92,,open_close -2022-08-07 01:54:00,1677.25,,open_close -2022-08-07 01:55:00,1676.0,,open_close -2022-08-07 01:56:00,1675.96,,open_close -2022-08-07 01:57:00,1676.98,,open_close -2022-08-07 01:58:00,1677.9,,open_close -2022-08-07 01:59:00,1677.35,,open_close -2022-08-07 02:00:00,1677.21,,open_close -2022-08-07 02:01:00,1675.62,,open_close -2022-08-07 02:02:00,1675.69,,open_close -2022-08-07 02:03:00,1676.77,,open_close -2022-08-07 02:04:00,1677.63,,open_close -2022-08-07 02:05:00,1679.16,,open_close -2022-08-07 02:06:00,1680.53,,open_close -2022-08-07 02:07:00,1679.29,,open_close -2022-08-07 02:08:00,1679.36,,open_close -2022-08-07 02:09:00,1679.86,,open_close -2022-08-07 02:10:00,1679.83,,open_close -2022-08-07 02:11:00,1680.55,,open_close -2022-08-07 02:12:00,1679.1,,open_close -2022-08-07 02:13:00,1679.59,,open_close -2022-08-07 02:14:00,1679.2,,open_close -2022-08-07 02:15:00,1678.28,,open_close -2022-08-07 02:16:00,1678.05,,open_close -2022-08-07 02:17:00,1677.48,,open_close -2022-08-07 02:18:00,1678.02,,open_close -2022-08-07 02:19:00,1678.3,,open_close -2022-08-07 02:20:00,1678.35,,open_close -2022-08-07 02:21:00,1678.88,,open_close -2022-08-07 02:22:00,1679.12,,open_close -2022-08-07 02:23:00,1678.84,,open_close -2022-08-07 02:24:00,1679.6,,open_close -2022-08-07 02:25:00,1679.73,,open_close -2022-08-07 02:26:00,1679.61,,open_close -2022-08-07 02:27:00,1680.03,,open_close -2022-08-07 02:28:00,1678.68,,open_close -2022-08-07 02:29:00,1679.53,,open_close -2022-08-07 02:30:00,1679.21,,open_close -2022-08-07 02:31:00,1679.54,,open_close -2022-08-07 02:32:00,1680.71,,open_close -2022-08-07 02:33:00,1680.83,,open_close -2022-08-07 02:34:00,1679.8,,open_close -2022-08-07 02:35:00,1680.81,,open_close -2022-08-07 02:36:00,1680.78,,open_close -2022-08-07 02:37:00,1681.91,,infty -2022-08-07 02:38:00,1681.18,,infty -2022-08-07 02:39:00,1681.15,,open_close -2022-08-07 02:40:00,1680.28,,open_close -2022-08-07 02:41:00,1680.79,,open_close -2022-08-07 02:42:00,1681.62,,infty -2022-08-07 02:43:00,1681.59,,infty -2022-08-07 02:44:00,1681.4,,infty -2022-08-07 02:45:00,1682.03,,infty -2022-08-07 02:46:00,1682.28,,infty -2022-08-07 02:47:00,1683.17,,infty -2022-08-07 02:48:00,1683.53,,infty -2022-08-07 02:49:00,1683.47,,infty -2022-08-07 02:50:00,1683.96,,infty -2022-08-07 02:51:00,1684.45,,infty -2022-08-07 02:52:00,1685.4,,infty -2022-08-07 02:53:00,1685.77,,infty -2022-08-07 02:54:00,1687.09,,infty -2022-08-07 02:55:00,1687.29,,infty -2022-08-07 02:56:00,1685.47,,infty -2022-08-07 02:57:00,1685.93,,infty -2022-08-07 02:58:00,1685.92,,infty -2022-08-07 02:59:00,1686.29,,infty -2022-08-07 03:00:00,1685.72,,infty -2022-08-07 03:01:00,1687.2,,infty -2022-08-07 03:02:00,1688.24,,infty -2022-08-07 03:03:00,1689.08,,infty -2022-08-07 03:04:00,1687.28,,infty -2022-08-07 03:05:00,1686.69,,infty -2022-08-07 03:06:00,1686.7,,infty -2022-08-07 03:07:00,1684.21,,infty -2022-08-07 03:08:00,1685.48,,infty -2022-08-07 03:09:00,1686.06,,infty -2022-08-07 03:10:00,1685.1,,infty -2022-08-07 03:11:00,1685.68,,infty -2022-08-07 03:12:00,1686.2,,infty -2022-08-07 03:13:00,1686.55,,infty -2022-08-07 03:14:00,1686.54,,infty -2022-08-07 03:15:00,1686.23,,infty -2022-08-07 03:16:00,1686.61,,infty -2022-08-07 03:17:00,1687.46,,infty -2022-08-07 03:18:00,1686.97,,infty -2022-08-07 03:19:00,1686.46,,infty -2022-08-07 03:20:00,1684.42,,infty -2022-08-07 03:21:00,1683.03,,infty -2022-08-07 03:22:00,1683.03,,infty -2022-08-07 03:23:00,1684.11,,infty 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04:18:00,1684.84,,infty -2022-08-07 04:19:00,1683.13,,infty -2022-08-07 04:20:00,1683.55,,infty -2022-08-07 04:21:00,1682.12,,infty -2022-08-07 04:22:00,1682.71,,infty -2022-08-07 04:23:00,1682.74,,infty -2022-08-07 04:24:00,1683.55,,infty -2022-08-07 04:25:00,1683.81,,infty -2022-08-07 04:26:00,1684.63,,infty -2022-08-07 04:27:00,1684.2,,infty -2022-08-07 04:28:00,1684.35,,infty -2022-08-07 04:29:00,1683.78,,infty -2022-08-07 04:30:00,1684.16,,infty -2022-08-07 04:31:00,1684.54,,infty -2022-08-07 04:32:00,1684.54,,infty -2022-08-07 04:33:00,1684.54,,infty -2022-08-07 04:34:00,1685.09,,infty -2022-08-07 04:35:00,1686.21,,infty -2022-08-07 04:36:00,1686.38,,infty -2022-08-07 04:37:00,1687.55,,infty -2022-08-07 04:38:00,1686.37,,infty -2022-08-07 04:39:00,1685.65,,infty -2022-08-07 04:40:00,1686.22,,infty -2022-08-07 04:41:00,1686.01,,infty -2022-08-07 04:42:00,1684.77,,infty -2022-08-07 04:43:00,1679.41,,open_close -2022-08-07 04:44:00,1680.43,,open_close -2022-08-07 04:45:00,1677.58,,open_close -2022-08-07 04:46:00,1679.22,,open_close -2022-08-07 04:47:00,1680.82,,open_close -2022-08-07 04:48:00,1681.52,,infty -2022-08-07 04:49:00,1680.6,,open_close -2022-08-07 04:50:00,1680.11,,open_close -2022-08-07 04:51:00,1678.68,,open_close -2022-08-07 04:52:00,1675.76,,open_close -2022-08-07 04:53:00,1673.6,,open_close -2022-08-07 04:54:00,1675.52,,open_close -2022-08-07 04:55:00,1677.83,,open_close -2022-08-07 04:56:00,1677.82,,open_close -2022-08-07 04:57:00,1677.54,,open_close -2022-08-07 04:58:00,1677.87,,open_close -2022-08-07 04:59:00,1675.31,,open_close -2022-08-07 05:00:00,1675.63,,open_close -2022-08-07 05:01:00,1676.73,,open_close -2022-08-07 05:02:00,1676.7,,open_close -2022-08-07 05:03:00,1677.98,,open_close -2022-08-07 05:04:00,1678.42,,open_close -2022-08-07 05:05:00,1678.98,,open_close -2022-08-07 05:06:00,1680.47,,open_close -2022-08-07 05:07:00,1680.78,,open_close -2022-08-07 05:08:00,1681.68,,infty -2022-08-07 05:09:00,1680.56,,open_close -2022-08-07 05:10:00,1680.2,,open_close -2022-08-07 05:11:00,1679.71,,open_close -2022-08-07 05:12:00,1680.06,,open_close -2022-08-07 05:13:00,1680.87,,open_close -2022-08-07 05:14:00,1680.21,,open_close -2022-08-07 05:15:00,1680.72,,open_close -2022-08-07 05:16:00,1679.72,,open_close -2022-08-07 05:17:00,1678.12,,open_close -2022-08-07 05:18:00,1678.22,,open_close -2022-08-07 05:19:00,1679.49,,open_close -2022-08-07 05:20:00,1680.39,,open_close -2022-08-07 05:21:00,1680.83,,open_close -2022-08-07 05:22:00,1681.28,,infty -2022-08-07 05:23:00,1681.33,,infty -2022-08-07 05:24:00,1682.38,,infty -2022-08-07 05:25:00,1682.16,,infty -2022-08-07 05:26:00,1684.39,,infty -2022-08-07 05:27:00,1683.42,,infty -2022-08-07 05:28:00,1683.82,,infty -2022-08-07 05:29:00,1684.39,,infty -2022-08-07 05:30:00,1685.26,,infty -2022-08-07 05:31:00,1685.06,,infty -2022-08-07 05:32:00,1683.96,,infty -2022-08-07 05:33:00,1684.43,,infty -2022-08-07 05:34:00,1683.66,,infty -2022-08-07 05:35:00,1683.71,,infty -2022-08-07 05:36:00,1683.07,,infty -2022-08-07 05:37:00,1683.45,,infty -2022-08-07 05:38:00,1683.09,,infty -2022-08-07 05:39:00,1683.38,,infty -2022-08-07 05:40:00,1683.82,,infty -2022-08-07 05:41:00,1683.1,,infty -2022-08-07 05:42:00,1682.28,,infty -2022-08-07 05:43:00,1682.59,,infty -2022-08-07 05:44:00,1681.07,,open_close -2022-08-07 05:45:00,1680.42,,open_close -2022-08-07 05:46:00,1680.4,,open_close -2022-08-07 05:47:00,1681.44,,infty -2022-08-07 05:48:00,1682.74,,infty -2022-08-07 05:49:00,1682.29,,infty -2022-08-07 05:50:00,1683.32,,infty -2022-08-07 05:51:00,1682.08,,infty -2022-08-07 05:52:00,1681.88,,infty -2022-08-07 05:53:00,1681.6,,infty -2022-08-07 05:54:00,1682.01,,infty -2022-08-07 05:55:00,1682.36,,infty -2022-08-07 05:56:00,1681.63,,infty -2022-08-07 05:57:00,1681.02,,open_close -2022-08-07 05:58:00,1680.79,,open_close -2022-08-07 05:59:00,1681.68,,infty -2022-08-07 06:00:00,1682.02,,infty -2022-08-07 06:01:00,1682.67,,infty -2022-08-07 06:02:00,1684.1,,infty -2022-08-07 06:03:00,1684.7,,infty -2022-08-07 06:04:00,1683.79,,infty -2022-08-07 06:05:00,1683.02,,infty -2022-08-07 06:06:00,1683.02,,infty -2022-08-07 06:07:00,1682.58,,infty -2022-08-07 06:08:00,1682.49,,infty -2022-08-07 06:09:00,1683.12,,infty -2022-08-07 06:10:00,1681.92,,infty -2022-08-07 06:11:00,1681.99,,infty -2022-08-07 06:12:00,1681.86,,infty -2022-08-07 06:13:00,1681.95,,infty -2022-08-07 06:14:00,1683.05,,infty -2022-08-07 06:15:00,1683.0,,infty -2022-08-07 06:16:00,1683.41,,infty -2022-08-07 06:17:00,1682.92,,infty -2022-08-07 06:18:00,1683.16,,infty -2022-08-07 06:19:00,1682.66,,infty -2022-08-07 06:20:00,1682.49,,infty -2022-08-07 06:21:00,1682.56,,infty -2022-08-07 06:22:00,1682.96,,infty -2022-08-07 06:23:00,1682.83,,infty -2022-08-07 06:24:00,1682.85,,infty -2022-08-07 06:25:00,1683.01,,infty -2022-08-07 06:26:00,1682.63,,infty -2022-08-07 06:27:00,1681.5,,infty -2022-08-07 06:28:00,1681.41,,infty -2022-08-07 06:29:00,1681.36,,infty -2022-08-07 06:30:00,1680.33,,open_close -2022-08-07 06:31:00,1681.01,,open_close -2022-08-07 06:32:00,1682.27,,infty -2022-08-07 06:33:00,1683.64,,infty -2022-08-07 06:34:00,1683.55,,infty -2022-08-07 06:35:00,1684.6,,infty -2022-08-07 06:36:00,1684.94,,infty -2022-08-07 06:37:00,1684.27,,infty -2022-08-07 06:38:00,1683.28,,infty -2022-08-07 06:39:00,1683.37,,infty -2022-08-07 06:40:00,1683.29,,infty -2022-08-07 06:41:00,1682.78,,infty -2022-08-07 06:42:00,1683.3,,infty -2022-08-07 06:43:00,1682.73,,infty -2022-08-07 06:44:00,1682.62,,infty -2022-08-07 06:45:00,1685.11,,infty -2022-08-07 06:46:00,1683.9,,infty -2022-08-07 06:47:00,1683.36,,infty -2022-08-07 06:48:00,1683.61,,infty -2022-08-07 06:49:00,1683.06,,infty -2022-08-07 06:50:00,1682.69,,infty -2022-08-07 06:51:00,1683.56,,infty -2022-08-07 06:52:00,1683.05,,infty -2022-08-07 06:53:00,1683.05,,infty -2022-08-07 06:54:00,1683.04,,infty -2022-08-07 06:55:00,1684.42,,infty -2022-08-07 06:56:00,1683.66,,infty -2022-08-07 06:57:00,1683.44,,infty -2022-08-07 06:58:00,1682.93,,infty -2022-08-07 06:59:00,1683.77,,infty -2022-08-07 07:00:00,1684.04,,infty -2022-08-07 07:01:00,1684.35,,infty -2022-08-07 07:02:00,1684.22,,infty -2022-08-07 07:03:00,1684.37,,infty -2022-08-07 07:04:00,1684.65,,infty -2022-08-07 07:05:00,1685.23,,infty -2022-08-07 07:06:00,1685.12,,infty -2022-08-07 07:07:00,1684.55,,infty -2022-08-07 07:08:00,1683.45,,infty -2022-08-07 07:09:00,1684.47,,infty -2022-08-07 07:10:00,1684.39,,infty -2022-08-07 07:11:00,1687.27,,infty -2022-08-07 07:12:00,1686.57,,infty -2022-08-07 07:13:00,1687.26,,infty -2022-08-07 07:14:00,1687.08,,infty -2022-08-07 07:15:00,1686.6,,infty -2022-08-07 07:16:00,1685.36,,infty -2022-08-07 07:17:00,1685.78,,infty -2022-08-07 07:18:00,1685.78,,infty -2022-08-07 07:19:00,1685.37,,infty -2022-08-07 07:20:00,1685.37,,infty -2022-08-07 07:21:00,1685.93,,infty -2022-08-07 07:22:00,1685.16,,infty -2022-08-07 07:23:00,1685.24,,infty -2022-08-07 07:24:00,1685.32,,infty -2022-08-07 07:25:00,1685.94,,infty -2022-08-07 07:26:00,1686.43,,infty -2022-08-07 07:27:00,1685.03,,infty -2022-08-07 07:28:00,1685.62,,infty -2022-08-07 07:29:00,1684.84,,infty -2022-08-07 07:30:00,1685.46,,infty -2022-08-07 07:31:00,1686.86,,infty -2022-08-07 07:32:00,1685.87,,infty -2022-08-07 07:33:00,1686.76,,infty -2022-08-07 07:34:00,1686.49,,infty -2022-08-07 07:35:00,1686.17,,infty -2022-08-07 07:36:00,1685.86,,infty -2022-08-07 07:37:00,1685.7,,infty -2022-08-07 07:38:00,1685.97,,infty -2022-08-07 07:39:00,1686.6,,infty -2022-08-07 07:40:00,1687.0,,infty -2022-08-07 07:41:00,1687.39,,infty -2022-08-07 07:42:00,1687.53,,infty -2022-08-07 07:43:00,1687.01,,infty -2022-08-07 07:44:00,1686.6,,infty -2022-08-07 07:45:00,1686.81,,infty -2022-08-07 07:46:00,1687.23,,infty -2022-08-07 07:47:00,1688.48,,infty -2022-08-07 07:48:00,1687.54,,infty -2022-08-07 07:49:00,1688.96,,infty -2022-08-07 07:50:00,1688.76,,infty -2022-08-07 07:51:00,1687.97,,infty -2022-08-07 07:52:00,1688.53,,infty -2022-08-07 07:53:00,1687.89,,infty -2022-08-07 07:54:00,1686.36,,infty -2022-08-07 07:55:00,1685.02,,infty -2022-08-07 07:56:00,1685.51,,infty -2022-08-07 07:57:00,1685.63,,infty -2022-08-07 07:58:00,1686.32,,infty -2022-08-07 07:59:00,1686.45,,infty -2022-08-07 08:00:00,1686.3,,infty -2022-08-07 08:01:00,1684.65,,infty -2022-08-07 08:02:00,1685.27,,infty -2022-08-07 08:03:00,1684.48,,infty -2022-08-07 08:04:00,1685.21,,infty -2022-08-07 08:05:00,1683.46,,infty -2022-08-07 08:06:00,1682.04,,infty -2022-08-07 08:07:00,1682.12,,infty -2022-08-07 08:08:00,1681.45,,infty -2022-08-07 08:09:00,1681.57,,infty -2022-08-07 08:10:00,1683.41,,infty -2022-08-07 08:11:00,1684.07,,infty -2022-08-07 08:12:00,1683.7,,infty -2022-08-07 08:13:00,1682.03,,infty -2022-08-07 08:14:00,1679.03,,open_close -2022-08-07 08:15:00,1679.72,,open_close -2022-08-07 08:16:00,1682.58,,infty -2022-08-07 08:17:00,1681.53,,infty -2022-08-07 08:18:00,1680.29,,open_close -2022-08-07 08:19:00,1681.57,,infty -2022-08-07 08:20:00,1682.02,,infty -2022-08-07 08:21:00,1682.06,,infty -2022-08-07 08:22:00,1681.97,,infty -2022-08-07 08:23:00,1681.69,,infty -2022-08-07 08:24:00,1681.89,,infty -2022-08-07 08:25:00,1680.69,,open_close -2022-08-07 08:26:00,1680.89,,open_close -2022-08-07 08:27:00,1681.41,,infty -2022-08-07 08:28:00,1681.89,,infty -2022-08-07 08:29:00,1682.39,,infty -2022-08-07 08:30:00,1681.75,,infty -2022-08-07 08:31:00,1682.17,,infty -2022-08-07 08:32:00,1681.43,,infty -2022-08-07 08:33:00,1679.34,,open_close -2022-08-07 08:34:00,1680.34,,open_close -2022-08-07 08:35:00,1679.48,,open_close -2022-08-07 08:36:00,1680.44,,open_close -2022-08-07 08:37:00,1681.01,,open_close -2022-08-07 08:38:00,1681.4,,infty -2022-08-07 08:39:00,1681.45,,infty -2022-08-07 08:40:00,1680.98,,open_close -2022-08-07 08:41:00,1681.71,,infty -2022-08-07 08:42:00,1680.61,,open_close -2022-08-07 08:43:00,1680.52,,open_close -2022-08-07 08:44:00,1681.54,,infty -2022-08-07 08:45:00,1681.01,,open_close -2022-08-07 08:46:00,1681.59,,infty -2022-08-07 08:47:00,1682.13,,infty -2022-08-07 08:48:00,1682.36,,infty -2022-08-07 08:49:00,1683.0,,infty -2022-08-07 08:50:00,1682.59,,infty -2022-08-07 08:51:00,1683.36,,infty -2022-08-07 08:52:00,1681.63,,infty -2022-08-07 08:53:00,1682.05,,infty -2022-08-07 08:54:00,1682.29,,infty -2022-08-07 08:55:00,1680.99,,open_close -2022-08-07 08:56:00,1678.42,,open_close -2022-08-07 08:57:00,1679.75,,open_close -2022-08-07 08:58:00,1679.39,,open_close -2022-08-07 08:59:00,1679.33,,open_close -2022-08-07 09:00:00,1679.61,,open_close -2022-08-07 09:01:00,1679.61,,open_close -2022-08-07 09:02:00,1679.56,,open_close -2022-08-07 09:03:00,1678.39,,open_close -2022-08-07 09:04:00,1676.0,,open_close -2022-08-07 09:05:00,1677.07,,open_close -2022-08-07 09:06:00,1678.46,,open_close -2022-08-07 09:07:00,1678.9,,open_close -2022-08-07 09:08:00,1679.79,,open_close -2022-08-07 09:09:00,1681.29,,infty -2022-08-07 09:10:00,1680.25,,open_close -2022-08-07 09:11:00,1680.12,,open_close -2022-08-07 09:12:00,1678.33,,open_close -2022-08-07 09:13:00,1679.1,,open_close -2022-08-07 09:14:00,1677.52,,open_close -2022-08-07 09:15:00,1678.32,,open_close -2022-08-07 09:16:00,1679.29,,open_close -2022-08-07 09:17:00,1680.35,,open_close -2022-08-07 09:18:00,1678.68,,open_close -2022-08-07 09:19:00,1679.48,,open_close -2022-08-07 09:20:00,1680.39,,open_close -2022-08-07 09:21:00,1681.66,,infty -2022-08-07 09:22:00,1680.64,,open_close -2022-08-07 09:23:00,1680.74,,open_close -2022-08-07 09:24:00,1680.04,,open_close -2022-08-07 09:25:00,1679.71,,open_close -2022-08-07 09:26:00,1679.54,,open_close -2022-08-07 09:27:00,1679.98,,open_close -2022-08-07 09:28:00,1679.66,,open_close -2022-08-07 09:29:00,1681.1,,open_close -2022-08-07 09:30:00,1682.42,,infty -2022-08-07 09:31:00,1681.95,,infty -2022-08-07 09:32:00,1683.01,,infty -2022-08-07 09:33:00,1685.47,,infty -2022-08-07 09:34:00,1685.23,,infty -2022-08-07 09:35:00,1683.92,,infty -2022-08-07 09:36:00,1684.48,,infty -2022-08-07 09:37:00,1683.33,,infty -2022-08-07 09:38:00,1684.22,,infty -2022-08-07 09:39:00,1682.95,,infty -2022-08-07 09:40:00,1683.92,,infty -2022-08-07 09:41:00,1683.51,,infty -2022-08-07 09:42:00,1683.6,,infty -2022-08-07 09:43:00,1682.39,,infty -2022-08-07 09:44:00,1681.54,,infty -2022-08-07 09:45:00,1683.25,,infty -2022-08-07 09:46:00,1685.08,,infty -2022-08-07 09:47:00,1684.18,,infty -2022-08-07 09:48:00,1684.17,,infty -2022-08-07 09:49:00,1684.16,,infty -2022-08-07 09:50:00,1682.83,,infty -2022-08-07 09:51:00,1682.73,,infty -2022-08-07 09:52:00,1682.55,,infty -2022-08-07 09:53:00,1683.66,,infty -2022-08-07 09:54:00,1683.25,,infty -2022-08-07 09:55:00,1683.21,,infty -2022-08-07 09:56:00,1684.14,,infty -2022-08-07 09:57:00,1683.77,,infty -2022-08-07 09:58:00,1684.68,,infty -2022-08-07 09:59:00,1684.57,,infty -2022-08-07 10:00:00,1684.25,,infty -2022-08-07 10:01:00,1683.78,,infty -2022-08-07 10:02:00,1684.19,,infty -2022-08-07 10:03:00,1684.08,,infty -2022-08-07 10:04:00,1682.21,,infty -2022-08-07 10:05:00,1682.72,,infty -2022-08-07 10:06:00,1682.21,,infty -2022-08-07 10:07:00,1683.21,,infty -2022-08-07 10:08:00,1683.85,,infty -2022-08-07 10:09:00,1683.23,,infty -2022-08-07 10:10:00,1682.86,,infty -2022-08-07 10:11:00,1682.02,,infty -2022-08-07 10:12:00,1681.92,,infty -2022-08-07 10:13:00,1681.73,,infty -2022-08-07 10:14:00,1681.75,,infty -2022-08-07 10:15:00,1683.5,,infty -2022-08-07 10:16:00,1683.72,,infty -2022-08-07 10:17:00,1684.1,,infty -2022-08-07 10:18:00,1683.93,,infty -2022-08-07 10:19:00,1683.97,,infty -2022-08-07 10:20:00,1684.76,,infty -2022-08-07 10:21:00,1684.17,,infty -2022-08-07 10:22:00,1683.32,,infty -2022-08-07 10:23:00,1683.73,,infty -2022-08-07 10:24:00,1683.88,,infty -2022-08-07 10:25:00,1684.29,,infty -2022-08-07 10:26:00,1685.77,,infty -2022-08-07 10:27:00,1685.84,,infty -2022-08-07 10:28:00,1684.56,,infty -2022-08-07 10:29:00,1684.74,,infty -2022-08-07 10:30:00,1685.69,,infty -2022-08-07 10:31:00,1685.43,,infty -2022-08-07 10:32:00,1685.61,,infty -2022-08-07 10:33:00,1685.9,,infty -2022-08-07 10:34:00,1685.91,,infty -2022-08-07 10:35:00,1686.08,,infty -2022-08-07 10:36:00,1685.31,,infty -2022-08-07 10:37:00,1685.69,,infty -2022-08-07 10:38:00,1685.23,,infty -2022-08-07 10:39:00,1684.32,,infty -2022-08-07 10:40:00,1684.42,,infty -2022-08-07 10:41:00,1684.44,,infty -2022-08-07 10:42:00,1683.77,,infty -2022-08-07 10:43:00,1683.98,,infty -2022-08-07 10:44:00,1683.98,,infty -2022-08-07 10:45:00,1684.45,,infty -2022-08-07 10:46:00,1685.04,,infty -2022-08-07 10:47:00,1685.57,,infty -2022-08-07 10:48:00,1685.45,,infty -2022-08-07 10:49:00,1685.17,,infty -2022-08-07 10:50:00,1685.48,,infty -2022-08-07 10:51:00,1685.2,,infty -2022-08-07 10:52:00,1684.65,,infty -2022-08-07 10:53:00,1684.32,,infty -2022-08-07 10:54:00,1684.39,,infty -2022-08-07 10:55:00,1684.13,,infty -2022-08-07 10:56:00,1685.99,,infty -2022-08-07 10:57:00,1685.79,,infty -2022-08-07 10:58:00,1685.64,,infty -2022-08-07 10:59:00,1685.45,,infty -2022-08-07 11:00:00,1685.07,,infty -2022-08-07 11:01:00,1684.24,,infty -2022-08-07 11:02:00,1685.29,,infty -2022-08-07 11:03:00,1685.56,,infty -2022-08-07 11:04:00,1686.96,,infty -2022-08-07 11:05:00,1685.62,,infty -2022-08-07 11:06:00,1684.79,,infty -2022-08-07 11:07:00,1684.69,,infty -2022-08-07 11:08:00,1685.36,,infty -2022-08-07 11:09:00,1686.18,,infty -2022-08-07 11:10:00,1686.62,,infty -2022-08-07 11:11:00,1685.62,,infty -2022-08-07 11:12:00,1685.78,,infty -2022-08-07 11:13:00,1684.49,,infty -2022-08-07 11:14:00,1684.96,,infty -2022-08-07 11:15:00,1684.78,,infty -2022-08-07 11:16:00,1684.93,,infty -2022-08-07 11:17:00,1683.89,,infty -2022-08-07 11:18:00,1684.11,,infty -2022-08-07 11:19:00,1682.32,,infty -2022-08-07 11:20:00,1682.8,,infty -2022-08-07 11:21:00,1683.14,,infty -2022-08-07 11:22:00,1683.05,,infty -2022-08-07 11:23:00,1683.15,,infty -2022-08-07 11:24:00,1683.6,,infty -2022-08-07 11:25:00,1682.86,,infty -2022-08-07 11:26:00,1683.89,,infty -2022-08-07 11:27:00,1684.39,,infty -2022-08-07 11:28:00,1684.39,,infty -2022-08-07 11:29:00,1683.59,,infty -2022-08-07 11:30:00,1683.18,,infty -2022-08-07 11:31:00,1683.71,,infty -2022-08-07 11:32:00,1683.59,,infty -2022-08-07 11:33:00,1683.85,,infty -2022-08-07 11:34:00,1683.86,,infty -2022-08-07 11:35:00,1683.91,,infty -2022-08-07 11:36:00,1684.75,,infty -2022-08-07 11:37:00,1685.07,,infty -2022-08-07 11:38:00,1684.78,,infty -2022-08-07 11:39:00,1684.71,,infty -2022-08-07 11:40:00,1684.71,,infty -2022-08-07 11:41:00,1684.42,,infty -2022-08-07 11:42:00,1684.25,,infty -2022-08-07 11:43:00,1684.29,,infty -2022-08-07 11:44:00,1684.65,,infty -2022-08-07 11:45:00,1685.32,,infty -2022-08-07 11:46:00,1690.64,,infty -2022-08-07 11:47:00,1689.52,,infty -2022-08-07 11:48:00,1687.84,,infty -2022-08-07 11:49:00,1691.33,,infty -2022-08-07 11:50:00,1692.28,,infty -2022-08-07 11:51:00,1692.35,,infty -2022-08-07 11:52:00,1691.77,,infty -2022-08-07 11:53:00,1691.87,,infty -2022-08-07 11:54:00,1693.55,,infty -2022-08-07 11:55:00,1692.66,,infty -2022-08-07 11:56:00,1691.56,,infty -2022-08-07 11:57:00,1692.45,,infty -2022-08-07 11:58:00,1690.8,,infty -2022-08-07 11:59:00,1692.96,,infty -2022-08-07 12:00:00,1695.46,,infty -2022-08-07 12:01:00,1698.97,,infty -2022-08-07 12:02:00,1699.37,,infty -2022-08-07 12:03:00,1701.48,,infty -2022-08-07 12:04:00,1703.92,,infty -2022-08-07 12:05:00,1700.83,,infty -2022-08-07 12:06:00,1700.72,,infty -2022-08-07 12:07:00,1700.9,,infty -2022-08-07 12:08:00,1701.63,,infty -2022-08-07 12:09:00,1700.56,,infty -2022-08-07 12:10:00,1700.22,,infty -2022-08-07 12:11:00,1699.84,,infty -2022-08-07 12:12:00,1700.82,,infty -2022-08-07 12:13:00,1701.53,,infty -2022-08-07 12:14:00,1702.48,,infty -2022-08-07 12:15:00,1703.81,,infty -2022-08-07 12:16:00,1702.77,,infty -2022-08-07 12:17:00,1700.95,,infty -2022-08-07 12:18:00,1699.99,,infty -2022-08-07 12:19:00,1699.19,,infty -2022-08-07 12:20:00,1694.5,,infty -2022-08-07 12:21:00,1696.57,,infty -2022-08-07 12:22:00,1696.53,,infty -2022-08-07 12:23:00,1696.92,,infty -2022-08-07 12:24:00,1697.56,,infty -2022-08-07 12:25:00,1697.21,,infty -2022-08-07 12:26:00,1697.37,,infty -2022-08-07 12:27:00,1695.87,,infty -2022-08-07 12:28:00,1694.36,,infty -2022-08-07 12:29:00,1695.86,,infty -2022-08-07 12:30:00,1695.37,,infty -2022-08-07 12:31:00,1694.48,,infty -2022-08-07 12:32:00,1693.6,,infty -2022-08-07 12:33:00,1693.72,,infty -2022-08-07 12:34:00,1694.2,,infty -2022-08-07 12:35:00,1694.87,,infty -2022-08-07 12:36:00,1695.68,,infty -2022-08-07 12:37:00,1696.43,,infty -2022-08-07 12:38:00,1696.0,,infty -2022-08-07 12:39:00,1696.08,,infty -2022-08-07 12:40:00,1695.77,,infty -2022-08-07 12:41:00,1696.0,,infty -2022-08-07 12:42:00,1696.2,,infty -2022-08-07 12:43:00,1697.24,,infty -2022-08-07 12:44:00,1698.22,,infty -2022-08-07 12:45:00,1697.04,,infty -2022-08-07 12:46:00,1696.88,,infty -2022-08-07 12:47:00,1696.96,,infty -2022-08-07 12:48:00,1697.88,,infty -2022-08-07 12:49:00,1698.09,,infty -2022-08-07 12:50:00,1699.8,,infty -2022-08-07 12:51:00,1708.07,,infty -2022-08-07 12:52:00,1706.31,,infty -2022-08-07 12:53:00,1704.91,,infty -2022-08-07 12:54:00,1704.63,,infty -2022-08-07 12:55:00,1703.97,,infty -2022-08-07 12:56:00,1705.97,,infty -2022-08-07 12:57:00,1706.9,,infty -2022-08-07 12:58:00,1709.4,,infty -2022-08-07 12:59:00,1708.28,,infty -2022-08-07 13:00:00,1711.49,,infty -2022-08-07 13:01:00,1712.7,,infty -2022-08-07 13:02:00,1712.55,,infty -2022-08-07 13:03:00,1713.75,,infty -2022-08-07 13:04:00,1711.77,,infty -2022-08-07 13:05:00,1711.67,,infty -2022-08-07 13:06:00,1711.41,,infty -2022-08-07 13:07:00,1711.96,,infty -2022-08-07 13:08:00,1708.83,,infty -2022-08-07 13:09:00,1709.71,,infty -2022-08-07 13:10:00,1709.69,,infty -2022-08-07 13:11:00,1707.86,,infty -2022-08-07 13:12:00,1707.79,,infty -2022-08-07 13:13:00,1708.77,,infty -2022-08-07 13:14:00,1709.58,,infty -2022-08-07 13:15:00,1708.29,,infty -2022-08-07 13:16:00,1705.58,,infty -2022-08-07 13:17:00,1706.05,,infty -2022-08-07 13:18:00,1706.4,,infty -2022-08-07 13:19:00,1704.73,,infty -2022-08-07 13:20:00,1706.9,,infty -2022-08-07 13:21:00,1706.66,,infty -2022-08-07 13:22:00,1705.47,,infty -2022-08-07 13:23:00,1707.91,,infty -2022-08-07 13:24:00,1707.49,,infty -2022-08-07 13:25:00,1709.22,,infty -2022-08-07 13:26:00,1707.66,,infty -2022-08-07 13:27:00,1707.64,,infty -2022-08-07 13:28:00,1707.29,,infty -2022-08-07 13:29:00,1706.38,,infty -2022-08-07 13:30:00,1707.46,,infty -2022-08-07 13:31:00,1707.78,,infty -2022-08-07 13:32:00,1706.05,,infty -2022-08-07 13:33:00,1706.23,,infty -2022-08-07 13:34:00,1705.04,,infty -2022-08-07 13:35:00,1706.59,,infty -2022-08-07 13:36:00,1707.39,,infty -2022-08-07 13:37:00,1707.53,,infty -2022-08-07 13:38:00,1707.8,,infty -2022-08-07 13:39:00,1707.57,,infty -2022-08-07 13:40:00,1707.67,,infty -2022-08-07 13:41:00,1707.48,,infty -2022-08-07 13:42:00,1706.89,,infty -2022-08-07 13:43:00,1705.87,,infty 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14:11:00,1705.44,,infty -2022-08-07 14:12:00,1706.14,,infty -2022-08-07 14:13:00,1707.27,,infty -2022-08-07 14:14:00,1707.83,,infty -2022-08-07 14:15:00,1706.71,,infty -2022-08-07 14:16:00,1706.2,,infty -2022-08-07 14:17:00,1705.67,,infty -2022-08-07 14:18:00,1705.35,,infty -2022-08-07 14:19:00,1704.88,,infty -2022-08-07 14:20:00,1705.23,,infty -2022-08-07 14:21:00,1704.17,,infty -2022-08-07 14:22:00,1704.29,,infty -2022-08-07 14:23:00,1703.41,,infty -2022-08-07 14:24:00,1703.39,,infty -2022-08-07 14:25:00,1703.4,,infty -2022-08-07 14:26:00,1700.32,,infty -2022-08-07 14:27:00,1699.45,,infty -2022-08-07 14:28:00,1699.4,,infty -2022-08-07 14:29:00,1700.16,,infty -2022-08-07 14:30:00,1700.42,,infty -2022-08-07 14:31:00,1699.39,,infty -2022-08-07 14:32:00,1698.38,,infty -2022-08-07 14:33:00,1696.46,,infty -2022-08-07 14:34:00,1697.65,,infty -2022-08-07 14:35:00,1697.98,,infty -2022-08-07 14:36:00,1698.75,,infty -2022-08-07 14:37:00,1697.68,,infty -2022-08-07 14:38:00,1698.31,,infty 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15:06:00,1702.84,,infty -2022-08-07 15:07:00,1704.85,,infty -2022-08-07 15:08:00,1703.47,,infty -2022-08-07 15:09:00,1704.37,,infty -2022-08-07 15:10:00,1704.86,,infty -2022-08-07 15:11:00,1703.73,,infty -2022-08-07 15:12:00,1703.36,,infty -2022-08-07 15:13:00,1703.37,,infty -2022-08-07 15:14:00,1703.87,,infty -2022-08-07 15:15:00,1703.0,,infty -2022-08-07 15:16:00,1702.55,,infty -2022-08-07 15:17:00,1703.41,,infty -2022-08-07 15:18:00,1701.24,,infty -2022-08-07 15:19:00,1702.15,,infty -2022-08-07 15:20:00,1701.95,,infty -2022-08-07 15:21:00,1702.42,,infty -2022-08-07 15:22:00,1702.49,,infty -2022-08-07 15:23:00,1699.94,,infty -2022-08-07 15:24:00,1698.36,,infty -2022-08-07 15:25:00,1699.21,,infty -2022-08-07 15:26:00,1700.29,,infty -2022-08-07 15:27:00,1699.16,,infty -2022-08-07 15:28:00,1698.82,,infty -2022-08-07 15:29:00,1699.97,,infty -2022-08-07 15:30:00,1700.82,,infty -2022-08-07 15:31:00,1701.4,,infty -2022-08-07 15:32:00,1700.49,,infty -2022-08-07 15:33:00,1698.32,,infty 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16:01:00,1700.23,,infty -2022-08-07 16:02:00,1698.55,,infty -2022-08-07 16:03:00,1696.83,,infty -2022-08-07 16:04:00,1696.46,,infty -2022-08-07 16:05:00,1696.72,,infty -2022-08-07 16:06:00,1698.24,,infty -2022-08-07 16:07:00,1698.38,,infty -2022-08-07 16:08:00,1697.86,,infty -2022-08-07 16:09:00,1699.21,,infty -2022-08-07 16:10:00,1700.26,,infty -2022-08-07 16:11:00,1701.09,,infty -2022-08-07 16:12:00,1703.63,,infty -2022-08-07 16:13:00,1704.57,,infty -2022-08-07 16:14:00,1705.37,,infty -2022-08-07 16:15:00,1705.74,,infty -2022-08-07 16:16:00,1705.1,,infty -2022-08-07 16:17:00,1703.51,,infty -2022-08-07 16:18:00,1703.78,,infty -2022-08-07 16:19:00,1704.47,,infty -2022-08-07 16:20:00,1705.99,,infty -2022-08-07 16:21:00,1705.98,,infty -2022-08-07 16:22:00,1706.55,,infty -2022-08-07 16:23:00,1705.59,,infty -2022-08-07 16:24:00,1704.63,,infty -2022-08-07 16:25:00,1705.8,,infty -2022-08-07 16:26:00,1706.81,,infty -2022-08-07 16:27:00,1705.62,,infty -2022-08-07 16:28:00,1705.38,,infty 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16:56:00,1706.32,,infty -2022-08-07 16:57:00,1706.47,,infty -2022-08-07 16:58:00,1707.02,,infty -2022-08-07 16:59:00,1706.41,,infty -2022-08-07 17:00:00,1708.62,,infty -2022-08-07 17:01:00,1708.87,,infty -2022-08-07 17:02:00,1708.04,,infty -2022-08-07 17:03:00,1706.93,,infty -2022-08-07 17:04:00,1706.86,,infty -2022-08-07 17:05:00,1707.44,,infty -2022-08-07 17:06:00,1707.67,,infty -2022-08-07 17:07:00,1708.69,,infty -2022-08-07 17:08:00,1707.22,,infty -2022-08-07 17:09:00,1707.99,,infty -2022-08-07 17:10:00,1710.36,,infty -2022-08-07 17:11:00,1709.55,,infty -2022-08-07 17:12:00,1710.06,,infty -2022-08-07 17:13:00,1716.5,,infty -2022-08-07 17:14:00,1712.98,,infty -2022-08-07 17:15:00,1714.45,,infty -2022-08-07 17:16:00,1713.79,,infty -2022-08-07 17:17:00,1713.24,,infty -2022-08-07 17:18:00,1713.58,,infty -2022-08-07 17:19:00,1712.65,,infty -2022-08-07 17:20:00,1709.12,,infty -2022-08-07 17:21:00,1710.53,,infty -2022-08-07 17:22:00,1710.42,,infty -2022-08-07 17:23:00,1711.0,,infty 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17:51:00,1711.59,,infty -2022-08-07 17:52:00,1711.18,,infty -2022-08-07 17:53:00,1710.57,,infty -2022-08-07 17:54:00,1712.5,,infty -2022-08-07 17:55:00,1712.55,,infty -2022-08-07 17:56:00,1713.37,,infty -2022-08-07 17:57:00,1712.56,,infty -2022-08-07 17:58:00,1712.57,,infty -2022-08-07 17:59:00,1712.46,,infty -2022-08-07 18:00:00,1713.16,,infty -2022-08-07 18:01:00,1714.47,,infty -2022-08-07 18:02:00,1713.09,,infty -2022-08-07 18:03:00,1712.66,,infty -2022-08-07 18:04:00,1711.79,,infty -2022-08-07 18:05:00,1711.81,,infty -2022-08-07 18:06:00,1711.65,,infty -2022-08-07 18:07:00,1710.81,,infty -2022-08-07 18:08:00,1710.48,,infty -2022-08-07 18:09:00,1710.0,,infty -2022-08-07 18:10:00,1710.49,,infty -2022-08-07 18:11:00,1710.07,,infty -2022-08-07 18:12:00,1709.39,,infty -2022-08-07 18:13:00,1709.42,,infty -2022-08-07 18:14:00,1710.49,,infty -2022-08-07 18:15:00,1710.66,,infty -2022-08-07 18:16:00,1710.58,,infty -2022-08-07 18:17:00,1710.52,,infty -2022-08-07 18:18:00,1711.4,,infty 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18:46:00,1709.35,,infty -2022-08-07 18:47:00,1707.29,,infty -2022-08-07 18:48:00,1707.94,,infty -2022-08-07 18:49:00,1708.67,,infty -2022-08-07 18:50:00,1707.94,,infty -2022-08-07 18:51:00,1706.7,,infty -2022-08-07 18:52:00,1708.81,,infty -2022-08-07 18:53:00,1708.53,,infty -2022-08-07 18:54:00,1708.54,,infty -2022-08-07 18:55:00,1708.53,,infty -2022-08-07 18:56:00,1707.75,,infty -2022-08-07 18:57:00,1708.33,,infty -2022-08-07 18:58:00,1707.47,,infty -2022-08-07 18:59:00,1708.07,,infty -2022-08-07 19:00:00,1707.57,,infty -2022-08-07 19:01:00,1707.4,,infty -2022-08-07 19:02:00,1707.61,,infty -2022-08-07 19:03:00,1707.06,,infty -2022-08-07 19:04:00,1707.29,,infty -2022-08-07 19:05:00,1706.75,,infty -2022-08-07 19:06:00,1707.1,,infty -2022-08-07 19:07:00,1707.33,,infty -2022-08-07 19:08:00,1707.7,,infty -2022-08-07 19:09:00,1707.53,,infty -2022-08-07 19:10:00,1707.87,,infty -2022-08-07 19:11:00,1706.73,,infty -2022-08-07 19:12:00,1707.51,,infty -2022-08-07 19:13:00,1708.73,,infty 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19:41:00,1710.62,,infty -2022-08-07 19:42:00,1710.56,,infty -2022-08-07 19:43:00,1710.09,,infty -2022-08-07 19:44:00,1710.14,,infty -2022-08-07 19:45:00,1709.57,,infty -2022-08-07 19:46:00,1709.95,,infty -2022-08-07 19:47:00,1709.99,,infty -2022-08-07 19:48:00,1710.5,,infty -2022-08-07 19:49:00,1709.61,,infty -2022-08-07 19:50:00,1708.56,,infty -2022-08-07 19:51:00,1708.54,,infty -2022-08-07 19:52:00,1709.11,,infty -2022-08-07 19:53:00,1709.46,,infty -2022-08-07 19:54:00,1709.14,,infty -2022-08-07 19:55:00,1708.69,,infty -2022-08-07 19:56:00,1708.72,,infty -2022-08-07 19:57:00,1708.73,,infty -2022-08-07 19:58:00,1708.14,,infty -2022-08-07 19:59:00,1708.44,,infty -2022-08-07 20:00:00,1708.45,,infty -2022-08-07 20:01:00,1708.96,,infty -2022-08-07 20:02:00,1709.69,,infty -2022-08-07 20:03:00,1711.46,,infty -2022-08-07 20:04:00,1712.86,,infty -2022-08-07 20:05:00,1713.06,,infty -2022-08-07 20:06:00,1714.59,,infty -2022-08-07 20:07:00,1714.17,,infty -2022-08-07 20:08:00,1713.47,,infty 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20:36:00,1719.04,,infty -2022-08-07 20:37:00,1719.57,,infty -2022-08-07 20:38:00,1718.13,,infty -2022-08-07 20:39:00,1718.55,,infty -2022-08-07 20:40:00,1718.2,,infty -2022-08-07 20:41:00,1719.13,,infty -2022-08-07 20:42:00,1718.7,,infty -2022-08-07 20:43:00,1718.26,,infty -2022-08-07 20:44:00,1718.65,,infty -2022-08-07 20:45:00,1719.01,,infty -2022-08-07 20:46:00,1719.02,,infty -2022-08-07 20:47:00,1717.82,,infty -2022-08-07 20:48:00,1719.8,,infty -2022-08-07 20:49:00,1720.51,,infty -2022-08-07 20:50:00,1720.26,,infty -2022-08-07 20:51:00,1721.14,,infty -2022-08-07 20:52:00,1722.24,,infty -2022-08-07 20:53:00,1720.78,,infty -2022-08-07 20:54:00,1719.99,,infty -2022-08-07 20:55:00,1720.69,,infty -2022-08-07 20:56:00,1720.92,,infty -2022-08-07 20:57:00,1722.62,,infty -2022-08-07 20:58:00,1722.74,,infty -2022-08-07 20:59:00,1722.49,,infty -2022-08-07 21:00:00,1721.25,,infty -2022-08-07 21:01:00,1722.78,,infty -2022-08-07 21:02:00,1723.26,,infty -2022-08-07 21:03:00,1724.19,,infty 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21:31:00,1718.37,,infty -2022-08-07 21:32:00,1719.02,,infty -2022-08-07 21:33:00,1718.79,,infty -2022-08-07 21:34:00,1717.02,,infty -2022-08-07 21:35:00,1717.02,,infty -2022-08-07 21:36:00,1717.87,,infty -2022-08-07 21:37:00,1714.98,,infty -2022-08-07 21:38:00,1714.66,,infty -2022-08-07 21:39:00,1713.64,,infty -2022-08-07 21:40:00,1714.2,,infty -2022-08-07 21:41:00,1716.68,,infty -2022-08-07 21:42:00,1715.78,,infty -2022-08-07 21:43:00,1716.49,,infty -2022-08-07 21:44:00,1716.24,,infty -2022-08-07 21:45:00,1716.21,,infty -2022-08-07 21:46:00,1715.59,,infty -2022-08-07 21:47:00,1716.99,,infty -2022-08-07 21:48:00,1716.45,,infty -2022-08-07 21:49:00,1717.2,,infty -2022-08-07 21:50:00,1717.14,,infty -2022-08-07 21:51:00,1717.11,,infty -2022-08-07 21:52:00,1716.75,,infty -2022-08-07 21:53:00,1715.47,,infty -2022-08-07 21:54:00,1716.59,,infty -2022-08-07 21:55:00,1717.34,,infty -2022-08-07 21:56:00,1717.34,,infty -2022-08-07 21:57:00,1716.71,,infty -2022-08-07 21:58:00,1717.28,,infty 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22:26:00,1711.68,,infty -2022-08-07 22:27:00,1711.5,,infty -2022-08-07 22:28:00,1711.29,,infty -2022-08-07 22:29:00,1710.27,,infty -2022-08-07 22:30:00,1710.19,,infty -2022-08-07 22:31:00,1709.96,,infty -2022-08-07 22:32:00,1709.27,,infty -2022-08-07 22:33:00,1707.22,,infty -2022-08-07 22:34:00,1706.59,,infty -2022-08-07 22:35:00,1708.06,,infty -2022-08-07 22:36:00,1707.44,,infty -2022-08-07 22:37:00,1708.4,,infty -2022-08-07 22:38:00,1707.48,,infty -2022-08-07 22:39:00,1706.52,,infty -2022-08-07 22:40:00,1705.91,,infty -2022-08-07 22:41:00,1707.42,,infty -2022-08-07 22:42:00,1706.19,,infty -2022-08-07 22:43:00,1705.62,,infty -2022-08-07 22:44:00,1703.4,,infty -2022-08-07 22:45:00,1705.19,,infty -2022-08-07 22:46:00,1704.79,,infty -2022-08-07 22:47:00,1705.13,,infty -2022-08-07 22:48:00,1706.16,,infty -2022-08-07 22:49:00,1705.33,,infty -2022-08-07 22:50:00,1705.84,,infty -2022-08-07 22:51:00,1704.75,,infty -2022-08-07 22:52:00,1705.96,,infty -2022-08-07 22:53:00,1705.27,,infty 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03:56:00,1712.15,,infty -2022-08-08 03:57:00,1710.92,,infty -2022-08-08 03:58:00,1710.01,,infty -2022-08-08 03:59:00,1710.06,,infty -2022-08-08 04:00:00,1710.38,,infty -2022-08-08 04:01:00,1709.41,,infty -2022-08-08 04:02:00,1709.74,,infty -2022-08-08 04:03:00,1709.74,,infty -2022-08-08 04:04:00,1709.79,,infty -2022-08-08 04:05:00,1709.77,,infty -2022-08-08 04:06:00,1709.67,,infty -2022-08-08 04:07:00,1711.38,,infty -2022-08-08 04:08:00,1711.65,,infty -2022-08-08 04:09:00,1712.01,,infty -2022-08-08 04:10:00,1711.11,,infty -2022-08-08 04:11:00,1710.41,,infty -2022-08-08 04:12:00,1711.04,,infty -2022-08-08 04:13:00,1711.38,,infty -2022-08-08 04:14:00,1711.31,,infty -2022-08-08 04:15:00,1712.22,,infty -2022-08-08 04:16:00,1711.84,,infty -2022-08-08 04:17:00,1712.02,,infty -2022-08-08 04:18:00,1711.86,,infty -2022-08-08 04:19:00,1710.72,,infty -2022-08-08 04:20:00,1710.63,,infty -2022-08-08 04:21:00,1709.85,,infty -2022-08-08 04:22:00,1709.85,,infty -2022-08-08 04:23:00,1710.13,,infty 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05:19:00,1716.81,,infty -2022-08-08 05:20:00,1717.53,,infty -2022-08-08 05:21:00,1717.82,,infty -2022-08-08 05:22:00,1718.14,,infty -2022-08-08 05:23:00,1719.95,,infty -2022-08-08 05:24:00,1720.53,,infty -2022-08-08 05:25:00,1720.09,,infty -2022-08-08 05:26:00,1720.85,,infty -2022-08-08 05:27:00,1721.27,,infty -2022-08-08 05:28:00,1724.0,,infty -2022-08-08 05:29:00,1723.5,,infty -2022-08-08 05:30:00,1721.54,,infty -2022-08-08 05:31:00,1724.53,,infty -2022-08-08 05:32:00,1722.41,,infty -2022-08-08 05:33:00,1722.95,,infty -2022-08-08 05:34:00,1723.08,,infty -2022-08-08 05:35:00,1720.87,,infty -2022-08-08 05:36:00,1719.59,,infty -2022-08-08 05:37:00,1720.13,,infty -2022-08-08 05:38:00,1720.84,,infty -2022-08-08 05:39:00,1722.04,,infty -2022-08-08 05:40:00,1720.97,,infty -2022-08-08 05:41:00,1723.27,,infty -2022-08-08 05:42:00,1723.09,,infty -2022-08-08 05:43:00,1722.62,,infty -2022-08-08 05:44:00,1722.27,,infty -2022-08-08 05:45:00,1723.73,,infty -2022-08-08 05:46:00,1725.16,,infty 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06:14:00,1729.11,,infty -2022-08-08 06:15:00,1729.42,,infty -2022-08-08 06:16:00,1729.19,,infty -2022-08-08 06:17:00,1729.25,,infty -2022-08-08 06:18:00,1730.42,,infty -2022-08-08 06:19:00,1730.95,,infty -2022-08-08 06:20:00,1730.93,,infty -2022-08-08 06:21:00,1731.13,,infty -2022-08-08 06:22:00,1732.31,,infty -2022-08-08 06:23:00,1732.24,,infty -2022-08-08 06:24:00,1731.32,,infty -2022-08-08 06:25:00,1731.87,,infty -2022-08-08 06:26:00,1732.82,,infty -2022-08-08 06:27:00,1731.72,,infty -2022-08-08 06:28:00,1732.05,,infty -2022-08-08 06:29:00,1732.11,,infty -2022-08-08 06:30:00,1734.2,,infty -2022-08-08 06:31:00,1737.32,,infty -2022-08-08 06:32:00,1739.0,,infty -2022-08-08 06:33:00,1738.65,,infty -2022-08-08 06:34:00,1736.81,,infty -2022-08-08 06:35:00,1733.78,,infty -2022-08-08 06:36:00,1733.7,,infty -2022-08-08 06:37:00,1732.32,,infty -2022-08-08 06:38:00,1730.7,,infty -2022-08-08 06:39:00,1728.77,,infty -2022-08-08 06:40:00,1731.4,,infty -2022-08-08 06:41:00,1733.42,,infty 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07:09:00,1736.34,,infty -2022-08-08 07:10:00,1736.59,,infty -2022-08-08 07:11:00,1737.76,,infty -2022-08-08 07:12:00,1736.41,,infty -2022-08-08 07:13:00,1736.74,,infty -2022-08-08 07:14:00,1735.98,,infty -2022-08-08 07:15:00,1734.36,,infty -2022-08-08 07:16:00,1732.64,,infty -2022-08-08 07:17:00,1734.11,,infty -2022-08-08 07:18:00,1733.57,,infty -2022-08-08 07:19:00,1731.61,,infty -2022-08-08 07:20:00,1731.34,,infty -2022-08-08 07:21:00,1730.55,,infty -2022-08-08 07:22:00,1733.33,,infty -2022-08-08 07:23:00,1735.44,,infty -2022-08-08 07:24:00,1735.73,,infty -2022-08-08 07:25:00,1736.9,,infty -2022-08-08 07:26:00,1737.06,,infty -2022-08-08 07:27:00,1736.41,,infty -2022-08-08 07:28:00,1737.5,,infty -2022-08-08 07:29:00,1737.08,,infty -2022-08-08 07:30:00,1737.65,,infty -2022-08-08 07:31:00,1737.04,,infty -2022-08-08 07:32:00,1735.63,,infty -2022-08-08 07:33:00,1735.09,,infty -2022-08-08 07:34:00,1734.0,,infty -2022-08-08 07:35:00,1734.34,,infty -2022-08-08 07:36:00,1733.12,,infty 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08:04:00,1735.78,,infty -2022-08-08 08:05:00,1736.3,,infty -2022-08-08 08:06:00,1734.53,,infty -2022-08-08 08:07:00,1733.32,,infty -2022-08-08 08:08:00,1732.24,,infty -2022-08-08 08:09:00,1732.05,,infty -2022-08-08 08:10:00,1731.06,,infty -2022-08-08 08:11:00,1730.52,,infty -2022-08-08 08:12:00,1731.52,,infty -2022-08-08 08:13:00,1731.63,,infty -2022-08-08 08:14:00,1730.8,,infty -2022-08-08 08:15:00,1730.01,,infty -2022-08-08 08:16:00,1730.08,,infty -2022-08-08 08:17:00,1731.23,,infty -2022-08-08 08:18:00,1732.01,,infty -2022-08-08 08:19:00,1731.41,,infty -2022-08-08 08:20:00,1732.26,,infty -2022-08-08 08:21:00,1733.6,,infty -2022-08-08 08:22:00,1733.77,,infty -2022-08-08 08:23:00,1735.62,,infty -2022-08-08 08:24:00,1735.28,,infty -2022-08-08 08:25:00,1735.35,,infty -2022-08-08 08:26:00,1735.52,,infty -2022-08-08 08:27:00,1736.04,,infty -2022-08-08 08:28:00,1735.93,,infty -2022-08-08 08:29:00,1737.19,,infty -2022-08-08 08:30:00,1740.15,,infty -2022-08-08 08:31:00,1741.72,,infty 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08:59:00,1773.95,,infty -2022-08-08 09:00:00,1776.37,,infty -2022-08-08 09:01:00,1777.49,,infty -2022-08-08 09:02:00,1775.56,,infty -2022-08-08 09:03:00,1777.14,,infty -2022-08-08 09:04:00,1775.0,,infty -2022-08-08 09:05:00,1777.19,,infty -2022-08-08 09:06:00,1777.32,,infty -2022-08-08 09:07:00,1777.19,,infty -2022-08-08 09:08:00,1775.29,,infty -2022-08-08 09:09:00,1775.82,,infty -2022-08-08 09:10:00,1777.21,,infty -2022-08-08 09:11:00,1775.11,,infty -2022-08-08 09:12:00,1774.23,,infty -2022-08-08 09:13:00,1771.62,,infty -2022-08-08 09:14:00,1769.0,,infty -2022-08-08 09:15:00,1771.6,,infty -2022-08-08 09:16:00,1772.94,,infty -2022-08-08 09:17:00,1773.3,,infty -2022-08-08 09:18:00,1770.74,,infty -2022-08-08 09:19:00,1772.59,,infty -2022-08-08 09:20:00,1772.39,,infty -2022-08-08 09:21:00,1775.43,,infty -2022-08-08 09:22:00,1775.34,,infty -2022-08-08 09:23:00,1775.83,,infty -2022-08-08 09:24:00,1775.94,,infty -2022-08-08 09:25:00,1774.57,,infty -2022-08-08 09:26:00,1774.16,,infty 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09:54:00,1771.4,,infty -2022-08-08 09:55:00,1770.61,,infty -2022-08-08 09:56:00,1768.61,,infty -2022-08-08 09:57:00,1769.09,,infty -2022-08-08 09:58:00,1768.42,,infty -2022-08-08 09:59:00,1769.26,,infty -2022-08-08 10:00:00,1770.33,,infty -2022-08-08 10:01:00,1768.13,,infty -2022-08-08 10:02:00,1768.12,,infty -2022-08-08 10:03:00,1769.91,,infty -2022-08-08 10:04:00,1771.59,,infty -2022-08-08 10:05:00,1771.6,,infty -2022-08-08 10:06:00,1770.76,,infty -2022-08-08 10:07:00,1771.12,,infty -2022-08-08 10:08:00,1771.94,,infty -2022-08-08 10:09:00,1771.43,,infty -2022-08-08 10:10:00,1773.25,,infty -2022-08-08 10:11:00,1772.23,,infty -2022-08-08 10:12:00,1771.47,,infty -2022-08-08 10:13:00,1770.57,,infty -2022-08-08 10:14:00,1769.17,,infty -2022-08-08 10:15:00,1770.35,,infty -2022-08-08 10:16:00,1769.92,,infty -2022-08-08 10:17:00,1770.05,,infty -2022-08-08 10:18:00,1770.46,,infty -2022-08-08 10:19:00,1769.76,,infty -2022-08-08 10:20:00,1770.12,,infty -2022-08-08 10:21:00,1769.1,,infty 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10:49:00,1774.22,,infty -2022-08-08 10:50:00,1773.57,,infty -2022-08-08 10:51:00,1773.5,,infty -2022-08-08 10:52:00,1773.02,,infty -2022-08-08 10:53:00,1773.41,,infty -2022-08-08 10:54:00,1772.62,,infty -2022-08-08 10:55:00,1772.8,,infty -2022-08-08 10:56:00,1772.39,,infty -2022-08-08 10:57:00,1773.48,,infty -2022-08-08 10:58:00,1773.5,,infty -2022-08-08 10:59:00,1773.12,,infty -2022-08-08 11:00:00,1772.58,,infty -2022-08-08 11:01:00,1772.19,,infty -2022-08-08 11:02:00,1771.48,,infty -2022-08-08 11:03:00,1770.16,,infty -2022-08-08 11:04:00,1771.02,,infty -2022-08-08 11:05:00,1769.17,,infty -2022-08-08 11:06:00,1770.21,,infty -2022-08-08 11:07:00,1770.8,,infty -2022-08-08 11:08:00,1771.31,,infty -2022-08-08 11:09:00,1770.86,,infty -2022-08-08 11:10:00,1771.47,,infty -2022-08-08 11:11:00,1770.5,,infty -2022-08-08 11:12:00,1770.29,,infty -2022-08-08 11:13:00,1770.09,,infty -2022-08-08 11:14:00,1770.18,,infty -2022-08-08 11:15:00,1769.31,,infty -2022-08-08 11:16:00,1770.39,,infty 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11:44:00,1783.39,,infty -2022-08-08 11:45:00,1783.16,,infty -2022-08-08 11:46:00,1784.02,,infty -2022-08-08 11:47:00,1783.75,,infty -2022-08-08 11:48:00,1783.36,,infty -2022-08-08 11:49:00,1782.65,,infty -2022-08-08 11:50:00,1783.09,,infty -2022-08-08 11:51:00,1783.46,,infty -2022-08-08 11:52:00,1784.32,,infty -2022-08-08 11:53:00,1785.01,,infty -2022-08-08 11:54:00,1786.27,,infty -2022-08-08 11:55:00,1785.74,,infty -2022-08-08 11:56:00,1788.15,,infty -2022-08-08 11:57:00,1786.17,,infty -2022-08-08 11:58:00,1787.73,,infty -2022-08-08 11:59:00,1789.69,,infty -2022-08-08 12:00:00,1788.51,,infty -2022-08-08 12:01:00,1787.23,,infty -2022-08-08 12:02:00,1787.83,,infty -2022-08-08 12:03:00,1790.27,,infty -2022-08-08 12:04:00,1793.7,,infty -2022-08-08 12:05:00,1799.21,,infty -2022-08-08 12:06:00,1796.49,,infty -2022-08-08 12:07:00,1798.06,,infty -2022-08-08 12:08:00,1803.8,,infty -2022-08-08 12:09:00,1814.69,,infty -2022-08-08 12:10:00,1811.11,,infty -2022-08-08 12:11:00,1809.14,,infty 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12:39:00,1799.31,,infty -2022-08-08 12:40:00,1800.6,,infty -2022-08-08 12:41:00,1801.71,,infty -2022-08-08 12:42:00,1801.73,,infty -2022-08-08 12:43:00,1803.46,,infty -2022-08-08 12:44:00,1804.0,,infty -2022-08-08 12:45:00,1803.22,,infty -2022-08-08 12:46:00,1801.09,,infty -2022-08-08 12:47:00,1802.1,,infty -2022-08-08 12:48:00,1802.79,,infty -2022-08-08 12:49:00,1803.04,,infty -2022-08-08 12:50:00,1805.12,,infty -2022-08-08 12:51:00,1803.94,,infty -2022-08-08 12:52:00,1806.02,,infty -2022-08-08 12:53:00,1808.64,,infty -2022-08-08 12:54:00,1809.69,,infty -2022-08-08 12:55:00,1806.0,,infty -2022-08-08 12:56:00,1807.55,,infty -2022-08-08 12:57:00,1808.17,,infty -2022-08-08 12:58:00,1808.41,,infty -2022-08-08 12:59:00,1808.49,,infty -2022-08-08 13:00:00,1806.62,,infty -2022-08-08 13:01:00,1805.48,,infty -2022-08-08 13:02:00,1800.02,,infty -2022-08-08 13:03:00,1798.81,,infty -2022-08-08 13:04:00,1798.05,,infty -2022-08-08 13:05:00,1797.33,,infty -2022-08-08 13:06:00,1799.48,,infty 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13:34:00,1799.26,,infty -2022-08-08 13:35:00,1799.04,,infty -2022-08-08 13:36:00,1800.55,,infty -2022-08-08 13:37:00,1799.51,,infty -2022-08-08 13:38:00,1800.14,,infty -2022-08-08 13:39:00,1802.64,,infty -2022-08-08 13:40:00,1802.46,,infty -2022-08-08 13:41:00,1803.23,,infty -2022-08-08 13:42:00,1804.85,,infty -2022-08-08 13:43:00,1804.39,,infty -2022-08-08 13:44:00,1805.11,,infty -2022-08-08 13:45:00,1803.02,,infty -2022-08-08 13:46:00,1805.06,,infty -2022-08-08 13:47:00,1803.07,,infty -2022-08-08 13:48:00,1805.36,,infty -2022-08-08 13:49:00,1805.81,,infty -2022-08-08 13:50:00,1805.96,,infty -2022-08-08 13:51:00,1804.58,,infty -2022-08-08 13:52:00,1801.95,,infty -2022-08-08 13:53:00,1801.11,,infty -2022-08-08 13:54:00,1802.97,,infty -2022-08-08 13:55:00,1804.02,,infty -2022-08-08 13:56:00,1803.32,,infty -2022-08-08 13:57:00,1804.81,,infty -2022-08-08 13:58:00,1802.11,,infty -2022-08-08 13:59:00,1801.21,,infty -2022-08-08 14:00:00,1801.91,,infty -2022-08-08 14:01:00,1801.9,,infty 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14:29:00,1798.11,,infty -2022-08-08 14:30:00,1798.39,,infty -2022-08-08 14:31:00,1799.17,,infty -2022-08-08 14:32:00,1799.06,,infty -2022-08-08 14:33:00,1798.64,,infty -2022-08-08 14:34:00,1796.41,,infty -2022-08-08 14:35:00,1794.96,,infty -2022-08-08 14:36:00,1797.99,,infty -2022-08-08 14:37:00,1796.44,,infty -2022-08-08 14:38:00,1799.38,,infty -2022-08-08 14:39:00,1799.12,,infty -2022-08-08 14:40:00,1798.36,,infty -2022-08-08 14:41:00,1796.99,,infty -2022-08-08 14:42:00,1796.58,,infty -2022-08-08 14:43:00,1798.74,,infty -2022-08-08 14:44:00,1801.41,,infty -2022-08-08 14:45:00,1800.39,,infty -2022-08-08 14:46:00,1799.05,,infty -2022-08-08 14:47:00,1796.61,,infty -2022-08-08 14:48:00,1795.0,,infty -2022-08-08 14:49:00,1794.4,,infty -2022-08-08 14:50:00,1796.6,,infty -2022-08-08 14:51:00,1795.36,,infty -2022-08-08 14:52:00,1796.92,,infty -2022-08-08 14:53:00,1795.68,,infty -2022-08-08 14:54:00,1797.89,,infty -2022-08-08 14:55:00,1796.77,,infty -2022-08-08 14:56:00,1795.15,,infty 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15:52:00,1770.13,,infty -2022-08-08 15:53:00,1772.01,,infty -2022-08-08 15:54:00,1775.13,,infty -2022-08-08 15:55:00,1775.94,,infty -2022-08-08 15:56:00,1774.83,,infty -2022-08-08 15:57:00,1774.65,,infty -2022-08-08 15:58:00,1774.89,,infty -2022-08-08 15:59:00,1775.65,,infty -2022-08-08 16:00:00,1777.15,,infty -2022-08-08 16:01:00,1776.48,,infty -2022-08-08 16:02:00,1776.19,,infty -2022-08-08 16:03:00,1777.25,,infty -2022-08-08 16:04:00,1776.8,,infty -2022-08-08 16:05:00,1776.23,,infty -2022-08-08 16:06:00,1773.07,,infty -2022-08-08 16:07:00,1771.42,,infty -2022-08-08 16:08:00,1769.16,,infty -2022-08-08 16:09:00,1767.29,,infty -2022-08-08 16:10:00,1769.6,,infty -2022-08-08 16:11:00,1768.47,,infty -2022-08-08 16:12:00,1766.97,,infty -2022-08-08 16:13:00,1768.32,,infty -2022-08-08 16:14:00,1770.85,,infty -2022-08-08 16:15:00,1771.49,,infty -2022-08-08 16:16:00,1770.79,,infty -2022-08-08 16:17:00,1767.09,,infty -2022-08-08 16:18:00,1762.99,,infty -2022-08-08 16:19:00,1762.06,,infty 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16:47:00,1770.27,,infty -2022-08-08 16:48:00,1771.93,,infty -2022-08-08 16:49:00,1771.33,,infty -2022-08-08 16:50:00,1772.04,,infty -2022-08-08 16:51:00,1771.82,,infty -2022-08-08 16:52:00,1770.96,,infty -2022-08-08 16:53:00,1769.57,,infty -2022-08-08 16:54:00,1770.95,,infty -2022-08-08 16:55:00,1769.78,,infty -2022-08-08 16:56:00,1768.65,,infty -2022-08-08 16:57:00,1767.28,,infty -2022-08-08 16:58:00,1766.17,,infty -2022-08-08 16:59:00,1768.0,,infty -2022-08-08 17:00:00,1765.69,,infty -2022-08-08 17:01:00,1766.17,,infty -2022-08-08 17:02:00,1763.95,,infty -2022-08-08 17:03:00,1765.43,,infty -2022-08-08 17:04:00,1766.89,,infty -2022-08-08 17:05:00,1767.95,,infty -2022-08-08 17:06:00,1768.96,,infty -2022-08-08 17:07:00,1771.78,,infty -2022-08-08 17:08:00,1771.54,,infty -2022-08-08 17:09:00,1770.73,,infty -2022-08-08 17:10:00,1771.31,,infty -2022-08-08 17:11:00,1770.4,,infty -2022-08-08 17:12:00,1772.2,,infty -2022-08-08 17:13:00,1770.54,,infty -2022-08-08 17:14:00,1770.75,,infty 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17:42:00,1768.37,,infty -2022-08-08 17:43:00,1768.08,,infty -2022-08-08 17:44:00,1767.85,,infty -2022-08-08 17:45:00,1769.72,,infty -2022-08-08 17:46:00,1769.55,,infty -2022-08-08 17:47:00,1767.13,,infty -2022-08-08 17:48:00,1765.99,,infty -2022-08-08 17:49:00,1765.99,,infty -2022-08-08 17:50:00,1766.02,,infty -2022-08-08 17:51:00,1767.96,,infty -2022-08-08 17:52:00,1768.66,,infty -2022-08-08 17:53:00,1770.06,,infty -2022-08-08 17:54:00,1771.41,,infty -2022-08-08 17:55:00,1771.24,,infty -2022-08-08 17:56:00,1770.91,,infty -2022-08-08 17:57:00,1770.13,,infty -2022-08-08 17:58:00,1770.47,,infty -2022-08-08 17:59:00,1770.81,,infty -2022-08-08 18:00:00,1769.85,,infty -2022-08-08 18:01:00,1769.65,,infty -2022-08-08 18:02:00,1770.01,,infty -2022-08-08 18:03:00,1767.01,,infty -2022-08-08 18:04:00,1766.03,,infty -2022-08-08 18:05:00,1763.47,,infty -2022-08-08 18:06:00,1765.97,,infty -2022-08-08 18:07:00,1766.79,,infty -2022-08-08 18:08:00,1764.83,,infty -2022-08-08 18:09:00,1765.04,,infty 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18:37:00,1777.76,,infty -2022-08-08 18:38:00,1780.52,,infty -2022-08-08 18:39:00,1777.49,,infty -2022-08-08 18:40:00,1778.51,,infty -2022-08-08 18:41:00,1778.86,,infty -2022-08-08 18:42:00,1778.91,,infty -2022-08-08 18:43:00,1777.61,,infty -2022-08-08 18:44:00,1778.7,,infty -2022-08-08 18:45:00,1774.02,,infty -2022-08-08 18:46:00,1775.65,,infty -2022-08-08 18:47:00,1775.12,,infty -2022-08-08 18:48:00,1772.83,,infty -2022-08-08 18:49:00,1774.86,,infty -2022-08-08 18:50:00,1774.3,,infty -2022-08-08 18:51:00,1772.16,,infty -2022-08-08 18:52:00,1771.67,,infty -2022-08-08 18:53:00,1770.26,,infty -2022-08-08 18:54:00,1769.37,,infty -2022-08-08 18:55:00,1772.12,,infty -2022-08-08 18:56:00,1770.73,,infty -2022-08-08 18:57:00,1772.55,,infty -2022-08-08 18:58:00,1774.15,,infty -2022-08-08 18:59:00,1773.82,,infty -2022-08-08 19:00:00,1774.43,,infty -2022-08-08 19:01:00,1773.28,,infty -2022-08-08 19:02:00,1775.35,,infty -2022-08-08 19:03:00,1776.36,,infty -2022-08-08 19:04:00,1773.85,,infty 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19:32:00,1779.72,,infty -2022-08-08 19:33:00,1783.72,,infty -2022-08-08 19:34:00,1782.52,,infty -2022-08-08 19:35:00,1782.77,,infty -2022-08-08 19:36:00,1783.86,,infty -2022-08-08 19:37:00,1784.26,,infty -2022-08-08 19:38:00,1784.64,,infty -2022-08-08 19:39:00,1785.95,,infty -2022-08-08 19:40:00,1785.0,,infty -2022-08-08 19:41:00,1785.58,,infty -2022-08-08 19:42:00,1785.76,,infty -2022-08-08 19:43:00,1784.68,,infty -2022-08-08 19:44:00,1786.03,,infty -2022-08-08 19:45:00,1785.72,,infty -2022-08-08 19:46:00,1786.36,,infty -2022-08-08 19:47:00,1784.89,,infty -2022-08-08 19:48:00,1784.41,,infty -2022-08-08 19:49:00,1782.84,,infty -2022-08-08 19:50:00,1784.65,,infty -2022-08-08 19:51:00,1784.23,,infty -2022-08-08 19:52:00,1782.68,,infty -2022-08-08 19:53:00,1782.87,,infty -2022-08-08 19:54:00,1783.57,,infty -2022-08-08 19:55:00,1782.81,,infty -2022-08-08 19:56:00,1782.63,,infty -2022-08-08 19:57:00,1782.34,,infty -2022-08-08 19:58:00,1783.1,,infty -2022-08-08 19:59:00,1783.75,,infty 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20:27:00,1784.95,,infty -2022-08-08 20:28:00,1786.05,,infty -2022-08-08 20:29:00,1787.43,,infty -2022-08-08 20:30:00,1788.33,,infty -2022-08-08 20:31:00,1786.87,,infty -2022-08-08 20:32:00,1787.68,,infty -2022-08-08 20:33:00,1788.28,,infty -2022-08-08 20:34:00,1788.04,,infty -2022-08-08 20:35:00,1789.95,,infty -2022-08-08 20:36:00,1790.95,,infty -2022-08-08 20:37:00,1791.08,,infty -2022-08-08 20:38:00,1789.66,,infty -2022-08-08 20:39:00,1791.03,,infty -2022-08-08 20:40:00,1790.31,,infty -2022-08-08 20:41:00,1789.77,,infty -2022-08-08 20:42:00,1790.55,,infty -2022-08-08 20:43:00,1788.67,,infty -2022-08-08 20:44:00,1787.36,,infty -2022-08-08 20:45:00,1785.46,,infty -2022-08-08 20:46:00,1785.74,,infty -2022-08-08 20:47:00,1787.19,,infty -2022-08-08 20:48:00,1788.11,,infty -2022-08-08 20:49:00,1790.22,,infty -2022-08-08 20:50:00,1791.36,,infty -2022-08-08 20:51:00,1791.54,,infty -2022-08-08 20:52:00,1792.3,,infty -2022-08-08 20:53:00,1794.55,,infty -2022-08-08 20:54:00,1792.21,,infty 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00:07:00,1779.82,,infty -2022-08-09 00:08:00,1778.31,,infty -2022-08-09 00:09:00,1778.98,,infty -2022-08-09 00:10:00,1776.14,,infty -2022-08-09 00:11:00,1773.53,,infty -2022-08-09 00:12:00,1770.99,,infty -2022-08-09 00:13:00,1772.72,,infty -2022-08-09 00:14:00,1776.99,,infty -2022-08-09 00:15:00,1775.91,,infty -2022-08-09 00:16:00,1777.35,,infty -2022-08-09 00:17:00,1777.33,,infty -2022-08-09 00:18:00,1779.11,,infty -2022-08-09 00:19:00,1783.32,,infty -2022-08-09 00:20:00,1782.65,,infty -2022-08-09 00:21:00,1784.75,,infty -2022-08-09 00:22:00,1784.01,,infty -2022-08-09 00:23:00,1783.63,,infty -2022-08-09 00:24:00,1781.04,,infty -2022-08-09 00:25:00,1782.47,,infty -2022-08-09 00:26:00,1781.81,,infty -2022-08-09 00:27:00,1782.98,,infty -2022-08-09 00:28:00,1781.93,,infty -2022-08-09 00:29:00,1783.78,,infty -2022-08-09 00:30:00,1784.19,,infty -2022-08-09 00:31:00,1783.56,,infty -2022-08-09 00:32:00,1782.99,,infty -2022-08-09 00:33:00,1784.49,,infty -2022-08-09 00:34:00,1785.73,,infty 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01:57:00,1765.25,,infty -2022-08-09 01:58:00,1766.05,,infty -2022-08-09 01:59:00,1766.41,,infty -2022-08-09 02:00:00,1765.58,,infty -2022-08-09 02:01:00,1766.82,,infty -2022-08-09 02:02:00,1767.56,,infty -2022-08-09 02:03:00,1767.77,,infty -2022-08-09 02:04:00,1767.98,,infty -2022-08-09 02:05:00,1767.74,,infty -2022-08-09 02:06:00,1768.31,,infty -2022-08-09 02:07:00,1769.66,,infty -2022-08-09 02:08:00,1765.75,,infty -2022-08-09 02:09:00,1764.79,,infty -2022-08-09 02:10:00,1764.54,,infty -2022-08-09 02:11:00,1766.53,,infty -2022-08-09 02:12:00,1765.57,,infty -2022-08-09 02:13:00,1765.96,,infty -2022-08-09 02:14:00,1764.04,,infty -2022-08-09 02:15:00,1763.47,,infty -2022-08-09 02:16:00,1767.13,,infty -2022-08-09 02:17:00,1768.0,,infty -2022-08-09 02:18:00,1766.74,,infty -2022-08-09 02:19:00,1768.22,,infty -2022-08-09 02:20:00,1768.39,,infty -2022-08-09 02:21:00,1770.15,,infty -2022-08-09 02:22:00,1770.45,,infty -2022-08-09 02:23:00,1771.11,,infty -2022-08-09 02:24:00,1770.61,,infty 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02:52:00,1772.83,,infty -2022-08-09 02:53:00,1772.88,,infty -2022-08-09 02:54:00,1772.96,,infty -2022-08-09 02:55:00,1772.81,,infty -2022-08-09 02:56:00,1774.46,,infty -2022-08-09 02:57:00,1773.61,,infty -2022-08-09 02:58:00,1773.93,,infty -2022-08-09 02:59:00,1774.43,,infty -2022-08-09 03:00:00,1774.27,,infty -2022-08-09 03:01:00,1773.51,,infty -2022-08-09 03:02:00,1774.23,,infty -2022-08-09 03:03:00,1773.57,,infty -2022-08-09 03:04:00,1778.98,,infty -2022-08-09 03:05:00,1777.19,,infty -2022-08-09 03:06:00,1774.94,,infty -2022-08-09 03:07:00,1775.17,,infty -2022-08-09 03:08:00,1775.61,,infty -2022-08-09 03:09:00,1775.97,,infty -2022-08-09 03:10:00,1774.74,,infty -2022-08-09 03:11:00,1774.42,,infty -2022-08-09 03:12:00,1776.28,,infty -2022-08-09 03:13:00,1775.36,,infty -2022-08-09 03:14:00,1777.04,,infty -2022-08-09 03:15:00,1777.23,,infty -2022-08-09 03:16:00,1777.57,,infty -2022-08-09 03:17:00,1777.61,,infty -2022-08-09 03:18:00,1776.07,,infty -2022-08-09 03:19:00,1775.72,,infty 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03:47:00,1778.12,,infty -2022-08-09 03:48:00,1778.92,,infty -2022-08-09 03:49:00,1778.26,,infty -2022-08-09 03:50:00,1777.73,,infty -2022-08-09 03:51:00,1777.21,,infty -2022-08-09 03:52:00,1777.4,,infty -2022-08-09 03:53:00,1779.26,,infty -2022-08-09 03:54:00,1778.92,,infty -2022-08-09 03:55:00,1778.33,,infty -2022-08-09 03:56:00,1779.34,,infty -2022-08-09 03:57:00,1779.08,,infty -2022-08-09 03:58:00,1778.92,,infty -2022-08-09 03:59:00,1779.16,,infty -2022-08-09 04:00:00,1779.5,,infty -2022-08-09 04:01:00,1778.99,,infty -2022-08-09 04:02:00,1777.8,,infty -2022-08-09 04:03:00,1777.33,,infty -2022-08-09 04:04:00,1779.16,,infty -2022-08-09 04:05:00,1779.16,,infty -2022-08-09 04:06:00,1780.25,,infty -2022-08-09 04:07:00,1781.28,,infty -2022-08-09 04:08:00,1779.33,,infty -2022-08-09 04:09:00,1780.05,,infty -2022-08-09 04:10:00,1780.21,,infty -2022-08-09 04:11:00,1779.78,,infty -2022-08-09 04:12:00,1778.82,,infty -2022-08-09 04:13:00,1779.82,,infty -2022-08-09 04:14:00,1779.82,,infty 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05:37:00,1779.88,,infty -2022-08-09 05:38:00,1779.23,,infty -2022-08-09 05:39:00,1777.63,,infty -2022-08-09 05:40:00,1778.09,,infty -2022-08-09 05:41:00,1778.6,,infty -2022-08-09 05:42:00,1779.15,,infty -2022-08-09 05:43:00,1778.55,,infty -2022-08-09 05:44:00,1778.97,,infty -2022-08-09 05:45:00,1778.07,,infty -2022-08-09 05:46:00,1778.32,,infty -2022-08-09 05:47:00,1778.78,,infty -2022-08-09 05:48:00,1779.88,,infty -2022-08-09 05:49:00,1780.25,,infty -2022-08-09 05:50:00,1779.51,,infty -2022-08-09 05:51:00,1778.44,,infty -2022-08-09 05:52:00,1779.43,,infty -2022-08-09 05:53:00,1779.29,,infty -2022-08-09 05:54:00,1780.03,,infty -2022-08-09 05:55:00,1779.6,,infty -2022-08-09 05:56:00,1778.84,,infty -2022-08-09 05:57:00,1779.07,,infty -2022-08-09 05:58:00,1777.87,,infty -2022-08-09 05:59:00,1777.87,,infty -2022-08-09 06:00:00,1776.13,,infty -2022-08-09 06:01:00,1770.98,,infty -2022-08-09 06:02:00,1769.82,,infty -2022-08-09 06:03:00,1769.36,,infty -2022-08-09 06:04:00,1769.64,,infty 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06:32:00,1771.01,,infty -2022-08-09 06:33:00,1772.2,,infty -2022-08-09 06:34:00,1770.87,,infty -2022-08-09 06:35:00,1771.11,,infty -2022-08-09 06:36:00,1770.24,,infty -2022-08-09 06:37:00,1770.01,,infty -2022-08-09 06:38:00,1770.88,,infty -2022-08-09 06:39:00,1772.0,,infty -2022-08-09 06:40:00,1772.06,,infty -2022-08-09 06:41:00,1772.11,,infty -2022-08-09 06:42:00,1772.0,,infty -2022-08-09 06:43:00,1772.01,,infty -2022-08-09 06:44:00,1772.5,,infty -2022-08-09 06:45:00,1772.0,,infty -2022-08-09 06:46:00,1773.08,,infty -2022-08-09 06:47:00,1773.47,,infty -2022-08-09 06:48:00,1772.0,,infty -2022-08-09 06:49:00,1772.41,,infty -2022-08-09 06:50:00,1771.68,,infty -2022-08-09 06:51:00,1771.54,,infty -2022-08-09 06:52:00,1773.63,,infty -2022-08-09 06:53:00,1773.56,,infty -2022-08-09 06:54:00,1774.83,,infty -2022-08-09 06:55:00,1776.18,,infty -2022-08-09 06:56:00,1779.45,,infty -2022-08-09 06:57:00,1779.07,,infty -2022-08-09 06:58:00,1779.74,,infty -2022-08-09 06:59:00,1780.02,,infty 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07:27:00,1778.15,,infty -2022-08-09 07:28:00,1778.52,,infty -2022-08-09 07:29:00,1779.37,,infty -2022-08-09 07:30:00,1780.0,,infty -2022-08-09 07:31:00,1779.55,,infty -2022-08-09 07:32:00,1780.6,,infty -2022-08-09 07:33:00,1780.74,,infty -2022-08-09 07:34:00,1780.68,,infty -2022-08-09 07:35:00,1780.58,,infty -2022-08-09 07:36:00,1778.71,,infty -2022-08-09 07:37:00,1779.43,,infty -2022-08-09 07:38:00,1780.41,,infty -2022-08-09 07:39:00,1780.99,,infty -2022-08-09 07:40:00,1782.39,,infty -2022-08-09 07:41:00,1780.98,,infty -2022-08-09 07:42:00,1780.02,,infty -2022-08-09 07:43:00,1781.5,,infty -2022-08-09 07:44:00,1780.04,,infty -2022-08-09 07:45:00,1778.88,,infty -2022-08-09 07:46:00,1778.86,,infty -2022-08-09 07:47:00,1778.55,,infty -2022-08-09 07:48:00,1780.0,,infty -2022-08-09 07:49:00,1778.3,,infty -2022-08-09 07:50:00,1779.98,,infty -2022-08-09 07:51:00,1778.86,,infty -2022-08-09 07:52:00,1777.11,,infty -2022-08-09 07:53:00,1778.41,,infty -2022-08-09 07:54:00,1779.15,,infty 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08:22:00,1773.91,,infty -2022-08-09 08:23:00,1775.42,,infty -2022-08-09 08:24:00,1775.98,,infty -2022-08-09 08:25:00,1776.07,,infty -2022-08-09 08:26:00,1775.14,,infty -2022-08-09 08:27:00,1773.95,,infty -2022-08-09 08:28:00,1775.41,,infty -2022-08-09 08:29:00,1775.65,,infty -2022-08-09 08:30:00,1775.51,,infty -2022-08-09 08:31:00,1776.18,,infty -2022-08-09 08:32:00,1778.26,,infty -2022-08-09 08:33:00,1777.44,,infty -2022-08-09 08:34:00,1776.66,,infty -2022-08-09 08:35:00,1775.67,,infty -2022-08-09 08:36:00,1775.69,,infty -2022-08-09 08:37:00,1776.58,,infty -2022-08-09 08:38:00,1777.23,,infty -2022-08-09 08:39:00,1777.47,,infty -2022-08-09 08:40:00,1777.53,,infty -2022-08-09 08:41:00,1776.35,,infty -2022-08-09 08:42:00,1775.85,,infty -2022-08-09 08:43:00,1774.72,,infty -2022-08-09 08:44:00,1774.85,,infty -2022-08-09 08:45:00,1773.77,,infty -2022-08-09 08:46:00,1773.42,,infty -2022-08-09 08:47:00,1772.11,,infty -2022-08-09 08:48:00,1771.51,,infty -2022-08-09 08:49:00,1772.65,,infty 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09:17:00,1772.81,,infty -2022-08-09 09:18:00,1771.66,,infty -2022-08-09 09:19:00,1771.17,,infty -2022-08-09 09:20:00,1766.6,,infty -2022-08-09 09:21:00,1767.5,,infty -2022-08-09 09:22:00,1765.04,,infty -2022-08-09 09:23:00,1763.3,,infty -2022-08-09 09:24:00,1763.48,,infty -2022-08-09 09:25:00,1763.77,,infty -2022-08-09 09:26:00,1763.48,,infty -2022-08-09 09:27:00,1759.93,,infty -2022-08-09 09:28:00,1759.13,,infty -2022-08-09 09:29:00,1760.82,,infty -2022-08-09 09:30:00,1758.78,,infty -2022-08-09 09:31:00,1757.49,,infty -2022-08-09 09:32:00,1757.43,,infty -2022-08-09 09:33:00,1758.92,,infty -2022-08-09 09:34:00,1759.88,,infty -2022-08-09 09:35:00,1759.32,,infty -2022-08-09 09:36:00,1757.63,,infty -2022-08-09 09:37:00,1759.6,,infty -2022-08-09 09:38:00,1761.02,,infty -2022-08-09 09:39:00,1760.03,,infty -2022-08-09 09:40:00,1759.35,,infty -2022-08-09 09:41:00,1756.71,,infty -2022-08-09 09:42:00,1755.92,,infty -2022-08-09 09:43:00,1753.8,,infty -2022-08-09 09:44:00,1751.81,,infty 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10:12:00,1739.05,,infty -2022-08-09 10:13:00,1736.25,,infty -2022-08-09 10:14:00,1735.81,,infty -2022-08-09 10:15:00,1732.07,,infty -2022-08-09 10:16:00,1731.63,,infty -2022-08-09 10:17:00,1734.82,,infty -2022-08-09 10:18:00,1736.95,,infty -2022-08-09 10:19:00,1737.47,,infty -2022-08-09 10:20:00,1735.3,,infty -2022-08-09 10:21:00,1733.54,,infty -2022-08-09 10:22:00,1733.66,,infty -2022-08-09 10:23:00,1734.7,,infty -2022-08-09 10:24:00,1733.92,,infty -2022-08-09 10:25:00,1733.55,,infty -2022-08-09 10:26:00,1733.35,,infty -2022-08-09 10:27:00,1732.49,,infty -2022-08-09 10:28:00,1725.92,,infty -2022-08-09 10:29:00,1727.11,,infty -2022-08-09 10:30:00,1727.93,,infty -2022-08-09 10:31:00,1722.55,,infty -2022-08-09 10:32:00,1722.06,,infty -2022-08-09 10:33:00,1724.72,,infty -2022-08-09 10:34:00,1723.83,,infty -2022-08-09 10:35:00,1723.06,,infty -2022-08-09 10:36:00,1720.55,,infty -2022-08-09 10:37:00,1719.07,,infty -2022-08-09 10:38:00,1718.28,,infty -2022-08-09 10:39:00,1719.73,,infty 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11:07:00,1715.92,,infty -2022-08-09 11:08:00,1716.49,,infty -2022-08-09 11:09:00,1715.93,,infty -2022-08-09 11:10:00,1717.81,,infty -2022-08-09 11:11:00,1718.36,,infty -2022-08-09 11:12:00,1721.57,,infty -2022-08-09 11:13:00,1719.28,,infty -2022-08-09 11:14:00,1718.38,,infty -2022-08-09 11:15:00,1720.63,,infty -2022-08-09 11:16:00,1721.43,,infty -2022-08-09 11:17:00,1722.9,,infty -2022-08-09 11:18:00,1722.75,,infty -2022-08-09 11:19:00,1722.5,,infty -2022-08-09 11:20:00,1722.63,,infty -2022-08-09 11:21:00,1722.97,,infty -2022-08-09 11:22:00,1721.57,,infty -2022-08-09 11:23:00,1719.59,,infty -2022-08-09 11:24:00,1719.15,,infty -2022-08-09 11:25:00,1716.4,,infty -2022-08-09 11:26:00,1718.92,,infty -2022-08-09 11:27:00,1717.87,,infty -2022-08-09 11:28:00,1718.49,,infty -2022-08-09 11:29:00,1716.5,,infty -2022-08-09 11:30:00,1716.74,,infty -2022-08-09 11:31:00,1717.5,,infty -2022-08-09 11:32:00,1718.1,,infty -2022-08-09 11:33:00,1718.37,,infty -2022-08-09 11:34:00,1718.06,,infty 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12:02:00,1703.46,,infty -2022-08-09 12:03:00,1703.45,,infty -2022-08-09 12:04:00,1705.33,,infty -2022-08-09 12:05:00,1703.54,,infty -2022-08-09 12:06:00,1703.72,,infty -2022-08-09 12:07:00,1701.65,,infty -2022-08-09 12:08:00,1702.45,,infty -2022-08-09 12:09:00,1700.97,,infty -2022-08-09 12:10:00,1700.64,,infty -2022-08-09 12:11:00,1700.43,,infty -2022-08-09 12:12:00,1702.91,,infty -2022-08-09 12:13:00,1702.77,,infty -2022-08-09 12:14:00,1705.08,,infty -2022-08-09 12:15:00,1705.53,,infty -2022-08-09 12:16:00,1704.92,,infty -2022-08-09 12:17:00,1704.39,,infty -2022-08-09 12:18:00,1706.4,,infty -2022-08-09 12:19:00,1706.9,,infty -2022-08-09 12:20:00,1708.65,,infty -2022-08-09 12:21:00,1708.92,,infty -2022-08-09 12:22:00,1711.38,,infty -2022-08-09 12:23:00,1712.44,,infty -2022-08-09 12:24:00,1711.24,,infty -2022-08-09 12:25:00,1710.16,,infty -2022-08-09 12:26:00,1711.15,,infty -2022-08-09 12:27:00,1711.84,,infty -2022-08-09 12:28:00,1710.85,,infty -2022-08-09 12:29:00,1710.18,,infty 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12:57:00,1707.21,,infty -2022-08-09 12:58:00,1707.52,,infty -2022-08-09 12:59:00,1706.98,,infty -2022-08-09 13:00:00,1707.74,,infty -2022-08-09 13:01:00,1709.72,,infty -2022-08-09 13:02:00,1708.64,,infty -2022-08-09 13:03:00,1709.6,,infty -2022-08-09 13:04:00,1709.7,,infty -2022-08-09 13:05:00,1710.42,,infty -2022-08-09 13:06:00,1708.02,,infty -2022-08-09 13:07:00,1708.37,,infty -2022-08-09 13:08:00,1708.56,,infty -2022-08-09 13:09:00,1708.65,,infty -2022-08-09 13:10:00,1709.38,,infty -2022-08-09 13:11:00,1710.41,,infty -2022-08-09 13:12:00,1711.0,,infty -2022-08-09 13:13:00,1709.04,,infty -2022-08-09 13:14:00,1709.1,,infty -2022-08-09 13:15:00,1707.37,,infty -2022-08-09 13:16:00,1706.46,,infty -2022-08-09 13:17:00,1707.6,,infty -2022-08-09 13:18:00,1707.48,,infty -2022-08-09 13:19:00,1708.69,,infty -2022-08-09 13:20:00,1707.55,,infty -2022-08-09 13:21:00,1708.96,,infty -2022-08-09 13:22:00,1710.07,,infty -2022-08-09 13:23:00,1711.21,,infty -2022-08-09 13:24:00,1712.18,,infty 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13:52:00,1709.99,,infty -2022-08-09 13:53:00,1706.66,,infty -2022-08-09 13:54:00,1702.05,,infty -2022-08-09 13:55:00,1695.48,,infty -2022-08-09 13:56:00,1694.5,,infty -2022-08-09 13:57:00,1688.75,,infty -2022-08-09 13:58:00,1689.34,,infty -2022-08-09 13:59:00,1687.27,,infty -2022-08-09 14:00:00,1683.04,,infty -2022-08-09 14:01:00,1681.05,,open_close -2022-08-09 14:02:00,1681.26,,infty -2022-08-09 14:03:00,1684.62,,infty -2022-08-09 14:04:00,1681.34,,infty -2022-08-09 14:05:00,1681.06,,open_close -2022-08-09 14:06:00,1679.2,,open_close -2022-08-09 14:07:00,1675.91,,open_close -2022-08-09 14:08:00,1671.35,,open_close -2022-08-09 14:09:00,1671.89,,open_close -2022-08-09 14:10:00,1674.5,,open_close -2022-08-09 14:11:00,1677.18,,open_close -2022-08-09 14:12:00,1679.52,,open_close -2022-08-09 14:13:00,1680.11,,open_close -2022-08-09 14:14:00,1680.07,,open_close -2022-08-09 14:15:00,1679.31,,open_close -2022-08-09 14:16:00,1680.37,,open_close -2022-08-09 14:17:00,1680.19,,open_close 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14:45:00,1686.61,,infty -2022-08-09 14:46:00,1686.9,,infty -2022-08-09 14:47:00,1690.01,,infty -2022-08-09 14:48:00,1692.38,,infty -2022-08-09 14:49:00,1690.87,,infty -2022-08-09 14:50:00,1691.2,,infty -2022-08-09 14:51:00,1690.16,,infty -2022-08-09 14:52:00,1690.6,,infty -2022-08-09 14:53:00,1688.47,,infty -2022-08-09 14:54:00,1688.94,,infty -2022-08-09 14:55:00,1687.61,,infty -2022-08-09 14:56:00,1687.6,,infty -2022-08-09 14:57:00,1686.73,,infty -2022-08-09 14:58:00,1684.09,,infty -2022-08-09 14:59:00,1685.32,,infty -2022-08-09 15:00:00,1684.16,,infty -2022-08-09 15:01:00,1681.94,,infty -2022-08-09 15:02:00,1682.58,,infty -2022-08-09 15:03:00,1685.07,,infty -2022-08-09 15:04:00,1683.37,,infty -2022-08-09 15:05:00,1684.26,,infty -2022-08-09 15:06:00,1685.4,,infty -2022-08-09 15:07:00,1686.78,,infty -2022-08-09 15:08:00,1688.1,,infty -2022-08-09 15:09:00,1686.52,,infty -2022-08-09 15:10:00,1687.5,,infty -2022-08-09 15:11:00,1689.36,,infty -2022-08-09 15:12:00,1691.76,,infty -2022-08-09 15:13:00,1691.74,,infty -2022-08-09 15:14:00,1693.18,,infty -2022-08-09 15:15:00,1691.89,,infty -2022-08-09 15:16:00,1690.44,,infty -2022-08-09 15:17:00,1690.63,,infty -2022-08-09 15:18:00,1692.99,,infty -2022-08-09 15:19:00,1693.34,,infty -2022-08-09 15:20:00,1694.18,,infty -2022-08-09 15:21:00,1693.41,,infty -2022-08-09 15:22:00,1694.83,,infty -2022-08-09 15:23:00,1692.99,,infty -2022-08-09 15:24:00,1693.55,,infty -2022-08-09 15:25:00,1693.76,,infty -2022-08-09 15:26:00,1694.42,,infty -2022-08-09 15:27:00,1693.01,,infty -2022-08-09 15:28:00,1692.68,,infty -2022-08-09 15:29:00,1691.87,,infty -2022-08-09 15:30:00,1691.4,,infty -2022-08-09 15:31:00,1692.78,,infty -2022-08-09 15:32:00,1691.18,,infty -2022-08-09 15:33:00,1689.73,,infty -2022-08-09 15:34:00,1690.25,,infty -2022-08-09 15:35:00,1690.55,,infty -2022-08-09 15:36:00,1689.36,,infty -2022-08-09 15:37:00,1691.78,,infty -2022-08-09 15:38:00,1691.39,,infty -2022-08-09 15:39:00,1690.56,,infty -2022-08-09 15:40:00,1691.02,,infty 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16:08:00,1684.23,,infty -2022-08-09 16:09:00,1682.6,,infty -2022-08-09 16:10:00,1681.16,,open_close -2022-08-09 16:11:00,1682.32,,infty -2022-08-09 16:12:00,1681.54,,infty -2022-08-09 16:13:00,1683.76,,infty -2022-08-09 16:14:00,1682.74,,infty -2022-08-09 16:15:00,1683.84,,infty -2022-08-09 16:16:00,1683.8,,infty -2022-08-09 16:17:00,1684.25,,infty -2022-08-09 16:18:00,1682.18,,infty -2022-08-09 16:19:00,1679.08,,open_close -2022-08-09 16:20:00,1679.74,,open_close -2022-08-09 16:21:00,1675.73,,open_close -2022-08-09 16:22:00,1676.47,,open_close -2022-08-09 16:23:00,1676.21,,open_close -2022-08-09 16:24:00,1678.77,,open_close -2022-08-09 16:25:00,1684.05,,infty -2022-08-09 16:26:00,1684.99,,infty -2022-08-09 16:27:00,1681.05,,open_close -2022-08-09 16:28:00,1680.56,,open_close -2022-08-09 16:29:00,1679.71,,open_close -2022-08-09 16:30:00,1679.84,,open_close -2022-08-09 16:31:00,1683.59,,infty -2022-08-09 16:32:00,1682.71,,infty -2022-08-09 16:33:00,1685.26,,infty -2022-08-09 16:34:00,1686.47,,infty -2022-08-09 16:35:00,1686.58,,infty -2022-08-09 16:36:00,1688.71,,infty -2022-08-09 16:37:00,1689.52,,infty -2022-08-09 16:38:00,1690.68,,infty -2022-08-09 16:39:00,1689.72,,infty -2022-08-09 16:40:00,1687.98,,infty -2022-08-09 16:41:00,1688.09,,infty -2022-08-09 16:42:00,1688.64,,infty -2022-08-09 16:43:00,1689.62,,infty -2022-08-09 16:44:00,1688.62,,infty -2022-08-09 16:45:00,1687.07,,infty -2022-08-09 16:46:00,1688.73,,infty -2022-08-09 16:47:00,1688.32,,infty -2022-08-09 16:48:00,1689.69,,infty -2022-08-09 16:49:00,1689.07,,infty -2022-08-09 16:50:00,1687.79,,infty -2022-08-09 16:51:00,1686.94,,infty -2022-08-09 16:52:00,1687.25,,infty -2022-08-09 16:53:00,1687.76,,infty -2022-08-09 16:54:00,1689.36,,infty -2022-08-09 16:55:00,1688.61,,infty -2022-08-09 16:56:00,1687.44,,infty -2022-08-09 16:57:00,1688.68,,infty -2022-08-09 16:58:00,1686.74,,infty -2022-08-09 16:59:00,1688.63,,infty -2022-08-09 17:00:00,1689.88,,infty -2022-08-09 17:01:00,1692.06,,infty 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17:29:00,1697.86,,infty -2022-08-09 17:30:00,1696.66,,infty -2022-08-09 17:31:00,1696.66,,infty -2022-08-09 17:32:00,1697.21,,infty -2022-08-09 17:33:00,1696.48,,infty -2022-08-09 17:34:00,1695.2,,infty -2022-08-09 17:35:00,1694.11,,infty -2022-08-09 17:36:00,1692.23,,infty -2022-08-09 17:37:00,1692.93,,infty -2022-08-09 17:38:00,1691.47,,infty -2022-08-09 17:39:00,1690.75,,infty -2022-08-09 17:40:00,1688.25,,infty -2022-08-09 17:41:00,1686.04,,infty -2022-08-09 17:42:00,1685.22,,infty -2022-08-09 17:43:00,1687.43,,infty -2022-08-09 17:44:00,1687.77,,infty -2022-08-09 17:45:00,1685.92,,infty -2022-08-09 17:46:00,1685.28,,infty -2022-08-09 17:47:00,1686.37,,infty -2022-08-09 17:48:00,1688.18,,infty -2022-08-09 17:49:00,1689.12,,infty -2022-08-09 17:50:00,1690.79,,infty -2022-08-09 17:51:00,1692.03,,infty -2022-08-09 17:52:00,1690.51,,infty -2022-08-09 17:53:00,1690.87,,infty -2022-08-09 17:54:00,1690.07,,infty -2022-08-09 17:55:00,1692.52,,infty -2022-08-09 17:56:00,1692.59,,infty 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18:24:00,1688.18,,infty -2022-08-09 18:25:00,1687.93,,infty -2022-08-09 18:26:00,1689.32,,infty -2022-08-09 18:27:00,1688.01,,infty -2022-08-09 18:28:00,1686.63,,infty -2022-08-09 18:29:00,1686.85,,infty -2022-08-09 18:30:00,1688.25,,infty -2022-08-09 18:31:00,1686.49,,infty -2022-08-09 18:32:00,1686.86,,infty -2022-08-09 18:33:00,1687.06,,infty -2022-08-09 18:34:00,1688.93,,infty -2022-08-09 18:35:00,1688.94,,infty -2022-08-09 18:36:00,1690.03,,infty -2022-08-09 18:37:00,1689.3,,infty -2022-08-09 18:38:00,1689.7,,infty -2022-08-09 18:39:00,1690.38,,infty -2022-08-09 18:40:00,1690.2,,infty -2022-08-09 18:41:00,1688.21,,infty -2022-08-09 18:42:00,1687.69,,infty -2022-08-09 18:43:00,1686.81,,infty -2022-08-09 18:44:00,1685.5,,infty -2022-08-09 18:45:00,1687.69,,infty -2022-08-09 18:46:00,1687.79,,infty -2022-08-09 18:47:00,1687.58,,infty -2022-08-09 18:48:00,1689.28,,infty -2022-08-09 18:49:00,1690.52,,infty -2022-08-09 18:50:00,1691.38,,infty -2022-08-09 18:51:00,1692.05,,infty 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19:19:00,1686.21,,infty -2022-08-09 19:20:00,1687.53,,infty -2022-08-09 19:21:00,1686.19,,infty -2022-08-09 19:22:00,1685.77,,infty -2022-08-09 19:23:00,1686.62,,infty -2022-08-09 19:24:00,1688.06,,infty -2022-08-09 19:25:00,1688.51,,infty -2022-08-09 19:26:00,1690.03,,infty -2022-08-09 19:27:00,1690.07,,infty -2022-08-09 19:28:00,1688.15,,infty -2022-08-09 19:29:00,1686.29,,infty -2022-08-09 19:30:00,1686.3,,infty -2022-08-09 19:31:00,1686.52,,infty -2022-08-09 19:32:00,1687.25,,infty -2022-08-09 19:33:00,1686.02,,infty -2022-08-09 19:34:00,1685.93,,infty -2022-08-09 19:35:00,1686.81,,infty -2022-08-09 19:36:00,1687.35,,infty -2022-08-09 19:37:00,1686.45,,infty -2022-08-09 19:38:00,1686.57,,infty -2022-08-09 19:39:00,1686.97,,infty -2022-08-09 19:40:00,1688.68,,infty -2022-08-09 19:41:00,1688.99,,infty -2022-08-09 19:42:00,1688.86,,infty -2022-08-09 19:43:00,1686.81,,infty -2022-08-09 19:44:00,1686.41,,infty -2022-08-09 19:45:00,1687.8,,infty -2022-08-09 19:46:00,1686.41,,infty 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20:14:00,1688.6,,infty -2022-08-09 20:15:00,1690.32,,infty -2022-08-09 20:16:00,1689.47,,infty -2022-08-09 20:17:00,1691.79,,infty -2022-08-09 20:18:00,1691.3,,infty -2022-08-09 20:19:00,1691.76,,infty -2022-08-09 20:20:00,1691.58,,infty -2022-08-09 20:21:00,1692.81,,infty -2022-08-09 20:22:00,1692.84,,infty -2022-08-09 20:23:00,1692.12,,infty -2022-08-09 20:24:00,1692.87,,infty -2022-08-09 20:25:00,1692.21,,infty -2022-08-09 20:26:00,1692.29,,infty -2022-08-09 20:27:00,1692.24,,infty -2022-08-09 20:28:00,1693.96,,infty -2022-08-09 20:29:00,1693.49,,infty -2022-08-09 20:30:00,1692.69,,infty -2022-08-09 20:31:00,1693.31,,infty -2022-08-09 20:32:00,1692.23,,infty -2022-08-09 20:33:00,1693.86,,infty -2022-08-09 20:34:00,1694.37,,infty -2022-08-09 20:35:00,1694.52,,infty -2022-08-09 20:36:00,1693.79,,infty -2022-08-09 20:37:00,1694.13,,infty -2022-08-09 20:38:00,1695.72,,infty -2022-08-09 20:39:00,1696.24,,infty -2022-08-09 20:40:00,1696.23,,infty -2022-08-09 20:41:00,1695.52,,infty -2022-08-09 20:42:00,1693.89,,infty -2022-08-09 20:43:00,1694.21,,infty -2022-08-09 20:44:00,1694.47,,infty -2022-08-09 20:45:00,1694.08,,infty -2022-08-09 20:46:00,1694.46,,infty -2022-08-09 20:47:00,1693.78,,infty -2022-08-09 20:48:00,1693.58,,infty -2022-08-09 20:49:00,1694.48,,infty -2022-08-09 20:50:00,1694.28,,infty -2022-08-09 20:51:00,1695.31,,infty -2022-08-09 20:52:00,1695.69,,infty -2022-08-09 20:53:00,1696.66,,infty -2022-08-09 20:54:00,1698.08,,infty -2022-08-09 20:55:00,1698.35,,infty -2022-08-09 20:56:00,1698.78,,infty -2022-08-09 20:57:00,1696.2,,infty -2022-08-09 20:58:00,1697.5,,infty -2022-08-09 20:59:00,1696.72,,infty -2022-08-09 21:00:00,1696.46,,infty -2022-08-09 21:01:00,1698.38,,infty -2022-08-09 21:02:00,1699.14,,infty -2022-08-09 21:03:00,1701.5,,infty -2022-08-09 21:04:00,1701.23,,infty -2022-08-09 21:05:00,1700.82,,infty -2022-08-09 21:06:00,1702.42,,infty -2022-08-09 21:07:00,1701.61,,infty -2022-08-09 21:08:00,1701.9,,infty -2022-08-09 21:09:00,1699.03,,infty -2022-08-09 21:10:00,1698.83,,infty -2022-08-09 21:11:00,1699.18,,infty -2022-08-09 21:12:00,1700.36,,infty -2022-08-09 21:13:00,1699.1,,infty -2022-08-09 21:14:00,1700.17,,infty -2022-08-09 21:15:00,1700.74,,infty -2022-08-09 21:16:00,1700.32,,infty -2022-08-09 21:17:00,1700.32,,infty -2022-08-09 21:18:00,1701.31,,infty -2022-08-09 21:19:00,1700.4,,infty -2022-08-09 21:20:00,1700.19,,infty -2022-08-09 21:21:00,1702.36,,infty -2022-08-09 21:22:00,1702.36,,infty -2022-08-09 21:23:00,1702.86,,infty -2022-08-09 21:24:00,1707.74,,infty -2022-08-09 21:25:00,1707.13,,infty -2022-08-09 21:26:00,1707.71,,infty -2022-08-09 21:27:00,1708.31,,infty -2022-08-09 21:28:00,1710.17,,infty -2022-08-09 21:29:00,1708.98,,infty -2022-08-09 21:30:00,1706.33,,infty -2022-08-09 21:31:00,1707.62,,infty -2022-08-09 21:32:00,1706.94,,infty -2022-08-09 21:33:00,1707.58,,infty -2022-08-09 21:34:00,1708.14,,infty -2022-08-09 21:35:00,1705.89,,infty -2022-08-09 21:36:00,1706.0,,infty 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22:04:00,1705.24,,infty -2022-08-09 22:05:00,1707.39,,infty -2022-08-09 22:06:00,1709.05,,infty -2022-08-09 22:07:00,1708.68,,infty -2022-08-09 22:08:00,1707.94,,infty -2022-08-09 22:09:00,1707.59,,infty -2022-08-09 22:10:00,1709.36,,infty -2022-08-09 22:11:00,1709.77,,infty -2022-08-09 22:12:00,1710.85,,infty -2022-08-09 22:13:00,1711.47,,infty -2022-08-09 22:14:00,1713.33,,infty -2022-08-09 22:15:00,1711.5,,infty -2022-08-09 22:16:00,1714.68,,infty -2022-08-09 22:17:00,1713.55,,infty -2022-08-09 22:18:00,1714.89,,infty -2022-08-09 22:19:00,1715.3,,infty -2022-08-09 22:20:00,1715.76,,infty -2022-08-09 22:21:00,1715.78,,infty -2022-08-09 22:22:00,1715.62,,infty -2022-08-09 22:23:00,1714.41,,infty -2022-08-09 22:24:00,1712.54,,infty -2022-08-09 22:25:00,1711.71,,infty -2022-08-09 22:26:00,1712.49,,infty -2022-08-09 22:27:00,1711.73,,infty -2022-08-09 22:28:00,1711.34,,infty -2022-08-09 22:29:00,1711.02,,infty -2022-08-09 22:30:00,1711.88,,infty -2022-08-09 22:31:00,1711.51,,infty 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22:59:00,1703.72,,infty -2022-08-09 23:00:00,1705.84,,infty -2022-08-09 23:01:00,1705.45,,infty -2022-08-09 23:02:00,1707.67,,infty -2022-08-09 23:03:00,1706.94,,infty -2022-08-09 23:04:00,1707.83,,infty -2022-08-09 23:05:00,1709.98,,infty -2022-08-09 23:06:00,1710.4,,infty -2022-08-09 23:07:00,1708.77,,infty -2022-08-09 23:08:00,1707.39,,infty -2022-08-09 23:09:00,1708.21,,infty -2022-08-09 23:10:00,1707.71,,infty -2022-08-09 23:11:00,1709.76,,infty -2022-08-09 23:12:00,1708.6,,infty -2022-08-09 23:13:00,1710.91,,infty -2022-08-09 23:14:00,1710.24,,infty -2022-08-09 23:15:00,1708.63,,infty -2022-08-09 23:16:00,1707.51,,infty -2022-08-09 23:17:00,1707.55,,infty -2022-08-09 23:18:00,1708.04,,infty -2022-08-09 23:19:00,1704.93,,infty -2022-08-09 23:20:00,1706.12,,infty -2022-08-09 23:21:00,1706.02,,infty -2022-08-09 23:22:00,1706.2,,infty -2022-08-09 23:23:00,1704.86,,infty -2022-08-09 23:24:00,1701.86,,infty -2022-08-09 23:25:00,1705.78,,infty -2022-08-09 23:26:00,1704.0,,infty 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23:54:00,1702.51,,infty -2022-08-09 23:55:00,1703.2,,infty -2022-08-09 23:56:00,1703.27,,infty -2022-08-09 23:57:00,1703.51,,infty -2022-08-09 23:58:00,1702.17,,infty -2022-08-09 23:59:00,1703.28,,infty -2022-08-10 00:00:00,1701.39,,infty -2022-08-10 00:01:00,1700.95,,infty -2022-08-10 00:02:00,1699.94,,infty -2022-08-10 00:03:00,1697.57,,infty -2022-08-10 00:04:00,1697.4,,infty -2022-08-10 00:05:00,1697.31,,infty -2022-08-10 00:06:00,1699.53,,infty -2022-08-10 00:07:00,1697.87,,infty -2022-08-10 00:08:00,1698.5,,infty -2022-08-10 00:09:00,1699.93,,infty -2022-08-10 00:10:00,1700.33,,infty -2022-08-10 00:11:00,1701.24,,infty -2022-08-10 00:12:00,1701.98,,infty -2022-08-10 00:13:00,1700.77,,infty -2022-08-10 00:14:00,1699.94,,infty -2022-08-10 00:15:00,1700.49,,infty -2022-08-10 00:16:00,1699.21,,infty -2022-08-10 00:17:00,1697.1,,infty -2022-08-10 00:18:00,1692.68,,infty -2022-08-10 00:19:00,1693.49,,infty -2022-08-10 00:20:00,1690.95,,infty -2022-08-10 00:21:00,1693.75,,infty -2022-08-10 00:22:00,1693.17,,infty -2022-08-10 00:23:00,1693.85,,infty -2022-08-10 00:24:00,1694.05,,infty -2022-08-10 00:25:00,1692.26,,infty -2022-08-10 00:26:00,1693.82,,infty -2022-08-10 00:27:00,1694.44,,infty -2022-08-10 00:28:00,1693.66,,infty -2022-08-10 00:29:00,1692.97,,infty -2022-08-10 00:30:00,1691.66,,infty -2022-08-10 00:31:00,1691.43,,infty -2022-08-10 00:32:00,1689.19,,infty -2022-08-10 00:33:00,1688.34,,infty -2022-08-10 00:34:00,1683.33,,infty -2022-08-10 00:35:00,1683.14,,infty -2022-08-10 00:36:00,1676.31,,open_close -2022-08-10 00:37:00,1678.96,,open_close -2022-08-10 00:38:00,1675.3,,open_close -2022-08-10 00:39:00,1675.86,,open_close -2022-08-10 00:40:00,1664.0,,open_close -2022-08-10 00:41:00,1662.35,,open_close -2022-08-10 00:42:00,1665.13,,open_close -2022-08-10 00:43:00,1666.98,,open_close -2022-08-10 00:44:00,1668.92,,open_close -2022-08-10 00:45:00,1663.84,,open_close -2022-08-10 00:46:00,1665.06,,open_close -2022-08-10 00:47:00,1664.89,,open_close -2022-08-10 00:48:00,1666.14,,open_close -2022-08-10 00:49:00,1666.5,,open_close -2022-08-10 00:50:00,1669.77,,open_close -2022-08-10 00:51:00,1670.26,,open_close -2022-08-10 00:52:00,1672.0,,open_close -2022-08-10 00:53:00,1672.23,,open_close -2022-08-10 00:54:00,1670.75,,open_close -2022-08-10 00:55:00,1668.27,,open_close -2022-08-10 00:56:00,1670.9,,open_close -2022-08-10 00:57:00,1670.85,,open_close -2022-08-10 00:58:00,1672.29,,open_close -2022-08-10 00:59:00,1672.57,,open_close -2022-08-10 01:00:00,1675.17,,open_close -2022-08-10 01:01:00,1674.0,,open_close -2022-08-10 01:02:00,1675.05,,open_close -2022-08-10 01:03:00,1679.02,,open_close -2022-08-10 01:04:00,1680.67,,open_close -2022-08-10 01:05:00,1683.44,,infty -2022-08-10 01:06:00,1682.25,,infty -2022-08-10 01:07:00,1680.68,,open_close -2022-08-10 01:08:00,1679.64,,open_close -2022-08-10 01:09:00,1679.5,,open_close -2022-08-10 01:10:00,1679.79,,open_close -2022-08-10 01:11:00,1680.64,,open_close -2022-08-10 01:12:00,1682.17,,infty -2022-08-10 01:13:00,1686.47,,infty -2022-08-10 01:14:00,1686.56,,infty -2022-08-10 01:15:00,1686.0,,infty -2022-08-10 01:16:00,1685.05,,infty -2022-08-10 01:17:00,1685.8,,infty -2022-08-10 01:18:00,1687.76,,infty -2022-08-10 01:19:00,1686.79,,infty -2022-08-10 01:20:00,1685.62,,infty -2022-08-10 01:21:00,1687.78,,infty -2022-08-10 01:22:00,1686.95,,infty -2022-08-10 01:23:00,1687.04,,infty -2022-08-10 01:24:00,1689.11,,infty -2022-08-10 01:25:00,1689.65,,infty -2022-08-10 01:26:00,1688.43,,infty -2022-08-10 01:27:00,1690.35,,infty -2022-08-10 01:28:00,1688.86,,infty -2022-08-10 01:29:00,1688.99,,infty -2022-08-10 01:30:00,1688.54,,infty -2022-08-10 01:31:00,1687.59,,infty -2022-08-10 01:32:00,1685.73,,infty -2022-08-10 01:33:00,1687.65,,infty -2022-08-10 01:34:00,1685.88,,infty -2022-08-10 01:35:00,1687.09,,infty -2022-08-10 01:36:00,1687.21,,infty -2022-08-10 01:37:00,1686.04,,infty -2022-08-10 01:38:00,1685.97,,infty -2022-08-10 01:39:00,1683.03,,infty -2022-08-10 01:40:00,1683.6,,infty -2022-08-10 01:41:00,1681.16,,open_close -2022-08-10 01:42:00,1681.09,,open_close -2022-08-10 01:43:00,1682.02,,infty -2022-08-10 01:44:00,1679.38,,open_close -2022-08-10 01:45:00,1680.95,,open_close -2022-08-10 01:46:00,1682.19,,infty -2022-08-10 01:47:00,1682.57,,infty -2022-08-10 01:48:00,1684.95,,infty -2022-08-10 01:49:00,1684.78,,infty -2022-08-10 01:50:00,1683.88,,infty -2022-08-10 01:51:00,1679.83,,open_close -2022-08-10 01:52:00,1680.02,,open_close -2022-08-10 01:53:00,1678.89,,open_close -2022-08-10 01:54:00,1679.63,,open_close -2022-08-10 01:55:00,1677.74,,open_close -2022-08-10 01:56:00,1680.9,,open_close -2022-08-10 01:57:00,1681.68,,infty -2022-08-10 01:58:00,1682.79,,infty -2022-08-10 01:59:00,1681.07,,open_close -2022-08-10 02:00:00,1683.26,,infty -2022-08-10 02:01:00,1683.02,,infty -2022-08-10 02:02:00,1684.08,,infty -2022-08-10 02:03:00,1684.65,,infty -2022-08-10 02:04:00,1683.32,,infty -2022-08-10 02:05:00,1683.65,,infty -2022-08-10 02:06:00,1681.55,,infty -2022-08-10 02:07:00,1682.21,,infty -2022-08-10 02:08:00,1683.04,,infty -2022-08-10 02:09:00,1683.01,,infty -2022-08-10 02:10:00,1681.02,,open_close -2022-08-10 02:11:00,1680.96,,open_close -2022-08-10 02:12:00,1680.8,,open_close -2022-08-10 02:13:00,1680.67,,open_close -2022-08-10 02:14:00,1680.49,,open_close -2022-08-10 02:15:00,1679.11,,open_close -2022-08-10 02:16:00,1678.76,,open_close -2022-08-10 02:17:00,1681.31,,infty -2022-08-10 02:18:00,1680.78,,open_close -2022-08-10 02:19:00,1680.65,,open_close -2022-08-10 02:20:00,1680.01,,open_close -2022-08-10 02:21:00,1680.92,,open_close -2022-08-10 02:22:00,1680.86,,open_close -2022-08-10 02:23:00,1681.9,,infty -2022-08-10 02:24:00,1680.43,,open_close -2022-08-10 02:25:00,1670.32,,open_close -2022-08-10 02:26:00,1671.15,,open_close -2022-08-10 02:27:00,1670.99,,open_close -2022-08-10 02:28:00,1674.08,,open_close -2022-08-10 02:29:00,1674.75,,open_close -2022-08-10 02:30:00,1675.83,,open_close -2022-08-10 02:31:00,1673.31,,open_close -2022-08-10 02:32:00,1671.46,,open_close -2022-08-10 02:33:00,1668.3,,open_close -2022-08-10 02:34:00,1668.88,,open_close -2022-08-10 02:35:00,1669.87,,open_close -2022-08-10 02:36:00,1668.51,,open_close -2022-08-10 02:37:00,1669.16,,open_close -2022-08-10 02:38:00,1670.99,,open_close -2022-08-10 02:39:00,1670.95,,open_close -2022-08-10 02:40:00,1671.24,,open_close -2022-08-10 02:41:00,1673.11,,open_close -2022-08-10 02:42:00,1672.98,,open_close -2022-08-10 02:43:00,1675.56,,open_close -2022-08-10 02:44:00,1675.3,,open_close -2022-08-10 02:45:00,1677.94,,open_close -2022-08-10 02:46:00,1676.88,,open_close -2022-08-10 02:47:00,1676.01,,open_close -2022-08-10 02:48:00,1676.71,,open_close -2022-08-10 02:49:00,1676.25,,open_close -2022-08-10 02:50:00,1678.27,,open_close -2022-08-10 02:51:00,1677.87,,open_close -2022-08-10 02:52:00,1677.67,,open_close -2022-08-10 02:53:00,1678.55,,open_close -2022-08-10 02:54:00,1679.37,,open_close -2022-08-10 02:55:00,1678.37,,open_close -2022-08-10 02:56:00,1682.94,,infty -2022-08-10 02:57:00,1681.91,,infty -2022-08-10 02:58:00,1681.13,,open_close -2022-08-10 02:59:00,1680.98,,open_close -2022-08-10 03:00:00,1680.3,,open_close -2022-08-10 03:01:00,1681.67,,infty -2022-08-10 03:02:00,1683.57,,infty -2022-08-10 03:03:00,1682.34,,infty -2022-08-10 03:04:00,1682.25,,infty -2022-08-10 03:05:00,1680.69,,open_close -2022-08-10 03:06:00,1680.53,,open_close -2022-08-10 03:07:00,1680.04,,open_close -2022-08-10 03:08:00,1679.46,,open_close -2022-08-10 03:09:00,1680.18,,open_close -2022-08-10 03:10:00,1676.7,,open_close -2022-08-10 03:11:00,1676.72,,open_close -2022-08-10 03:12:00,1677.56,,open_close -2022-08-10 03:13:00,1674.7,,open_close -2022-08-10 03:14:00,1676.36,,open_close -2022-08-10 03:15:00,1678.68,,open_close -2022-08-10 03:16:00,1677.29,,open_close -2022-08-10 03:17:00,1677.16,,open_close -2022-08-10 03:18:00,1675.95,,open_close -2022-08-10 03:19:00,1675.47,,open_close -2022-08-10 03:20:00,1674.64,,open_close -2022-08-10 03:21:00,1672.54,,open_close -2022-08-10 03:22:00,1673.58,,open_close -2022-08-10 03:23:00,1670.62,,open_close -2022-08-10 03:24:00,1670.64,,open_close -2022-08-10 03:25:00,1670.88,,open_close -2022-08-10 03:26:00,1671.51,,open_close -2022-08-10 03:27:00,1671.92,,open_close -2022-08-10 03:28:00,1670.52,,open_close -2022-08-10 03:29:00,1670.86,,open_close -2022-08-10 03:30:00,1670.75,,open_close -2022-08-10 03:31:00,1670.67,,open_close -2022-08-10 03:32:00,1672.5,,open_close -2022-08-10 03:33:00,1674.24,,open_close -2022-08-10 03:34:00,1673.04,,open_close -2022-08-10 03:35:00,1673.85,,open_close -2022-08-10 03:36:00,1674.26,,open_close -2022-08-10 03:37:00,1673.0,,open_close -2022-08-10 03:38:00,1672.18,,open_close -2022-08-10 03:39:00,1671.97,,open_close -2022-08-10 03:40:00,1673.07,,open_close -2022-08-10 03:41:00,1672.9,,open_close -2022-08-10 03:42:00,1673.56,,open_close -2022-08-10 03:43:00,1674.31,,open_close -2022-08-10 03:44:00,1673.43,,open_close -2022-08-10 03:45:00,1673.92,,open_close -2022-08-10 03:46:00,1673.23,,open_close -2022-08-10 03:47:00,1674.33,,open_close -2022-08-10 03:48:00,1675.37,,open_close -2022-08-10 03:49:00,1673.46,,open_close -2022-08-10 03:50:00,1671.34,,open_close -2022-08-10 03:51:00,1672.29,,open_close -2022-08-10 03:52:00,1670.9,,open_close -2022-08-10 03:53:00,1669.95,,open_close -2022-08-10 03:54:00,1671.39,,open_close -2022-08-10 03:55:00,1670.74,,open_close -2022-08-10 03:56:00,1670.67,,open_close -2022-08-10 03:57:00,1671.47,,open_close -2022-08-10 03:58:00,1670.54,,open_close -2022-08-10 03:59:00,1669.48,,open_close -2022-08-10 04:00:00,1670.4,,open_close -2022-08-10 04:01:00,1671.82,,open_close -2022-08-10 04:02:00,1671.46,,open_close -2022-08-10 04:03:00,1669.14,,open_close -2022-08-10 04:04:00,1672.19,,open_close -2022-08-10 04:05:00,1673.6,,open_close -2022-08-10 04:06:00,1675.33,,open_close -2022-08-10 04:07:00,1671.66,,open_close -2022-08-10 04:08:00,1671.86,,open_close -2022-08-10 04:09:00,1670.89,,open_close -2022-08-10 04:10:00,1672.01,,open_close -2022-08-10 04:11:00,1672.08,,open_close -2022-08-10 04:12:00,1672.36,,open_close -2022-08-10 04:13:00,1672.83,,open_close -2022-08-10 04:14:00,1673.58,,open_close -2022-08-10 04:15:00,1672.84,,open_close -2022-08-10 04:16:00,1673.9,,open_close -2022-08-10 04:17:00,1674.5,,open_close -2022-08-10 04:18:00,1674.39,,open_close -2022-08-10 04:19:00,1674.24,,open_close -2022-08-10 04:20:00,1674.48,,open_close -2022-08-10 04:21:00,1674.53,,open_close -2022-08-10 04:22:00,1673.49,,open_close -2022-08-10 04:23:00,1674.73,,open_close -2022-08-10 04:24:00,1674.16,,open_close -2022-08-10 04:25:00,1674.31,,open_close -2022-08-10 04:26:00,1675.47,,open_close -2022-08-10 04:27:00,1673.87,,open_close -2022-08-10 04:28:00,1673.75,,open_close -2022-08-10 04:29:00,1672.85,,open_close -2022-08-10 04:30:00,1673.35,,open_close -2022-08-10 04:31:00,1674.46,,open_close -2022-08-10 04:32:00,1674.42,,open_close -2022-08-10 04:33:00,1675.41,,open_close -2022-08-10 04:34:00,1677.48,,open_close -2022-08-10 04:35:00,1677.53,,open_close -2022-08-10 04:36:00,1677.47,,open_close -2022-08-10 04:37:00,1676.6,,open_close -2022-08-10 04:38:00,1678.13,,open_close -2022-08-10 04:39:00,1680.31,,open_close -2022-08-10 04:40:00,1682.0,,infty -2022-08-10 04:41:00,1681.26,,infty -2022-08-10 04:42:00,1682.26,,infty -2022-08-10 04:43:00,1682.99,,infty -2022-08-10 04:44:00,1683.66,,infty -2022-08-10 04:45:00,1683.17,,infty -2022-08-10 04:46:00,1683.02,,infty -2022-08-10 04:47:00,1681.69,,infty -2022-08-10 04:48:00,1681.42,,infty -2022-08-10 04:49:00,1680.91,,open_close -2022-08-10 04:50:00,1681.76,,infty -2022-08-10 04:51:00,1681.46,,infty -2022-08-10 04:52:00,1681.66,,infty -2022-08-10 04:53:00,1682.07,,infty -2022-08-10 04:54:00,1682.18,,infty -2022-08-10 04:55:00,1681.65,,infty -2022-08-10 04:56:00,1678.86,,open_close -2022-08-10 04:57:00,1678.13,,open_close -2022-08-10 04:58:00,1679.21,,open_close -2022-08-10 04:59:00,1680.02,,open_close -2022-08-10 05:00:00,1680.1,,open_close -2022-08-10 05:01:00,1681.3,,infty -2022-08-10 05:02:00,1680.62,,open_close -2022-08-10 05:03:00,1681.37,,infty -2022-08-10 05:04:00,1681.55,,infty -2022-08-10 05:05:00,1681.87,,infty -2022-08-10 05:06:00,1680.76,,open_close -2022-08-10 05:07:00,1681.2,,infty -2022-08-10 05:08:00,1681.95,,infty -2022-08-10 05:09:00,1681.58,,infty -2022-08-10 05:10:00,1683.06,,infty -2022-08-10 05:11:00,1683.55,,infty -2022-08-10 05:12:00,1682.72,,infty -2022-08-10 05:13:00,1682.21,,infty -2022-08-10 05:14:00,1681.71,,infty -2022-08-10 05:15:00,1682.34,,infty -2022-08-10 05:16:00,1680.72,,open_close -2022-08-10 05:17:00,1680.58,,open_close -2022-08-10 05:18:00,1680.16,,open_close -2022-08-10 05:19:00,1679.52,,open_close -2022-08-10 05:20:00,1680.86,,open_close -2022-08-10 05:21:00,1680.25,,open_close -2022-08-10 05:22:00,1680.61,,open_close -2022-08-10 05:23:00,1679.83,,open_close -2022-08-10 05:24:00,1678.71,,open_close -2022-08-10 05:25:00,1679.28,,open_close -2022-08-10 05:26:00,1680.31,,open_close -2022-08-10 05:27:00,1681.35,,infty -2022-08-10 05:28:00,1681.97,,infty -2022-08-10 05:29:00,1680.56,,open_close -2022-08-10 05:30:00,1681.35,,infty -2022-08-10 05:31:00,1682.08,,infty -2022-08-10 05:32:00,1681.55,,infty -2022-08-10 05:33:00,1680.63,,open_close -2022-08-10 05:34:00,1681.26,,infty -2022-08-10 05:35:00,1679.76,,open_close -2022-08-10 05:36:00,1681.75,,infty -2022-08-10 05:37:00,1681.29,,infty -2022-08-10 05:38:00,1682.84,,infty -2022-08-10 05:39:00,1681.91,,infty -2022-08-10 05:40:00,1676.06,,open_close -2022-08-10 05:41:00,1677.38,,open_close -2022-08-10 05:42:00,1678.62,,open_close -2022-08-10 05:43:00,1678.02,,open_close -2022-08-10 05:44:00,1677.72,,open_close -2022-08-10 05:45:00,1676.98,,open_close -2022-08-10 05:46:00,1677.55,,open_close -2022-08-10 05:47:00,1677.75,,open_close -2022-08-10 05:48:00,1679.71,,open_close -2022-08-10 05:49:00,1679.8,,open_close -2022-08-10 05:50:00,1678.8,,open_close -2022-08-10 05:51:00,1682.06,,infty -2022-08-10 05:52:00,1681.59,,infty -2022-08-10 05:53:00,1681.32,,infty -2022-08-10 05:54:00,1681.35,,infty -2022-08-10 05:55:00,1680.7,,open_close -2022-08-10 05:56:00,1681.5,,infty -2022-08-10 05:57:00,1680.09,,open_close -2022-08-10 05:58:00,1680.55,,open_close -2022-08-10 05:59:00,1680.49,,open_close -2022-08-10 06:00:00,1679.62,,open_close -2022-08-10 06:01:00,1680.82,,open_close -2022-08-10 06:02:00,1681.14,,open_close -2022-08-10 06:03:00,1678.67,,open_close -2022-08-10 06:04:00,1678.44,,open_close -2022-08-10 06:05:00,1679.97,,open_close -2022-08-10 06:06:00,1679.68,,open_close -2022-08-10 06:07:00,1679.36,,open_close -2022-08-10 06:08:00,1679.69,,open_close -2022-08-10 06:09:00,1677.83,,open_close -2022-08-10 06:10:00,1677.8,,open_close -2022-08-10 06:11:00,1678.5,,open_close -2022-08-10 06:12:00,1678.17,,open_close -2022-08-10 06:13:00,1676.61,,open_close -2022-08-10 06:14:00,1676.93,,open_close -2022-08-10 06:15:00,1674.7,,open_close -2022-08-10 06:16:00,1675.03,,open_close -2022-08-10 06:17:00,1673.35,,open_close -2022-08-10 06:18:00,1673.83,,open_close -2022-08-10 06:19:00,1675.53,,open_close -2022-08-10 06:20:00,1673.96,,open_close -2022-08-10 06:21:00,1675.48,,open_close -2022-08-10 06:22:00,1676.46,,open_close -2022-08-10 06:23:00,1677.65,,open_close -2022-08-10 06:24:00,1679.59,,open_close -2022-08-10 06:25:00,1678.65,,open_close -2022-08-10 06:26:00,1679.54,,open_close -2022-08-10 06:27:00,1679.17,,open_close -2022-08-10 06:28:00,1677.25,,open_close -2022-08-10 06:29:00,1677.2,,open_close -2022-08-10 06:30:00,1678.02,,open_close -2022-08-10 06:31:00,1677.35,,open_close -2022-08-10 06:32:00,1676.68,,open_close -2022-08-10 06:33:00,1674.25,,open_close -2022-08-10 06:34:00,1674.33,,open_close -2022-08-10 06:35:00,1678.11,,open_close -2022-08-10 06:36:00,1678.39,,open_close -2022-08-10 06:37:00,1679.12,,open_close -2022-08-10 06:38:00,1678.91,,open_close -2022-08-10 06:39:00,1679.36,,open_close -2022-08-10 06:40:00,1679.14,,open_close -2022-08-10 06:41:00,1679.42,,open_close -2022-08-10 06:42:00,1678.73,,open_close -2022-08-10 06:43:00,1678.08,,open_close -2022-08-10 06:44:00,1678.0,,open_close -2022-08-10 06:45:00,1679.05,,open_close -2022-08-10 06:46:00,1678.05,,open_close -2022-08-10 06:47:00,1678.58,,open_close -2022-08-10 06:48:00,1679.89,,open_close -2022-08-10 06:49:00,1681.42,,infty -2022-08-10 06:50:00,1681.29,,infty -2022-08-10 06:51:00,1680.61,,open_close -2022-08-10 06:52:00,1680.21,,open_close -2022-08-10 06:53:00,1680.05,,open_close -2022-08-10 06:54:00,1680.79,,open_close -2022-08-10 06:55:00,1680.44,,open_close -2022-08-10 06:56:00,1680.59,,open_close -2022-08-10 06:57:00,1681.63,,infty -2022-08-10 06:58:00,1681.89,,infty -2022-08-10 06:59:00,1680.27,,open_close -2022-08-10 07:00:00,1680.83,,open_close -2022-08-10 07:01:00,1680.94,,open_close -2022-08-10 07:02:00,1684.18,,infty -2022-08-10 07:03:00,1683.19,,infty -2022-08-10 07:04:00,1682.56,,infty -2022-08-10 07:05:00,1683.4,,infty -2022-08-10 07:06:00,1683.15,,infty -2022-08-10 07:07:00,1682.17,,infty -2022-08-10 07:08:00,1681.43,,infty -2022-08-10 07:09:00,1682.12,,infty -2022-08-10 07:10:00,1682.61,,infty -2022-08-10 07:11:00,1681.93,,infty -2022-08-10 07:12:00,1683.33,,infty -2022-08-10 07:13:00,1684.06,,infty -2022-08-10 07:14:00,1686.62,,infty -2022-08-10 07:15:00,1684.4,,infty -2022-08-10 07:16:00,1684.8,,infty -2022-08-10 07:17:00,1684.69,,infty -2022-08-10 07:18:00,1684.42,,infty -2022-08-10 07:19:00,1684.71,,infty -2022-08-10 07:20:00,1686.3,,infty -2022-08-10 07:21:00,1684.95,,infty -2022-08-10 07:22:00,1684.52,,infty -2022-08-10 07:23:00,1685.15,,infty -2022-08-10 07:24:00,1684.06,,infty -2022-08-10 07:25:00,1684.88,,infty -2022-08-10 07:26:00,1684.34,,infty -2022-08-10 07:27:00,1686.1,,infty -2022-08-10 07:28:00,1686.89,,infty -2022-08-10 07:29:00,1684.95,,infty -2022-08-10 07:30:00,1686.22,,infty -2022-08-10 07:31:00,1686.39,,infty -2022-08-10 07:32:00,1685.05,,infty -2022-08-10 07:33:00,1686.53,,infty -2022-08-10 07:34:00,1688.76,,infty -2022-08-10 07:35:00,1689.76,,infty -2022-08-10 07:36:00,1688.83,,infty -2022-08-10 07:37:00,1689.84,,infty -2022-08-10 07:38:00,1689.06,,infty -2022-08-10 07:39:00,1687.25,,infty -2022-08-10 07:40:00,1687.99,,infty -2022-08-10 07:41:00,1687.33,,infty -2022-08-10 07:42:00,1688.11,,infty -2022-08-10 07:43:00,1688.61,,infty -2022-08-10 07:44:00,1689.16,,infty -2022-08-10 07:45:00,1689.07,,infty -2022-08-10 07:46:00,1687.94,,infty -2022-08-10 07:47:00,1688.53,,infty -2022-08-10 07:48:00,1688.53,,infty -2022-08-10 07:49:00,1689.61,,infty -2022-08-10 07:50:00,1689.38,,infty -2022-08-10 07:51:00,1689.38,,infty -2022-08-10 07:52:00,1689.05,,infty -2022-08-10 07:53:00,1689.41,,infty -2022-08-10 07:54:00,1687.96,,infty -2022-08-10 07:55:00,1686.26,,infty -2022-08-10 07:56:00,1685.53,,infty -2022-08-10 07:57:00,1684.64,,infty -2022-08-10 07:58:00,1685.54,,infty -2022-08-10 07:59:00,1686.39,,infty -2022-08-10 08:00:00,1687.5,,infty -2022-08-10 08:01:00,1686.56,,infty -2022-08-10 08:02:00,1689.07,,infty -2022-08-10 08:03:00,1687.67,,infty -2022-08-10 08:04:00,1687.02,,infty -2022-08-10 08:05:00,1687.48,,infty -2022-08-10 08:06:00,1688.36,,infty -2022-08-10 08:07:00,1687.5,,infty -2022-08-10 08:08:00,1686.92,,infty -2022-08-10 08:09:00,1685.67,,infty -2022-08-10 08:10:00,1685.91,,infty -2022-08-10 08:11:00,1685.38,,infty -2022-08-10 08:12:00,1685.92,,infty -2022-08-10 08:13:00,1687.7,,infty -2022-08-10 08:14:00,1686.34,,infty -2022-08-10 08:15:00,1688.15,,infty -2022-08-10 08:16:00,1689.39,,infty -2022-08-10 08:17:00,1689.18,,infty -2022-08-10 08:18:00,1688.55,,infty -2022-08-10 08:19:00,1689.12,,infty -2022-08-10 08:20:00,1689.96,,infty -2022-08-10 08:21:00,1691.96,,infty -2022-08-10 08:22:00,1693.44,,infty -2022-08-10 08:23:00,1692.6,,infty -2022-08-10 08:24:00,1693.16,,infty -2022-08-10 08:25:00,1695.72,,infty 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08:53:00,1702.88,,infty -2022-08-10 08:54:00,1703.32,,infty -2022-08-10 08:55:00,1703.76,,infty -2022-08-10 08:56:00,1703.62,,infty -2022-08-10 08:57:00,1705.13,,infty -2022-08-10 08:58:00,1704.35,,infty -2022-08-10 08:59:00,1702.42,,infty -2022-08-10 09:00:00,1703.84,,infty -2022-08-10 09:01:00,1703.44,,infty -2022-08-10 09:02:00,1704.19,,infty -2022-08-10 09:03:00,1704.27,,infty -2022-08-10 09:04:00,1704.35,,infty -2022-08-10 09:05:00,1704.03,,infty -2022-08-10 09:06:00,1702.55,,infty -2022-08-10 09:07:00,1702.8,,infty -2022-08-10 09:08:00,1703.35,,infty -2022-08-10 09:09:00,1701.92,,infty -2022-08-10 09:10:00,1702.41,,infty -2022-08-10 09:11:00,1702.83,,infty -2022-08-10 09:12:00,1702.3,,infty -2022-08-10 09:13:00,1702.88,,infty -2022-08-10 09:14:00,1702.87,,infty -2022-08-10 09:15:00,1701.06,,infty -2022-08-10 09:16:00,1701.65,,infty -2022-08-10 09:17:00,1704.18,,infty -2022-08-10 09:18:00,1702.86,,infty -2022-08-10 09:19:00,1704.27,,infty -2022-08-10 09:20:00,1702.94,,infty 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09:48:00,1696.75,,infty -2022-08-10 09:49:00,1696.21,,infty -2022-08-10 09:50:00,1695.2,,infty -2022-08-10 09:51:00,1695.32,,infty -2022-08-10 09:52:00,1694.77,,infty -2022-08-10 09:53:00,1695.52,,infty -2022-08-10 09:54:00,1695.85,,infty -2022-08-10 09:55:00,1696.08,,infty -2022-08-10 09:56:00,1696.35,,infty -2022-08-10 09:57:00,1697.87,,infty -2022-08-10 09:58:00,1699.18,,infty -2022-08-10 09:59:00,1700.5,,infty -2022-08-10 10:00:00,1699.83,,infty -2022-08-10 10:01:00,1700.13,,infty -2022-08-10 10:02:00,1698.85,,infty -2022-08-10 10:03:00,1698.37,,infty -2022-08-10 10:04:00,1697.58,,infty -2022-08-10 10:05:00,1695.76,,infty -2022-08-10 10:06:00,1695.77,,infty -2022-08-10 10:07:00,1695.45,,infty -2022-08-10 10:08:00,1695.19,,infty -2022-08-10 10:09:00,1695.76,,infty -2022-08-10 10:10:00,1695.21,,infty -2022-08-10 10:11:00,1695.58,,infty -2022-08-10 10:12:00,1694.66,,infty -2022-08-10 10:13:00,1695.07,,infty -2022-08-10 10:14:00,1695.77,,infty -2022-08-10 10:15:00,1696.45,,infty 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10:43:00,1695.64,,infty -2022-08-10 10:44:00,1694.92,,infty -2022-08-10 10:45:00,1696.37,,infty -2022-08-10 10:46:00,1696.18,,infty -2022-08-10 10:47:00,1695.69,,infty -2022-08-10 10:48:00,1695.31,,infty -2022-08-10 10:49:00,1693.19,,infty -2022-08-10 10:50:00,1694.48,,infty -2022-08-10 10:51:00,1695.16,,infty -2022-08-10 10:52:00,1692.51,,infty -2022-08-10 10:53:00,1692.7,,infty -2022-08-10 10:54:00,1692.83,,infty -2022-08-10 10:55:00,1694.1,,infty -2022-08-10 10:56:00,1693.79,,infty -2022-08-10 10:57:00,1694.07,,infty -2022-08-10 10:58:00,1694.25,,infty -2022-08-10 10:59:00,1694.18,,infty -2022-08-10 11:00:00,1692.94,,infty -2022-08-10 11:01:00,1693.42,,infty -2022-08-10 11:02:00,1693.68,,infty -2022-08-10 11:03:00,1694.0,,infty -2022-08-10 11:04:00,1696.23,,infty -2022-08-10 11:05:00,1696.14,,infty -2022-08-10 11:06:00,1697.64,,infty -2022-08-10 11:07:00,1696.73,,infty -2022-08-10 11:08:00,1696.24,,infty -2022-08-10 11:09:00,1696.65,,infty -2022-08-10 11:10:00,1696.87,,infty 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11:38:00,1703.54,,infty -2022-08-10 11:39:00,1701.28,,infty -2022-08-10 11:40:00,1700.18,,infty -2022-08-10 11:41:00,1702.4,,infty -2022-08-10 11:42:00,1702.68,,infty -2022-08-10 11:43:00,1702.4,,infty -2022-08-10 11:44:00,1702.96,,infty -2022-08-10 11:45:00,1704.18,,infty -2022-08-10 11:46:00,1704.42,,infty -2022-08-10 11:47:00,1705.0,,infty -2022-08-10 11:48:00,1704.8,,infty -2022-08-10 11:49:00,1706.44,,infty -2022-08-10 11:50:00,1705.98,,infty -2022-08-10 11:51:00,1706.59,,infty -2022-08-10 11:52:00,1708.1,,infty -2022-08-10 11:53:00,1709.74,,infty -2022-08-10 11:54:00,1708.43,,infty -2022-08-10 11:55:00,1708.78,,infty -2022-08-10 11:56:00,1707.8,,infty -2022-08-10 11:57:00,1707.42,,infty -2022-08-10 11:58:00,1707.19,,infty -2022-08-10 11:59:00,1707.73,,infty -2022-08-10 12:00:00,1706.89,,infty -2022-08-10 12:01:00,1706.46,,infty -2022-08-10 12:02:00,1704.33,,infty -2022-08-10 12:03:00,1706.66,,infty -2022-08-10 12:04:00,1706.55,,infty -2022-08-10 12:05:00,1708.12,,infty 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12:33:00,1770.4,,infty -2022-08-10 12:34:00,1767.25,,infty -2022-08-10 12:35:00,1759.95,,infty -2022-08-10 12:36:00,1770.25,,infty -2022-08-10 12:37:00,1771.51,,infty -2022-08-10 12:38:00,1776.8,,infty -2022-08-10 12:39:00,1781.34,,infty -2022-08-10 12:40:00,1780.92,,infty -2022-08-10 12:41:00,1781.99,,infty -2022-08-10 12:42:00,1778.1,,infty -2022-08-10 12:43:00,1775.37,,infty -2022-08-10 12:44:00,1777.42,,infty -2022-08-10 12:45:00,1783.87,,infty -2022-08-10 12:46:00,1794.07,,infty -2022-08-10 12:47:00,1811.0,,infty -2022-08-10 12:48:00,1802.42,,infty -2022-08-10 12:49:00,1796.55,,infty -2022-08-10 12:50:00,1799.7,,infty -2022-08-10 12:51:00,1800.54,,infty -2022-08-10 12:52:00,1799.48,,infty -2022-08-10 12:53:00,1797.45,,infty -2022-08-10 12:54:00,1799.73,,infty -2022-08-10 12:55:00,1806.9,,infty -2022-08-10 12:56:00,1813.19,,infty -2022-08-10 12:57:00,1826.08,,infty -2022-08-10 12:58:00,1830.0,,infty -2022-08-10 12:59:00,1824.53,,infty -2022-08-10 13:00:00,1827.22,,infty -2022-08-10 13:01:00,1832.75,,infty -2022-08-10 13:02:00,1827.42,,infty -2022-08-10 13:03:00,1828.41,,infty -2022-08-10 13:04:00,1830.27,,infty -2022-08-10 13:05:00,1823.86,,infty -2022-08-10 13:06:00,1819.12,,infty -2022-08-10 13:07:00,1819.59,,infty -2022-08-10 13:08:00,1821.62,,infty -2022-08-10 13:09:00,1819.26,,infty -2022-08-10 13:10:00,1817.76,,infty -2022-08-10 13:11:00,1811.45,,infty -2022-08-10 13:12:00,1817.07,,infty -2022-08-10 13:13:00,1820.27,,infty -2022-08-10 13:14:00,1820.64,,infty -2022-08-10 13:15:00,1825.33,,infty -2022-08-10 13:16:00,1824.04,,infty -2022-08-10 13:17:00,1825.6,,infty -2022-08-10 13:18:00,1829.93,,infty -2022-08-10 13:19:00,1828.73,,infty -2022-08-10 13:20:00,1832.88,,infty -2022-08-10 13:21:00,1832.05,,infty -2022-08-10 13:22:00,1833.94,,infty -2022-08-10 13:23:00,1844.86,,infty -2022-08-10 13:24:00,1839.8,,infty -2022-08-10 13:25:00,1837.43,,infty -2022-08-10 13:26:00,1844.84,,infty -2022-08-10 13:27:00,1842.06,,infty -2022-08-10 13:28:00,1842.39,,infty 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14:24:00,1828.21,,infty -2022-08-10 14:25:00,1828.49,,infty -2022-08-10 14:26:00,1831.06,,infty -2022-08-10 14:27:00,1830.49,,infty -2022-08-10 14:28:00,1830.19,,infty -2022-08-10 14:29:00,1830.11,,infty -2022-08-10 14:30:00,1828.69,,infty -2022-08-10 14:31:00,1828.69,,infty -2022-08-10 14:32:00,1827.79,,infty -2022-08-10 14:33:00,1828.8,,infty -2022-08-10 14:34:00,1830.79,,infty -2022-08-10 14:35:00,1830.54,,infty -2022-08-10 14:36:00,1832.2,,infty -2022-08-10 14:37:00,1833.51,,infty -2022-08-10 14:38:00,1830.71,,infty -2022-08-10 14:39:00,1831.5,,infty -2022-08-10 14:40:00,1833.34,,infty -2022-08-10 14:41:00,1835.65,,infty -2022-08-10 14:42:00,1836.17,,infty -2022-08-10 14:43:00,1839.52,,infty -2022-08-10 14:44:00,1841.71,,infty -2022-08-10 14:45:00,1840.0,,infty -2022-08-10 14:46:00,1841.24,,infty -2022-08-10 14:47:00,1852.75,,infty -2022-08-10 14:48:00,1845.57,,infty -2022-08-10 14:49:00,1844.97,,infty -2022-08-10 14:50:00,1846.53,,infty -2022-08-10 14:51:00,1845.4,,infty 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15:47:00,1840.94,,infty -2022-08-10 15:48:00,1839.75,,infty -2022-08-10 15:49:00,1838.32,,infty -2022-08-10 15:50:00,1838.21,,infty -2022-08-10 15:51:00,1838.9,,infty -2022-08-10 15:52:00,1838.74,,infty -2022-08-10 15:53:00,1837.59,,infty -2022-08-10 15:54:00,1839.19,,infty -2022-08-10 15:55:00,1840.76,,infty -2022-08-10 15:56:00,1840.67,,infty -2022-08-10 15:57:00,1840.92,,infty -2022-08-10 15:58:00,1838.44,,infty -2022-08-10 15:59:00,1837.98,,infty -2022-08-10 16:00:00,1838.73,,infty -2022-08-10 16:01:00,1834.11,,infty -2022-08-10 16:02:00,1837.72,,infty -2022-08-10 16:03:00,1842.21,,infty -2022-08-10 16:04:00,1840.99,,infty -2022-08-10 16:05:00,1839.63,,infty -2022-08-10 16:06:00,1842.38,,infty -2022-08-10 16:07:00,1841.93,,infty -2022-08-10 16:08:00,1844.3,,infty -2022-08-10 16:09:00,1847.24,,infty -2022-08-10 16:10:00,1846.9,,infty -2022-08-10 16:11:00,1843.66,,infty -2022-08-10 16:12:00,1844.35,,infty -2022-08-10 16:13:00,1845.01,,infty -2022-08-10 16:14:00,1845.02,,infty 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16:42:00,1827.85,,infty -2022-08-10 16:43:00,1827.82,,infty -2022-08-10 16:44:00,1829.5,,infty -2022-08-10 16:45:00,1831.51,,infty -2022-08-10 16:46:00,1831.31,,infty -2022-08-10 16:47:00,1828.66,,infty -2022-08-10 16:48:00,1829.22,,infty -2022-08-10 16:49:00,1831.16,,infty -2022-08-10 16:50:00,1829.3,,infty -2022-08-10 16:51:00,1828.4,,infty -2022-08-10 16:52:00,1826.39,,infty -2022-08-10 16:53:00,1827.73,,infty -2022-08-10 16:54:00,1829.04,,infty -2022-08-10 16:55:00,1830.72,,infty -2022-08-10 16:56:00,1831.62,,infty -2022-08-10 16:57:00,1833.14,,infty -2022-08-10 16:58:00,1831.48,,infty -2022-08-10 16:59:00,1831.07,,infty -2022-08-10 17:00:00,1831.19,,infty -2022-08-10 17:01:00,1829.78,,infty -2022-08-10 17:02:00,1830.27,,infty -2022-08-10 17:03:00,1827.66,,infty -2022-08-10 17:04:00,1827.33,,infty -2022-08-10 17:05:00,1824.29,,infty -2022-08-10 17:06:00,1824.57,,infty -2022-08-10 17:07:00,1827.89,,infty -2022-08-10 17:08:00,1828.92,,infty -2022-08-10 17:09:00,1829.05,,infty 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17:37:00,1834.22,,infty -2022-08-10 17:38:00,1834.42,,infty -2022-08-10 17:39:00,1833.82,,infty -2022-08-10 17:40:00,1835.95,,infty -2022-08-10 17:41:00,1835.37,,infty -2022-08-10 17:42:00,1835.16,,infty -2022-08-10 17:43:00,1834.43,,infty -2022-08-10 17:44:00,1836.87,,infty -2022-08-10 17:45:00,1836.86,,infty -2022-08-10 17:46:00,1837.75,,infty -2022-08-10 17:47:00,1839.56,,infty -2022-08-10 17:48:00,1836.74,,infty -2022-08-10 17:49:00,1836.77,,infty -2022-08-10 17:50:00,1836.85,,infty -2022-08-10 17:51:00,1836.46,,infty -2022-08-10 17:52:00,1837.2,,infty -2022-08-10 17:53:00,1836.23,,infty -2022-08-10 17:54:00,1835.05,,infty -2022-08-10 17:55:00,1836.5,,infty -2022-08-10 17:56:00,1834.51,,infty -2022-08-10 17:57:00,1833.68,,infty -2022-08-10 17:58:00,1833.93,,infty -2022-08-10 17:59:00,1833.9,,infty -2022-08-10 18:00:00,1834.2,,infty -2022-08-10 18:01:00,1834.27,,infty -2022-08-10 18:02:00,1833.95,,infty -2022-08-10 18:03:00,1833.06,,infty -2022-08-10 18:04:00,1832.29,,infty 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18:32:00,1826.93,,infty -2022-08-10 18:33:00,1825.78,,infty -2022-08-10 18:34:00,1823.76,,infty -2022-08-10 18:35:00,1824.66,,infty -2022-08-10 18:36:00,1825.04,,infty -2022-08-10 18:37:00,1826.07,,infty -2022-08-10 18:38:00,1824.56,,infty -2022-08-10 18:39:00,1820.76,,infty -2022-08-10 18:40:00,1823.1,,infty -2022-08-10 18:41:00,1820.56,,infty -2022-08-10 18:42:00,1823.4,,infty -2022-08-10 18:43:00,1822.47,,infty -2022-08-10 18:44:00,1818.8,,infty -2022-08-10 18:45:00,1814.73,,infty -2022-08-10 18:46:00,1813.49,,infty -2022-08-10 18:47:00,1810.15,,infty -2022-08-10 18:48:00,1806.24,,infty -2022-08-10 18:49:00,1806.73,,infty -2022-08-10 18:50:00,1809.55,,infty -2022-08-10 18:51:00,1807.43,,infty -2022-08-10 18:52:00,1805.95,,infty -2022-08-10 18:53:00,1808.39,,infty -2022-08-10 18:54:00,1807.52,,infty -2022-08-10 18:55:00,1808.44,,infty -2022-08-10 18:56:00,1810.36,,infty -2022-08-10 18:57:00,1807.89,,infty -2022-08-10 18:58:00,1810.94,,infty -2022-08-10 18:59:00,1813.74,,infty 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19:27:00,1823.27,,infty -2022-08-10 19:28:00,1823.53,,infty -2022-08-10 19:29:00,1822.06,,infty -2022-08-10 19:30:00,1823.93,,infty -2022-08-10 19:31:00,1826.2,,infty -2022-08-10 19:32:00,1825.01,,infty -2022-08-10 19:33:00,1824.39,,infty -2022-08-10 19:34:00,1823.69,,infty -2022-08-10 19:35:00,1824.52,,infty -2022-08-10 19:36:00,1823.73,,infty -2022-08-10 19:37:00,1824.12,,infty -2022-08-10 19:38:00,1824.8,,infty -2022-08-10 19:39:00,1824.98,,infty -2022-08-10 19:40:00,1822.63,,infty -2022-08-10 19:41:00,1823.16,,infty -2022-08-10 19:42:00,1824.26,,infty -2022-08-10 19:43:00,1823.94,,infty -2022-08-10 19:44:00,1822.35,,infty -2022-08-10 19:45:00,1822.41,,infty -2022-08-10 19:46:00,1821.61,,infty -2022-08-10 19:47:00,1819.79,,infty -2022-08-10 19:48:00,1819.29,,infty -2022-08-10 19:49:00,1815.79,,infty -2022-08-10 19:50:00,1817.36,,infty -2022-08-10 19:51:00,1817.41,,infty -2022-08-10 19:52:00,1815.81,,infty -2022-08-10 19:53:00,1817.28,,infty -2022-08-10 19:54:00,1818.14,,infty 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20:22:00,1822.95,,infty -2022-08-10 20:23:00,1823.67,,infty -2022-08-10 20:24:00,1822.22,,infty -2022-08-10 20:25:00,1822.17,,infty -2022-08-10 20:26:00,1821.87,,infty -2022-08-10 20:27:00,1822.95,,infty -2022-08-10 20:28:00,1823.69,,infty -2022-08-10 20:29:00,1822.03,,infty -2022-08-10 20:30:00,1825.79,,infty -2022-08-10 20:31:00,1825.04,,infty -2022-08-10 20:32:00,1831.19,,infty -2022-08-10 20:33:00,1826.83,,infty -2022-08-10 20:34:00,1828.14,,infty -2022-08-10 20:35:00,1828.17,,infty -2022-08-10 20:36:00,1825.84,,infty -2022-08-10 20:37:00,1827.46,,infty -2022-08-10 20:38:00,1827.32,,infty -2022-08-10 20:39:00,1826.96,,infty -2022-08-10 20:40:00,1826.6,,infty -2022-08-10 20:41:00,1828.83,,infty -2022-08-10 20:42:00,1830.15,,infty -2022-08-10 20:43:00,1834.51,,infty -2022-08-10 20:44:00,1831.17,,infty -2022-08-10 20:45:00,1831.84,,infty -2022-08-10 20:46:00,1833.19,,infty -2022-08-10 20:47:00,1834.63,,infty -2022-08-10 20:48:00,1834.62,,infty -2022-08-10 20:49:00,1834.46,,infty 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21:17:00,1832.95,,infty -2022-08-10 21:18:00,1833.6,,infty -2022-08-10 21:19:00,1838.86,,infty -2022-08-10 21:20:00,1839.73,,infty -2022-08-10 21:21:00,1839.72,,infty -2022-08-10 21:22:00,1841.09,,infty -2022-08-10 21:23:00,1841.27,,infty -2022-08-10 21:24:00,1840.15,,infty -2022-08-10 21:25:00,1843.12,,infty -2022-08-10 21:26:00,1842.35,,infty -2022-08-10 21:27:00,1849.28,,infty -2022-08-10 21:28:00,1871.16,,infty -2022-08-10 21:29:00,1863.45,,infty -2022-08-10 21:30:00,1877.75,,infty -2022-08-10 21:31:00,1885.72,,infty -2022-08-10 21:32:00,1876.53,,infty -2022-08-10 21:33:00,1863.89,,infty -2022-08-10 21:34:00,1862.25,,infty -2022-08-10 21:35:00,1862.64,,infty -2022-08-10 21:36:00,1852.36,,infty -2022-08-10 21:37:00,1854.41,,infty -2022-08-10 21:38:00,1854.0,,infty -2022-08-10 21:39:00,1853.22,,infty -2022-08-10 21:40:00,1857.2,,infty -2022-08-10 21:41:00,1851.71,,infty -2022-08-10 21:42:00,1850.34,,infty -2022-08-10 21:43:00,1854.55,,infty -2022-08-10 21:44:00,1851.5,,infty 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22:12:00,1863.31,,infty -2022-08-10 22:13:00,1860.25,,infty -2022-08-10 22:14:00,1859.95,,infty -2022-08-10 22:15:00,1854.16,,infty -2022-08-10 22:16:00,1853.77,,infty -2022-08-10 22:17:00,1856.34,,infty -2022-08-10 22:18:00,1855.44,,infty -2022-08-10 22:19:00,1856.03,,infty -2022-08-10 22:20:00,1857.2,,infty -2022-08-10 22:21:00,1857.54,,infty -2022-08-10 22:22:00,1858.82,,infty -2022-08-10 22:23:00,1859.72,,infty -2022-08-10 22:24:00,1858.36,,infty -2022-08-10 22:25:00,1858.82,,infty -2022-08-10 22:26:00,1859.56,,infty -2022-08-10 22:27:00,1862.26,,infty -2022-08-10 22:28:00,1862.27,,infty -2022-08-10 22:29:00,1863.31,,infty -2022-08-10 22:30:00,1862.68,,infty -2022-08-10 22:31:00,1860.41,,infty -2022-08-10 22:32:00,1859.02,,infty -2022-08-10 22:33:00,1857.07,,infty -2022-08-10 22:34:00,1855.92,,infty -2022-08-10 22:35:00,1855.42,,infty -2022-08-10 22:36:00,1856.94,,infty -2022-08-10 22:37:00,1855.72,,infty -2022-08-10 22:38:00,1854.61,,infty -2022-08-10 22:39:00,1856.01,,infty 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23:07:00,1853.14,,infty -2022-08-10 23:08:00,1853.64,,infty -2022-08-10 23:09:00,1853.47,,infty -2022-08-10 23:10:00,1852.27,,infty -2022-08-10 23:11:00,1852.08,,infty -2022-08-10 23:12:00,1852.3,,infty -2022-08-10 23:13:00,1852.45,,infty -2022-08-10 23:14:00,1855.04,,infty -2022-08-10 23:15:00,1854.2,,infty -2022-08-10 23:16:00,1851.72,,infty -2022-08-10 23:17:00,1852.7,,infty -2022-08-10 23:18:00,1852.45,,infty -2022-08-10 23:19:00,1853.21,,infty -2022-08-10 23:20:00,1852.23,,infty -2022-08-10 23:21:00,1852.22,,infty -2022-08-10 23:22:00,1852.49,,infty -2022-08-10 23:23:00,1851.35,,infty -2022-08-10 23:24:00,1849.14,,infty -2022-08-10 23:25:00,1851.86,,infty -2022-08-10 23:26:00,1850.14,,infty -2022-08-10 23:27:00,1850.35,,infty -2022-08-10 23:28:00,1849.55,,infty -2022-08-10 23:29:00,1847.58,,infty -2022-08-10 23:30:00,1848.92,,infty -2022-08-10 23:31:00,1849.87,,infty -2022-08-10 23:32:00,1849.64,,infty -2022-08-10 23:33:00,1849.84,,infty -2022-08-10 23:34:00,1850.24,,infty 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00:02:00,1855.37,,infty -2022-08-11 00:03:00,1855.5,,infty -2022-08-11 00:04:00,1852.93,,infty -2022-08-11 00:05:00,1853.43,,infty -2022-08-11 00:06:00,1851.33,,infty -2022-08-11 00:07:00,1856.34,,infty -2022-08-11 00:08:00,1858.01,,infty -2022-08-11 00:09:00,1856.76,,infty -2022-08-11 00:10:00,1856.82,,infty -2022-08-11 00:11:00,1857.64,,infty -2022-08-11 00:12:00,1857.56,,infty -2022-08-11 00:13:00,1857.09,,infty -2022-08-11 00:14:00,1858.0,,infty -2022-08-11 00:15:00,1857.82,,infty -2022-08-11 00:16:00,1862.69,,infty -2022-08-11 00:17:00,1860.07,,infty -2022-08-11 00:18:00,1861.76,,infty -2022-08-11 00:19:00,1860.61,,infty -2022-08-11 00:20:00,1860.28,,infty -2022-08-11 00:21:00,1859.66,,infty -2022-08-11 00:22:00,1858.81,,infty -2022-08-11 00:23:00,1855.59,,infty -2022-08-11 00:24:00,1857.42,,infty -2022-08-11 00:25:00,1854.56,,infty -2022-08-11 00:26:00,1855.05,,infty -2022-08-11 00:27:00,1854.76,,infty -2022-08-11 00:28:00,1853.24,,infty -2022-08-11 00:29:00,1854.08,,infty 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00:57:00,1879.91,,infty -2022-08-11 00:58:00,1876.86,,infty -2022-08-11 00:59:00,1876.17,,infty -2022-08-11 01:00:00,1878.62,,infty -2022-08-11 01:01:00,1879.41,,infty -2022-08-11 01:02:00,1879.89,,infty -2022-08-11 01:03:00,1881.06,,infty -2022-08-11 01:04:00,1878.7,,infty -2022-08-11 01:05:00,1879.26,,infty -2022-08-11 01:06:00,1877.73,,infty -2022-08-11 01:07:00,1878.11,,infty -2022-08-11 01:08:00,1872.94,,infty -2022-08-11 01:09:00,1871.82,,infty -2022-08-11 01:10:00,1872.53,,infty -2022-08-11 01:11:00,1876.6,,infty -2022-08-11 01:12:00,1875.5,,infty -2022-08-11 01:13:00,1876.1,,infty -2022-08-11 01:14:00,1875.03,,infty -2022-08-11 01:15:00,1872.81,,infty -2022-08-11 01:16:00,1873.19,,infty -2022-08-11 01:17:00,1872.02,,infty -2022-08-11 01:18:00,1871.47,,infty -2022-08-11 01:19:00,1871.81,,infty -2022-08-11 01:20:00,1871.62,,infty -2022-08-11 01:21:00,1870.52,,infty -2022-08-11 01:22:00,1872.68,,infty -2022-08-11 01:23:00,1876.27,,infty -2022-08-11 01:24:00,1875.95,,infty 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01:52:00,1914.28,,infty -2022-08-11 01:53:00,1909.72,,infty -2022-08-11 01:54:00,1911.25,,infty -2022-08-11 01:55:00,1906.52,,infty -2022-08-11 01:56:00,1905.94,,infty -2022-08-11 01:57:00,1902.0,,infty -2022-08-11 01:58:00,1903.08,,infty -2022-08-11 01:59:00,1906.53,,infty -2022-08-11 02:00:00,1906.97,,infty -2022-08-11 02:01:00,1905.24,,infty -2022-08-11 02:02:00,1905.4,,infty -2022-08-11 02:03:00,1902.05,,infty -2022-08-11 02:04:00,1895.88,,infty -2022-08-11 02:05:00,1896.93,,infty -2022-08-11 02:06:00,1884.96,,infty -2022-08-11 02:07:00,1887.04,,infty -2022-08-11 02:08:00,1888.17,,infty -2022-08-11 02:09:00,1887.45,,infty -2022-08-11 02:10:00,1885.88,,infty -2022-08-11 02:11:00,1880.94,,infty -2022-08-11 02:12:00,1881.18,,infty -2022-08-11 02:13:00,1876.83,,infty -2022-08-11 02:14:00,1879.18,,infty -2022-08-11 02:15:00,1876.42,,infty -2022-08-11 02:16:00,1870.92,,infty -2022-08-11 02:17:00,1868.74,,infty -2022-08-11 02:18:00,1866.57,,infty -2022-08-11 02:19:00,1865.86,,infty 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02:47:00,1873.86,,infty -2022-08-11 02:48:00,1876.23,,infty -2022-08-11 02:49:00,1874.47,,infty -2022-08-11 02:50:00,1875.77,,infty -2022-08-11 02:51:00,1878.04,,infty -2022-08-11 02:52:00,1878.95,,infty -2022-08-11 02:53:00,1880.13,,infty -2022-08-11 02:54:00,1878.14,,infty -2022-08-11 02:55:00,1878.33,,infty -2022-08-11 02:56:00,1875.64,,infty -2022-08-11 02:57:00,1878.04,,infty -2022-08-11 02:58:00,1879.01,,infty -2022-08-11 02:59:00,1879.64,,infty -2022-08-11 03:00:00,1881.61,,infty -2022-08-11 03:01:00,1877.79,,infty -2022-08-11 03:02:00,1878.96,,infty -2022-08-11 03:03:00,1879.39,,infty -2022-08-11 03:04:00,1880.22,,infty -2022-08-11 03:05:00,1880.99,,infty -2022-08-11 03:06:00,1880.56,,infty -2022-08-11 03:07:00,1878.78,,infty -2022-08-11 03:08:00,1874.04,,infty -2022-08-11 03:09:00,1876.77,,infty -2022-08-11 03:10:00,1877.24,,infty -2022-08-11 03:11:00,1877.88,,infty -2022-08-11 03:12:00,1880.18,,infty -2022-08-11 03:13:00,1880.21,,infty -2022-08-11 03:14:00,1875.87,,infty 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03:42:00,1879.5,,infty -2022-08-11 03:43:00,1879.85,,infty -2022-08-11 03:44:00,1881.48,,infty -2022-08-11 03:45:00,1881.7,,infty -2022-08-11 03:46:00,1881.72,,infty -2022-08-11 03:47:00,1883.36,,infty -2022-08-11 03:48:00,1882.62,,infty -2022-08-11 03:49:00,1883.09,,infty -2022-08-11 03:50:00,1883.24,,infty -2022-08-11 03:51:00,1883.45,,infty -2022-08-11 03:52:00,1885.83,,infty -2022-08-11 03:53:00,1884.25,,infty -2022-08-11 03:54:00,1881.1,,infty -2022-08-11 03:55:00,1876.41,,infty -2022-08-11 03:56:00,1877.0,,infty -2022-08-11 03:57:00,1880.7,,infty -2022-08-11 03:58:00,1882.8,,infty -2022-08-11 03:59:00,1880.78,,infty -2022-08-11 04:00:00,1882.51,,infty -2022-08-11 04:01:00,1882.92,,infty -2022-08-11 04:02:00,1883.65,,infty -2022-08-11 04:03:00,1885.44,,infty -2022-08-11 04:04:00,1887.59,,infty -2022-08-11 04:05:00,1886.86,,infty -2022-08-11 04:06:00,1886.15,,infty -2022-08-11 04:07:00,1886.06,,infty -2022-08-11 04:08:00,1886.47,,infty -2022-08-11 04:09:00,1886.05,,infty -2022-08-11 04:10:00,1885.95,,infty -2022-08-11 04:11:00,1885.79,,infty -2022-08-11 04:12:00,1886.33,,infty -2022-08-11 04:13:00,1885.28,,infty -2022-08-11 04:14:00,1885.54,,infty -2022-08-11 04:15:00,1885.03,,infty -2022-08-11 04:16:00,1884.39,,infty -2022-08-11 04:17:00,1883.67,,infty -2022-08-11 04:18:00,1885.46,,infty -2022-08-11 04:19:00,1885.46,,infty -2022-08-11 04:20:00,1886.39,,infty -2022-08-11 04:21:00,1884.9,,infty -2022-08-11 04:22:00,1884.04,,infty -2022-08-11 04:23:00,1885.97,,infty -2022-08-11 04:24:00,1883.96,,infty -2022-08-11 04:25:00,1883.78,,infty -2022-08-11 04:26:00,1880.58,,infty -2022-08-11 04:27:00,1881.49,,infty -2022-08-11 04:28:00,1882.12,,infty -2022-08-11 04:29:00,1882.64,,infty -2022-08-11 04:30:00,1881.48,,infty -2022-08-11 04:31:00,1880.01,,infty -2022-08-11 04:32:00,1881.71,,infty -2022-08-11 04:33:00,1884.82,,infty -2022-08-11 04:34:00,1884.19,,infty -2022-08-11 04:35:00,1883.46,,infty -2022-08-11 04:36:00,1884.16,,infty -2022-08-11 04:37:00,1881.76,,infty -2022-08-11 04:38:00,1883.02,,infty -2022-08-11 04:39:00,1880.49,,infty -2022-08-11 04:40:00,1881.55,,infty -2022-08-11 04:41:00,1885.87,,infty -2022-08-11 04:42:00,1886.78,,infty -2022-08-11 04:43:00,1886.33,,infty -2022-08-11 04:44:00,1886.94,,infty -2022-08-11 04:45:00,1886.4,,infty -2022-08-11 04:46:00,1886.02,,infty -2022-08-11 04:47:00,1886.15,,infty -2022-08-11 04:48:00,1885.98,,infty -2022-08-11 04:49:00,1885.31,,infty -2022-08-11 04:50:00,1884.66,,infty -2022-08-11 04:51:00,1885.6,,infty -2022-08-11 04:52:00,1887.11,,infty -2022-08-11 04:53:00,1886.1,,infty -2022-08-11 04:54:00,1888.58,,infty -2022-08-11 04:55:00,1887.48,,infty -2022-08-11 04:56:00,1887.43,,infty -2022-08-11 04:57:00,1888.73,,infty -2022-08-11 04:58:00,1889.74,,infty -2022-08-11 04:59:00,1892.81,,infty -2022-08-11 05:00:00,1890.73,,infty -2022-08-11 05:01:00,1890.26,,infty -2022-08-11 05:02:00,1891.17,,infty -2022-08-11 05:03:00,1891.7,,infty -2022-08-11 05:04:00,1891.52,,infty 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05:32:00,1904.74,,infty -2022-08-11 05:33:00,1903.89,,infty -2022-08-11 05:34:00,1903.24,,infty -2022-08-11 05:35:00,1905.04,,infty -2022-08-11 05:36:00,1904.93,,infty -2022-08-11 05:37:00,1902.75,,infty -2022-08-11 05:38:00,1902.96,,infty -2022-08-11 05:39:00,1903.96,,infty -2022-08-11 05:40:00,1902.69,,infty -2022-08-11 05:41:00,1901.98,,infty -2022-08-11 05:42:00,1903.76,,infty -2022-08-11 05:43:00,1899.37,,infty -2022-08-11 05:44:00,1898.57,,infty -2022-08-11 05:45:00,1895.89,,infty -2022-08-11 05:46:00,1897.88,,infty -2022-08-11 05:47:00,1901.69,,infty -2022-08-11 05:48:00,1901.69,,infty -2022-08-11 05:49:00,1901.53,,infty -2022-08-11 05:50:00,1901.59,,infty -2022-08-11 05:51:00,1898.78,,infty -2022-08-11 05:52:00,1899.69,,infty -2022-08-11 05:53:00,1896.34,,infty -2022-08-11 05:54:00,1884.58,,infty -2022-08-11 05:55:00,1890.66,,infty -2022-08-11 05:56:00,1890.2,,infty -2022-08-11 05:57:00,1892.14,,infty -2022-08-11 05:58:00,1892.54,,infty -2022-08-11 05:59:00,1893.05,,infty -2022-08-11 06:00:00,1893.28,,infty -2022-08-11 06:01:00,1891.44,,infty -2022-08-11 06:02:00,1892.48,,infty -2022-08-11 06:03:00,1892.88,,infty -2022-08-11 06:04:00,1892.05,,infty -2022-08-11 06:05:00,1893.53,,infty -2022-08-11 06:06:00,1897.57,,infty -2022-08-11 06:07:00,1897.68,,infty -2022-08-11 06:08:00,1896.49,,infty -2022-08-11 06:09:00,1897.42,,infty -2022-08-11 06:10:00,1897.35,,infty -2022-08-11 06:11:00,1895.99,,infty -2022-08-11 06:12:00,1894.64,,infty -2022-08-11 06:13:00,1894.62,,infty -2022-08-11 06:14:00,1895.0,,infty -2022-08-11 06:15:00,1893.13,,infty -2022-08-11 06:16:00,1893.6,,infty -2022-08-11 06:17:00,1890.0,,infty -2022-08-11 06:18:00,1889.16,,infty -2022-08-11 06:19:00,1890.0,,infty -2022-08-11 06:20:00,1892.54,,infty -2022-08-11 06:21:00,1894.91,,infty -2022-08-11 06:22:00,1897.07,,infty -2022-08-11 06:23:00,1898.71,,infty -2022-08-11 06:24:00,1898.73,,infty -2022-08-11 06:25:00,1899.12,,infty -2022-08-11 06:26:00,1897.28,,infty -2022-08-11 06:27:00,1898.26,,infty -2022-08-11 06:28:00,1894.11,,infty -2022-08-11 06:29:00,1894.44,,infty -2022-08-11 06:30:00,1893.5,,infty -2022-08-11 06:31:00,1895.04,,infty -2022-08-11 06:32:00,1895.5,,infty -2022-08-11 06:33:00,1891.27,,infty -2022-08-11 06:34:00,1888.21,,infty -2022-08-11 06:35:00,1885.43,,infty -2022-08-11 06:36:00,1886.11,,infty -2022-08-11 06:37:00,1886.79,,infty -2022-08-11 06:38:00,1887.01,,infty -2022-08-11 06:39:00,1886.76,,infty -2022-08-11 06:40:00,1887.25,,infty -2022-08-11 06:41:00,1884.12,,infty -2022-08-11 06:42:00,1885.48,,infty -2022-08-11 06:43:00,1887.47,,infty -2022-08-11 06:44:00,1887.54,,infty -2022-08-11 06:45:00,1885.99,,infty -2022-08-11 06:46:00,1887.63,,infty -2022-08-11 06:47:00,1886.62,,infty -2022-08-11 06:48:00,1889.04,,infty -2022-08-11 06:49:00,1888.5,,infty -2022-08-11 06:50:00,1889.28,,infty -2022-08-11 06:51:00,1891.11,,infty -2022-08-11 06:52:00,1891.96,,infty -2022-08-11 06:53:00,1891.62,,infty -2022-08-11 06:54:00,1890.01,,infty 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07:22:00,1897.27,,infty -2022-08-11 07:23:00,1896.48,,infty -2022-08-11 07:24:00,1896.02,,infty -2022-08-11 07:25:00,1895.14,,infty -2022-08-11 07:26:00,1894.97,,infty -2022-08-11 07:27:00,1895.05,,infty -2022-08-11 07:28:00,1893.96,,infty -2022-08-11 07:29:00,1896.19,,infty -2022-08-11 07:30:00,1896.85,,infty -2022-08-11 07:31:00,1898.87,,infty -2022-08-11 07:32:00,1903.58,,infty -2022-08-11 07:33:00,1899.59,,infty -2022-08-11 07:34:00,1891.58,,infty -2022-08-11 07:35:00,1889.1,,infty -2022-08-11 07:36:00,1890.31,,infty -2022-08-11 07:37:00,1891.69,,infty -2022-08-11 07:38:00,1890.14,,infty -2022-08-11 07:39:00,1890.61,,infty -2022-08-11 07:40:00,1887.45,,infty -2022-08-11 07:41:00,1882.53,,infty -2022-08-11 07:42:00,1880.91,,infty -2022-08-11 07:43:00,1877.44,,infty -2022-08-11 07:44:00,1877.76,,infty -2022-08-11 07:45:00,1879.34,,infty -2022-08-11 07:46:00,1876.33,,infty -2022-08-11 07:47:00,1873.5,,infty -2022-08-11 07:48:00,1877.35,,infty -2022-08-11 07:49:00,1878.23,,infty 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08:17:00,1884.9,,infty -2022-08-11 08:18:00,1884.51,,infty -2022-08-11 08:19:00,1885.49,,infty -2022-08-11 08:20:00,1884.79,,infty -2022-08-11 08:21:00,1883.46,,infty -2022-08-11 08:22:00,1883.99,,infty -2022-08-11 08:23:00,1884.12,,infty -2022-08-11 08:24:00,1884.36,,infty -2022-08-11 08:25:00,1884.39,,infty -2022-08-11 08:26:00,1883.86,,infty -2022-08-11 08:27:00,1883.34,,infty -2022-08-11 08:28:00,1884.18,,infty -2022-08-11 08:29:00,1883.3,,infty -2022-08-11 08:30:00,1882.57,,infty -2022-08-11 08:31:00,1881.82,,infty -2022-08-11 08:32:00,1886.18,,infty -2022-08-11 08:33:00,1887.36,,infty -2022-08-11 08:34:00,1885.6,,infty -2022-08-11 08:35:00,1886.39,,infty -2022-08-11 08:36:00,1886.89,,infty -2022-08-11 08:37:00,1888.65,,infty -2022-08-11 08:38:00,1887.09,,infty -2022-08-11 08:39:00,1887.26,,infty -2022-08-11 08:40:00,1887.06,,infty -2022-08-11 08:41:00,1887.39,,infty -2022-08-11 08:42:00,1886.48,,infty -2022-08-11 08:43:00,1887.24,,infty -2022-08-11 08:44:00,1885.35,,infty 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09:12:00,1889.91,,infty -2022-08-11 09:13:00,1890.59,,infty -2022-08-11 09:14:00,1890.03,,infty -2022-08-11 09:15:00,1890.09,,infty -2022-08-11 09:16:00,1890.01,,infty -2022-08-11 09:17:00,1889.69,,infty -2022-08-11 09:18:00,1888.54,,infty -2022-08-11 09:19:00,1886.23,,infty -2022-08-11 09:20:00,1886.21,,infty -2022-08-11 09:21:00,1886.22,,infty -2022-08-11 09:22:00,1886.41,,infty -2022-08-11 09:23:00,1886.17,,infty -2022-08-11 09:24:00,1884.16,,infty -2022-08-11 09:25:00,1885.55,,infty -2022-08-11 09:26:00,1884.81,,infty -2022-08-11 09:27:00,1885.0,,infty -2022-08-11 09:28:00,1886.19,,infty -2022-08-11 09:29:00,1885.66,,infty -2022-08-11 09:30:00,1886.14,,infty -2022-08-11 09:31:00,1885.83,,infty -2022-08-11 09:32:00,1886.85,,infty -2022-08-11 09:33:00,1887.15,,infty -2022-08-11 09:34:00,1889.18,,infty -2022-08-11 09:35:00,1887.53,,infty -2022-08-11 09:36:00,1889.56,,infty -2022-08-11 09:37:00,1891.55,,infty -2022-08-11 09:38:00,1891.04,,infty -2022-08-11 09:39:00,1889.5,,infty 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10:07:00,1884.85,,infty -2022-08-11 10:08:00,1882.63,,infty -2022-08-11 10:09:00,1880.69,,infty -2022-08-11 10:10:00,1878.76,,infty -2022-08-11 10:11:00,1879.74,,infty -2022-08-11 10:12:00,1881.22,,infty -2022-08-11 10:13:00,1882.62,,infty -2022-08-11 10:14:00,1882.32,,infty -2022-08-11 10:15:00,1882.93,,infty -2022-08-11 10:16:00,1883.56,,infty -2022-08-11 10:17:00,1883.33,,infty -2022-08-11 10:18:00,1886.14,,infty -2022-08-11 10:19:00,1884.73,,infty -2022-08-11 10:20:00,1884.52,,infty -2022-08-11 10:21:00,1886.19,,infty -2022-08-11 10:22:00,1884.92,,infty -2022-08-11 10:23:00,1885.9,,infty -2022-08-11 10:24:00,1884.97,,infty -2022-08-11 10:25:00,1885.8,,infty -2022-08-11 10:26:00,1884.6,,infty -2022-08-11 10:27:00,1884.56,,infty -2022-08-11 10:28:00,1884.2,,infty -2022-08-11 10:29:00,1884.22,,infty -2022-08-11 10:30:00,1886.08,,infty -2022-08-11 10:31:00,1885.0,,infty -2022-08-11 10:32:00,1886.02,,infty -2022-08-11 10:33:00,1886.07,,infty -2022-08-11 10:34:00,1885.91,,infty 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11:02:00,1888.63,,infty -2022-08-11 11:03:00,1889.65,,infty -2022-08-11 11:04:00,1890.86,,infty -2022-08-11 11:05:00,1890.59,,infty -2022-08-11 11:06:00,1890.98,,infty -2022-08-11 11:07:00,1892.59,,infty -2022-08-11 11:08:00,1889.92,,infty -2022-08-11 11:09:00,1890.78,,infty -2022-08-11 11:10:00,1892.26,,infty -2022-08-11 11:11:00,1890.83,,infty -2022-08-11 11:12:00,1890.48,,infty -2022-08-11 11:13:00,1890.68,,infty -2022-08-11 11:14:00,1890.96,,infty -2022-08-11 11:15:00,1890.61,,infty -2022-08-11 11:16:00,1893.58,,infty -2022-08-11 11:17:00,1892.3,,infty -2022-08-11 11:18:00,1891.25,,infty -2022-08-11 11:19:00,1891.23,,infty -2022-08-11 11:20:00,1891.39,,infty -2022-08-11 11:21:00,1890.83,,infty -2022-08-11 11:22:00,1891.36,,infty -2022-08-11 11:23:00,1893.36,,infty -2022-08-11 11:24:00,1892.26,,infty -2022-08-11 11:25:00,1893.03,,infty -2022-08-11 11:26:00,1895.79,,infty -2022-08-11 11:27:00,1895.62,,infty -2022-08-11 11:28:00,1896.12,,infty -2022-08-11 11:29:00,1896.88,,infty 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11:57:00,1903.53,,infty -2022-08-11 11:58:00,1904.12,,infty -2022-08-11 11:59:00,1904.45,,infty -2022-08-11 12:00:00,1907.97,,infty -2022-08-11 12:01:00,1910.53,,infty -2022-08-11 12:02:00,1911.22,,infty -2022-08-11 12:03:00,1909.78,,infty -2022-08-11 12:04:00,1910.4,,infty -2022-08-11 12:05:00,1910.25,,infty -2022-08-11 12:06:00,1910.65,,infty -2022-08-11 12:07:00,1910.61,,infty -2022-08-11 12:08:00,1911.04,,infty -2022-08-11 12:09:00,1913.79,,infty -2022-08-11 12:10:00,1911.25,,infty -2022-08-11 12:11:00,1914.41,,infty -2022-08-11 12:12:00,1922.85,,infty -2022-08-11 12:13:00,1919.42,,infty -2022-08-11 12:14:00,1921.03,,infty -2022-08-11 12:15:00,1922.97,,infty -2022-08-11 12:16:00,1928.08,,infty -2022-08-11 12:17:00,1921.34,,infty -2022-08-11 12:18:00,1919.64,,infty -2022-08-11 12:19:00,1923.69,,infty -2022-08-11 12:20:00,1926.71,,infty -2022-08-11 12:21:00,1924.83,,infty -2022-08-11 12:22:00,1934.4,,infty -2022-08-11 12:23:00,1929.87,,infty -2022-08-11 12:24:00,1927.09,,infty 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12:52:00,1927.54,,infty -2022-08-11 12:53:00,1927.44,,infty -2022-08-11 12:54:00,1925.11,,infty -2022-08-11 12:55:00,1927.81,,infty -2022-08-11 12:56:00,1926.74,,infty -2022-08-11 12:57:00,1925.34,,infty -2022-08-11 12:58:00,1927.59,,infty -2022-08-11 12:59:00,1928.02,,infty -2022-08-11 13:00:00,1926.07,,infty -2022-08-11 13:01:00,1925.75,,infty -2022-08-11 13:02:00,1922.4,,infty -2022-08-11 13:03:00,1920.97,,infty -2022-08-11 13:04:00,1921.1,,infty -2022-08-11 13:05:00,1919.07,,infty -2022-08-11 13:06:00,1920.95,,infty -2022-08-11 13:07:00,1920.6,,infty -2022-08-11 13:08:00,1919.42,,infty -2022-08-11 13:09:00,1922.46,,infty -2022-08-11 13:10:00,1921.13,,infty -2022-08-11 13:11:00,1919.37,,infty -2022-08-11 13:12:00,1914.36,,infty -2022-08-11 13:13:00,1914.3,,infty -2022-08-11 13:14:00,1913.01,,infty -2022-08-11 13:15:00,1910.0,,infty -2022-08-11 13:16:00,1912.27,,infty -2022-08-11 13:17:00,1911.14,,infty -2022-08-11 13:18:00,1907.73,,infty -2022-08-11 13:19:00,1909.53,,infty -2022-08-11 13:20:00,1908.92,,infty -2022-08-11 13:21:00,1910.61,,infty -2022-08-11 13:22:00,1909.71,,infty -2022-08-11 13:23:00,1910.77,,infty -2022-08-11 13:24:00,1910.08,,infty -2022-08-11 13:25:00,1909.13,,infty -2022-08-11 13:26:00,1910.95,,infty -2022-08-11 13:27:00,1914.52,,infty -2022-08-11 13:28:00,1913.41,,infty -2022-08-11 13:29:00,1915.67,,infty -2022-08-11 13:30:00,1913.71,,infty -2022-08-11 13:31:00,1913.72,,infty -2022-08-11 13:32:00,1913.83,,infty -2022-08-11 13:33:00,1911.62,,infty -2022-08-11 13:34:00,1904.93,,infty -2022-08-11 13:35:00,1907.97,,infty -2022-08-11 13:36:00,1906.38,,infty -2022-08-11 13:37:00,1904.3,,infty -2022-08-11 13:38:00,1904.19,,infty -2022-08-11 13:39:00,1905.35,,infty -2022-08-11 13:40:00,1908.1,,infty -2022-08-11 13:41:00,1908.49,,infty -2022-08-11 13:42:00,1911.35,,infty -2022-08-11 13:43:00,1908.12,,infty -2022-08-11 13:44:00,1910.69,,infty -2022-08-11 13:45:00,1910.7,,infty -2022-08-11 13:46:00,1912.23,,infty -2022-08-11 13:47:00,1913.14,,infty -2022-08-11 13:48:00,1913.27,,infty -2022-08-11 13:49:00,1911.26,,infty -2022-08-11 13:50:00,1910.43,,infty -2022-08-11 13:51:00,1911.13,,infty -2022-08-11 13:52:00,1913.41,,infty -2022-08-11 13:53:00,1913.62,,infty -2022-08-11 13:54:00,1914.55,,infty -2022-08-11 13:55:00,1913.5,,infty -2022-08-11 13:56:00,1912.73,,infty -2022-08-11 13:57:00,1911.55,,infty -2022-08-11 13:58:00,1911.81,,infty -2022-08-11 13:59:00,1912.09,,infty -2022-08-11 14:00:00,1916.68,,infty -2022-08-11 14:01:00,1912.77,,infty -2022-08-11 14:02:00,1913.82,,infty -2022-08-11 14:03:00,1915.3,,infty -2022-08-11 14:04:00,1916.27,,infty -2022-08-11 14:05:00,1920.47,,infty -2022-08-11 14:06:00,1918.97,,infty -2022-08-11 14:07:00,1919.35,,infty -2022-08-11 14:08:00,1921.61,,infty -2022-08-11 14:09:00,1920.79,,infty -2022-08-11 14:10:00,1919.17,,infty -2022-08-11 14:11:00,1925.07,,infty -2022-08-11 14:12:00,1922.97,,infty -2022-08-11 14:13:00,1923.76,,infty -2022-08-11 14:14:00,1922.48,,infty -2022-08-11 14:15:00,1923.71,,infty -2022-08-11 14:16:00,1922.98,,infty -2022-08-11 14:17:00,1920.25,,infty -2022-08-11 14:18:00,1919.77,,infty -2022-08-11 14:19:00,1919.55,,infty -2022-08-11 14:20:00,1917.21,,infty -2022-08-11 14:21:00,1914.35,,infty -2022-08-11 14:22:00,1912.76,,infty -2022-08-11 14:23:00,1912.75,,infty -2022-08-11 14:24:00,1912.36,,infty -2022-08-11 14:25:00,1912.2,,infty -2022-08-11 14:26:00,1910.22,,infty -2022-08-11 14:27:00,1911.08,,infty -2022-08-11 14:28:00,1907.08,,infty -2022-08-11 14:29:00,1905.4,,infty -2022-08-11 14:30:00,1909.21,,infty -2022-08-11 14:31:00,1908.14,,infty -2022-08-11 14:32:00,1908.76,,infty -2022-08-11 14:33:00,1905.06,,infty -2022-08-11 14:34:00,1900.72,,infty -2022-08-11 14:35:00,1900.5,,infty -2022-08-11 14:36:00,1898.93,,infty -2022-08-11 14:37:00,1899.01,,infty -2022-08-11 14:38:00,1898.21,,infty -2022-08-11 14:39:00,1895.55,,infty -2022-08-11 14:40:00,1895.78,,infty -2022-08-11 14:41:00,1888.64,,infty -2022-08-11 14:42:00,1893.41,,infty -2022-08-11 14:43:00,1892.35,,infty -2022-08-11 14:44:00,1893.15,,infty -2022-08-11 14:45:00,1891.68,,infty -2022-08-11 14:46:00,1897.71,,infty -2022-08-11 14:47:00,1896.16,,infty -2022-08-11 14:48:00,1893.89,,infty -2022-08-11 14:49:00,1897.18,,infty -2022-08-11 14:50:00,1901.58,,infty -2022-08-11 14:51:00,1905.18,,infty -2022-08-11 14:52:00,1904.71,,infty -2022-08-11 14:53:00,1906.99,,infty -2022-08-11 14:54:00,1905.73,,infty -2022-08-11 14:55:00,1902.88,,infty -2022-08-11 14:56:00,1903.0,,infty -2022-08-11 14:57:00,1902.61,,infty -2022-08-11 14:58:00,1902.87,,infty -2022-08-11 14:59:00,1903.28,,infty -2022-08-11 15:00:00,1901.95,,infty -2022-08-11 15:01:00,1905.45,,infty -2022-08-11 15:02:00,1901.28,,infty -2022-08-11 15:03:00,1901.43,,infty -2022-08-11 15:04:00,1901.98,,infty -2022-08-11 15:05:00,1898.13,,infty -2022-08-11 15:06:00,1895.5,,infty -2022-08-11 15:07:00,1896.39,,infty -2022-08-11 15:08:00,1898.0,,infty -2022-08-11 15:09:00,1898.56,,infty -2022-08-11 15:10:00,1900.46,,infty -2022-08-11 15:11:00,1898.77,,infty -2022-08-11 15:12:00,1901.39,,infty -2022-08-11 15:13:00,1898.83,,infty -2022-08-11 15:14:00,1895.48,,infty -2022-08-11 15:15:00,1898.28,,infty -2022-08-11 15:16:00,1899.36,,infty -2022-08-11 15:17:00,1894.81,,infty -2022-08-11 15:18:00,1891.48,,infty -2022-08-11 15:19:00,1891.41,,infty -2022-08-11 15:20:00,1895.38,,infty -2022-08-11 15:21:00,1897.71,,infty -2022-08-11 15:22:00,1900.38,,infty -2022-08-11 15:23:00,1900.18,,infty -2022-08-11 15:24:00,1898.92,,infty -2022-08-11 15:25:00,1897.4,,infty -2022-08-11 15:26:00,1898.61,,infty -2022-08-11 15:27:00,1900.94,,infty -2022-08-11 15:28:00,1900.84,,infty -2022-08-11 15:29:00,1901.51,,infty -2022-08-11 15:30:00,1902.09,,infty -2022-08-11 15:31:00,1905.66,,infty -2022-08-11 15:32:00,1905.94,,infty -2022-08-11 15:33:00,1905.77,,infty -2022-08-11 15:34:00,1906.78,,infty -2022-08-11 15:35:00,1908.37,,infty -2022-08-11 15:36:00,1906.54,,infty -2022-08-11 15:37:00,1901.66,,infty -2022-08-11 15:38:00,1901.0,,infty -2022-08-11 15:39:00,1902.41,,infty -2022-08-11 15:40:00,1899.57,,infty -2022-08-11 15:41:00,1899.91,,infty -2022-08-11 15:42:00,1900.55,,infty -2022-08-11 15:43:00,1899.27,,infty -2022-08-11 15:44:00,1898.49,,infty -2022-08-11 15:45:00,1892.18,,infty -2022-08-11 15:46:00,1889.23,,infty -2022-08-11 15:47:00,1893.68,,infty -2022-08-11 15:48:00,1892.64,,infty -2022-08-11 15:49:00,1891.97,,infty -2022-08-11 15:50:00,1885.72,,infty -2022-08-11 15:51:00,1887.25,,infty -2022-08-11 15:52:00,1891.83,,infty -2022-08-11 15:53:00,1889.9,,infty -2022-08-11 15:54:00,1889.72,,infty -2022-08-11 15:55:00,1890.53,,infty -2022-08-11 15:56:00,1890.77,,infty -2022-08-11 15:57:00,1892.3,,infty -2022-08-11 15:58:00,1892.27,,infty -2022-08-11 15:59:00,1891.22,,infty -2022-08-11 16:00:00,1894.27,,infty -2022-08-11 16:01:00,1893.76,,infty -2022-08-11 16:02:00,1893.91,,infty -2022-08-11 16:03:00,1894.58,,infty -2022-08-11 16:04:00,1892.12,,infty -2022-08-11 16:05:00,1890.75,,infty -2022-08-11 16:06:00,1888.88,,infty -2022-08-11 16:07:00,1890.85,,infty -2022-08-11 16:08:00,1894.64,,infty -2022-08-11 16:09:00,1893.24,,infty -2022-08-11 16:10:00,1893.99,,infty -2022-08-11 16:11:00,1892.94,,infty -2022-08-11 16:12:00,1889.66,,infty -2022-08-11 16:13:00,1890.55,,infty -2022-08-11 16:14:00,1888.4,,infty -2022-08-11 16:15:00,1888.77,,infty -2022-08-11 16:16:00,1891.36,,infty -2022-08-11 16:17:00,1894.29,,infty -2022-08-11 16:18:00,1893.43,,infty -2022-08-11 16:19:00,1892.3,,infty -2022-08-11 16:20:00,1893.68,,infty -2022-08-11 16:21:00,1891.92,,infty -2022-08-11 16:22:00,1891.24,,infty -2022-08-11 16:23:00,1894.04,,infty -2022-08-11 16:24:00,1892.13,,infty -2022-08-11 16:25:00,1892.48,,infty -2022-08-11 16:26:00,1893.73,,infty -2022-08-11 16:27:00,1892.78,,infty -2022-08-11 16:28:00,1893.16,,infty -2022-08-11 16:29:00,1895.45,,infty -2022-08-11 16:30:00,1897.13,,infty -2022-08-11 16:31:00,1898.63,,infty -2022-08-11 16:32:00,1897.93,,infty -2022-08-11 16:33:00,1903.01,,infty -2022-08-11 16:34:00,1900.24,,infty -2022-08-11 16:35:00,1898.96,,infty -2022-08-11 16:36:00,1899.3,,infty -2022-08-11 16:37:00,1896.72,,infty -2022-08-11 16:38:00,1894.94,,infty -2022-08-11 16:39:00,1893.52,,infty -2022-08-11 16:40:00,1895.14,,infty -2022-08-11 16:41:00,1895.42,,infty -2022-08-11 16:42:00,1891.43,,infty -2022-08-11 16:43:00,1894.65,,infty -2022-08-11 16:44:00,1896.21,,infty -2022-08-11 16:45:00,1893.67,,infty -2022-08-11 16:46:00,1894.89,,infty -2022-08-11 16:47:00,1894.2,,infty -2022-08-11 16:48:00,1896.37,,infty -2022-08-11 16:49:00,1897.41,,infty -2022-08-11 16:50:00,1897.73,,infty -2022-08-11 16:51:00,1898.77,,infty -2022-08-11 16:52:00,1897.46,,infty -2022-08-11 16:53:00,1897.85,,infty -2022-08-11 16:54:00,1898.47,,infty -2022-08-11 16:55:00,1900.14,,infty -2022-08-11 16:56:00,1900.4,,infty -2022-08-11 16:57:00,1900.66,,infty -2022-08-11 16:58:00,1900.27,,infty -2022-08-11 16:59:00,1901.14,,infty -2022-08-11 17:00:00,1902.78,,infty -2022-08-11 17:01:00,1903.58,,infty -2022-08-11 17:02:00,1899.65,,infty -2022-08-11 17:03:00,1898.5,,infty -2022-08-11 17:04:00,1900.5,,infty -2022-08-11 17:05:00,1903.29,,infty -2022-08-11 17:06:00,1902.99,,infty -2022-08-11 17:07:00,1900.82,,infty -2022-08-11 17:08:00,1902.73,,infty -2022-08-11 17:09:00,1901.18,,infty -2022-08-11 17:10:00,1900.34,,infty -2022-08-11 17:11:00,1901.85,,infty -2022-08-11 17:12:00,1904.27,,infty -2022-08-11 17:13:00,1905.45,,infty -2022-08-11 17:14:00,1904.52,,infty -2022-08-11 17:15:00,1907.68,,infty -2022-08-11 17:16:00,1907.79,,infty -2022-08-11 17:17:00,1907.71,,infty -2022-08-11 17:18:00,1907.06,,infty -2022-08-11 17:19:00,1907.88,,infty -2022-08-11 17:20:00,1905.14,,infty -2022-08-11 17:21:00,1907.22,,infty -2022-08-11 17:22:00,1908.35,,infty -2022-08-11 17:23:00,1906.79,,infty -2022-08-11 17:24:00,1905.85,,infty -2022-08-11 17:25:00,1903.6,,infty -2022-08-11 17:26:00,1904.32,,infty -2022-08-11 17:27:00,1904.61,,infty -2022-08-11 17:28:00,1904.68,,infty -2022-08-11 17:29:00,1902.35,,infty -2022-08-11 17:30:00,1900.89,,infty -2022-08-11 17:31:00,1903.71,,infty -2022-08-11 17:32:00,1903.86,,infty -2022-08-11 17:33:00,1902.54,,infty -2022-08-11 17:34:00,1900.5,,infty -2022-08-11 17:35:00,1902.01,,infty -2022-08-11 17:36:00,1902.62,,infty -2022-08-11 17:37:00,1906.76,,infty -2022-08-11 17:38:00,1907.15,,infty -2022-08-11 17:39:00,1905.77,,infty -2022-08-11 17:40:00,1906.27,,infty -2022-08-11 17:41:00,1908.35,,infty -2022-08-11 17:42:00,1907.74,,infty -2022-08-11 17:43:00,1908.1,,infty -2022-08-11 17:44:00,1906.51,,infty -2022-08-11 17:45:00,1907.03,,infty -2022-08-11 17:46:00,1903.44,,infty -2022-08-11 17:47:00,1902.63,,infty -2022-08-11 17:48:00,1901.25,,infty -2022-08-11 17:49:00,1899.42,,infty -2022-08-11 17:50:00,1901.69,,infty -2022-08-11 17:51:00,1899.47,,infty -2022-08-11 17:52:00,1899.22,,infty -2022-08-11 17:53:00,1898.73,,infty -2022-08-11 17:54:00,1899.45,,infty -2022-08-11 17:55:00,1899.63,,infty -2022-08-11 17:56:00,1900.51,,infty -2022-08-11 17:57:00,1901.68,,infty -2022-08-11 17:58:00,1901.92,,infty -2022-08-11 17:59:00,1902.13,,infty -2022-08-11 18:00:00,1898.91,,infty -2022-08-11 18:01:00,1896.65,,infty -2022-08-11 18:02:00,1898.36,,infty -2022-08-11 18:03:00,1894.28,,infty -2022-08-11 18:04:00,1893.85,,infty -2022-08-11 18:05:00,1895.38,,infty -2022-08-11 18:06:00,1895.64,,infty -2022-08-11 18:07:00,1895.67,,infty -2022-08-11 18:08:00,1894.6,,infty -2022-08-11 18:09:00,1894.71,,infty -2022-08-11 18:10:00,1892.57,,infty -2022-08-11 18:11:00,1890.77,,infty -2022-08-11 18:12:00,1887.0,,infty -2022-08-11 18:13:00,1887.15,,infty -2022-08-11 18:14:00,1881.75,,infty -2022-08-11 18:15:00,1880.26,,infty -2022-08-11 18:16:00,1878.93,,infty -2022-08-11 18:17:00,1876.67,,infty -2022-08-11 18:18:00,1879.39,,infty -2022-08-11 18:19:00,1882.68,,infty -2022-08-11 18:20:00,1879.27,,infty -2022-08-11 18:21:00,1880.83,,infty -2022-08-11 18:22:00,1882.39,,infty -2022-08-11 18:23:00,1887.13,,infty -2022-08-11 18:24:00,1888.01,,infty -2022-08-11 18:25:00,1886.58,,infty -2022-08-11 18:26:00,1887.54,,infty -2022-08-11 18:27:00,1888.41,,infty -2022-08-11 18:28:00,1887.57,,infty -2022-08-11 18:29:00,1889.59,,infty -2022-08-11 18:30:00,1890.7,,infty -2022-08-11 18:31:00,1888.78,,infty -2022-08-11 18:32:00,1887.07,,infty -2022-08-11 18:33:00,1886.18,,infty -2022-08-11 18:34:00,1886.04,,infty -2022-08-11 18:35:00,1885.59,,infty -2022-08-11 18:36:00,1887.54,,infty -2022-08-11 18:37:00,1890.08,,infty -2022-08-11 18:38:00,1890.32,,infty -2022-08-11 18:39:00,1890.79,,infty -2022-08-11 18:40:00,1889.66,,infty -2022-08-11 18:41:00,1891.24,,infty -2022-08-11 18:42:00,1889.89,,infty -2022-08-11 18:43:00,1888.82,,infty -2022-08-11 18:44:00,1890.25,,infty -2022-08-11 18:45:00,1890.06,,infty -2022-08-11 18:46:00,1888.89,,infty -2022-08-11 18:47:00,1889.83,,infty -2022-08-11 18:48:00,1890.22,,infty -2022-08-11 18:49:00,1890.05,,infty -2022-08-11 18:50:00,1887.62,,infty -2022-08-11 18:51:00,1885.38,,infty -2022-08-11 18:52:00,1882.59,,infty -2022-08-11 18:53:00,1884.22,,infty -2022-08-11 18:54:00,1883.63,,infty -2022-08-11 18:55:00,1883.04,,infty -2022-08-11 18:56:00,1883.89,,infty -2022-08-11 18:57:00,1884.77,,infty -2022-08-11 18:58:00,1888.98,,infty -2022-08-11 18:59:00,1887.72,,infty -2022-08-11 19:00:00,1886.92,,infty -2022-08-11 19:01:00,1886.03,,infty -2022-08-11 19:02:00,1885.04,,infty -2022-08-11 19:03:00,1884.87,,infty -2022-08-11 19:04:00,1886.46,,infty -2022-08-11 19:05:00,1885.7,,infty -2022-08-11 19:06:00,1883.12,,infty -2022-08-11 19:07:00,1883.05,,infty -2022-08-11 19:08:00,1881.16,,infty -2022-08-11 19:09:00,1884.09,,infty -2022-08-11 19:10:00,1882.38,,infty -2022-08-11 19:11:00,1879.33,,infty -2022-08-11 19:12:00,1877.61,,infty -2022-08-11 19:13:00,1879.65,,infty -2022-08-11 19:14:00,1882.13,,infty -2022-08-11 19:15:00,1882.62,,infty -2022-08-11 19:16:00,1882.76,,infty -2022-08-11 19:17:00,1886.01,,infty -2022-08-11 19:18:00,1886.17,,infty -2022-08-11 19:19:00,1885.34,,infty -2022-08-11 19:20:00,1889.55,,infty -2022-08-11 19:21:00,1889.0,,infty -2022-08-11 19:22:00,1888.37,,infty -2022-08-11 19:23:00,1887.91,,infty -2022-08-11 19:24:00,1887.16,,infty -2022-08-11 19:25:00,1887.72,,infty -2022-08-11 19:26:00,1887.18,,infty -2022-08-11 19:27:00,1888.61,,infty -2022-08-11 19:28:00,1889.04,,infty -2022-08-11 19:29:00,1888.53,,infty -2022-08-11 19:30:00,1889.31,,infty -2022-08-11 19:31:00,1890.35,,infty -2022-08-11 19:32:00,1889.65,,infty -2022-08-11 19:33:00,1890.44,,infty -2022-08-11 19:34:00,1891.25,,infty -2022-08-11 19:35:00,1889.74,,infty -2022-08-11 19:36:00,1889.43,,infty -2022-08-11 19:37:00,1888.0,,infty -2022-08-11 19:38:00,1888.59,,infty -2022-08-11 19:39:00,1889.37,,infty -2022-08-11 19:40:00,1889.85,,infty -2022-08-11 19:41:00,1891.07,,infty -2022-08-11 19:42:00,1891.34,,infty -2022-08-11 19:43:00,1890.94,,infty -2022-08-11 19:44:00,1894.2,,infty 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20:12:00,1891.44,,infty -2022-08-11 20:13:00,1889.63,,infty -2022-08-11 20:14:00,1891.29,,infty -2022-08-11 20:15:00,1890.5,,infty -2022-08-11 20:16:00,1888.47,,infty -2022-08-11 20:17:00,1888.92,,infty -2022-08-11 20:18:00,1889.27,,infty -2022-08-11 20:19:00,1886.84,,infty -2022-08-11 20:20:00,1886.18,,infty -2022-08-11 20:21:00,1886.77,,infty -2022-08-11 20:22:00,1887.86,,infty -2022-08-11 20:23:00,1887.87,,infty -2022-08-11 20:24:00,1887.38,,infty -2022-08-11 20:25:00,1887.84,,infty -2022-08-11 20:26:00,1890.38,,infty -2022-08-11 20:27:00,1893.88,,infty -2022-08-11 20:28:00,1893.98,,infty -2022-08-11 20:29:00,1896.46,,infty -2022-08-11 20:30:00,1896.87,,infty -2022-08-11 20:31:00,1895.09,,infty -2022-08-11 20:32:00,1897.6,,infty -2022-08-11 20:33:00,1898.51,,infty -2022-08-11 20:34:00,1897.79,,infty -2022-08-11 20:35:00,1896.39,,infty -2022-08-11 20:36:00,1896.89,,infty -2022-08-11 20:37:00,1896.89,,infty -2022-08-11 20:38:00,1897.75,,infty -2022-08-11 20:39:00,1896.59,,infty 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21:07:00,1902.98,,infty -2022-08-11 21:08:00,1904.06,,infty -2022-08-11 21:09:00,1903.49,,infty -2022-08-11 21:10:00,1902.83,,infty -2022-08-11 21:11:00,1901.67,,infty -2022-08-11 21:12:00,1903.24,,infty -2022-08-11 21:13:00,1902.13,,infty -2022-08-11 21:14:00,1901.37,,infty -2022-08-11 21:15:00,1900.75,,infty -2022-08-11 21:16:00,1901.0,,infty -2022-08-11 21:17:00,1901.02,,infty -2022-08-11 21:18:00,1901.31,,infty -2022-08-11 21:19:00,1902.86,,infty -2022-08-11 21:20:00,1903.15,,infty -2022-08-11 21:21:00,1901.79,,infty -2022-08-11 21:22:00,1901.49,,infty -2022-08-11 21:23:00,1902.3,,infty -2022-08-11 21:24:00,1903.81,,infty -2022-08-11 21:25:00,1903.32,,infty -2022-08-11 21:26:00,1899.45,,infty -2022-08-11 21:27:00,1900.66,,infty -2022-08-11 21:28:00,1899.03,,infty -2022-08-11 21:29:00,1898.25,,infty -2022-08-11 21:30:00,1898.04,,infty -2022-08-11 21:31:00,1900.65,,infty -2022-08-11 21:32:00,1904.01,,infty -2022-08-11 21:33:00,1905.47,,infty -2022-08-11 21:34:00,1904.35,,infty 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22:02:00,1899.0,,infty -2022-08-11 22:03:00,1897.18,,infty -2022-08-11 22:04:00,1896.16,,infty -2022-08-11 22:05:00,1896.63,,infty -2022-08-11 22:06:00,1892.92,,infty -2022-08-11 22:07:00,1894.44,,infty -2022-08-11 22:08:00,1893.0,,infty -2022-08-11 22:09:00,1893.1,,infty -2022-08-11 22:10:00,1889.12,,infty -2022-08-11 22:11:00,1890.62,,infty -2022-08-11 22:12:00,1892.26,,infty -2022-08-11 22:13:00,1890.91,,infty -2022-08-11 22:14:00,1892.01,,infty -2022-08-11 22:15:00,1892.38,,infty -2022-08-11 22:16:00,1890.25,,infty -2022-08-11 22:17:00,1889.56,,infty -2022-08-11 22:18:00,1890.01,,infty -2022-08-11 22:19:00,1888.47,,infty -2022-08-11 22:20:00,1890.76,,infty -2022-08-11 22:21:00,1887.56,,infty -2022-08-11 22:22:00,1886.36,,infty -2022-08-11 22:23:00,1888.02,,infty -2022-08-11 22:24:00,1888.99,,infty -2022-08-11 22:25:00,1889.33,,infty -2022-08-11 22:26:00,1888.5,,infty -2022-08-11 22:27:00,1886.05,,infty -2022-08-11 22:28:00,1886.79,,infty -2022-08-11 22:29:00,1885.6,,infty 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22:57:00,1875.4,,infty -2022-08-11 22:58:00,1875.51,,infty -2022-08-11 22:59:00,1874.88,,infty -2022-08-11 23:00:00,1874.63,,infty -2022-08-11 23:01:00,1873.74,,infty -2022-08-11 23:02:00,1871.49,,infty -2022-08-11 23:03:00,1870.32,,infty -2022-08-11 23:04:00,1872.84,,infty -2022-08-11 23:05:00,1869.49,,infty -2022-08-11 23:06:00,1870.0,,infty -2022-08-11 23:07:00,1869.21,,infty -2022-08-11 23:08:00,1869.27,,infty -2022-08-11 23:09:00,1868.55,,infty -2022-08-11 23:10:00,1871.46,,infty -2022-08-11 23:11:00,1870.51,,infty -2022-08-11 23:12:00,1874.06,,infty -2022-08-11 23:13:00,1876.47,,infty -2022-08-11 23:14:00,1876.47,,infty -2022-08-11 23:15:00,1875.95,,infty -2022-08-11 23:16:00,1878.74,,infty -2022-08-11 23:17:00,1879.0,,infty -2022-08-11 23:18:00,1877.39,,infty -2022-08-11 23:19:00,1877.53,,infty -2022-08-11 23:20:00,1876.38,,infty -2022-08-11 23:21:00,1877.2,,infty -2022-08-11 23:22:00,1877.49,,infty -2022-08-11 23:23:00,1874.92,,infty -2022-08-11 23:24:00,1875.04,,infty 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23:52:00,1882.92,,infty -2022-08-11 23:53:00,1882.92,,infty -2022-08-11 23:54:00,1881.87,,infty -2022-08-11 23:55:00,1882.97,,infty -2022-08-11 23:56:00,1881.56,,infty -2022-08-11 23:57:00,1881.82,,infty -2022-08-11 23:58:00,1881.95,,infty -2022-08-11 23:59:00,1880.95,,infty -2022-08-12 00:00:00,1882.14,,infty -2022-08-12 00:01:00,1883.96,,infty -2022-08-12 00:02:00,1883.79,,infty -2022-08-12 00:03:00,1881.84,,infty -2022-08-12 00:04:00,1880.76,,infty -2022-08-12 00:05:00,1878.62,,infty -2022-08-12 00:06:00,1878.43,,infty -2022-08-12 00:07:00,1875.35,,infty -2022-08-12 00:08:00,1876.88,,infty -2022-08-12 00:09:00,1874.47,,infty -2022-08-12 00:10:00,1875.25,,infty -2022-08-12 00:11:00,1874.64,,infty -2022-08-12 00:12:00,1871.46,,infty -2022-08-12 00:13:00,1870.01,,infty -2022-08-12 00:14:00,1862.84,,infty -2022-08-12 00:15:00,1867.78,,infty -2022-08-12 00:16:00,1863.56,,infty -2022-08-12 00:17:00,1859.9,,infty -2022-08-12 00:18:00,1866.67,,infty -2022-08-12 00:19:00,1864.97,,infty 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00:47:00,1883.64,,infty -2022-08-12 00:48:00,1884.41,,infty -2022-08-12 00:49:00,1884.44,,infty -2022-08-12 00:50:00,1884.22,,infty -2022-08-12 00:51:00,1883.53,,infty -2022-08-12 00:52:00,1884.4,,infty -2022-08-12 00:53:00,1887.96,,infty -2022-08-12 00:54:00,1889.04,,infty -2022-08-12 00:55:00,1888.76,,infty -2022-08-12 00:56:00,1886.52,,infty -2022-08-12 00:57:00,1886.9,,infty -2022-08-12 00:58:00,1887.0,,infty -2022-08-12 00:59:00,1886.11,,infty -2022-08-12 01:00:00,1885.82,,infty -2022-08-12 01:01:00,1889.24,,infty -2022-08-12 01:02:00,1886.47,,infty -2022-08-12 01:03:00,1888.78,,infty -2022-08-12 01:04:00,1887.77,,infty -2022-08-12 01:05:00,1890.5,,infty -2022-08-12 01:06:00,1889.59,,infty -2022-08-12 01:07:00,1888.0,,infty -2022-08-12 01:08:00,1886.24,,infty -2022-08-12 01:09:00,1886.36,,infty -2022-08-12 01:10:00,1887.3,,infty -2022-08-12 01:11:00,1889.44,,infty -2022-08-12 01:12:00,1888.37,,infty -2022-08-12 01:13:00,1887.23,,infty -2022-08-12 01:14:00,1883.53,,infty 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02:10:00,1878.2,,infty -2022-08-12 02:11:00,1877.22,,infty -2022-08-12 02:12:00,1872.84,,infty -2022-08-12 02:13:00,1874.15,,infty -2022-08-12 02:14:00,1873.05,,infty -2022-08-12 02:15:00,1873.42,,infty -2022-08-12 02:16:00,1874.56,,infty -2022-08-12 02:17:00,1874.98,,infty -2022-08-12 02:18:00,1873.49,,infty -2022-08-12 02:19:00,1873.97,,infty -2022-08-12 02:20:00,1874.86,,infty -2022-08-12 02:21:00,1874.28,,infty -2022-08-12 02:22:00,1878.36,,infty -2022-08-12 02:23:00,1877.33,,infty -2022-08-12 02:24:00,1880.29,,infty -2022-08-12 02:25:00,1879.34,,infty -2022-08-12 02:26:00,1882.62,,infty -2022-08-12 02:27:00,1884.38,,infty -2022-08-12 02:28:00,1884.8,,infty -2022-08-12 02:29:00,1887.96,,infty -2022-08-12 02:30:00,1890.62,,infty -2022-08-12 02:31:00,1888.55,,infty -2022-08-12 02:32:00,1888.85,,infty -2022-08-12 02:33:00,1892.52,,infty -2022-08-12 02:34:00,1893.97,,infty -2022-08-12 02:35:00,1895.16,,infty -2022-08-12 02:36:00,1896.24,,infty -2022-08-12 02:37:00,1896.14,,infty 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03:05:00,1904.5,,infty -2022-08-12 03:06:00,1904.34,,infty -2022-08-12 03:07:00,1903.27,,infty -2022-08-12 03:08:00,1900.59,,infty -2022-08-12 03:09:00,1901.58,,infty -2022-08-12 03:10:00,1901.57,,infty -2022-08-12 03:11:00,1901.98,,infty -2022-08-12 03:12:00,1901.89,,infty -2022-08-12 03:13:00,1900.91,,infty -2022-08-12 03:14:00,1902.09,,infty -2022-08-12 03:15:00,1902.36,,infty -2022-08-12 03:16:00,1902.3,,infty -2022-08-12 03:17:00,1902.61,,infty -2022-08-12 03:18:00,1899.91,,infty -2022-08-12 03:19:00,1900.64,,infty -2022-08-12 03:20:00,1898.87,,infty -2022-08-12 03:21:00,1899.45,,infty -2022-08-12 03:22:00,1899.11,,infty -2022-08-12 03:23:00,1899.1,,infty -2022-08-12 03:24:00,1899.96,,infty -2022-08-12 03:25:00,1900.71,,infty -2022-08-12 03:26:00,1900.21,,infty -2022-08-12 03:27:00,1900.04,,infty -2022-08-12 03:28:00,1899.53,,infty -2022-08-12 03:29:00,1900.27,,infty -2022-08-12 03:30:00,1898.22,,infty -2022-08-12 03:31:00,1897.79,,infty -2022-08-12 03:32:00,1896.2,,infty 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04:00:00,1905.28,,infty -2022-08-12 04:01:00,1905.4,,infty -2022-08-12 04:02:00,1906.66,,infty -2022-08-12 04:03:00,1905.75,,infty -2022-08-12 04:04:00,1908.59,,infty -2022-08-12 04:05:00,1907.68,,infty -2022-08-12 04:06:00,1904.05,,infty -2022-08-12 04:07:00,1906.08,,infty -2022-08-12 04:08:00,1904.01,,infty -2022-08-12 04:09:00,1903.7,,infty -2022-08-12 04:10:00,1904.8,,infty -2022-08-12 04:11:00,1903.9,,infty -2022-08-12 04:12:00,1903.97,,infty -2022-08-12 04:13:00,1903.64,,infty -2022-08-12 04:14:00,1903.7,,infty -2022-08-12 04:15:00,1901.68,,infty -2022-08-12 04:16:00,1902.05,,infty -2022-08-12 04:17:00,1901.04,,infty -2022-08-12 04:18:00,1901.56,,infty -2022-08-12 04:19:00,1902.76,,infty -2022-08-12 04:20:00,1902.06,,infty -2022-08-12 04:21:00,1901.03,,infty -2022-08-12 04:22:00,1899.84,,infty -2022-08-12 04:23:00,1899.18,,infty -2022-08-12 04:24:00,1898.81,,infty -2022-08-12 04:25:00,1897.04,,infty -2022-08-12 04:26:00,1897.9,,infty -2022-08-12 04:27:00,1897.52,,infty 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04:55:00,1891.37,,infty -2022-08-12 04:56:00,1890.58,,infty -2022-08-12 04:57:00,1891.25,,infty -2022-08-12 04:58:00,1891.42,,infty -2022-08-12 04:59:00,1891.63,,infty -2022-08-12 05:00:00,1891.61,,infty -2022-08-12 05:01:00,1892.45,,infty -2022-08-12 05:02:00,1892.43,,infty -2022-08-12 05:03:00,1893.64,,infty -2022-08-12 05:04:00,1893.63,,infty -2022-08-12 05:05:00,1894.99,,infty -2022-08-12 05:06:00,1896.0,,infty -2022-08-12 05:07:00,1896.28,,infty -2022-08-12 05:08:00,1896.42,,infty -2022-08-12 05:09:00,1896.0,,infty -2022-08-12 05:10:00,1897.13,,infty -2022-08-12 05:11:00,1898.04,,infty -2022-08-12 05:12:00,1898.07,,infty -2022-08-12 05:13:00,1898.13,,infty -2022-08-12 05:14:00,1898.22,,infty -2022-08-12 05:15:00,1898.69,,infty -2022-08-12 05:16:00,1900.34,,infty -2022-08-12 05:17:00,1900.0,,infty -2022-08-12 05:18:00,1900.0,,infty -2022-08-12 05:19:00,1900.98,,infty -2022-08-12 05:20:00,1899.68,,infty -2022-08-12 05:21:00,1900.03,,infty -2022-08-12 05:22:00,1901.75,,infty 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05:50:00,1894.73,,infty -2022-08-12 05:51:00,1894.03,,infty -2022-08-12 05:52:00,1894.34,,infty -2022-08-12 05:53:00,1894.28,,infty -2022-08-12 05:54:00,1895.81,,infty -2022-08-12 05:55:00,1893.5,,infty -2022-08-12 05:56:00,1893.02,,infty -2022-08-12 05:57:00,1892.29,,infty -2022-08-12 05:58:00,1893.33,,infty -2022-08-12 05:59:00,1894.35,,infty -2022-08-12 06:00:00,1896.52,,infty -2022-08-12 06:01:00,1896.3,,infty -2022-08-12 06:02:00,1896.12,,infty -2022-08-12 06:03:00,1897.09,,infty -2022-08-12 06:04:00,1896.31,,infty -2022-08-12 06:05:00,1896.59,,infty -2022-08-12 06:06:00,1896.54,,infty -2022-08-12 06:07:00,1892.73,,infty -2022-08-12 06:08:00,1890.98,,infty -2022-08-12 06:09:00,1888.69,,infty -2022-08-12 06:10:00,1889.85,,infty -2022-08-12 06:11:00,1888.94,,infty -2022-08-12 06:12:00,1890.36,,infty -2022-08-12 06:13:00,1890.2,,infty -2022-08-12 06:14:00,1890.03,,infty -2022-08-12 06:15:00,1889.87,,infty -2022-08-12 06:16:00,1889.85,,infty -2022-08-12 06:17:00,1889.94,,infty 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06:45:00,1885.98,,infty -2022-08-12 06:46:00,1885.11,,infty -2022-08-12 06:47:00,1886.51,,infty -2022-08-12 06:48:00,1888.54,,infty -2022-08-12 06:49:00,1889.26,,infty -2022-08-12 06:50:00,1889.62,,infty -2022-08-12 06:51:00,1887.07,,infty -2022-08-12 06:52:00,1888.58,,infty -2022-08-12 06:53:00,1888.38,,infty -2022-08-12 06:54:00,1889.53,,infty -2022-08-12 06:55:00,1889.06,,infty -2022-08-12 06:56:00,1889.3,,infty -2022-08-12 06:57:00,1889.05,,infty -2022-08-12 06:58:00,1892.08,,infty -2022-08-12 06:59:00,1892.86,,infty -2022-08-12 07:00:00,1890.04,,infty -2022-08-12 07:01:00,1890.3,,infty -2022-08-12 07:02:00,1887.62,,infty -2022-08-12 07:03:00,1888.77,,infty -2022-08-12 07:04:00,1887.63,,infty -2022-08-12 07:05:00,1887.01,,infty -2022-08-12 07:06:00,1887.05,,infty -2022-08-12 07:07:00,1890.09,,infty -2022-08-12 07:08:00,1891.29,,infty -2022-08-12 07:09:00,1893.9,,infty -2022-08-12 07:10:00,1893.66,,infty -2022-08-12 07:11:00,1891.96,,infty -2022-08-12 07:12:00,1892.8,,infty 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07:40:00,1886.94,,infty -2022-08-12 07:41:00,1885.55,,infty -2022-08-12 07:42:00,1885.0,,infty -2022-08-12 07:43:00,1885.03,,infty -2022-08-12 07:44:00,1883.54,,infty -2022-08-12 07:45:00,1883.65,,infty -2022-08-12 07:46:00,1884.63,,infty -2022-08-12 07:47:00,1884.17,,infty -2022-08-12 07:48:00,1884.94,,infty -2022-08-12 07:49:00,1887.01,,infty -2022-08-12 07:50:00,1887.88,,infty -2022-08-12 07:51:00,1888.22,,infty -2022-08-12 07:52:00,1888.0,,infty -2022-08-12 07:53:00,1888.61,,infty -2022-08-12 07:54:00,1889.26,,infty -2022-08-12 07:55:00,1887.65,,infty -2022-08-12 07:56:00,1887.51,,infty -2022-08-12 07:57:00,1887.87,,infty -2022-08-12 07:58:00,1888.67,,infty -2022-08-12 07:59:00,1889.31,,infty -2022-08-12 08:00:00,1888.49,,infty -2022-08-12 08:01:00,1885.41,,infty -2022-08-12 08:02:00,1888.41,,infty -2022-08-12 08:03:00,1888.39,,infty -2022-08-12 08:04:00,1890.53,,infty -2022-08-12 08:05:00,1892.5,,infty -2022-08-12 08:06:00,1892.82,,infty -2022-08-12 08:07:00,1893.9,,infty 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08:35:00,1889.83,,infty -2022-08-12 08:36:00,1890.82,,infty -2022-08-12 08:37:00,1890.77,,infty -2022-08-12 08:38:00,1890.94,,infty -2022-08-12 08:39:00,1890.33,,infty -2022-08-12 08:40:00,1892.15,,infty -2022-08-12 08:41:00,1891.66,,infty -2022-08-12 08:42:00,1893.76,,infty -2022-08-12 08:43:00,1894.04,,infty -2022-08-12 08:44:00,1892.51,,infty -2022-08-12 08:45:00,1890.85,,infty -2022-08-12 08:46:00,1892.41,,infty -2022-08-12 08:47:00,1893.35,,infty -2022-08-12 08:48:00,1892.38,,infty -2022-08-12 08:49:00,1894.05,,infty -2022-08-12 08:50:00,1893.87,,infty -2022-08-12 08:51:00,1892.37,,infty -2022-08-12 08:52:00,1890.76,,infty -2022-08-12 08:53:00,1891.72,,infty -2022-08-12 08:54:00,1891.41,,infty -2022-08-12 08:55:00,1891.94,,infty -2022-08-12 08:56:00,1892.09,,infty -2022-08-12 08:57:00,1890.74,,infty -2022-08-12 08:58:00,1890.47,,infty -2022-08-12 08:59:00,1888.86,,infty -2022-08-12 09:00:00,1889.62,,infty -2022-08-12 09:01:00,1889.7,,infty -2022-08-12 09:02:00,1890.56,,infty 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09:30:00,1899.01,,infty -2022-08-12 09:31:00,1899.44,,infty -2022-08-12 09:32:00,1898.41,,infty -2022-08-12 09:33:00,1899.33,,infty -2022-08-12 09:34:00,1899.7,,infty -2022-08-12 09:35:00,1899.59,,infty -2022-08-12 09:36:00,1899.25,,infty -2022-08-12 09:37:00,1898.2,,infty -2022-08-12 09:38:00,1898.72,,infty -2022-08-12 09:39:00,1900.58,,infty -2022-08-12 09:40:00,1901.27,,infty -2022-08-12 09:41:00,1899.95,,infty -2022-08-12 09:42:00,1898.6,,infty -2022-08-12 09:43:00,1897.76,,infty -2022-08-12 09:44:00,1897.59,,infty -2022-08-12 09:45:00,1895.72,,infty -2022-08-12 09:46:00,1893.4,,infty -2022-08-12 09:47:00,1895.29,,infty -2022-08-12 09:48:00,1894.55,,infty -2022-08-12 09:49:00,1897.34,,infty -2022-08-12 09:50:00,1902.14,,infty -2022-08-12 09:51:00,1902.46,,infty -2022-08-12 09:52:00,1900.15,,infty -2022-08-12 09:53:00,1901.56,,infty -2022-08-12 09:54:00,1901.55,,infty -2022-08-12 09:55:00,1902.68,,infty -2022-08-12 09:56:00,1901.43,,infty -2022-08-12 09:57:00,1895.04,,infty 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10:25:00,1887.42,,infty -2022-08-12 10:26:00,1887.19,,infty -2022-08-12 10:27:00,1889.56,,infty -2022-08-12 10:28:00,1886.34,,infty -2022-08-12 10:29:00,1886.73,,infty -2022-08-12 10:30:00,1888.29,,infty -2022-08-12 10:31:00,1888.45,,infty -2022-08-12 10:32:00,1888.04,,infty -2022-08-12 10:33:00,1888.62,,infty -2022-08-12 10:34:00,1888.62,,infty -2022-08-12 10:35:00,1889.39,,infty -2022-08-12 10:36:00,1887.09,,infty -2022-08-12 10:37:00,1887.96,,infty -2022-08-12 10:38:00,1887.22,,infty -2022-08-12 10:39:00,1886.48,,infty -2022-08-12 10:40:00,1886.82,,infty -2022-08-12 10:41:00,1885.61,,infty -2022-08-12 10:42:00,1887.12,,infty -2022-08-12 10:43:00,1885.91,,infty -2022-08-12 10:44:00,1885.65,,infty -2022-08-12 10:45:00,1887.3,,infty -2022-08-12 10:46:00,1886.36,,infty -2022-08-12 10:47:00,1885.52,,infty -2022-08-12 10:48:00,1884.86,,infty -2022-08-12 10:49:00,1885.19,,infty -2022-08-12 10:50:00,1884.26,,infty -2022-08-12 10:51:00,1882.4,,infty -2022-08-12 10:52:00,1881.6,,infty 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11:48:00,1869.29,,infty -2022-08-12 11:49:00,1870.23,,infty -2022-08-12 11:50:00,1868.09,,infty -2022-08-12 11:51:00,1869.08,,infty -2022-08-12 11:52:00,1868.0,,infty -2022-08-12 11:53:00,1869.06,,infty -2022-08-12 11:54:00,1868.64,,infty -2022-08-12 11:55:00,1868.49,,infty -2022-08-12 11:56:00,1870.0,,infty -2022-08-12 11:57:00,1870.04,,infty -2022-08-12 11:58:00,1871.35,,infty -2022-08-12 11:59:00,1869.59,,infty -2022-08-12 12:00:00,1869.34,,infty -2022-08-12 12:01:00,1869.96,,infty -2022-08-12 12:02:00,1870.23,,infty -2022-08-12 12:03:00,1868.63,,infty -2022-08-12 12:04:00,1869.04,,infty -2022-08-12 12:05:00,1868.33,,infty -2022-08-12 12:06:00,1868.74,,infty -2022-08-12 12:07:00,1873.24,,infty -2022-08-12 12:08:00,1875.03,,infty -2022-08-12 12:09:00,1877.62,,infty -2022-08-12 12:10:00,1877.4,,infty -2022-08-12 12:11:00,1876.08,,infty -2022-08-12 12:12:00,1876.36,,infty -2022-08-12 12:13:00,1877.41,,infty -2022-08-12 12:14:00,1877.2,,infty -2022-08-12 12:15:00,1874.46,,infty 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12:43:00,1880.03,,infty -2022-08-12 12:44:00,1879.57,,infty -2022-08-12 12:45:00,1876.87,,infty -2022-08-12 12:46:00,1879.01,,infty -2022-08-12 12:47:00,1880.47,,infty -2022-08-12 12:48:00,1883.13,,infty -2022-08-12 12:49:00,1882.65,,infty -2022-08-12 12:50:00,1881.87,,infty -2022-08-12 12:51:00,1882.49,,infty -2022-08-12 12:52:00,1881.63,,infty -2022-08-12 12:53:00,1882.2,,infty -2022-08-12 12:54:00,1881.93,,infty -2022-08-12 12:55:00,1881.48,,infty -2022-08-12 12:56:00,1880.65,,infty -2022-08-12 12:57:00,1880.45,,infty -2022-08-12 12:58:00,1880.84,,infty -2022-08-12 12:59:00,1881.64,,infty -2022-08-12 13:00:00,1882.58,,infty -2022-08-12 13:01:00,1882.98,,infty -2022-08-12 13:02:00,1882.83,,infty -2022-08-12 13:03:00,1884.37,,infty -2022-08-12 13:04:00,1881.98,,infty -2022-08-12 13:05:00,1883.82,,infty -2022-08-12 13:06:00,1885.37,,infty -2022-08-12 13:07:00,1886.08,,infty -2022-08-12 13:08:00,1883.71,,infty -2022-08-12 13:09:00,1882.66,,infty -2022-08-12 13:10:00,1879.67,,infty 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13:38:00,1876.8,,infty -2022-08-12 13:39:00,1877.98,,infty -2022-08-12 13:40:00,1887.83,,infty -2022-08-12 13:41:00,1891.21,,infty -2022-08-12 13:42:00,1888.54,,infty -2022-08-12 13:43:00,1887.84,,infty -2022-08-12 13:44:00,1888.97,,infty -2022-08-12 13:45:00,1885.68,,infty -2022-08-12 13:46:00,1886.58,,infty -2022-08-12 13:47:00,1885.04,,infty -2022-08-12 13:48:00,1880.56,,infty -2022-08-12 13:49:00,1880.38,,infty -2022-08-12 13:50:00,1883.85,,infty -2022-08-12 13:51:00,1885.17,,infty -2022-08-12 13:52:00,1885.78,,infty -2022-08-12 13:53:00,1882.61,,infty -2022-08-12 13:54:00,1881.41,,infty -2022-08-12 13:55:00,1882.13,,infty -2022-08-12 13:56:00,1880.24,,infty -2022-08-12 13:57:00,1881.57,,infty -2022-08-12 13:58:00,1882.7,,infty -2022-08-12 13:59:00,1884.42,,infty -2022-08-12 14:00:00,1875.23,,infty -2022-08-12 14:01:00,1876.69,,infty -2022-08-12 14:02:00,1880.17,,infty -2022-08-12 14:03:00,1881.07,,infty -2022-08-12 14:04:00,1879.81,,infty -2022-08-12 14:05:00,1877.07,,infty 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14:33:00,1888.13,,infty -2022-08-12 14:34:00,1886.3,,infty -2022-08-12 14:35:00,1886.67,,infty -2022-08-12 14:36:00,1884.43,,infty -2022-08-12 14:37:00,1885.14,,infty -2022-08-12 14:38:00,1883.12,,infty -2022-08-12 14:39:00,1884.01,,infty -2022-08-12 14:40:00,1881.53,,infty -2022-08-12 14:41:00,1880.3,,infty -2022-08-12 14:42:00,1881.41,,infty -2022-08-12 14:43:00,1881.12,,infty -2022-08-12 14:44:00,1881.92,,infty -2022-08-12 14:45:00,1882.44,,infty -2022-08-12 14:46:00,1880.2,,infty -2022-08-12 14:47:00,1878.3,,infty -2022-08-12 14:48:00,1877.59,,infty -2022-08-12 14:49:00,1878.29,,infty -2022-08-12 14:50:00,1880.13,,infty -2022-08-12 14:51:00,1880.42,,infty -2022-08-12 14:52:00,1880.93,,infty -2022-08-12 14:53:00,1880.9,,infty -2022-08-12 14:54:00,1881.8,,infty -2022-08-12 14:55:00,1881.42,,infty -2022-08-12 14:56:00,1881.28,,infty -2022-08-12 14:57:00,1882.73,,infty -2022-08-12 14:58:00,1882.69,,infty -2022-08-12 14:59:00,1882.04,,infty -2022-08-12 15:00:00,1881.52,,infty 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15:28:00,1898.86,,infty -2022-08-12 15:29:00,1898.76,,infty -2022-08-12 15:30:00,1904.37,,infty -2022-08-12 15:31:00,1901.38,,infty -2022-08-12 15:32:00,1897.4,,infty -2022-08-12 15:33:00,1895.81,,infty -2022-08-12 15:34:00,1896.0,,infty -2022-08-12 15:35:00,1895.88,,infty -2022-08-12 15:36:00,1897.66,,infty -2022-08-12 15:37:00,1900.34,,infty -2022-08-12 15:38:00,1898.94,,infty -2022-08-12 15:39:00,1899.73,,infty -2022-08-12 15:40:00,1899.0,,infty -2022-08-12 15:41:00,1898.71,,infty -2022-08-12 15:42:00,1898.22,,infty -2022-08-12 15:43:00,1896.6,,infty -2022-08-12 15:44:00,1896.86,,infty -2022-08-12 15:45:00,1897.36,,infty -2022-08-12 15:46:00,1897.43,,infty -2022-08-12 15:47:00,1897.79,,infty -2022-08-12 15:48:00,1900.78,,infty -2022-08-12 15:49:00,1899.69,,infty -2022-08-12 15:50:00,1900.78,,infty -2022-08-12 15:51:00,1898.69,,infty -2022-08-12 15:52:00,1899.35,,infty -2022-08-12 15:53:00,1899.92,,infty -2022-08-12 15:54:00,1899.93,,infty -2022-08-12 15:55:00,1901.68,,infty -2022-08-12 15:56:00,1900.69,,infty -2022-08-12 15:57:00,1899.59,,infty -2022-08-12 15:58:00,1898.86,,infty -2022-08-12 15:59:00,1898.81,,infty -2022-08-12 16:00:00,1901.02,,infty -2022-08-12 16:01:00,1898.93,,infty -2022-08-12 16:02:00,1900.56,,infty -2022-08-12 16:03:00,1901.03,,infty -2022-08-12 16:04:00,1905.66,,infty -2022-08-12 16:05:00,1904.41,,infty -2022-08-12 16:06:00,1905.34,,infty -2022-08-12 16:07:00,1905.33,,infty -2022-08-12 16:08:00,1903.53,,infty -2022-08-12 16:09:00,1903.43,,infty -2022-08-12 16:10:00,1903.06,,infty -2022-08-12 16:11:00,1900.86,,infty -2022-08-12 16:12:00,1901.11,,infty -2022-08-12 16:13:00,1901.89,,infty -2022-08-12 16:14:00,1901.45,,infty -2022-08-12 16:15:00,1904.47,,infty -2022-08-12 16:16:00,1903.97,,infty -2022-08-12 16:17:00,1903.5,,infty -2022-08-12 16:18:00,1903.48,,infty -2022-08-12 16:19:00,1901.97,,infty -2022-08-12 16:20:00,1900.31,,infty -2022-08-12 16:21:00,1901.08,,infty -2022-08-12 16:22:00,1901.16,,infty -2022-08-12 16:23:00,1899.34,,infty -2022-08-12 16:24:00,1900.99,,infty -2022-08-12 16:25:00,1900.11,,infty -2022-08-12 16:26:00,1899.76,,infty -2022-08-12 16:27:00,1897.27,,infty -2022-08-12 16:28:00,1898.84,,infty -2022-08-12 16:29:00,1899.33,,infty -2022-08-12 16:30:00,1897.2,,infty -2022-08-12 16:31:00,1896.08,,infty -2022-08-12 16:32:00,1893.37,,infty -2022-08-12 16:33:00,1896.08,,infty -2022-08-12 16:34:00,1894.99,,infty -2022-08-12 16:35:00,1896.19,,infty -2022-08-12 16:36:00,1894.68,,infty -2022-08-12 16:37:00,1895.3,,infty -2022-08-12 16:38:00,1897.13,,infty -2022-08-12 16:39:00,1898.77,,infty -2022-08-12 16:40:00,1897.15,,infty -2022-08-12 16:41:00,1896.4,,infty -2022-08-12 16:42:00,1896.29,,infty -2022-08-12 16:43:00,1897.53,,infty -2022-08-12 16:44:00,1897.69,,infty -2022-08-12 16:45:00,1897.32,,infty -2022-08-12 16:46:00,1896.9,,infty -2022-08-12 16:47:00,1896.48,,infty -2022-08-12 16:48:00,1898.22,,infty -2022-08-12 16:49:00,1898.23,,infty -2022-08-12 16:50:00,1898.09,,infty -2022-08-12 16:51:00,1896.92,,infty -2022-08-12 16:52:00,1896.28,,infty -2022-08-12 16:53:00,1895.7,,infty -2022-08-12 16:54:00,1895.38,,infty -2022-08-12 16:55:00,1893.8,,infty -2022-08-12 16:56:00,1894.42,,infty -2022-08-12 16:57:00,1894.49,,infty -2022-08-12 16:58:00,1894.05,,infty -2022-08-12 16:59:00,1894.37,,infty -2022-08-12 17:00:00,1894.55,,infty -2022-08-12 17:01:00,1892.83,,infty -2022-08-12 17:02:00,1893.18,,infty -2022-08-12 17:03:00,1894.88,,infty -2022-08-12 17:04:00,1893.88,,infty -2022-08-12 17:05:00,1892.43,,infty -2022-08-12 17:06:00,1893.12,,infty -2022-08-12 17:07:00,1894.11,,infty -2022-08-12 17:08:00,1895.61,,infty -2022-08-12 17:09:00,1894.84,,infty -2022-08-12 17:10:00,1894.72,,infty -2022-08-12 17:11:00,1894.0,,infty -2022-08-12 17:12:00,1893.89,,infty -2022-08-12 17:13:00,1893.04,,infty -2022-08-12 17:14:00,1893.95,,infty -2022-08-12 17:15:00,1894.96,,infty -2022-08-12 17:16:00,1896.12,,infty -2022-08-12 17:17:00,1895.33,,infty -2022-08-12 17:18:00,1894.5,,infty -2022-08-12 17:19:00,1897.5,,infty -2022-08-12 17:20:00,1898.93,,infty -2022-08-12 17:21:00,1897.55,,infty -2022-08-12 17:22:00,1896.02,,infty -2022-08-12 17:23:00,1895.27,,infty -2022-08-12 17:24:00,1894.41,,infty -2022-08-12 17:25:00,1896.19,,infty -2022-08-12 17:26:00,1896.39,,infty -2022-08-12 17:27:00,1897.11,,infty -2022-08-12 17:28:00,1896.23,,infty -2022-08-12 17:29:00,1896.92,,infty -2022-08-12 17:30:00,1898.33,,infty -2022-08-12 17:31:00,1897.64,,infty -2022-08-12 17:32:00,1895.49,,infty -2022-08-12 17:33:00,1897.9,,infty -2022-08-12 17:34:00,1897.85,,infty -2022-08-12 17:35:00,1898.02,,infty -2022-08-12 17:36:00,1898.19,,infty -2022-08-12 17:37:00,1897.42,,infty -2022-08-12 17:38:00,1898.45,,infty -2022-08-12 17:39:00,1898.31,,infty -2022-08-12 17:40:00,1899.76,,infty -2022-08-12 17:41:00,1898.17,,infty -2022-08-12 17:42:00,1896.87,,infty -2022-08-12 17:43:00,1897.55,,infty -2022-08-12 17:44:00,1897.15,,infty -2022-08-12 17:45:00,1897.18,,infty -2022-08-12 17:46:00,1897.46,,infty -2022-08-12 17:47:00,1897.87,,infty -2022-08-12 17:48:00,1897.36,,infty -2022-08-12 17:49:00,1896.77,,infty -2022-08-12 17:50:00,1895.57,,infty -2022-08-12 17:51:00,1895.86,,infty -2022-08-12 17:52:00,1894.81,,infty -2022-08-12 17:53:00,1896.06,,infty -2022-08-12 17:54:00,1897.41,,infty -2022-08-12 17:55:00,1899.81,,infty -2022-08-12 17:56:00,1902.14,,infty -2022-08-12 17:57:00,1900.4,,infty -2022-08-12 17:58:00,1899.69,,infty -2022-08-12 17:59:00,1899.7,,infty -2022-08-12 18:00:00,1900.27,,infty -2022-08-12 18:01:00,1901.22,,infty -2022-08-12 18:02:00,1899.86,,infty -2022-08-12 18:03:00,1901.51,,infty -2022-08-12 18:04:00,1903.28,,infty -2022-08-12 18:05:00,1901.33,,infty -2022-08-12 18:06:00,1898.36,,infty -2022-08-12 18:07:00,1898.36,,infty -2022-08-12 18:08:00,1897.26,,infty -2022-08-12 18:09:00,1897.91,,infty -2022-08-12 18:10:00,1898.16,,infty -2022-08-12 18:11:00,1898.68,,infty -2022-08-12 18:12:00,1899.55,,infty -2022-08-12 18:13:00,1899.21,,infty -2022-08-12 18:14:00,1900.13,,infty -2022-08-12 18:15:00,1899.37,,infty -2022-08-12 18:16:00,1900.71,,infty -2022-08-12 18:17:00,1901.25,,infty -2022-08-12 18:18:00,1901.97,,infty -2022-08-12 18:19:00,1900.67,,infty -2022-08-12 18:20:00,1899.37,,infty -2022-08-12 18:21:00,1901.58,,infty -2022-08-12 18:22:00,1900.75,,infty -2022-08-12 18:23:00,1901.14,,infty -2022-08-12 18:24:00,1901.44,,infty -2022-08-12 18:25:00,1902.47,,infty -2022-08-12 18:26:00,1901.31,,infty -2022-08-12 18:27:00,1902.25,,infty -2022-08-12 18:28:00,1903.48,,infty -2022-08-12 18:29:00,1903.87,,infty -2022-08-12 18:30:00,1905.18,,infty -2022-08-12 18:31:00,1903.33,,infty -2022-08-12 18:32:00,1903.29,,infty -2022-08-12 18:33:00,1905.92,,infty -2022-08-12 18:34:00,1908.8,,infty -2022-08-12 18:35:00,1909.54,,infty -2022-08-12 18:36:00,1907.76,,infty -2022-08-12 18:37:00,1907.57,,infty -2022-08-12 18:38:00,1904.64,,infty -2022-08-12 18:39:00,1905.67,,infty -2022-08-12 18:40:00,1906.43,,infty -2022-08-12 18:41:00,1908.15,,infty -2022-08-12 18:42:00,1922.23,,infty -2022-08-12 18:43:00,1922.21,,infty -2022-08-12 18:44:00,1918.44,,infty -2022-08-12 18:45:00,1921.56,,infty -2022-08-12 18:46:00,1916.13,,infty -2022-08-12 18:47:00,1908.99,,infty -2022-08-12 18:48:00,1909.53,,infty -2022-08-12 18:49:00,1909.57,,infty -2022-08-12 18:50:00,1911.53,,infty -2022-08-12 18:51:00,1913.89,,infty -2022-08-12 18:52:00,1919.26,,infty -2022-08-12 18:53:00,1917.96,,infty -2022-08-12 18:54:00,1918.66,,infty -2022-08-12 18:55:00,1917.35,,infty -2022-08-12 18:56:00,1917.09,,infty -2022-08-12 18:57:00,1915.67,,infty -2022-08-12 18:58:00,1915.31,,infty -2022-08-12 18:59:00,1912.53,,infty -2022-08-12 19:00:00,1916.87,,infty -2022-08-12 19:01:00,1915.8,,infty -2022-08-12 19:02:00,1915.46,,infty -2022-08-12 19:03:00,1915.9,,infty -2022-08-12 19:04:00,1915.03,,infty -2022-08-12 19:05:00,1917.16,,infty -2022-08-12 19:06:00,1921.91,,infty -2022-08-12 19:07:00,1921.61,,infty -2022-08-12 19:08:00,1922.74,,infty -2022-08-12 19:09:00,1921.14,,infty -2022-08-12 19:10:00,1921.33,,infty -2022-08-12 19:11:00,1922.91,,infty -2022-08-12 19:12:00,1922.0,,infty -2022-08-12 19:13:00,1921.48,,infty -2022-08-12 19:14:00,1921.16,,infty -2022-08-12 19:15:00,1920.92,,infty -2022-08-12 19:16:00,1921.43,,infty -2022-08-12 19:17:00,1928.56,,infty -2022-08-12 19:18:00,1924.07,,infty -2022-08-12 19:19:00,1924.36,,infty -2022-08-12 19:20:00,1924.33,,infty -2022-08-12 19:21:00,1924.59,,infty -2022-08-12 19:22:00,1925.21,,infty -2022-08-12 19:23:00,1924.27,,infty -2022-08-12 19:24:00,1923.9,,infty -2022-08-12 19:25:00,1923.02,,infty -2022-08-12 19:26:00,1923.08,,infty -2022-08-12 19:27:00,1925.24,,infty -2022-08-12 19:28:00,1925.46,,infty -2022-08-12 19:29:00,1926.21,,infty -2022-08-12 19:30:00,1927.92,,infty -2022-08-12 19:31:00,1927.0,,infty -2022-08-12 19:32:00,1925.0,,infty -2022-08-12 19:33:00,1925.42,,infty -2022-08-12 19:34:00,1924.86,,infty -2022-08-12 19:35:00,1924.88,,infty -2022-08-12 19:36:00,1925.57,,infty -2022-08-12 19:37:00,1924.38,,infty -2022-08-12 19:38:00,1922.6,,infty -2022-08-12 19:39:00,1923.58,,infty -2022-08-12 19:40:00,1923.88,,infty -2022-08-12 19:41:00,1924.68,,infty -2022-08-12 19:42:00,1926.13,,infty -2022-08-12 19:43:00,1927.67,,infty -2022-08-12 19:44:00,1927.12,,infty -2022-08-12 19:45:00,1925.52,,infty -2022-08-12 19:46:00,1928.02,,infty -2022-08-12 19:47:00,1923.26,,infty -2022-08-12 19:48:00,1923.23,,infty -2022-08-12 19:49:00,1922.09,,infty -2022-08-12 19:50:00,1923.62,,infty -2022-08-12 19:51:00,1923.29,,infty -2022-08-12 19:52:00,1923.55,,infty -2022-08-12 19:53:00,1925.15,,infty -2022-08-12 19:54:00,1924.59,,infty -2022-08-12 19:55:00,1924.48,,infty -2022-08-12 19:56:00,1925.58,,infty -2022-08-12 19:57:00,1926.68,,infty -2022-08-12 19:58:00,1927.4,,infty -2022-08-12 19:59:00,1926.98,,infty -2022-08-12 20:00:00,1925.13,,infty -2022-08-12 20:01:00,1924.78,,infty -2022-08-12 20:02:00,1923.03,,infty -2022-08-12 20:03:00,1922.39,,infty -2022-08-12 20:04:00,1920.59,,infty -2022-08-12 20:05:00,1922.22,,infty -2022-08-12 20:06:00,1922.0,,infty -2022-08-12 20:07:00,1921.76,,infty -2022-08-12 20:08:00,1923.63,,infty -2022-08-12 20:09:00,1925.11,,infty -2022-08-12 20:10:00,1925.17,,infty -2022-08-12 20:11:00,1923.49,,infty -2022-08-12 20:12:00,1922.9,,infty -2022-08-12 20:13:00,1923.87,,infty -2022-08-12 20:14:00,1923.25,,infty -2022-08-12 20:15:00,1923.44,,infty -2022-08-12 20:16:00,1922.38,,infty -2022-08-12 20:17:00,1920.51,,infty -2022-08-12 20:18:00,1920.78,,infty -2022-08-12 20:19:00,1921.3,,infty -2022-08-12 20:20:00,1919.81,,infty -2022-08-12 20:21:00,1920.38,,infty -2022-08-12 20:22:00,1918.31,,infty -2022-08-12 20:23:00,1919.32,,infty -2022-08-12 20:24:00,1920.43,,infty -2022-08-12 20:25:00,1918.06,,infty -2022-08-12 20:26:00,1918.17,,infty -2022-08-12 20:27:00,1918.65,,infty -2022-08-12 20:28:00,1917.67,,infty -2022-08-12 20:29:00,1921.59,,infty -2022-08-12 20:30:00,1923.03,,infty -2022-08-12 20:31:00,1922.64,,infty -2022-08-12 20:32:00,1919.75,,infty -2022-08-12 20:33:00,1920.15,,infty -2022-08-12 20:34:00,1920.72,,infty -2022-08-12 20:35:00,1921.22,,infty -2022-08-12 20:36:00,1926.53,,infty -2022-08-12 20:37:00,1924.62,,infty -2022-08-12 20:38:00,1924.81,,infty -2022-08-12 20:39:00,1923.98,,infty -2022-08-12 20:40:00,1923.19,,infty -2022-08-12 20:41:00,1925.17,,infty -2022-08-12 20:42:00,1923.56,,infty -2022-08-12 20:43:00,1923.6,,infty -2022-08-12 20:44:00,1924.21,,infty -2022-08-12 20:45:00,1925.08,,infty -2022-08-12 20:46:00,1927.07,,infty -2022-08-12 20:47:00,1925.74,,infty -2022-08-12 20:48:00,1924.05,,infty -2022-08-12 20:49:00,1923.89,,infty -2022-08-12 20:50:00,1923.47,,infty -2022-08-12 20:51:00,1923.25,,infty -2022-08-12 20:52:00,1922.68,,infty -2022-08-12 20:53:00,1924.59,,infty -2022-08-12 20:54:00,1925.64,,infty -2022-08-12 20:55:00,1932.64,,infty -2022-08-12 20:56:00,1935.0,,infty -2022-08-12 20:57:00,1935.39,,infty -2022-08-12 20:58:00,1933.3,,infty -2022-08-12 20:59:00,1932.75,,infty -2022-08-12 21:00:00,1931.38,,infty -2022-08-12 21:01:00,1929.19,,infty -2022-08-12 21:02:00,1930.94,,infty -2022-08-12 21:03:00,1929.8,,infty -2022-08-12 21:04:00,1928.76,,infty -2022-08-12 21:05:00,1929.8,,infty -2022-08-12 21:06:00,1929.68,,infty -2022-08-12 21:07:00,1931.23,,infty -2022-08-12 21:08:00,1929.49,,infty -2022-08-12 21:09:00,1930.82,,infty -2022-08-12 21:10:00,1931.56,,infty -2022-08-12 21:11:00,1931.86,,infty -2022-08-12 21:12:00,1929.88,,infty -2022-08-12 21:13:00,1928.74,,infty -2022-08-12 21:14:00,1928.4,,infty -2022-08-12 21:15:00,1928.77,,infty -2022-08-12 21:16:00,1928.87,,infty -2022-08-12 21:17:00,1928.21,,infty -2022-08-12 21:18:00,1927.43,,infty -2022-08-12 21:19:00,1927.57,,infty -2022-08-12 21:20:00,1927.42,,infty -2022-08-12 21:21:00,1924.86,,infty -2022-08-12 21:22:00,1925.88,,infty -2022-08-12 21:23:00,1924.36,,infty -2022-08-12 21:24:00,1925.01,,infty -2022-08-12 21:25:00,1924.66,,infty -2022-08-12 21:26:00,1924.31,,infty -2022-08-12 21:27:00,1924.26,,infty -2022-08-12 21:28:00,1925.18,,infty -2022-08-12 21:29:00,1925.29,,infty -2022-08-12 21:30:00,1925.12,,infty -2022-08-12 21:31:00,1925.37,,infty -2022-08-12 21:32:00,1927.18,,infty -2022-08-12 21:33:00,1927.41,,infty -2022-08-12 21:34:00,1926.8,,infty -2022-08-12 21:35:00,1926.59,,infty -2022-08-12 21:36:00,1925.67,,infty -2022-08-12 21:37:00,1923.19,,infty -2022-08-12 21:38:00,1923.51,,infty -2022-08-12 21:39:00,1923.44,,infty -2022-08-12 21:40:00,1923.05,,infty -2022-08-12 21:41:00,1923.95,,infty -2022-08-12 21:42:00,1923.76,,infty -2022-08-12 21:43:00,1924.24,,infty -2022-08-12 21:44:00,1922.93,,infty -2022-08-12 21:45:00,1922.61,,infty -2022-08-12 21:46:00,1924.13,,infty -2022-08-12 21:47:00,1925.08,,infty -2022-08-12 21:48:00,1926.33,,infty -2022-08-12 21:49:00,1926.75,,infty -2022-08-12 21:50:00,1925.89,,infty -2022-08-12 21:51:00,1925.77,,infty -2022-08-12 21:52:00,1925.06,,infty -2022-08-12 21:53:00,1925.08,,infty -2022-08-12 21:54:00,1924.76,,infty -2022-08-12 21:55:00,1924.12,,infty -2022-08-12 21:56:00,1924.38,,infty -2022-08-12 21:57:00,1923.82,,infty -2022-08-12 21:58:00,1923.31,,infty -2022-08-12 21:59:00,1923.13,,infty -2022-08-12 22:00:00,1925.8,,infty -2022-08-12 22:01:00,1924.12,,infty -2022-08-12 22:02:00,1923.18,,infty -2022-08-12 22:03:00,1922.6,,infty -2022-08-12 22:04:00,1920.72,,infty -2022-08-12 22:05:00,1920.73,,infty -2022-08-12 22:06:00,1920.87,,infty -2022-08-12 22:07:00,1921.44,,infty -2022-08-12 22:08:00,1920.75,,infty -2022-08-12 22:09:00,1918.99,,infty -2022-08-12 22:10:00,1921.19,,infty -2022-08-12 22:11:00,1921.01,,infty -2022-08-12 22:12:00,1921.59,,infty -2022-08-12 22:13:00,1922.28,,infty -2022-08-12 22:14:00,1921.9,,infty -2022-08-12 22:15:00,1924.52,,infty -2022-08-12 22:16:00,1923.9,,infty -2022-08-12 22:17:00,1923.63,,infty -2022-08-12 22:18:00,1923.02,,infty -2022-08-12 22:19:00,1922.14,,infty -2022-08-12 22:20:00,1922.58,,infty -2022-08-12 22:21:00,1923.76,,infty -2022-08-12 22:22:00,1922.42,,infty -2022-08-12 22:23:00,1921.86,,infty -2022-08-12 22:24:00,1920.38,,infty -2022-08-12 22:25:00,1921.48,,infty -2022-08-12 22:26:00,1921.6,,infty -2022-08-12 22:27:00,1921.98,,infty -2022-08-12 22:28:00,1921.95,,infty -2022-08-12 22:29:00,1920.76,,infty -2022-08-12 22:30:00,1920.89,,infty -2022-08-12 22:31:00,1920.31,,infty -2022-08-12 22:32:00,1923.43,,infty -2022-08-12 22:33:00,1922.56,,infty -2022-08-12 22:34:00,1923.69,,infty -2022-08-12 22:35:00,1926.86,,infty -2022-08-12 22:36:00,1926.68,,infty -2022-08-12 22:37:00,1924.28,,infty -2022-08-12 22:38:00,1924.1,,infty -2022-08-12 22:39:00,1925.29,,infty -2022-08-12 22:40:00,1925.73,,infty -2022-08-12 22:41:00,1925.53,,infty -2022-08-12 22:42:00,1927.89,,infty -2022-08-12 22:43:00,1928.92,,infty -2022-08-12 22:44:00,1930.49,,infty -2022-08-12 22:45:00,1927.74,,infty -2022-08-12 22:46:00,1927.29,,infty -2022-08-12 22:47:00,1926.72,,infty -2022-08-12 22:48:00,1928.71,,infty -2022-08-12 22:49:00,1928.45,,infty -2022-08-12 22:50:00,1930.62,,infty -2022-08-12 22:51:00,1931.45,,infty -2022-08-12 22:52:00,1929.69,,infty -2022-08-12 22:53:00,1930.03,,infty -2022-08-12 22:54:00,1928.94,,infty -2022-08-12 22:55:00,1929.84,,infty -2022-08-12 22:56:00,1931.2,,infty -2022-08-12 22:57:00,1931.21,,infty -2022-08-12 22:58:00,1931.27,,infty -2022-08-12 22:59:00,1932.13,,infty -2022-08-12 23:00:00,1930.88,,infty -2022-08-12 23:01:00,1929.23,,infty -2022-08-12 23:02:00,1928.16,,infty -2022-08-12 23:03:00,1929.5,,infty -2022-08-12 23:04:00,1928.95,,infty -2022-08-12 23:05:00,1928.4,,infty -2022-08-12 23:06:00,1929.31,,infty -2022-08-12 23:07:00,1929.78,,infty -2022-08-12 23:08:00,1930.48,,infty -2022-08-12 23:09:00,1935.19,,infty -2022-08-12 23:10:00,1930.93,,infty -2022-08-12 23:11:00,1931.07,,infty -2022-08-12 23:12:00,1933.02,,infty -2022-08-12 23:13:00,1935.17,,infty -2022-08-12 23:14:00,1946.33,,infty -2022-08-12 23:15:00,1948.6,,infty -2022-08-12 23:16:00,1945.64,,infty -2022-08-12 23:17:00,1947.37,,infty -2022-08-12 23:18:00,1945.51,,infty -2022-08-12 23:19:00,1945.21,,infty -2022-08-12 23:20:00,1952.0,,infty -2022-08-12 23:21:00,1949.35,,infty -2022-08-12 23:22:00,1954.25,,infty -2022-08-12 23:23:00,1952.03,,infty -2022-08-12 23:24:00,1952.98,,infty -2022-08-12 23:25:00,1956.23,,infty -2022-08-12 23:26:00,1955.18,,infty -2022-08-12 23:27:00,1953.71,,infty -2022-08-12 23:28:00,1955.83,,infty -2022-08-12 23:29:00,1954.67,,infty -2022-08-12 23:30:00,1953.07,,infty -2022-08-12 23:31:00,1951.89,,infty -2022-08-12 23:32:00,1955.13,,infty -2022-08-12 23:33:00,1956.01,,infty -2022-08-12 23:34:00,1958.07,,infty -2022-08-12 23:35:00,1957.21,,infty -2022-08-12 23:36:00,1955.42,,infty -2022-08-12 23:37:00,1957.0,,infty -2022-08-12 23:38:00,1955.82,,infty -2022-08-12 23:39:00,1954.02,,infty -2022-08-12 23:40:00,1953.82,,infty -2022-08-12 23:41:00,1951.3,,infty -2022-08-12 23:42:00,1950.21,,infty -2022-08-12 23:43:00,1950.6,,infty -2022-08-12 23:44:00,1951.8,,infty -2022-08-12 23:45:00,1953.64,,infty -2022-08-12 23:46:00,1949.7,,infty -2022-08-12 23:47:00,1952.18,,infty -2022-08-12 23:48:00,1952.61,,infty -2022-08-12 23:49:00,1955.08,,infty -2022-08-12 23:50:00,1954.72,,infty -2022-08-12 23:51:00,1957.7,,infty -2022-08-12 23:52:00,1957.38,,infty -2022-08-12 23:53:00,1961.78,,infty -2022-08-12 23:54:00,1960.23,,infty -2022-08-12 23:55:00,1959.7,,infty -2022-08-12 23:56:00,1959.47,,infty -2022-08-12 23:57:00,1961.48,,infty -2022-08-12 23:58:00,1960.63,,infty -2022-08-12 23:59:00,1959.0,,infty -2022-08-13 00:00:00,1957.03,,infty -2022-08-13 00:01:00,1958.28,,infty -2022-08-13 00:02:00,1957.25,,infty -2022-08-13 00:03:00,1956.25,,infty -2022-08-13 00:04:00,1955.65,,infty -2022-08-13 00:05:00,1956.72,,infty -2022-08-13 00:06:00,1956.22,,infty -2022-08-13 00:07:00,1953.75,,infty -2022-08-13 00:08:00,1954.98,,infty -2022-08-13 00:09:00,1954.57,,infty -2022-08-13 00:10:00,1955.95,,infty -2022-08-13 00:11:00,1957.51,,infty -2022-08-13 00:12:00,1957.62,,infty -2022-08-13 00:13:00,1955.87,,infty -2022-08-13 00:14:00,1952.65,,infty -2022-08-13 00:15:00,1951.44,,infty -2022-08-13 00:16:00,1950.5,,infty -2022-08-13 00:17:00,1951.95,,infty -2022-08-13 00:18:00,1949.38,,infty -2022-08-13 00:19:00,1949.7,,infty -2022-08-13 00:20:00,1948.93,,infty -2022-08-13 00:21:00,1948.28,,infty -2022-08-13 00:22:00,1948.0,,infty -2022-08-13 00:23:00,1947.92,,infty -2022-08-13 00:24:00,1948.97,,infty -2022-08-13 00:25:00,1950.35,,infty -2022-08-13 00:26:00,1951.45,,infty -2022-08-13 00:27:00,1950.0,,infty -2022-08-13 00:28:00,1950.97,,infty -2022-08-13 00:29:00,1952.63,,infty -2022-08-13 00:30:00,1952.6,,infty -2022-08-13 00:31:00,1948.65,,infty -2022-08-13 00:32:00,1950.27,,infty -2022-08-13 00:33:00,1950.5,,infty -2022-08-13 00:34:00,1950.45,,infty -2022-08-13 00:35:00,1949.58,,infty -2022-08-13 00:36:00,1951.94,,infty -2022-08-13 00:37:00,1952.97,,infty -2022-08-13 00:38:00,1953.27,,infty -2022-08-13 00:39:00,1952.44,,infty -2022-08-13 00:40:00,1952.11,,infty -2022-08-13 00:41:00,1953.79,,infty -2022-08-13 00:42:00,1955.19,,infty -2022-08-13 00:43:00,1956.74,,infty -2022-08-13 00:44:00,1960.22,,infty -2022-08-13 00:45:00,1958.04,,infty -2022-08-13 00:46:00,1953.93,,infty -2022-08-13 00:47:00,1954.97,,infty -2022-08-13 00:48:00,1955.82,,infty -2022-08-13 00:49:00,1955.74,,infty -2022-08-13 00:50:00,1958.06,,infty -2022-08-13 00:51:00,1957.84,,infty -2022-08-13 00:52:00,1958.59,,infty -2022-08-13 00:53:00,1958.88,,infty -2022-08-13 00:54:00,1957.29,,infty -2022-08-13 00:55:00,1958.22,,infty -2022-08-13 00:56:00,1958.77,,infty -2022-08-13 00:57:00,1962.25,,infty -2022-08-13 00:58:00,1963.04,,infty -2022-08-13 00:59:00,1969.74,,infty -2022-08-13 01:00:00,1967.76,,infty -2022-08-13 01:01:00,1966.99,,infty -2022-08-13 01:02:00,1965.24,,infty -2022-08-13 01:03:00,1963.99,,infty -2022-08-13 01:04:00,1965.59,,infty -2022-08-13 01:05:00,1967.75,,infty -2022-08-13 01:06:00,1970.57,,infty -2022-08-13 01:07:00,1967.48,,infty -2022-08-13 01:08:00,1968.01,,infty -2022-08-13 01:09:00,1969.25,,infty -2022-08-13 01:10:00,1965.55,,infty -2022-08-13 01:11:00,1966.51,,infty -2022-08-13 01:12:00,1965.96,,infty -2022-08-13 01:13:00,1965.91,,infty -2022-08-13 01:14:00,1965.04,,infty -2022-08-13 01:15:00,1969.58,,infty -2022-08-13 01:16:00,1967.65,,infty -2022-08-13 01:17:00,1967.08,,infty -2022-08-13 01:18:00,1967.7,,infty -2022-08-13 01:19:00,1967.46,,infty -2022-08-13 01:20:00,1967.34,,infty -2022-08-13 01:21:00,1967.63,,infty -2022-08-13 01:22:00,1968.18,,infty -2022-08-13 01:23:00,1965.54,,infty -2022-08-13 01:24:00,1966.28,,infty -2022-08-13 01:25:00,1966.4,,infty -2022-08-13 01:26:00,1966.16,,infty -2022-08-13 01:27:00,1963.78,,infty -2022-08-13 01:28:00,1963.55,,infty -2022-08-13 01:29:00,1964.3,,infty -2022-08-13 01:30:00,1964.84,,infty -2022-08-13 01:31:00,1962.94,,infty -2022-08-13 01:32:00,1959.51,,infty -2022-08-13 01:33:00,1959.73,,infty -2022-08-13 01:34:00,1959.27,,infty -2022-08-13 01:35:00,1958.24,,infty -2022-08-13 01:36:00,1959.06,,infty -2022-08-13 01:37:00,1957.9,,infty -2022-08-13 01:38:00,1960.52,,infty -2022-08-13 01:39:00,1960.2,,infty -2022-08-13 01:40:00,1959.92,,infty -2022-08-13 01:41:00,1961.62,,infty -2022-08-13 01:42:00,1960.78,,infty -2022-08-13 01:43:00,1959.72,,infty -2022-08-13 01:44:00,1967.62,,infty -2022-08-13 01:45:00,1964.85,,infty -2022-08-13 01:46:00,1962.46,,infty -2022-08-13 01:47:00,1966.42,,infty -2022-08-13 01:48:00,1966.46,,infty -2022-08-13 01:49:00,1963.66,,infty -2022-08-13 01:50:00,1963.33,,infty -2022-08-13 01:51:00,1965.25,,infty -2022-08-13 01:52:00,1964.67,,infty -2022-08-13 01:53:00,1966.72,,infty -2022-08-13 01:54:00,1968.28,,infty -2022-08-13 01:55:00,1966.31,,infty -2022-08-13 01:56:00,1967.0,,infty -2022-08-13 01:57:00,1965.35,,infty -2022-08-13 01:58:00,1964.44,,infty -2022-08-13 01:59:00,1964.96,,infty -2022-08-13 02:00:00,1964.36,,infty -2022-08-13 02:01:00,1964.36,,infty -2022-08-13 02:02:00,1962.52,,infty -2022-08-13 02:03:00,1963.26,,infty -2022-08-13 02:04:00,1963.77,,infty -2022-08-13 02:05:00,1963.03,,infty -2022-08-13 02:06:00,1964.34,,infty -2022-08-13 02:07:00,1964.79,,infty -2022-08-13 02:08:00,1964.21,,infty -2022-08-13 02:09:00,1965.15,,infty -2022-08-13 02:10:00,1963.31,,infty -2022-08-13 02:11:00,1964.88,,infty -2022-08-13 02:12:00,1962.38,,infty -2022-08-13 02:13:00,1962.83,,infty -2022-08-13 02:14:00,1962.05,,infty -2022-08-13 02:15:00,1961.37,,infty -2022-08-13 02:16:00,1961.48,,infty -2022-08-13 02:17:00,1964.9,,infty -2022-08-13 02:18:00,1964.03,,infty -2022-08-13 02:19:00,1965.08,,infty -2022-08-13 02:20:00,1963.51,,infty -2022-08-13 02:21:00,1965.48,,infty -2022-08-13 02:22:00,1964.95,,infty -2022-08-13 02:23:00,1965.05,,infty -2022-08-13 02:24:00,1964.74,,infty -2022-08-13 02:25:00,1964.03,,infty -2022-08-13 02:26:00,1964.14,,infty -2022-08-13 02:27:00,1963.22,,infty -2022-08-13 02:28:00,1962.76,,infty -2022-08-13 02:29:00,1963.69,,infty -2022-08-13 02:30:00,1961.5,,infty -2022-08-13 02:31:00,1963.09,,infty -2022-08-13 02:32:00,1965.7,,infty -2022-08-13 02:33:00,1964.4,,infty -2022-08-13 02:34:00,1963.9,,infty -2022-08-13 02:35:00,1965.48,,infty -2022-08-13 02:36:00,1965.8,,infty -2022-08-13 02:37:00,1965.39,,infty -2022-08-13 02:38:00,1967.25,,infty -2022-08-13 02:39:00,1964.47,,infty -2022-08-13 02:40:00,1965.67,,infty -2022-08-13 02:41:00,1963.41,,infty -2022-08-13 02:42:00,1963.48,,infty -2022-08-13 02:43:00,1963.78,,infty -2022-08-13 02:44:00,1965.8,,infty -2022-08-13 02:45:00,1965.59,,infty -2022-08-13 02:46:00,1966.29,,infty -2022-08-13 02:47:00,1966.13,,infty -2022-08-13 02:48:00,1966.31,,infty -2022-08-13 02:49:00,1968.73,,infty -2022-08-13 02:50:00,1968.0,,infty -2022-08-13 02:51:00,1967.72,,infty -2022-08-13 02:52:00,1969.49,,infty -2022-08-13 02:53:00,1969.1,,infty -2022-08-13 02:54:00,1968.97,,infty -2022-08-13 02:55:00,1967.41,,infty -2022-08-13 02:56:00,1966.8,,infty -2022-08-13 02:57:00,1968.36,,infty -2022-08-13 02:58:00,1968.47,,infty -2022-08-13 02:59:00,1978.45,,infty -2022-08-13 03:00:00,1975.2,,infty -2022-08-13 03:01:00,1975.46,,infty -2022-08-13 03:02:00,1972.0,,infty -2022-08-13 03:03:00,1970.51,,infty -2022-08-13 03:04:00,1970.6,,infty -2022-08-13 03:05:00,1973.45,,infty -2022-08-13 03:06:00,1974.33,,infty -2022-08-13 03:07:00,1975.8,,infty -2022-08-13 03:08:00,1975.15,,infty -2022-08-13 03:09:00,1974.56,,infty -2022-08-13 03:10:00,1971.59,,infty -2022-08-13 03:11:00,1972.5,,infty -2022-08-13 03:12:00,1971.26,,infty -2022-08-13 03:13:00,1970.92,,infty -2022-08-13 03:14:00,1970.03,,infty -2022-08-13 03:15:00,1971.62,,infty -2022-08-13 03:16:00,1973.16,,infty -2022-08-13 03:17:00,1973.14,,infty -2022-08-13 03:18:00,1972.46,,infty -2022-08-13 03:19:00,1970.33,,infty -2022-08-13 03:20:00,1970.24,,infty -2022-08-13 03:21:00,1969.61,,infty -2022-08-13 03:22:00,1971.49,,infty -2022-08-13 03:23:00,1973.0,,infty -2022-08-13 03:24:00,1973.85,,infty -2022-08-13 03:25:00,1973.3,,infty -2022-08-13 03:26:00,1972.81,,infty -2022-08-13 03:27:00,1975.2,,infty -2022-08-13 03:28:00,1974.64,,infty -2022-08-13 03:29:00,1973.38,,infty -2022-08-13 03:30:00,1973.69,,infty -2022-08-13 03:31:00,1972.49,,infty -2022-08-13 03:32:00,1971.77,,infty -2022-08-13 03:33:00,1975.69,,infty -2022-08-13 03:34:00,1974.7,,infty -2022-08-13 03:35:00,1976.36,,infty -2022-08-13 03:36:00,1976.36,,infty -2022-08-13 03:37:00,1975.37,,infty -2022-08-13 03:38:00,1976.99,,infty -2022-08-13 03:39:00,1975.36,,infty -2022-08-13 03:40:00,1978.06,,infty -2022-08-13 03:41:00,1979.23,,infty -2022-08-13 03:42:00,1985.0,,infty -2022-08-13 03:43:00,1984.87,,infty -2022-08-13 03:44:00,1982.49,,infty -2022-08-13 03:45:00,1986.6,,infty -2022-08-13 03:46:00,1982.66,,infty -2022-08-13 03:47:00,1982.87,,infty -2022-08-13 03:48:00,1983.24,,infty -2022-08-13 03:49:00,1985.51,,infty -2022-08-13 03:50:00,1982.2,,infty -2022-08-13 03:51:00,1984.02,,infty -2022-08-13 03:52:00,1981.65,,infty -2022-08-13 03:53:00,1979.43,,infty -2022-08-13 03:54:00,1979.2,,infty -2022-08-13 03:55:00,1979.33,,infty -2022-08-13 03:56:00,1983.63,,infty -2022-08-13 03:57:00,1992.44,,infty -2022-08-13 03:58:00,1994.74,,infty -2022-08-13 03:59:00,1997.22,,infty -2022-08-13 04:00:00,1996.28,,infty -2022-08-13 04:01:00,1994.91,,infty -2022-08-13 04:02:00,1995.0,,infty -2022-08-13 04:03:00,1997.61,,infty -2022-08-13 04:04:00,1995.16,,infty -2022-08-13 04:05:00,1994.06,,infty -2022-08-13 04:06:00,1992.46,,infty -2022-08-13 04:07:00,1991.68,,infty -2022-08-13 04:08:00,1992.01,,infty -2022-08-13 04:09:00,1990.0,,infty -2022-08-13 04:10:00,1989.22,,infty -2022-08-13 04:11:00,1992.04,,infty -2022-08-13 04:12:00,1990.97,,infty -2022-08-13 04:13:00,1991.66,,infty -2022-08-13 04:14:00,1991.82,,infty -2022-08-13 04:15:00,1990.02,,infty -2022-08-13 04:16:00,1988.42,,infty -2022-08-13 04:17:00,1989.06,,infty -2022-08-13 04:18:00,1990.6,,infty -2022-08-13 04:19:00,1990.17,,infty -2022-08-13 04:20:00,1991.99,,infty -2022-08-13 04:21:00,1997.13,,infty -2022-08-13 04:22:00,1999.48,,infty -2022-08-13 04:23:00,2000.35,,infty -2022-08-13 04:24:00,2006.14,,infty -2022-08-13 04:25:00,2000.85,,infty -2022-08-13 04:26:00,2001.69,,infty -2022-08-13 04:27:00,2003.97,,infty -2022-08-13 04:28:00,2000.63,,infty -2022-08-13 04:29:00,1999.29,,infty -2022-08-13 04:30:00,2000.22,,infty -2022-08-13 04:31:00,2000.71,,infty -2022-08-13 04:32:00,1996.58,,infty -2022-08-13 04:33:00,1994.68,,infty -2022-08-13 04:34:00,1993.91,,infty -2022-08-13 04:35:00,1992.74,,infty -2022-08-13 04:36:00,1992.72,,infty -2022-08-13 04:37:00,1991.49,,infty -2022-08-13 04:38:00,1991.5,,infty -2022-08-13 04:39:00,1992.39,,infty -2022-08-13 04:40:00,1991.36,,infty -2022-08-13 04:41:00,1992.96,,infty -2022-08-13 04:42:00,1992.65,,infty -2022-08-13 04:43:00,1993.58,,infty -2022-08-13 04:44:00,1993.21,,infty -2022-08-13 04:45:00,1992.14,,infty -2022-08-13 04:46:00,1990.59,,infty -2022-08-13 04:47:00,1990.09,,infty -2022-08-13 04:48:00,1990.21,,infty -2022-08-13 04:49:00,1990.9,,infty -2022-08-13 04:50:00,1992.42,,infty -2022-08-13 04:51:00,1991.34,,infty -2022-08-13 04:52:00,1991.83,,infty -2022-08-13 04:53:00,1991.87,,infty -2022-08-13 04:54:00,1992.17,,infty -2022-08-13 04:55:00,1992.03,,infty -2022-08-13 04:56:00,1991.0,,infty -2022-08-13 04:57:00,1990.95,,infty -2022-08-13 04:58:00,1993.57,,infty -2022-08-13 04:59:00,1993.08,,infty -2022-08-13 05:00:00,1992.74,,infty -2022-08-13 05:01:00,1992.38,,infty -2022-08-13 05:02:00,1993.54,,infty -2022-08-13 05:03:00,1993.35,,infty -2022-08-13 05:04:00,1995.07,,infty -2022-08-13 05:05:00,1996.0,,infty -2022-08-13 05:06:00,1996.0,,infty -2022-08-13 05:07:00,1993.22,,infty -2022-08-13 05:08:00,1991.89,,infty -2022-08-13 05:09:00,1994.46,,infty -2022-08-13 05:10:00,1995.82,,infty -2022-08-13 05:11:00,1996.0,,infty -2022-08-13 05:12:00,1995.67,,infty -2022-08-13 05:13:00,1994.8,,infty -2022-08-13 05:14:00,1995.01,,infty -2022-08-13 05:15:00,1996.28,,infty -2022-08-13 05:16:00,1996.55,,infty -2022-08-13 05:17:00,1995.14,,infty -2022-08-13 05:18:00,1994.64,,infty -2022-08-13 05:19:00,1995.79,,infty -2022-08-13 05:20:00,1994.64,,infty -2022-08-13 05:21:00,1996.27,,infty -2022-08-13 05:22:00,1994.69,,infty -2022-08-13 05:23:00,1994.65,,infty -2022-08-13 05:24:00,1996.1,,infty -2022-08-13 05:25:00,1996.67,,infty -2022-08-13 05:26:00,1998.0,,infty -2022-08-13 05:27:00,1998.11,,infty -2022-08-13 05:28:00,1996.63,,infty -2022-08-13 05:29:00,1996.12,,infty -2022-08-13 05:30:00,1996.83,,infty -2022-08-13 05:31:00,1997.18,,infty -2022-08-13 05:32:00,1999.34,,infty -2022-08-13 05:33:00,1996.74,,infty -2022-08-13 05:34:00,1997.3,,infty -2022-08-13 05:35:00,1997.21,,infty -2022-08-13 05:36:00,1994.64,,infty -2022-08-13 05:37:00,1993.55,,infty -2022-08-13 05:38:00,1992.85,,infty -2022-08-13 05:39:00,1991.22,,infty -2022-08-13 05:40:00,1991.1,,infty -2022-08-13 05:41:00,1991.15,,infty -2022-08-13 05:42:00,1992.54,,infty -2022-08-13 05:43:00,1993.84,,infty -2022-08-13 05:44:00,1993.73,,infty -2022-08-13 05:45:00,1995.56,,infty -2022-08-13 05:46:00,1994.31,,infty -2022-08-13 05:47:00,1994.38,,infty -2022-08-13 05:48:00,1992.93,,infty -2022-08-13 05:49:00,1992.69,,infty -2022-08-13 05:50:00,1994.01,,infty -2022-08-13 05:51:00,1994.21,,infty -2022-08-13 05:52:00,1994.47,,infty -2022-08-13 05:53:00,1995.26,,infty -2022-08-13 05:54:00,1995.71,,infty -2022-08-13 05:55:00,1994.29,,infty -2022-08-13 05:56:00,1995.92,,infty -2022-08-13 05:57:00,1995.74,,infty -2022-08-13 05:58:00,1996.76,,infty -2022-08-13 05:59:00,1995.04,,infty -2022-08-13 06:00:00,1997.22,,infty -2022-08-13 06:01:00,2001.99,,infty -2022-08-13 06:02:00,2000.64,,infty -2022-08-13 06:03:00,2000.93,,infty -2022-08-13 06:04:00,1999.85,,infty -2022-08-13 06:05:00,2003.68,,infty -2022-08-13 06:06:00,2001.33,,infty -2022-08-13 06:07:00,2003.92,,infty -2022-08-13 06:08:00,2005.01,,infty -2022-08-13 06:09:00,2005.18,,infty -2022-08-13 06:10:00,2006.06,,infty -2022-08-13 06:11:00,2006.0,,infty -2022-08-13 06:12:00,2006.0,,infty -2022-08-13 06:13:00,2008.1,,infty -2022-08-13 06:14:00,2008.41,,infty -2022-08-13 06:15:00,2012.0,,infty -2022-08-13 06:16:00,2017.0,,infty -2022-08-13 06:17:00,2017.0,,infty -2022-08-13 06:18:00,2020.47,,infty -2022-08-13 06:19:00,2020.89,,infty -2022-08-13 06:20:00,2016.94,,infty -2022-08-13 06:21:00,2018.6,,infty -2022-08-13 06:22:00,2014.43,,infty -2022-08-13 06:23:00,2012.86,,infty -2022-08-13 06:24:00,2010.0,,infty -2022-08-13 06:25:00,2013.81,,infty -2022-08-13 06:26:00,2012.28,,infty -2022-08-13 06:27:00,2014.68,,infty -2022-08-13 06:28:00,2017.17,,infty -2022-08-13 06:29:00,2015.83,,infty -2022-08-13 06:30:00,2013.28,,infty -2022-08-13 06:31:00,2012.35,,infty -2022-08-13 06:32:00,2013.7,,infty -2022-08-13 06:33:00,2014.56,,infty -2022-08-13 06:34:00,2013.54,,infty -2022-08-13 06:35:00,2012.8,,infty -2022-08-13 06:36:00,2011.17,,infty -2022-08-13 06:37:00,2011.35,,infty -2022-08-13 06:38:00,2011.44,,infty -2022-08-13 06:39:00,2014.29,,infty -2022-08-13 06:40:00,2014.5,,infty -2022-08-13 06:41:00,2015.27,,infty -2022-08-13 06:42:00,2014.46,,infty -2022-08-13 06:43:00,2013.17,,infty -2022-08-13 06:44:00,2013.58,,infty -2022-08-13 06:45:00,2013.15,,infty -2022-08-13 06:46:00,2011.31,,infty -2022-08-13 06:47:00,2007.01,,infty -2022-08-13 06:48:00,2008.15,,infty -2022-08-13 06:49:00,2006.77,,infty -2022-08-13 06:50:00,2005.52,,infty -2022-08-13 06:51:00,2004.61,,infty -2022-08-13 06:52:00,2002.68,,infty -2022-08-13 06:53:00,1999.16,,infty -2022-08-13 06:54:00,2002.64,,infty -2022-08-13 06:55:00,2004.97,,infty -2022-08-13 06:56:00,2007.18,,infty -2022-08-13 06:57:00,2007.22,,infty -2022-08-13 06:58:00,2007.97,,infty -2022-08-13 06:59:00,2006.85,,infty -2022-08-13 07:00:00,2009.7,,infty -2022-08-13 07:01:00,2011.54,,infty -2022-08-13 07:02:00,2010.27,,infty -2022-08-13 07:03:00,2009.27,,infty -2022-08-13 07:04:00,2007.8,,infty -2022-08-13 07:05:00,2008.82,,infty -2022-08-13 07:06:00,2008.43,,infty -2022-08-13 07:07:00,2010.68,,infty -2022-08-13 07:08:00,2009.47,,infty -2022-08-13 07:09:00,2008.41,,infty -2022-08-13 07:10:00,2007.64,,infty -2022-08-13 07:11:00,2011.22,,infty -2022-08-13 07:12:00,2010.64,,infty -2022-08-13 07:13:00,2008.48,,infty -2022-08-13 07:14:00,2009.4,,infty -2022-08-13 07:15:00,2008.96,,infty -2022-08-13 07:16:00,2007.24,,infty -2022-08-13 07:17:00,2006.05,,infty -2022-08-13 07:18:00,2006.38,,infty -2022-08-13 07:19:00,2005.04,,infty -2022-08-13 07:20:00,2003.81,,infty -2022-08-13 07:21:00,2003.75,,infty -2022-08-13 07:22:00,2005.36,,infty -2022-08-13 07:23:00,2004.65,,infty -2022-08-13 07:24:00,2003.55,,infty -2022-08-13 07:25:00,2004.86,,infty -2022-08-13 07:26:00,2006.16,,infty -2022-08-13 07:27:00,2006.41,,infty -2022-08-13 07:28:00,2006.55,,infty -2022-08-13 07:29:00,2007.31,,infty -2022-08-13 07:30:00,2006.94,,infty -2022-08-13 07:31:00,2005.7,,infty -2022-08-13 07:32:00,2007.09,,infty -2022-08-13 07:33:00,2005.7,,infty -2022-08-13 07:34:00,2005.72,,infty -2022-08-13 07:35:00,2001.34,,infty -2022-08-13 07:36:00,2000.43,,infty -2022-08-13 07:37:00,2001.45,,infty -2022-08-13 07:38:00,2002.03,,infty -2022-08-13 07:39:00,2002.2,,infty -2022-08-13 07:40:00,2001.14,,infty -2022-08-13 07:41:00,2003.33,,infty -2022-08-13 07:42:00,2002.58,,infty -2022-08-13 07:43:00,2002.56,,infty -2022-08-13 07:44:00,2002.43,,infty -2022-08-13 07:45:00,2002.38,,infty -2022-08-13 07:46:00,2002.6,,infty -2022-08-13 07:47:00,2001.08,,infty -2022-08-13 07:48:00,1999.74,,infty -2022-08-13 07:49:00,2000.26,,infty -2022-08-13 07:50:00,1999.0,,infty -2022-08-13 07:51:00,1998.36,,infty -2022-08-13 07:52:00,1998.62,,infty -2022-08-13 07:53:00,1996.58,,infty -2022-08-13 07:54:00,1995.89,,infty -2022-08-13 07:55:00,1996.28,,infty -2022-08-13 07:56:00,1997.01,,infty -2022-08-13 07:57:00,1997.36,,infty -2022-08-13 07:58:00,1997.26,,infty -2022-08-13 07:59:00,1996.83,,infty -2022-08-13 08:00:00,1999.2,,infty -2022-08-13 08:01:00,2003.22,,infty -2022-08-13 08:02:00,2002.66,,infty -2022-08-13 08:03:00,2002.5,,infty -2022-08-13 08:04:00,2002.54,,infty -2022-08-13 08:05:00,2003.83,,infty -2022-08-13 08:06:00,2003.93,,infty -2022-08-13 08:07:00,2003.79,,infty -2022-08-13 08:08:00,2003.73,,infty -2022-08-13 08:09:00,2003.0,,infty -2022-08-13 08:10:00,2004.69,,infty -2022-08-13 08:11:00,2003.55,,infty -2022-08-13 08:12:00,2004.33,,infty -2022-08-13 08:13:00,2002.54,,infty -2022-08-13 08:14:00,2001.92,,infty -2022-08-13 08:15:00,2004.11,,infty -2022-08-13 08:16:00,2004.48,,infty -2022-08-13 08:17:00,2003.18,,infty -2022-08-13 08:18:00,2002.02,,infty -2022-08-13 08:19:00,2001.17,,infty -2022-08-13 08:20:00,1999.98,,infty -2022-08-13 08:21:00,2000.91,,infty -2022-08-13 08:22:00,2001.1,,infty -2022-08-13 08:23:00,1998.93,,infty -2022-08-13 08:24:00,1999.79,,infty -2022-08-13 08:25:00,1999.86,,infty -2022-08-13 08:26:00,1998.98,,infty -2022-08-13 08:27:00,1999.22,,infty -2022-08-13 08:28:00,1999.46,,infty -2022-08-13 08:29:00,2000.81,,infty -2022-08-13 08:30:00,1999.69,,infty -2022-08-13 08:31:00,2000.0,,infty -2022-08-13 08:32:00,1999.65,,infty -2022-08-13 08:33:00,2000.31,,infty -2022-08-13 08:34:00,2001.29,,infty -2022-08-13 08:35:00,2000.27,,infty -2022-08-13 08:36:00,1999.22,,infty -2022-08-13 08:37:00,1998.4,,infty -2022-08-13 08:38:00,1998.4,,infty -2022-08-13 08:39:00,1995.31,,infty -2022-08-13 08:40:00,1995.09,,infty -2022-08-13 08:41:00,1993.32,,infty -2022-08-13 08:42:00,1996.04,,infty -2022-08-13 08:43:00,1995.89,,infty -2022-08-13 08:44:00,1993.44,,infty -2022-08-13 08:45:00,1995.27,,infty -2022-08-13 08:46:00,1993.89,,infty -2022-08-13 08:47:00,1995.2,,infty -2022-08-13 08:48:00,1995.31,,infty -2022-08-13 08:49:00,1994.52,,infty -2022-08-13 08:50:00,1993.87,,infty -2022-08-13 08:51:00,1993.28,,infty -2022-08-13 08:52:00,1991.64,,infty -2022-08-13 08:53:00,1989.49,,infty -2022-08-13 08:54:00,1987.91,,infty -2022-08-13 08:55:00,1982.16,,infty -2022-08-13 08:56:00,1984.33,,infty -2022-08-13 08:57:00,1986.54,,infty -2022-08-13 08:58:00,1989.17,,infty -2022-08-13 08:59:00,1988.57,,infty -2022-08-13 09:00:00,1990.63,,infty -2022-08-13 09:01:00,1988.74,,infty -2022-08-13 09:02:00,1988.97,,infty -2022-08-13 09:03:00,1990.48,,infty -2022-08-13 09:04:00,1990.88,,infty -2022-08-13 09:05:00,1987.74,,infty -2022-08-13 09:06:00,1985.59,,infty -2022-08-13 09:07:00,1989.0,,infty -2022-08-13 09:08:00,1988.67,,infty -2022-08-13 09:09:00,1989.57,,infty -2022-08-13 09:10:00,1992.02,,infty -2022-08-13 09:11:00,1991.0,,infty -2022-08-13 09:12:00,1990.87,,infty -2022-08-13 09:13:00,1989.46,,infty -2022-08-13 09:14:00,1992.04,,infty -2022-08-13 09:15:00,1990.7,,infty -2022-08-13 09:16:00,1989.64,,infty -2022-08-13 09:17:00,1992.05,,infty -2022-08-13 09:18:00,1994.19,,infty -2022-08-13 09:19:00,1995.65,,infty -2022-08-13 09:20:00,1994.86,,infty -2022-08-13 09:21:00,1998.29,,infty -2022-08-13 09:22:00,1997.25,,infty -2022-08-13 09:23:00,1997.77,,infty -2022-08-13 09:24:00,1998.04,,infty -2022-08-13 09:25:00,1998.05,,infty -2022-08-13 09:26:00,1997.0,,infty -2022-08-13 09:27:00,1998.26,,infty -2022-08-13 09:28:00,1996.9,,infty -2022-08-13 09:29:00,1996.38,,infty -2022-08-13 09:30:00,1998.72,,infty -2022-08-13 09:31:00,2000.08,,infty -2022-08-13 09:32:00,2002.24,,infty -2022-08-13 09:33:00,2000.5,,infty -2022-08-13 09:34:00,2000.2,,infty -2022-08-13 09:35:00,1999.18,,infty -2022-08-13 09:36:00,2000.51,,infty -2022-08-13 09:37:00,2000.29,,infty -2022-08-13 09:38:00,1999.58,,infty -2022-08-13 09:39:00,1999.94,,infty -2022-08-13 09:40:00,1999.16,,infty -2022-08-13 09:41:00,1999.97,,infty -2022-08-13 09:42:00,1999.12,,infty -2022-08-13 09:43:00,2000.2,,infty -2022-08-13 09:44:00,2000.26,,infty -2022-08-13 09:45:00,1999.91,,infty -2022-08-13 09:46:00,2001.39,,infty -2022-08-13 09:47:00,2001.66,,infty -2022-08-13 09:48:00,2001.92,,infty -2022-08-13 09:49:00,2002.55,,infty -2022-08-13 09:50:00,2001.73,,infty -2022-08-13 09:51:00,2002.19,,infty -2022-08-13 09:52:00,2005.22,,infty -2022-08-13 09:53:00,2003.31,,infty -2022-08-13 09:54:00,2006.75,,infty -2022-08-13 09:55:00,2006.57,,infty -2022-08-13 09:56:00,2005.0,,infty -2022-08-13 09:57:00,2005.32,,infty -2022-08-13 09:58:00,2004.61,,infty -2022-08-13 09:59:00,2003.14,,infty -2022-08-13 10:00:00,2003.54,,infty -2022-08-13 10:01:00,2002.48,,infty -2022-08-13 10:02:00,2002.07,,infty -2022-08-13 10:03:00,2000.07,,infty -2022-08-13 10:04:00,2000.38,,infty -2022-08-13 10:05:00,2001.94,,infty -2022-08-13 10:06:00,1999.69,,infty -2022-08-13 10:07:00,1999.08,,infty -2022-08-13 10:08:00,2000.31,,infty -2022-08-13 10:09:00,1998.45,,infty -2022-08-13 10:10:00,1999.48,,infty -2022-08-13 10:11:00,1999.49,,infty -2022-08-13 10:12:00,1999.55,,infty -2022-08-13 10:13:00,1996.96,,infty -2022-08-13 10:14:00,1997.49,,infty -2022-08-13 10:15:00,1993.97,,infty -2022-08-13 10:16:00,1994.67,,infty -2022-08-13 10:17:00,1993.13,,infty -2022-08-13 10:18:00,1994.41,,infty -2022-08-13 10:19:00,1996.08,,infty -2022-08-13 10:20:00,1995.74,,infty -2022-08-13 10:21:00,1997.28,,infty -2022-08-13 10:22:00,1998.23,,infty -2022-08-13 10:23:00,1998.53,,infty -2022-08-13 10:24:00,1998.26,,infty -2022-08-13 10:25:00,1998.29,,infty -2022-08-13 10:26:00,1999.03,,infty -2022-08-13 10:27:00,1998.25,,infty -2022-08-13 10:28:00,1995.34,,infty -2022-08-13 10:29:00,1997.64,,infty -2022-08-13 10:30:00,2000.04,,infty -2022-08-13 10:31:00,2000.74,,infty -2022-08-13 10:32:00,2000.25,,infty -2022-08-13 10:33:00,2000.67,,infty -2022-08-13 10:34:00,2001.34,,infty -2022-08-13 10:35:00,2002.16,,infty -2022-08-13 10:36:00,2001.88,,infty -2022-08-13 10:37:00,2000.06,,infty -2022-08-13 10:38:00,2000.42,,infty -2022-08-13 10:39:00,2000.02,,infty -2022-08-13 10:40:00,1997.35,,infty -2022-08-13 10:41:00,1999.78,,infty -2022-08-13 10:42:00,1999.82,,infty -2022-08-13 10:43:00,2000.0,,infty -2022-08-13 10:44:00,1998.85,,infty -2022-08-13 10:45:00,1998.64,,infty -2022-08-13 10:46:00,1998.04,,infty -2022-08-13 10:47:00,1997.0,,infty -2022-08-13 10:48:00,1996.51,,infty -2022-08-13 10:49:00,1994.23,,infty -2022-08-13 10:50:00,1994.78,,infty -2022-08-13 10:51:00,1994.04,,infty -2022-08-13 10:52:00,1992.34,,infty -2022-08-13 10:53:00,1992.13,,infty -2022-08-13 10:54:00,1991.57,,infty -2022-08-13 10:55:00,1993.27,,infty -2022-08-13 10:56:00,1992.7,,infty -2022-08-13 10:57:00,1994.67,,infty -2022-08-13 10:58:00,1994.26,,infty -2022-08-13 10:59:00,1994.84,,infty -2022-08-13 11:00:00,1995.49,,infty -2022-08-13 11:01:00,1993.35,,infty -2022-08-13 11:02:00,1994.05,,infty -2022-08-13 11:03:00,1995.81,,infty -2022-08-13 11:04:00,1994.35,,infty -2022-08-13 11:05:00,1992.7,,infty -2022-08-13 11:06:00,1991.81,,infty -2022-08-13 11:07:00,1991.45,,infty -2022-08-13 11:08:00,1991.88,,infty -2022-08-13 11:09:00,1992.36,,infty -2022-08-13 11:10:00,1992.39,,infty -2022-08-13 11:11:00,1990.01,,infty -2022-08-13 11:12:00,1990.58,,infty -2022-08-13 11:13:00,1988.86,,infty -2022-08-13 11:14:00,1989.03,,infty -2022-08-13 11:15:00,1988.03,,infty -2022-08-13 11:16:00,1990.69,,infty -2022-08-13 11:17:00,1991.28,,infty -2022-08-13 11:18:00,1991.33,,infty -2022-08-13 11:19:00,1991.17,,infty -2022-08-13 11:20:00,1989.68,,infty -2022-08-13 11:21:00,1987.73,,infty -2022-08-13 11:22:00,1986.88,,infty -2022-08-13 11:23:00,1986.78,,infty -2022-08-13 11:24:00,1989.8,,infty -2022-08-13 11:25:00,1990.77,,infty -2022-08-13 11:26:00,1990.04,,infty -2022-08-13 11:27:00,1987.95,,infty -2022-08-13 11:28:00,1986.67,,infty -2022-08-13 11:29:00,1988.16,,infty -2022-08-13 11:30:00,1988.56,,infty -2022-08-13 11:31:00,1986.69,,infty -2022-08-13 11:32:00,1986.93,,infty -2022-08-13 11:33:00,1986.18,,infty -2022-08-13 11:34:00,1986.58,,infty -2022-08-13 11:35:00,1986.41,,infty -2022-08-13 11:36:00,1985.52,,infty -2022-08-13 11:37:00,1984.29,,infty -2022-08-13 11:38:00,1982.49,,infty -2022-08-13 11:39:00,1983.11,,infty -2022-08-13 11:40:00,1980.66,,infty -2022-08-13 11:41:00,1978.16,,infty -2022-08-13 11:42:00,1978.86,,infty -2022-08-13 11:43:00,1977.68,,infty -2022-08-13 11:44:00,1976.83,,infty -2022-08-13 11:45:00,1979.72,,infty -2022-08-13 11:46:00,1979.46,,infty -2022-08-13 11:47:00,1979.06,,infty -2022-08-13 11:48:00,1978.85,,infty -2022-08-13 11:49:00,1980.26,,infty -2022-08-13 11:50:00,1981.83,,infty -2022-08-13 11:51:00,1982.41,,infty -2022-08-13 11:52:00,1986.39,,infty -2022-08-13 11:53:00,1985.55,,infty -2022-08-13 11:54:00,1984.73,,infty -2022-08-13 11:55:00,1985.68,,infty -2022-08-13 11:56:00,1985.83,,infty -2022-08-13 11:57:00,1984.74,,infty -2022-08-13 11:58:00,1987.16,,infty -2022-08-13 11:59:00,1986.93,,infty -2022-08-13 12:00:00,1985.97,,infty -2022-08-13 12:01:00,1984.0,,infty -2022-08-13 12:02:00,1982.77,,infty -2022-08-13 12:03:00,1982.87,,infty -2022-08-13 12:04:00,1984.21,,infty -2022-08-13 12:05:00,1984.72,,infty -2022-08-13 12:06:00,1982.99,,infty -2022-08-13 12:07:00,1984.34,,infty -2022-08-13 12:08:00,1984.71,,infty -2022-08-13 12:09:00,1985.85,,infty -2022-08-13 12:10:00,1984.77,,infty -2022-08-13 12:11:00,1984.59,,infty -2022-08-13 12:12:00,1982.05,,infty -2022-08-13 12:13:00,1980.36,,infty -2022-08-13 12:14:00,1982.54,,infty -2022-08-13 12:15:00,1981.62,,infty -2022-08-13 12:16:00,1981.87,,infty -2022-08-13 12:17:00,1981.7,,infty -2022-08-13 12:18:00,1980.69,,infty -2022-08-13 12:19:00,1982.46,,infty -2022-08-13 12:20:00,1982.17,,infty -2022-08-13 12:21:00,1980.39,,infty -2022-08-13 12:22:00,1981.09,,infty -2022-08-13 12:23:00,1980.08,,infty -2022-08-13 12:24:00,1978.87,,infty -2022-08-13 12:25:00,1974.04,,infty -2022-08-13 12:26:00,1973.67,,infty -2022-08-13 12:27:00,1971.92,,infty -2022-08-13 12:28:00,1975.19,,infty -2022-08-13 12:29:00,1972.11,,infty -2022-08-13 12:30:00,1974.56,,infty -2022-08-13 12:31:00,1976.36,,infty -2022-08-13 12:32:00,1973.67,,infty -2022-08-13 12:33:00,1971.42,,infty -2022-08-13 12:34:00,1968.94,,infty -2022-08-13 12:35:00,1965.4,,infty -2022-08-13 12:36:00,1968.1,,infty -2022-08-13 12:37:00,1969.6,,infty -2022-08-13 12:38:00,1968.66,,infty -2022-08-13 12:39:00,1972.67,,infty -2022-08-13 12:40:00,1974.26,,infty -2022-08-13 12:41:00,1974.27,,infty -2022-08-13 12:42:00,1974.13,,infty -2022-08-13 12:43:00,1973.66,,infty -2022-08-13 12:44:00,1975.33,,infty -2022-08-13 12:45:00,1978.24,,infty -2022-08-13 12:46:00,1978.04,,infty -2022-08-13 12:47:00,1977.22,,infty -2022-08-13 12:48:00,1976.92,,infty -2022-08-13 12:49:00,1976.48,,infty -2022-08-13 12:50:00,1978.53,,infty -2022-08-13 12:51:00,1981.35,,infty -2022-08-13 12:52:00,1982.9,,infty -2022-08-13 12:53:00,1982.52,,infty -2022-08-13 12:54:00,1980.76,,infty -2022-08-13 12:55:00,1980.25,,infty -2022-08-13 12:56:00,1981.23,,infty -2022-08-13 12:57:00,1980.81,,infty -2022-08-13 12:58:00,1981.21,,infty -2022-08-13 12:59:00,1981.38,,infty -2022-08-13 13:00:00,1982.02,,infty -2022-08-13 13:01:00,1980.93,,infty -2022-08-13 13:02:00,1980.95,,infty -2022-08-13 13:03:00,1981.82,,infty -2022-08-13 13:04:00,1983.17,,infty -2022-08-13 13:05:00,1984.38,,infty -2022-08-13 13:06:00,1983.57,,infty -2022-08-13 13:07:00,1984.91,,infty -2022-08-13 13:08:00,1984.13,,infty -2022-08-13 13:09:00,1983.97,,infty -2022-08-13 13:10:00,1983.91,,infty -2022-08-13 13:11:00,1982.24,,infty -2022-08-13 13:12:00,1980.91,,infty -2022-08-13 13:13:00,1981.17,,infty -2022-08-13 13:14:00,1983.1,,infty -2022-08-13 13:15:00,1982.16,,infty -2022-08-13 13:16:00,1980.84,,infty -2022-08-13 13:17:00,1980.33,,infty -2022-08-13 13:18:00,1981.33,,infty -2022-08-13 13:19:00,1981.75,,infty -2022-08-13 13:20:00,1982.72,,infty -2022-08-13 13:21:00,1986.81,,infty -2022-08-13 13:22:00,1984.69,,infty -2022-08-13 13:23:00,1984.02,,infty -2022-08-13 13:24:00,1983.64,,infty -2022-08-13 13:25:00,1985.85,,infty -2022-08-13 13:26:00,1983.84,,infty -2022-08-13 13:27:00,1983.9,,infty -2022-08-13 13:28:00,1984.3,,infty -2022-08-13 13:29:00,1983.88,,infty -2022-08-13 13:30:00,1982.02,,infty -2022-08-13 13:31:00,1982.01,,infty -2022-08-13 13:32:00,1980.09,,infty -2022-08-13 13:33:00,1979.55,,infty -2022-08-13 13:34:00,1979.37,,infty -2022-08-13 13:35:00,1980.8,,infty -2022-08-13 13:36:00,1979.66,,infty -2022-08-13 13:37:00,1980.22,,infty -2022-08-13 13:38:00,1984.01,,infty -2022-08-13 13:39:00,1986.18,,infty -2022-08-13 13:40:00,1987.95,,infty -2022-08-13 13:41:00,1989.01,,infty -2022-08-13 13:42:00,1986.78,,infty -2022-08-13 13:43:00,1988.55,,infty -2022-08-13 13:44:00,1988.83,,infty -2022-08-13 13:45:00,1985.96,,infty -2022-08-13 13:46:00,1984.97,,infty -2022-08-13 13:47:00,1983.31,,infty -2022-08-13 13:48:00,1981.95,,infty -2022-08-13 13:49:00,1981.13,,infty -2022-08-13 13:50:00,1980.36,,infty -2022-08-13 13:51:00,1982.6,,infty -2022-08-13 13:52:00,1981.35,,infty -2022-08-13 13:53:00,1981.77,,infty -2022-08-13 13:54:00,1979.76,,infty -2022-08-13 13:55:00,1977.47,,infty -2022-08-13 13:56:00,1978.11,,infty -2022-08-13 13:57:00,1978.56,,infty -2022-08-13 13:58:00,1980.22,,infty -2022-08-13 13:59:00,1978.66,,infty -2022-08-13 14:00:00,1978.47,,infty -2022-08-13 14:01:00,1978.86,,infty -2022-08-13 14:02:00,1978.91,,infty -2022-08-13 14:03:00,1979.68,,infty -2022-08-13 14:04:00,1979.27,,infty -2022-08-13 14:05:00,1979.9,,infty -2022-08-13 14:06:00,1980.5,,infty -2022-08-13 14:07:00,1981.23,,infty -2022-08-13 14:08:00,1982.34,,infty -2022-08-13 14:09:00,1982.66,,infty -2022-08-13 14:10:00,1980.18,,infty -2022-08-13 14:11:00,1980.94,,infty -2022-08-13 14:12:00,1981.9,,infty -2022-08-13 14:13:00,1983.16,,infty -2022-08-13 14:14:00,1983.17,,infty -2022-08-13 14:15:00,1982.13,,infty -2022-08-13 14:16:00,1983.63,,infty -2022-08-13 14:17:00,1984.5,,infty -2022-08-13 14:18:00,1983.63,,infty -2022-08-13 14:19:00,1983.2,,infty -2022-08-13 14:20:00,1982.89,,infty -2022-08-13 14:21:00,1984.76,,infty -2022-08-13 14:22:00,1985.3,,infty -2022-08-13 14:23:00,1984.31,,infty -2022-08-13 14:24:00,1984.07,,infty -2022-08-13 14:25:00,1983.59,,infty -2022-08-13 14:26:00,1983.1,,infty -2022-08-13 14:27:00,1981.78,,infty -2022-08-13 14:28:00,1983.32,,infty -2022-08-13 14:29:00,1982.92,,infty -2022-08-13 14:30:00,1983.05,,infty -2022-08-13 14:31:00,1981.84,,infty -2022-08-13 14:32:00,1982.25,,infty -2022-08-13 14:33:00,1983.07,,infty -2022-08-13 14:34:00,1983.27,,infty -2022-08-13 14:35:00,1984.79,,infty -2022-08-13 14:36:00,1984.97,,infty -2022-08-13 14:37:00,1986.24,,infty -2022-08-13 14:38:00,1986.17,,infty -2022-08-13 14:39:00,1986.48,,infty -2022-08-13 14:40:00,1985.74,,infty -2022-08-13 14:41:00,1984.12,,infty -2022-08-13 14:42:00,1982.72,,infty -2022-08-13 14:43:00,1979.48,,infty -2022-08-13 14:44:00,1979.04,,infty -2022-08-13 14:45:00,1980.6,,infty -2022-08-13 14:46:00,1980.19,,infty -2022-08-13 14:47:00,1981.68,,infty -2022-08-13 14:48:00,1982.46,,infty -2022-08-13 14:49:00,1983.17,,infty -2022-08-13 14:50:00,1983.91,,infty -2022-08-13 14:51:00,1984.88,,infty -2022-08-13 14:52:00,1985.32,,infty -2022-08-13 14:53:00,1982.87,,infty -2022-08-13 14:54:00,1984.17,,infty -2022-08-13 14:55:00,1983.2,,infty -2022-08-13 14:56:00,1981.37,,infty -2022-08-13 14:57:00,1981.3,,infty -2022-08-13 14:58:00,1981.57,,infty -2022-08-13 14:59:00,1982.23,,infty -2022-08-13 15:00:00,1981.64,,infty -2022-08-13 15:01:00,1985.25,,infty -2022-08-13 15:02:00,1983.61,,infty -2022-08-13 15:03:00,1987.28,,infty -2022-08-13 15:04:00,1987.51,,infty -2022-08-13 15:05:00,1988.65,,infty -2022-08-13 15:06:00,1991.97,,infty -2022-08-13 15:07:00,1991.41,,infty -2022-08-13 15:08:00,1992.28,,infty -2022-08-13 15:09:00,1993.46,,infty -2022-08-13 15:10:00,1993.98,,infty -2022-08-13 15:11:00,1992.99,,infty -2022-08-13 15:12:00,1991.56,,infty -2022-08-13 15:13:00,1988.57,,infty -2022-08-13 15:14:00,1985.0,,infty -2022-08-13 15:15:00,1987.74,,infty -2022-08-13 15:16:00,1988.87,,infty -2022-08-13 15:17:00,1988.48,,infty -2022-08-13 15:18:00,1987.84,,infty -2022-08-13 15:19:00,1985.79,,infty -2022-08-13 15:20:00,1984.87,,infty -2022-08-13 15:21:00,1984.18,,infty -2022-08-13 15:22:00,1985.88,,infty -2022-08-13 15:23:00,1983.79,,infty -2022-08-13 15:24:00,1985.25,,infty -2022-08-13 15:25:00,1982.79,,infty -2022-08-13 15:26:00,1981.39,,infty -2022-08-13 15:27:00,1982.02,,infty -2022-08-13 15:28:00,1982.0,,infty -2022-08-13 15:29:00,1981.1,,infty -2022-08-13 15:30:00,1982.39,,infty -2022-08-13 15:31:00,1983.05,,infty -2022-08-13 15:32:00,1982.34,,infty -2022-08-13 15:33:00,1979.91,,infty -2022-08-13 15:34:00,1981.27,,infty -2022-08-13 15:35:00,1982.02,,infty -2022-08-13 15:36:00,1983.06,,infty -2022-08-13 15:37:00,1984.85,,infty -2022-08-13 15:38:00,1984.12,,infty -2022-08-13 15:39:00,1985.11,,infty -2022-08-13 15:40:00,1984.09,,infty -2022-08-13 15:41:00,1984.18,,infty -2022-08-13 15:42:00,1984.89,,infty -2022-08-13 15:43:00,1986.08,,infty -2022-08-13 15:44:00,1986.7,,infty -2022-08-13 15:45:00,1986.15,,infty -2022-08-13 15:46:00,1987.37,,infty -2022-08-13 15:47:00,1986.9,,infty -2022-08-13 15:48:00,1988.65,,infty -2022-08-13 15:49:00,1988.93,,infty -2022-08-13 15:50:00,1988.84,,infty -2022-08-13 15:51:00,1989.81,,infty -2022-08-13 15:52:00,1988.81,,infty -2022-08-13 15:53:00,1988.75,,infty -2022-08-13 15:54:00,1988.56,,infty -2022-08-13 15:55:00,1990.12,,infty -2022-08-13 15:56:00,1990.7,,infty -2022-08-13 15:57:00,1990.78,,infty -2022-08-13 15:58:00,1990.54,,infty -2022-08-13 15:59:00,1989.2,,infty -2022-08-13 16:00:00,1990.35,,infty -2022-08-13 16:01:00,1992.96,,infty -2022-08-13 16:02:00,1992.4,,infty -2022-08-13 16:03:00,1993.37,,infty -2022-08-13 16:04:00,1991.5,,infty -2022-08-13 16:05:00,1990.93,,infty -2022-08-13 16:06:00,1989.05,,infty -2022-08-13 16:07:00,1990.38,,infty -2022-08-13 16:08:00,1989.43,,infty -2022-08-13 16:09:00,1992.23,,infty -2022-08-13 16:10:00,1991.74,,infty -2022-08-13 16:11:00,1992.71,,infty -2022-08-13 16:12:00,1992.48,,infty -2022-08-13 16:13:00,1991.44,,infty -2022-08-13 16:14:00,1991.77,,infty -2022-08-13 16:15:00,1989.69,,infty -2022-08-13 16:16:00,1989.99,,infty -2022-08-13 16:17:00,1990.53,,infty -2022-08-13 16:18:00,1990.46,,infty -2022-08-13 16:19:00,1990.43,,infty -2022-08-13 16:20:00,1991.77,,infty -2022-08-13 16:21:00,1991.06,,infty -2022-08-13 16:22:00,1989.73,,infty -2022-08-13 16:23:00,1987.7,,infty -2022-08-13 16:24:00,1986.47,,infty -2022-08-13 16:25:00,1986.59,,infty -2022-08-13 16:26:00,1988.45,,infty -2022-08-13 16:27:00,1989.04,,infty -2022-08-13 16:28:00,1987.79,,infty -2022-08-13 16:29:00,1987.67,,infty -2022-08-13 16:30:00,1987.33,,infty -2022-08-13 16:31:00,1986.1,,infty -2022-08-13 16:32:00,1987.79,,infty -2022-08-13 16:33:00,1986.32,,infty -2022-08-13 16:34:00,1986.8,,infty -2022-08-13 16:35:00,1989.95,,infty -2022-08-13 16:36:00,1991.61,,infty -2022-08-13 16:37:00,1994.13,,infty -2022-08-13 16:38:00,1995.08,,infty -2022-08-13 16:39:00,1992.68,,infty -2022-08-13 16:40:00,1994.45,,infty -2022-08-13 16:41:00,1993.85,,infty -2022-08-13 16:42:00,1993.99,,infty -2022-08-13 16:43:00,1991.59,,infty -2022-08-13 16:44:00,1990.01,,infty -2022-08-13 16:45:00,1988.59,,infty -2022-08-13 16:46:00,1989.57,,infty -2022-08-13 16:47:00,1988.27,,infty -2022-08-13 16:48:00,1988.27,,infty -2022-08-13 16:49:00,1987.73,,infty -2022-08-13 16:50:00,1988.56,,infty -2022-08-13 16:51:00,1990.39,,infty -2022-08-13 16:52:00,1989.72,,infty -2022-08-13 16:53:00,1989.43,,infty -2022-08-13 16:54:00,1988.72,,infty -2022-08-13 16:55:00,1987.16,,infty -2022-08-13 16:56:00,1987.64,,infty -2022-08-13 16:57:00,1988.19,,infty -2022-08-13 16:58:00,1989.27,,infty -2022-08-13 16:59:00,1990.48,,infty -2022-08-13 17:00:00,1987.95,,infty -2022-08-13 17:01:00,1987.67,,infty -2022-08-13 17:02:00,1989.68,,infty -2022-08-13 17:03:00,1991.17,,infty -2022-08-13 17:04:00,1993.18,,infty -2022-08-13 17:05:00,1991.41,,infty -2022-08-13 17:06:00,1991.17,,infty -2022-08-13 17:07:00,1988.44,,infty -2022-08-13 17:08:00,1989.47,,infty -2022-08-13 17:09:00,1988.71,,infty -2022-08-13 17:10:00,1988.53,,infty -2022-08-13 17:11:00,1989.04,,infty -2022-08-13 17:12:00,1991.21,,infty -2022-08-13 17:13:00,1991.11,,infty -2022-08-13 17:14:00,1990.83,,infty -2022-08-13 17:15:00,1991.75,,infty -2022-08-13 17:16:00,1991.53,,infty -2022-08-13 17:17:00,1990.29,,infty -2022-08-13 17:18:00,1992.0,,infty -2022-08-13 17:19:00,1991.91,,infty -2022-08-13 17:20:00,1992.19,,infty -2022-08-13 17:21:00,1992.37,,infty -2022-08-13 17:22:00,1993.66,,infty -2022-08-13 17:23:00,1993.77,,infty -2022-08-13 17:24:00,1993.37,,infty -2022-08-13 17:25:00,1993.57,,infty -2022-08-13 17:26:00,1993.81,,infty -2022-08-13 17:27:00,1993.01,,infty -2022-08-13 17:28:00,1994.08,,infty -2022-08-13 17:29:00,1993.83,,infty -2022-08-13 17:30:00,1994.14,,infty -2022-08-13 17:31:00,1995.64,,infty -2022-08-13 17:32:00,1996.45,,infty -2022-08-13 17:33:00,1997.0,,infty -2022-08-13 17:34:00,1995.92,,infty -2022-08-13 17:35:00,1994.96,,infty -2022-08-13 17:36:00,1998.33,,infty -2022-08-13 17:37:00,1997.44,,infty -2022-08-13 17:38:00,1998.64,,infty -2022-08-13 17:39:00,1997.7,,infty -2022-08-13 17:40:00,1996.85,,infty -2022-08-13 17:41:00,1998.81,,infty -2022-08-13 17:42:00,1999.54,,infty -2022-08-13 17:43:00,1999.79,,infty -2022-08-13 17:44:00,1998.31,,infty -2022-08-13 17:45:00,1997.34,,infty -2022-08-13 17:46:00,1996.25,,infty -2022-08-13 17:47:00,1995.21,,infty -2022-08-13 17:48:00,1996.48,,infty -2022-08-13 17:49:00,1994.68,,infty -2022-08-13 17:50:00,1994.51,,infty -2022-08-13 17:51:00,1994.91,,infty -2022-08-13 17:52:00,1995.26,,infty -2022-08-13 17:53:00,1997.18,,infty -2022-08-13 17:54:00,1997.52,,infty -2022-08-13 17:55:00,1999.1,,infty -2022-08-13 17:56:00,1998.84,,infty -2022-08-13 17:57:00,1997.45,,infty -2022-08-13 17:58:00,1997.82,,infty -2022-08-13 17:59:00,2000.31,,infty -2022-08-13 18:00:00,1998.48,,infty -2022-08-13 18:01:00,1997.27,,infty -2022-08-13 18:02:00,1997.03,,infty -2022-08-13 18:03:00,1998.32,,infty -2022-08-13 18:04:00,1999.95,,infty -2022-08-13 18:05:00,2002.62,,infty -2022-08-13 18:06:00,2001.43,,infty -2022-08-13 18:07:00,2001.2,,infty -2022-08-13 18:08:00,2000.81,,infty -2022-08-13 18:09:00,2000.35,,infty -2022-08-13 18:10:00,1999.78,,infty -2022-08-13 18:11:00,2000.27,,infty -2022-08-13 18:12:00,2000.79,,infty -2022-08-13 18:13:00,2000.76,,infty -2022-08-13 18:14:00,1999.71,,infty -2022-08-13 18:15:00,1998.0,,infty -2022-08-13 18:16:00,1997.34,,infty -2022-08-13 18:17:00,1996.22,,infty -2022-08-13 18:18:00,1997.15,,infty -2022-08-13 18:19:00,1996.72,,infty -2022-08-13 18:20:00,1995.58,,infty -2022-08-13 18:21:00,1995.79,,infty -2022-08-13 18:22:00,1992.98,,infty -2022-08-13 18:23:00,1988.91,,infty -2022-08-13 18:24:00,1988.76,,infty -2022-08-13 18:25:00,1986.93,,infty -2022-08-13 18:26:00,1989.15,,infty -2022-08-13 18:27:00,1987.69,,infty -2022-08-13 18:28:00,1989.88,,infty -2022-08-13 18:29:00,1991.5,,infty -2022-08-13 18:30:00,1990.21,,infty -2022-08-13 18:31:00,1987.11,,infty -2022-08-13 18:32:00,1987.21,,infty -2022-08-13 18:33:00,1985.97,,infty -2022-08-13 18:34:00,1984.07,,infty -2022-08-13 18:35:00,1986.37,,infty -2022-08-13 18:36:00,1983.17,,infty -2022-08-13 18:37:00,1986.3,,infty -2022-08-13 18:38:00,1985.85,,infty -2022-08-13 18:39:00,1986.88,,infty -2022-08-13 18:40:00,1982.19,,infty -2022-08-13 18:41:00,1982.33,,infty -2022-08-13 18:42:00,1979.53,,infty -2022-08-13 18:43:00,1978.98,,infty -2022-08-13 18:44:00,1979.75,,infty -2022-08-13 18:45:00,1980.74,,infty -2022-08-13 18:46:00,1977.96,,infty -2022-08-13 18:47:00,1979.56,,infty -2022-08-13 18:48:00,1980.74,,infty -2022-08-13 18:49:00,1981.62,,infty -2022-08-13 18:50:00,1980.1,,infty -2022-08-13 18:51:00,1980.99,,infty -2022-08-13 18:52:00,1980.61,,infty -2022-08-13 18:53:00,1980.62,,infty -2022-08-13 18:54:00,1980.98,,infty -2022-08-13 18:55:00,1979.27,,infty -2022-08-13 18:56:00,1981.3,,infty -2022-08-13 18:57:00,1983.39,,infty -2022-08-13 18:58:00,1984.52,,infty -2022-08-13 18:59:00,1984.28,,infty -2022-08-13 19:00:00,1985.27,,infty -2022-08-13 19:01:00,1983.81,,infty -2022-08-13 19:02:00,1982.91,,infty -2022-08-13 19:03:00,1982.55,,infty -2022-08-13 19:04:00,1983.42,,infty -2022-08-13 19:05:00,1982.36,,infty -2022-08-13 19:06:00,1983.89,,infty -2022-08-13 19:07:00,1984.5,,infty -2022-08-13 19:08:00,1983.09,,infty -2022-08-13 19:09:00,1983.2,,infty -2022-08-13 19:10:00,1983.29,,infty -2022-08-13 19:11:00,1980.66,,infty -2022-08-13 19:12:00,1980.57,,infty -2022-08-13 19:13:00,1982.54,,infty -2022-08-13 19:14:00,1984.22,,infty -2022-08-13 19:15:00,1983.42,,infty -2022-08-13 19:16:00,1983.25,,infty -2022-08-13 19:17:00,1983.24,,infty -2022-08-13 19:18:00,1981.41,,infty -2022-08-13 19:19:00,1982.93,,infty -2022-08-13 19:20:00,1983.25,,infty -2022-08-13 19:21:00,1981.85,,infty -2022-08-13 19:22:00,1981.48,,infty -2022-08-13 19:23:00,1981.46,,infty -2022-08-13 19:24:00,1981.22,,infty -2022-08-13 19:25:00,1979.56,,infty -2022-08-13 19:26:00,1980.02,,infty -2022-08-13 19:27:00,1976.61,,infty -2022-08-13 19:28:00,1973.81,,infty -2022-08-13 19:29:00,1977.65,,infty -2022-08-13 19:30:00,1976.65,,infty -2022-08-13 19:31:00,1975.1,,infty -2022-08-13 19:32:00,1976.54,,infty -2022-08-13 19:33:00,1975.1,,infty -2022-08-13 19:34:00,1978.87,,infty -2022-08-13 19:35:00,1981.23,,infty -2022-08-13 19:36:00,1983.12,,infty -2022-08-13 19:37:00,1983.02,,infty -2022-08-13 19:38:00,1983.04,,infty -2022-08-13 19:39:00,1985.25,,infty -2022-08-13 19:40:00,1984.56,,infty -2022-08-13 19:41:00,1986.1,,infty -2022-08-13 19:42:00,1984.81,,infty -2022-08-13 19:43:00,1985.02,,infty -2022-08-13 19:44:00,1984.8,,infty -2022-08-13 19:45:00,1985.67,,infty -2022-08-13 19:46:00,1986.81,,infty -2022-08-13 19:47:00,1986.12,,infty -2022-08-13 19:48:00,1985.44,,infty -2022-08-13 19:49:00,1984.86,,infty -2022-08-13 19:50:00,1984.75,,infty -2022-08-13 19:51:00,1985.88,,infty -2022-08-13 19:52:00,1985.38,,infty -2022-08-13 19:53:00,1985.19,,infty -2022-08-13 19:54:00,1977.6,,infty -2022-08-13 19:55:00,1980.29,,infty -2022-08-13 19:56:00,1979.95,,infty -2022-08-13 19:57:00,1979.19,,infty -2022-08-13 19:58:00,1978.91,,infty -2022-08-13 19:59:00,1976.09,,infty -2022-08-13 20:00:00,1974.25,,infty -2022-08-13 20:01:00,1975.2,,infty -2022-08-13 20:02:00,1973.68,,infty -2022-08-13 20:03:00,1972.88,,infty -2022-08-13 20:04:00,1974.17,,infty -2022-08-13 20:05:00,1972.07,,infty -2022-08-13 20:06:00,1972.49,,infty -2022-08-13 20:07:00,1970.53,,infty -2022-08-13 20:08:00,1974.14,,infty -2022-08-13 20:09:00,1976.27,,infty -2022-08-13 20:10:00,1974.38,,infty -2022-08-13 20:11:00,1976.18,,infty -2022-08-13 20:12:00,1976.96,,infty -2022-08-13 20:13:00,1975.52,,infty -2022-08-13 20:14:00,1976.92,,infty -2022-08-13 20:15:00,1978.11,,infty -2022-08-13 20:16:00,1981.17,,infty -2022-08-13 20:17:00,1981.9,,infty -2022-08-13 20:18:00,1980.88,,infty -2022-08-13 20:19:00,1981.53,,infty -2022-08-13 20:20:00,1980.31,,infty -2022-08-13 20:21:00,1980.75,,infty -2022-08-13 20:22:00,1981.11,,infty -2022-08-13 20:23:00,1982.41,,infty -2022-08-13 20:24:00,1981.82,,infty -2022-08-13 20:25:00,1981.42,,infty -2022-08-13 20:26:00,1982.75,,infty -2022-08-13 20:27:00,1983.98,,infty -2022-08-13 20:28:00,1983.09,,infty -2022-08-13 20:29:00,1983.1,,infty -2022-08-13 20:30:00,1982.87,,infty -2022-08-13 20:31:00,1982.75,,infty -2022-08-13 20:32:00,1982.63,,infty -2022-08-13 20:33:00,1982.63,,infty -2022-08-13 20:34:00,1982.91,,infty -2022-08-13 20:35:00,1982.55,,infty -2022-08-13 20:36:00,1984.59,,infty -2022-08-13 20:37:00,1984.82,,infty -2022-08-13 20:38:00,1986.84,,infty -2022-08-13 20:39:00,1986.19,,infty -2022-08-13 20:40:00,1985.92,,infty -2022-08-13 20:41:00,1987.48,,infty -2022-08-13 20:42:00,1988.34,,infty -2022-08-13 20:43:00,1988.32,,infty -2022-08-13 20:44:00,1989.25,,infty -2022-08-13 20:45:00,1988.51,,infty -2022-08-13 20:46:00,1987.05,,infty -2022-08-13 20:47:00,1988.19,,infty -2022-08-13 20:48:00,1989.37,,infty -2022-08-13 20:49:00,1990.42,,infty -2022-08-13 20:50:00,1988.93,,infty -2022-08-13 20:51:00,1989.34,,infty -2022-08-13 20:52:00,1991.17,,infty -2022-08-13 20:53:00,1991.85,,infty -2022-08-13 20:54:00,1991.89,,infty -2022-08-13 20:55:00,1990.63,,infty -2022-08-13 20:56:00,1994.34,,infty -2022-08-13 20:57:00,1992.54,,infty -2022-08-13 20:58:00,1992.57,,infty -2022-08-13 20:59:00,1990.14,,infty -2022-08-13 21:00:00,1991.28,,infty -2022-08-13 21:01:00,1991.9,,infty -2022-08-13 21:02:00,1992.77,,infty -2022-08-13 21:03:00,1992.59,,infty -2022-08-13 21:04:00,1992.19,,infty -2022-08-13 21:05:00,1991.1,,infty -2022-08-13 21:06:00,1990.62,,infty -2022-08-13 21:07:00,1991.02,,infty -2022-08-13 21:08:00,1989.78,,infty -2022-08-13 21:09:00,1991.11,,infty -2022-08-13 21:10:00,1992.08,,infty -2022-08-13 21:11:00,1992.41,,infty -2022-08-13 21:12:00,1993.1,,infty -2022-08-13 21:13:00,1993.1,,infty -2022-08-13 21:14:00,1993.18,,infty -2022-08-13 21:15:00,1993.17,,infty -2022-08-13 21:16:00,1992.42,,infty -2022-08-13 21:17:00,1992.78,,infty -2022-08-13 21:18:00,1992.82,,infty -2022-08-13 21:19:00,1992.58,,infty -2022-08-13 21:20:00,1992.52,,infty -2022-08-13 21:21:00,1991.21,,infty -2022-08-13 21:22:00,1990.4,,infty -2022-08-13 21:23:00,1991.3,,infty -2022-08-13 21:24:00,1990.55,,infty -2022-08-13 21:25:00,1989.55,,infty -2022-08-13 21:26:00,1989.55,,infty -2022-08-13 21:27:00,1989.72,,infty -2022-08-13 21:28:00,1988.05,,infty -2022-08-13 21:29:00,1988.32,,infty -2022-08-13 21:30:00,1989.24,,infty -2022-08-13 21:31:00,1989.51,,infty -2022-08-13 21:32:00,1989.51,,infty -2022-08-13 21:33:00,1991.31,,infty -2022-08-13 21:34:00,1992.62,,infty -2022-08-13 21:35:00,1992.68,,infty -2022-08-13 21:36:00,1991.69,,infty -2022-08-13 21:37:00,1990.49,,infty -2022-08-13 21:38:00,1988.31,,infty -2022-08-13 21:39:00,1986.38,,infty -2022-08-13 21:40:00,1985.36,,infty -2022-08-13 21:41:00,1987.34,,infty -2022-08-13 21:42:00,1987.65,,infty -2022-08-13 21:43:00,1987.65,,infty -2022-08-13 21:44:00,1989.31,,infty -2022-08-13 21:45:00,1989.48,,infty -2022-08-13 21:46:00,1988.64,,infty -2022-08-13 21:47:00,1987.97,,infty -2022-08-13 21:48:00,1987.07,,infty -2022-08-13 21:49:00,1985.99,,infty -2022-08-13 21:50:00,1986.05,,infty -2022-08-13 21:51:00,1984.89,,infty -2022-08-13 21:52:00,1985.4,,infty -2022-08-13 21:53:00,1985.25,,infty -2022-08-13 21:54:00,1986.97,,infty -2022-08-13 21:55:00,1986.02,,infty -2022-08-13 21:56:00,1985.01,,infty -2022-08-13 21:57:00,1985.14,,infty -2022-08-13 21:58:00,1986.31,,infty -2022-08-13 21:59:00,1987.52,,infty -2022-08-13 22:00:00,1989.31,,infty -2022-08-13 22:01:00,1987.22,,infty -2022-08-13 22:02:00,1985.48,,infty -2022-08-13 22:03:00,1984.8,,infty -2022-08-13 22:04:00,1985.95,,infty -2022-08-13 22:05:00,1989.93,,infty -2022-08-13 22:06:00,1990.73,,infty -2022-08-13 22:07:00,1989.1,,infty -2022-08-13 22:08:00,1989.63,,infty -2022-08-13 22:09:00,1990.37,,infty -2022-08-13 22:10:00,1991.46,,infty -2022-08-13 22:11:00,1992.57,,infty -2022-08-13 22:12:00,1991.57,,infty -2022-08-13 22:13:00,1991.15,,infty -2022-08-13 22:14:00,1991.22,,infty -2022-08-13 22:15:00,1988.11,,infty -2022-08-13 22:16:00,1988.36,,infty -2022-08-13 22:17:00,1989.04,,infty -2022-08-13 22:18:00,1990.82,,infty -2022-08-13 22:19:00,1991.13,,infty -2022-08-13 22:20:00,1990.71,,infty -2022-08-13 22:21:00,1991.39,,infty -2022-08-13 22:22:00,1989.81,,infty -2022-08-13 22:23:00,1986.44,,infty -2022-08-13 22:24:00,1984.47,,infty -2022-08-13 22:25:00,1985.6,,infty -2022-08-13 22:26:00,1984.31,,infty -2022-08-13 22:27:00,1984.35,,infty -2022-08-13 22:28:00,1983.98,,infty -2022-08-13 22:29:00,1983.88,,infty -2022-08-13 22:30:00,1982.87,,infty -2022-08-13 22:31:00,1983.21,,infty -2022-08-13 22:32:00,1982.63,,infty -2022-08-13 22:33:00,1983.72,,infty -2022-08-13 22:34:00,1982.78,,infty -2022-08-13 22:35:00,1982.84,,infty -2022-08-13 22:36:00,1981.94,,infty -2022-08-13 22:37:00,1981.78,,infty -2022-08-13 22:38:00,1984.53,,infty -2022-08-13 22:39:00,1984.0,,infty -2022-08-13 22:40:00,1983.64,,infty -2022-08-13 22:41:00,1983.03,,infty -2022-08-13 22:42:00,1983.41,,infty -2022-08-13 22:43:00,1984.39,,infty -2022-08-13 22:44:00,1985.07,,infty -2022-08-13 22:45:00,1984.93,,infty -2022-08-13 22:46:00,1984.44,,infty -2022-08-13 22:47:00,1983.89,,infty -2022-08-13 22:48:00,1984.62,,infty -2022-08-13 22:49:00,1982.99,,infty -2022-08-13 22:50:00,1982.01,,infty -2022-08-13 22:51:00,1981.51,,infty -2022-08-13 22:52:00,1982.05,,infty -2022-08-13 22:53:00,1980.75,,infty -2022-08-13 22:54:00,1981.59,,infty -2022-08-13 22:55:00,1980.76,,infty -2022-08-13 22:56:00,1980.05,,infty -2022-08-13 22:57:00,1979.77,,infty -2022-08-13 22:58:00,1979.55,,infty -2022-08-13 22:59:00,1979.17,,infty -2022-08-13 23:00:00,1978.86,,infty -2022-08-13 23:01:00,1978.23,,infty -2022-08-13 23:02:00,1979.65,,infty -2022-08-13 23:03:00,1982.29,,infty -2022-08-13 23:04:00,1981.58,,infty -2022-08-13 23:05:00,1982.61,,infty -2022-08-13 23:06:00,1984.33,,infty -2022-08-13 23:07:00,1981.8,,infty -2022-08-13 23:08:00,1982.66,,infty -2022-08-13 23:09:00,1984.11,,infty -2022-08-13 23:10:00,1984.31,,infty -2022-08-13 23:11:00,1984.73,,infty -2022-08-13 23:12:00,1986.07,,infty -2022-08-13 23:13:00,1985.01,,infty -2022-08-13 23:14:00,1985.91,,infty -2022-08-13 23:15:00,1987.21,,infty -2022-08-13 23:16:00,1986.32,,infty -2022-08-13 23:17:00,1987.99,,infty -2022-08-13 23:18:00,1986.56,,infty -2022-08-13 23:19:00,1986.98,,infty -2022-08-13 23:20:00,1987.29,,infty -2022-08-13 23:21:00,1986.94,,infty -2022-08-13 23:22:00,1983.24,,infty -2022-08-13 23:23:00,1982.35,,infty -2022-08-13 23:24:00,1982.24,,infty -2022-08-13 23:25:00,1978.57,,infty -2022-08-13 23:26:00,1978.09,,infty -2022-08-13 23:27:00,1975.69,,infty -2022-08-13 23:28:00,1977.59,,infty -2022-08-13 23:29:00,1980.5,,infty -2022-08-13 23:30:00,1980.41,,infty -2022-08-13 23:31:00,1979.44,,infty -2022-08-13 23:32:00,1977.56,,infty -2022-08-13 23:33:00,1977.96,,infty -2022-08-13 23:34:00,1977.06,,infty -2022-08-13 23:35:00,1977.04,,infty -2022-08-13 23:36:00,1975.75,,infty -2022-08-13 23:37:00,1978.62,,infty -2022-08-13 23:38:00,1977.6,,infty -2022-08-13 23:39:00,1977.17,,infty -2022-08-13 23:40:00,1977.28,,infty -2022-08-13 23:41:00,1977.01,,infty -2022-08-13 23:42:00,1976.14,,infty -2022-08-13 23:43:00,1979.22,,infty -2022-08-13 23:44:00,1979.52,,infty -2022-08-13 23:45:00,1980.3,,infty -2022-08-13 23:46:00,1981.57,,infty -2022-08-13 23:47:00,1982.06,,infty -2022-08-13 23:48:00,1983.9,,infty -2022-08-13 23:49:00,1982.57,,infty -2022-08-13 23:50:00,1982.06,,infty -2022-08-13 23:51:00,1981.95,,infty -2022-08-13 23:52:00,1981.67,,infty -2022-08-13 23:53:00,1983.27,,infty -2022-08-13 23:54:00,1981.8,,infty -2022-08-13 23:55:00,1979.49,,infty -2022-08-13 23:56:00,1980.94,,infty -2022-08-13 23:57:00,1983.2,,infty -2022-08-13 23:58:00,1984.8,,infty -2022-08-13 23:59:00,1984.21,,infty -2022-08-14 00:00:00,1983.12,,infty -2022-08-14 00:01:00,1983.91,,infty -2022-08-14 00:02:00,1984.61,,infty -2022-08-14 00:03:00,1983.25,,infty -2022-08-14 00:04:00,1982.0,,infty -2022-08-14 00:05:00,1983.04,,infty -2022-08-14 00:06:00,1983.14,,infty -2022-08-14 00:07:00,1980.99,,infty -2022-08-14 00:08:00,1978.77,,infty -2022-08-14 00:09:00,1978.86,,infty -2022-08-14 00:10:00,1977.72,,infty -2022-08-14 00:11:00,1977.03,,infty -2022-08-14 00:12:00,1978.02,,infty -2022-08-14 00:13:00,1977.92,,infty -2022-08-14 00:14:00,1977.62,,infty -2022-08-14 00:15:00,1976.64,,infty -2022-08-14 00:16:00,1977.82,,infty -2022-08-14 00:17:00,1978.81,,infty -2022-08-14 00:18:00,1979.79,,infty -2022-08-14 00:19:00,1978.41,,infty -2022-08-14 00:20:00,1981.05,,infty -2022-08-14 00:21:00,1981.77,,infty -2022-08-14 00:22:00,1982.76,,infty -2022-08-14 00:23:00,1983.24,,infty -2022-08-14 00:24:00,1982.73,,infty -2022-08-14 00:25:00,1981.68,,infty -2022-08-14 00:26:00,1982.34,,infty -2022-08-14 00:27:00,1983.58,,infty -2022-08-14 00:28:00,1984.16,,infty -2022-08-14 00:29:00,1983.99,,infty -2022-08-14 00:30:00,1985.13,,infty -2022-08-14 00:31:00,1985.01,,infty -2022-08-14 00:32:00,1987.89,,infty -2022-08-14 00:33:00,1986.85,,infty -2022-08-14 00:34:00,1987.55,,infty -2022-08-14 00:35:00,1987.87,,infty -2022-08-14 00:36:00,1989.08,,infty -2022-08-14 00:37:00,1991.28,,infty -2022-08-14 00:38:00,1988.55,,infty -2022-08-14 00:39:00,1989.23,,infty -2022-08-14 00:40:00,1987.78,,infty -2022-08-14 00:41:00,1989.64,,infty -2022-08-14 00:42:00,1989.62,,infty -2022-08-14 00:43:00,1988.56,,infty -2022-08-14 00:44:00,1987.32,,infty -2022-08-14 00:45:00,1989.1,,infty -2022-08-14 00:46:00,1988.88,,infty -2022-08-14 00:47:00,1991.26,,infty -2022-08-14 00:48:00,1988.55,,infty -2022-08-14 00:49:00,1990.7,,infty -2022-08-14 00:50:00,1989.0,,infty -2022-08-14 00:51:00,1988.77,,infty -2022-08-14 00:52:00,1990.53,,infty -2022-08-14 00:53:00,1988.95,,infty -2022-08-14 00:54:00,1990.42,,infty -2022-08-14 00:55:00,1990.41,,infty -2022-08-14 00:56:00,1988.85,,infty -2022-08-14 00:57:00,1988.05,,infty -2022-08-14 00:58:00,1985.81,,infty -2022-08-14 00:59:00,1987.2,,infty -2022-08-14 01:00:00,1988.15,,infty -2022-08-14 01:01:00,1988.59,,infty -2022-08-14 01:02:00,1989.22,,infty -2022-08-14 01:03:00,1991.15,,infty -2022-08-14 01:04:00,1991.99,,infty -2022-08-14 01:05:00,1992.55,,infty -2022-08-14 01:06:00,1992.06,,infty -2022-08-14 01:07:00,1992.79,,infty -2022-08-14 01:08:00,1994.65,,infty -2022-08-14 01:09:00,1993.26,,infty -2022-08-14 01:10:00,1990.63,,infty -2022-08-14 01:11:00,1988.89,,infty -2022-08-14 01:12:00,1990.9,,infty -2022-08-14 01:13:00,1989.68,,infty -2022-08-14 01:14:00,1987.97,,infty -2022-08-14 01:15:00,1990.1,,infty -2022-08-14 01:16:00,1988.89,,infty -2022-08-14 01:17:00,1989.95,,infty -2022-08-14 01:18:00,1988.83,,infty -2022-08-14 01:19:00,1988.94,,infty -2022-08-14 01:20:00,1987.9,,infty -2022-08-14 01:21:00,1987.29,,infty -2022-08-14 01:22:00,1986.11,,infty -2022-08-14 01:23:00,1986.45,,infty -2022-08-14 01:24:00,1986.29,,infty -2022-08-14 01:25:00,1984.8,,infty -2022-08-14 01:26:00,1982.96,,infty -2022-08-14 01:27:00,1980.71,,infty -2022-08-14 01:28:00,1978.68,,infty -2022-08-14 01:29:00,1976.69,,infty -2022-08-14 01:30:00,1974.65,,infty -2022-08-14 01:31:00,1973.07,,infty -2022-08-14 01:32:00,1976.36,,infty -2022-08-14 01:33:00,1978.83,,infty -2022-08-14 01:34:00,1980.43,,infty -2022-08-14 01:35:00,1983.36,,infty -2022-08-14 01:36:00,1983.29,,infty -2022-08-14 01:37:00,1982.58,,infty -2022-08-14 01:38:00,1983.49,,infty -2022-08-14 01:39:00,1981.75,,infty -2022-08-14 01:40:00,1983.14,,infty -2022-08-14 01:41:00,1983.24,,infty -2022-08-14 01:42:00,1984.73,,infty -2022-08-14 01:43:00,1985.75,,infty -2022-08-14 01:44:00,1987.37,,infty -2022-08-14 01:45:00,1987.51,,infty -2022-08-14 01:46:00,1987.84,,infty -2022-08-14 01:47:00,1984.61,,infty -2022-08-14 01:48:00,1985.83,,infty -2022-08-14 01:49:00,1987.0,,infty -2022-08-14 01:50:00,1987.75,,infty -2022-08-14 01:51:00,1985.8,,infty -2022-08-14 01:52:00,1987.61,,infty -2022-08-14 01:53:00,1987.82,,infty -2022-08-14 01:54:00,1987.68,,infty -2022-08-14 01:55:00,1988.2,,infty -2022-08-14 01:56:00,1988.77,,infty -2022-08-14 01:57:00,1989.83,,infty -2022-08-14 01:58:00,1990.16,,infty -2022-08-14 01:59:00,1988.95,,infty -2022-08-14 02:00:00,1990.77,,infty -2022-08-14 02:01:00,1990.73,,infty -2022-08-14 02:02:00,1989.17,,infty -2022-08-14 02:03:00,1987.77,,infty -2022-08-14 02:04:00,1987.4,,infty -2022-08-14 02:05:00,1987.89,,infty -2022-08-14 02:06:00,1988.22,,infty -2022-08-14 02:07:00,1989.18,,infty -2022-08-14 02:08:00,1990.04,,infty -2022-08-14 02:09:00,1988.48,,infty -2022-08-14 02:10:00,1988.21,,infty -2022-08-14 02:11:00,1988.4,,infty -2022-08-14 02:12:00,1985.99,,infty -2022-08-14 02:13:00,1987.23,,infty -2022-08-14 02:14:00,1985.0,,infty -2022-08-14 02:15:00,1986.22,,infty -2022-08-14 02:16:00,1983.55,,infty -2022-08-14 02:17:00,1985.19,,infty -2022-08-14 02:18:00,1984.29,,infty -2022-08-14 02:19:00,1984.33,,infty -2022-08-14 02:20:00,1983.12,,infty -2022-08-14 02:21:00,1983.56,,infty -2022-08-14 02:22:00,1985.11,,infty -2022-08-14 02:23:00,1985.6,,infty -2022-08-14 02:24:00,1986.74,,infty -2022-08-14 02:25:00,1987.03,,infty -2022-08-14 02:26:00,1987.84,,infty -2022-08-14 02:27:00,1986.75,,infty -2022-08-14 02:28:00,1987.79,,infty -2022-08-14 02:29:00,1986.74,,infty -2022-08-14 02:30:00,1986.89,,infty -2022-08-14 02:31:00,1986.11,,infty -2022-08-14 02:32:00,1985.96,,infty -2022-08-14 02:33:00,1987.47,,infty -2022-08-14 02:34:00,1987.32,,infty -2022-08-14 02:35:00,1988.15,,infty -2022-08-14 02:36:00,1989.59,,infty -2022-08-14 02:37:00,1990.63,,infty -2022-08-14 02:38:00,1988.88,,infty -2022-08-14 02:39:00,1988.03,,infty -2022-08-14 02:40:00,1989.78,,infty -2022-08-14 02:41:00,1989.35,,infty -2022-08-14 02:42:00,1988.48,,infty -2022-08-14 02:43:00,1990.09,,infty -2022-08-14 02:44:00,1990.86,,infty -2022-08-14 02:45:00,1989.79,,infty -2022-08-14 02:46:00,1990.25,,infty -2022-08-14 02:47:00,1988.41,,infty -2022-08-14 02:48:00,1989.24,,infty -2022-08-14 02:49:00,1988.35,,infty -2022-08-14 02:50:00,1989.54,,infty -2022-08-14 02:51:00,1989.29,,infty -2022-08-14 02:52:00,1990.04,,infty -2022-08-14 02:53:00,1988.17,,infty -2022-08-14 02:54:00,1988.29,,infty -2022-08-14 02:55:00,1988.61,,infty -2022-08-14 02:56:00,1987.83,,infty -2022-08-14 02:57:00,1989.15,,infty -2022-08-14 02:58:00,1988.17,,infty -2022-08-14 02:59:00,1988.7,,infty -2022-08-14 03:00:00,1989.41,,infty -2022-08-14 03:01:00,1989.62,,infty -2022-08-14 03:02:00,1989.38,,infty -2022-08-14 03:03:00,1988.0,,infty -2022-08-14 03:04:00,1989.25,,infty -2022-08-14 03:05:00,1989.4,,infty -2022-08-14 03:06:00,1987.41,,infty -2022-08-14 03:07:00,1984.29,,infty -2022-08-14 03:08:00,1984.92,,infty -2022-08-14 03:09:00,1985.43,,infty -2022-08-14 03:10:00,1985.89,,infty -2022-08-14 03:11:00,1987.31,,infty -2022-08-14 03:12:00,1985.75,,infty -2022-08-14 03:13:00,1987.1,,infty -2022-08-14 03:14:00,1986.4,,infty -2022-08-14 03:15:00,1984.36,,infty -2022-08-14 03:16:00,1985.57,,infty -2022-08-14 03:17:00,1985.83,,infty -2022-08-14 03:18:00,1985.24,,infty -2022-08-14 03:19:00,1985.49,,infty -2022-08-14 03:20:00,1985.29,,infty -2022-08-14 03:21:00,1985.85,,infty -2022-08-14 03:22:00,1987.45,,infty -2022-08-14 03:23:00,1987.68,,infty -2022-08-14 03:24:00,1987.61,,infty -2022-08-14 03:25:00,1987.97,,infty -2022-08-14 03:26:00,1986.31,,infty -2022-08-14 03:27:00,1986.15,,infty -2022-08-14 03:28:00,1985.88,,infty -2022-08-14 03:29:00,1986.61,,infty -2022-08-14 03:30:00,1987.15,,infty -2022-08-14 03:31:00,1987.79,,infty -2022-08-14 03:32:00,1987.75,,infty -2022-08-14 03:33:00,1988.02,,infty -2022-08-14 03:34:00,1987.29,,infty -2022-08-14 03:35:00,1988.92,,infty -2022-08-14 03:36:00,1986.37,,infty -2022-08-14 03:37:00,1985.6,,infty -2022-08-14 03:38:00,1985.96,,infty -2022-08-14 03:39:00,1987.47,,infty -2022-08-14 03:40:00,1985.82,,infty -2022-08-14 03:41:00,1986.6,,infty -2022-08-14 03:42:00,1986.36,,infty -2022-08-14 03:43:00,1986.71,,infty -2022-08-14 03:44:00,1987.65,,infty -2022-08-14 03:45:00,1989.02,,infty -2022-08-14 03:46:00,1988.26,,infty -2022-08-14 03:47:00,1988.93,,infty -2022-08-14 03:48:00,1989.05,,infty -2022-08-14 03:49:00,1988.91,,infty -2022-08-14 03:50:00,1988.55,,infty -2022-08-14 03:51:00,1988.21,,infty -2022-08-14 03:52:00,1987.64,,infty -2022-08-14 03:53:00,1987.06,,infty -2022-08-14 03:54:00,1987.19,,infty -2022-08-14 03:55:00,1987.44,,infty -2022-08-14 03:56:00,1988.33,,infty -2022-08-14 03:57:00,1988.03,,infty -2022-08-14 03:58:00,1986.75,,infty -2022-08-14 03:59:00,1986.47,,infty -2022-08-14 04:00:00,1986.41,,infty -2022-08-14 04:01:00,1986.31,,infty -2022-08-14 04:02:00,1985.88,,infty -2022-08-14 04:03:00,1986.3,,infty -2022-08-14 04:04:00,1985.99,,infty -2022-08-14 04:05:00,1986.0,,infty -2022-08-14 04:06:00,1985.91,,infty -2022-08-14 04:07:00,1987.18,,infty -2022-08-14 04:08:00,1987.0,,infty -2022-08-14 04:09:00,1987.74,,infty -2022-08-14 04:10:00,1987.67,,infty -2022-08-14 04:11:00,1987.99,,infty -2022-08-14 04:12:00,1988.51,,infty -2022-08-14 04:13:00,1988.84,,infty -2022-08-14 04:14:00,1989.7,,infty -2022-08-14 04:15:00,1990.5,,infty -2022-08-14 04:16:00,1995.36,,infty -2022-08-14 04:17:00,1995.59,,infty -2022-08-14 04:18:00,1998.1,,infty -2022-08-14 04:19:00,1997.61,,infty -2022-08-14 04:20:00,1995.64,,infty -2022-08-14 04:21:00,1996.04,,infty -2022-08-14 04:22:00,1996.37,,infty -2022-08-14 04:23:00,1996.77,,infty -2022-08-14 04:24:00,1993.91,,infty -2022-08-14 04:25:00,1992.53,,infty -2022-08-14 04:26:00,1988.92,,infty -2022-08-14 04:27:00,1985.05,,infty -2022-08-14 04:28:00,1985.02,,infty -2022-08-14 04:29:00,1982.77,,infty -2022-08-14 04:30:00,1983.71,,infty -2022-08-14 04:31:00,1986.1,,infty -2022-08-14 04:32:00,1986.91,,infty -2022-08-14 04:33:00,1986.0,,infty -2022-08-14 04:34:00,1986.2,,infty -2022-08-14 04:35:00,1985.96,,infty -2022-08-14 04:36:00,1984.39,,infty -2022-08-14 04:37:00,1984.9,,infty -2022-08-14 04:38:00,1985.27,,infty -2022-08-14 04:39:00,1986.29,,infty -2022-08-14 04:40:00,1984.99,,infty -2022-08-14 04:41:00,1984.12,,infty -2022-08-14 04:42:00,1984.09,,infty -2022-08-14 04:43:00,1985.76,,infty -2022-08-14 04:44:00,1985.52,,infty -2022-08-14 04:45:00,1985.77,,infty -2022-08-14 04:46:00,1987.23,,infty -2022-08-14 04:47:00,1986.89,,infty -2022-08-14 04:48:00,1988.34,,infty -2022-08-14 04:49:00,1987.09,,infty -2022-08-14 04:50:00,1987.55,,infty -2022-08-14 04:51:00,1989.52,,infty -2022-08-14 04:52:00,1990.54,,infty -2022-08-14 04:53:00,1988.71,,infty -2022-08-14 04:54:00,1988.56,,infty -2022-08-14 04:55:00,1987.65,,infty -2022-08-14 04:56:00,1989.25,,infty -2022-08-14 04:57:00,1988.65,,infty -2022-08-14 04:58:00,1990.49,,infty -2022-08-14 04:59:00,1992.37,,infty -2022-08-14 05:00:00,1992.85,,infty -2022-08-14 05:01:00,1992.24,,infty -2022-08-14 05:02:00,1990.82,,infty -2022-08-14 05:03:00,1991.55,,infty -2022-08-14 05:04:00,1990.84,,infty -2022-08-14 05:05:00,1990.21,,infty -2022-08-14 05:06:00,1989.63,,infty -2022-08-14 05:07:00,1991.94,,infty -2022-08-14 05:08:00,1993.54,,infty -2022-08-14 05:09:00,1992.04,,infty -2022-08-14 05:10:00,1993.46,,infty -2022-08-14 05:11:00,1994.36,,infty -2022-08-14 05:12:00,1994.77,,infty -2022-08-14 05:13:00,1993.68,,infty -2022-08-14 05:14:00,1992.2,,infty -2022-08-14 05:15:00,1990.85,,infty -2022-08-14 05:16:00,1992.4,,infty -2022-08-14 05:17:00,1993.14,,infty -2022-08-14 05:18:00,1994.26,,infty -2022-08-14 05:19:00,1993.04,,infty -2022-08-14 05:20:00,1994.23,,infty -2022-08-14 05:21:00,1995.08,,infty -2022-08-14 05:22:00,1993.5,,infty -2022-08-14 05:23:00,1994.6,,infty -2022-08-14 05:24:00,1993.72,,infty -2022-08-14 05:25:00,1993.55,,infty -2022-08-14 05:26:00,1990.56,,infty -2022-08-14 05:27:00,1991.29,,infty -2022-08-14 05:28:00,1990.81,,infty -2022-08-14 05:29:00,1991.0,,infty -2022-08-14 05:30:00,1989.94,,infty -2022-08-14 05:31:00,1987.5,,infty -2022-08-14 05:32:00,1989.21,,infty -2022-08-14 05:33:00,1988.58,,infty -2022-08-14 05:34:00,1989.03,,infty -2022-08-14 05:35:00,1989.43,,infty -2022-08-14 05:36:00,1989.52,,infty -2022-08-14 05:37:00,1990.51,,infty -2022-08-14 05:38:00,1990.0,,infty -2022-08-14 05:39:00,1991.64,,infty -2022-08-14 05:40:00,1994.07,,infty -2022-08-14 05:41:00,1993.4,,infty -2022-08-14 05:42:00,1994.75,,infty -2022-08-14 05:43:00,1993.52,,infty -2022-08-14 05:44:00,1994.7,,infty -2022-08-14 05:45:00,1994.41,,infty -2022-08-14 05:46:00,1992.26,,infty -2022-08-14 05:47:00,1993.84,,infty -2022-08-14 05:48:00,1994.46,,infty -2022-08-14 05:49:00,1994.0,,infty -2022-08-14 05:50:00,1993.63,,infty -2022-08-14 05:51:00,1993.43,,infty -2022-08-14 05:52:00,1994.53,,infty -2022-08-14 05:53:00,1995.24,,infty -2022-08-14 05:54:00,1995.78,,infty -2022-08-14 05:55:00,1995.38,,infty -2022-08-14 05:56:00,1993.95,,infty -2022-08-14 05:57:00,1994.08,,infty -2022-08-14 05:58:00,1993.72,,infty -2022-08-14 05:59:00,1993.15,,infty -2022-08-14 06:00:00,1994.43,,infty -2022-08-14 06:01:00,1995.08,,infty -2022-08-14 06:02:00,1994.43,,infty -2022-08-14 06:03:00,1994.31,,infty -2022-08-14 06:04:00,1994.69,,infty -2022-08-14 06:05:00,1996.58,,infty -2022-08-14 06:06:00,1995.65,,infty -2022-08-14 06:07:00,1994.97,,infty -2022-08-14 06:08:00,1994.6,,infty -2022-08-14 06:09:00,1995.28,,infty -2022-08-14 06:10:00,1994.2,,infty -2022-08-14 06:11:00,1994.46,,infty -2022-08-14 06:12:00,1992.6,,infty -2022-08-14 06:13:00,1994.7,,infty -2022-08-14 06:14:00,1996.09,,infty -2022-08-14 06:15:00,1995.41,,infty -2022-08-14 06:16:00,1995.5,,infty -2022-08-14 06:17:00,1994.6,,infty -2022-08-14 06:18:00,1995.14,,infty -2022-08-14 06:19:00,1994.24,,infty -2022-08-14 06:20:00,1993.98,,infty -2022-08-14 06:21:00,1993.53,,infty -2022-08-14 06:22:00,1994.36,,infty -2022-08-14 06:23:00,1994.6,,infty -2022-08-14 06:24:00,1994.23,,infty -2022-08-14 06:25:00,1993.63,,infty -2022-08-14 06:26:00,1992.54,,infty -2022-08-14 06:27:00,1991.0,,infty -2022-08-14 06:28:00,1991.9,,infty -2022-08-14 06:29:00,1992.98,,infty -2022-08-14 06:30:00,1992.99,,infty -2022-08-14 06:31:00,1994.19,,infty -2022-08-14 06:32:00,1993.6,,infty -2022-08-14 06:33:00,1991.38,,infty -2022-08-14 06:34:00,1990.74,,infty -2022-08-14 06:35:00,1990.74,,infty -2022-08-14 06:36:00,1991.92,,infty -2022-08-14 06:37:00,1992.76,,infty -2022-08-14 06:38:00,1991.96,,infty -2022-08-14 06:39:00,1992.3,,infty -2022-08-14 06:40:00,1991.12,,infty -2022-08-14 06:41:00,1990.81,,infty -2022-08-14 06:42:00,1991.35,,infty -2022-08-14 06:43:00,1990.36,,infty -2022-08-14 06:44:00,1990.35,,infty -2022-08-14 06:45:00,1989.99,,infty -2022-08-14 06:46:00,1991.26,,infty -2022-08-14 06:47:00,1990.52,,infty -2022-08-14 06:48:00,1991.51,,infty -2022-08-14 06:49:00,1990.94,,infty -2022-08-14 06:50:00,1990.77,,infty -2022-08-14 06:51:00,1991.23,,infty -2022-08-14 06:52:00,1991.25,,infty -2022-08-14 06:53:00,1990.9,,infty -2022-08-14 06:54:00,1991.25,,infty -2022-08-14 06:55:00,1991.06,,infty -2022-08-14 06:56:00,1991.42,,infty -2022-08-14 06:57:00,1992.81,,infty -2022-08-14 06:58:00,1993.78,,infty -2022-08-14 06:59:00,1993.73,,infty -2022-08-14 07:00:00,1993.7,,infty -2022-08-14 07:01:00,1992.82,,infty -2022-08-14 07:02:00,1992.82,,infty -2022-08-14 07:03:00,1998.22,,infty -2022-08-14 07:04:00,1996.97,,infty -2022-08-14 07:05:00,1997.22,,infty -2022-08-14 07:06:00,1994.92,,infty -2022-08-14 07:07:00,1994.69,,infty -2022-08-14 07:08:00,1992.82,,infty -2022-08-14 07:09:00,1993.74,,infty -2022-08-14 07:10:00,1995.0,,infty -2022-08-14 07:11:00,1995.75,,infty -2022-08-14 07:12:00,1995.73,,infty -2022-08-14 07:13:00,1995.06,,infty -2022-08-14 07:14:00,1994.87,,infty -2022-08-14 07:15:00,1994.58,,infty -2022-08-14 07:16:00,1996.08,,infty -2022-08-14 07:17:00,1995.62,,infty -2022-08-14 07:18:00,1994.54,,infty -2022-08-14 07:19:00,1997.25,,infty -2022-08-14 07:20:00,1997.19,,infty -2022-08-14 07:21:00,1996.3,,infty -2022-08-14 07:22:00,1997.95,,infty -2022-08-14 07:23:00,1998.36,,infty -2022-08-14 07:24:00,2001.53,,infty -2022-08-14 07:25:00,2003.92,,infty -2022-08-14 07:26:00,2003.19,,infty -2022-08-14 07:27:00,1999.52,,infty -2022-08-14 07:28:00,2000.81,,infty -2022-08-14 07:29:00,2001.12,,infty -2022-08-14 07:30:00,2003.63,,infty -2022-08-14 07:31:00,2003.7,,infty -2022-08-14 07:32:00,2006.18,,infty -2022-08-14 07:33:00,2006.5,,infty -2022-08-14 07:34:00,2008.09,,infty -2022-08-14 07:35:00,2011.07,,infty -2022-08-14 07:36:00,2013.41,,infty -2022-08-14 07:37:00,2010.82,,infty -2022-08-14 07:38:00,2011.24,,infty -2022-08-14 07:39:00,2012.87,,infty -2022-08-14 07:40:00,2011.02,,infty -2022-08-14 07:41:00,2010.73,,infty -2022-08-14 07:42:00,2016.05,,infty -2022-08-14 07:43:00,2014.04,,infty -2022-08-14 07:44:00,2016.53,,infty -2022-08-14 07:45:00,2012.83,,infty -2022-08-14 07:46:00,2014.86,,infty -2022-08-14 07:47:00,2017.84,,infty -2022-08-14 07:48:00,2017.31,,infty -2022-08-14 07:49:00,2018.18,,infty -2022-08-14 07:50:00,2018.78,,infty -2022-08-14 07:51:00,2022.48,,infty -2022-08-14 07:52:00,2024.73,,infty -2022-08-14 07:53:00,2026.04,,infty -2022-08-14 07:54:00,2023.63,,infty -2022-08-14 07:55:00,2017.81,,infty -2022-08-14 07:56:00,2015.81,,infty -2022-08-14 07:57:00,2019.66,,infty -2022-08-14 07:58:00,2017.17,,infty -2022-08-14 07:59:00,2017.94,,infty -2022-08-14 08:00:00,2015.99,,infty -2022-08-14 08:01:00,2010.19,,infty -2022-08-14 08:02:00,2013.3,,infty -2022-08-14 08:03:00,2011.74,,infty -2022-08-14 08:04:00,2011.41,,infty -2022-08-14 08:05:00,2008.91,,infty -2022-08-14 08:06:00,2005.97,,infty -2022-08-14 08:07:00,2009.75,,infty -2022-08-14 08:08:00,2008.55,,infty -2022-08-14 08:09:00,2007.89,,infty -2022-08-14 08:10:00,2006.51,,infty -2022-08-14 08:11:00,2009.3,,infty -2022-08-14 08:12:00,2008.64,,infty -2022-08-14 08:13:00,2012.42,,infty -2022-08-14 08:14:00,2009.4,,infty -2022-08-14 08:15:00,2009.71,,infty -2022-08-14 08:16:00,2013.01,,infty -2022-08-14 08:17:00,2012.06,,infty -2022-08-14 08:18:00,2012.68,,infty -2022-08-14 08:19:00,2009.55,,infty -2022-08-14 08:20:00,2011.24,,infty -2022-08-14 08:21:00,2011.28,,infty -2022-08-14 08:22:00,2010.85,,infty -2022-08-14 08:23:00,2013.05,,infty -2022-08-14 08:24:00,2012.97,,infty -2022-08-14 08:25:00,2015.09,,infty -2022-08-14 08:26:00,2015.49,,infty -2022-08-14 08:27:00,2014.25,,infty -2022-08-14 08:28:00,2013.23,,infty -2022-08-14 08:29:00,2012.47,,infty -2022-08-14 08:30:00,2014.56,,infty -2022-08-14 08:31:00,2013.15,,infty -2022-08-14 08:32:00,2013.12,,infty -2022-08-14 08:33:00,2010.0,,infty -2022-08-14 08:34:00,2007.98,,infty -2022-08-14 08:35:00,2008.18,,infty -2022-08-14 08:36:00,2008.18,,infty -2022-08-14 08:37:00,2008.22,,infty -2022-08-14 08:38:00,2006.01,,infty -2022-08-14 08:39:00,2007.75,,infty -2022-08-14 08:40:00,2004.87,,infty -2022-08-14 08:41:00,2005.06,,infty -2022-08-14 08:42:00,2004.36,,infty -2022-08-14 08:43:00,2002.85,,infty -2022-08-14 08:44:00,2003.0,,infty -2022-08-14 08:45:00,1998.66,,infty -2022-08-14 08:46:00,1998.64,,infty -2022-08-14 08:47:00,1997.17,,infty -2022-08-14 08:48:00,1998.03,,infty -2022-08-14 08:49:00,2000.17,,infty -2022-08-14 08:50:00,1998.81,,infty -2022-08-14 08:51:00,1997.03,,infty -2022-08-14 08:52:00,1996.56,,infty -2022-08-14 08:53:00,1998.0,,infty -2022-08-14 08:54:00,1999.27,,infty -2022-08-14 08:55:00,1997.68,,infty -2022-08-14 08:56:00,1999.17,,infty -2022-08-14 08:57:00,1999.9,,infty -2022-08-14 08:58:00,2002.05,,infty -2022-08-14 08:59:00,2000.2,,infty -2022-08-14 09:00:00,2002.16,,infty -2022-08-14 09:01:00,2000.23,,infty -2022-08-14 09:02:00,2000.52,,infty -2022-08-14 09:03:00,2001.29,,infty -2022-08-14 09:04:00,2003.76,,infty -2022-08-14 09:05:00,2004.7,,infty -2022-08-14 09:06:00,2003.91,,infty -2022-08-14 09:07:00,2004.74,,infty -2022-08-14 09:08:00,2005.08,,infty -2022-08-14 09:09:00,2006.1,,infty -2022-08-14 09:10:00,2004.5,,infty -2022-08-14 09:11:00,2005.5,,infty -2022-08-14 09:12:00,2005.33,,infty -2022-08-14 09:13:00,2004.2,,infty -2022-08-14 09:14:00,2004.68,,infty -2022-08-14 09:15:00,2004.58,,infty -2022-08-14 09:16:00,2007.02,,infty -2022-08-14 09:17:00,2004.72,,infty -2022-08-14 09:18:00,2005.9,,infty -2022-08-14 09:19:00,2006.83,,infty -2022-08-14 09:20:00,2006.21,,infty -2022-08-14 09:21:00,2005.06,,infty -2022-08-14 09:22:00,2005.41,,infty -2022-08-14 09:23:00,2004.76,,infty -2022-08-14 09:24:00,2004.45,,infty -2022-08-14 09:25:00,2005.6,,infty -2022-08-14 09:26:00,2005.52,,infty -2022-08-14 09:27:00,2005.62,,infty -2022-08-14 09:28:00,2005.61,,infty -2022-08-14 09:29:00,2006.64,,infty -2022-08-14 09:30:00,2006.39,,infty -2022-08-14 09:31:00,2008.37,,infty -2022-08-14 09:32:00,2008.46,,infty -2022-08-14 09:33:00,2007.93,,infty -2022-08-14 09:34:00,2009.79,,infty -2022-08-14 09:35:00,2009.0,,infty -2022-08-14 09:36:00,2010.26,,infty -2022-08-14 09:37:00,2009.76,,infty -2022-08-14 09:38:00,2007.34,,infty -2022-08-14 09:39:00,2009.6,,infty -2022-08-14 09:40:00,2008.65,,infty -2022-08-14 09:41:00,2008.8,,infty -2022-08-14 09:42:00,2009.19,,infty -2022-08-14 09:43:00,2009.38,,infty -2022-08-14 09:44:00,2009.78,,infty -2022-08-14 09:45:00,2009.41,,infty -2022-08-14 09:46:00,2009.74,,infty -2022-08-14 09:47:00,2010.12,,infty -2022-08-14 09:48:00,2012.12,,infty -2022-08-14 09:49:00,2011.98,,infty -2022-08-14 09:50:00,2010.25,,infty -2022-08-14 09:51:00,2011.11,,infty -2022-08-14 09:52:00,2009.95,,infty -2022-08-14 09:53:00,2009.63,,infty -2022-08-14 09:54:00,2008.58,,infty -2022-08-14 09:55:00,2006.61,,infty -2022-08-14 09:56:00,2007.61,,infty -2022-08-14 09:57:00,2007.98,,infty -2022-08-14 09:58:00,2008.66,,infty -2022-08-14 09:59:00,2009.72,,infty -2022-08-14 10:00:00,2009.01,,infty -2022-08-14 10:01:00,2008.14,,infty -2022-08-14 10:02:00,2007.84,,infty -2022-08-14 10:03:00,2005.95,,infty -2022-08-14 10:04:00,2005.3,,infty -2022-08-14 10:05:00,2004.41,,infty -2022-08-14 10:06:00,2003.96,,infty -2022-08-14 10:07:00,2004.09,,infty -2022-08-14 10:08:00,2005.42,,infty -2022-08-14 10:09:00,2005.78,,infty -2022-08-14 10:10:00,2004.64,,infty -2022-08-14 10:11:00,2004.27,,infty -2022-08-14 10:12:00,2005.27,,infty -2022-08-14 10:13:00,2005.02,,infty -2022-08-14 10:14:00,2006.07,,infty -2022-08-14 10:15:00,2004.4,,infty -2022-08-14 10:16:00,2002.55,,infty -2022-08-14 10:17:00,2001.72,,infty -2022-08-14 10:18:00,2002.02,,infty -2022-08-14 10:19:00,2000.06,,infty -2022-08-14 10:20:00,2000.58,,infty -2022-08-14 10:21:00,1999.79,,infty -2022-08-14 10:22:00,2003.06,,infty -2022-08-14 10:23:00,2001.43,,infty -2022-08-14 10:24:00,2001.2,,infty -2022-08-14 10:25:00,2003.0,,infty -2022-08-14 10:26:00,2003.67,,infty -2022-08-14 10:27:00,2003.74,,infty -2022-08-14 10:28:00,2006.34,,infty -2022-08-14 10:29:00,2005.5,,infty -2022-08-14 10:30:00,2004.84,,infty -2022-08-14 10:31:00,2004.46,,infty -2022-08-14 10:32:00,2004.71,,infty -2022-08-14 10:33:00,2004.67,,infty -2022-08-14 10:34:00,2003.65,,infty -2022-08-14 10:35:00,2002.41,,infty -2022-08-14 10:36:00,2000.72,,infty -2022-08-14 10:37:00,2000.59,,infty -2022-08-14 10:38:00,1999.99,,infty -2022-08-14 10:39:00,1999.17,,infty -2022-08-14 10:40:00,1999.45,,infty -2022-08-14 10:41:00,2000.46,,infty -2022-08-14 10:42:00,1998.38,,infty -2022-08-14 10:43:00,1998.49,,infty -2022-08-14 10:44:00,1998.5,,infty -2022-08-14 10:45:00,1997.89,,infty -2022-08-14 10:46:00,1999.0,,infty -2022-08-14 10:47:00,1998.76,,infty -2022-08-14 10:48:00,1998.56,,infty -2022-08-14 10:49:00,1999.86,,infty -2022-08-14 10:50:00,1999.38,,infty -2022-08-14 10:51:00,1999.24,,infty -2022-08-14 10:52:00,1998.55,,infty -2022-08-14 10:53:00,1998.4,,infty -2022-08-14 10:54:00,1999.96,,infty -2022-08-14 10:55:00,2000.76,,infty -2022-08-14 10:56:00,1999.5,,infty -2022-08-14 10:57:00,1998.43,,infty -2022-08-14 10:58:00,1998.09,,infty -2022-08-14 10:59:00,1998.39,,infty -2022-08-14 11:00:00,1998.26,,infty -2022-08-14 11:01:00,1997.74,,infty -2022-08-14 11:02:00,1997.58,,infty -2022-08-14 11:03:00,2000.14,,infty -2022-08-14 11:04:00,2000.21,,infty -2022-08-14 11:05:00,1999.13,,infty -2022-08-14 11:06:00,1999.55,,infty -2022-08-14 11:07:00,1998.73,,infty -2022-08-14 11:08:00,1998.01,,infty -2022-08-14 11:09:00,1997.36,,infty -2022-08-14 11:10:00,1997.97,,infty -2022-08-14 11:11:00,1998.2,,infty -2022-08-14 11:12:00,1998.4,,infty -2022-08-14 11:13:00,1999.37,,infty -2022-08-14 11:14:00,1999.29,,infty -2022-08-14 11:15:00,1999.0,,infty -2022-08-14 11:16:00,2000.7,,infty -2022-08-14 11:17:00,2000.96,,infty -2022-08-14 11:18:00,2002.47,,infty -2022-08-14 11:19:00,2000.91,,infty -2022-08-14 11:20:00,2001.24,,infty -2022-08-14 11:21:00,2000.7,,infty -2022-08-14 11:22:00,2001.38,,infty -2022-08-14 11:23:00,2001.1,,infty -2022-08-14 11:24:00,2001.2,,infty -2022-08-14 11:25:00,2000.85,,infty -2022-08-14 11:26:00,1999.01,,infty -2022-08-14 11:27:00,2000.42,,infty -2022-08-14 11:28:00,2001.57,,infty -2022-08-14 11:29:00,2002.17,,infty -2022-08-14 11:30:00,2001.97,,infty -2022-08-14 11:31:00,2001.75,,infty -2022-08-14 11:32:00,2001.45,,infty -2022-08-14 11:33:00,2000.27,,infty -2022-08-14 11:34:00,1999.56,,infty -2022-08-14 11:35:00,1998.41,,infty -2022-08-14 11:36:00,1996.35,,infty -2022-08-14 11:37:00,1993.51,,infty -2022-08-14 11:38:00,1988.44,,infty -2022-08-14 11:39:00,1989.85,,infty -2022-08-14 11:40:00,1989.45,,infty -2022-08-14 11:41:00,1985.77,,infty -2022-08-14 11:42:00,1984.42,,infty -2022-08-14 11:43:00,1985.83,,infty -2022-08-14 11:44:00,1984.54,,infty -2022-08-14 11:45:00,1988.12,,infty -2022-08-14 11:46:00,1986.72,,infty -2022-08-14 11:47:00,1988.4,,infty -2022-08-14 11:48:00,1987.25,,infty -2022-08-14 11:49:00,1988.54,,infty -2022-08-14 11:50:00,1990.61,,infty -2022-08-14 11:51:00,1991.15,,infty -2022-08-14 11:52:00,1991.47,,infty -2022-08-14 11:53:00,1992.64,,infty -2022-08-14 11:54:00,1990.83,,infty -2022-08-14 11:55:00,1989.43,,infty -2022-08-14 11:56:00,1991.09,,infty -2022-08-14 11:57:00,1990.92,,infty -2022-08-14 11:58:00,1990.58,,infty -2022-08-14 11:59:00,1990.94,,infty -2022-08-14 12:00:00,1989.44,,infty -2022-08-14 12:01:00,1989.09,,infty -2022-08-14 12:02:00,1988.0,,infty -2022-08-14 12:03:00,1988.59,,infty -2022-08-14 12:04:00,1991.5,,infty -2022-08-14 12:05:00,1991.75,,infty -2022-08-14 12:06:00,1991.42,,infty -2022-08-14 12:07:00,1992.22,,infty -2022-08-14 12:08:00,1991.06,,infty -2022-08-14 12:09:00,1988.22,,infty -2022-08-14 12:10:00,1987.67,,infty -2022-08-14 12:11:00,1985.86,,infty -2022-08-14 12:12:00,1984.61,,infty -2022-08-14 12:13:00,1983.64,,infty -2022-08-14 12:14:00,1986.16,,infty -2022-08-14 12:15:00,1988.98,,infty -2022-08-14 12:16:00,1986.04,,infty -2022-08-14 12:17:00,1987.44,,infty -2022-08-14 12:18:00,1987.47,,infty -2022-08-14 12:19:00,1984.93,,infty -2022-08-14 12:20:00,1985.49,,infty -2022-08-14 12:21:00,1987.11,,infty -2022-08-14 12:22:00,1986.42,,infty -2022-08-14 12:23:00,1986.94,,infty -2022-08-14 12:24:00,1986.13,,infty -2022-08-14 12:25:00,1987.35,,infty -2022-08-14 12:26:00,1987.77,,infty -2022-08-14 12:27:00,1985.55,,infty -2022-08-14 12:28:00,1986.32,,infty -2022-08-14 12:29:00,1985.0,,infty -2022-08-14 12:30:00,1984.22,,infty -2022-08-14 12:31:00,1983.86,,infty -2022-08-14 12:32:00,1984.11,,infty -2022-08-14 12:33:00,1984.9,,infty -2022-08-14 12:34:00,1985.26,,infty -2022-08-14 12:35:00,1986.91,,infty -2022-08-14 12:36:00,1987.72,,infty -2022-08-14 12:37:00,1987.11,,infty -2022-08-14 12:38:00,1988.07,,infty -2022-08-14 12:39:00,1986.89,,infty -2022-08-14 12:40:00,1985.7,,infty -2022-08-14 12:41:00,1986.63,,infty -2022-08-14 12:42:00,1986.59,,infty -2022-08-14 12:43:00,1985.98,,infty -2022-08-14 12:44:00,1984.87,,infty -2022-08-14 12:45:00,1985.92,,infty -2022-08-14 12:46:00,1984.56,,infty -2022-08-14 12:47:00,1982.79,,infty -2022-08-14 12:48:00,1983.16,,infty -2022-08-14 12:49:00,1982.37,,infty -2022-08-14 12:50:00,1983.6,,infty -2022-08-14 12:51:00,1984.03,,infty -2022-08-14 12:52:00,1985.91,,infty -2022-08-14 12:53:00,1986.06,,infty -2022-08-14 12:54:00,1986.54,,infty -2022-08-14 12:55:00,1987.12,,infty -2022-08-14 12:56:00,1986.86,,infty -2022-08-14 12:57:00,1986.44,,infty -2022-08-14 12:58:00,1985.53,,infty -2022-08-14 12:59:00,1986.41,,infty -2022-08-14 13:00:00,1985.81,,infty -2022-08-14 13:01:00,1987.32,,infty -2022-08-14 13:02:00,1986.65,,infty -2022-08-14 13:03:00,1987.42,,infty -2022-08-14 13:04:00,1986.86,,infty -2022-08-14 13:05:00,1986.59,,infty -2022-08-14 13:06:00,1986.31,,infty -2022-08-14 13:07:00,1985.08,,infty -2022-08-14 13:08:00,1985.63,,infty -2022-08-14 13:09:00,1984.63,,infty -2022-08-14 13:10:00,1985.15,,infty -2022-08-14 13:11:00,1984.44,,infty -2022-08-14 13:12:00,1985.17,,infty -2022-08-14 13:13:00,1984.69,,infty -2022-08-14 13:14:00,1983.41,,infty -2022-08-14 13:15:00,1981.02,,infty -2022-08-14 13:16:00,1975.74,,infty -2022-08-14 13:17:00,1979.5,,infty -2022-08-14 13:18:00,1983.61,,infty -2022-08-14 13:19:00,1979.46,,infty -2022-08-14 13:20:00,1979.21,,infty -2022-08-14 13:21:00,1980.59,,infty -2022-08-14 13:22:00,1977.74,,infty -2022-08-14 13:23:00,1981.6,,infty -2022-08-14 13:24:00,1977.62,,infty -2022-08-14 13:25:00,1975.77,,infty -2022-08-14 13:26:00,1976.31,,infty -2022-08-14 13:27:00,1977.46,,infty -2022-08-14 13:28:00,1978.32,,infty -2022-08-14 13:29:00,1980.01,,infty -2022-08-14 13:30:00,1978.05,,infty -2022-08-14 13:31:00,1977.88,,infty -2022-08-14 13:32:00,1975.58,,infty -2022-08-14 13:33:00,1974.2,,infty -2022-08-14 13:34:00,1973.11,,infty -2022-08-14 13:35:00,1972.1,,infty -2022-08-14 13:36:00,1973.77,,infty -2022-08-14 13:37:00,1977.92,,infty -2022-08-14 13:38:00,1977.3,,infty -2022-08-14 13:39:00,1976.84,,infty -2022-08-14 13:40:00,1977.6,,infty -2022-08-14 13:41:00,1975.24,,infty -2022-08-14 13:42:00,1974.82,,infty -2022-08-14 13:43:00,1976.14,,infty -2022-08-14 13:44:00,1977.81,,infty -2022-08-14 13:45:00,1979.99,,infty -2022-08-14 13:46:00,1981.34,,infty -2022-08-14 13:47:00,1982.04,,infty -2022-08-14 13:48:00,1980.78,,infty -2022-08-14 13:49:00,1981.17,,infty -2022-08-14 13:50:00,1981.86,,infty -2022-08-14 13:51:00,1981.91,,infty -2022-08-14 13:52:00,1979.81,,infty -2022-08-14 13:53:00,1979.76,,infty -2022-08-14 13:54:00,1979.32,,infty -2022-08-14 13:55:00,1979.11,,infty -2022-08-14 13:56:00,1978.52,,infty -2022-08-14 13:57:00,1978.21,,infty -2022-08-14 13:58:00,1976.77,,infty -2022-08-14 13:59:00,1977.34,,infty -2022-08-14 14:00:00,1976.08,,infty -2022-08-14 14:01:00,1978.16,,infty -2022-08-14 14:02:00,1978.68,,infty -2022-08-14 14:03:00,1979.19,,infty -2022-08-14 14:04:00,1979.93,,infty -2022-08-14 14:05:00,1981.59,,infty -2022-08-14 14:06:00,1981.82,,infty -2022-08-14 14:07:00,1979.58,,infty -2022-08-14 14:08:00,1980.92,,infty -2022-08-14 14:09:00,1980.75,,infty -2022-08-14 14:10:00,1981.36,,infty -2022-08-14 14:11:00,1983.42,,infty -2022-08-14 14:12:00,1982.86,,infty -2022-08-14 14:13:00,1982.32,,infty -2022-08-14 14:14:00,1982.05,,infty -2022-08-14 14:15:00,1983.73,,infty -2022-08-14 14:16:00,1984.33,,infty -2022-08-14 14:17:00,1984.88,,infty -2022-08-14 14:18:00,1985.17,,infty -2022-08-14 14:19:00,1985.85,,infty -2022-08-14 14:20:00,1983.95,,infty -2022-08-14 14:21:00,1983.32,,infty -2022-08-14 14:22:00,1983.95,,infty -2022-08-14 14:23:00,1984.61,,infty -2022-08-14 14:24:00,1983.92,,infty -2022-08-14 14:25:00,1983.97,,infty -2022-08-14 14:26:00,1981.95,,infty -2022-08-14 14:27:00,1982.22,,infty -2022-08-14 14:28:00,1981.88,,infty -2022-08-14 14:29:00,1982.05,,infty -2022-08-14 14:30:00,1978.87,,infty -2022-08-14 14:31:00,1979.57,,infty -2022-08-14 14:32:00,1979.61,,infty -2022-08-14 14:33:00,1980.84,,infty -2022-08-14 14:34:00,1980.5,,infty -2022-08-14 14:35:00,1979.91,,infty -2022-08-14 14:36:00,1979.25,,infty -2022-08-14 14:37:00,1980.63,,infty -2022-08-14 14:38:00,1980.64,,infty -2022-08-14 14:39:00,1980.82,,infty -2022-08-14 14:40:00,1981.78,,infty -2022-08-14 14:41:00,1983.12,,infty -2022-08-14 14:42:00,1982.19,,infty -2022-08-14 14:43:00,1982.08,,infty -2022-08-14 14:44:00,1983.3,,infty -2022-08-14 14:45:00,1984.21,,infty -2022-08-14 14:46:00,1982.26,,infty -2022-08-14 14:47:00,1981.13,,infty -2022-08-14 14:48:00,1982.42,,infty -2022-08-14 14:49:00,1983.2,,infty -2022-08-14 14:50:00,1982.2,,infty -2022-08-14 14:51:00,1982.45,,infty -2022-08-14 14:52:00,1981.42,,infty -2022-08-14 14:53:00,1981.22,,infty -2022-08-14 14:54:00,1982.07,,infty -2022-08-14 14:55:00,1982.74,,infty -2022-08-14 14:56:00,1982.19,,infty -2022-08-14 14:57:00,1982.93,,infty -2022-08-14 14:58:00,1982.61,,infty -2022-08-14 14:59:00,1982.51,,infty -2022-08-14 15:00:00,1982.4,,infty -2022-08-14 15:01:00,1982.82,,infty -2022-08-14 15:02:00,1984.38,,infty -2022-08-14 15:03:00,1985.07,,infty -2022-08-14 15:04:00,1986.18,,infty -2022-08-14 15:05:00,1985.69,,infty -2022-08-14 15:06:00,1984.22,,infty -2022-08-14 15:07:00,1983.12,,infty -2022-08-14 15:08:00,1982.55,,infty -2022-08-14 15:09:00,1981.8,,infty -2022-08-14 15:10:00,1980.67,,infty -2022-08-14 15:11:00,1981.03,,infty -2022-08-14 15:12:00,1979.86,,infty -2022-08-14 15:13:00,1979.37,,infty -2022-08-14 15:14:00,1978.74,,infty -2022-08-14 15:15:00,1981.13,,infty -2022-08-14 15:16:00,1981.34,,infty -2022-08-14 15:17:00,1981.97,,infty -2022-08-14 15:18:00,1982.98,,infty -2022-08-14 15:19:00,1984.04,,infty -2022-08-14 15:20:00,1981.79,,infty -2022-08-14 15:21:00,1980.17,,infty -2022-08-14 15:22:00,1980.68,,infty -2022-08-14 15:23:00,1981.21,,infty -2022-08-14 15:24:00,1984.22,,infty -2022-08-14 15:25:00,1983.21,,infty -2022-08-14 15:26:00,1982.99,,infty -2022-08-14 15:27:00,1981.72,,infty -2022-08-14 15:28:00,1981.72,,infty -2022-08-14 15:29:00,1981.8,,infty -2022-08-14 15:30:00,1983.56,,infty -2022-08-14 15:31:00,1984.25,,infty -2022-08-14 15:32:00,1983.15,,infty -2022-08-14 15:33:00,1982.6,,infty -2022-08-14 15:34:00,1983.13,,infty -2022-08-14 15:35:00,1984.64,,infty -2022-08-14 15:36:00,1984.66,,infty -2022-08-14 15:37:00,1984.72,,infty -2022-08-14 15:38:00,1985.0,,infty -2022-08-14 15:39:00,1984.7,,infty -2022-08-14 15:40:00,1984.83,,infty -2022-08-14 15:41:00,1984.1,,infty -2022-08-14 15:42:00,1983.41,,infty -2022-08-14 15:43:00,1983.7,,infty -2022-08-14 15:44:00,1982.1,,infty -2022-08-14 15:45:00,1982.72,,infty -2022-08-14 15:46:00,1982.56,,infty -2022-08-14 15:47:00,1983.56,,infty -2022-08-14 15:48:00,1984.22,,infty -2022-08-14 15:49:00,1984.51,,infty -2022-08-14 15:50:00,1984.77,,infty -2022-08-14 15:51:00,1983.71,,infty -2022-08-14 15:52:00,1983.13,,infty -2022-08-14 15:53:00,1982.75,,infty -2022-08-14 15:54:00,1982.42,,infty -2022-08-14 15:55:00,1984.01,,infty -2022-08-14 15:56:00,1983.38,,infty -2022-08-14 15:57:00,1983.8,,infty -2022-08-14 15:58:00,1983.35,,infty -2022-08-14 15:59:00,1984.52,,infty -2022-08-14 16:00:00,1982.59,,infty -2022-08-14 16:01:00,1983.94,,infty -2022-08-14 16:02:00,1984.51,,infty -2022-08-14 16:03:00,1982.66,,infty -2022-08-14 16:04:00,1979.9,,infty -2022-08-14 16:05:00,1980.97,,infty -2022-08-14 16:06:00,1980.97,,infty -2022-08-14 16:07:00,1980.95,,infty -2022-08-14 16:08:00,1980.57,,infty -2022-08-14 16:09:00,1980.71,,infty -2022-08-14 16:10:00,1982.45,,infty -2022-08-14 16:11:00,1983.47,,infty -2022-08-14 16:12:00,1982.93,,infty -2022-08-14 16:13:00,1979.81,,infty -2022-08-14 16:14:00,1980.81,,infty -2022-08-14 16:15:00,1979.15,,infty -2022-08-14 16:16:00,1977.68,,infty -2022-08-14 16:17:00,1977.07,,infty -2022-08-14 16:18:00,1979.13,,infty -2022-08-14 16:19:00,1983.32,,infty -2022-08-14 16:20:00,1984.48,,infty -2022-08-14 16:21:00,1983.84,,infty -2022-08-14 16:22:00,1983.2,,infty -2022-08-14 16:23:00,1982.81,,infty -2022-08-14 16:24:00,1981.81,,infty -2022-08-14 16:25:00,1980.37,,infty -2022-08-14 16:26:00,1980.15,,infty -2022-08-14 16:27:00,1980.54,,infty -2022-08-14 16:28:00,1981.66,,infty -2022-08-14 16:29:00,1979.89,,infty -2022-08-14 16:30:00,1980.14,,infty -2022-08-14 16:31:00,1980.37,,infty -2022-08-14 16:32:00,1980.38,,infty -2022-08-14 16:33:00,1978.5,,infty -2022-08-14 16:34:00,1979.59,,infty -2022-08-14 16:35:00,1978.84,,infty -2022-08-14 16:36:00,1977.0,,infty -2022-08-14 16:37:00,1977.6,,infty -2022-08-14 16:38:00,1975.96,,infty -2022-08-14 16:39:00,1971.9,,infty -2022-08-14 16:40:00,1974.16,,infty -2022-08-14 16:41:00,1975.36,,infty -2022-08-14 16:42:00,1972.57,,infty -2022-08-14 16:43:00,1970.75,,infty -2022-08-14 16:44:00,1969.17,,infty -2022-08-14 16:45:00,1970.81,,infty -2022-08-14 16:46:00,1967.44,,infty -2022-08-14 16:47:00,1962.96,,infty -2022-08-14 16:48:00,1958.8,,infty -2022-08-14 16:49:00,1956.83,,infty -2022-08-14 16:50:00,1958.31,,infty -2022-08-14 16:51:00,1957.77,,infty -2022-08-14 16:52:00,1958.58,,infty -2022-08-14 16:53:00,1954.55,,infty -2022-08-14 16:54:00,1953.34,,infty -2022-08-14 16:55:00,1951.15,,infty -2022-08-14 16:56:00,1946.38,,infty -2022-08-14 16:57:00,1949.32,,infty -2022-08-14 16:58:00,1946.48,,infty -2022-08-14 16:59:00,1942.03,,infty -2022-08-14 17:00:00,1934.58,,infty -2022-08-14 17:01:00,1933.87,,infty -2022-08-14 17:02:00,1930.81,,infty -2022-08-14 17:03:00,1934.62,,infty -2022-08-14 17:04:00,1933.21,,infty -2022-08-14 17:05:00,1934.67,,infty -2022-08-14 17:06:00,1934.13,,infty -2022-08-14 17:07:00,1935.28,,infty -2022-08-14 17:08:00,1932.74,,infty -2022-08-14 17:09:00,1929.7,,infty -2022-08-14 17:10:00,1933.26,,infty -2022-08-14 17:11:00,1935.55,,infty -2022-08-14 17:12:00,1937.13,,infty -2022-08-14 17:13:00,1936.04,,infty -2022-08-14 17:14:00,1937.61,,infty -2022-08-14 17:15:00,1940.39,,infty -2022-08-14 17:16:00,1944.62,,infty -2022-08-14 17:17:00,1939.8,,infty -2022-08-14 17:18:00,1940.87,,infty -2022-08-14 17:19:00,1940.0,,infty -2022-08-14 17:20:00,1941.4,,infty -2022-08-14 17:21:00,1940.24,,infty -2022-08-14 17:22:00,1938.66,,infty -2022-08-14 17:23:00,1937.28,,infty -2022-08-14 17:24:00,1935.76,,infty -2022-08-14 17:25:00,1930.0,,infty -2022-08-14 17:26:00,1932.23,,infty -2022-08-14 17:27:00,1932.63,,infty -2022-08-14 17:28:00,1931.72,,infty -2022-08-14 17:29:00,1935.56,,infty -2022-08-14 17:30:00,1932.28,,infty -2022-08-14 17:31:00,1935.51,,infty -2022-08-14 17:32:00,1937.45,,infty -2022-08-14 17:33:00,1938.5,,infty -2022-08-14 17:34:00,1936.33,,infty -2022-08-14 17:35:00,1937.61,,infty -2022-08-14 17:36:00,1936.0,,infty -2022-08-14 17:37:00,1936.93,,infty -2022-08-14 17:38:00,1938.36,,infty -2022-08-14 17:39:00,1939.19,,infty -2022-08-14 17:40:00,1936.9,,infty -2022-08-14 17:41:00,1938.28,,infty -2022-08-14 17:42:00,1936.29,,infty -2022-08-14 17:43:00,1936.01,,infty -2022-08-14 17:44:00,1936.82,,infty -2022-08-14 17:45:00,1935.1,,infty -2022-08-14 17:46:00,1934.51,,infty -2022-08-14 17:47:00,1935.12,,infty -2022-08-14 17:48:00,1934.95,,infty -2022-08-14 17:49:00,1935.1,,infty -2022-08-14 17:50:00,1933.0,,infty -2022-08-14 17:51:00,1931.72,,infty -2022-08-14 17:52:00,1932.56,,infty -2022-08-14 17:53:00,1930.19,,infty -2022-08-14 17:54:00,1930.71,,infty -2022-08-14 17:55:00,1926.05,,infty -2022-08-14 17:56:00,1922.92,,infty -2022-08-14 17:57:00,1923.22,,infty -2022-08-14 17:58:00,1924.26,,infty -2022-08-14 17:59:00,1923.23,,infty -2022-08-14 18:00:00,1925.27,,infty -2022-08-14 18:01:00,1924.1,,infty -2022-08-14 18:02:00,1919.31,,infty -2022-08-14 18:03:00,1913.0,,infty -2022-08-14 18:04:00,1912.53,,infty -2022-08-14 18:05:00,1916.73,,infty -2022-08-14 18:06:00,1919.88,,infty -2022-08-14 18:07:00,1918.24,,infty -2022-08-14 18:08:00,1923.07,,infty -2022-08-14 18:09:00,1922.4,,infty -2022-08-14 18:10:00,1922.68,,infty -2022-08-14 18:11:00,1922.86,,infty -2022-08-14 18:12:00,1922.19,,infty -2022-08-14 18:13:00,1921.67,,infty -2022-08-14 18:14:00,1919.89,,infty -2022-08-14 18:15:00,1918.17,,infty -2022-08-14 18:16:00,1921.43,,infty -2022-08-14 18:17:00,1921.76,,infty -2022-08-14 18:18:00,1919.58,,infty -2022-08-14 18:19:00,1920.24,,infty -2022-08-14 18:20:00,1923.03,,infty -2022-08-14 18:21:00,1923.15,,infty -2022-08-14 18:22:00,1921.92,,infty -2022-08-14 18:23:00,1922.64,,infty -2022-08-14 18:24:00,1922.33,,infty -2022-08-14 18:25:00,1925.77,,infty -2022-08-14 18:26:00,1924.59,,infty -2022-08-14 18:27:00,1925.89,,infty -2022-08-14 18:28:00,1925.8,,infty -2022-08-14 18:29:00,1928.39,,infty -2022-08-14 18:30:00,1929.5,,infty -2022-08-14 18:31:00,1927.77,,infty -2022-08-14 18:32:00,1928.54,,infty -2022-08-14 18:33:00,1928.22,,infty -2022-08-14 18:34:00,1928.46,,infty -2022-08-14 18:35:00,1928.53,,infty -2022-08-14 18:36:00,1929.1,,infty -2022-08-14 18:37:00,1930.34,,infty -2022-08-14 18:38:00,1932.0,,infty -2022-08-14 18:39:00,1931.2,,infty -2022-08-14 18:40:00,1932.06,,infty -2022-08-14 18:41:00,1931.1,,infty -2022-08-14 18:42:00,1931.21,,infty -2022-08-14 18:43:00,1931.23,,infty -2022-08-14 18:44:00,1929.08,,infty -2022-08-14 18:45:00,1928.97,,infty -2022-08-14 18:46:00,1929.89,,infty -2022-08-14 18:47:00,1931.13,,infty -2022-08-14 18:48:00,1930.82,,infty -2022-08-14 18:49:00,1929.13,,infty -2022-08-14 18:50:00,1929.61,,infty -2022-08-14 18:51:00,1928.51,,infty -2022-08-14 18:52:00,1930.33,,infty -2022-08-14 18:53:00,1929.55,,infty -2022-08-14 18:54:00,1930.38,,infty -2022-08-14 18:55:00,1929.06,,infty -2022-08-14 18:56:00,1929.88,,infty -2022-08-14 18:57:00,1930.29,,infty -2022-08-14 18:58:00,1930.93,,infty -2022-08-14 18:59:00,1930.57,,infty -2022-08-14 19:00:00,1928.8,,infty -2022-08-14 19:01:00,1930.09,,infty -2022-08-14 19:02:00,1930.89,,infty -2022-08-14 19:03:00,1930.89,,infty -2022-08-14 19:04:00,1930.19,,infty -2022-08-14 19:05:00,1929.35,,infty -2022-08-14 19:06:00,1929.33,,infty -2022-08-14 19:07:00,1929.73,,infty -2022-08-14 19:08:00,1930.41,,infty -2022-08-14 19:09:00,1932.04,,infty -2022-08-14 19:10:00,1931.43,,infty -2022-08-14 19:11:00,1931.3,,infty -2022-08-14 19:12:00,1931.03,,infty -2022-08-14 19:13:00,1933.01,,infty -2022-08-14 19:14:00,1934.18,,infty -2022-08-14 19:15:00,1934.94,,infty -2022-08-14 19:16:00,1933.58,,infty -2022-08-14 19:17:00,1931.91,,infty -2022-08-14 19:18:00,1931.93,,infty -2022-08-14 19:19:00,1931.78,,infty -2022-08-14 19:20:00,1929.0,,infty -2022-08-14 19:21:00,1930.23,,infty -2022-08-14 19:22:00,1930.4,,infty -2022-08-14 19:23:00,1929.79,,infty -2022-08-14 19:24:00,1930.92,,infty -2022-08-14 19:25:00,1930.86,,infty -2022-08-14 19:26:00,1930.77,,infty -2022-08-14 19:27:00,1930.12,,infty -2022-08-14 19:28:00,1929.57,,infty -2022-08-14 19:29:00,1929.15,,infty -2022-08-14 19:30:00,1928.41,,infty -2022-08-14 19:31:00,1928.73,,infty -2022-08-14 19:32:00,1927.3,,infty -2022-08-14 19:33:00,1928.79,,infty -2022-08-14 19:34:00,1929.02,,infty -2022-08-14 19:35:00,1929.86,,infty -2022-08-14 19:36:00,1928.72,,infty -2022-08-14 19:37:00,1927.87,,infty -2022-08-14 19:38:00,1929.52,,infty -2022-08-14 19:39:00,1928.56,,infty -2022-08-14 19:40:00,1929.05,,infty -2022-08-14 19:41:00,1930.98,,infty -2022-08-14 19:42:00,1929.67,,infty -2022-08-14 19:43:00,1929.91,,infty -2022-08-14 19:44:00,1929.7,,infty -2022-08-14 19:45:00,1929.2,,infty -2022-08-14 19:46:00,1929.99,,infty -2022-08-14 19:47:00,1930.59,,infty -2022-08-14 19:48:00,1932.05,,infty -2022-08-14 19:49:00,1933.26,,infty -2022-08-14 19:50:00,1931.53,,infty -2022-08-14 19:51:00,1931.85,,infty -2022-08-14 19:52:00,1933.73,,infty -2022-08-14 19:53:00,1933.85,,infty -2022-08-14 19:54:00,1933.26,,infty -2022-08-14 19:55:00,1933.37,,infty -2022-08-14 19:56:00,1933.92,,infty -2022-08-14 19:57:00,1932.91,,infty -2022-08-14 19:58:00,1934.09,,infty -2022-08-14 19:59:00,1931.79,,infty -2022-08-14 20:00:00,1931.27,,infty -2022-08-14 20:01:00,1932.24,,infty -2022-08-14 20:02:00,1932.55,,infty -2022-08-14 20:03:00,1931.24,,infty -2022-08-14 20:04:00,1932.68,,infty -2022-08-14 20:05:00,1931.83,,infty -2022-08-14 20:06:00,1932.45,,infty -2022-08-14 20:07:00,1932.94,,infty -2022-08-14 20:08:00,1931.88,,infty -2022-08-14 20:09:00,1931.89,,infty -2022-08-14 20:10:00,1931.79,,infty -2022-08-14 20:11:00,1934.6,,infty -2022-08-14 20:12:00,1936.48,,infty -2022-08-14 20:13:00,1936.47,,infty -2022-08-14 20:14:00,1939.56,,infty -2022-08-14 20:15:00,1941.12,,infty -2022-08-14 20:16:00,1943.69,,infty -2022-08-14 20:17:00,1944.02,,infty -2022-08-14 20:18:00,1943.03,,infty -2022-08-14 20:19:00,1940.85,,infty -2022-08-14 20:20:00,1940.72,,infty -2022-08-14 20:21:00,1940.77,,infty -2022-08-14 20:22:00,1937.12,,infty -2022-08-14 20:23:00,1939.48,,infty -2022-08-14 20:24:00,1939.09,,infty -2022-08-14 20:25:00,1936.89,,infty -2022-08-14 20:26:00,1937.33,,infty -2022-08-14 20:27:00,1938.38,,infty -2022-08-14 20:28:00,1938.82,,infty -2022-08-14 20:29:00,1939.73,,infty -2022-08-14 20:30:00,1938.68,,infty -2022-08-14 20:31:00,1938.84,,infty -2022-08-14 20:32:00,1939.43,,infty -2022-08-14 20:33:00,1938.94,,infty -2022-08-14 20:34:00,1938.86,,infty -2022-08-14 20:35:00,1938.86,,infty -2022-08-14 20:36:00,1938.5,,infty -2022-08-14 20:37:00,1937.62,,infty -2022-08-14 20:38:00,1938.61,,infty -2022-08-14 20:39:00,1939.89,,infty -2022-08-14 20:40:00,1939.67,,infty -2022-08-14 20:41:00,1940.83,,infty -2022-08-14 20:42:00,1940.86,,infty -2022-08-14 20:43:00,1941.29,,infty -2022-08-14 20:44:00,1939.96,,infty -2022-08-14 20:45:00,1940.26,,infty -2022-08-14 20:46:00,1939.65,,infty -2022-08-14 20:47:00,1939.9,,infty -2022-08-14 20:48:00,1939.62,,infty -2022-08-14 20:49:00,1940.44,,infty -2022-08-14 20:50:00,1939.9,,infty -2022-08-14 20:51:00,1940.49,,infty -2022-08-14 20:52:00,1940.35,,infty -2022-08-14 20:53:00,1940.6,,infty -2022-08-14 20:54:00,1942.22,,infty -2022-08-14 20:55:00,1941.5,,infty -2022-08-14 20:56:00,1941.98,,infty -2022-08-14 20:57:00,1941.79,,infty -2022-08-14 20:58:00,1942.0,,infty -2022-08-14 20:59:00,1941.14,,infty -2022-08-14 21:00:00,1941.93,,infty -2022-08-14 21:01:00,1942.3,,infty -2022-08-14 21:02:00,1941.54,,infty -2022-08-14 21:03:00,1941.71,,infty -2022-08-14 21:04:00,1939.43,,infty -2022-08-14 21:05:00,1940.35,,infty -2022-08-14 21:06:00,1939.74,,infty -2022-08-14 21:07:00,1940.22,,infty -2022-08-14 21:08:00,1941.55,,infty -2022-08-14 21:09:00,1942.45,,infty -2022-08-14 21:10:00,1944.44,,infty -2022-08-14 21:11:00,1944.25,,infty -2022-08-14 21:12:00,1946.73,,infty -2022-08-14 21:13:00,1947.35,,infty -2022-08-14 21:14:00,1948.16,,infty -2022-08-14 21:15:00,1947.98,,infty -2022-08-14 21:16:00,1949.24,,infty -2022-08-14 21:17:00,1949.26,,infty -2022-08-14 21:18:00,1945.81,,infty -2022-08-14 21:19:00,1946.03,,infty -2022-08-14 21:20:00,1944.66,,infty -2022-08-14 21:21:00,1947.05,,infty -2022-08-14 21:22:00,1948.62,,infty -2022-08-14 21:23:00,1948.2,,infty -2022-08-14 21:24:00,1952.49,,infty -2022-08-14 21:25:00,1954.0,,infty -2022-08-14 21:26:00,1954.87,,infty -2022-08-14 21:27:00,1954.26,,infty -2022-08-14 21:28:00,1951.22,,infty -2022-08-14 21:29:00,1954.58,,infty -2022-08-14 21:30:00,1958.76,,infty -2022-08-14 21:31:00,1957.54,,infty -2022-08-14 21:32:00,1957.89,,infty -2022-08-14 21:33:00,1958.0,,infty -2022-08-14 21:34:00,1954.15,,infty -2022-08-14 21:35:00,1953.74,,infty -2022-08-14 21:36:00,1951.74,,infty -2022-08-14 21:37:00,1951.07,,infty -2022-08-14 21:38:00,1946.21,,infty -2022-08-14 21:39:00,1946.24,,infty -2022-08-14 21:40:00,1948.35,,infty -2022-08-14 21:41:00,1950.16,,infty -2022-08-14 21:42:00,1950.94,,infty -2022-08-14 21:43:00,1950.0,,infty -2022-08-14 21:44:00,1951.58,,infty -2022-08-14 21:45:00,1951.0,,infty -2022-08-14 21:46:00,1948.43,,infty -2022-08-14 21:47:00,1950.18,,infty -2022-08-14 21:48:00,1950.03,,infty -2022-08-14 21:49:00,1950.03,,infty -2022-08-14 21:50:00,1949.28,,infty -2022-08-14 21:51:00,1949.5,,infty -2022-08-14 21:52:00,1950.05,,infty -2022-08-14 21:53:00,1949.8,,infty -2022-08-14 21:54:00,1947.52,,infty -2022-08-14 21:55:00,1945.11,,infty -2022-08-14 21:56:00,1946.56,,infty -2022-08-14 21:57:00,1946.9,,infty -2022-08-14 21:58:00,1947.3,,infty -2022-08-14 21:59:00,1948.93,,infty -2022-08-14 22:00:00,1950.23,,infty -2022-08-14 22:01:00,1947.91,,infty -2022-08-14 22:02:00,1944.31,,infty -2022-08-14 22:03:00,1941.1,,infty -2022-08-14 22:04:00,1935.77,,infty -2022-08-14 22:05:00,1929.59,,infty -2022-08-14 22:06:00,1932.21,,infty -2022-08-14 22:07:00,1933.5,,infty -2022-08-14 22:08:00,1935.48,,infty -2022-08-14 22:09:00,1935.66,,infty -2022-08-14 22:10:00,1929.78,,infty -2022-08-14 22:11:00,1928.45,,infty -2022-08-14 22:12:00,1923.96,,infty -2022-08-14 22:13:00,1924.32,,infty -2022-08-14 22:14:00,1924.28,,infty -2022-08-14 22:15:00,1922.08,,infty -2022-08-14 22:16:00,1924.26,,infty -2022-08-14 22:17:00,1926.87,,infty -2022-08-14 22:18:00,1925.91,,infty -2022-08-14 22:19:00,1928.2,,infty -2022-08-14 22:20:00,1928.79,,infty -2022-08-14 22:21:00,1929.31,,infty -2022-08-14 22:22:00,1930.39,,infty -2022-08-14 22:23:00,1929.29,,infty -2022-08-14 22:24:00,1935.54,,infty -2022-08-14 22:25:00,1937.01,,infty -2022-08-14 22:26:00,1938.06,,infty -2022-08-14 22:27:00,1937.36,,infty -2022-08-14 22:28:00,1937.45,,infty -2022-08-14 22:29:00,1936.18,,infty -2022-08-14 22:30:00,1938.99,,infty -2022-08-14 22:31:00,1941.44,,infty -2022-08-14 22:32:00,1938.73,,infty -2022-08-14 22:33:00,1939.58,,infty -2022-08-14 22:34:00,1939.05,,infty -2022-08-14 22:35:00,1940.26,,infty -2022-08-14 22:36:00,1939.13,,infty -2022-08-14 22:37:00,1938.11,,infty -2022-08-14 22:38:00,1941.21,,infty -2022-08-14 22:39:00,1942.06,,infty -2022-08-14 22:40:00,1943.08,,infty -2022-08-14 22:41:00,1941.04,,infty -2022-08-14 22:42:00,1941.43,,infty -2022-08-14 22:43:00,1941.54,,infty -2022-08-14 22:44:00,1941.63,,infty -2022-08-14 22:45:00,1942.66,,infty -2022-08-14 22:46:00,1939.82,,infty -2022-08-14 22:47:00,1940.79,,infty -2022-08-14 22:48:00,1940.42,,infty -2022-08-14 22:49:00,1941.01,,infty -2022-08-14 22:50:00,1941.44,,infty -2022-08-14 22:51:00,1941.25,,infty -2022-08-14 22:52:00,1941.05,,infty -2022-08-14 22:53:00,1942.93,,infty -2022-08-14 22:54:00,1941.48,,infty -2022-08-14 22:55:00,1940.51,,infty -2022-08-14 22:56:00,1941.04,,infty -2022-08-14 22:57:00,1941.3,,infty -2022-08-14 22:58:00,1936.87,,infty -2022-08-14 22:59:00,1933.04,,infty -2022-08-14 23:00:00,1934.76,,infty -2022-08-14 23:01:00,1930.92,,infty -2022-08-14 23:02:00,1931.72,,infty -2022-08-14 23:03:00,1934.8,,infty -2022-08-14 23:04:00,1937.96,,infty -2022-08-14 23:05:00,1937.53,,infty -2022-08-14 23:06:00,1936.28,,infty -2022-08-14 23:07:00,1934.99,,infty -2022-08-14 23:08:00,1935.13,,infty -2022-08-14 23:09:00,1933.08,,infty -2022-08-14 23:10:00,1934.99,,infty -2022-08-14 23:11:00,1934.55,,infty -2022-08-14 23:12:00,1934.37,,infty -2022-08-14 23:13:00,1934.97,,infty -2022-08-14 23:14:00,1935.39,,infty -2022-08-14 23:15:00,1937.43,,infty -2022-08-14 23:16:00,1936.92,,infty -2022-08-14 23:17:00,1935.88,,infty -2022-08-14 23:18:00,1937.53,,infty -2022-08-14 23:19:00,1936.2,,infty -2022-08-14 23:20:00,1937.81,,infty -2022-08-14 23:21:00,1936.88,,infty -2022-08-14 23:22:00,1934.52,,infty -2022-08-14 23:23:00,1935.63,,infty -2022-08-14 23:24:00,1935.77,,infty -2022-08-14 23:25:00,1934.35,,infty -2022-08-14 23:26:00,1932.43,,infty -2022-08-14 23:27:00,1931.33,,infty -2022-08-14 23:28:00,1932.19,,infty -2022-08-14 23:29:00,1931.55,,infty -2022-08-14 23:30:00,1931.7,,infty -2022-08-14 23:31:00,1931.69,,infty -2022-08-14 23:32:00,1930.85,,infty -2022-08-14 23:33:00,1932.05,,infty -2022-08-14 23:34:00,1933.52,,infty -2022-08-14 23:35:00,1932.73,,infty -2022-08-14 23:36:00,1935.33,,infty -2022-08-14 23:37:00,1937.29,,infty -2022-08-14 23:38:00,1935.61,,infty -2022-08-14 23:39:00,1935.97,,infty -2022-08-14 23:40:00,1935.62,,infty -2022-08-14 23:41:00,1934.95,,infty -2022-08-14 23:42:00,1935.41,,infty -2022-08-14 23:43:00,1936.81,,infty -2022-08-14 23:44:00,1938.22,,infty -2022-08-14 23:45:00,1940.93,,infty -2022-08-14 23:46:00,1939.43,,infty -2022-08-14 23:47:00,1939.44,,infty -2022-08-14 23:48:00,1937.79,,infty -2022-08-14 23:49:00,1938.73,,infty -2022-08-14 23:50:00,1938.41,,infty -2022-08-14 23:51:00,1938.68,,infty -2022-08-14 23:52:00,1938.74,,infty -2022-08-14 23:53:00,1937.22,,infty -2022-08-14 23:54:00,1938.27,,infty -2022-08-14 23:55:00,1938.54,,infty -2022-08-14 23:56:00,1936.75,,infty -2022-08-14 23:57:00,1937.05,,infty -2022-08-14 23:58:00,1935.35,,infty -2022-08-14 23:59:00,1935.81,,infty -2022-08-15 00:00:00,1935.34,,infty -2022-08-15 00:01:00,1935.33,,infty -2022-08-15 00:02:00,1933.2,,infty -2022-08-15 00:03:00,1933.7,,infty -2022-08-15 00:04:00,1934.42,,infty -2022-08-15 00:05:00,1932.04,,infty -2022-08-15 00:06:00,1933.42,,infty -2022-08-15 00:07:00,1931.33,,infty -2022-08-15 00:08:00,1929.82,,infty -2022-08-15 00:09:00,1927.3,,infty -2022-08-15 00:10:00,1927.67,,infty -2022-08-15 00:11:00,1927.44,,infty -2022-08-15 00:12:00,1927.09,,infty -2022-08-15 00:13:00,1926.49,,infty -2022-08-15 00:14:00,1927.45,,infty -2022-08-15 00:15:00,1928.98,,infty -2022-08-15 00:16:00,1925.95,,infty -2022-08-15 00:17:00,1928.33,,infty -2022-08-15 00:18:00,1931.66,,infty -2022-08-15 00:19:00,1931.81,,infty -2022-08-15 00:20:00,1934.77,,infty -2022-08-15 00:21:00,1934.55,,infty -2022-08-15 00:22:00,1934.27,,infty -2022-08-15 00:23:00,1932.06,,infty -2022-08-15 00:24:00,1930.3,,infty -2022-08-15 00:25:00,1929.83,,infty -2022-08-15 00:26:00,1930.27,,infty -2022-08-15 00:27:00,1930.8,,infty -2022-08-15 00:28:00,1931.58,,infty -2022-08-15 00:29:00,1931.78,,infty -2022-08-15 00:30:00,1931.21,,infty -2022-08-15 00:31:00,1934.0,,infty -2022-08-15 00:32:00,1936.28,,infty -2022-08-15 00:33:00,1940.49,,infty -2022-08-15 00:34:00,1943.72,,infty -2022-08-15 00:35:00,1949.4,,infty -2022-08-15 00:36:00,1949.58,,infty -2022-08-15 00:37:00,1949.53,,infty -2022-08-15 00:38:00,1948.94,,infty -2022-08-15 00:39:00,1947.88,,infty -2022-08-15 00:40:00,1947.32,,infty -2022-08-15 00:41:00,1949.92,,infty -2022-08-15 00:42:00,1949.64,,infty -2022-08-15 00:43:00,1951.11,,infty -2022-08-15 00:44:00,1951.81,,infty -2022-08-15 00:45:00,1950.11,,infty -2022-08-15 00:46:00,1952.46,,infty -2022-08-15 00:47:00,1951.99,,infty -2022-08-15 00:48:00,1950.97,,infty -2022-08-15 00:49:00,1950.06,,infty -2022-08-15 00:50:00,1952.09,,infty -2022-08-15 00:51:00,1950.62,,infty -2022-08-15 00:52:00,1951.99,,infty -2022-08-15 00:53:00,1952.07,,infty -2022-08-15 00:54:00,1950.98,,infty -2022-08-15 00:55:00,1953.06,,infty -2022-08-15 00:56:00,1954.82,,infty -2022-08-15 00:57:00,1955.94,,infty -2022-08-15 00:58:00,1956.8,,infty -2022-08-15 00:59:00,1957.97,,infty -2022-08-15 01:00:00,1955.19,,infty -2022-08-15 01:01:00,1956.45,,infty -2022-08-15 01:02:00,1955.44,,infty -2022-08-15 01:03:00,1956.59,,infty -2022-08-15 01:04:00,1955.13,,infty -2022-08-15 01:05:00,1955.72,,infty -2022-08-15 01:06:00,1952.52,,infty -2022-08-15 01:07:00,1953.66,,infty -2022-08-15 01:08:00,1953.84,,infty -2022-08-15 01:09:00,1955.53,,infty -2022-08-15 01:10:00,1954.75,,infty -2022-08-15 01:11:00,1955.24,,infty -2022-08-15 01:12:00,1953.92,,infty -2022-08-15 01:13:00,1949.56,,infty -2022-08-15 01:14:00,1949.08,,infty -2022-08-15 01:15:00,1949.09,,infty -2022-08-15 01:16:00,1949.79,,infty -2022-08-15 01:17:00,1951.06,,infty -2022-08-15 01:18:00,1952.91,,infty -2022-08-15 01:19:00,1956.62,,infty -2022-08-15 01:20:00,1955.94,,infty -2022-08-15 01:21:00,1956.35,,infty -2022-08-15 01:22:00,1954.68,,infty -2022-08-15 01:23:00,1953.51,,infty -2022-08-15 01:24:00,1954.67,,infty -2022-08-15 01:25:00,1954.01,,infty -2022-08-15 01:26:00,1953.04,,infty -2022-08-15 01:27:00,1953.91,,infty -2022-08-15 01:28:00,1953.55,,infty -2022-08-15 01:29:00,1953.96,,infty -2022-08-15 01:30:00,1955.6,,infty -2022-08-15 01:31:00,1963.17,,infty -2022-08-15 01:32:00,1959.8,,infty -2022-08-15 01:33:00,1961.57,,infty -2022-08-15 01:34:00,1960.35,,infty -2022-08-15 01:35:00,1963.57,,infty -2022-08-15 01:36:00,1964.3,,infty -2022-08-15 01:37:00,1963.67,,infty -2022-08-15 01:38:00,1962.83,,infty -2022-08-15 01:39:00,1965.18,,infty -2022-08-15 01:40:00,1963.18,,infty -2022-08-15 01:41:00,1967.69,,infty -2022-08-15 01:42:00,1964.42,,infty -2022-08-15 01:43:00,1963.68,,infty -2022-08-15 01:44:00,1965.66,,infty -2022-08-15 01:45:00,1964.92,,infty -2022-08-15 01:46:00,1966.5,,infty -2022-08-15 01:47:00,1969.18,,infty -2022-08-15 01:48:00,1969.32,,infty -2022-08-15 01:49:00,1968.59,,infty -2022-08-15 01:50:00,1970.75,,infty -2022-08-15 01:51:00,1972.97,,infty -2022-08-15 01:52:00,1971.94,,infty -2022-08-15 01:53:00,1970.55,,infty -2022-08-15 01:54:00,1970.39,,infty -2022-08-15 01:55:00,1972.91,,infty -2022-08-15 01:56:00,1972.93,,infty -2022-08-15 01:57:00,1974.06,,infty -2022-08-15 01:58:00,1973.13,,infty -2022-08-15 01:59:00,1974.63,,infty -2022-08-15 02:00:00,1973.67,,infty -2022-08-15 02:01:00,1973.0,,infty -2022-08-15 02:02:00,1973.78,,infty -2022-08-15 02:03:00,1972.99,,infty -2022-08-15 02:04:00,1972.09,,infty -2022-08-15 02:05:00,1970.68,,infty -2022-08-15 02:06:00,1971.65,,infty -2022-08-15 02:07:00,1973.56,,infty -2022-08-15 02:08:00,1977.67,,infty -2022-08-15 02:09:00,1977.11,,infty -2022-08-15 02:10:00,1975.45,,infty -2022-08-15 02:11:00,1976.06,,infty -2022-08-15 02:12:00,1977.58,,infty -2022-08-15 02:13:00,1980.33,,infty -2022-08-15 02:14:00,1979.45,,infty -2022-08-15 02:15:00,1979.65,,infty -2022-08-15 02:16:00,1978.48,,infty -2022-08-15 02:17:00,1979.81,,infty -2022-08-15 02:18:00,1979.34,,infty -2022-08-15 02:19:00,1978.87,,infty -2022-08-15 02:20:00,1977.21,,infty -2022-08-15 02:21:00,1978.48,,infty -2022-08-15 02:22:00,1978.92,,infty -2022-08-15 02:23:00,1982.24,,infty -2022-08-15 02:24:00,1983.65,,infty -2022-08-15 02:25:00,1983.03,,infty -2022-08-15 02:26:00,1982.6,,infty -2022-08-15 02:27:00,1982.86,,infty -2022-08-15 02:28:00,1987.17,,infty -2022-08-15 02:29:00,1987.74,,infty -2022-08-15 02:30:00,1984.93,,infty -2022-08-15 02:31:00,1985.48,,infty -2022-08-15 02:32:00,1993.53,,infty -2022-08-15 02:33:00,1994.31,,infty -2022-08-15 02:34:00,1997.2,,infty -2022-08-15 02:35:00,1999.52,,infty -2022-08-15 02:36:00,2001.18,,infty -2022-08-15 02:37:00,2002.7,,infty -2022-08-15 02:38:00,2003.21,,infty -2022-08-15 02:39:00,2001.26,,infty -2022-08-15 02:40:00,2005.18,,infty -2022-08-15 02:41:00,2004.6,,infty -2022-08-15 02:42:00,2002.75,,infty -2022-08-15 02:43:00,1998.77,,infty -2022-08-15 02:44:00,2000.27,,infty -2022-08-15 02:45:00,1995.14,,infty -2022-08-15 02:46:00,1995.31,,infty -2022-08-15 02:47:00,1996.33,,infty -2022-08-15 02:48:00,1998.85,,infty -2022-08-15 02:49:00,2001.69,,infty -2022-08-15 02:50:00,2001.65,,infty -2022-08-15 02:51:00,2003.07,,infty -2022-08-15 02:52:00,2001.71,,infty -2022-08-15 02:53:00,2003.72,,infty -2022-08-15 02:54:00,2002.36,,infty -2022-08-15 02:55:00,2002.28,,infty -2022-08-15 02:56:00,2000.7,,infty -2022-08-15 02:57:00,2001.1,,infty -2022-08-15 02:58:00,2001.89,,infty -2022-08-15 02:59:00,2000.92,,infty -2022-08-15 03:00:00,2000.71,,infty -2022-08-15 03:01:00,1999.56,,infty -2022-08-15 03:02:00,2000.74,,infty -2022-08-15 03:03:00,2000.82,,infty -2022-08-15 03:04:00,2002.14,,infty -2022-08-15 03:05:00,1998.5,,infty -2022-08-15 03:06:00,1995.81,,infty -2022-08-15 03:07:00,1997.16,,infty -2022-08-15 03:08:00,1993.57,,infty -2022-08-15 03:09:00,1994.86,,infty -2022-08-15 03:10:00,1997.21,,infty -2022-08-15 03:11:00,2000.78,,infty -2022-08-15 03:12:00,2005.54,,infty -2022-08-15 03:13:00,2007.4,,infty -2022-08-15 03:14:00,2010.89,,infty -2022-08-15 03:15:00,2009.36,,infty -2022-08-15 03:16:00,2008.94,,infty -2022-08-15 03:17:00,2008.2,,infty -2022-08-15 03:18:00,2005.39,,infty -2022-08-15 03:19:00,2002.44,,infty -2022-08-15 03:20:00,2004.34,,infty -2022-08-15 03:21:00,1999.47,,infty -2022-08-15 03:22:00,2000.79,,infty -2022-08-15 03:23:00,1995.42,,infty -2022-08-15 03:24:00,1998.53,,infty -2022-08-15 03:25:00,2000.05,,infty -2022-08-15 03:26:00,1998.75,,infty -2022-08-15 03:27:00,1997.81,,infty -2022-08-15 03:28:00,1999.35,,infty -2022-08-15 03:29:00,2000.1,,infty -2022-08-15 03:30:00,1999.59,,infty -2022-08-15 03:31:00,2000.41,,infty -2022-08-15 03:32:00,1999.8,,infty -2022-08-15 03:33:00,1999.29,,infty -2022-08-15 03:34:00,1996.79,,infty -2022-08-15 03:35:00,1998.21,,infty -2022-08-15 03:36:00,1995.47,,infty -2022-08-15 03:37:00,1995.24,,infty -2022-08-15 03:38:00,1993.94,,infty -2022-08-15 03:39:00,1992.92,,infty -2022-08-15 03:40:00,1988.01,,infty -2022-08-15 03:41:00,1987.99,,infty -2022-08-15 03:42:00,1984.53,,infty -2022-08-15 03:43:00,1977.51,,infty -2022-08-15 03:44:00,1981.72,,infty -2022-08-15 03:45:00,1983.59,,infty -2022-08-15 03:46:00,1984.05,,infty -2022-08-15 03:47:00,1985.51,,infty -2022-08-15 03:48:00,1983.74,,infty -2022-08-15 03:49:00,1981.36,,infty -2022-08-15 03:50:00,1984.82,,infty -2022-08-15 03:51:00,1985.74,,infty -2022-08-15 03:52:00,1986.95,,infty -2022-08-15 03:53:00,1985.82,,infty -2022-08-15 03:54:00,1984.86,,infty -2022-08-15 03:55:00,1986.52,,infty -2022-08-15 03:56:00,1986.91,,infty -2022-08-15 03:57:00,1987.15,,infty -2022-08-15 03:58:00,1987.74,,infty -2022-08-15 03:59:00,1987.0,,infty -2022-08-15 04:00:00,1986.26,,infty -2022-08-15 04:01:00,1985.66,,infty -2022-08-15 04:02:00,1983.7,,infty -2022-08-15 04:03:00,1985.44,,infty -2022-08-15 04:04:00,1984.14,,infty -2022-08-15 04:05:00,1983.18,,infty -2022-08-15 04:06:00,1980.79,,infty -2022-08-15 04:07:00,1980.44,,infty -2022-08-15 04:08:00,1977.63,,infty -2022-08-15 04:09:00,1980.21,,infty -2022-08-15 04:10:00,1980.7,,infty -2022-08-15 04:11:00,1981.57,,infty -2022-08-15 04:12:00,1980.4,,infty -2022-08-15 04:13:00,1982.91,,infty -2022-08-15 04:14:00,1983.32,,infty -2022-08-15 04:15:00,1983.66,,infty -2022-08-15 04:16:00,1982.58,,infty -2022-08-15 04:17:00,1979.96,,infty -2022-08-15 04:18:00,1978.27,,infty -2022-08-15 04:19:00,1979.67,,infty -2022-08-15 04:20:00,1980.41,,infty -2022-08-15 04:21:00,1978.7,,infty -2022-08-15 04:22:00,1977.93,,infty -2022-08-15 04:23:00,1978.81,,infty -2022-08-15 04:24:00,1979.92,,infty -2022-08-15 04:25:00,1978.53,,infty -2022-08-15 04:26:00,1978.55,,infty -2022-08-15 04:27:00,1977.7,,infty -2022-08-15 04:28:00,1974.97,,infty -2022-08-15 04:29:00,1976.58,,infty -2022-08-15 04:30:00,1977.91,,infty -2022-08-15 04:31:00,1979.76,,infty -2022-08-15 04:32:00,1980.66,,infty -2022-08-15 04:33:00,1980.06,,infty -2022-08-15 04:34:00,1979.76,,infty -2022-08-15 04:35:00,1981.87,,infty -2022-08-15 04:36:00,1981.61,,infty -2022-08-15 04:37:00,1981.52,,infty -2022-08-15 04:38:00,1980.65,,infty -2022-08-15 04:39:00,1980.91,,infty -2022-08-15 04:40:00,1980.99,,infty -2022-08-15 04:41:00,1981.73,,infty -2022-08-15 04:42:00,1981.41,,infty -2022-08-15 04:43:00,1981.24,,infty -2022-08-15 04:44:00,1981.15,,infty -2022-08-15 04:45:00,1980.73,,infty -2022-08-15 04:46:00,1979.14,,infty -2022-08-15 04:47:00,1979.9,,infty -2022-08-15 04:48:00,1977.69,,infty -2022-08-15 04:49:00,1976.84,,infty -2022-08-15 04:50:00,1978.02,,infty -2022-08-15 04:51:00,1978.49,,infty -2022-08-15 04:52:00,1982.16,,infty -2022-08-15 04:53:00,1983.7,,infty -2022-08-15 04:54:00,1981.43,,infty -2022-08-15 04:55:00,1984.25,,infty -2022-08-15 04:56:00,1984.38,,infty -2022-08-15 04:57:00,1983.71,,infty -2022-08-15 04:58:00,1983.41,,infty -2022-08-15 04:59:00,1983.74,,infty -2022-08-15 05:00:00,1984.31,,infty -2022-08-15 05:01:00,1983.27,,infty -2022-08-15 05:02:00,1984.6,,infty -2022-08-15 05:03:00,1988.16,,infty -2022-08-15 05:04:00,1987.28,,infty -2022-08-15 05:05:00,1987.71,,infty -2022-08-15 05:06:00,1988.36,,infty -2022-08-15 05:07:00,1988.25,,infty -2022-08-15 05:08:00,1987.21,,infty -2022-08-15 05:09:00,1986.51,,infty -2022-08-15 05:10:00,1983.5,,infty -2022-08-15 05:11:00,1981.89,,infty -2022-08-15 05:12:00,1982.25,,infty -2022-08-15 05:13:00,1983.82,,infty -2022-08-15 05:14:00,1984.43,,infty -2022-08-15 05:15:00,1983.17,,infty -2022-08-15 05:16:00,1982.33,,infty -2022-08-15 05:17:00,1981.67,,infty -2022-08-15 05:18:00,1981.09,,infty -2022-08-15 05:19:00,1979.28,,infty -2022-08-15 05:20:00,1979.11,,infty -2022-08-15 05:21:00,1979.87,,infty -2022-08-15 05:22:00,1981.66,,infty -2022-08-15 05:23:00,1979.55,,infty -2022-08-15 05:24:00,1976.02,,infty -2022-08-15 05:25:00,1975.65,,infty -2022-08-15 05:26:00,1977.65,,infty -2022-08-15 05:27:00,1978.16,,infty -2022-08-15 05:28:00,1976.03,,infty -2022-08-15 05:29:00,1977.37,,infty -2022-08-15 05:30:00,1977.16,,infty -2022-08-15 05:31:00,1974.98,,infty -2022-08-15 05:32:00,1971.43,,infty -2022-08-15 05:33:00,1972.07,,infty -2022-08-15 05:34:00,1969.71,,infty -2022-08-15 05:35:00,1968.98,,infty -2022-08-15 05:36:00,1970.51,,infty -2022-08-15 05:37:00,1971.89,,infty -2022-08-15 05:38:00,1973.48,,infty -2022-08-15 05:39:00,1968.08,,infty -2022-08-15 05:40:00,1970.0,,infty -2022-08-15 05:41:00,1969.31,,infty -2022-08-15 05:42:00,1970.11,,infty -2022-08-15 05:43:00,1970.07,,infty -2022-08-15 05:44:00,1971.25,,infty -2022-08-15 05:45:00,1968.67,,infty -2022-08-15 05:46:00,1966.93,,infty -2022-08-15 05:47:00,1965.66,,infty -2022-08-15 05:48:00,1965.92,,infty -2022-08-15 05:49:00,1967.45,,infty -2022-08-15 05:50:00,1968.77,,infty -2022-08-15 05:51:00,1969.14,,infty -2022-08-15 05:52:00,1968.16,,infty -2022-08-15 05:53:00,1969.45,,infty -2022-08-15 05:54:00,1969.76,,infty -2022-08-15 05:55:00,1968.92,,infty -2022-08-15 05:56:00,1969.23,,infty -2022-08-15 05:57:00,1969.19,,infty -2022-08-15 05:58:00,1969.69,,infty -2022-08-15 05:59:00,1970.25,,infty -2022-08-15 06:00:00,1969.81,,infty -2022-08-15 06:01:00,1969.04,,infty -2022-08-15 06:02:00,1968.21,,infty -2022-08-15 06:03:00,1965.36,,infty -2022-08-15 06:04:00,1961.0,,infty -2022-08-15 06:05:00,1961.48,,infty -2022-08-15 06:06:00,1957.21,,infty -2022-08-15 06:07:00,1950.88,,infty -2022-08-15 06:08:00,1943.83,,infty -2022-08-15 06:09:00,1944.95,,infty -2022-08-15 06:10:00,1943.48,,infty -2022-08-15 06:11:00,1944.95,,infty -2022-08-15 06:12:00,1940.23,,infty -2022-08-15 06:13:00,1935.64,,infty -2022-08-15 06:14:00,1934.18,,infty -2022-08-15 06:15:00,1935.5,,infty -2022-08-15 06:16:00,1938.41,,infty -2022-08-15 06:17:00,1932.18,,infty -2022-08-15 06:18:00,1931.89,,infty -2022-08-15 06:19:00,1933.63,,infty 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06:47:00,1917.6,,infty -2022-08-15 06:48:00,1918.81,,infty -2022-08-15 06:49:00,1917.05,,infty -2022-08-15 06:50:00,1917.81,,infty -2022-08-15 06:51:00,1905.91,,infty -2022-08-15 06:52:00,1898.75,,infty -2022-08-15 06:53:00,1897.11,,infty -2022-08-15 06:54:00,1903.52,,infty -2022-08-15 06:55:00,1907.03,,infty -2022-08-15 06:56:00,1905.99,,infty -2022-08-15 06:57:00,1909.99,,infty -2022-08-15 06:58:00,1903.82,,infty -2022-08-15 06:59:00,1907.3,,infty -2022-08-15 07:00:00,1907.78,,infty -2022-08-15 07:01:00,1905.49,,infty -2022-08-15 07:02:00,1908.51,,infty -2022-08-15 07:03:00,1915.43,,infty -2022-08-15 07:04:00,1912.56,,infty -2022-08-15 07:05:00,1911.02,,infty -2022-08-15 07:06:00,1914.1,,infty -2022-08-15 07:07:00,1911.75,,infty -2022-08-15 07:08:00,1910.46,,infty -2022-08-15 07:09:00,1908.47,,infty -2022-08-15 07:10:00,1905.64,,infty -2022-08-15 07:11:00,1909.49,,infty -2022-08-15 07:12:00,1909.02,,infty -2022-08-15 07:13:00,1910.2,,infty -2022-08-15 07:14:00,1908.42,,infty -2022-08-15 07:15:00,1907.96,,infty -2022-08-15 07:16:00,1909.41,,infty -2022-08-15 07:17:00,1910.37,,infty -2022-08-15 07:18:00,1912.9,,infty -2022-08-15 07:19:00,1913.79,,infty -2022-08-15 07:20:00,1912.54,,infty -2022-08-15 07:21:00,1911.7,,infty -2022-08-15 07:22:00,1912.39,,infty -2022-08-15 07:23:00,1911.58,,infty -2022-08-15 07:24:00,1911.85,,infty -2022-08-15 07:25:00,1912.32,,infty -2022-08-15 07:26:00,1910.63,,infty -2022-08-15 07:27:00,1909.22,,infty -2022-08-15 07:28:00,1909.56,,infty -2022-08-15 07:29:00,1911.44,,infty -2022-08-15 07:30:00,1912.26,,infty -2022-08-15 07:31:00,1911.48,,infty -2022-08-15 07:32:00,1911.98,,infty -2022-08-15 07:33:00,1913.35,,infty -2022-08-15 07:34:00,1915.34,,infty -2022-08-15 07:35:00,1909.76,,infty -2022-08-15 07:36:00,1907.75,,infty -2022-08-15 07:37:00,1900.81,,infty -2022-08-15 07:38:00,1895.29,,infty -2022-08-15 07:39:00,1887.97,,infty -2022-08-15 07:40:00,1885.71,,infty -2022-08-15 07:41:00,1898.22,,infty -2022-08-15 07:42:00,1903.32,,infty -2022-08-15 07:43:00,1902.27,,infty -2022-08-15 07:44:00,1901.4,,infty -2022-08-15 07:45:00,1903.73,,infty -2022-08-15 07:46:00,1903.26,,infty -2022-08-15 07:47:00,1902.15,,infty -2022-08-15 07:48:00,1902.01,,infty -2022-08-15 07:49:00,1900.62,,infty -2022-08-15 07:50:00,1897.12,,infty -2022-08-15 07:51:00,1901.04,,infty -2022-08-15 07:52:00,1903.13,,infty -2022-08-15 07:53:00,1902.67,,infty -2022-08-15 07:54:00,1903.46,,infty -2022-08-15 07:55:00,1900.37,,infty -2022-08-15 07:56:00,1899.87,,infty -2022-08-15 07:57:00,1899.24,,infty -2022-08-15 07:58:00,1900.1,,infty -2022-08-15 07:59:00,1900.13,,infty -2022-08-15 08:00:00,1902.06,,infty -2022-08-15 08:01:00,1903.45,,infty -2022-08-15 08:02:00,1901.4,,infty -2022-08-15 08:03:00,1900.22,,infty -2022-08-15 08:04:00,1900.93,,infty -2022-08-15 08:05:00,1900.31,,infty -2022-08-15 08:06:00,1904.31,,infty -2022-08-15 08:07:00,1903.41,,infty -2022-08-15 08:08:00,1898.13,,infty -2022-08-15 08:09:00,1897.26,,infty -2022-08-15 08:10:00,1893.06,,infty -2022-08-15 08:11:00,1892.91,,infty -2022-08-15 08:12:00,1892.7,,infty -2022-08-15 08:13:00,1891.4,,infty -2022-08-15 08:14:00,1890.45,,infty -2022-08-15 08:15:00,1892.13,,infty -2022-08-15 08:16:00,1881.26,,infty -2022-08-15 08:17:00,1885.26,,infty -2022-08-15 08:18:00,1894.92,,infty -2022-08-15 08:19:00,1896.85,,infty -2022-08-15 08:20:00,1898.9,,infty -2022-08-15 08:21:00,1900.29,,infty -2022-08-15 08:22:00,1898.61,,infty -2022-08-15 08:23:00,1900.71,,infty -2022-08-15 08:24:00,1897.73,,infty -2022-08-15 08:25:00,1900.14,,infty -2022-08-15 08:26:00,1899.43,,infty -2022-08-15 08:27:00,1900.68,,infty -2022-08-15 08:28:00,1898.8,,infty -2022-08-15 08:29:00,1900.92,,infty -2022-08-15 08:30:00,1900.76,,infty -2022-08-15 08:31:00,1902.11,,infty -2022-08-15 08:32:00,1902.38,,infty -2022-08-15 08:33:00,1906.89,,infty -2022-08-15 08:34:00,1904.29,,infty -2022-08-15 08:35:00,1906.18,,infty -2022-08-15 08:36:00,1906.54,,infty -2022-08-15 08:37:00,1907.17,,infty -2022-08-15 08:38:00,1905.55,,infty -2022-08-15 08:39:00,1906.69,,infty -2022-08-15 08:40:00,1906.19,,infty -2022-08-15 08:41:00,1904.41,,infty -2022-08-15 08:42:00,1905.06,,infty -2022-08-15 08:43:00,1904.29,,infty -2022-08-15 08:44:00,1904.19,,infty -2022-08-15 08:45:00,1902.09,,infty -2022-08-15 08:46:00,1903.2,,infty -2022-08-15 08:47:00,1903.33,,infty -2022-08-15 08:48:00,1902.21,,infty -2022-08-15 08:49:00,1903.51,,infty -2022-08-15 08:50:00,1905.82,,infty -2022-08-15 08:51:00,1904.42,,infty -2022-08-15 08:52:00,1905.22,,infty -2022-08-15 08:53:00,1905.61,,infty -2022-08-15 08:54:00,1907.11,,infty -2022-08-15 08:55:00,1905.68,,infty -2022-08-15 08:56:00,1905.44,,infty -2022-08-15 08:57:00,1905.38,,infty -2022-08-15 08:58:00,1903.64,,infty -2022-08-15 08:59:00,1905.36,,infty -2022-08-15 09:00:00,1906.44,,infty -2022-08-15 09:01:00,1906.66,,infty -2022-08-15 09:02:00,1902.99,,infty -2022-08-15 09:03:00,1903.41,,infty -2022-08-15 09:04:00,1903.48,,infty -2022-08-15 09:05:00,1904.39,,infty -2022-08-15 09:06:00,1905.33,,infty -2022-08-15 09:07:00,1903.08,,infty -2022-08-15 09:08:00,1900.06,,infty -2022-08-15 09:09:00,1900.5,,infty -2022-08-15 09:10:00,1902.72,,infty -2022-08-15 09:11:00,1902.48,,infty -2022-08-15 09:12:00,1902.41,,infty -2022-08-15 09:13:00,1900.94,,infty -2022-08-15 09:14:00,1900.57,,infty -2022-08-15 09:15:00,1898.76,,infty -2022-08-15 09:16:00,1897.31,,infty -2022-08-15 09:17:00,1898.12,,infty -2022-08-15 09:18:00,1896.58,,infty -2022-08-15 09:19:00,1897.87,,infty -2022-08-15 09:20:00,1898.53,,infty -2022-08-15 09:21:00,1898.18,,infty -2022-08-15 09:22:00,1899.1,,infty -2022-08-15 09:23:00,1898.7,,infty -2022-08-15 09:24:00,1898.18,,infty -2022-08-15 09:25:00,1898.41,,infty -2022-08-15 09:26:00,1896.71,,infty -2022-08-15 09:27:00,1896.99,,infty -2022-08-15 09:28:00,1895.23,,infty -2022-08-15 09:29:00,1892.66,,infty -2022-08-15 09:30:00,1893.73,,infty -2022-08-15 09:31:00,1893.85,,infty -2022-08-15 09:32:00,1894.02,,infty -2022-08-15 09:33:00,1893.06,,infty -2022-08-15 09:34:00,1889.7,,infty -2022-08-15 09:35:00,1893.13,,infty -2022-08-15 09:36:00,1896.22,,infty -2022-08-15 09:37:00,1894.88,,infty -2022-08-15 09:38:00,1893.87,,infty -2022-08-15 09:39:00,1890.62,,infty -2022-08-15 09:40:00,1892.18,,infty -2022-08-15 09:41:00,1893.68,,infty -2022-08-15 09:42:00,1893.16,,infty -2022-08-15 09:43:00,1894.08,,infty -2022-08-15 09:44:00,1896.79,,infty -2022-08-15 09:45:00,1896.32,,infty -2022-08-15 09:46:00,1899.17,,infty -2022-08-15 09:47:00,1898.26,,infty -2022-08-15 09:48:00,1903.67,,infty -2022-08-15 09:49:00,1902.38,,infty -2022-08-15 09:50:00,1901.99,,infty -2022-08-15 09:51:00,1903.28,,infty -2022-08-15 09:52:00,1906.82,,infty -2022-08-15 09:53:00,1907.15,,infty -2022-08-15 09:54:00,1907.57,,infty -2022-08-15 09:55:00,1902.99,,infty -2022-08-15 09:56:00,1903.9,,infty -2022-08-15 09:57:00,1905.64,,infty -2022-08-15 09:58:00,1908.45,,infty -2022-08-15 09:59:00,1910.47,,infty -2022-08-15 10:00:00,1909.91,,infty -2022-08-15 10:01:00,1908.64,,infty -2022-08-15 10:02:00,1905.99,,infty -2022-08-15 10:03:00,1906.13,,infty -2022-08-15 10:04:00,1906.06,,infty -2022-08-15 10:05:00,1906.78,,infty -2022-08-15 10:06:00,1906.06,,infty -2022-08-15 10:07:00,1903.66,,infty -2022-08-15 10:08:00,1903.07,,infty -2022-08-15 10:09:00,1903.69,,infty -2022-08-15 10:10:00,1903.92,,infty -2022-08-15 10:11:00,1903.44,,infty -2022-08-15 10:12:00,1903.49,,infty -2022-08-15 10:13:00,1905.05,,infty -2022-08-15 10:14:00,1905.9,,infty -2022-08-15 10:15:00,1904.5,,infty -2022-08-15 10:16:00,1905.9,,infty -2022-08-15 10:17:00,1905.15,,infty -2022-08-15 10:18:00,1906.4,,infty -2022-08-15 10:19:00,1906.05,,infty -2022-08-15 10:20:00,1904.21,,infty -2022-08-15 10:21:00,1904.14,,infty -2022-08-15 10:22:00,1899.28,,infty -2022-08-15 10:23:00,1903.26,,infty -2022-08-15 10:24:00,1903.34,,infty -2022-08-15 10:25:00,1904.02,,infty -2022-08-15 10:26:00,1904.92,,infty -2022-08-15 10:27:00,1905.92,,infty -2022-08-15 10:28:00,1905.25,,infty -2022-08-15 10:29:00,1905.28,,infty -2022-08-15 10:30:00,1906.8,,infty -2022-08-15 10:31:00,1905.8,,infty -2022-08-15 10:32:00,1902.0,,infty -2022-08-15 10:33:00,1901.13,,infty -2022-08-15 10:34:00,1902.48,,infty -2022-08-15 10:35:00,1902.73,,infty -2022-08-15 10:36:00,1901.55,,infty -2022-08-15 10:37:00,1902.73,,infty -2022-08-15 10:38:00,1902.87,,infty -2022-08-15 10:39:00,1902.64,,infty -2022-08-15 10:40:00,1901.06,,infty -2022-08-15 10:41:00,1899.89,,infty -2022-08-15 10:42:00,1899.77,,infty -2022-08-15 10:43:00,1898.63,,infty -2022-08-15 10:44:00,1896.97,,infty -2022-08-15 10:45:00,1898.62,,infty -2022-08-15 10:46:00,1900.59,,infty -2022-08-15 10:47:00,1903.31,,infty -2022-08-15 10:48:00,1904.33,,infty -2022-08-15 10:49:00,1902.86,,infty -2022-08-15 10:50:00,1902.92,,infty -2022-08-15 10:51:00,1902.62,,infty -2022-08-15 10:52:00,1904.07,,infty -2022-08-15 10:53:00,1905.1,,infty -2022-08-15 10:54:00,1906.66,,infty -2022-08-15 10:55:00,1908.53,,infty -2022-08-15 10:56:00,1906.66,,infty -2022-08-15 10:57:00,1906.65,,infty -2022-08-15 10:58:00,1905.81,,infty -2022-08-15 10:59:00,1907.31,,infty -2022-08-15 11:00:00,1907.06,,infty -2022-08-15 11:01:00,1909.81,,infty -2022-08-15 11:02:00,1910.13,,infty -2022-08-15 11:03:00,1911.28,,infty -2022-08-15 11:04:00,1909.73,,infty -2022-08-15 11:05:00,1909.96,,infty -2022-08-15 11:06:00,1907.22,,infty -2022-08-15 11:07:00,1904.41,,infty -2022-08-15 11:08:00,1903.87,,infty -2022-08-15 11:09:00,1906.35,,infty -2022-08-15 11:10:00,1905.96,,infty -2022-08-15 11:11:00,1906.42,,infty -2022-08-15 11:12:00,1906.06,,infty -2022-08-15 11:13:00,1904.21,,infty -2022-08-15 11:14:00,1903.91,,infty -2022-08-15 11:15:00,1905.34,,infty -2022-08-15 11:16:00,1905.12,,infty -2022-08-15 11:17:00,1906.41,,infty -2022-08-15 11:18:00,1906.66,,infty -2022-08-15 11:19:00,1905.17,,infty -2022-08-15 11:20:00,1903.14,,infty -2022-08-15 11:21:00,1903.07,,infty -2022-08-15 11:22:00,1901.67,,infty -2022-08-15 11:23:00,1901.81,,infty -2022-08-15 11:24:00,1900.41,,infty -2022-08-15 11:25:00,1902.45,,infty -2022-08-15 11:26:00,1902.38,,infty -2022-08-15 11:27:00,1903.16,,infty -2022-08-15 11:28:00,1903.0,,infty -2022-08-15 11:29:00,1901.72,,infty -2022-08-15 11:30:00,1900.65,,infty -2022-08-15 11:31:00,1902.16,,infty -2022-08-15 11:32:00,1901.3,,infty -2022-08-15 11:33:00,1896.31,,infty -2022-08-15 11:34:00,1898.35,,infty -2022-08-15 11:35:00,1899.14,,infty -2022-08-15 11:36:00,1899.16,,infty -2022-08-15 11:37:00,1896.24,,infty -2022-08-15 11:38:00,1894.93,,infty -2022-08-15 11:39:00,1896.0,,infty -2022-08-15 11:40:00,1897.35,,infty -2022-08-15 11:41:00,1897.56,,infty -2022-08-15 11:42:00,1898.22,,infty -2022-08-15 11:43:00,1898.44,,infty -2022-08-15 11:44:00,1899.03,,infty -2022-08-15 11:45:00,1898.96,,infty -2022-08-15 11:46:00,1895.49,,infty -2022-08-15 11:47:00,1896.21,,infty -2022-08-15 11:48:00,1895.96,,infty -2022-08-15 11:49:00,1895.24,,infty -2022-08-15 11:50:00,1893.31,,infty -2022-08-15 11:51:00,1889.71,,infty -2022-08-15 11:52:00,1890.73,,infty -2022-08-15 11:53:00,1888.63,,infty -2022-08-15 11:54:00,1888.23,,infty -2022-08-15 11:55:00,1890.69,,infty -2022-08-15 11:56:00,1893.2,,infty -2022-08-15 11:57:00,1893.43,,infty -2022-08-15 11:58:00,1893.19,,infty -2022-08-15 11:59:00,1891.8,,infty -2022-08-15 12:00:00,1892.11,,infty -2022-08-15 12:01:00,1891.95,,infty -2022-08-15 12:02:00,1891.73,,infty -2022-08-15 12:03:00,1889.54,,infty -2022-08-15 12:04:00,1888.46,,infty -2022-08-15 12:05:00,1892.0,,infty -2022-08-15 12:06:00,1893.83,,infty -2022-08-15 12:07:00,1895.44,,infty -2022-08-15 12:08:00,1896.48,,infty -2022-08-15 12:09:00,1896.26,,infty -2022-08-15 12:10:00,1894.18,,infty -2022-08-15 12:11:00,1894.49,,infty -2022-08-15 12:12:00,1889.13,,infty -2022-08-15 12:13:00,1892.37,,infty -2022-08-15 12:14:00,1893.17,,infty -2022-08-15 12:15:00,1893.64,,infty -2022-08-15 12:16:00,1895.04,,infty -2022-08-15 12:17:00,1895.79,,infty -2022-08-15 12:18:00,1896.65,,infty -2022-08-15 12:19:00,1897.16,,infty -2022-08-15 12:20:00,1896.87,,infty -2022-08-15 12:21:00,1896.8,,infty -2022-08-15 12:22:00,1894.63,,infty -2022-08-15 12:23:00,1896.18,,infty -2022-08-15 12:24:00,1896.09,,infty -2022-08-15 12:25:00,1897.11,,infty -2022-08-15 12:26:00,1898.21,,infty -2022-08-15 12:27:00,1897.97,,infty -2022-08-15 12:28:00,1899.83,,infty -2022-08-15 12:29:00,1902.43,,infty -2022-08-15 12:30:00,1903.89,,infty -2022-08-15 12:31:00,1900.04,,infty -2022-08-15 12:32:00,1898.92,,infty -2022-08-15 12:33:00,1893.85,,infty -2022-08-15 12:34:00,1896.61,,infty -2022-08-15 12:35:00,1893.79,,infty -2022-08-15 12:36:00,1896.66,,infty -2022-08-15 12:37:00,1895.64,,infty -2022-08-15 12:38:00,1895.9,,infty -2022-08-15 12:39:00,1895.74,,infty -2022-08-15 12:40:00,1897.47,,infty -2022-08-15 12:41:00,1894.99,,infty -2022-08-15 12:42:00,1893.69,,infty -2022-08-15 12:43:00,1895.03,,infty -2022-08-15 12:44:00,1895.8,,infty -2022-08-15 12:45:00,1894.69,,infty -2022-08-15 12:46:00,1894.3,,infty -2022-08-15 12:47:00,1893.17,,infty -2022-08-15 12:48:00,1894.75,,infty -2022-08-15 12:49:00,1894.41,,infty -2022-08-15 12:50:00,1891.45,,infty -2022-08-15 12:51:00,1890.48,,infty -2022-08-15 12:52:00,1885.31,,infty -2022-08-15 12:53:00,1879.87,,infty -2022-08-15 12:54:00,1880.76,,infty -2022-08-15 12:55:00,1874.28,,infty -2022-08-15 12:56:00,1878.27,,infty -2022-08-15 12:57:00,1881.8,,infty -2022-08-15 12:58:00,1883.05,,infty -2022-08-15 12:59:00,1877.72,,infty -2022-08-15 13:00:00,1874.83,,infty -2022-08-15 13:01:00,1883.17,,infty -2022-08-15 13:02:00,1884.53,,infty -2022-08-15 13:03:00,1884.26,,infty -2022-08-15 13:04:00,1888.3,,infty -2022-08-15 13:05:00,1888.12,,infty -2022-08-15 13:06:00,1891.49,,infty -2022-08-15 13:07:00,1894.75,,infty -2022-08-15 13:08:00,1896.72,,infty -2022-08-15 13:09:00,1895.09,,infty -2022-08-15 13:10:00,1900.2,,infty -2022-08-15 13:11:00,1898.1,,infty -2022-08-15 13:12:00,1899.0,,infty -2022-08-15 13:13:00,1899.39,,infty -2022-08-15 13:14:00,1899.21,,infty -2022-08-15 13:15:00,1903.64,,infty -2022-08-15 13:16:00,1902.0,,infty -2022-08-15 13:17:00,1901.08,,infty -2022-08-15 13:18:00,1901.2,,infty -2022-08-15 13:19:00,1900.92,,infty -2022-08-15 13:20:00,1900.97,,infty -2022-08-15 13:21:00,1900.35,,infty -2022-08-15 13:22:00,1898.61,,infty -2022-08-15 13:23:00,1902.25,,infty -2022-08-15 13:24:00,1900.21,,infty -2022-08-15 13:25:00,1900.69,,infty -2022-08-15 13:26:00,1899.09,,infty -2022-08-15 13:27:00,1898.44,,infty -2022-08-15 13:28:00,1898.45,,infty -2022-08-15 13:29:00,1900.26,,infty -2022-08-15 13:30:00,1904.19,,infty -2022-08-15 13:31:00,1901.42,,infty -2022-08-15 13:32:00,1905.88,,infty -2022-08-15 13:33:00,1905.0,,infty -2022-08-15 13:34:00,1907.75,,infty -2022-08-15 13:35:00,1905.12,,infty -2022-08-15 13:36:00,1903.75,,infty -2022-08-15 13:37:00,1902.33,,infty -2022-08-15 13:38:00,1900.27,,infty -2022-08-15 13:39:00,1901.5,,infty -2022-08-15 13:40:00,1896.54,,infty -2022-08-15 13:41:00,1893.85,,infty -2022-08-15 13:42:00,1886.74,,infty -2022-08-15 13:43:00,1892.56,,infty -2022-08-15 13:44:00,1891.84,,infty -2022-08-15 13:45:00,1896.38,,infty -2022-08-15 13:46:00,1899.17,,infty -2022-08-15 13:47:00,1896.79,,infty -2022-08-15 13:48:00,1894.99,,infty -2022-08-15 13:49:00,1891.27,,infty -2022-08-15 13:50:00,1896.68,,infty -2022-08-15 13:51:00,1896.89,,infty -2022-08-15 13:52:00,1897.31,,infty -2022-08-15 13:53:00,1902.21,,infty -2022-08-15 13:54:00,1905.4,,infty -2022-08-15 13:55:00,1907.65,,infty -2022-08-15 13:56:00,1915.69,,infty -2022-08-15 13:57:00,1918.18,,infty -2022-08-15 13:58:00,1917.27,,infty -2022-08-15 13:59:00,1916.99,,infty -2022-08-15 14:00:00,1914.38,,infty -2022-08-15 14:01:00,1914.59,,infty -2022-08-15 14:02:00,1910.42,,infty -2022-08-15 14:03:00,1912.51,,infty -2022-08-15 14:04:00,1912.04,,infty -2022-08-15 14:05:00,1912.41,,infty -2022-08-15 14:06:00,1912.45,,infty -2022-08-15 14:07:00,1910.52,,infty -2022-08-15 14:08:00,1913.8,,infty -2022-08-15 14:09:00,1914.69,,infty -2022-08-15 14:10:00,1913.16,,infty -2022-08-15 14:11:00,1910.74,,infty -2022-08-15 14:12:00,1907.94,,infty -2022-08-15 14:13:00,1905.92,,infty -2022-08-15 14:14:00,1903.52,,infty -2022-08-15 14:15:00,1904.57,,infty -2022-08-15 14:16:00,1904.08,,infty -2022-08-15 14:17:00,1906.99,,infty -2022-08-15 14:18:00,1907.98,,infty -2022-08-15 14:19:00,1907.96,,infty -2022-08-15 14:20:00,1906.56,,infty -2022-08-15 14:21:00,1903.65,,infty -2022-08-15 14:22:00,1902.26,,infty -2022-08-15 14:23:00,1904.54,,infty -2022-08-15 14:24:00,1902.2,,infty -2022-08-15 14:25:00,1905.63,,infty -2022-08-15 14:26:00,1904.83,,infty -2022-08-15 14:27:00,1903.58,,infty -2022-08-15 14:28:00,1905.52,,infty -2022-08-15 14:29:00,1906.48,,infty -2022-08-15 14:30:00,1908.84,,infty -2022-08-15 14:31:00,1910.79,,infty -2022-08-15 14:32:00,1910.51,,infty -2022-08-15 14:33:00,1912.52,,infty -2022-08-15 14:34:00,1910.48,,infty -2022-08-15 14:35:00,1909.52,,infty -2022-08-15 14:36:00,1906.87,,infty -2022-08-15 14:37:00,1905.86,,infty -2022-08-15 14:38:00,1904.81,,infty -2022-08-15 14:39:00,1903.97,,infty -2022-08-15 14:40:00,1906.1,,infty -2022-08-15 14:41:00,1905.84,,infty -2022-08-15 14:42:00,1908.52,,infty -2022-08-15 14:43:00,1907.45,,infty -2022-08-15 14:44:00,1906.93,,infty -2022-08-15 14:45:00,1905.19,,infty -2022-08-15 14:46:00,1906.45,,infty -2022-08-15 14:47:00,1907.73,,infty -2022-08-15 14:48:00,1904.69,,infty -2022-08-15 14:49:00,1902.86,,infty -2022-08-15 14:50:00,1903.5,,infty -2022-08-15 14:51:00,1902.76,,infty -2022-08-15 14:52:00,1905.88,,infty -2022-08-15 14:53:00,1904.82,,infty -2022-08-15 14:54:00,1904.77,,infty -2022-08-15 14:55:00,1903.53,,infty -2022-08-15 14:56:00,1899.58,,infty -2022-08-15 14:57:00,1900.9,,infty -2022-08-15 14:58:00,1903.48,,infty -2022-08-15 14:59:00,1903.22,,infty -2022-08-15 15:00:00,1902.53,,infty -2022-08-15 15:01:00,1906.43,,infty -2022-08-15 15:02:00,1906.31,,infty -2022-08-15 15:03:00,1901.74,,infty -2022-08-15 15:04:00,1901.79,,infty -2022-08-15 15:05:00,1903.12,,infty -2022-08-15 15:06:00,1903.6,,infty -2022-08-15 15:07:00,1901.64,,infty -2022-08-15 15:08:00,1901.95,,infty -2022-08-15 15:09:00,1899.76,,infty -2022-08-15 15:10:00,1897.23,,infty -2022-08-15 15:11:00,1897.03,,infty -2022-08-15 15:12:00,1894.58,,infty -2022-08-15 15:13:00,1897.07,,infty -2022-08-15 15:14:00,1898.14,,infty -2022-08-15 15:15:00,1897.77,,infty -2022-08-15 15:16:00,1899.65,,infty -2022-08-15 15:17:00,1896.94,,infty -2022-08-15 15:18:00,1900.38,,infty -2022-08-15 15:19:00,1899.21,,infty -2022-08-15 15:20:00,1901.06,,infty -2022-08-15 15:21:00,1903.32,,infty -2022-08-15 15:22:00,1903.84,,infty -2022-08-15 15:23:00,1904.52,,infty -2022-08-15 15:24:00,1902.92,,infty -2022-08-15 15:25:00,1903.92,,infty -2022-08-15 15:26:00,1906.05,,infty -2022-08-15 15:27:00,1904.17,,infty -2022-08-15 15:28:00,1902.2,,infty -2022-08-15 15:29:00,1904.73,,infty -2022-08-15 15:30:00,1904.32,,infty -2022-08-15 15:31:00,1906.51,,infty -2022-08-15 15:32:00,1907.88,,infty -2022-08-15 15:33:00,1907.86,,infty -2022-08-15 15:34:00,1907.01,,infty -2022-08-15 15:35:00,1908.16,,infty -2022-08-15 15:36:00,1909.4,,infty -2022-08-15 15:37:00,1909.93,,infty -2022-08-15 15:38:00,1910.62,,infty -2022-08-15 15:39:00,1910.3,,infty -2022-08-15 15:40:00,1908.54,,infty -2022-08-15 15:41:00,1907.66,,infty -2022-08-15 15:42:00,1907.37,,infty -2022-08-15 15:43:00,1909.77,,infty -2022-08-15 15:44:00,1910.49,,infty -2022-08-15 15:45:00,1912.16,,infty -2022-08-15 15:46:00,1908.49,,infty -2022-08-15 15:47:00,1912.47,,infty -2022-08-15 15:48:00,1911.11,,infty -2022-08-15 15:49:00,1913.02,,infty -2022-08-15 15:50:00,1912.8,,infty -2022-08-15 15:51:00,1912.26,,infty -2022-08-15 15:52:00,1912.45,,infty -2022-08-15 15:53:00,1913.69,,infty -2022-08-15 15:54:00,1916.77,,infty -2022-08-15 15:55:00,1919.29,,infty -2022-08-15 15:56:00,1918.74,,infty -2022-08-15 15:57:00,1918.67,,infty -2022-08-15 15:58:00,1920.21,,infty -2022-08-15 15:59:00,1918.65,,infty -2022-08-15 16:00:00,1919.35,,infty -2022-08-15 16:01:00,1920.33,,infty -2022-08-15 16:02:00,1915.36,,infty -2022-08-15 16:03:00,1918.69,,infty -2022-08-15 16:04:00,1921.3,,infty -2022-08-15 16:05:00,1918.93,,infty -2022-08-15 16:06:00,1917.44,,infty -2022-08-15 16:07:00,1917.18,,infty -2022-08-15 16:08:00,1916.09,,infty -2022-08-15 16:09:00,1916.7,,infty -2022-08-15 16:10:00,1916.69,,infty -2022-08-15 16:11:00,1917.97,,infty -2022-08-15 16:12:00,1916.93,,infty -2022-08-15 16:13:00,1915.56,,infty -2022-08-15 16:14:00,1911.41,,infty -2022-08-15 16:15:00,1914.29,,infty -2022-08-15 16:16:00,1914.44,,infty -2022-08-15 16:17:00,1916.47,,infty -2022-08-15 16:18:00,1912.78,,infty -2022-08-15 16:19:00,1912.4,,infty -2022-08-15 16:20:00,1913.31,,infty -2022-08-15 16:21:00,1912.92,,infty -2022-08-15 16:22:00,1912.49,,infty -2022-08-15 16:23:00,1911.67,,infty -2022-08-15 16:24:00,1912.46,,infty -2022-08-15 16:25:00,1913.67,,infty -2022-08-15 16:26:00,1912.83,,infty -2022-08-15 16:27:00,1914.32,,infty -2022-08-15 16:28:00,1915.55,,infty -2022-08-15 16:29:00,1916.03,,infty -2022-08-15 16:30:00,1915.45,,infty -2022-08-15 16:31:00,1916.8,,infty -2022-08-15 16:32:00,1916.81,,infty -2022-08-15 16:33:00,1917.14,,infty -2022-08-15 16:34:00,1917.49,,infty -2022-08-15 16:35:00,1919.35,,infty -2022-08-15 16:36:00,1916.42,,infty -2022-08-15 16:37:00,1913.33,,infty -2022-08-15 16:38:00,1913.98,,infty -2022-08-15 16:39:00,1912.43,,infty -2022-08-15 16:40:00,1915.57,,infty -2022-08-15 16:41:00,1915.4,,infty -2022-08-15 16:42:00,1914.17,,infty -2022-08-15 16:43:00,1916.27,,infty -2022-08-15 16:44:00,1915.75,,infty -2022-08-15 16:45:00,1914.05,,infty -2022-08-15 16:46:00,1914.47,,infty -2022-08-15 16:47:00,1916.25,,infty -2022-08-15 16:48:00,1916.61,,infty -2022-08-15 16:49:00,1915.38,,infty -2022-08-15 16:50:00,1917.8,,infty -2022-08-15 16:51:00,1917.79,,infty -2022-08-15 16:52:00,1918.23,,infty -2022-08-15 16:53:00,1917.8,,infty -2022-08-15 16:54:00,1918.75,,infty -2022-08-15 16:55:00,1923.88,,infty -2022-08-15 16:56:00,1926.16,,infty -2022-08-15 16:57:00,1928.39,,infty -2022-08-15 16:58:00,1929.9,,infty -2022-08-15 16:59:00,1929.52,,infty -2022-08-15 17:00:00,1929.7,,infty -2022-08-15 17:01:00,1931.02,,infty -2022-08-15 17:02:00,1927.6,,infty -2022-08-15 17:03:00,1924.87,,infty -2022-08-15 17:04:00,1924.51,,infty -2022-08-15 17:05:00,1927.35,,infty -2022-08-15 17:06:00,1928.41,,infty -2022-08-15 17:07:00,1928.97,,infty -2022-08-15 17:08:00,1927.57,,infty -2022-08-15 17:09:00,1928.02,,infty -2022-08-15 17:10:00,1927.46,,infty -2022-08-15 17:11:00,1926.2,,infty -2022-08-15 17:12:00,1927.73,,infty -2022-08-15 17:13:00,1926.73,,infty -2022-08-15 17:14:00,1924.98,,infty -2022-08-15 17:15:00,1925.96,,infty -2022-08-15 17:16:00,1926.65,,infty -2022-08-15 17:17:00,1926.88,,infty -2022-08-15 17:18:00,1923.53,,infty -2022-08-15 17:19:00,1917.75,,infty -2022-08-15 17:20:00,1916.37,,infty -2022-08-15 17:21:00,1909.86,,infty -2022-08-15 17:22:00,1913.08,,infty -2022-08-15 17:23:00,1914.36,,infty -2022-08-15 17:24:00,1913.84,,infty -2022-08-15 17:25:00,1915.6,,infty -2022-08-15 17:26:00,1915.49,,infty -2022-08-15 17:27:00,1914.12,,infty -2022-08-15 17:28:00,1914.77,,infty -2022-08-15 17:29:00,1913.93,,infty -2022-08-15 17:30:00,1910.88,,infty -2022-08-15 17:31:00,1907.68,,infty -2022-08-15 17:32:00,1905.44,,infty -2022-08-15 17:33:00,1905.28,,infty -2022-08-15 17:34:00,1904.63,,infty -2022-08-15 17:35:00,1906.85,,infty -2022-08-15 17:36:00,1907.67,,infty -2022-08-15 17:37:00,1905.86,,infty -2022-08-15 17:38:00,1907.3,,infty -2022-08-15 17:39:00,1906.49,,infty -2022-08-15 17:40:00,1909.0,,infty -2022-08-15 17:41:00,1908.19,,infty -2022-08-15 17:42:00,1905.19,,infty -2022-08-15 17:43:00,1904.39,,infty -2022-08-15 17:44:00,1904.99,,infty -2022-08-15 17:45:00,1905.7,,infty -2022-08-15 17:46:00,1904.93,,infty -2022-08-15 17:47:00,1906.16,,infty -2022-08-15 17:48:00,1907.19,,infty -2022-08-15 17:49:00,1906.17,,infty -2022-08-15 17:50:00,1904.97,,infty -2022-08-15 17:51:00,1905.5,,infty -2022-08-15 17:52:00,1905.76,,infty -2022-08-15 17:53:00,1903.19,,infty -2022-08-15 17:54:00,1902.22,,infty -2022-08-15 17:55:00,1899.05,,infty -2022-08-15 17:56:00,1902.72,,infty -2022-08-15 17:57:00,1902.45,,infty -2022-08-15 17:58:00,1903.94,,infty -2022-08-15 17:59:00,1903.44,,infty -2022-08-15 18:00:00,1901.86,,infty -2022-08-15 18:01:00,1901.78,,infty -2022-08-15 18:02:00,1901.12,,infty -2022-08-15 18:03:00,1899.31,,infty -2022-08-15 18:04:00,1900.7,,infty -2022-08-15 18:05:00,1898.82,,infty -2022-08-15 18:06:00,1896.72,,infty -2022-08-15 18:07:00,1895.83,,infty -2022-08-15 18:08:00,1895.4,,infty -2022-08-15 18:09:00,1892.65,,infty -2022-08-15 18:10:00,1894.28,,infty -2022-08-15 18:11:00,1894.07,,infty -2022-08-15 18:12:00,1895.92,,infty -2022-08-15 18:13:00,1895.54,,infty -2022-08-15 18:14:00,1896.34,,infty 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18:42:00,1897.26,,infty -2022-08-15 18:43:00,1895.92,,infty -2022-08-15 18:44:00,1898.15,,infty -2022-08-15 18:45:00,1896.91,,infty -2022-08-15 18:46:00,1899.89,,infty -2022-08-15 18:47:00,1899.2,,infty -2022-08-15 18:48:00,1898.55,,infty -2022-08-15 18:49:00,1898.51,,infty -2022-08-15 18:50:00,1898.23,,infty -2022-08-15 18:51:00,1897.88,,infty -2022-08-15 18:52:00,1896.86,,infty -2022-08-15 18:53:00,1896.45,,infty -2022-08-15 18:54:00,1897.53,,infty -2022-08-15 18:55:00,1897.05,,infty -2022-08-15 18:56:00,1897.75,,infty -2022-08-15 18:57:00,1896.65,,infty -2022-08-15 18:58:00,1897.57,,infty -2022-08-15 18:59:00,1896.8,,infty -2022-08-15 19:00:00,1894.47,,infty -2022-08-15 19:01:00,1897.86,,infty -2022-08-15 19:02:00,1899.66,,infty -2022-08-15 19:03:00,1898.91,,infty -2022-08-15 19:04:00,1898.4,,infty -2022-08-15 19:05:00,1897.33,,infty -2022-08-15 19:06:00,1898.02,,infty -2022-08-15 19:07:00,1896.1,,infty -2022-08-15 19:08:00,1893.57,,infty -2022-08-15 19:09:00,1889.25,,infty 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19:37:00,1897.06,,infty -2022-08-15 19:38:00,1897.94,,infty -2022-08-15 19:39:00,1897.8,,infty -2022-08-15 19:40:00,1891.89,,infty -2022-08-15 19:41:00,1888.99,,infty -2022-08-15 19:42:00,1890.92,,infty -2022-08-15 19:43:00,1892.23,,infty -2022-08-15 19:44:00,1891.12,,infty -2022-08-15 19:45:00,1892.25,,infty -2022-08-15 19:46:00,1890.94,,infty -2022-08-15 19:47:00,1893.11,,infty -2022-08-15 19:48:00,1894.18,,infty -2022-08-15 19:49:00,1895.19,,infty -2022-08-15 19:50:00,1896.07,,infty -2022-08-15 19:51:00,1895.25,,infty -2022-08-15 19:52:00,1893.6,,infty -2022-08-15 19:53:00,1892.97,,infty -2022-08-15 19:54:00,1889.81,,infty -2022-08-15 19:55:00,1890.21,,infty -2022-08-15 19:56:00,1891.81,,infty -2022-08-15 19:57:00,1891.23,,infty -2022-08-15 19:58:00,1890.8,,infty -2022-08-15 19:59:00,1889.47,,infty -2022-08-15 20:00:00,1888.42,,infty -2022-08-15 20:01:00,1892.75,,infty -2022-08-15 20:02:00,1892.83,,infty -2022-08-15 20:03:00,1894.03,,infty -2022-08-15 20:04:00,1894.39,,infty 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20:32:00,1904.02,,infty -2022-08-15 20:33:00,1900.69,,infty -2022-08-15 20:34:00,1899.56,,infty -2022-08-15 20:35:00,1900.78,,infty -2022-08-15 20:36:00,1901.39,,infty -2022-08-15 20:37:00,1899.36,,infty -2022-08-15 20:38:00,1899.81,,infty -2022-08-15 20:39:00,1900.43,,infty -2022-08-15 20:40:00,1899.35,,infty -2022-08-15 20:41:00,1900.64,,infty -2022-08-15 20:42:00,1900.39,,infty -2022-08-15 20:43:00,1899.98,,infty -2022-08-15 20:44:00,1901.52,,infty -2022-08-15 20:45:00,1899.54,,infty -2022-08-15 20:46:00,1896.74,,infty -2022-08-15 20:47:00,1894.93,,infty -2022-08-15 20:48:00,1894.32,,infty -2022-08-15 20:49:00,1896.65,,infty -2022-08-15 20:50:00,1895.95,,infty -2022-08-15 20:51:00,1896.33,,infty -2022-08-15 20:52:00,1898.63,,infty -2022-08-15 20:53:00,1898.98,,infty -2022-08-15 20:54:00,1900.58,,infty -2022-08-15 20:55:00,1899.09,,infty -2022-08-15 20:56:00,1901.48,,infty -2022-08-15 20:57:00,1901.16,,infty -2022-08-15 20:58:00,1902.8,,infty -2022-08-15 20:59:00,1903.66,,infty 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21:27:00,1909.93,,infty -2022-08-15 21:28:00,1909.33,,infty -2022-08-15 21:29:00,1912.78,,infty -2022-08-15 21:30:00,1910.85,,infty -2022-08-15 21:31:00,1908.79,,infty -2022-08-15 21:32:00,1908.17,,infty -2022-08-15 21:33:00,1908.66,,infty -2022-08-15 21:34:00,1906.01,,infty -2022-08-15 21:35:00,1905.59,,infty -2022-08-15 21:36:00,1908.74,,infty -2022-08-15 21:37:00,1909.34,,infty -2022-08-15 21:38:00,1909.34,,infty -2022-08-15 21:39:00,1906.06,,infty -2022-08-15 21:40:00,1907.93,,infty -2022-08-15 21:41:00,1908.8,,infty -2022-08-15 21:42:00,1909.71,,infty -2022-08-15 21:43:00,1908.84,,infty -2022-08-15 21:44:00,1908.89,,infty -2022-08-15 21:45:00,1909.14,,infty -2022-08-15 21:46:00,1907.1,,infty -2022-08-15 21:47:00,1903.25,,infty -2022-08-15 21:48:00,1904.9,,infty -2022-08-15 21:49:00,1905.93,,infty -2022-08-15 21:50:00,1905.4,,infty -2022-08-15 21:51:00,1905.16,,infty -2022-08-15 21:52:00,1905.96,,infty -2022-08-15 21:53:00,1906.83,,infty -2022-08-15 21:54:00,1903.95,,infty -2022-08-15 21:55:00,1900.01,,infty -2022-08-15 21:56:00,1901.13,,infty -2022-08-15 21:57:00,1902.92,,infty -2022-08-15 21:58:00,1903.56,,infty -2022-08-15 21:59:00,1903.49,,infty -2022-08-15 22:00:00,1904.91,,infty -2022-08-15 22:01:00,1900.98,,infty -2022-08-15 22:02:00,1901.52,,infty -2022-08-15 22:03:00,1900.6,,infty -2022-08-15 22:04:00,1901.29,,infty -2022-08-15 22:05:00,1900.8,,infty -2022-08-15 22:06:00,1902.06,,infty -2022-08-15 22:07:00,1903.1,,infty -2022-08-15 22:08:00,1902.55,,infty -2022-08-15 22:09:00,1902.47,,infty -2022-08-15 22:10:00,1902.35,,infty -2022-08-15 22:11:00,1904.15,,infty -2022-08-15 22:12:00,1904.9,,infty -2022-08-15 22:13:00,1912.74,,infty -2022-08-15 22:14:00,1915.7,,infty -2022-08-15 22:15:00,1915.16,,infty -2022-08-15 22:16:00,1913.77,,infty -2022-08-15 22:17:00,1913.02,,infty -2022-08-15 22:18:00,1915.14,,infty -2022-08-15 22:19:00,1917.61,,infty -2022-08-15 22:20:00,1913.69,,infty -2022-08-15 22:21:00,1911.9,,infty -2022-08-15 22:22:00,1912.65,,infty -2022-08-15 22:23:00,1913.56,,infty -2022-08-15 22:24:00,1912.5,,infty -2022-08-15 22:25:00,1911.0,,infty -2022-08-15 22:26:00,1910.46,,infty -2022-08-15 22:27:00,1910.23,,infty -2022-08-15 22:28:00,1909.77,,infty -2022-08-15 22:29:00,1910.25,,infty -2022-08-15 22:30:00,1909.2,,infty -2022-08-15 22:31:00,1913.23,,infty -2022-08-15 22:32:00,1911.82,,infty -2022-08-15 22:33:00,1912.01,,infty -2022-08-15 22:34:00,1913.0,,infty -2022-08-15 22:35:00,1910.25,,infty -2022-08-15 22:36:00,1911.25,,infty -2022-08-15 22:37:00,1911.39,,infty -2022-08-15 22:38:00,1910.92,,infty -2022-08-15 22:39:00,1910.81,,infty -2022-08-15 22:40:00,1910.63,,infty -2022-08-15 22:41:00,1908.47,,infty -2022-08-15 22:42:00,1909.18,,infty -2022-08-15 22:43:00,1908.9,,infty -2022-08-15 22:44:00,1905.43,,infty -2022-08-15 22:45:00,1904.36,,infty -2022-08-15 22:46:00,1904.15,,infty -2022-08-15 22:47:00,1904.47,,infty -2022-08-15 22:48:00,1902.13,,infty -2022-08-15 22:49:00,1903.25,,infty -2022-08-15 22:50:00,1902.65,,infty -2022-08-15 22:51:00,1902.6,,infty -2022-08-15 22:52:00,1902.06,,infty -2022-08-15 22:53:00,1902.44,,infty -2022-08-15 22:54:00,1903.08,,infty -2022-08-15 22:55:00,1902.04,,infty -2022-08-15 22:56:00,1902.0,,infty -2022-08-15 22:57:00,1901.8,,infty -2022-08-15 22:58:00,1898.83,,infty -2022-08-15 22:59:00,1895.67,,infty -2022-08-15 23:00:00,1891.93,,infty -2022-08-15 23:01:00,1891.07,,infty -2022-08-15 23:02:00,1888.21,,infty -2022-08-15 23:03:00,1887.85,,infty -2022-08-15 23:04:00,1888.25,,infty -2022-08-15 23:05:00,1883.23,,infty -2022-08-15 23:06:00,1883.89,,infty -2022-08-15 23:07:00,1883.0,,infty -2022-08-15 23:08:00,1884.25,,infty -2022-08-15 23:09:00,1885.19,,infty -2022-08-15 23:10:00,1880.18,,infty -2022-08-15 23:11:00,1880.94,,infty -2022-08-15 23:12:00,1880.7,,infty -2022-08-15 23:13:00,1880.53,,infty -2022-08-15 23:14:00,1879.69,,infty -2022-08-15 23:15:00,1883.07,,infty -2022-08-15 23:16:00,1881.85,,infty -2022-08-15 23:17:00,1880.35,,infty -2022-08-15 23:18:00,1882.94,,infty -2022-08-15 23:19:00,1885.38,,infty -2022-08-15 23:20:00,1886.49,,infty -2022-08-15 23:21:00,1886.0,,infty -2022-08-15 23:22:00,1887.64,,infty -2022-08-15 23:23:00,1893.81,,infty -2022-08-15 23:24:00,1896.77,,infty -2022-08-15 23:25:00,1897.85,,infty -2022-08-15 23:26:00,1903.55,,infty -2022-08-15 23:27:00,1902.43,,infty -2022-08-15 23:28:00,1899.89,,infty -2022-08-15 23:29:00,1898.27,,infty -2022-08-15 23:30:00,1899.27,,infty -2022-08-15 23:31:00,1902.73,,infty -2022-08-15 23:32:00,1903.7,,infty -2022-08-15 23:33:00,1904.72,,infty -2022-08-15 23:34:00,1904.25,,infty -2022-08-15 23:35:00,1905.19,,infty -2022-08-15 23:36:00,1908.74,,infty -2022-08-15 23:37:00,1907.69,,infty -2022-08-15 23:38:00,1909.25,,infty -2022-08-15 23:39:00,1908.34,,infty -2022-08-15 23:40:00,1908.65,,infty -2022-08-15 23:41:00,1908.94,,infty -2022-08-15 23:42:00,1907.19,,infty -2022-08-15 23:43:00,1908.79,,infty -2022-08-15 23:44:00,1908.85,,infty 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00:40:00,1889.81,,infty -2022-08-16 00:41:00,1891.71,,infty -2022-08-16 00:42:00,1895.34,,infty -2022-08-16 00:43:00,1893.88,,infty -2022-08-16 00:44:00,1895.26,,infty -2022-08-16 00:45:00,1893.49,,infty -2022-08-16 00:46:00,1896.95,,infty -2022-08-16 00:47:00,1897.28,,infty -2022-08-16 00:48:00,1896.72,,infty -2022-08-16 00:49:00,1896.76,,infty -2022-08-16 00:50:00,1897.97,,infty -2022-08-16 00:51:00,1898.39,,infty -2022-08-16 00:52:00,1898.14,,infty -2022-08-16 00:53:00,1899.55,,infty -2022-08-16 00:54:00,1900.16,,infty -2022-08-16 00:55:00,1897.71,,infty -2022-08-16 00:56:00,1896.48,,infty -2022-08-16 00:57:00,1896.67,,infty -2022-08-16 00:58:00,1897.98,,infty -2022-08-16 00:59:00,1896.94,,infty -2022-08-16 01:00:00,1899.18,,infty -2022-08-16 01:01:00,1899.71,,infty -2022-08-16 01:02:00,1899.96,,infty -2022-08-16 01:03:00,1905.56,,infty -2022-08-16 01:04:00,1903.83,,infty -2022-08-16 01:05:00,1905.55,,infty -2022-08-16 01:06:00,1906.76,,infty -2022-08-16 01:07:00,1907.43,,infty -2022-08-16 01:08:00,1906.15,,infty -2022-08-16 01:09:00,1906.44,,infty -2022-08-16 01:10:00,1907.67,,infty -2022-08-16 01:11:00,1907.97,,infty -2022-08-16 01:12:00,1909.37,,infty -2022-08-16 01:13:00,1908.95,,infty -2022-08-16 01:14:00,1909.6,,infty -2022-08-16 01:15:00,1908.21,,infty -2022-08-16 01:16:00,1908.39,,infty -2022-08-16 01:17:00,1907.93,,infty -2022-08-16 01:18:00,1907.3,,infty -2022-08-16 01:19:00,1907.62,,infty -2022-08-16 01:20:00,1906.72,,infty -2022-08-16 01:21:00,1908.6,,infty -2022-08-16 01:22:00,1908.78,,infty -2022-08-16 01:23:00,1908.92,,infty -2022-08-16 01:24:00,1909.54,,infty -2022-08-16 01:25:00,1908.03,,infty -2022-08-16 01:26:00,1909.41,,infty -2022-08-16 01:27:00,1908.04,,infty -2022-08-16 01:28:00,1907.12,,infty -2022-08-16 01:29:00,1906.33,,infty -2022-08-16 01:30:00,1908.23,,infty -2022-08-16 01:31:00,1911.66,,infty -2022-08-16 01:32:00,1914.48,,infty -2022-08-16 01:33:00,1912.8,,infty -2022-08-16 01:34:00,1912.72,,infty -2022-08-16 01:35:00,1911.67,,infty -2022-08-16 01:36:00,1909.51,,infty -2022-08-16 01:37:00,1909.79,,infty -2022-08-16 01:38:00,1907.67,,infty -2022-08-16 01:39:00,1909.71,,infty -2022-08-16 01:40:00,1909.64,,infty -2022-08-16 01:41:00,1911.54,,infty -2022-08-16 01:42:00,1912.57,,infty -2022-08-16 01:43:00,1910.91,,infty -2022-08-16 01:44:00,1911.35,,infty -2022-08-16 01:45:00,1910.46,,infty -2022-08-16 01:46:00,1911.35,,infty -2022-08-16 01:47:00,1908.41,,infty -2022-08-16 01:48:00,1907.72,,infty -2022-08-16 01:49:00,1905.34,,infty -2022-08-16 01:50:00,1905.77,,infty -2022-08-16 01:51:00,1905.57,,infty -2022-08-16 01:52:00,1905.18,,infty -2022-08-16 01:53:00,1897.59,,infty -2022-08-16 01:54:00,1897.61,,infty -2022-08-16 01:55:00,1900.21,,infty -2022-08-16 01:56:00,1902.2,,infty -2022-08-16 01:57:00,1901.31,,infty -2022-08-16 01:58:00,1900.7,,infty -2022-08-16 01:59:00,1899.26,,infty -2022-08-16 02:00:00,1897.62,,infty -2022-08-16 02:01:00,1898.96,,infty -2022-08-16 02:02:00,1900.54,,infty 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02:30:00,1903.39,,infty -2022-08-16 02:31:00,1904.79,,infty -2022-08-16 02:32:00,1904.78,,infty -2022-08-16 02:33:00,1902.96,,infty -2022-08-16 02:34:00,1902.43,,infty -2022-08-16 02:35:00,1903.5,,infty -2022-08-16 02:36:00,1903.64,,infty -2022-08-16 02:37:00,1903.27,,infty -2022-08-16 02:38:00,1900.77,,infty -2022-08-16 02:39:00,1894.7,,infty -2022-08-16 02:40:00,1893.6,,infty -2022-08-16 02:41:00,1893.41,,infty -2022-08-16 02:42:00,1892.05,,infty -2022-08-16 02:43:00,1892.62,,infty -2022-08-16 02:44:00,1890.08,,infty -2022-08-16 02:45:00,1892.26,,infty -2022-08-16 02:46:00,1890.57,,infty -2022-08-16 02:47:00,1890.09,,infty -2022-08-16 02:48:00,1890.78,,infty -2022-08-16 02:49:00,1892.0,,infty -2022-08-16 02:50:00,1890.0,,infty -2022-08-16 02:51:00,1890.73,,infty -2022-08-16 02:52:00,1891.12,,infty -2022-08-16 02:53:00,1890.88,,infty -2022-08-16 02:54:00,1892.53,,infty -2022-08-16 02:55:00,1893.18,,infty -2022-08-16 02:56:00,1891.71,,infty -2022-08-16 02:57:00,1891.24,,infty 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03:25:00,1879.99,,infty -2022-08-16 03:26:00,1879.26,,infty -2022-08-16 03:27:00,1884.52,,infty -2022-08-16 03:28:00,1887.99,,infty -2022-08-16 03:29:00,1887.97,,infty -2022-08-16 03:30:00,1884.95,,infty -2022-08-16 03:31:00,1887.84,,infty -2022-08-16 03:32:00,1888.71,,infty -2022-08-16 03:33:00,1886.65,,infty -2022-08-16 03:34:00,1887.43,,infty -2022-08-16 03:35:00,1885.26,,infty -2022-08-16 03:36:00,1886.3,,infty -2022-08-16 03:37:00,1886.81,,infty -2022-08-16 03:38:00,1887.59,,infty -2022-08-16 03:39:00,1889.21,,infty -2022-08-16 03:40:00,1890.52,,infty -2022-08-16 03:41:00,1889.75,,infty -2022-08-16 03:42:00,1887.53,,infty -2022-08-16 03:43:00,1888.36,,infty -2022-08-16 03:44:00,1887.01,,infty -2022-08-16 03:45:00,1885.86,,infty -2022-08-16 03:46:00,1888.03,,infty -2022-08-16 03:47:00,1888.56,,infty -2022-08-16 03:48:00,1888.73,,infty -2022-08-16 03:49:00,1889.33,,infty -2022-08-16 03:50:00,1888.22,,infty -2022-08-16 03:51:00,1887.89,,infty -2022-08-16 03:52:00,1886.61,,infty 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04:20:00,1879.74,,infty -2022-08-16 04:21:00,1878.9,,infty -2022-08-16 04:22:00,1877.96,,infty -2022-08-16 04:23:00,1879.0,,infty -2022-08-16 04:24:00,1880.43,,infty -2022-08-16 04:25:00,1880.5,,infty -2022-08-16 04:26:00,1881.47,,infty -2022-08-16 04:27:00,1881.4,,infty -2022-08-16 04:28:00,1877.62,,infty -2022-08-16 04:29:00,1876.21,,infty -2022-08-16 04:30:00,1875.07,,infty -2022-08-16 04:31:00,1873.75,,infty -2022-08-16 04:32:00,1871.79,,infty -2022-08-16 04:33:00,1872.3,,infty -2022-08-16 04:34:00,1871.4,,infty -2022-08-16 04:35:00,1872.67,,infty -2022-08-16 04:36:00,1874.34,,infty -2022-08-16 04:37:00,1874.72,,infty -2022-08-16 04:38:00,1876.1,,infty -2022-08-16 04:39:00,1875.93,,infty -2022-08-16 04:40:00,1875.12,,infty -2022-08-16 04:41:00,1873.56,,infty -2022-08-16 04:42:00,1872.97,,infty -2022-08-16 04:43:00,1871.58,,infty -2022-08-16 04:44:00,1871.81,,infty -2022-08-16 04:45:00,1873.35,,infty -2022-08-16 04:46:00,1872.26,,infty -2022-08-16 04:47:00,1876.15,,infty -2022-08-16 04:48:00,1875.28,,infty -2022-08-16 04:49:00,1876.21,,infty -2022-08-16 04:50:00,1876.42,,infty -2022-08-16 04:51:00,1875.95,,infty -2022-08-16 04:52:00,1875.26,,infty -2022-08-16 04:53:00,1873.84,,infty -2022-08-16 04:54:00,1873.73,,infty -2022-08-16 04:55:00,1875.42,,infty -2022-08-16 04:56:00,1875.6,,infty -2022-08-16 04:57:00,1873.22,,infty -2022-08-16 04:58:00,1872.5,,infty -2022-08-16 04:59:00,1872.34,,infty -2022-08-16 05:00:00,1870.61,,infty -2022-08-16 05:01:00,1870.92,,infty -2022-08-16 05:02:00,1870.0,,infty -2022-08-16 05:03:00,1866.93,,infty -2022-08-16 05:04:00,1867.6,,infty -2022-08-16 05:05:00,1866.32,,infty -2022-08-16 05:06:00,1869.27,,infty -2022-08-16 05:07:00,1870.71,,infty -2022-08-16 05:08:00,1869.74,,infty -2022-08-16 05:09:00,1870.59,,infty -2022-08-16 05:10:00,1868.91,,infty -2022-08-16 05:11:00,1866.74,,infty -2022-08-16 05:12:00,1866.37,,infty -2022-08-16 05:13:00,1867.0,,infty -2022-08-16 05:14:00,1865.35,,infty -2022-08-16 05:15:00,1863.89,,infty 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05:43:00,1865.86,,infty -2022-08-16 05:44:00,1864.65,,infty -2022-08-16 05:45:00,1865.07,,infty -2022-08-16 05:46:00,1861.22,,infty -2022-08-16 05:47:00,1864.18,,infty -2022-08-16 05:48:00,1864.94,,infty -2022-08-16 05:49:00,1866.17,,infty -2022-08-16 05:50:00,1865.56,,infty -2022-08-16 05:51:00,1866.7,,infty -2022-08-16 05:52:00,1866.58,,infty -2022-08-16 05:53:00,1869.37,,infty -2022-08-16 05:54:00,1875.38,,infty -2022-08-16 05:55:00,1873.16,,infty -2022-08-16 05:56:00,1874.89,,infty -2022-08-16 05:57:00,1871.05,,infty -2022-08-16 05:58:00,1871.33,,infty -2022-08-16 05:59:00,1871.55,,infty -2022-08-16 06:00:00,1872.66,,infty -2022-08-16 06:01:00,1873.84,,infty -2022-08-16 06:02:00,1876.78,,infty -2022-08-16 06:03:00,1877.64,,infty -2022-08-16 06:04:00,1877.9,,infty -2022-08-16 06:05:00,1876.31,,infty -2022-08-16 06:06:00,1873.72,,infty -2022-08-16 06:07:00,1873.01,,infty -2022-08-16 06:08:00,1870.47,,infty -2022-08-16 06:09:00,1870.29,,infty -2022-08-16 06:10:00,1870.93,,infty 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06:38:00,1880.61,,infty -2022-08-16 06:39:00,1880.15,,infty -2022-08-16 06:40:00,1880.13,,infty -2022-08-16 06:41:00,1879.1,,infty -2022-08-16 06:42:00,1881.06,,infty -2022-08-16 06:43:00,1884.61,,infty -2022-08-16 06:44:00,1883.99,,infty -2022-08-16 06:45:00,1882.67,,infty -2022-08-16 06:46:00,1881.43,,infty -2022-08-16 06:47:00,1884.79,,infty -2022-08-16 06:48:00,1883.6,,infty -2022-08-16 06:49:00,1885.39,,infty -2022-08-16 06:50:00,1886.2,,infty -2022-08-16 06:51:00,1885.38,,infty -2022-08-16 06:52:00,1885.36,,infty -2022-08-16 06:53:00,1884.32,,infty -2022-08-16 06:54:00,1881.7,,infty -2022-08-16 06:55:00,1883.37,,infty -2022-08-16 06:56:00,1884.31,,infty -2022-08-16 06:57:00,1883.72,,infty -2022-08-16 06:58:00,1882.75,,infty -2022-08-16 06:59:00,1881.0,,infty -2022-08-16 07:00:00,1880.66,,infty -2022-08-16 07:01:00,1881.22,,infty -2022-08-16 07:02:00,1882.71,,infty -2022-08-16 07:03:00,1881.76,,infty -2022-08-16 07:04:00,1881.86,,infty -2022-08-16 07:05:00,1879.64,,infty 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07:33:00,1881.0,,infty -2022-08-16 07:34:00,1882.15,,infty -2022-08-16 07:35:00,1881.41,,infty -2022-08-16 07:36:00,1882.74,,infty -2022-08-16 07:37:00,1883.91,,infty -2022-08-16 07:38:00,1883.35,,infty -2022-08-16 07:39:00,1883.98,,infty -2022-08-16 07:40:00,1883.52,,infty -2022-08-16 07:41:00,1882.06,,infty -2022-08-16 07:42:00,1880.23,,infty -2022-08-16 07:43:00,1880.43,,infty -2022-08-16 07:44:00,1876.75,,infty -2022-08-16 07:45:00,1878.9,,infty -2022-08-16 07:46:00,1881.07,,infty -2022-08-16 07:47:00,1880.5,,infty -2022-08-16 07:48:00,1880.83,,infty -2022-08-16 07:49:00,1880.96,,infty -2022-08-16 07:50:00,1880.39,,infty -2022-08-16 07:51:00,1880.09,,infty -2022-08-16 07:52:00,1880.66,,infty -2022-08-16 07:53:00,1880.36,,infty -2022-08-16 07:54:00,1880.49,,infty -2022-08-16 07:55:00,1881.66,,infty -2022-08-16 07:56:00,1882.78,,infty -2022-08-16 07:57:00,1883.81,,infty -2022-08-16 07:58:00,1884.37,,infty -2022-08-16 07:59:00,1886.94,,infty -2022-08-16 08:00:00,1887.03,,infty 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08:28:00,1882.03,,infty -2022-08-16 08:29:00,1882.44,,infty -2022-08-16 08:30:00,1881.29,,infty -2022-08-16 08:31:00,1882.24,,infty -2022-08-16 08:32:00,1880.52,,infty -2022-08-16 08:33:00,1879.58,,infty -2022-08-16 08:34:00,1880.66,,infty -2022-08-16 08:35:00,1881.07,,infty -2022-08-16 08:36:00,1882.3,,infty -2022-08-16 08:37:00,1880.94,,infty -2022-08-16 08:38:00,1880.89,,infty -2022-08-16 08:39:00,1881.41,,infty -2022-08-16 08:40:00,1882.93,,infty -2022-08-16 08:41:00,1883.01,,infty -2022-08-16 08:42:00,1882.74,,infty -2022-08-16 08:43:00,1879.68,,infty -2022-08-16 08:44:00,1878.99,,infty -2022-08-16 08:45:00,1879.57,,infty -2022-08-16 08:46:00,1879.02,,infty -2022-08-16 08:47:00,1879.15,,infty -2022-08-16 08:48:00,1880.03,,infty -2022-08-16 08:49:00,1881.0,,infty -2022-08-16 08:50:00,1881.15,,infty -2022-08-16 08:51:00,1881.71,,infty -2022-08-16 08:52:00,1882.5,,infty -2022-08-16 08:53:00,1880.79,,infty -2022-08-16 08:54:00,1879.45,,infty -2022-08-16 08:55:00,1878.74,,infty 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09:23:00,1893.5,,infty -2022-08-16 09:24:00,1893.26,,infty -2022-08-16 09:25:00,1893.99,,infty -2022-08-16 09:26:00,1892.53,,infty -2022-08-16 09:27:00,1891.8,,infty -2022-08-16 09:28:00,1892.67,,infty -2022-08-16 09:29:00,1892.0,,infty -2022-08-16 09:30:00,1892.21,,infty -2022-08-16 09:31:00,1891.47,,infty -2022-08-16 09:32:00,1891.79,,infty -2022-08-16 09:33:00,1890.02,,infty -2022-08-16 09:34:00,1892.0,,infty -2022-08-16 09:35:00,1892.33,,infty -2022-08-16 09:36:00,1893.38,,infty -2022-08-16 09:37:00,1892.22,,infty -2022-08-16 09:38:00,1890.77,,infty -2022-08-16 09:39:00,1891.91,,infty -2022-08-16 09:40:00,1894.08,,infty -2022-08-16 09:41:00,1893.78,,infty -2022-08-16 09:42:00,1894.7,,infty -2022-08-16 09:43:00,1896.03,,infty -2022-08-16 09:44:00,1896.85,,infty -2022-08-16 09:45:00,1895.36,,infty -2022-08-16 09:46:00,1894.07,,infty -2022-08-16 09:47:00,1894.86,,infty -2022-08-16 09:48:00,1896.32,,infty -2022-08-16 09:49:00,1895.18,,infty -2022-08-16 09:50:00,1895.59,,infty 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10:18:00,1894.89,,infty -2022-08-16 10:19:00,1893.33,,infty -2022-08-16 10:20:00,1894.07,,infty -2022-08-16 10:21:00,1894.21,,infty -2022-08-16 10:22:00,1892.44,,infty -2022-08-16 10:23:00,1892.88,,infty -2022-08-16 10:24:00,1894.63,,infty -2022-08-16 10:25:00,1895.19,,infty -2022-08-16 10:26:00,1895.09,,infty -2022-08-16 10:27:00,1893.64,,infty -2022-08-16 10:28:00,1893.09,,infty -2022-08-16 10:29:00,1892.44,,infty -2022-08-16 10:30:00,1893.73,,infty -2022-08-16 10:31:00,1895.53,,infty -2022-08-16 10:32:00,1895.72,,infty -2022-08-16 10:33:00,1894.91,,infty -2022-08-16 10:34:00,1895.61,,infty -2022-08-16 10:35:00,1894.1,,infty -2022-08-16 10:36:00,1893.89,,infty -2022-08-16 10:37:00,1893.49,,infty -2022-08-16 10:38:00,1893.67,,infty -2022-08-16 10:39:00,1894.65,,infty -2022-08-16 10:40:00,1893.61,,infty -2022-08-16 10:41:00,1892.72,,infty -2022-08-16 10:42:00,1894.1,,infty -2022-08-16 10:43:00,1893.61,,infty -2022-08-16 10:44:00,1893.11,,infty -2022-08-16 10:45:00,1893.45,,infty 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11:13:00,1905.49,,infty -2022-08-16 11:14:00,1905.71,,infty -2022-08-16 11:15:00,1908.26,,infty -2022-08-16 11:16:00,1910.04,,infty -2022-08-16 11:17:00,1911.26,,infty -2022-08-16 11:18:00,1909.67,,infty -2022-08-16 11:19:00,1910.41,,infty -2022-08-16 11:20:00,1908.53,,infty -2022-08-16 11:21:00,1906.73,,infty -2022-08-16 11:22:00,1907.0,,infty -2022-08-16 11:23:00,1909.21,,infty -2022-08-16 11:24:00,1909.99,,infty -2022-08-16 11:25:00,1908.15,,infty -2022-08-16 11:26:00,1906.76,,infty -2022-08-16 11:27:00,1904.07,,infty -2022-08-16 11:28:00,1901.82,,infty -2022-08-16 11:29:00,1900.1,,infty -2022-08-16 11:30:00,1904.11,,infty -2022-08-16 11:31:00,1904.51,,infty -2022-08-16 11:32:00,1901.76,,infty -2022-08-16 11:33:00,1902.22,,infty -2022-08-16 11:34:00,1902.13,,infty -2022-08-16 11:35:00,1904.27,,infty -2022-08-16 11:36:00,1903.66,,infty -2022-08-16 11:37:00,1904.58,,infty -2022-08-16 11:38:00,1904.0,,infty -2022-08-16 11:39:00,1904.63,,infty -2022-08-16 11:40:00,1904.55,,infty 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12:08:00,1909.04,,infty -2022-08-16 12:09:00,1910.56,,infty -2022-08-16 12:10:00,1912.35,,infty -2022-08-16 12:11:00,1912.79,,infty -2022-08-16 12:12:00,1911.59,,infty -2022-08-16 12:13:00,1910.86,,infty -2022-08-16 12:14:00,1910.6,,infty -2022-08-16 12:15:00,1911.86,,infty -2022-08-16 12:16:00,1912.89,,infty -2022-08-16 12:17:00,1912.3,,infty -2022-08-16 12:18:00,1912.87,,infty -2022-08-16 12:19:00,1911.58,,infty -2022-08-16 12:20:00,1910.99,,infty -2022-08-16 12:21:00,1911.47,,infty -2022-08-16 12:22:00,1910.76,,infty -2022-08-16 12:23:00,1910.72,,infty -2022-08-16 12:24:00,1909.6,,infty -2022-08-16 12:25:00,1908.41,,infty -2022-08-16 12:26:00,1909.53,,infty -2022-08-16 12:27:00,1907.8,,infty -2022-08-16 12:28:00,1906.47,,infty -2022-08-16 12:29:00,1906.7,,infty -2022-08-16 12:30:00,1906.34,,infty -2022-08-16 12:31:00,1904.77,,infty -2022-08-16 12:32:00,1898.3,,infty -2022-08-16 12:33:00,1901.17,,infty -2022-08-16 12:34:00,1899.28,,infty -2022-08-16 12:35:00,1902.0,,infty -2022-08-16 12:36:00,1900.58,,infty -2022-08-16 12:37:00,1899.76,,infty -2022-08-16 12:38:00,1900.31,,infty -2022-08-16 12:39:00,1899.79,,infty -2022-08-16 12:40:00,1897.91,,infty -2022-08-16 12:41:00,1898.08,,infty -2022-08-16 12:42:00,1896.16,,infty -2022-08-16 12:43:00,1896.52,,infty -2022-08-16 12:44:00,1895.79,,infty -2022-08-16 12:45:00,1897.62,,infty -2022-08-16 12:46:00,1896.77,,infty -2022-08-16 12:47:00,1893.42,,infty -2022-08-16 12:48:00,1893.86,,infty -2022-08-16 12:49:00,1890.24,,infty -2022-08-16 12:50:00,1890.92,,infty -2022-08-16 12:51:00,1888.27,,infty -2022-08-16 12:52:00,1886.66,,infty -2022-08-16 12:53:00,1887.33,,infty -2022-08-16 12:54:00,1889.16,,infty -2022-08-16 12:55:00,1887.6,,infty -2022-08-16 12:56:00,1888.62,,infty -2022-08-16 12:57:00,1889.62,,infty -2022-08-16 12:58:00,1889.81,,infty -2022-08-16 12:59:00,1890.05,,infty -2022-08-16 13:00:00,1891.0,,infty -2022-08-16 13:01:00,1890.9,,infty -2022-08-16 13:02:00,1892.18,,infty -2022-08-16 13:03:00,1891.8,,infty 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13:31:00,1895.15,,infty -2022-08-16 13:32:00,1899.99,,infty -2022-08-16 13:33:00,1898.29,,infty -2022-08-16 13:34:00,1897.29,,infty -2022-08-16 13:35:00,1900.47,,infty -2022-08-16 13:36:00,1899.42,,infty -2022-08-16 13:37:00,1893.15,,infty -2022-08-16 13:38:00,1888.41,,infty -2022-08-16 13:39:00,1891.29,,infty -2022-08-16 13:40:00,1882.47,,infty -2022-08-16 13:41:00,1884.74,,infty -2022-08-16 13:42:00,1889.58,,infty -2022-08-16 13:43:00,1888.88,,infty -2022-08-16 13:44:00,1886.89,,infty -2022-08-16 13:45:00,1884.24,,infty -2022-08-16 13:46:00,1885.89,,infty -2022-08-16 13:47:00,1884.65,,infty -2022-08-16 13:48:00,1883.42,,infty -2022-08-16 13:49:00,1877.17,,infty -2022-08-16 13:50:00,1879.82,,infty -2022-08-16 13:51:00,1879.22,,infty -2022-08-16 13:52:00,1880.82,,infty -2022-08-16 13:53:00,1882.7,,infty -2022-08-16 13:54:00,1881.66,,infty -2022-08-16 13:55:00,1881.9,,infty -2022-08-16 13:56:00,1883.77,,infty -2022-08-16 13:57:00,1885.83,,infty -2022-08-16 13:58:00,1884.12,,infty 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14:26:00,1877.01,,infty -2022-08-16 14:27:00,1877.07,,infty -2022-08-16 14:28:00,1878.25,,infty -2022-08-16 14:29:00,1877.18,,infty -2022-08-16 14:30:00,1876.72,,infty -2022-08-16 14:31:00,1872.13,,infty -2022-08-16 14:32:00,1872.29,,infty -2022-08-16 14:33:00,1869.62,,infty -2022-08-16 14:34:00,1867.13,,infty -2022-08-16 14:35:00,1868.85,,infty -2022-08-16 14:36:00,1870.04,,infty -2022-08-16 14:37:00,1869.23,,infty -2022-08-16 14:38:00,1870.71,,infty -2022-08-16 14:39:00,1871.93,,infty -2022-08-16 14:40:00,1873.47,,infty -2022-08-16 14:41:00,1874.33,,infty -2022-08-16 14:42:00,1877.45,,infty -2022-08-16 14:43:00,1875.77,,infty -2022-08-16 14:44:00,1875.4,,infty -2022-08-16 14:45:00,1871.7,,infty -2022-08-16 14:46:00,1873.7,,infty -2022-08-16 14:47:00,1875.74,,infty -2022-08-16 14:48:00,1874.25,,infty -2022-08-16 14:49:00,1874.36,,infty -2022-08-16 14:50:00,1877.07,,infty -2022-08-16 14:51:00,1877.85,,infty -2022-08-16 14:52:00,1876.32,,infty -2022-08-16 14:53:00,1877.05,,infty 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15:21:00,1883.01,,infty -2022-08-16 15:22:00,1882.77,,infty -2022-08-16 15:23:00,1882.68,,infty -2022-08-16 15:24:00,1880.97,,infty -2022-08-16 15:25:00,1880.07,,infty -2022-08-16 15:26:00,1883.01,,infty -2022-08-16 15:27:00,1882.57,,infty -2022-08-16 15:28:00,1882.82,,infty -2022-08-16 15:29:00,1882.89,,infty -2022-08-16 15:30:00,1885.44,,infty -2022-08-16 15:31:00,1886.25,,infty -2022-08-16 15:32:00,1886.87,,infty -2022-08-16 15:33:00,1885.97,,infty -2022-08-16 15:34:00,1885.0,,infty -2022-08-16 15:35:00,1887.08,,infty -2022-08-16 15:36:00,1885.49,,infty -2022-08-16 15:37:00,1886.36,,infty -2022-08-16 15:38:00,1886.83,,infty -2022-08-16 15:39:00,1887.27,,infty -2022-08-16 15:40:00,1885.77,,infty -2022-08-16 15:41:00,1885.33,,infty -2022-08-16 15:42:00,1885.49,,infty -2022-08-16 15:43:00,1885.99,,infty -2022-08-16 15:44:00,1886.49,,infty -2022-08-16 15:45:00,1885.16,,infty -2022-08-16 15:46:00,1886.1,,infty -2022-08-16 15:47:00,1889.27,,infty -2022-08-16 15:48:00,1890.1,,infty 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16:16:00,1884.85,,infty -2022-08-16 16:17:00,1881.03,,infty -2022-08-16 16:18:00,1879.81,,infty -2022-08-16 16:19:00,1882.28,,infty -2022-08-16 16:20:00,1882.55,,infty -2022-08-16 16:21:00,1882.85,,infty -2022-08-16 16:22:00,1883.38,,infty -2022-08-16 16:23:00,1884.48,,infty -2022-08-16 16:24:00,1884.6,,infty -2022-08-16 16:25:00,1882.75,,infty -2022-08-16 16:26:00,1881.47,,infty -2022-08-16 16:27:00,1882.95,,infty -2022-08-16 16:28:00,1882.02,,infty -2022-08-16 16:29:00,1880.66,,infty -2022-08-16 16:30:00,1880.7,,infty -2022-08-16 16:31:00,1882.57,,infty -2022-08-16 16:32:00,1883.61,,infty -2022-08-16 16:33:00,1882.6,,infty -2022-08-16 16:34:00,1880.93,,infty -2022-08-16 16:35:00,1880.76,,infty -2022-08-16 16:36:00,1881.22,,infty -2022-08-16 16:37:00,1877.26,,infty -2022-08-16 16:38:00,1877.25,,infty -2022-08-16 16:39:00,1875.02,,infty -2022-08-16 16:40:00,1874.37,,infty -2022-08-16 16:41:00,1875.11,,infty -2022-08-16 16:42:00,1872.4,,infty -2022-08-16 16:43:00,1870.92,,infty 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17:11:00,1881.68,,infty -2022-08-16 17:12:00,1878.74,,infty -2022-08-16 17:13:00,1879.54,,infty -2022-08-16 17:14:00,1879.38,,infty -2022-08-16 17:15:00,1876.86,,infty -2022-08-16 17:16:00,1878.9,,infty -2022-08-16 17:17:00,1879.66,,infty -2022-08-16 17:18:00,1881.02,,infty -2022-08-16 17:19:00,1878.77,,infty -2022-08-16 17:20:00,1878.45,,infty -2022-08-16 17:21:00,1877.16,,infty -2022-08-16 17:22:00,1877.02,,infty -2022-08-16 17:23:00,1875.64,,infty -2022-08-16 17:24:00,1876.09,,infty -2022-08-16 17:25:00,1873.55,,infty -2022-08-16 17:26:00,1872.59,,infty -2022-08-16 17:27:00,1873.21,,infty -2022-08-16 17:28:00,1872.94,,infty -2022-08-16 17:29:00,1872.65,,infty -2022-08-16 17:30:00,1871.01,,infty -2022-08-16 17:31:00,1873.46,,infty -2022-08-16 17:32:00,1874.17,,infty -2022-08-16 17:33:00,1875.8,,infty -2022-08-16 17:34:00,1875.36,,infty -2022-08-16 17:35:00,1875.01,,infty -2022-08-16 17:36:00,1878.4,,infty -2022-08-16 17:37:00,1881.01,,infty -2022-08-16 17:38:00,1882.32,,infty 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18:06:00,1881.32,,infty -2022-08-16 18:07:00,1881.03,,infty -2022-08-16 18:08:00,1886.96,,infty -2022-08-16 18:09:00,1893.72,,infty -2022-08-16 18:10:00,1892.43,,infty -2022-08-16 18:11:00,1890.71,,infty -2022-08-16 18:12:00,1891.01,,infty -2022-08-16 18:13:00,1890.69,,infty -2022-08-16 18:14:00,1889.81,,infty -2022-08-16 18:15:00,1889.8,,infty -2022-08-16 18:16:00,1894.55,,infty -2022-08-16 18:17:00,1893.25,,infty -2022-08-16 18:18:00,1895.7,,infty -2022-08-16 18:19:00,1895.44,,infty -2022-08-16 18:20:00,1895.22,,infty -2022-08-16 18:21:00,1893.25,,infty -2022-08-16 18:22:00,1892.64,,infty -2022-08-16 18:23:00,1892.16,,infty -2022-08-16 18:24:00,1892.09,,infty -2022-08-16 18:25:00,1890.29,,infty -2022-08-16 18:26:00,1890.03,,infty -2022-08-16 18:27:00,1889.92,,infty -2022-08-16 18:28:00,1885.98,,infty -2022-08-16 18:29:00,1886.89,,infty -2022-08-16 18:30:00,1885.43,,infty -2022-08-16 18:31:00,1884.62,,infty -2022-08-16 18:32:00,1884.52,,infty -2022-08-16 18:33:00,1882.17,,infty 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19:01:00,1874.02,,infty -2022-08-16 19:02:00,1868.43,,infty -2022-08-16 19:03:00,1869.4,,infty -2022-08-16 19:04:00,1868.34,,infty -2022-08-16 19:05:00,1871.38,,infty -2022-08-16 19:06:00,1875.65,,infty -2022-08-16 19:07:00,1875.25,,infty -2022-08-16 19:08:00,1874.38,,infty -2022-08-16 19:09:00,1871.95,,infty -2022-08-16 19:10:00,1872.79,,infty -2022-08-16 19:11:00,1873.19,,infty -2022-08-16 19:12:00,1874.02,,infty -2022-08-16 19:13:00,1875.63,,infty -2022-08-16 19:14:00,1874.45,,infty -2022-08-16 19:15:00,1873.25,,infty -2022-08-16 19:16:00,1876.19,,infty -2022-08-16 19:17:00,1875.18,,infty -2022-08-16 19:18:00,1875.6,,infty -2022-08-16 19:19:00,1875.76,,infty -2022-08-16 19:20:00,1875.38,,infty -2022-08-16 19:21:00,1873.83,,infty -2022-08-16 19:22:00,1871.61,,infty -2022-08-16 19:23:00,1873.51,,infty -2022-08-16 19:24:00,1872.84,,infty -2022-08-16 19:25:00,1874.62,,infty -2022-08-16 19:26:00,1876.2,,infty -2022-08-16 19:27:00,1877.01,,infty -2022-08-16 19:28:00,1878.4,,infty 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19:56:00,1877.32,,infty -2022-08-16 19:57:00,1879.7,,infty -2022-08-16 19:58:00,1879.51,,infty -2022-08-16 19:59:00,1879.74,,infty -2022-08-16 20:00:00,1878.32,,infty -2022-08-16 20:01:00,1877.45,,infty -2022-08-16 20:02:00,1878.05,,infty -2022-08-16 20:03:00,1876.65,,infty -2022-08-16 20:04:00,1877.91,,infty -2022-08-16 20:05:00,1880.0,,infty -2022-08-16 20:06:00,1881.47,,infty -2022-08-16 20:07:00,1882.68,,infty -2022-08-16 20:08:00,1883.79,,infty -2022-08-16 20:09:00,1883.48,,infty -2022-08-16 20:10:00,1882.36,,infty -2022-08-16 20:11:00,1883.0,,infty -2022-08-16 20:12:00,1883.27,,infty -2022-08-16 20:13:00,1883.76,,infty -2022-08-16 20:14:00,1882.28,,infty -2022-08-16 20:15:00,1881.06,,infty -2022-08-16 20:16:00,1880.75,,infty -2022-08-16 20:17:00,1880.42,,infty -2022-08-16 20:18:00,1879.77,,infty -2022-08-16 20:19:00,1880.22,,infty -2022-08-16 20:20:00,1879.18,,infty -2022-08-16 20:21:00,1879.2,,infty -2022-08-16 20:22:00,1879.89,,infty -2022-08-16 20:23:00,1881.98,,infty 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20:51:00,1882.78,,infty -2022-08-16 20:52:00,1883.32,,infty -2022-08-16 20:53:00,1882.18,,infty -2022-08-16 20:54:00,1883.66,,infty -2022-08-16 20:55:00,1885.98,,infty -2022-08-16 20:56:00,1885.33,,infty -2022-08-16 20:57:00,1883.06,,infty -2022-08-16 20:58:00,1884.31,,infty -2022-08-16 20:59:00,1884.68,,infty -2022-08-16 21:00:00,1885.08,,infty -2022-08-16 21:01:00,1884.31,,infty -2022-08-16 21:02:00,1884.92,,infty -2022-08-16 21:03:00,1884.24,,infty -2022-08-16 21:04:00,1885.2,,infty -2022-08-16 21:05:00,1884.14,,infty -2022-08-16 21:06:00,1882.94,,infty -2022-08-16 21:07:00,1881.45,,infty -2022-08-16 21:08:00,1883.01,,infty -2022-08-16 21:09:00,1882.6,,infty -2022-08-16 21:10:00,1883.31,,infty -2022-08-16 21:11:00,1883.02,,infty -2022-08-16 21:12:00,1882.14,,infty -2022-08-16 21:13:00,1882.16,,infty -2022-08-16 21:14:00,1882.14,,infty -2022-08-16 21:15:00,1881.58,,infty -2022-08-16 21:16:00,1880.01,,infty -2022-08-16 21:17:00,1879.86,,infty -2022-08-16 21:18:00,1879.35,,infty -2022-08-16 21:19:00,1877.47,,infty -2022-08-16 21:20:00,1876.56,,infty -2022-08-16 21:21:00,1874.12,,infty -2022-08-16 21:22:00,1876.2,,infty -2022-08-16 21:23:00,1874.98,,infty -2022-08-16 21:24:00,1875.11,,infty -2022-08-16 21:25:00,1875.08,,infty -2022-08-16 21:26:00,1874.82,,infty -2022-08-16 21:27:00,1879.48,,infty -2022-08-16 21:28:00,1879.19,,infty -2022-08-16 21:29:00,1880.49,,infty -2022-08-16 21:30:00,1881.33,,infty -2022-08-16 21:31:00,1881.96,,infty -2022-08-16 21:32:00,1882.13,,infty -2022-08-16 21:33:00,1880.24,,infty -2022-08-16 21:34:00,1879.56,,infty -2022-08-16 21:35:00,1878.92,,infty -2022-08-16 21:36:00,1880.46,,infty -2022-08-16 21:37:00,1880.46,,infty -2022-08-16 21:38:00,1880.12,,infty -2022-08-16 21:39:00,1879.93,,infty -2022-08-16 21:40:00,1880.49,,infty -2022-08-16 21:41:00,1878.18,,infty -2022-08-16 21:42:00,1877.99,,infty -2022-08-16 21:43:00,1877.95,,infty -2022-08-16 21:44:00,1878.0,,infty -2022-08-16 21:45:00,1878.25,,infty -2022-08-16 21:46:00,1876.96,,infty -2022-08-16 21:47:00,1877.22,,infty -2022-08-16 21:48:00,1875.64,,infty -2022-08-16 21:49:00,1877.69,,infty -2022-08-16 21:50:00,1877.36,,infty -2022-08-16 21:51:00,1877.85,,infty -2022-08-16 21:52:00,1877.66,,infty -2022-08-16 21:53:00,1879.66,,infty -2022-08-16 21:54:00,1878.97,,infty -2022-08-16 21:55:00,1877.17,,infty -2022-08-16 21:56:00,1878.27,,infty -2022-08-16 21:57:00,1879.23,,infty -2022-08-16 21:58:00,1880.36,,infty -2022-08-16 21:59:00,1880.2,,infty -2022-08-16 22:00:00,1879.79,,infty -2022-08-16 22:01:00,1879.9,,infty -2022-08-16 22:02:00,1876.22,,infty -2022-08-16 22:03:00,1876.1,,infty -2022-08-16 22:04:00,1876.22,,infty -2022-08-16 22:05:00,1874.61,,infty -2022-08-16 22:06:00,1875.34,,infty -2022-08-16 22:07:00,1873.94,,infty -2022-08-16 22:08:00,1872.91,,infty -2022-08-16 22:09:00,1875.81,,infty -2022-08-16 22:10:00,1876.8,,infty -2022-08-16 22:11:00,1876.27,,infty -2022-08-16 22:12:00,1876.69,,infty -2022-08-16 22:13:00,1875.94,,infty 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22:41:00,1873.3,,infty -2022-08-16 22:42:00,1873.21,,infty -2022-08-16 22:43:00,1873.52,,infty -2022-08-16 22:44:00,1875.4,,infty -2022-08-16 22:45:00,1876.52,,infty -2022-08-16 22:46:00,1875.25,,infty -2022-08-16 22:47:00,1875.89,,infty -2022-08-16 22:48:00,1874.05,,infty -2022-08-16 22:49:00,1874.14,,infty -2022-08-16 22:50:00,1874.23,,infty -2022-08-16 22:51:00,1874.08,,infty -2022-08-16 22:52:00,1875.37,,infty -2022-08-16 22:53:00,1875.53,,infty -2022-08-16 22:54:00,1876.33,,infty -2022-08-16 22:55:00,1876.06,,infty -2022-08-16 22:56:00,1875.28,,infty -2022-08-16 22:57:00,1875.93,,infty -2022-08-16 22:58:00,1874.19,,infty -2022-08-16 22:59:00,1872.36,,infty -2022-08-16 23:00:00,1869.9,,infty -2022-08-16 23:01:00,1871.23,,infty -2022-08-16 23:02:00,1873.45,,infty -2022-08-16 23:03:00,1872.51,,infty -2022-08-16 23:04:00,1873.04,,infty -2022-08-16 23:05:00,1872.32,,infty -2022-08-16 23:06:00,1873.23,,infty -2022-08-16 23:07:00,1874.32,,infty -2022-08-16 23:08:00,1876.98,,infty 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23:36:00,1871.31,,infty -2022-08-16 23:37:00,1871.91,,infty -2022-08-16 23:38:00,1873.73,,infty -2022-08-16 23:39:00,1877.38,,infty -2022-08-16 23:40:00,1877.42,,infty -2022-08-16 23:41:00,1876.62,,infty -2022-08-16 23:42:00,1876.67,,infty -2022-08-16 23:43:00,1875.77,,infty -2022-08-16 23:44:00,1873.69,,infty -2022-08-16 23:45:00,1875.3,,infty -2022-08-16 23:46:00,1875.59,,infty -2022-08-16 23:47:00,1878.4,,infty -2022-08-16 23:48:00,1878.71,,infty -2022-08-16 23:49:00,1879.28,,infty -2022-08-16 23:50:00,1880.73,,infty -2022-08-16 23:51:00,1878.12,,infty -2022-08-16 23:52:00,1878.95,,infty -2022-08-16 23:53:00,1879.91,,infty -2022-08-16 23:54:00,1880.33,,infty -2022-08-16 23:55:00,1880.71,,infty -2022-08-16 23:56:00,1878.58,,infty -2022-08-16 23:57:00,1878.87,,infty -2022-08-16 23:58:00,1877.99,,infty -2022-08-16 23:59:00,1876.72,,infty -2022-08-17 00:00:00,1878.5,,infty -2022-08-17 00:01:00,1876.5,,infty -2022-08-17 00:02:00,1876.73,,infty -2022-08-17 00:03:00,1876.89,,infty 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00:31:00,1885.38,,infty -2022-08-17 00:32:00,1885.41,,infty -2022-08-17 00:33:00,1884.61,,infty -2022-08-17 00:34:00,1891.67,,infty -2022-08-17 00:35:00,1888.84,,infty -2022-08-17 00:36:00,1890.72,,infty -2022-08-17 00:37:00,1896.55,,infty -2022-08-17 00:38:00,1896.13,,infty -2022-08-17 00:39:00,1898.29,,infty -2022-08-17 00:40:00,1895.3,,infty -2022-08-17 00:41:00,1895.17,,infty -2022-08-17 00:42:00,1894.08,,infty -2022-08-17 00:43:00,1894.64,,infty -2022-08-17 00:44:00,1895.44,,infty -2022-08-17 00:45:00,1895.58,,infty -2022-08-17 00:46:00,1896.0,,infty -2022-08-17 00:47:00,1896.93,,infty -2022-08-17 00:48:00,1895.25,,infty -2022-08-17 00:49:00,1892.8,,infty -2022-08-17 00:50:00,1892.9,,infty -2022-08-17 00:51:00,1894.07,,infty -2022-08-17 00:52:00,1894.78,,infty -2022-08-17 00:53:00,1894.91,,infty -2022-08-17 00:54:00,1895.55,,infty -2022-08-17 00:55:00,1898.87,,infty -2022-08-17 00:56:00,1896.32,,infty -2022-08-17 00:57:00,1896.4,,infty -2022-08-17 00:58:00,1895.51,,infty 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01:26:00,1893.13,,infty -2022-08-17 01:27:00,1894.43,,infty -2022-08-17 01:28:00,1893.9,,infty -2022-08-17 01:29:00,1894.01,,infty -2022-08-17 01:30:00,1893.4,,infty -2022-08-17 01:31:00,1894.33,,infty -2022-08-17 01:32:00,1894.23,,infty -2022-08-17 01:33:00,1894.84,,infty -2022-08-17 01:34:00,1895.58,,infty -2022-08-17 01:35:00,1896.54,,infty -2022-08-17 01:36:00,1896.99,,infty -2022-08-17 01:37:00,1894.97,,infty -2022-08-17 01:38:00,1888.85,,infty -2022-08-17 01:39:00,1887.89,,infty -2022-08-17 01:40:00,1890.39,,infty -2022-08-17 01:41:00,1888.53,,infty -2022-08-17 01:42:00,1888.0,,infty -2022-08-17 01:43:00,1889.8,,infty -2022-08-17 01:44:00,1889.33,,infty -2022-08-17 01:45:00,1890.22,,infty -2022-08-17 01:46:00,1889.1,,infty -2022-08-17 01:47:00,1889.0,,infty -2022-08-17 01:48:00,1889.4,,infty -2022-08-17 01:49:00,1888.96,,infty -2022-08-17 01:50:00,1887.77,,infty -2022-08-17 01:51:00,1887.15,,infty -2022-08-17 01:52:00,1888.34,,infty -2022-08-17 01:53:00,1887.75,,infty -2022-08-17 01:54:00,1889.02,,infty -2022-08-17 01:55:00,1886.98,,infty -2022-08-17 01:56:00,1887.89,,infty -2022-08-17 01:57:00,1888.05,,infty -2022-08-17 01:58:00,1887.44,,infty -2022-08-17 01:59:00,1887.66,,infty -2022-08-17 02:00:00,1886.8,,infty -2022-08-17 02:01:00,1887.15,,infty -2022-08-17 02:02:00,1886.08,,infty -2022-08-17 02:03:00,1884.62,,infty -2022-08-17 02:04:00,1885.56,,infty -2022-08-17 02:05:00,1884.13,,infty -2022-08-17 02:06:00,1882.56,,infty -2022-08-17 02:07:00,1884.65,,infty -2022-08-17 02:08:00,1884.9,,infty -2022-08-17 02:09:00,1885.99,,infty -2022-08-17 02:10:00,1886.8,,infty -2022-08-17 02:11:00,1885.94,,infty -2022-08-17 02:12:00,1885.94,,infty -2022-08-17 02:13:00,1885.4,,infty -2022-08-17 02:14:00,1884.02,,infty -2022-08-17 02:15:00,1885.21,,infty -2022-08-17 02:16:00,1882.95,,infty -2022-08-17 02:17:00,1883.11,,infty -2022-08-17 02:18:00,1882.35,,infty -2022-08-17 02:19:00,1881.9,,infty -2022-08-17 02:20:00,1882.44,,infty -2022-08-17 02:21:00,1882.8,,infty 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03:17:00,1898.72,,infty -2022-08-17 03:18:00,1898.24,,infty -2022-08-17 03:19:00,1899.36,,infty -2022-08-17 03:20:00,1899.23,,infty -2022-08-17 03:21:00,1898.89,,infty -2022-08-17 03:22:00,1899.32,,infty -2022-08-17 03:23:00,1897.72,,infty -2022-08-17 03:24:00,1898.56,,infty -2022-08-17 03:25:00,1899.14,,infty -2022-08-17 03:26:00,1899.95,,infty -2022-08-17 03:27:00,1899.85,,infty -2022-08-17 03:28:00,1898.84,,infty -2022-08-17 03:29:00,1898.55,,infty -2022-08-17 03:30:00,1901.36,,infty -2022-08-17 03:31:00,1900.74,,infty -2022-08-17 03:32:00,1901.1,,infty -2022-08-17 03:33:00,1898.94,,infty -2022-08-17 03:34:00,1898.83,,infty -2022-08-17 03:35:00,1899.31,,infty -2022-08-17 03:36:00,1899.28,,infty -2022-08-17 03:37:00,1899.14,,infty -2022-08-17 03:38:00,1896.89,,infty -2022-08-17 03:39:00,1897.15,,infty -2022-08-17 03:40:00,1897.7,,infty -2022-08-17 03:41:00,1897.69,,infty -2022-08-17 03:42:00,1896.87,,infty -2022-08-17 03:43:00,1897.83,,infty -2022-08-17 03:44:00,1896.5,,infty 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04:12:00,1896.62,,infty -2022-08-17 04:13:00,1896.85,,infty -2022-08-17 04:14:00,1896.08,,infty -2022-08-17 04:15:00,1896.7,,infty -2022-08-17 04:16:00,1897.84,,infty -2022-08-17 04:17:00,1898.44,,infty -2022-08-17 04:18:00,1898.79,,infty -2022-08-17 04:19:00,1898.59,,infty -2022-08-17 04:20:00,1897.87,,infty -2022-08-17 04:21:00,1897.0,,infty -2022-08-17 04:22:00,1898.15,,infty -2022-08-17 04:23:00,1898.02,,infty -2022-08-17 04:24:00,1897.0,,infty -2022-08-17 04:25:00,1897.4,,infty -2022-08-17 04:26:00,1896.13,,infty -2022-08-17 04:27:00,1897.32,,infty -2022-08-17 04:28:00,1898.03,,infty -2022-08-17 04:29:00,1897.51,,infty -2022-08-17 04:30:00,1897.32,,infty -2022-08-17 04:31:00,1896.54,,infty -2022-08-17 04:32:00,1896.33,,infty -2022-08-17 04:33:00,1896.54,,infty -2022-08-17 04:34:00,1896.38,,infty -2022-08-17 04:35:00,1896.74,,infty -2022-08-17 04:36:00,1896.99,,infty -2022-08-17 04:37:00,1898.81,,infty -2022-08-17 04:38:00,1897.82,,infty -2022-08-17 04:39:00,1898.01,,infty 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05:07:00,1907.14,,infty -2022-08-17 05:08:00,1905.04,,infty -2022-08-17 05:09:00,1906.3,,infty -2022-08-17 05:10:00,1909.53,,infty -2022-08-17 05:11:00,1910.32,,infty -2022-08-17 05:12:00,1909.01,,infty -2022-08-17 05:13:00,1909.42,,infty -2022-08-17 05:14:00,1906.67,,infty -2022-08-17 05:15:00,1906.38,,infty -2022-08-17 05:16:00,1906.47,,infty -2022-08-17 05:17:00,1905.64,,infty -2022-08-17 05:18:00,1904.83,,infty -2022-08-17 05:19:00,1908.44,,infty -2022-08-17 05:20:00,1909.03,,infty -2022-08-17 05:21:00,1911.37,,infty -2022-08-17 05:22:00,1912.3,,infty -2022-08-17 05:23:00,1911.16,,infty -2022-08-17 05:24:00,1910.92,,infty -2022-08-17 05:25:00,1910.12,,infty -2022-08-17 05:26:00,1909.1,,infty -2022-08-17 05:27:00,1909.87,,infty -2022-08-17 05:28:00,1909.56,,infty -2022-08-17 05:29:00,1912.44,,infty -2022-08-17 05:30:00,1911.61,,infty -2022-08-17 05:31:00,1910.82,,infty -2022-08-17 05:32:00,1909.68,,infty -2022-08-17 05:33:00,1913.55,,infty -2022-08-17 05:34:00,1912.45,,infty 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06:02:00,1939.32,,infty -2022-08-17 06:03:00,1938.61,,infty -2022-08-17 06:04:00,1940.03,,infty -2022-08-17 06:05:00,1939.69,,infty -2022-08-17 06:06:00,1943.24,,infty -2022-08-17 06:07:00,1940.5,,infty -2022-08-17 06:08:00,1943.24,,infty -2022-08-17 06:09:00,1942.57,,infty -2022-08-17 06:10:00,1943.59,,infty -2022-08-17 06:11:00,1946.43,,infty -2022-08-17 06:12:00,1946.66,,infty -2022-08-17 06:13:00,1943.94,,infty -2022-08-17 06:14:00,1941.48,,infty -2022-08-17 06:15:00,1944.46,,infty -2022-08-17 06:16:00,1945.33,,infty -2022-08-17 06:17:00,1943.77,,infty -2022-08-17 06:18:00,1943.1,,infty -2022-08-17 06:19:00,1939.63,,infty -2022-08-17 06:20:00,1942.78,,infty -2022-08-17 06:21:00,1942.99,,infty -2022-08-17 06:22:00,1943.67,,infty -2022-08-17 06:23:00,1943.18,,infty -2022-08-17 06:24:00,1944.86,,infty -2022-08-17 06:25:00,1945.28,,infty -2022-08-17 06:26:00,1946.86,,infty -2022-08-17 06:27:00,1946.96,,infty -2022-08-17 06:28:00,1945.31,,infty -2022-08-17 06:29:00,1946.79,,infty 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06:57:00,1952.79,,infty -2022-08-17 06:58:00,1953.93,,infty -2022-08-17 06:59:00,1951.88,,infty -2022-08-17 07:00:00,1952.62,,infty -2022-08-17 07:01:00,1949.23,,infty -2022-08-17 07:02:00,1950.14,,infty -2022-08-17 07:03:00,1951.16,,infty -2022-08-17 07:04:00,1949.62,,infty -2022-08-17 07:05:00,1948.68,,infty -2022-08-17 07:06:00,1945.66,,infty -2022-08-17 07:07:00,1942.18,,infty -2022-08-17 07:08:00,1943.75,,infty -2022-08-17 07:09:00,1945.81,,infty -2022-08-17 07:10:00,1943.71,,infty -2022-08-17 07:11:00,1943.03,,infty -2022-08-17 07:12:00,1943.45,,infty -2022-08-17 07:13:00,1944.82,,infty -2022-08-17 07:14:00,1943.66,,infty -2022-08-17 07:15:00,1945.79,,infty -2022-08-17 07:16:00,1945.82,,infty -2022-08-17 07:17:00,1946.38,,infty -2022-08-17 07:18:00,1948.24,,infty -2022-08-17 07:19:00,1943.96,,infty -2022-08-17 07:20:00,1945.84,,infty -2022-08-17 07:21:00,1944.52,,infty -2022-08-17 07:22:00,1944.1,,infty -2022-08-17 07:23:00,1940.11,,infty -2022-08-17 07:24:00,1938.59,,infty 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07:52:00,1895.87,,infty -2022-08-17 07:53:00,1894.14,,infty -2022-08-17 07:54:00,1891.61,,infty -2022-08-17 07:55:00,1892.03,,infty -2022-08-17 07:56:00,1893.39,,infty -2022-08-17 07:57:00,1890.8,,infty -2022-08-17 07:58:00,1892.48,,infty -2022-08-17 07:59:00,1890.76,,infty -2022-08-17 08:00:00,1890.0,,infty -2022-08-17 08:01:00,1885.41,,infty -2022-08-17 08:02:00,1888.85,,infty -2022-08-17 08:03:00,1890.4,,infty -2022-08-17 08:04:00,1888.96,,infty -2022-08-17 08:05:00,1886.06,,infty -2022-08-17 08:06:00,1885.4,,infty -2022-08-17 08:07:00,1888.42,,infty -2022-08-17 08:08:00,1890.19,,infty -2022-08-17 08:09:00,1889.45,,infty -2022-08-17 08:10:00,1891.93,,infty -2022-08-17 08:11:00,1894.62,,infty -2022-08-17 08:12:00,1896.81,,infty -2022-08-17 08:13:00,1896.08,,infty -2022-08-17 08:14:00,1894.44,,infty -2022-08-17 08:15:00,1897.5,,infty -2022-08-17 08:16:00,1901.65,,infty -2022-08-17 08:17:00,1901.19,,infty -2022-08-17 08:18:00,1901.11,,infty -2022-08-17 08:19:00,1899.48,,infty 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09:15:00,1886.33,,infty -2022-08-17 09:16:00,1885.3,,infty -2022-08-17 09:17:00,1885.86,,infty -2022-08-17 09:18:00,1885.1,,infty -2022-08-17 09:19:00,1885.1,,infty -2022-08-17 09:20:00,1884.45,,infty -2022-08-17 09:21:00,1885.89,,infty -2022-08-17 09:22:00,1888.32,,infty -2022-08-17 09:23:00,1888.78,,infty -2022-08-17 09:24:00,1889.81,,infty -2022-08-17 09:25:00,1889.24,,infty -2022-08-17 09:26:00,1891.04,,infty -2022-08-17 09:27:00,1891.81,,infty -2022-08-17 09:28:00,1890.38,,infty -2022-08-17 09:29:00,1891.26,,infty -2022-08-17 09:30:00,1895.48,,infty -2022-08-17 09:31:00,1895.06,,infty -2022-08-17 09:32:00,1895.24,,infty -2022-08-17 09:33:00,1894.24,,infty -2022-08-17 09:34:00,1892.63,,infty -2022-08-17 09:35:00,1892.67,,infty -2022-08-17 09:36:00,1890.27,,infty -2022-08-17 09:37:00,1892.1,,infty -2022-08-17 09:38:00,1892.76,,infty -2022-08-17 09:39:00,1892.18,,infty -2022-08-17 09:40:00,1890.75,,infty -2022-08-17 09:41:00,1891.83,,infty -2022-08-17 09:42:00,1890.63,,infty 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10:10:00,1884.8,,infty -2022-08-17 10:11:00,1884.98,,infty -2022-08-17 10:12:00,1883.06,,infty -2022-08-17 10:13:00,1881.65,,infty -2022-08-17 10:14:00,1876.69,,infty -2022-08-17 10:15:00,1876.59,,infty -2022-08-17 10:16:00,1877.55,,infty -2022-08-17 10:17:00,1879.24,,infty -2022-08-17 10:18:00,1877.77,,infty -2022-08-17 10:19:00,1875.7,,infty -2022-08-17 10:20:00,1876.61,,infty -2022-08-17 10:21:00,1877.9,,infty -2022-08-17 10:22:00,1877.44,,infty -2022-08-17 10:23:00,1876.34,,infty -2022-08-17 10:24:00,1877.06,,infty -2022-08-17 10:25:00,1875.78,,infty -2022-08-17 10:26:00,1877.19,,infty -2022-08-17 10:27:00,1878.43,,infty -2022-08-17 10:28:00,1876.86,,infty -2022-08-17 10:29:00,1879.87,,infty -2022-08-17 10:30:00,1876.89,,infty -2022-08-17 10:31:00,1880.08,,infty -2022-08-17 10:32:00,1880.98,,infty -2022-08-17 10:33:00,1881.56,,infty -2022-08-17 10:34:00,1881.4,,infty -2022-08-17 10:35:00,1881.82,,infty -2022-08-17 10:36:00,1880.97,,infty -2022-08-17 10:37:00,1881.23,,infty 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11:05:00,1879.54,,infty -2022-08-17 11:06:00,1880.0,,infty -2022-08-17 11:07:00,1879.18,,infty -2022-08-17 11:08:00,1880.48,,infty -2022-08-17 11:09:00,1879.63,,infty -2022-08-17 11:10:00,1881.51,,infty -2022-08-17 11:11:00,1881.8,,infty -2022-08-17 11:12:00,1881.55,,infty -2022-08-17 11:13:00,1881.35,,infty -2022-08-17 11:14:00,1881.46,,infty -2022-08-17 11:15:00,1882.16,,infty -2022-08-17 11:16:00,1883.3,,infty -2022-08-17 11:17:00,1878.53,,infty -2022-08-17 11:18:00,1879.61,,infty -2022-08-17 11:19:00,1880.24,,infty -2022-08-17 11:20:00,1879.8,,infty -2022-08-17 11:21:00,1878.42,,infty -2022-08-17 11:22:00,1878.86,,infty -2022-08-17 11:23:00,1878.79,,infty -2022-08-17 11:24:00,1877.84,,infty -2022-08-17 11:25:00,1876.87,,infty -2022-08-17 11:26:00,1877.84,,infty -2022-08-17 11:27:00,1876.04,,infty -2022-08-17 11:28:00,1876.89,,infty -2022-08-17 11:29:00,1877.23,,infty -2022-08-17 11:30:00,1877.88,,infty -2022-08-17 11:31:00,1876.68,,infty -2022-08-17 11:32:00,1875.95,,infty 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12:00:00,1877.06,,infty -2022-08-17 12:01:00,1878.35,,infty -2022-08-17 12:02:00,1876.57,,infty -2022-08-17 12:03:00,1875.48,,infty -2022-08-17 12:04:00,1875.27,,infty -2022-08-17 12:05:00,1873.91,,infty -2022-08-17 12:06:00,1873.95,,infty -2022-08-17 12:07:00,1875.44,,infty -2022-08-17 12:08:00,1876.34,,infty -2022-08-17 12:09:00,1875.93,,infty -2022-08-17 12:10:00,1876.62,,infty -2022-08-17 12:11:00,1876.22,,infty -2022-08-17 12:12:00,1875.97,,infty -2022-08-17 12:13:00,1874.77,,infty -2022-08-17 12:14:00,1872.58,,infty -2022-08-17 12:15:00,1871.8,,infty -2022-08-17 12:16:00,1867.2,,infty -2022-08-17 12:17:00,1866.22,,infty -2022-08-17 12:18:00,1867.36,,infty -2022-08-17 12:19:00,1863.82,,infty -2022-08-17 12:20:00,1866.86,,infty -2022-08-17 12:21:00,1862.62,,infty -2022-08-17 12:22:00,1864.96,,infty -2022-08-17 12:23:00,1862.83,,infty -2022-08-17 12:24:00,1865.09,,infty -2022-08-17 12:25:00,1862.68,,infty -2022-08-17 12:26:00,1862.1,,infty -2022-08-17 12:27:00,1858.72,,infty 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12:55:00,1873.26,,infty -2022-08-17 12:56:00,1872.12,,infty -2022-08-17 12:57:00,1871.1,,infty -2022-08-17 12:58:00,1872.67,,infty -2022-08-17 12:59:00,1872.22,,infty -2022-08-17 13:00:00,1870.9,,infty -2022-08-17 13:01:00,1870.15,,infty -2022-08-17 13:02:00,1868.28,,infty -2022-08-17 13:03:00,1866.82,,infty -2022-08-17 13:04:00,1867.08,,infty -2022-08-17 13:05:00,1865.66,,infty -2022-08-17 13:06:00,1866.29,,infty -2022-08-17 13:07:00,1864.63,,infty -2022-08-17 13:08:00,1868.16,,infty -2022-08-17 13:09:00,1870.08,,infty -2022-08-17 13:10:00,1867.93,,infty -2022-08-17 13:11:00,1866.57,,infty -2022-08-17 13:12:00,1861.39,,infty -2022-08-17 13:13:00,1861.25,,infty -2022-08-17 13:14:00,1860.86,,infty -2022-08-17 13:15:00,1859.92,,infty -2022-08-17 13:16:00,1858.53,,infty -2022-08-17 13:17:00,1856.71,,infty -2022-08-17 13:18:00,1862.49,,infty -2022-08-17 13:19:00,1862.46,,infty -2022-08-17 13:20:00,1863.99,,infty -2022-08-17 13:21:00,1865.36,,infty -2022-08-17 13:22:00,1863.85,,infty 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13:50:00,1822.6,,infty -2022-08-17 13:51:00,1824.65,,infty -2022-08-17 13:52:00,1827.49,,infty -2022-08-17 13:53:00,1829.63,,infty -2022-08-17 13:54:00,1832.29,,infty -2022-08-17 13:55:00,1833.61,,infty -2022-08-17 13:56:00,1837.39,,infty -2022-08-17 13:57:00,1834.03,,infty -2022-08-17 13:58:00,1836.63,,infty -2022-08-17 13:59:00,1834.77,,infty -2022-08-17 14:00:00,1832.11,,infty -2022-08-17 14:01:00,1830.53,,infty -2022-08-17 14:02:00,1829.1,,infty -2022-08-17 14:03:00,1833.62,,infty -2022-08-17 14:04:00,1834.48,,infty -2022-08-17 14:05:00,1835.47,,infty -2022-08-17 14:06:00,1838.32,,infty -2022-08-17 14:07:00,1836.89,,infty -2022-08-17 14:08:00,1833.65,,infty -2022-08-17 14:09:00,1830.73,,infty -2022-08-17 14:10:00,1831.65,,infty -2022-08-17 14:11:00,1827.13,,infty -2022-08-17 14:12:00,1827.08,,infty -2022-08-17 14:13:00,1830.2,,infty -2022-08-17 14:14:00,1829.76,,infty -2022-08-17 14:15:00,1827.53,,infty -2022-08-17 14:16:00,1830.0,,infty -2022-08-17 14:17:00,1831.01,,infty 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14:45:00,1826.24,,infty -2022-08-17 14:46:00,1826.78,,infty -2022-08-17 14:47:00,1825.58,,infty -2022-08-17 14:48:00,1824.94,,infty -2022-08-17 14:49:00,1825.18,,infty -2022-08-17 14:50:00,1826.5,,infty -2022-08-17 14:51:00,1826.77,,infty -2022-08-17 14:52:00,1826.04,,infty -2022-08-17 14:53:00,1822.62,,infty -2022-08-17 14:54:00,1820.34,,infty -2022-08-17 14:55:00,1822.91,,infty -2022-08-17 14:56:00,1820.09,,infty -2022-08-17 14:57:00,1820.68,,infty -2022-08-17 14:58:00,1822.9,,infty -2022-08-17 14:59:00,1823.35,,infty -2022-08-17 15:00:00,1822.72,,infty -2022-08-17 15:01:00,1825.91,,infty -2022-08-17 15:02:00,1826.75,,infty -2022-08-17 15:03:00,1827.09,,infty -2022-08-17 15:04:00,1829.85,,infty -2022-08-17 15:05:00,1830.02,,infty -2022-08-17 15:06:00,1828.09,,infty -2022-08-17 15:07:00,1829.49,,infty -2022-08-17 15:08:00,1829.14,,infty -2022-08-17 15:09:00,1828.3,,infty -2022-08-17 15:10:00,1829.09,,infty -2022-08-17 15:11:00,1831.81,,infty -2022-08-17 15:12:00,1832.26,,infty 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15:40:00,1832.94,,infty -2022-08-17 15:41:00,1830.61,,infty -2022-08-17 15:42:00,1830.21,,infty -2022-08-17 15:43:00,1827.25,,infty -2022-08-17 15:44:00,1829.59,,infty -2022-08-17 15:45:00,1830.64,,infty -2022-08-17 15:46:00,1829.15,,infty -2022-08-17 15:47:00,1830.37,,infty -2022-08-17 15:48:00,1829.19,,infty -2022-08-17 15:49:00,1829.97,,infty -2022-08-17 15:50:00,1830.55,,infty -2022-08-17 15:51:00,1829.89,,infty -2022-08-17 15:52:00,1828.79,,infty -2022-08-17 15:53:00,1830.11,,infty -2022-08-17 15:54:00,1831.38,,infty -2022-08-17 15:55:00,1830.94,,infty -2022-08-17 15:56:00,1830.82,,infty -2022-08-17 15:57:00,1829.66,,infty -2022-08-17 15:58:00,1829.37,,infty -2022-08-17 15:59:00,1831.05,,infty -2022-08-17 16:00:00,1829.24,,infty -2022-08-17 16:01:00,1829.34,,infty -2022-08-17 16:02:00,1830.48,,infty -2022-08-17 16:03:00,1832.64,,infty -2022-08-17 16:04:00,1833.55,,infty -2022-08-17 16:05:00,1832.72,,infty -2022-08-17 16:06:00,1832.4,,infty -2022-08-17 16:07:00,1831.0,,infty 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16:35:00,1835.6,,infty -2022-08-17 16:36:00,1835.1,,infty -2022-08-17 16:37:00,1834.11,,infty -2022-08-17 16:38:00,1837.24,,infty -2022-08-17 16:39:00,1836.92,,infty -2022-08-17 16:40:00,1838.34,,infty -2022-08-17 16:41:00,1839.16,,infty -2022-08-17 16:42:00,1837.13,,infty -2022-08-17 16:43:00,1837.56,,infty -2022-08-17 16:44:00,1836.13,,infty -2022-08-17 16:45:00,1836.63,,infty -2022-08-17 16:46:00,1840.03,,infty -2022-08-17 16:47:00,1840.2,,infty -2022-08-17 16:48:00,1839.83,,infty -2022-08-17 16:49:00,1840.0,,infty -2022-08-17 16:50:00,1838.32,,infty -2022-08-17 16:51:00,1839.37,,infty -2022-08-17 16:52:00,1838.5,,infty -2022-08-17 16:53:00,1838.81,,infty -2022-08-17 16:54:00,1839.06,,infty -2022-08-17 16:55:00,1839.74,,infty -2022-08-17 16:56:00,1841.14,,infty -2022-08-17 16:57:00,1839.67,,infty -2022-08-17 16:58:00,1840.61,,infty -2022-08-17 16:59:00,1840.34,,infty -2022-08-17 17:00:00,1843.7,,infty -2022-08-17 17:01:00,1840.0,,infty -2022-08-17 17:02:00,1835.85,,infty -2022-08-17 17:03:00,1839.01,,infty -2022-08-17 17:04:00,1839.77,,infty -2022-08-17 17:05:00,1842.27,,infty -2022-08-17 17:06:00,1840.53,,infty -2022-08-17 17:07:00,1839.57,,infty -2022-08-17 17:08:00,1838.82,,infty -2022-08-17 17:09:00,1838.14,,infty -2022-08-17 17:10:00,1838.18,,infty -2022-08-17 17:11:00,1839.05,,infty -2022-08-17 17:12:00,1838.19,,infty -2022-08-17 17:13:00,1839.19,,infty -2022-08-17 17:14:00,1838.53,,infty -2022-08-17 17:15:00,1840.31,,infty -2022-08-17 17:16:00,1840.97,,infty -2022-08-17 17:17:00,1837.69,,infty -2022-08-17 17:18:00,1835.73,,infty -2022-08-17 17:19:00,1835.15,,infty -2022-08-17 17:20:00,1837.1,,infty -2022-08-17 17:21:00,1840.84,,infty -2022-08-17 17:22:00,1841.46,,infty -2022-08-17 17:23:00,1843.95,,infty -2022-08-17 17:24:00,1842.02,,infty -2022-08-17 17:25:00,1843.67,,infty -2022-08-17 17:26:00,1844.34,,infty -2022-08-17 17:27:00,1843.81,,infty -2022-08-17 17:28:00,1845.57,,infty -2022-08-17 17:29:00,1842.94,,infty -2022-08-17 17:30:00,1844.65,,infty 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17:58:00,1845.6,,infty -2022-08-17 17:59:00,1850.7,,infty -2022-08-17 18:00:00,1842.04,,infty -2022-08-17 18:01:00,1847.45,,infty -2022-08-17 18:02:00,1853.04,,infty -2022-08-17 18:03:00,1851.56,,infty -2022-08-17 18:04:00,1850.57,,infty -2022-08-17 18:05:00,1847.67,,infty -2022-08-17 18:06:00,1841.26,,infty -2022-08-17 18:07:00,1837.23,,infty -2022-08-17 18:08:00,1840.56,,infty -2022-08-17 18:09:00,1843.73,,infty -2022-08-17 18:10:00,1843.5,,infty -2022-08-17 18:11:00,1851.82,,infty -2022-08-17 18:12:00,1851.03,,infty -2022-08-17 18:13:00,1848.88,,infty -2022-08-17 18:14:00,1854.02,,infty -2022-08-17 18:15:00,1853.3,,infty -2022-08-17 18:16:00,1850.62,,infty -2022-08-17 18:17:00,1848.45,,infty -2022-08-17 18:18:00,1853.98,,infty -2022-08-17 18:19:00,1853.89,,infty -2022-08-17 18:20:00,1853.83,,infty -2022-08-17 18:21:00,1849.59,,infty -2022-08-17 18:22:00,1852.05,,infty -2022-08-17 18:23:00,1848.87,,infty -2022-08-17 18:24:00,1851.65,,infty -2022-08-17 18:25:00,1850.24,,infty 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18:53:00,1859.55,,infty -2022-08-17 18:54:00,1856.76,,infty -2022-08-17 18:55:00,1856.21,,infty -2022-08-17 18:56:00,1858.2,,infty -2022-08-17 18:57:00,1856.82,,infty -2022-08-17 18:58:00,1853.82,,infty -2022-08-17 18:59:00,1850.47,,infty -2022-08-17 19:00:00,1848.56,,infty -2022-08-17 19:01:00,1848.97,,infty -2022-08-17 19:02:00,1851.26,,infty -2022-08-17 19:03:00,1852.31,,infty -2022-08-17 19:04:00,1853.79,,infty -2022-08-17 19:05:00,1854.65,,infty -2022-08-17 19:06:00,1852.46,,infty -2022-08-17 19:07:00,1851.27,,infty -2022-08-17 19:08:00,1851.45,,infty -2022-08-17 19:09:00,1849.24,,infty -2022-08-17 19:10:00,1844.95,,infty -2022-08-17 19:11:00,1839.15,,infty -2022-08-17 19:12:00,1841.62,,infty -2022-08-17 19:13:00,1836.37,,infty -2022-08-17 19:14:00,1836.8,,infty -2022-08-17 19:15:00,1833.78,,infty -2022-08-17 19:16:00,1836.76,,infty -2022-08-17 19:17:00,1838.59,,infty -2022-08-17 19:18:00,1837.81,,infty -2022-08-17 19:19:00,1835.94,,infty -2022-08-17 19:20:00,1835.25,,infty 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19:48:00,1839.05,,infty -2022-08-17 19:49:00,1838.84,,infty -2022-08-17 19:50:00,1841.29,,infty -2022-08-17 19:51:00,1838.93,,infty -2022-08-17 19:52:00,1839.92,,infty -2022-08-17 19:53:00,1839.41,,infty -2022-08-17 19:54:00,1837.23,,infty -2022-08-17 19:55:00,1837.65,,infty -2022-08-17 19:56:00,1836.99,,infty -2022-08-17 19:57:00,1837.46,,infty -2022-08-17 19:58:00,1839.4,,infty -2022-08-17 19:59:00,1838.38,,infty -2022-08-17 20:00:00,1836.7,,infty -2022-08-17 20:01:00,1837.12,,infty -2022-08-17 20:02:00,1833.86,,infty -2022-08-17 20:03:00,1831.92,,infty -2022-08-17 20:04:00,1829.89,,infty -2022-08-17 20:05:00,1831.13,,infty -2022-08-17 20:06:00,1831.61,,infty -2022-08-17 20:07:00,1832.39,,infty -2022-08-17 20:08:00,1835.07,,infty -2022-08-17 20:09:00,1836.58,,infty -2022-08-17 20:10:00,1837.57,,infty -2022-08-17 20:11:00,1844.57,,infty -2022-08-17 20:12:00,1843.59,,infty -2022-08-17 20:13:00,1842.95,,infty -2022-08-17 20:14:00,1842.35,,infty -2022-08-17 20:15:00,1844.6,,infty 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20:43:00,1849.8,,infty -2022-08-17 20:44:00,1850.0,,infty -2022-08-17 20:45:00,1848.87,,infty -2022-08-17 20:46:00,1851.0,,infty -2022-08-17 20:47:00,1851.61,,infty -2022-08-17 20:48:00,1851.73,,infty -2022-08-17 20:49:00,1850.24,,infty -2022-08-17 20:50:00,1850.62,,infty -2022-08-17 20:51:00,1850.68,,infty -2022-08-17 20:52:00,1849.64,,infty -2022-08-17 20:53:00,1849.69,,infty -2022-08-17 20:54:00,1849.78,,infty -2022-08-17 20:55:00,1849.91,,infty -2022-08-17 20:56:00,1850.83,,infty -2022-08-17 20:57:00,1850.33,,infty -2022-08-17 20:58:00,1850.63,,infty -2022-08-17 20:59:00,1851.39,,infty -2022-08-17 21:00:00,1849.72,,infty -2022-08-17 21:01:00,1849.84,,infty -2022-08-17 21:02:00,1849.68,,infty -2022-08-17 21:03:00,1848.7,,infty -2022-08-17 21:04:00,1845.42,,infty -2022-08-17 21:05:00,1847.41,,infty -2022-08-17 21:06:00,1847.4,,infty -2022-08-17 21:07:00,1847.93,,infty -2022-08-17 21:08:00,1847.27,,infty -2022-08-17 21:09:00,1846.87,,infty -2022-08-17 21:10:00,1846.7,,infty 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21:38:00,1835.05,,infty -2022-08-17 21:39:00,1834.7,,infty -2022-08-17 21:40:00,1837.54,,infty -2022-08-17 21:41:00,1838.36,,infty -2022-08-17 21:42:00,1837.72,,infty -2022-08-17 21:43:00,1837.09,,infty -2022-08-17 21:44:00,1839.73,,infty -2022-08-17 21:45:00,1839.64,,infty -2022-08-17 21:46:00,1838.72,,infty -2022-08-17 21:47:00,1840.73,,infty -2022-08-17 21:48:00,1842.83,,infty -2022-08-17 21:49:00,1841.35,,infty -2022-08-17 21:50:00,1840.81,,infty -2022-08-17 21:51:00,1838.69,,infty -2022-08-17 21:52:00,1838.57,,infty -2022-08-17 21:53:00,1838.18,,infty -2022-08-17 21:54:00,1839.52,,infty -2022-08-17 21:55:00,1840.27,,infty -2022-08-17 21:56:00,1839.92,,infty -2022-08-17 21:57:00,1839.84,,infty -2022-08-17 21:58:00,1840.61,,infty -2022-08-17 21:59:00,1840.75,,infty -2022-08-17 22:00:00,1837.65,,infty -2022-08-17 22:01:00,1838.17,,infty -2022-08-17 22:02:00,1836.59,,infty -2022-08-17 22:03:00,1836.17,,infty -2022-08-17 22:04:00,1832.0,,infty -2022-08-17 22:05:00,1835.2,,infty 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22:33:00,1835.37,,infty -2022-08-17 22:34:00,1835.37,,infty -2022-08-17 22:35:00,1834.95,,infty -2022-08-17 22:36:00,1833.0,,infty -2022-08-17 22:37:00,1834.73,,infty -2022-08-17 22:38:00,1835.16,,infty -2022-08-17 22:39:00,1835.0,,infty -2022-08-17 22:40:00,1836.1,,infty -2022-08-17 22:41:00,1837.45,,infty -2022-08-17 22:42:00,1837.13,,infty -2022-08-17 22:43:00,1835.99,,infty -2022-08-17 22:44:00,1834.16,,infty -2022-08-17 22:45:00,1835.44,,infty -2022-08-17 22:46:00,1834.67,,infty -2022-08-17 22:47:00,1835.89,,infty -2022-08-17 22:48:00,1836.34,,infty -2022-08-17 22:49:00,1836.29,,infty -2022-08-17 22:50:00,1834.66,,infty -2022-08-17 22:51:00,1833.16,,infty -2022-08-17 22:52:00,1832.57,,infty -2022-08-17 22:53:00,1833.86,,infty -2022-08-17 22:54:00,1831.61,,infty -2022-08-17 22:55:00,1832.1,,infty -2022-08-17 22:56:00,1831.55,,infty -2022-08-17 22:57:00,1829.51,,infty -2022-08-17 22:58:00,1827.18,,infty -2022-08-17 22:59:00,1828.96,,infty -2022-08-17 23:00:00,1830.73,,infty 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23:28:00,1829.24,,infty -2022-08-17 23:29:00,1832.37,,infty -2022-08-17 23:30:00,1834.19,,infty -2022-08-17 23:31:00,1834.92,,infty -2022-08-17 23:32:00,1836.28,,infty -2022-08-17 23:33:00,1835.51,,infty -2022-08-17 23:34:00,1835.18,,infty -2022-08-17 23:35:00,1836.46,,infty -2022-08-17 23:36:00,1836.79,,infty -2022-08-17 23:37:00,1836.45,,infty -2022-08-17 23:38:00,1834.43,,infty -2022-08-17 23:39:00,1834.32,,infty -2022-08-17 23:40:00,1835.85,,infty -2022-08-17 23:41:00,1834.76,,infty -2022-08-17 23:42:00,1834.83,,infty -2022-08-17 23:43:00,1834.12,,infty -2022-08-17 23:44:00,1836.23,,infty -2022-08-17 23:45:00,1835.21,,infty -2022-08-17 23:46:00,1835.91,,infty -2022-08-17 23:47:00,1835.31,,infty -2022-08-17 23:48:00,1835.29,,infty -2022-08-17 23:49:00,1834.18,,infty -2022-08-17 23:50:00,1835.57,,infty -2022-08-17 23:51:00,1832.62,,infty -2022-08-17 23:52:00,1831.0,,infty -2022-08-17 23:53:00,1829.6,,infty -2022-08-17 23:54:00,1830.05,,infty -2022-08-17 23:55:00,1830.48,,infty 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00:23:00,1834.43,,infty -2022-08-18 00:24:00,1831.3,,infty -2022-08-18 00:25:00,1826.2,,infty -2022-08-18 00:26:00,1822.62,,infty -2022-08-18 00:27:00,1823.84,,infty -2022-08-18 00:28:00,1826.92,,infty -2022-08-18 00:29:00,1827.47,,infty -2022-08-18 00:30:00,1829.61,,infty -2022-08-18 00:31:00,1836.49,,infty -2022-08-18 00:32:00,1838.52,,infty -2022-08-18 00:33:00,1837.69,,infty -2022-08-18 00:34:00,1838.82,,infty -2022-08-18 00:35:00,1842.61,,infty -2022-08-18 00:36:00,1843.52,,infty -2022-08-18 00:37:00,1845.68,,infty -2022-08-18 00:38:00,1846.53,,infty -2022-08-18 00:39:00,1846.12,,infty -2022-08-18 00:40:00,1843.8,,infty -2022-08-18 00:41:00,1844.61,,infty -2022-08-18 00:42:00,1843.31,,infty -2022-08-18 00:43:00,1843.46,,infty -2022-08-18 00:44:00,1848.31,,infty -2022-08-18 00:45:00,1849.54,,infty -2022-08-18 00:46:00,1847.58,,infty -2022-08-18 00:47:00,1848.22,,infty -2022-08-18 00:48:00,1848.65,,infty -2022-08-18 00:49:00,1847.29,,infty -2022-08-18 00:50:00,1845.87,,infty 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01:18:00,1845.63,,infty -2022-08-18 01:19:00,1851.64,,infty -2022-08-18 01:20:00,1850.47,,infty -2022-08-18 01:21:00,1851.06,,infty -2022-08-18 01:22:00,1848.2,,infty -2022-08-18 01:23:00,1848.6,,infty -2022-08-18 01:24:00,1847.36,,infty -2022-08-18 01:25:00,1847.51,,infty -2022-08-18 01:26:00,1848.94,,infty -2022-08-18 01:27:00,1848.21,,infty -2022-08-18 01:28:00,1850.21,,infty -2022-08-18 01:29:00,1855.13,,infty -2022-08-18 01:30:00,1852.14,,infty -2022-08-18 01:31:00,1850.35,,infty -2022-08-18 01:32:00,1850.66,,infty -2022-08-18 01:33:00,1849.46,,infty -2022-08-18 01:34:00,1850.73,,infty -2022-08-18 01:35:00,1850.32,,infty -2022-08-18 01:36:00,1849.92,,infty -2022-08-18 01:37:00,1850.15,,infty -2022-08-18 01:38:00,1851.2,,infty -2022-08-18 01:39:00,1850.88,,infty -2022-08-18 01:40:00,1849.51,,infty -2022-08-18 01:41:00,1850.6,,infty -2022-08-18 01:42:00,1851.81,,infty -2022-08-18 01:43:00,1850.62,,infty -2022-08-18 01:44:00,1851.71,,infty -2022-08-18 01:45:00,1849.7,,infty 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02:13:00,1861.53,,infty -2022-08-18 02:14:00,1861.65,,infty -2022-08-18 02:15:00,1862.28,,infty -2022-08-18 02:16:00,1862.66,,infty -2022-08-18 02:17:00,1860.96,,infty -2022-08-18 02:18:00,1860.57,,infty -2022-08-18 02:19:00,1860.3,,infty -2022-08-18 02:20:00,1857.49,,infty -2022-08-18 02:21:00,1856.3,,infty -2022-08-18 02:22:00,1853.67,,infty -2022-08-18 02:23:00,1855.07,,infty -2022-08-18 02:24:00,1854.22,,infty -2022-08-18 02:25:00,1853.65,,infty -2022-08-18 02:26:00,1853.88,,infty -2022-08-18 02:27:00,1846.72,,infty -2022-08-18 02:28:00,1847.65,,infty -2022-08-18 02:29:00,1845.19,,infty -2022-08-18 02:30:00,1846.84,,infty -2022-08-18 02:31:00,1847.23,,infty -2022-08-18 02:32:00,1849.43,,infty -2022-08-18 02:33:00,1848.77,,infty -2022-08-18 02:34:00,1850.53,,infty -2022-08-18 02:35:00,1849.36,,infty -2022-08-18 02:36:00,1847.7,,infty -2022-08-18 02:37:00,1847.14,,infty -2022-08-18 02:38:00,1848.47,,infty -2022-08-18 02:39:00,1848.91,,infty -2022-08-18 02:40:00,1849.58,,infty 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03:08:00,1852.93,,infty -2022-08-18 03:09:00,1850.24,,infty -2022-08-18 03:10:00,1851.05,,infty -2022-08-18 03:11:00,1850.74,,infty -2022-08-18 03:12:00,1849.47,,infty -2022-08-18 03:13:00,1849.87,,infty -2022-08-18 03:14:00,1850.41,,infty -2022-08-18 03:15:00,1852.39,,infty -2022-08-18 03:16:00,1853.07,,infty -2022-08-18 03:17:00,1851.0,,infty -2022-08-18 03:18:00,1851.08,,infty -2022-08-18 03:19:00,1851.78,,infty -2022-08-18 03:20:00,1852.47,,infty -2022-08-18 03:21:00,1851.93,,infty -2022-08-18 03:22:00,1853.75,,infty -2022-08-18 03:23:00,1854.01,,infty -2022-08-18 03:24:00,1852.93,,infty -2022-08-18 03:25:00,1852.8,,infty -2022-08-18 03:26:00,1850.3,,infty -2022-08-18 03:27:00,1850.03,,infty -2022-08-18 03:28:00,1850.92,,infty -2022-08-18 03:29:00,1851.51,,infty -2022-08-18 03:30:00,1851.38,,infty -2022-08-18 03:31:00,1851.01,,infty -2022-08-18 03:32:00,1852.83,,infty -2022-08-18 03:33:00,1852.06,,infty -2022-08-18 03:34:00,1852.44,,infty -2022-08-18 03:35:00,1853.28,,infty 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04:03:00,1848.17,,infty -2022-08-18 04:04:00,1849.29,,infty -2022-08-18 04:05:00,1848.36,,infty -2022-08-18 04:06:00,1849.99,,infty -2022-08-18 04:07:00,1850.39,,infty -2022-08-18 04:08:00,1850.26,,infty -2022-08-18 04:09:00,1851.01,,infty -2022-08-18 04:10:00,1851.21,,infty -2022-08-18 04:11:00,1852.1,,infty -2022-08-18 04:12:00,1849.23,,infty -2022-08-18 04:13:00,1849.65,,infty -2022-08-18 04:14:00,1849.08,,infty -2022-08-18 04:15:00,1849.26,,infty -2022-08-18 04:16:00,1850.91,,infty -2022-08-18 04:17:00,1850.18,,infty -2022-08-18 04:18:00,1851.18,,infty -2022-08-18 04:19:00,1850.72,,infty -2022-08-18 04:20:00,1851.63,,infty -2022-08-18 04:21:00,1849.49,,infty -2022-08-18 04:22:00,1848.27,,infty -2022-08-18 04:23:00,1848.0,,infty -2022-08-18 04:24:00,1848.65,,infty -2022-08-18 04:25:00,1847.72,,infty -2022-08-18 04:26:00,1847.47,,infty -2022-08-18 04:27:00,1848.34,,infty -2022-08-18 04:28:00,1848.41,,infty -2022-08-18 04:29:00,1847.06,,infty -2022-08-18 04:30:00,1847.38,,infty 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04:58:00,1844.83,,infty -2022-08-18 04:59:00,1844.0,,infty -2022-08-18 05:00:00,1844.27,,infty -2022-08-18 05:01:00,1844.36,,infty -2022-08-18 05:02:00,1843.31,,infty -2022-08-18 05:03:00,1848.86,,infty -2022-08-18 05:04:00,1847.99,,infty -2022-08-18 05:05:00,1847.11,,infty -2022-08-18 05:06:00,1849.13,,infty -2022-08-18 05:07:00,1848.36,,infty -2022-08-18 05:08:00,1847.74,,infty -2022-08-18 05:09:00,1850.79,,infty -2022-08-18 05:10:00,1849.28,,infty -2022-08-18 05:11:00,1851.39,,infty -2022-08-18 05:12:00,1851.09,,infty -2022-08-18 05:13:00,1849.49,,infty -2022-08-18 05:14:00,1848.86,,infty -2022-08-18 05:15:00,1848.14,,infty -2022-08-18 05:16:00,1850.21,,infty -2022-08-18 05:17:00,1850.15,,infty -2022-08-18 05:18:00,1850.48,,infty -2022-08-18 05:19:00,1850.98,,infty -2022-08-18 05:20:00,1850.98,,infty -2022-08-18 05:21:00,1851.07,,infty -2022-08-18 05:22:00,1850.42,,infty -2022-08-18 05:23:00,1849.29,,infty -2022-08-18 05:24:00,1850.4,,infty -2022-08-18 05:25:00,1850.22,,infty 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05:53:00,1845.24,,infty -2022-08-18 05:54:00,1846.44,,infty -2022-08-18 05:55:00,1846.8,,infty -2022-08-18 05:56:00,1847.46,,infty -2022-08-18 05:57:00,1845.86,,infty -2022-08-18 05:58:00,1844.99,,infty -2022-08-18 05:59:00,1844.16,,infty -2022-08-18 06:00:00,1843.24,,infty -2022-08-18 06:01:00,1846.12,,infty -2022-08-18 06:02:00,1847.16,,infty -2022-08-18 06:03:00,1850.13,,infty -2022-08-18 06:04:00,1849.54,,infty -2022-08-18 06:05:00,1849.03,,infty -2022-08-18 06:06:00,1849.18,,infty -2022-08-18 06:07:00,1847.98,,infty -2022-08-18 06:08:00,1849.17,,infty -2022-08-18 06:09:00,1851.58,,infty -2022-08-18 06:10:00,1851.76,,infty -2022-08-18 06:11:00,1851.43,,infty -2022-08-18 06:12:00,1850.2,,infty -2022-08-18 06:13:00,1849.92,,infty -2022-08-18 06:14:00,1850.67,,infty -2022-08-18 06:15:00,1851.56,,infty -2022-08-18 06:16:00,1850.99,,infty -2022-08-18 06:17:00,1850.21,,infty -2022-08-18 06:18:00,1851.21,,infty -2022-08-18 06:19:00,1851.27,,infty -2022-08-18 06:20:00,1849.38,,infty 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06:48:00,1844.65,,infty -2022-08-18 06:49:00,1845.21,,infty -2022-08-18 06:50:00,1844.63,,infty -2022-08-18 06:51:00,1845.21,,infty -2022-08-18 06:52:00,1841.75,,infty -2022-08-18 06:53:00,1840.74,,infty -2022-08-18 06:54:00,1840.03,,infty -2022-08-18 06:55:00,1839.46,,infty -2022-08-18 06:56:00,1839.7,,infty -2022-08-18 06:57:00,1839.35,,infty -2022-08-18 06:58:00,1839.23,,infty -2022-08-18 06:59:00,1838.97,,infty -2022-08-18 07:00:00,1839.84,,infty -2022-08-18 07:01:00,1841.59,,infty -2022-08-18 07:02:00,1842.28,,infty -2022-08-18 07:03:00,1839.33,,infty -2022-08-18 07:04:00,1837.98,,infty -2022-08-18 07:05:00,1836.39,,infty -2022-08-18 07:06:00,1839.45,,infty -2022-08-18 07:07:00,1841.51,,infty -2022-08-18 07:08:00,1843.66,,infty -2022-08-18 07:09:00,1843.45,,infty -2022-08-18 07:10:00,1843.14,,infty -2022-08-18 07:11:00,1841.94,,infty -2022-08-18 07:12:00,1841.15,,infty -2022-08-18 07:13:00,1839.32,,infty -2022-08-18 07:14:00,1840.82,,infty -2022-08-18 07:15:00,1841.96,,infty 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07:43:00,1844.82,,infty -2022-08-18 07:44:00,1844.45,,infty -2022-08-18 07:45:00,1844.72,,infty -2022-08-18 07:46:00,1847.24,,infty -2022-08-18 07:47:00,1846.65,,infty -2022-08-18 07:48:00,1845.68,,infty -2022-08-18 07:49:00,1847.03,,infty -2022-08-18 07:50:00,1846.12,,infty -2022-08-18 07:51:00,1844.35,,infty -2022-08-18 07:52:00,1845.29,,infty -2022-08-18 07:53:00,1844.12,,infty -2022-08-18 07:54:00,1844.0,,infty -2022-08-18 07:55:00,1845.41,,infty -2022-08-18 07:56:00,1845.13,,infty -2022-08-18 07:57:00,1845.45,,infty -2022-08-18 07:58:00,1845.38,,infty -2022-08-18 07:59:00,1844.98,,infty -2022-08-18 08:00:00,1845.06,,infty -2022-08-18 08:01:00,1846.61,,infty -2022-08-18 08:02:00,1846.43,,infty -2022-08-18 08:03:00,1847.36,,infty -2022-08-18 08:04:00,1845.89,,infty -2022-08-18 08:05:00,1847.35,,infty -2022-08-18 08:06:00,1844.28,,infty -2022-08-18 08:07:00,1846.28,,infty -2022-08-18 08:08:00,1846.26,,infty -2022-08-18 08:09:00,1845.91,,infty -2022-08-18 08:10:00,1844.57,,infty 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08:38:00,1837.03,,infty -2022-08-18 08:39:00,1836.4,,infty -2022-08-18 08:40:00,1835.4,,infty -2022-08-18 08:41:00,1836.62,,infty -2022-08-18 08:42:00,1835.05,,infty -2022-08-18 08:43:00,1836.68,,infty -2022-08-18 08:44:00,1842.72,,infty -2022-08-18 08:45:00,1845.02,,infty -2022-08-18 08:46:00,1848.03,,infty -2022-08-18 08:47:00,1848.51,,infty -2022-08-18 08:48:00,1848.93,,infty -2022-08-18 08:49:00,1846.61,,infty -2022-08-18 08:50:00,1845.74,,infty -2022-08-18 08:51:00,1843.56,,infty -2022-08-18 08:52:00,1841.64,,infty -2022-08-18 08:53:00,1843.82,,infty -2022-08-18 08:54:00,1843.46,,infty -2022-08-18 08:55:00,1843.29,,infty -2022-08-18 08:56:00,1845.99,,infty -2022-08-18 08:57:00,1846.56,,infty -2022-08-18 08:58:00,1848.23,,infty -2022-08-18 08:59:00,1848.69,,infty -2022-08-18 09:00:00,1848.11,,infty -2022-08-18 09:01:00,1848.4,,infty -2022-08-18 09:02:00,1847.86,,infty -2022-08-18 09:03:00,1847.71,,infty -2022-08-18 09:04:00,1847.65,,infty -2022-08-18 09:05:00,1849.08,,infty 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09:33:00,1848.26,,infty -2022-08-18 09:34:00,1847.69,,infty -2022-08-18 09:35:00,1847.99,,infty -2022-08-18 09:36:00,1849.78,,infty -2022-08-18 09:37:00,1850.0,,infty -2022-08-18 09:38:00,1850.29,,infty -2022-08-18 09:39:00,1849.69,,infty -2022-08-18 09:40:00,1849.43,,infty -2022-08-18 09:41:00,1850.78,,infty -2022-08-18 09:42:00,1850.41,,infty -2022-08-18 09:43:00,1850.34,,infty -2022-08-18 09:44:00,1849.31,,infty -2022-08-18 09:45:00,1848.77,,infty -2022-08-18 09:46:00,1850.61,,infty -2022-08-18 09:47:00,1852.25,,infty -2022-08-18 09:48:00,1850.6,,infty -2022-08-18 09:49:00,1852.08,,infty -2022-08-18 09:50:00,1850.55,,infty -2022-08-18 09:51:00,1851.74,,infty -2022-08-18 09:52:00,1853.44,,infty -2022-08-18 09:53:00,1854.49,,infty -2022-08-18 09:54:00,1853.97,,infty -2022-08-18 09:55:00,1855.1,,infty -2022-08-18 09:56:00,1854.74,,infty -2022-08-18 09:57:00,1854.44,,infty -2022-08-18 09:58:00,1855.75,,infty -2022-08-18 09:59:00,1856.49,,infty -2022-08-18 10:00:00,1856.8,,infty 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23:18:00,1555.43,,open_close +2022-08-31 23:19:00,1553.02,,open_close +2022-08-31 23:20:00,1554.65,,open_close +2022-08-31 23:21:00,1553.45,,open_close +2022-08-31 23:22:00,1552.58,,open_close +2022-08-31 23:23:00,1553.04,,open_close +2022-08-31 23:24:00,1555.06,,open_close +2022-08-31 23:25:00,1554.17,,open_close +2022-08-31 23:26:00,1555.54,,open_close +2022-08-31 23:27:00,1554.92,,open_close +2022-08-31 23:28:00,1552.89,,open_close +2022-08-31 23:29:00,1552.24,,open_close +2022-08-31 23:30:00,1553.2,,open_close +2022-08-31 23:31:00,1548.57,,minus_infty +2022-08-31 23:32:00,1549.59,,open_close +2022-08-31 23:33:00,1547.77,,minus_infty +2022-08-31 23:34:00,1548.16,,minus_infty +2022-08-31 23:35:00,1549.27,,minus_infty +2022-08-31 23:36:00,1545.66,,minus_infty +2022-08-31 23:37:00,1546.76,,minus_infty +2022-08-31 23:38:00,1546.78,,minus_infty +2022-08-31 23:39:00,1546.2,,minus_infty +2022-08-31 23:40:00,1545.05,,minus_infty +2022-08-31 23:41:00,1543.41,,minus_infty +2022-08-31 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03:07:00,1563.64,,infty +2022-09-01 03:08:00,1559.49,,infty +2022-09-01 03:09:00,1560.59,,infty +2022-09-01 03:10:00,1558.58,,infty +2022-09-01 03:11:00,1559.05,,infty +2022-09-01 03:12:00,1560.11,,infty +2022-09-01 03:13:00,1560.42,,infty +2022-09-01 03:14:00,1559.42,,infty +2022-09-01 03:15:00,1557.46,,open_close +2022-09-01 03:16:00,1556.98,,open_close +2022-09-01 03:17:00,1555.76,,open_close +2022-09-01 03:18:00,1557.39,,open_close +2022-09-01 03:19:00,1559.21,,infty +2022-09-01 03:20:00,1561.32,,infty +2022-09-01 03:21:00,1560.66,,infty +2022-09-01 03:22:00,1558.63,,infty +2022-09-01 03:23:00,1559.81,,infty +2022-09-01 03:24:00,1559.52,,infty +2022-09-01 03:25:00,1559.16,,infty +2022-09-01 03:26:00,1556.61,,open_close +2022-09-01 03:27:00,1555.74,,open_close +2022-09-01 03:28:00,1553.96,,open_close +2022-09-01 03:29:00,1555.71,,open_close +2022-09-01 03:30:00,1554.89,,open_close +2022-09-01 03:31:00,1553.98,,open_close +2022-09-01 03:32:00,1556.55,,open_close +2022-09-01 03:33:00,1554.61,,open_close +2022-09-01 03:34:00,1556.53,,open_close +2022-09-01 03:35:00,1554.49,,open_close +2022-09-01 03:36:00,1554.05,,open_close +2022-09-01 03:37:00,1554.79,,open_close +2022-09-01 03:38:00,1555.47,,open_close +2022-09-01 03:39:00,1555.03,,open_close +2022-09-01 03:40:00,1553.53,,open_close +2022-09-01 03:41:00,1552.7,,open_close +2022-09-01 03:42:00,1550.1,,open_close +2022-09-01 03:43:00,1546.38,,minus_infty +2022-09-01 03:44:00,1544.57,,minus_infty +2022-09-01 03:45:00,1543.95,,minus_infty +2022-09-01 03:46:00,1541.32,,minus_infty +2022-09-01 03:47:00,1539.82,,minus_infty +2022-09-01 03:48:00,1539.91,,minus_infty +2022-09-01 03:49:00,1547.33,,minus_infty +2022-09-01 03:50:00,1548.33,,minus_infty +2022-09-01 03:51:00,1546.82,,minus_infty +2022-09-01 03:52:00,1548.1,,minus_infty +2022-09-01 03:53:00,1552.7,,open_close +2022-09-01 03:54:00,1554.2,,open_close +2022-09-01 03:55:00,1552.1,,open_close +2022-09-01 03:56:00,1549.33,,minus_infty +2022-09-01 03:57:00,1547.89,,minus_infty +2022-09-01 03:58:00,1548.03,,minus_infty +2022-09-01 03:59:00,1548.72,,minus_infty +2022-09-01 04:00:00,1550.2,,open_close +2022-09-01 04:01:00,1552.21,,open_close +2022-09-01 04:02:00,1551.22,,open_close +2022-09-01 04:03:00,1551.24,,open_close +2022-09-01 04:04:00,1551.64,,open_close +2022-09-01 04:05:00,1552.56,,open_close +2022-09-01 04:06:00,1554.18,,open_close +2022-09-01 04:07:00,1553.58,,open_close +2022-09-01 04:08:00,1553.77,,open_close +2022-09-01 04:09:00,1550.0,,open_close +2022-09-01 04:10:00,1547.48,,minus_infty +2022-09-01 04:11:00,1545.42,,minus_infty +2022-09-01 04:12:00,1545.72,,minus_infty +2022-09-01 04:13:00,1545.22,,minus_infty +2022-09-01 04:14:00,1545.22,,minus_infty +2022-09-01 04:15:00,1543.6,,minus_infty +2022-09-01 04:16:00,1543.33,,minus_infty +2022-09-01 04:17:00,1545.49,,minus_infty +2022-09-01 04:18:00,1547.95,,minus_infty +2022-09-01 04:19:00,1547.9,,minus_infty +2022-09-01 04:20:00,1548.21,,minus_infty +2022-09-01 04:21:00,1545.51,,minus_infty +2022-09-01 04:22:00,1547.28,,minus_infty +2022-09-01 04:23:00,1547.07,,minus_infty +2022-09-01 04:24:00,1546.95,,minus_infty +2022-09-01 04:25:00,1546.26,,minus_infty +2022-09-01 04:26:00,1551.12,,open_close +2022-09-01 04:27:00,1550.68,,open_close +2022-09-01 04:28:00,1552.58,,open_close +2022-09-01 04:29:00,1551.33,,open_close +2022-09-01 04:30:00,1552.63,,open_close +2022-09-01 04:31:00,1551.56,,open_close +2022-09-01 04:32:00,1553.01,,open_close +2022-09-01 04:33:00,1552.12,,open_close +2022-09-01 04:34:00,1552.98,,open_close +2022-09-01 04:35:00,1552.29,,open_close +2022-09-01 04:36:00,1552.41,,open_close +2022-09-01 04:37:00,1553.61,,open_close +2022-09-01 04:38:00,1554.79,,open_close +2022-09-01 04:39:00,1553.72,,open_close +2022-09-01 04:40:00,1552.83,,open_close +2022-09-01 04:41:00,1552.24,,open_close +2022-09-01 04:42:00,1553.11,,open_close +2022-09-01 04:43:00,1553.37,,open_close +2022-09-01 04:44:00,1554.25,,open_close +2022-09-01 04:45:00,1555.02,,open_close +2022-09-01 04:46:00,1556.88,,open_close +2022-09-01 04:47:00,1553.33,,open_close +2022-09-01 04:48:00,1554.46,,open_close +2022-09-01 04:49:00,1555.1,,open_close +2022-09-01 04:50:00,1555.16,,open_close +2022-09-01 04:51:00,1554.79,,open_close +2022-09-01 04:52:00,1556.0,,open_close +2022-09-01 04:53:00,1554.16,,open_close +2022-09-01 04:54:00,1556.72,,open_close +2022-09-01 04:55:00,1555.51,,open_close +2022-09-01 04:56:00,1555.54,,open_close +2022-09-01 04:57:00,1552.8,,open_close +2022-09-01 04:58:00,1551.87,,open_close +2022-09-01 04:59:00,1551.59,,open_close +2022-09-01 05:00:00,1551.16,,open_close +2022-09-01 05:01:00,1553.28,,open_close +2022-09-01 05:02:00,1551.19,,open_close +2022-09-01 05:03:00,1550.18,,open_close +2022-09-01 05:04:00,1549.15,,minus_infty +2022-09-01 05:05:00,1552.76,,open_close +2022-09-01 05:06:00,1555.12,,open_close +2022-09-01 05:07:00,1552.83,,open_close +2022-09-01 05:08:00,1553.86,,open_close +2022-09-01 05:09:00,1551.03,,open_close +2022-09-01 05:10:00,1551.17,,open_close +2022-09-01 05:11:00,1552.08,,open_close +2022-09-01 05:12:00,1554.3,,open_close +2022-09-01 05:13:00,1555.23,,open_close +2022-09-01 05:14:00,1558.67,,infty +2022-09-01 05:15:00,1560.22,,infty +2022-09-01 05:16:00,1559.54,,infty +2022-09-01 05:17:00,1558.61,,infty +2022-09-01 05:18:00,1560.15,,infty +2022-09-01 05:19:00,1561.86,,infty +2022-09-01 05:20:00,1560.16,,infty +2022-09-01 05:21:00,1559.62,,infty +2022-09-01 05:22:00,1559.66,,infty +2022-09-01 05:23:00,1559.7,,infty +2022-09-01 05:24:00,1560.41,,infty +2022-09-01 05:25:00,1561.23,,infty +2022-09-01 05:26:00,1561.26,,infty +2022-09-01 05:27:00,1562.53,,infty +2022-09-01 05:28:00,1561.4,,infty +2022-09-01 05:29:00,1561.08,,infty +2022-09-01 05:30:00,1560.1,,infty +2022-09-01 05:31:00,1558.39,,infty +2022-09-01 05:32:00,1557.04,,open_close +2022-09-01 05:33:00,1556.59,,open_close +2022-09-01 05:34:00,1558.42,,infty +2022-09-01 05:35:00,1557.34,,open_close +2022-09-01 05:36:00,1557.51,,open_close +2022-09-01 05:37:00,1559.14,,infty +2022-09-01 05:38:00,1561.01,,infty +2022-09-01 05:39:00,1560.13,,infty +2022-09-01 05:40:00,1560.25,,infty +2022-09-01 05:41:00,1559.32,,infty +2022-09-01 05:42:00,1559.18,,infty +2022-09-01 05:43:00,1559.75,,infty +2022-09-01 05:44:00,1559.15,,infty +2022-09-01 05:45:00,1558.34,,infty +2022-09-01 05:46:00,1558.11,,infty +2022-09-01 05:47:00,1558.5,,infty +2022-09-01 05:48:00,1555.47,,open_close +2022-09-01 05:49:00,1552.77,,open_close +2022-09-01 05:50:00,1551.44,,open_close +2022-09-01 05:51:00,1551.88,,open_close +2022-09-01 05:52:00,1555.11,,open_close +2022-09-01 05:53:00,1555.6,,open_close +2022-09-01 05:54:00,1555.0,,open_close +2022-09-01 05:55:00,1555.08,,open_close +2022-09-01 05:56:00,1554.47,,open_close +2022-09-01 05:57:00,1552.83,,open_close +2022-09-01 05:58:00,1554.23,,open_close +2022-09-01 05:59:00,1553.33,,open_close +2022-09-01 06:00:00,1554.83,,open_close +2022-09-01 06:01:00,1553.69,,open_close +2022-09-01 06:02:00,1554.9,,open_close +2022-09-01 06:03:00,1554.03,,open_close +2022-09-01 06:04:00,1552.61,,open_close +2022-09-01 06:05:00,1549.07,,minus_infty +2022-09-01 06:06:00,1550.83,,open_close +2022-09-01 06:07:00,1549.68,,open_close +2022-09-01 06:08:00,1549.98,,open_close +2022-09-01 06:09:00,1551.94,,open_close +2022-09-01 06:10:00,1549.48,,open_close +2022-09-01 06:11:00,1548.76,,minus_infty +2022-09-01 06:12:00,1548.99,,minus_infty +2022-09-01 06:13:00,1550.94,,open_close +2022-09-01 06:14:00,1551.27,,open_close +2022-09-01 06:15:00,1549.64,,open_close +2022-09-01 06:16:00,1545.97,,minus_infty +2022-09-01 06:17:00,1549.24,,minus_infty +2022-09-01 06:18:00,1545.83,,minus_infty +2022-09-01 06:19:00,1545.11,,minus_infty +2022-09-01 06:20:00,1545.79,,minus_infty +2022-09-01 06:21:00,1546.22,,minus_infty +2022-09-01 06:22:00,1546.98,,minus_infty +2022-09-01 06:23:00,1548.89,,minus_infty +2022-09-01 06:24:00,1547.31,,minus_infty +2022-09-01 06:25:00,1544.75,,minus_infty +2022-09-01 06:26:00,1548.37,,minus_infty +2022-09-01 06:27:00,1550.28,,open_close +2022-09-01 06:28:00,1552.03,,open_close +2022-09-01 06:29:00,1553.16,,open_close +2022-09-01 06:30:00,1553.55,,open_close +2022-09-01 06:31:00,1552.7,,open_close +2022-09-01 06:32:00,1552.59,,open_close +2022-09-01 06:33:00,1551.34,,open_close +2022-09-01 06:34:00,1553.47,,open_close +2022-09-01 06:35:00,1553.41,,open_close +2022-09-01 06:36:00,1555.13,,open_close +2022-09-01 06:37:00,1552.9,,open_close +2022-09-01 06:38:00,1551.49,,open_close +2022-09-01 06:39:00,1552.65,,open_close +2022-09-01 06:40:00,1554.53,,open_close +2022-09-01 06:41:00,1555.06,,open_close +2022-09-01 06:42:00,1554.48,,open_close +2022-09-01 06:43:00,1553.33,,open_close +2022-09-01 06:44:00,1551.56,,open_close +2022-09-01 06:45:00,1551.38,,open_close +2022-09-01 06:46:00,1552.84,,open_close +2022-09-01 06:47:00,1553.75,,open_close +2022-09-01 06:48:00,1553.01,,open_close +2022-09-01 06:49:00,1550.41,,open_close +2022-09-01 06:50:00,1548.41,,minus_infty +2022-09-01 06:51:00,1546.69,,minus_infty +2022-09-01 06:52:00,1548.38,,minus_infty +2022-09-01 06:53:00,1547.53,,minus_infty +2022-09-01 06:54:00,1548.67,,minus_infty +2022-09-01 06:55:00,1549.04,,minus_infty +2022-09-01 06:56:00,1549.81,,open_close +2022-09-01 06:57:00,1550.17,,open_close +2022-09-01 06:58:00,1547.18,,minus_infty +2022-09-01 06:59:00,1550.26,,open_close +2022-09-01 07:00:00,1546.99,,minus_infty +2022-09-01 07:01:00,1544.41,,minus_infty +2022-09-01 07:02:00,1539.98,,minus_infty +2022-09-01 07:03:00,1544.06,,minus_infty +2022-09-01 07:04:00,1544.04,,minus_infty +2022-09-01 07:05:00,1542.71,,minus_infty +2022-09-01 07:06:00,1543.27,,minus_infty +2022-09-01 07:07:00,1544.03,,minus_infty +2022-09-01 07:08:00,1542.19,,minus_infty +2022-09-01 07:09:00,1544.68,,minus_infty +2022-09-01 07:10:00,1542.96,,minus_infty +2022-09-01 07:11:00,1541.83,,minus_infty +2022-09-01 07:12:00,1543.17,,minus_infty +2022-09-01 07:13:00,1543.09,,minus_infty +2022-09-01 07:14:00,1545.21,,minus_infty +2022-09-01 07:15:00,1547.99,,minus_infty +2022-09-01 07:16:00,1545.67,,minus_infty +2022-09-01 07:17:00,1541.2,,minus_infty +2022-09-01 07:18:00,1541.06,,minus_infty +2022-09-01 07:19:00,1541.19,,minus_infty +2022-09-01 07:20:00,1542.37,,minus_infty +2022-09-01 07:21:00,1542.98,,minus_infty +2022-09-01 07:22:00,1544.22,,minus_infty +2022-09-01 07:23:00,1543.45,,minus_infty +2022-09-01 07:24:00,1542.2,,minus_infty +2022-09-01 07:25:00,1542.67,,minus_infty +2022-09-01 07:26:00,1542.87,,minus_infty +2022-09-01 07:27:00,1542.07,,minus_infty +2022-09-01 07:28:00,1542.59,,minus_infty +2022-09-01 07:29:00,1539.23,,minus_infty +2022-09-01 07:30:00,1536.83,,minus_infty +2022-09-01 07:31:00,1535.81,,minus_infty +2022-09-01 07:32:00,1534.81,,minus_infty +2022-09-01 07:33:00,1536.42,,minus_infty +2022-09-01 07:34:00,1537.16,,minus_infty +2022-09-01 07:35:00,1538.46,,minus_infty +2022-09-01 07:36:00,1539.66,,minus_infty +2022-09-01 07:37:00,1541.38,,minus_infty +2022-09-01 07:38:00,1540.93,,minus_infty +2022-09-01 07:39:00,1542.19,,minus_infty +2022-09-01 07:40:00,1541.77,,minus_infty +2022-09-01 07:41:00,1540.9,,minus_infty +2022-09-01 07:42:00,1539.4,,minus_infty +2022-09-01 07:43:00,1542.31,,minus_infty +2022-09-01 07:44:00,1542.55,,minus_infty +2022-09-01 07:45:00,1541.57,,minus_infty +2022-09-01 07:46:00,1541.22,,minus_infty +2022-09-01 07:47:00,1545.46,,minus_infty +2022-09-01 07:48:00,1545.63,,minus_infty +2022-09-01 07:49:00,1545.83,,minus_infty +2022-09-01 07:50:00,1544.94,,minus_infty +2022-09-01 07:51:00,1543.82,,minus_infty +2022-09-01 07:52:00,1542.91,,minus_infty +2022-09-01 07:53:00,1543.65,,minus_infty +2022-09-01 07:54:00,1544.04,,minus_infty +2022-09-01 07:55:00,1543.47,,minus_infty +2022-09-01 07:56:00,1543.69,,minus_infty +2022-09-01 07:57:00,1543.2,,minus_infty +2022-09-01 07:58:00,1543.69,,minus_infty +2022-09-01 07:59:00,1544.65,,minus_infty +2022-09-01 08:00:00,1545.3,,minus_infty +2022-09-01 08:01:00,1541.9,,minus_infty +2022-09-01 08:02:00,1539.94,,minus_infty +2022-09-01 08:03:00,1543.0,,minus_infty +2022-09-01 08:04:00,1543.62,,minus_infty +2022-09-01 08:05:00,1544.4,,minus_infty +2022-09-01 08:06:00,1543.24,,minus_infty +2022-09-01 08:07:00,1543.34,,minus_infty +2022-09-01 08:08:00,1542.62,,minus_infty +2022-09-01 08:09:00,1541.59,,minus_infty +2022-09-01 08:10:00,1541.21,,minus_infty +2022-09-01 08:11:00,1541.78,,minus_infty +2022-09-01 08:12:00,1543.93,,minus_infty +2022-09-01 08:13:00,1548.11,,minus_infty +2022-09-01 08:14:00,1548.85,,minus_infty +2022-09-01 08:15:00,1548.52,,minus_infty +2022-09-01 08:16:00,1546.51,,minus_infty +2022-09-01 08:17:00,1548.1,,minus_infty +2022-09-01 08:18:00,1547.52,,minus_infty +2022-09-01 08:19:00,1545.94,,minus_infty +2022-09-01 08:20:00,1545.79,,minus_infty +2022-09-01 08:21:00,1547.22,,minus_infty +2022-09-01 08:22:00,1546.91,,minus_infty +2022-09-01 08:23:00,1546.61,,minus_infty +2022-09-01 08:24:00,1546.92,,minus_infty +2022-09-01 08:25:00,1546.83,,minus_infty +2022-09-01 08:26:00,1546.51,,minus_infty +2022-09-01 08:27:00,1546.46,,minus_infty +2022-09-01 08:28:00,1545.52,,minus_infty +2022-09-01 08:29:00,1542.77,,minus_infty +2022-09-01 08:30:00,1542.63,,minus_infty +2022-09-01 08:31:00,1540.0,,minus_infty +2022-09-01 08:32:00,1538.15,,minus_infty +2022-09-01 08:33:00,1536.66,,minus_infty +2022-09-01 08:34:00,1533.72,,minus_infty +2022-09-01 08:35:00,1536.4,,minus_infty +2022-09-01 08:36:00,1538.62,,minus_infty +2022-09-01 08:37:00,1544.46,,minus_infty +2022-09-01 08:38:00,1543.42,,minus_infty +2022-09-01 08:39:00,1541.55,,minus_infty +2022-09-01 08:40:00,1541.97,,minus_infty +2022-09-01 08:41:00,1540.62,,minus_infty +2022-09-01 08:42:00,1541.84,,minus_infty +2022-09-01 08:43:00,1539.63,,minus_infty +2022-09-01 08:44:00,1540.28,,minus_infty +2022-09-01 08:45:00,1540.02,,minus_infty +2022-09-01 08:46:00,1539.67,,minus_infty +2022-09-01 08:47:00,1540.23,,minus_infty +2022-09-01 08:48:00,1540.96,,minus_infty +2022-09-01 08:49:00,1542.86,,minus_infty +2022-09-01 08:50:00,1543.44,,minus_infty +2022-09-01 08:51:00,1543.23,,minus_infty +2022-09-01 08:52:00,1542.91,,minus_infty +2022-09-01 08:53:00,1542.41,,minus_infty +2022-09-01 08:54:00,1542.26,,minus_infty +2022-09-01 08:55:00,1543.54,,minus_infty +2022-09-01 08:56:00,1545.15,,minus_infty +2022-09-01 08:57:00,1545.2,,minus_infty +2022-09-01 08:58:00,1545.72,,minus_infty +2022-09-01 08:59:00,1547.42,,minus_infty +2022-09-01 09:00:00,1548.61,,minus_infty +2022-09-01 09:01:00,1548.71,,minus_infty +2022-09-01 09:02:00,1548.05,,minus_infty +2022-09-01 09:03:00,1554.27,,open_close +2022-09-01 09:04:00,1554.09,,open_close +2022-09-01 09:05:00,1554.06,,open_close +2022-09-01 09:06:00,1552.66,,open_close +2022-09-01 09:07:00,1550.31,,open_close +2022-09-01 09:08:00,1550.21,,open_close +2022-09-01 09:09:00,1550.02,,open_close +2022-09-01 09:10:00,1547.91,,minus_infty +2022-09-01 09:11:00,1549.95,,open_close +2022-09-01 09:12:00,1547.38,,minus_infty +2022-09-01 09:13:00,1544.96,,minus_infty +2022-09-01 09:14:00,1543.42,,minus_infty +2022-09-01 09:15:00,1543.64,,minus_infty +2022-09-01 09:16:00,1544.93,,minus_infty +2022-09-01 09:17:00,1543.02,,minus_infty +2022-09-01 09:18:00,1540.78,,minus_infty +2022-09-01 09:19:00,1539.34,,minus_infty +2022-09-01 09:20:00,1539.93,,minus_infty +2022-09-01 09:21:00,1538.89,,minus_infty +2022-09-01 09:22:00,1539.93,,minus_infty +2022-09-01 09:23:00,1542.37,,minus_infty +2022-09-01 09:24:00,1544.02,,minus_infty +2022-09-01 09:25:00,1544.21,,minus_infty +2022-09-01 09:26:00,1545.69,,minus_infty +2022-09-01 09:27:00,1545.73,,minus_infty +2022-09-01 09:28:00,1545.52,,minus_infty +2022-09-01 09:29:00,1546.44,,minus_infty +2022-09-01 09:30:00,1546.85,,minus_infty +2022-09-01 09:31:00,1544.17,,minus_infty +2022-09-01 09:32:00,1543.08,,minus_infty +2022-09-01 09:33:00,1544.88,,minus_infty +2022-09-01 09:34:00,1543.71,,minus_infty +2022-09-01 09:35:00,1544.94,,minus_infty +2022-09-01 09:36:00,1545.78,,minus_infty +2022-09-01 09:37:00,1545.98,,minus_infty +2022-09-01 09:38:00,1545.89,,minus_infty +2022-09-01 09:39:00,1546.91,,minus_infty +2022-09-01 09:40:00,1547.18,,minus_infty +2022-09-01 09:41:00,1545.59,,minus_infty +2022-09-01 09:42:00,1545.07,,minus_infty +2022-09-01 09:43:00,1546.73,,minus_infty +2022-09-01 09:44:00,1547.46,,minus_infty +2022-09-01 09:45:00,1547.09,,minus_infty +2022-09-01 09:46:00,1545.4,,minus_infty +2022-09-01 09:47:00,1546.87,,minus_infty +2022-09-01 09:48:00,1547.31,,minus_infty +2022-09-01 09:49:00,1548.88,,minus_infty +2022-09-01 09:50:00,1551.05,,open_close +2022-09-01 09:51:00,1550.56,,open_close +2022-09-01 09:52:00,1551.64,,open_close +2022-09-01 09:53:00,1552.57,,open_close +2022-09-01 09:54:00,1552.51,,open_close +2022-09-01 09:55:00,1551.38,,open_close +2022-09-01 09:56:00,1550.48,,open_close +2022-09-01 09:57:00,1551.43,,open_close +2022-09-01 09:58:00,1550.11,,open_close +2022-09-01 09:59:00,1550.17,,open_close +2022-09-01 10:00:00,1551.43,,open_close +2022-09-01 10:01:00,1551.69,,open_close +2022-09-01 10:02:00,1550.06,,open_close +2022-09-01 10:03:00,1550.95,,open_close +2022-09-01 10:04:00,1549.64,,open_close +2022-09-01 10:05:00,1549.8,,open_close +2022-09-01 10:06:00,1548.96,,minus_infty +2022-09-01 10:07:00,1549.73,,open_close +2022-09-01 10:08:00,1548.72,,minus_infty +2022-09-01 10:09:00,1549.93,,open_close +2022-09-01 10:10:00,1550.4,,open_close +2022-09-01 10:11:00,1549.93,,open_close +2022-09-01 10:12:00,1551.17,,open_close +2022-09-01 10:13:00,1551.07,,open_close +2022-09-01 10:14:00,1552.09,,open_close +2022-09-01 10:15:00,1551.57,,open_close +2022-09-01 10:16:00,1551.65,,open_close +2022-09-01 10:17:00,1551.25,,open_close +2022-09-01 10:18:00,1553.76,,open_close +2022-09-01 10:19:00,1553.65,,open_close +2022-09-01 10:20:00,1556.45,,open_close +2022-09-01 10:21:00,1552.96,,open_close +2022-09-01 10:22:00,1551.11,,open_close +2022-09-01 10:23:00,1550.71,,open_close +2022-09-01 10:24:00,1552.16,,open_close +2022-09-01 10:25:00,1552.87,,open_close +2022-09-01 10:26:00,1552.45,,open_close +2022-09-01 10:27:00,1553.48,,open_close +2022-09-01 10:28:00,1554.92,,open_close +2022-09-01 10:29:00,1553.94,,open_close +2022-09-01 10:30:00,1552.35,,open_close +2022-09-01 10:31:00,1555.62,,open_close +2022-09-01 10:32:00,1557.9,,open_close +2022-09-01 10:33:00,1557.6,,open_close +2022-09-01 10:34:00,1557.78,,open_close +2022-09-01 10:35:00,1558.81,,infty +2022-09-01 10:36:00,1558.69,,infty +2022-09-01 10:37:00,1557.21,,open_close +2022-09-01 10:38:00,1556.6,,open_close +2022-09-01 10:39:00,1557.65,,open_close +2022-09-01 10:40:00,1557.15,,open_close +2022-09-01 10:41:00,1559.03,,infty +2022-09-01 10:42:00,1558.43,,infty +2022-09-01 10:43:00,1559.97,,infty +2022-09-01 10:44:00,1560.08,,infty +2022-09-01 10:45:00,1560.09,,infty +2022-09-01 10:46:00,1561.47,,infty +2022-09-01 10:47:00,1560.4,,infty +2022-09-01 10:48:00,1558.87,,infty +2022-09-01 10:49:00,1559.56,,infty +2022-09-01 10:50:00,1558.64,,infty +2022-09-01 10:51:00,1559.19,,infty +2022-09-01 10:52:00,1557.52,,open_close +2022-09-01 10:53:00,1556.69,,open_close +2022-09-01 10:54:00,1558.23,,infty +2022-09-01 10:55:00,1559.41,,infty +2022-09-01 10:56:00,1560.14,,infty +2022-09-01 10:57:00,1560.99,,infty +2022-09-01 10:58:00,1563.86,,infty +2022-09-01 10:59:00,1564.97,,infty +2022-09-01 11:00:00,1563.87,,infty +2022-09-01 11:01:00,1566.61,,infty +2022-09-01 11:02:00,1566.94,,infty +2022-09-01 11:03:00,1569.25,,infty +2022-09-01 11:04:00,1568.21,,infty +2022-09-01 11:05:00,1568.15,,infty +2022-09-01 11:06:00,1564.66,,infty +2022-09-01 11:07:00,1561.35,,infty +2022-09-01 11:08:00,1562.42,,infty +2022-09-01 11:09:00,1564.42,,infty +2022-09-01 11:10:00,1563.23,,infty +2022-09-01 11:11:00,1562.58,,infty +2022-09-01 11:12:00,1561.55,,infty +2022-09-01 11:13:00,1562.71,,infty +2022-09-01 11:14:00,1563.43,,infty +2022-09-01 11:15:00,1561.98,,infty +2022-09-01 11:16:00,1563.2,,infty +2022-09-01 11:17:00,1566.48,,infty +2022-09-01 11:18:00,1568.7,,infty +2022-09-01 11:19:00,1567.21,,infty +2022-09-01 11:20:00,1566.14,,infty +2022-09-01 11:21:00,1566.32,,infty +2022-09-01 11:22:00,1566.47,,infty +2022-09-01 11:23:00,1566.47,,infty +2022-09-01 11:24:00,1568.73,,infty +2022-09-01 11:25:00,1566.8,,infty +2022-09-01 11:26:00,1568.11,,infty +2022-09-01 11:27:00,1566.33,,infty +2022-09-01 11:28:00,1565.2,,infty +2022-09-01 11:29:00,1565.19,,infty diff --git a/hedge_scripts/aave.py b/hedge_scripts/aave.py deleted file mode 100644 index c96d9dd..0000000 --- a/hedge_scripts/aave.py +++ /dev/null @@ -1,233 +0,0 @@ -import math -import random -import numpy as np -from hedge_scripts import interval -# import time - -class Aave(object): - - def __init__(self, config): - # assert self.dydx_class_instance == isinstance(dydx) - # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg'] - self.market_price = config['market_price'] - self.interval_current = config['interval_current'] - - self.entry_price = config['entry_price'] - - self.collateral_eth_initial = config['collateral_eth'] - self.collateral_eth = config['collateral_eth'] - self.collateral_usdc = config['collateral_usdc'] - - self.reserve_margin_eth = 0 - self.reserve_margin_usdc = 0 - - self.borrowed_percentage = config['borrowed_pcg'] - self.usdc_status = config['usdc_status'] - - self.debt = config['debt'] - self.debt_initial = config['debt'] - - self.ltv = config['ltv'] - self.price_to_ltv_limit = config['price_to_ltv_limit'] - - self.lending_rate = 0 - self.lending_rate_hourly = 0 - self.interest_on_lending_eth = 0 # aggregated fees - self.interest_on_lending_usd = 0 - self.lending_fees_eth = 0 # fees between last 2 prices - self.lending_fees_usd = 0 - - self.borrowing_rate = 0 - self.borrowing_rate_hourly = 0 - self.interest_on_borrowing = 0 # aggregated fees - self.borrowing_fees = 0 # fees between last 2 prices - - self.lend_minus_borrow_interest = 0 - - self.costs = 0 - # self.historical = pd.DataFrame() - # self.dydx_class_instance = dydx_class_instance - # self.staked_in_protocol = stk - - def collateral_usd(self): - return self.collateral_eth * self.market_price - - def update_debt(self): - """ - it requires having called borrowing_fees_calc() in order to use updated values of last earned fees - """ - self.debt = self.debt + self.borrowing_fees - - def update_collateral(self): - """ - it requires having called lending_fees_calc() in order to use updated values of last earned fees - """ - self.collateral_eth = self.collateral_eth + self.lending_fees_eth - self.collateral_usdc = self.collateral_usd() - - def track_lend_borrow_interest(self): - """ - it requires having called borrowing_fees_calc() and lending_fees_calc() - in order to use updated values of last earned fees - """ - self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing - - def lending_fees_calc(self, freq): - self.simulate_lending_rate() - self.lending_rate_hourly = self.lending_rate / freq - self.lending_fees_eth = self.collateral_eth * self.lending_rate_hourly - self.lending_fees_usd = self.lending_fees_eth * self.market_price - self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth - self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd - - def borrowing_fees_calc(self, freq): - self.simulate_borrowing_rate() - self.borrowing_rate_hourly = self.borrowing_rate / freq - self.borrowing_fees = self.collateral_eth * self.entry_price * self.borrowed_percentage * self.borrowing_rate_hourly - self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees - - def simulate_lending_rate(self): - # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate'] - - # best case - # self.lending_rate = 1.5 / 100 - - # worst case - self.lending_rate = 0.5 / 100 - - def simulate_borrowing_rate(self): - # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate'] - - # best case - # self.borrowing_rate = 1.5/100 - - # worst case - self.borrowing_rate = 2.5/100 - - def ltv_calc(self): - if self.collateral_usd() == 0: - return 0 - else: - return self.debt / self.collateral_usd() - - def price_to_liquidation(self, dydx_class_instance): - return self.entry_price - (dydx_class_instance.pnl() - + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth - - def price_to_ltv_limit_calc(self): - return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3) - - def buffer_for_repay(self): - return 0.01 - - def ltv_limit(self): - return 0.5 - - # Actions to take - def return_usdc(self, new_market_price, new_interval_current, stgy_instance): - gas_fees = stgy_instance.gas_fees - time = 0 - if self.usdc_status: - # simulate 2min delay for tx - # update parameters - # AAVE parameters - self.usdc_status = False - # self.collateral_eth = 0 - # self.collateral_usdc = 0 - self.debt = 0 - self.ltv = 0 - self.price_to_ltv_limit = 0 - # self.lending_rate = 0 - # self.borrowing_rate = 0 - - # fees - self.costs = self.costs + gas_fees - - time = 1 - return time - - def borrow_usdc(self, new_market_price, new_interval_current, stgy_instance): - gas_fees = stgy_instance.gas_fees - intervals = stgy_instance.intervals - time = 0 - if not self.usdc_status: - # AAVE parameters - # update parameters - self.usdc_status = True - self.entry_price = self.market_price - self.debt = self.collateral_eth_initial * self.borrowed_percentage * stgy_instance.target_prices['open_close'] - self.debt_initial = self.debt - self.ltv = self.ltv_calc() - - # ltv_limit = 0.85 - # vol = stgy_instance.historical_data['vol'] - # benchmark_vol = 0.05 - # for i in range(5): - # if i*benchmark_vol < vol <= (i+1)*benchmark_vol: - # ltv_limit = 0.85 * 1/(i+1) = debt / coll(t) = debt / p_eth*coll = debt/p_eth_-1 * vol * coll - self.price_to_ltv_limit = self.price_to_ltv_limit_calc() # We have to define the criteria for this price - # self.lending_rate = 0 - # self.borrowing_rate = 0 - - # fees - self.costs = self.costs + gas_fees - - price_floor = intervals['open_close'].left_border - previous_position_order = intervals['open_close'].position_order - intervals['floor'] = interval.Interval(self.price_to_ltv_limit, price_floor, - 'floor', previous_position_order+1) - intervals['minus_infty'] = interval.Interval(-math.inf, self.price_to_ltv_limit, - 'minus_infty', previous_position_order+2) - # simulate 2min delay for tx - time = 1 - return time - - def repay_aave(self, new_market_price, new_interval_current, - stgy_instance): - gas_fees = stgy_instance.gas_fees - dydx_class_instance = stgy_instance.dydx - # aave_class_instance = stgy_instance.aave - # dydx_client_class_instance = stgy_instance.dydx_client - # - time = 0 - if self.usdc_status: - # update parameters - short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price) - new_short_size = dydx_class_instance.short_size - short_size_for_debt - - # pnl_for_debt = dydx_class_instance.pnl() - # We have to repeat the calculations for pnl and notional methods, but using different size_eth - pnl_for_debt = short_size_for_debt * (new_market_price - dydx_class_instance.entry_price) - self.debt = self.debt - pnl_for_debt - self.ltv = self.ltv_calc() - - self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3) - self.costs = self.costs + gas_fees - - dydx_class_instance.market_price = self.market_price - dydx_class_instance.interval_current = new_interval_current - dydx_class_instance.short_size = new_short_size - dydx_class_instance.notional = dydx_class_instance.notional_calc() - dydx_class_instance.equity = dydx_class_instance.equity_calc() - dydx_class_instance.leverage = dydx_class_instance.leverage_calc() - dydx_class_instance.pnl = dydx_class_instance.pnl_calc() - # dydx_class_instance.price_to_liquidation = \ - # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance) - - # fees - # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees - dydx_class_instance.simulate_maker_taker_fees() - notional_for_fees = abs(short_size_for_debt) * self.market_price - dydx_class_instance.costs = dydx_class_instance.costs \ - + dydx_class_instance.maker_taker_fees * notional_for_fees \ - + pnl_for_debt * dydx_class_instance.withdrawal_fees - - # Note that a negative self.debt is actually a profit - # We update the parameters - if self.debt > 0: - self.usdc_status = True - else: - self.usdc_status = False - # simulate 2min delay for tx - time = 1 - return time \ No newline at end of file diff --git a/hedge_scripts/binance_client_.py b/hedge_scripts/binance_client_.py deleted file mode 100644 index e5493d3..0000000 --- a/hedge_scripts/binance_client_.py +++ /dev/null @@ -1,89 +0,0 @@ -import math -import pandas as pd -import os.path -from datetime import timedelta, datetime -from dateutil import parser -from binance.client import Client as Client_binance - - -class BinanceClient(object): - - def __init__(self, - config): - self.binance_api_key = config['binance_api_key'] - self.binance_api_secret = config['binance_api_secret'] - - self.client = Client_binance(api_key=self.binance_api_key, api_secret=self.binance_api_secret) - # self.initial_date = config['initial_date'] - # self.symbol = config['symbol'] - # self.freq = config['freq'] - ### FUNCTIONS - def minutes_of_new_data(self, symbol, kline_size, - initial_date, data, source): - if len(data) > 0: - old = parser.parse(data["timestamp"].iloc[-1]) - elif source == "binance": - old = datetime.strptime(initial_date, '%d %b %Y') - if source == "binance": - new = pd.to_datetime(self.client.get_klines(symbol=symbol, interval=kline_size)[-1][0], unit='ms') - return old, new - - def get_all_binance(self, symbol, freq, - initial_date, save=False): - binsizes = {"1m": 1, "5m": 5, "10m": 10, "15m": 15, "1h": 60, "6h": 360, "12h": 720, "1d": 1440} - filename = '/home/agustin/Git-Repos/HedgingScripts/files/%s-%s-data_since_%s.csv' % (symbol, freq, initial_date) - data_df = pd.DataFrame() - oldest_point, newest_point = self.minutes_of_new_data(symbol, freq, - initial_date, data_df, source="binance") - delta_min = (newest_point - oldest_point).total_seconds() / 60 - available_data = math.ceil(delta_min / binsizes[freq]) - if oldest_point == datetime.strptime(initial_date, '%d %b %Y'): - print('Downloading all available %s data for %s. Be patient..!' % (freq, symbol)) - else: - print('Downloading %d minutes of new data available for %s, i.e. %d instances of %s data.' - % (delta_min, symbol, available_data, freq)) - klines = self.client.get_historical_klines(symbol, freq, - oldest_point.strftime("%d %b %Y %H:%M:%S"), - newest_point.strftime("%d %b %Y %H:%M:%S")) - data = pd.DataFrame(klines, - columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', - 'trades', 'tb_base_av', 'tb_quote_av', 'ignore']) - data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms') - # data.index = pd.to_datetime(data['timestamp'], unit='ms') - if len(data_df) > 0: - temp_df = pd.DataFrame(data) - data_df = data_df.append(temp_df) - else: - data_df = data - data_df.set_index('timestamp', inplace=True) - if save: - data_df.to_csv(filename) - print('All caught up..!') - print(initial_date) - return data_df - -# import json -# -# with open('/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json') as json_file: -# config = json.load(json_file) -# _binance_client_ = BinanceClient(config['binance_client']) -# eth_historical = _binance_client_.get_all_binance(save=True) -# -# -# eth_prices = eth_historical[-2000:]['close'] -# for i in range(len(eth_prices)): -# eth_prices[i] = float(eth_prices[i]) -# historical_data = eth_prices -# -# Track historical data -# symbol = 'ETHUSDC' -# freq = '1m' -# initial_date = "1 Jan 2019" -# _binance_client_ = BinanceClient(config['binance_client']) -# eth_historical = _binance_client_.get_all_binance(symbol=symbol, freq=freq, -# initial_date=initial_date, save=True) -# eth_prices = eth_historical['close'] -# for i in range(len(eth_prices)): -# eth_prices[i] = float(eth_prices[i]) -# historical_data = eth_prices -# # print(historical_data) \ No newline at end of file diff --git a/hedge_scripts/checking_var.py b/hedge_scripts/checking_var.py new file mode 100644 index 0000000..6b819f1 --- /dev/null +++ b/hedge_scripts/checking_var.py @@ -0,0 +1,179 @@ +import json +import math +import numpy as np +from scipy.stats import norm +import pandas as pd + +from stgyapp import StgyApp + +def parametric_var(data, confidence, case): + N_1y = 365 * 24 * 60 + N_6m = 180 * 24 * 60 + N_3m = 90 * 24 * 60 + if case == "lognormal returns": + returns = pd.DataFrame(list(round(data.pct_change().dropna()['close']+1, 3)))[0] # pct_change(1) = p_t+1 / p_t -1 = return - 1 + ewm_1y = returns[-N_1y:].ewm(alpha=0.8, adjust=False) + ewm_6m = returns[-N_6m:].ewm(alpha=0.8, adjust=False) + ewm_3m = returns[-N_3m:].ewm(alpha=0.8, adjust=False) + elif case == "normal logreturns": + log_returns = np.log(data['close']) - np.log(data['close'].shift(1)) + ewm_1y = log_returns[-N_1y:].ewm(alpha=0.8, adjust=False) + ewm_6m = log_returns[-N_6m:].ewm(alpha=0.8, adjust=False) + ewm_3m = log_returns[-N_3m:].ewm(alpha=0.8, adjust=False) + else: + print("Enter a valid case") + return + mean_1y = ewm_1y.mean().mean() + std_1y = ewm_1y.std().mean() + mean_6m = ewm_6m.mean().mean() + std_6m = ewm_6m.std().mean() + mean_3m = ewm_3m.mean().mean() + std_3m = ewm_3m.std().mean() + factor_add = round(norm.ppf(confidence), 3) + # We use a weighted linea combination of 1y, 6m and 3m drift and vol + # We convert it to 10m metrics as we are updating it every 10m + if case == "lognormal returns": + # In this case we need to take drift_T = (mu-sigma^2/2)*T, vol_T = sigma * sqrt(T) + drift_10_weighted = ((mean_3m-std_3m**2/2) * 10) * 0.6 \ + + ((mean_6m-std_6m**2/2) * 10) * 0.3 \ + + ((mean_1y-std_1y**2/2) * 10) * 0.1 + vol_10_weighted = (std_3m * np.sqrt(10)) * 0.6 \ + + (std_6m * np.sqrt(10)) * 0.3 \ + + (std_1y * np.sqrt(10)) * 0.1 + return math.e ** (drift_10_weighted + factor_add * vol_10_weighted) + elif case == "normal logreturns": + drift_10_weighted = (mean_3m * 10) * 0.6 \ + + (mean_6m * 10) * 0.3 \ + + (mean_1y * 10) * 0.1 + vol_10_weighted = (std_3m * np.sqrt(10)) * 0.6 \ + + (std_6m * np.sqrt(10)) * 0.3 \ + + (std_1y * np.sqrt(10)) * 0.1 + return math.e ** (drift_10_weighted + factor_add * vol_10_weighted) + +def historical_var(data, confidence, case): + # This is just the X-percentil in the historical changes + if case == "var of returns": + returns = pd.DataFrame(list(data.pct_change(10).dropna()['close']+1))[0] # pct_change(1) = p_t+1 / p_t -1 = return - 1 + changes_for_var = returns + elif case == "var of log returns": + log_returns = np.log(data['close']) - np.log(data['close'].shift(10)) + changes_for_var = log_returns + else: + print("Enter a valid case") + return + # difference_in_portf_value_pcg = [] + # for i in range(len(changes_for_var)): + # # if we use pct_change we should sum 1 in order to get returns + # difference_in_portf_value_pcg.append([changes_for_var[i], i]) + # difference_in_portf_value_pcg.sort() + changes_for_var = changes_for_var.sort_values(ascending=True) + changes_for_var.index = range(len(changes_for_var)) + index_for_var = int(len(changes_for_var) * confidence) + return {'var': changes_for_var[index_for_var], + 'index_in_data_for_that_var': index_for_var} + +def weighted_var(data, confidence, method, case): + if method == "parametric": + return parametric_var(data, confidence, case) + elif method == "historical": + var_3m = historical_var(data[-3 * 30 * 24 * 60:], confidence, case)['var'] + var_6m = historical_var(data[-6 * 30 * 24 * 60:], confidence, case)['var'] + var_1y = historical_var(data[-12 * 30 * 24 * 60:], confidence, case)['var'] + return 0.6 * var_3m + 0.3 * var_6m + 0.1 * var_1y + +def run_through_dataset(data_set, historical_dataset): + var_misses = {'total_misses': 0, + 'index_of_miss': []} + index_copy = list(data_set.index) + data_set.index = range(len(data_set)) + var = weighted_var(data_set, 0.99, "historical", "var of returns") + # var = weighted_var(data_set, 0.99, "parametric", "normal logreturns") + i = 10 + # Let's count var misses while current price is above p_add_current + new_p_add = p_open_close*var + while data_set["close"][i] > new_p_add: + print("current index: ", i) + # print(var) + print("var misses:", var_misses) + current_price = data_set["close"][i] + # last_10min_price = data_set["close"][i-10] + next_10min_price = data_set["close"][i + 10] + ######################### + # Count the number of times current 10min change was greater than current var + if current_price/next_10min_price > var: + print("curre price: ", current_price) + print("next 10m price: ", next_10min_price) + print("change:", current_price/next_10min_price) + print("var:", var) + print("difference: ", current_price / next_10min_price - var) + var_misses['total_misses'] += 1 + var_misses['index_of_miss'].append(i) + ######################### + N_1y = 12 * 30 * 24 * 60 + actual_current_data_set_index = index_copy[i] + last_year_data = historical_dataset.loc[:actual_current_data_set_index][-N_1y:].copy() + var = weighted_var(last_year_data, 0.99, "historical", "var of returns") + # var = weighted_var(last_year_data, 0.99, "parametric", "normal logreturns") + new_p_add = p_open_close*var + i += 1 + return {"var misses": var_misses, + "P_add when reached by P_current": new_p_add, + "Index at which P_current reached P_add": i} + +if __name__ == '__main__': + data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv") + historical_data = pd.DataFrame(data["close"], columns=['close']) + timestamp = pd.to_datetime(data['timestamp']) + historical_data.index = timestamp + + # data for var check + # + data_for_var = historical_data[-3 * 30 * 24 * 60:] + + # Define floor. We set the floor to be 80% of the month of data previous to our data_for_var + # We will update floor for every new price + floor = 1100#historical_data[-4 * 30 * 24 * 60:-3 * 30 * 24 * 60]['close'].max() * 0.8 + p_open_close = floor * 1.01 + ####################### + # import matplotlib.pyplot as plt + # var = weighted_var(data_for_var, 0.99, "parametric", "normal logreturns") + # i = 10 + # # Let's count var misses while current price is above p_add_current + # new_p_add = p_open_close * var + # fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + # # fig.suptitle("Factors = (%s, %s, %s), Vol=%s, Period=%s to %s" % (factors[0], factors[1], factors[2], + # # vol, period[0], period[1])) + # axs.plot(historical_data[-4 * 30 * 24 * 60:], color='tab:blue', label='market price') + # axs.axhline(y=floor, color='red', linestyle='--', label='floor') + # axs.axhline(y=p_open_close, color='darkred', linestyle='--', label='open_close') + # axs.axhline(y=new_p_add, color='darkred', linestyle='--', label='p_add') + # # axs.plot(data_for_var.iloc[10]['close']) + # axs.grid() + # axs.legend(loc='lower left') + # plt.show() + ############# + # data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv") + # historical_data = pd.DataFrame(data["close"], columns=['close']) + # timestamp = pd.to_datetime(data['timestamp']) + # historical_data.index = timestamp + + # data for var check + # data_for_var = historical_data[-3 * 30 * 24 * 60:] + # print(historical_var(data_for_var, 0.99, "var of returns")) + # print(historical_var(data_for_var, 0.99, "Hull")) + # print(parametric_var(data_for_var, 0.99)) + # print(data_for_var.pct_change().dropna()['close'][-1], data_for_var['close'][-1]/data_for_var['close'][-2]-1) + + var_misses = run_through_dataset(data_for_var, + historical_data)["var misses"] + print(var_misses) + + # Parallel execution. We divide out whole dataset into smaller datasets of 60000 prices (~ 41 days of data) + # from joblib import Parallel, delayed + # parallel_pool = Parallel(n_jobs=9) + # delayed_function = [delayed(run_through_dataset)( + # data_set=stgy.historical_data[first_index+60000*i:first_index+60000*(i+1)], + # historical_dataset=stgy.historical_data) + # for i in range(9)] + # var_misses_total = parallel_pool(delayed_function) + # print('var_misses', var_misses_total) \ No newline at end of file diff --git a/hedge_scripts/data_dumper.py b/hedge_scripts/data_dumper.py deleted file mode 100644 index 13e70c4..0000000 --- a/hedge_scripts/data_dumper.py +++ /dev/null @@ -1,302 +0,0 @@ -import os -import pygsheets -import matplotlib.pyplot as plt -from scipy.stats import norm -import csv -import pandas as pd -import numpy as np - -import interval - - -class DataDamperNPlotter: - def __init__(self): - self.historical_data = None - - @staticmethod - def write_data(stgy_instance, - new_interval_previous, interval_old, mkt_price_index, - sheet=False): - aave_instance = stgy_instance.aave - dydx_instance = stgy_instance.dydx - data_aave = [] - data_dydx = [] - aave_wanted_keys = [ - "market_price", - "interval_current", - "entry_price", - "collateral_eth", - "usdc_status", - "debt", - "ltv", - "lending_rate", - "interest_on_lending_usd", - "borrowing_rate", - "interest_on_borrowing", - "lend_minus_borrow_interest", - "costs"] - - for i in range(len(aave_instance.__dict__.values())): - if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys: - # print(list(aave_instance.__dict__.keys())[i]) - if isinstance(list(aave_instance.__dict__.values())[i], interval.Interval): - data_aave.append(str(list(aave_instance.__dict__.values())[i].name)) - # data_aave.append(new_interval_previous.name) - data_aave.append(interval_old.name) - else: - data_aave.append(str(list(aave_instance.__dict__.values())[i])) - for i in range(len(dydx_instance.__dict__.values())): - if isinstance(list(dydx_instance.__dict__.values())[i], interval.Interval): - data_dydx.append(str(list(dydx_instance.__dict__.values())[i].name)) - # data_dydx.append(new_interval_previous.name) - data_dydx.append(interval_old.name) - else: - data_dydx.append(str(list(dydx_instance.__dict__.values())[i])) - # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker - data_aave.append(stgy_instance.gas_fees) - data_aave.append(stgy_instance.total_costs) - data_aave.append(mkt_price_index) - data_dydx.append(stgy_instance.gas_fees) - data_dydx.append(stgy_instance.total_costs) - data_dydx.append(mkt_price_index) - # print(data_aave, list(dydx_instance.__dict__.keys())) - if sheet == True: - gc = pygsheets.authorize(service_file= - '/home/agustin/Git-Repos/HedgingScripts/files/stgy-1-simulations-e0ee0453ddf8.json') - sh = gc.open('aave/dydx simulations') - sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False) - sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False) - else: - with open('/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv', 'a') as file: - writer = csv.writer(file, lineterminator='\n') - writer.writerow(data_aave) - with open('/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv', 'a', - newline='', encoding='utf-8') as file: - writer = csv.writer(file, lineterminator='\n') - writer.writerow(data_dydx) - - @staticmethod - def delete_results(): - file_aave = '/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv' - file_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv' - if (os.path.exists(file_aave) and os.path.isfile(file_aave)): - os.remove(file_aave) - if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)): - os.remove(file_dydx) - - @staticmethod - def add_header(): - aave_headers = [ - "market_price", - "I_current", - # "I_previous", - "I_old", - "entry_price", - "collateral_eth", - "usdc_status", - "debt", - "ltv", - "lending_rate", - "interest_on_lending_usd", - "borrowing_rate", - "interest_on_borrowing", - "lend_minus_borrow_interest", - "costs", - "gas_fees", - "total_costs", - "index_of_mkt_price"] - dydx_headers = [ - "market_price", - "I_current", - # "I_previous", - "I_old", - "entry_price", - "short_size", - "collateral", - "notional", - "equity", - "leverage", - "pnl", - "price_to_liquidation", - "collateral_status", - "short_status", - "order_status", - "withdrawal_fees", - "funding_rates", - "maker_taker_fees", - "costs", - "gas_fees", - "total_costs", - "index_of_mkt_price"] - with open('/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv', 'a') as file: - writer = csv.writer(file, lineterminator='\n') - writer.writerow(aave_headers) - with open('/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv', 'a', - newline='', encoding='utf-8') as file: - writer = csv.writer(file, lineterminator='\n') - writer.writerow(dydx_headers) - - @staticmethod - def historical_parameters_data(aave_instance, dydx_instance): - aave_df = pd.DataFrame(aave_instance.historical_data, columns=list(aave_instance.__dict__.keys())) - dydx_df = pd.DataFrame(dydx_instance.historical_data, columns=list(dydx_instance.__dict__.keys())) - return {"aave_df": aave_df, - "dydx_df": dydx_df} - - @staticmethod - def plot_data(stgy_instance):#, - # save, - # factors, vol, period): - # colors https://datascientyst.com/full-list-named-colors-pandas-python-matplotlib/ - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - # fig.suptitle("Factors = (%s, %s, %s), Vol=%s, Period=%s to %s" % (factors[0], factors[1], factors[2], - # vol, period[0], period[1])) - axs.plot(stgy_instance.historical_data['close'], color='tab:blue', label='market price') - # axs.plot(list(pnl_), label='DyDx pnl') - # p_rtrn_usdc_n_rmv_coll_dydx = stgy_instance.target_prices['rtrn_usdc_n_rmv_coll_dydx'] - p_borrow_usdc_n_add_coll = stgy_instance.target_prices['borrow_usdc_n_add_coll'] - # p_add_collateral_dydx = stgy_instance.target_prices['p_borrow_usdc_n_add_coll'] - # p_close_short = stgy_instance.target_prices['close_short'] - p_open_close = stgy_instance.target_prices['open_close'] - floor = min(list(stgy_instance.target_prices.values())) - # axs.axhline(y=p_rtrn_usdc_n_rmv_coll_dydx, color='black', linestyle='--', - # label='rtrn_usdc_n_rmv_coll_dydx') - axs.axhline(y=p_borrow_usdc_n_add_coll, color='darkgoldenrod', linestyle='--', label='borrow_usdc_n_add_coll') - # axs.axhline(y=p_add_collateral_dydx, color='tab:orange', linestyle='--', label='add_collateral_dydx') - # axs.axhline(y=p_close_short, color='olive', linestyle='--', label='close_short') - axs.axhline(y=p_open_close, color='darkred', linestyle='--', label='open_close') - axs.axhline(y=floor, color='red', linestyle='--', label='floor') - if 'repay_aave' in list(stgy_instance.target_prices.keys()): - p_repay_aave = stgy_instance.target_prices['repay_aave'] - axs.axhline(y=p_repay_aave, color='magenta', linestyle='--', label='repay_aave') - if 'ltv_limit' in list(stgy_instance.target_prices.keys()): - p_ltv_limit = stgy_instance.target_prices['ltv_limit'] - axs.axhline(y=p_ltv_limit, color='purple', linestyle='--', label='ltv_limit') - # print(list(stgy_instance.target_prices.keys())) - axs.grid() - axs.legend(loc='lower left') - # if save: - # plt.savefig('/home/agustin/Git-Repos/HedgingScripts/files/simulated_plot_index_%s_to_%s.png' - # % (period[0], period[1])) - # else: - plt.show() - - def get_gif(self): - import numpy as np - from matplotlib.animation import FuncAnimation - from IPython import display - import matplotlib.pyplot as plt - Figure = plt.figure() - lines_plotted = plt.plot([]) - self.line_plotted = lines_plotted[0] - anim_created = FuncAnimation(Figure, self.AnimationFunction, frames=100, interval=25) - video = anim_created.to_html5_video() - plot = display.HTML(video) - # plot.save() - display.display(plot) - # with open('plot.html', 'w') as f: - # f.write(plot.text) - # with open("plot.html", "w") as file: - # file.write(plot) - - # function takes frame as an input - def AnimationFunction(self, frame): - - # setting y according to frame - # number and + x. It's logic - y = self.historical_data['close'][frame] - x = self.historical_data.index[frame] - - # line is set with new values of x and y - self.line_plotted.set_data((x, y)) - - @staticmethod - def plot_price_distribution(stgy_instance): - # fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - # from https://stackoverflow.com/questions/6855710/how-to-have-logarithmic-bins-in-a-python-histogram - data = np.log(stgy_instance.historical_data['close']) - MIN, MAX = data.min(), data.max() - data.hist(bins=np.linspace(MIN, MAX, 50)) - plt.gca().set_xscale("log") - plt.show() - # print(np.log(historical_data['close'])) - - # @staticmethod - def plot_returns_distribution(self):#stgy_instance): - """ - We assume returns are normally distributed - """ - - historical = self.historical_data#stgy_instance.historical_data.copy() - pct_change = historical['close'].pct_change().fillna(method='bfill') - log_returns = np.log(historical['close']) - np.log(historical['close'].shift(60)) - historical['pct_change'] = pct_change - historical['log_returns'] = log_returns - - x = np.linspace(pct_change.min(), 1, 100) - mean = np.mean(pct_change) - std = np.std(pct_change) - norm_dist = norm.pdf(x, mean, std) - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - log_returns.hist(bins=50, ax=axs) - # pct_change.hist(bins=50, ax=axs) - # axs.set_xlabel('Return') - # axs.set_ylabel('Sample') - # axs.set_title('Return distribution') - # axs.plot(x, norm_dist, color='tab:blue', label='Returns dist') - - # To check if its normally distributed + understate the likelihood of returns beyond -2/+2 quantiles - # import scipy.stats as stats - # stats.probplot(historical['returns'], dist='norm', plot=axs) - # axs.grid() - plt.show() - # print(historical.describe()) - - @staticmethod - def prob_return_in_range(stgy_instance, range): - """ - range = [a, b] with a < b - Recall: - cumulative distribution function of a random variable X is F_X(x) := P(X <= x) - So the probability of returns (R) falling in range is P(a <= R <= b) = P(R <= b) - P(R < a) = F_R(b) - F_R(a) - If we assume returns are normally distributed then F could be estimated using norm(mean, std).cdf function - """ - returns = stgy_instance.historical_data['returns'] - mean = np.mean(returns) - std = np.std(returns) - norm_cdf = norm(mean, std).cdf - return norm_cdf(range[1]) - norm_cdf(range[0]) - - @staticmethod - def plot_volatility(stgy_instance, method): - """ - We assume returns are normally distributed - """ - if method == 'arch': - vol = stgy_instance.volatility_calculator.get_arch(stgy_instance.historical_data, 1, 0, 0) - elif method == 'garch': - vol = stgy_instance.volatility_calculator.get_garch(stgy_instance.historical_data) - elif method == 'emwa': - vol = stgy_instance.volatility_calculator.get_emwa(stgy_instance.historical_data, 1, 0, 0) - historical = stgy_instance.historical_data.copy() - pct_change = historical['close'].pct_change().fillna(method='bfill') - log_returns = np.log(historical['close']) - np.log(historical['close'].shift(1)) - historical['pct_change'] = pct_change - historical['log_returns'] = log_returns - - x = np.linspace(pct_change.min(), 1, 100) - mean = np.mean(pct_change) - std = np.std(pct_change) - norm_dist = norm.pdf(x, mean, std) - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - log_returns.hist(bins=50, ax=axs) - -if __name__ == '__main__': - data_dumper = DataDamperNPlotter() - historical_daily = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv") - historical_hourly = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1h-data.csv") - historical_minutes = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv") - # assign data to stgy instance + define index as dates - data_dumper.historical_data = pd.DataFrame(historical_minutes["close"], columns=['close']) - # data_dumper.historical_data = pd.DataFrame(historical_hourly["close"], columns=['close']) - data_dumper.plot_returns_distribution() \ No newline at end of file diff --git a/hedge_scripts/dydx.py b/hedge_scripts/dydx.py deleted file mode 100644 index 619afd6..0000000 --- a/hedge_scripts/dydx.py +++ /dev/null @@ -1,189 +0,0 @@ -import math -import random -import numpy as np -import interval - - -class Dydx(object): - - def __init__(self, config): - # assert aave_class == isinstance(aave) - self.market_price = config['market_price'] - self.interval_current = config['interval_current'] - self.entry_price = config['entry_price'] - self.short_size = config['short_size'] - self.collateral = config['collateral'] - self.notional = config['notional'] - self.equity = config['equity'] - self.leverage = config['leverage'] - self.pnl = config['pnl'] - # self.price_to_liquidation = config['price_to_liquidation'] - self.collateral_status = config['collateral_status'] - self.short_status = config['short_status'] - self.order_status = True - self.withdrawal_fees = 0.01/100 - self.funding_rates = 0 - self.maker_taker_fees = 0 - self.costs = 0 - # self.historical = pd.DataFrame() - # self.aave_class_instance = aave_class_instance - # self.staked_in_protocol = stk - - # auxiliary functions - def pnl_calc(self): - return self.short_size * (self.market_price-self.entry_price) - - def notional_calc(self): - return abs(self.short_size)*self.market_price - - def equity_calc(self): - return self.collateral + self.pnl_calc() - - def leverage_calc(self): - if self.equity_calc() == 0: - return 0 - else: - return self.notional_calc() / self.equity_calc() - - def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance): - return self.entry_price \ - + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size - - @staticmethod - def price_to_liquidation_calc(dydx_client_class_instance): - return dydx_client_class_instance.dydx_margin_parameters["liquidation_price"] - - def add_funding_rates(self): - self.simulate_funding_rates() - self.costs = self.costs - self.funding_rates - - def simulate_funding_rates(self): - # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6) - - # best case - # self.funding_rates = 0.0075 / 100 - - # worst case - self.funding_rates = -0.0075 / 100 - - def simulate_maker_taker_fees(self): - # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6) - - # best case - # self.maker_taker_fees = 0.01 / 100 - - # worst case - self.maker_taker_fees = 0.035 / 100 - - # Actions to take - def remove_collateral(self, new_market_price, new_interval_current, stgy_instance): - self.cancel_order() - time = 0 - if self.collateral_status: - self.collateral_status = False - withdrawal_fees = self.collateral * self.withdrawal_fees - self.collateral = 0 - # self.price_to_liquidation = 0 - - # fees - self.costs = self.costs + withdrawal_fees - - time = 1 - return time - - def add_collateral(self, new_market_price, new_interval_current, - stgy_instance): - gas_fees = stgy_instance.gas_fees - aave_class_instance = stgy_instance.aave - time = 0 - if not self.collateral_status: - self.collateral_status = True - self.collateral = aave_class_instance.debt_initial - # fees - self.costs = self.costs + gas_fees - # We place an order in open_close - self.place_order(stgy_instance.target_prices['open_close']) - # add time - time = 10 - return time - - def open_short(self, new_market_price, new_interval_current, - stgy_instance): - aave_class_instance = stgy_instance.aave - # dydx_client_class_instance = stgy_instance.dydx_client - intervals = stgy_instance.intervals - if (not self.short_status) and self.order_status: - self.short_status = True - # dydx parameters - if self.market_price <= stgy_instance.target_prices['floor']: - print("CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!") - print("Difference of: ", stgy_instance.target_prices['floor'] - self.market_price) - - if self.market_price <= stgy_instance.target_prices['open_close']: - print("CAUTION: OPEN PRICE LOWER THAN open_close!") - print("Difference of: ", stgy_instance.target_prices['open_close'] - self.market_price) - self.entry_price = self.market_price - self.short_size = -aave_class_instance.collateral_eth_initial - # self.collateral = aave_class_instance.debt_initial - self.notional = self.notional_calc() - self.equity = self.equity_calc() - self.leverage = self.leverage_calc() - # Simulate maker taker fees - self.simulate_maker_taker_fees() - # Add costs - self.costs = self.costs + self.maker_taker_fees * self.notional - - - price_floor = intervals['open_close'].left_border - floor_position = intervals['floor'].position_order - - price_to_repay_debt = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(), - aave_class_instance) - price_to_ltv_limit = intervals['floor'].left_border - stgy_instance.target_prices['repay_aave'] = price_to_repay_debt - stgy_instance.target_prices['ltv_limit'] = price_to_ltv_limit - if price_to_ltv_limit < price_to_repay_debt: - intervals['floor'] = interval.Interval(price_to_repay_debt, price_floor, - 'floor', floor_position) - intervals['repay_aave'] = interval.Interval(price_to_ltv_limit, price_to_repay_debt, - 'repay_aave', floor_position + 1) - intervals['minus_infty'] = interval.Interval(-math.inf, price_to_ltv_limit, - 'minus_infty', floor_position + 2) - else: - print("CAUTION: P_ltv > P_repay") - print("Difference of: ", price_to_ltv_limit - price_to_repay_debt) - price_to_repay_debt = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance) - intervals['floor'] = interval.Interval(price_to_ltv_limit, price_floor, - 'floor', floor_position) - intervals['ltv_limit'] = interval.Interval(price_to_repay_debt, price_to_ltv_limit, - 'repay_aave', floor_position + 1) - intervals['minus_infty'] = interval.Interval(-math.inf, price_to_repay_debt, - 'minus_infty', floor_position + 2) - self.order_status = False - - def close_short(self, new_market_price, new_interval_current, stgy_instance): - if self.short_status: - # Next if is to move up the threshold if we didnt execute at exactly open_close - if self.market_price >= stgy_instance.target_prices['open_close']: - # new_open_close = self.market_price - print("CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!") - print("Difference of: ", self.market_price - stgy_instance.target_prices['open_close']) - # stgy_instance.target_prices['open_close'] = self.market_price - self.notional = self.notional_calc() - self.equity = self.equity_calc() - self.leverage = self.leverage_calc() - self.pnl = self.pnl_calc() - # We update short parameters after the calculation of pnl - self.entry_price = 0 - self.short_status = False - self.short_size = 0 - self.simulate_maker_taker_fees() - self.costs = self.costs + self.maker_taker_fees * self.notional - self.place_order(stgy_instance.target_prices['open_close']) - - def place_order(self, price): - self.order_status = True - # self. - - def cancel_order(self): - self.order_status = False \ No newline at end of file diff --git a/hedge_scripts/dydx_client.py b/hedge_scripts/dydx_client.py index 63095af..df24715 100644 --- a/hedge_scripts/dydx_client.py +++ b/hedge_scripts/dydx_client.py @@ -3,11 +3,9 @@ class DydxClient(object): - - def __init__(self, - config): + def __init__(self, config): self.dydx_margin_parameters = {} - self.host = config['host'] + self.host = config["host"] self.client = Client_dydx(self.host) # self.dydx_instance = dydx_class @@ -15,34 +13,67 @@ def get_dydx_parameters(self, dydx_class_instance): # We bring the necessary parameters market = self.client.public.get_markets() dydx_info = pd.DataFrame.from_dict(market.data).T - dydx_ETH_USD_data = dydx_info['ETH-USD'][0] - self.dydx_margin_parameters['incrementalInitialMarginFraction'] = float(dydx_ETH_USD_data['incrementalInitialMarginFraction']) + dydx_ETH_USD_data = dydx_info["ETH-USD"][0] + self.dydx_margin_parameters["incrementalInitialMarginFraction"] = float( + dydx_ETH_USD_data["incrementalInitialMarginFraction"] + ) - self.dydx_margin_parameters['initialMarginFraction'] = float(dydx_ETH_USD_data['initialMarginFraction']) - self.dydx_margin_parameters["maintenanceMarginFraction"] = float(dydx_ETH_USD_data['maintenanceMarginFraction']) - self.dydx_margin_parameters["oraclePrice"] = float(dydx_ETH_USD_data['oraclePrice']) - self.dydx_margin_parameters["next_funding_at"] = dydx_ETH_USD_data['nextFundingAt'] - self.dydx_margin_parameters["next_funding_rate"] = float(dydx_ETH_USD_data['nextFundingRate']) + self.dydx_margin_parameters["initialMarginFraction"] = float( + dydx_ETH_USD_data["initialMarginFraction"] + ) + self.dydx_margin_parameters["maintenanceMarginFraction"] = float( + dydx_ETH_USD_data["maintenanceMarginFraction"] + ) + self.dydx_margin_parameters["oraclePrice"] = float( + dydx_ETH_USD_data["oraclePrice"] + ) + self.dydx_margin_parameters["next_funding_at"] = dydx_ETH_USD_data[ + "nextFundingAt" + ] + self.dydx_margin_parameters["next_funding_rate"] = float( + dydx_ETH_USD_data["nextFundingRate"] + ) # initial_margin_requirement - self.dydx_margin_parameters["Initial_Margin_Requirement"] = abs(dydx_class_instance.short_size - * self.dydx_margin_parameters["oraclePrice"] - * self.dydx_margin_parameters['initialMarginFraction']) - self.dydx_margin_parameters["Total_Initial_Margin_Requirement"] = self.dydx_margin_parameters["Initial_Margin_Requirement"] + self.dydx_margin_parameters["Initial_Margin_Requirement"] = abs( + dydx_class_instance.short_size + * self.dydx_margin_parameters["oraclePrice"] + * self.dydx_margin_parameters["initialMarginFraction"] + ) + self.dydx_margin_parameters[ + "Total_Initial_Margin_Requirement" + ] = self.dydx_margin_parameters["Initial_Margin_Requirement"] # maintenance_margin_requirement - self.dydx_margin_parameters["Maintenance_Margin_Requirement"] = abs(dydx_class_instance.short_size - * self.dydx_margin_parameters["oraclePrice"] - * self.dydx_margin_parameters["maintenanceMarginFraction"]) - self.dydx_margin_parameters["Total_Maintenance_Margin_Requirement"] = self.dydx_margin_parameters["Maintenance_Margin_Requirement"] + self.dydx_margin_parameters["Maintenance_Margin_Requirement"] = abs( + dydx_class_instance.short_size + * self.dydx_margin_parameters["oraclePrice"] + * self.dydx_margin_parameters["maintenanceMarginFraction"] + ) + self.dydx_margin_parameters[ + "Total_Maintenance_Margin_Requirement" + ] = self.dydx_margin_parameters["Maintenance_Margin_Requirement"] - # total_account_value - self.dydx_margin_parameters["total_account_value"] = dydx_class_instance.collateral + dydx_class_instance.notional - self.dydx_margin_parameters["Free_collateral"] = self.dydx_margin_parameters["total_account_value"] \ - - self.dydx_margin_parameters["Total_Maintenance_Margin_Requirement"] + # total_account_value + self.dydx_margin_parameters["total_account_value"] = ( + dydx_class_instance.collateral + dydx_class_instance.notional + ) + self.dydx_margin_parameters["Free_collateral"] = ( + self.dydx_margin_parameters["total_account_value"] + - self.dydx_margin_parameters["Total_Maintenance_Margin_Requirement"] + ) if self.dydx_margin_parameters["Total_Maintenance_Margin_Requirement"] != 0: - self.dydx_margin_parameters["liquidation_price"] = self.dydx_margin_parameters["oraclePrice"] * ( - 1 + (self.dydx_margin_parameters["maintenanceMarginFraction"] * self.dydx_margin_parameters["total_account_value"] - / self.dydx_margin_parameters["Total_Maintenance_Margin_Requirement"])) + self.dydx_margin_parameters[ + "liquidation_price" + ] = self.dydx_margin_parameters["oraclePrice"] * ( + 1 + + ( + self.dydx_margin_parameters["maintenanceMarginFraction"] + * self.dydx_margin_parameters["total_account_value"] + / self.dydx_margin_parameters[ + "Total_Maintenance_Margin_Requirement" + ] + ) + ) else: - self.dydx_margin_parameters["liquidation_price"] = 0 \ No newline at end of file + self.dydx_margin_parameters["liquidation_price"] = 0 diff --git a/hedge_scripts/interval.py b/hedge_scripts/interval.py index 485e018..f892bc6 100644 --- a/hedge_scripts/interval.py +++ b/hedge_scripts/interval.py @@ -1,10 +1,5 @@ class Interval(object): - - def __init__(self, - left_border, - right_border, - name, - position_order): + def __init__(self, left_border, right_border, name, position_order): self.left_border = left_border self.right_border = right_border self.name = name diff --git a/hedge_scripts/metrics_calculator.py b/hedge_scripts/metrics_calculator.py new file mode 100644 index 0000000..109ade0 --- /dev/null +++ b/hedge_scripts/metrics_calculator.py @@ -0,0 +1,76 @@ +import math +import random +import numpy as np +from scipy.stats import norm +import pandas as pd +import matplotlib.pyplot as plt + +import interval + + +class MetricsCalculator(object): + + def ATR(self, df, n): + "function to calculate True Range and Average True Range" + df = df.copy() + + df['H-L'] = abs(df['high'] - df['low']) + df['H-PO'] = abs(df['high'] - df['open'].shift(1)) + df['L-PO'] = abs(df['low'] - df['open'].shift(1)) + + df['TR'] = df[['H-L', 'H-PO', 'L-PO']].max(axis=1, skipna=False) + df['ATR_SMA'] = df['TR'].rolling(n).mean() + df['ATR_EMA'] = df['TR'].ewm(alpha=0.8, adjust=False).mean() + + df2 = df.drop(['H-L', 'H-PO', 'L-PO'], axis=1) + return df2 + + def CES(self, df, n, m): + df2 = self.ATR(df, n) + df2['CES_SMA_' + str(n) + '_' + str(m)] = [None] * len(df2) + df2['CES_EMA_' + str(n) + '_' + str(m)] = [None] * len(df2) + for i in range(n, len(df2)): + df2['CES_SMA_' + str(n) + '_' + str(m)][i] = df2[-n:]['low'].min() + m * df2['ATR_SMA'][i] + df2['CES_EMA_' + str(n) + '_' + str(m)][i] = df2[-n:]['low'].min() + m * df2['ATR_EMA'][i] + return df2 + + def CES_test(self, df_with_ces, n, m): + pnl = 0 + i = 0 + while i < len(df_with_ces): + current_price = df_with_ces['close'][i] + # search for index st price>CES + j = 0 + if isinstance(df_with_ces['CES_EMA_' + str(n) + '_' + str(m)][i+j], type(None)): + j += 1 + else: + while(df_with_ces['close'][i+j] < df_with_ces['CES_EMA_' + str(n) + '_' + str(m)][i+j]): + if i+j == len(df_with_ces)-1: + return current_price - df_with_ces['close'][i+j] + j += 1 + pnl += current_price - df_with_ces['close'][i+j] + i = i+j + return pnl + +if __name__ == '__main__': + metric_calculator = MetricsCalculator() + metric_calculator.df = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/" + "ETHUSDC-1m-data_since_1 Sep 2019.csv")[-1000:] + # # assign data to stgy instance + define index as dates + # df = pd.DataFrame(historical_data["close"], columns=['close']) + timestamp = pd.to_datetime(metric_calculator.df['timestamp']) + metric_calculator.df.index = timestamp + metric_calculator.df = metric_calculator.df.drop(['timestamp'], axis=1) + df2 = metric_calculator.CES(metric_calculator.df, 30, 3) + # print(df2[['close', 'CES_SMA_30_3','CES_EMA_30_3', 'ATR_EMA', 'ATR_SMA']]) + # print(metric_calculator.CES_test(df2, 30, 3)) + # print((df2['CES_SMA_30_3']/df2['close']-1)*100) + # print((df2['CES_EMA_30_3']/df2['close']-1)*100) + fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + axs.plot(df2['close'], color='tab:blue', label='market price') + axs.plot(df2['CES_SMA_30_3'], color='tab:red', label='CES_SMA_30_3') + # axs.plot(df2['CES_EMA_30_3'], color='green', label='CES_EMA_30_3') + axs.grid() + axs.legend(loc='lower left') + plt.show() + diff --git a/hedge_scripts/parameter_manager.py b/hedge_scripts/parameter_manager.py deleted file mode 100644 index 7063167..0000000 --- a/hedge_scripts/parameter_manager.py +++ /dev/null @@ -1,360 +0,0 @@ -import math -import random -import numpy as np -import interval -from scipy.stats import norm -import pandas as pd -import matplotlib.pyplot as plt - -class ParameterManager(object): - # auxiliary functions - @staticmethod - def define_target_prices(stgy_instance, N_week, data_for_thresholds, floor): - # P_open_close to be P_floor * e^(mu + factor * sigma) where mu, sigma are calculated - # based on last 3 month of data. Factor is calculated using the VaR approach in which we choose a confidence - # level X (a probability of ensurance) and we calculate the maximum loss we are X % sure we wont lose more than - # that. - log_returns_1_week = np.log(data_for_thresholds['close']) - np.log( - data_for_thresholds['close'].shift(1)) - ewm_log_returns = log_returns_1_week[-N_week:].ewm(alpha=0.8, adjust=False) - mean_ema_log_returns = round(ewm_log_returns.mean().mean() * 365, 3) - std_ema_log_returns = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - - mu = mean_ema_log_returns / 365 * 24 * 60 - sigma = (std_ema_log_returns / np.sqrt(365)) * np.sqrt(24 * 60) - - factor_open_close = round(norm.ppf(0.90), 3) - p_open_close = floor * math.e ** (mu + factor_open_close * sigma) - ########################################################## - # P_borrow_usdc_n_add_coll to be P_open_close * e^(mu + factor * sigma) where mu, sigma are calculated - # based on last 3 month of data. Factor is calculated using the VaR approach in which we choose a confidence - # level X (a probability of ensurance) and we calculate the maximum loss we are X % sure we wont lose more than - # that. - log_returns_10min_last_3_months = np.log(stgy_instance.historical_data[-3 * 30 * 24 * 60:]['close']) - np.log( - data_for_thresholds[-3 * 30 * 24 * 60:]['close'].shift(10)) - - # vol benchmark: daily version of last 3month 2min vol (mean std) - ewm_log_returns = log_returns_10min_last_3_months.ewm(alpha=0.8, adjust=False) - std_10min_ema_mean_value = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - mean_10min_ema = round(ewm_log_returns.mean().mean() * 365, 3) - mu_10min_mean_daily = mean_10min_ema / 365 * 24 * 6 - sigma_10min_mean_daily = round((std_10min_ema_mean_value / np.sqrt(365) * np.sqrt(24 * 6)), 3) - - factor_add = round(norm.ppf(0.90), 3) - - p_borrow_usdc_n_add_coll = p_open_close * math.e**(mu_10min_mean_daily + factor_add * sigma_10min_mean_daily) - - stgy_instance.target_prices_copy = stgy_instance.target_prices - list_of_intervals = [#"rtrn_usdc_n_rmv_coll_dydx", - "borrow_usdc_n_add_coll", - "open_close", - # "open_short", - "floor"] - list_of_trigger_prices = [#p_rtrn_usdc_n_rmv_coll_dydx, - p_borrow_usdc_n_add_coll, - p_open_close, - # p_open_short, - floor] - # We define/update trigger prices - for i in range(len(list_of_intervals)): - interval_name = list_of_intervals[i] - trigger_price = list_of_trigger_prices[i] - stgy_instance.target_prices[interval_name] = trigger_price - - @staticmethod - def define_intervals(stgy_instance): - stgy_instance.intervals = {"infty": interval.Interval(stgy_instance.target_prices['borrow_usdc_n_add_coll'], - math.inf, - "infty", 0), - } - # By reading current names and values (instead of defining the list of names and values at hand) we can - # use this method both for defining the thresholds the first time and for updating them every day - names = list(stgy_instance.target_prices.keys()) - values = list(stgy_instance.target_prices.values()) - - # We define/update thresholds - for i in range(len(stgy_instance.target_prices) - 1): - stgy_instance.intervals[names[i]] = interval.Interval( - values[i + 1], - values[i], - names[i], i + 1) - stgy_instance.intervals["minus_infty"] = interval.Interval(-math.inf, - values[-1], - "minus_infty", - len(values)) - # print(stgy_instance.intervals.keys()) - - # function to assign interval_current to each market_price in historical data - @staticmethod - def load_intervals(stgy_instance): - stgy_instance.historical_data["interval"] = [[0, 0]] * len(stgy_instance.historical_data["close"]) - stgy_instance.historical_data["interval_name"] = ['nan'] * len(stgy_instance.historical_data["close"]) - for loc in range(len(stgy_instance.historical_data["close"])): - market_price = stgy_instance.historical_data["close"][loc] - for i in list(stgy_instance.intervals.values()): - if i.left_border < market_price <= i.right_border: - stgy_instance.historical_data["interval"][loc] = i - stgy_instance.historical_data["interval_name"][loc] = i.name - @staticmethod - # Checking and updating data - def update_parameters(stgy_instance, new_market_price, new_interval_current): - # AAVE - stgy_instance.aave.market_price = new_market_price - stgy_instance.aave.interval_current = new_interval_current - # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now - # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24 - stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60) - stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60) - # We have to execute track_ first because we need the fees for current collateral and debt values - stgy_instance.aave.track_lend_borrow_interest() - stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt - stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values - stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc() - - # DYDX - stgy_instance.dydx.market_price = new_market_price - stgy_instance.dydx.interval_current = new_interval_current - stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc() - stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc() - stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc() - stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc() - # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client) - - def find_scenario(self, stgy_instance, new_market_price, new_interval_current, interval_old, index): - actions = self.actions_to_take(stgy_instance, new_interval_current, interval_old) - self.simulate_fees(stgy_instance) - # We reset the costs in order to always start in 0 - stgy_instance.aave.costs = 0 - stgy_instance.dydx.costs = 0 - time = 0 - time_aave = 0 - time_dydx = 0 - for action in actions: - # if action == "rtrn_usdc_n_rmv_coll_dydx": - # time = stgy_instance.dydx.remove_collateral_dydx(new_market_price, new_interval_current, stgy_instance) - # stgy_instance.aave.return_usdc(new_market_price, new_interval_current, stgy_instance) - if action == "borrow_usdc_n_add_coll": - time_aave = stgy_instance.aave.borrow_usdc(new_market_price, new_interval_current, stgy_instance) - market_price = stgy_instance.historical_data["close"][index + time_aave] - interval_current = stgy_instance.historical_data["interval"][index + time_aave] - time_dydx = stgy_instance.dydx.add_collateral(market_price, - interval_current, stgy_instance) - time_aave = 0 - elif action in stgy_instance.aave_features["methods"]: - time_aave = getattr(stgy_instance.aave, action)(new_market_price, new_interval_current, stgy_instance) - elif action in stgy_instance.dydx_features["methods"]: - time_dydx = getattr(stgy_instance.dydx, action)(new_market_price, new_interval_current, stgy_instance) - time += time_aave + time_dydx - return time - # stgy_instance.append(action) - - @staticmethod - def actions_to_take(stgy_instance, new_interval_current, interval_old): - actions = [] - if interval_old.is_lower(new_interval_current): - for i in reversed(range(new_interval_current.position_order, interval_old.position_order)): - actions.append(list(stgy_instance.intervals.keys())[i+1]) # when P goes up we execute the name of previous intervals - # print(list(stgy_instance.intervals.keys())[i+1]) - else: - for i in range(interval_old.position_order + 1, new_interval_current.position_order + 1): - actions.append(list(stgy_instance.intervals.keys())[i]) - print(actions) - return actions - - @staticmethod - def simulate_fees(stgy_instance): - # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6) - - # best case - # stgy_instance.gas_fees = 1 - - # stgy_instance.gas_fees = 3 - - # stgy_instance.gas_fees = 6 - - # worst case - stgy_instance.gas_fees = 10 - - @staticmethod - def add_costs(stgy_instance): - stgy_instance.total_costs = stgy_instance.total_costs + stgy_instance.aave.costs + stgy_instance.dydx.costs - - @staticmethod - def value_at_risk(data, method, # T, - X): - # exposure = abs(stgy_instance.dydx.short_size) # we are exposed to an amount equal to the size - # window_to_use = 3 * 30 * 24 * 60 # 3 months of data - # data = stgy_instance.historical_data[-window_to_use:]['close'] - # vol benchmark: daily version of last 3month 2min vol (mean std) - if method == "parametric": - """ - We assume portfolio value is log-normally distributed - ln(V_T / V_0) ~ N((mu-sigma^2/2)*T, sigma^2*T) --> ln V_T ~ N(ln V_0 +(mu-sigma^2/2)*T, sigma^2*T) - Then, using that 95% of values under normal dist falls between 1.96 sigmas, - we can say that with a 95% confidence - |ln V_T| < [ln V_0 +(mu-sigma^2/2)*T] +- 1.96 * sigma * T^1/2 - V_T < e^{[ln V_0 +(mu-sigma^2/2)*T] +- 1.96 * sigma * T^1/2} - - In general, given a c-level X we can say the same using factor = F^-1(X) = norm.ppf(X) - """ - log_returns = np.log(data) - np.log(data.shift(1)) - sigma = round(log_returns.ewm(alpha=0.8, adjust=False).std().mean(), 3) - mu = round(log_returns.ewm(alpha=0.8, adjust=False).mean().mean(), 3) - factor = round(norm.ppf(X), 3) - var = mu + sigma * factor - return var['close'] - elif method == "non_parametric": - """ - We dont assume anything here. The idea will be to use past data for simulating different - today portfolio's value by taking - change_i = price_i / price_{i-1} --> change on i-th day - simulated_price_i = today_price * change_i - --> simulated a new price assuming yesterday/today's change is equal to i-th/i-1-th's change - portf_value_i = exposure * simulated_price_i / today_price - [ = exposure * change_i ] - Then, we calculate our potential profits/losses taking - loss_i = exposure - portf_value_i - [ = exposure * (1 - simulated_price_i / today_price) - = exposure * (1 - today_price * change_i / today_price - = exposure * (1 - change_i ] - i.e. we calculate the potential loss by comparing a portf value with actual exposure against - portf value with a different exposure (exposure * change_i) - That will give us a dataset of daily losses and therefore a distribution for daily losses in the value of - our portf. - We take the VaR as the X-th percentile of this dist. That will be our 1-day VaR. In order to - calculate N-day potential loss we take 1-day VaR * N^1/2. - So we will be X% confident that we wil not take a loss greater than this VaR estimate if market behaviour - is according to last data. - Everywhere day can be changed by any other time freq, in our case by minutes. - We repeat this for every new price, ie for every new data-set of last data to keep an - up to date VaR estimation. - """ - changes = list(round(data.pct_change().dropna()['close'], 3)) # returns - today = data.iloc[-1]['close'] - # print(today, changes) - scenarios = [] - portf_value = [] - difference_in_portf_value = [] - difference_in_portf_value_pcg = [] - for i in range(len(changes)): - scenarios.append(today * changes[i]) - # portf_value.append(exposure*scenarios[i]/today) - # difference_in_portf_value.append(exposure - portf_value[i]) - difference_in_portf_value_pcg.append([changes[i], i]) - difference_in_portf_value_pcg.sort() - plt.hist(changes) - return difference_in_portf_value_pcg[-10:] - -if __name__ == '__main__': - #######################################3 - # get historical data in seconds - import requests - from requests import Request - from datetime import datetime - import pandas as pd - import numpy as np - # import json - # url = 'https://api.coinbase.com/v2/prices/BTC-USD/historic?2018-07-15T00:00:00-04:00' - # request = Request('GET', url) - # s = requests.Session() - # prepared = request.prepare() - # response = s.send(prepared).json()['data']['prices'] - # historical_seconds = {'prices': [], 'date': []} - # for i in range(len(response)): - # item = response[i] - # historical_seconds['prices'].append(float(item['price'])) - # historical_seconds['date'].append(datetime.strptime(item['time'], '%Y-%m-%dT%H:%M:%SZ')) - # historical_seconds = pd.DataFrame(historical_seconds['prices'], - # index=historical_seconds['date'], - # columns=['close']).iloc[::-1] - historical_daily = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv") - historical_hourly = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1h-data.csv") - historical_minutes = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv") - # assign data to stgy instance + define index as dates - historical_data_daily = pd.DataFrame(historical_daily["close"], columns=['close']) - historical_data_hourly = pd.DataFrame(historical_hourly["close"], columns=['close']) - historical_data_minutes = pd.DataFrame(historical_minutes["close"], columns=['close']) - - ######################################################3 - # check historical 2min vol as benchmark to define add threshold - # manager = ParameterManager() - # N_week = 1 * 1 * 7 * 24 * 60 # 7 days - # data_for_thresholds = historical_data_minutes[:N_week].copy() # First week of data - - # log_returns_10_minutes = np.log(historical_minutes['close']) - np.log( - # historical_minutes['close'].shift(10)) - # log_returns = np.log(historical_minutes['close']) - np.log( - # historical_minutes['close'].shift(1)) - # - # # ema log returns - # ewm_log_returns = log_returns_10_minutes.ewm(alpha=0.8, adjust=False) - # - # mean_ema_log_returns_mean_value = round(ewm_log_returns.mean().mean() * 365, 3) - # mean_ema_log_returns_max_value = round(ewm_log_returns.mean().max() * 365, 3) - # mean_ema_log_returns_min_value = round(ewm_log_returns.mean().min() * 365, 3) - # std_ema_log_returns_mean_value = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - # std_ema_log_returns_max_value = round(ewm_log_returns.std().max() * np.sqrt(365), 3) - # std_ema_log_returns_min_value = round(ewm_log_returns.std().min() * np.sqrt(365), 3) - # mu_2min_mean = round(mean_ema_log_returns_mean_value / 365 * 24 * 30, 3) - # mu_2min_max = round(mean_ema_log_returns_max_value / 365 * 24 * 30, 3) - # mu_2min_min = round(mean_ema_log_returns_min_value / 365 * 24 * 30, 3) - # sigma_2min_mean = round((std_ema_log_returns_mean_value / np.sqrt(365)), 3) - # sigma_2min_max = round((std_ema_log_returns_max_value / np.sqrt(365)), 3) - # sigma_2min_min = round((std_ema_log_returns_min_value / np.sqrt(365)), 3) - # std = ewm_log_returns.std() - # # print(std[std==std.max()]) - # # print(historical_minutes['close'][9413-10:9413+10]) - # - # print('Hist_2min_mean_vol_last_3_month + daily v:', [sigma_2min_mean, sigma_2min_mean * np.sqrt(24*30)]) - # print('Hist_2min_max_vol_last_3_month + daily v:', [sigma_2min_max, sigma_2min_max * np.sqrt(24*30)]) - # print('Hist_2min_min_vol_last_3_month + daily v:', [sigma_2min_min, sigma_2min_min * np.sqrt(24*30)]) - - ###################################################### - # check P_open / P_borrow to define ltv_0 - # N_week = 1 * 1 * 7 * 24 * 60 # 7 days - # data_for_thresholds = historical_data_minutes[:N_week].copy() # First week of data - # log_returns = np.log(data_for_thresholds['close']) - np.log( - # data_for_thresholds['close'].shift(1)) - # # ema log returns - # ewm_log_returns = log_returns.ewm(alpha=0.8, adjust=False) - # mean_ema_log_returns = round(ewm_log_returns.mean().mean() * 365, 3) - # std_ema_log_returns = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - # - # mu = mean_ema_log_returns / 365 * 24 * 60 - # sigma = (std_ema_log_returns / np.sqrt(365)) * np.sqrt(24 * 60) - # - # factor_close_open = round(norm.ppf(0.99), 3) - # print('1+mu+factor_99 * sigma:', 1+mu+factor_close_open*sigma) - # - # top_pcg_open = 0.02 - # number_of_sigmas_open = (top_pcg_open - mu) / sigma - # confidence_for_close = norm.cdf(number_of_sigmas_open) - # - # print('f_confidence:', number_of_sigmas_open) - # print('confidence:', confidence_for_close) - - ################################################### - # Check VaR results - manager = ParameterManager() - historical_daily = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/BTCUSDC-1d-data_since_1 Jan 2021.csv")[-500:] - # assign data to stgy instance + define index as dates - historical_data_daily = pd.DataFrame(historical_daily["close"], columns=['close']) - data = historical_data_daily - print("VaR_99 Parametric:", manager.value_at_risk(data, "parametric", 0.99)) - print("VaR_99 historical:", manager.value_at_risk(data, "non_parametric", 0.99)) - print(historical_daily['timestamp'][319]) - plt.show() - - ################################################## - # Plot - # axs.axhline(y=p_rtrn_usdc_n_rmv_coll_dydx, color='black', linestyle='--', - # label='rtrn_usdc_n_rmv_coll_dydx') - # axs.axhline(y=p_borrow_usdc_n_add_coll, color='darkgoldenrod', linestyle='--', label='borrow_usdc_n_add_coll') - # axs.axhline(y=p_close_short, color='olive', linestyle='--', label='close_short') - # axs.axhline(y=p_close_short_pcg, color='darkgoldenrod', linestyle='--', label='close_short_pcg') - # axs.axhline(y=p_open_short, color='darkred', linestyle='--', label='open_short') - # axs.axhline(y=p_open_short_pcg, color='black', linestyle='--', label='open_short_pcg') - # axs.axhline(y=floor, color='red', linestyle='--', label='floor') - # axs.grid() - # axs.legend(loc='lower left') - # plt.show() \ No newline at end of file diff --git a/hedge_scripts/sm_interactor.py b/hedge_scripts/sm_interactor.py index a92ed8e..ca95aab 100644 --- a/hedge_scripts/sm_interactor.py +++ b/hedge_scripts/sm_interactor.py @@ -4,40 +4,47 @@ class SmInteractor: def __init__(self, config): - infura_node_as_http = config['infura_node_as_http'] - pool_address = config['pool_parameters']['pool_address'] - pool_abi = config['pool_parameters']['pool_abi'] + infura_node_as_http = config["infura_node_as_http"] + pool_address = config["pool_parameters"]["pool_address"] + pool_abi = config["pool_parameters"]["pool_abi"] web3_provider = web3.Web3.HTTPProvider(infura_node_as_http) w3_object = web3.Web3(web3_provider) # Conectamos con los contratos self.pool_contract = w3_object.eth.contract(address=pool_address, abi=pool_abi) - self.weth_address = config['weth_address'] - self.usdc_address = config['usdc_address'] - + self.weth_address = config["weth_address"] + self.usdc_address = config["usdc_address"] + def get_rates(self): - usdc_reserve_data = self.pool_contract.functions['getReserveData'](self.usdc_address).call() + usdc_reserve_data = self.pool_contract.functions["getReserveData"]( + self.usdc_address + ).call() # usdc_liquidity_index = usdc_reserve_data[1] / 10 ** 18 # usdc_variable_borrow_index = usdc_reserve_data[2] / 10 ** 18 # usdc_liquidity_rate = usdc_reserve_data[3] / 10 ** 27 - usdc_variable_borrow_rate = usdc_reserve_data[4] / 10 ** 27 - usdc_stable_borrow_rate = usdc_reserve_data[5] / 10 ** 27 - weth_reserve_data = self.pool_contract.functions['getReserveData'](self.weth_address).call() + usdc_variable_borrow_rate = usdc_reserve_data[4] / 10**27 + usdc_stable_borrow_rate = usdc_reserve_data[5] / 10**27 + weth_reserve_data = self.pool_contract.functions["getReserveData"]( + self.weth_address + ).call() # weth_liquidity_index = weth_reserve_data[1] / 10 ** 18 # weth_variable_borrow_index = weth_reserve_data[2] / 10 ** 18 # weth_liquidity_rate = weth_reserve_data[3] / 10 ** 27 - weth_variable_borrow_rate = weth_reserve_data[4] / 10 ** 27 - weth_stable_borrow_rate = weth_reserve_data[5] / 10 ** 27 - rates = {"usdc": { - "borrow_rates": { - "variable": usdc_variable_borrow_rate, - "stable": usdc_stable_borrow_rate - }}, + weth_variable_borrow_rate = weth_reserve_data[4] / 10**27 + weth_stable_borrow_rate = weth_reserve_data[5] / 10**27 + rates = { + "usdc": { + "borrow_rates": { + "variable": usdc_variable_borrow_rate, + "stable": usdc_stable_borrow_rate, + } + }, "weth": { - "borrowing_rates": { - "variable": weth_variable_borrow_rate, - "stable": weth_stable_borrow_rate - }} + "borrowing_rates": { + "variable": weth_variable_borrow_rate, + "stable": weth_stable_borrow_rate, + } + }, } return rates diff --git a/hedge_scripts/stgyapp.py b/hedge_scripts/stgyapp.py deleted file mode 100644 index 19827ed..0000000 --- a/hedge_scripts/stgyapp.py +++ /dev/null @@ -1,265 +0,0 @@ -import json -import pandas as pd -import math - -import aave -import dydx -import binance_client_ -import dydx_client -import sm_interactor -import volatility_calculator -import data_dumper -import parameter_manager -import interval - - -class StgyApp(object): - - def __init__(self, config): - - self.stk = config["stk"] - self.total_costs = 0 - self.gas_fees = 0 - - # prices and intervals - self.target_prices = {} - self.intervals = {} - - # clients for data - self.binance_client = binance_client_.BinanceClient(config["binance_client"]) - self.dydx_client = dydx_client.DydxClient(config["dydx_client"]) - self.sm_interactor = sm_interactor.SmInteractor(config["sm_interactor"]) - # self.historical_data = - - # We create attributes to fill later - self.aave = None - self.aave_features = None - self.aave_historical_data = None - self.aave_rates = None - self.aave_df = None - - self.dydx = None - self.dydx_features = None - self.dydx_historical_data = None - self.dydx_df = None - - self.volatility_calculator = None - - self.parameter_manager = parameter_manager.ParameterManager() - - self.historical_data = None - - self.data_dumper = data_dumper.DataDamperNPlotter() - - def launch(self, config): - # self.call_binance_data_loader() - self.initialize_aave(config['initial_parameters']['aave']) - self.initialize_dydx(config['initial_parameters']['dydx']) - self.call_dydx_client() - self.call_sm_interactor() - # self.initialize_volatility_calculator() - # floor = 1300 - # self.define_target_prices(floor) - # self.define_intervals() - - # def run_simulations(self): - # interval_old = self.intervals["infty"] - # for i in range(1, len(self.historical_data["close"]) - 1): - # new_interval_previous = self.historical_data["interval"][i - 1] - # new_interval_current = self.historical_data["interval"][i] - # new_market_price = self.historical_data["close"][i] - # # We could pass the whole AAVE_historical_df, DyDx_historical_df as parameters for scenarios if necessary - # self.find_scenario(new_market_price, new_interval_current, interval_old) - # if new_interval_previous != new_interval_current: - # interval_old = new_interval_previous - - # call clients functions - def call_binance_data_loader(self, symbol, freq, - initial_date, save): - eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq, - initial_date=initial_date, save=save) - # self.historical_data = eth_historical - self.historical_data = eth_historical["close"] - for i in range(len(self.historical_data)): - self.historical_data[i] = float(self.historical_data[i]) - # self.load_intervals() - - def call_dydx_client(self): - self.dydx_client.get_dydx_parameters(self.dydx) - - def call_sm_interactor(self): - self.aave_rates = self.sm_interactor.get_rates() - - - # initialize classes - def initialize_aave(self, config): - # We initialize aave and dydx classes instances - self.aave = aave.Aave(config) - # We load methods and attributes for aave and dydx to use later - self.aave_features = {"methods": [func for func in dir(self.aave) - if (callable(getattr(self.aave, func))) & (not func.startswith('__'))], - "attributes": {"values": list(self.aave.__dict__.values()), - "keys": list(self.aave.__dict__.keys())}} - # We create an attribute for historical data - self.aave_historical_data = [] - - def initialize_dydx(self, config): - self.dydx = dydx.Dydx(config) - self.dydx_features = {"methods": [func for func in dir(self.dydx) - if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))], - "attributes": {"values": list(self.dydx.__dict__.values()), - "keys": list(self.dydx.__dict__.keys())}} - self.dydx_historical_data = [] - - def initialize_volatility_calculator(self): - self.volatility_calculator = volatility_calculator.VolatilityCalculator() - - -if __name__ == "__main__": - # load configurations - with open("/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json") as json_file: - config = json.load(json_file) - - # Initialize stgyApp - stgy = StgyApp(config) - - # Track historical data - # symbol = 'ETHUSDC' - # freq = '1m' - # initial_date = "1 Jan 2019" - # stgy.call_binance_data_loader(symbol=symbol, freq=freq, - # initial_date=initial_date, save=True) - - # Load historical data if previously tracked and saved - historical_data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv")[-30000:] - # # assign data to stgy instance + define index as dates - stgy.historical_data = pd.DataFrame(historical_data["close"], columns=['close']) - timestamp = pd.to_datetime(historical_data['timestamp']) - stgy.historical_data.index = timestamp - # - # ####################################################### - # # Simulations - - # Define floor - floor = stgy.historical_data['close'].max() * 0.8 - ######################### - # Define trigger prices and thresholds - N_week = 1 * 1 * 7 * 24 * 60 # 7 days - data_for_thresholds = stgy.historical_data[:N_week].copy() # First week of data - stgy.parameter_manager.define_target_prices(stgy, N_week, data_for_thresholds, floor) - stgy.parameter_manager.define_intervals(stgy) - stgy.parameter_manager.load_intervals(stgy) - ######################### - # Save historical data with trigger prices and thresholds loaded - # stgy.historical_data.to_csv("/home/agustin/Git-Repos/HedgingScripts/files/stgy.historical_data.csv") - ######################### - # Here we define initial parameters for AAVE and DyDx depending on at which price we are starting simulations - - # Define initial and final index if needed in order to only run simulations in periods of several trigger prices - # As we calculate vol using first week of data, we initialize simulations from that week on - initial_index = N_week + 1 - # final_index = 3923 - 1 - # print(config['stk']) - stgy.launch(config) - - # AAVE - stgy.aave.market_price = stgy.historical_data['close'][initial_index] - stgy.aave.interval_current = stgy.historical_data['interval'][initial_index] - stgy.aave.entry_price = stgy.target_prices['open_close'] - stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3) - stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3) - stgy.reserve_margin_eth = stgy.stk * 0.1 - stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price - stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price - stgy.aave.usdc_status = True - stgy.aave.debt = stgy.aave.collateral_eth_initial * stgy.target_prices['open_close'] * stgy.aave.borrowed_percentage - # debt_initial - stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / 0.5, 3) - # stgy.total_costs = 104 - - # DyDx - stgy.dydx.market_price = stgy.historical_data['close'][initial_index] - stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index] - stgy.dydx.collateral = stgy.aave.debt - stgy.dydx.equity = stgy.dydx.collateral - stgy.dydx.collateral_status = True - ######################### - # Change or define prices that aren't defined yet if the period of simulations involves those prices - # For ex if we are executing periods of time in which ltv_limit or repay_aave are already defined - - # price_floor = stgy.intervals['open_close'].left_border - previous_position_order = stgy.intervals['open_close'].position_order - stgy.intervals['floor'] = interval.Interval(stgy.aave.price_to_ltv_limit, floor, - 'floor', previous_position_order + 1) - stgy.intervals['minus_infty'] = interval.Interval(-math.inf, stgy.aave.price_to_ltv_limit, - 'minus_infty', previous_position_order + 2) - - ######################### - # Load interval_old - interval_old = stgy.intervals['infty'] - ######################### - # Clear previous csv data for aave and dydx - stgy.data_dumper.delete_results() - ######################### - # add header to csv of aave and dydx - stgy.data_dumper.add_header() - ######################### - import time - # run simulations - starttime = time.time() - print('starttime:', starttime) - # for i in range(initial_index, len(stgy.historical_data)): - i = initial_index - while(i < len(stgy.historical_data)): - # for i in range(initial_index, len(stgy.historical_data)): - # pass - new_interval_previous = stgy.historical_data["interval"][i-1] - new_interval_current = stgy.historical_data["interval"][i] - new_market_price = stgy.historical_data["close"][i] - ######################### - # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late - # therefore not taking the actions needed as soon as they are needed - if new_interval_previous != new_interval_current: - interval_old = new_interval_previous - ######################### - # Update parameters - # First we update everything in order to execute scenarios with updated values - stgy.parameter_manager.update_parameters(stgy, new_market_price, new_interval_current) - time_used = stgy.parameter_manager.find_scenario(stgy, new_market_price, new_interval_current, interval_old, i) - ######################### - # Funding rates - # We are using hourly data so we add funding rates every 8hs (every 8 new prices) - # Moreover, we need to call this method after find_scenarios in order to have all costs updated. - # Calling it before find_scenarios will overwrite the funding by 0 - # We have to check all the indexes between old index i and next index i+time_used - for index in range(i, i+time_used): - if (index - initial_index) % (8 * 60) == 0: - stgy.dydx.add_funding_rates() - # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates - ######################### - # Add costs - stgy.parameter_manager.add_costs(stgy) - ######################### - # Write data - # We write the data into the google sheet or csv file acording to sheet value - # (sheet = True --> sheet, sheet = False --> csv) - stgy.data_dumper.write_data(stgy, - new_interval_previous, interval_old, i, - sheet=False) - ######################### - # Update trigger prices and thresholds - # We update trigger prices and thresholds every day - if (i+time_used - initial_index) % (1*24*60) == 0: - # We call the paramater_manager instance with updated data - data_for_thresholds = stgy.historical_data[:i].copy() - stgy.parameter_manager.define_target_prices(stgy, N_week, data_for_thresholds, floor) - stgy.parameter_manager.define_intervals(stgy) - stgy.parameter_manager.load_intervals(stgy) - save = True - # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period) - - # we increment index by the time consumed in executing actions - i += time_used - - endtime = time.time() - print('endtime:', endtime) diff --git a/hedge_scripts/volatility_calculator.py b/hedge_scripts/volatility_calculator.py index c7a1702..a0adfeb 100644 --- a/hedge_scripts/volatility_calculator.py +++ b/hedge_scripts/volatility_calculator.py @@ -5,13 +5,12 @@ class VolatilityCalculator(object): - @staticmethod def get_std_vol(historical_data): """ historical data has to be a df OHLC data """ - returns = np.around(historical_data['close'].pct_change().dropna(), 3) + returns = np.around(historical_data["close"].pct_change().dropna(), 3) mu = np.mean(returns) sigma = np.std(returns) sigma_anualized = sigma * np.sqrt(365) @@ -22,28 +21,38 @@ def get_std_vol(historical_data): def get_atr(historical_data, atr_length): "function to calculate True Range and Average True Range" - historical_data['H-L'] = abs(historical_data['High'] - historical_data['Low']) - historical_data['H-PO'] = abs(historical_data['High'] - historical_data['Open'].shift(1)) - historical_data['L-PO'] = abs(historical_data['Low'] - historical_data['Open'].shift(1)) + historical_data["H-L"] = abs(historical_data["High"] - historical_data["Low"]) + historical_data["H-PO"] = abs( + historical_data["High"] - historical_data["Open"].shift(1) + ) + historical_data["L-PO"] = abs( + historical_data["Low"] - historical_data["Open"].shift(1) + ) - historical_data['TR'] = historical_data[['H-L', 'H-PO', 'L-PO']].max(axis=1, skipna=False) - historical_data['ATR'] = historical_data['TR'].rolling(atr_length).mean() + historical_data["TR"] = historical_data[["H-L", "H-PO", "L-PO"]].max( + axis=1, skipna=False + ) + historical_data["ATR"] = historical_data["TR"].rolling(atr_length).mean() - df2 = historical_data.drop(['H-L', 'H-PO', 'L-PO'], axis=1) + df2 = historical_data.drop(["H-L", "H-PO", "L-PO"], axis=1) return df2 @staticmethod def get_sma_std_vol_of_returns(historical_data, rolling_number=14): # Rolling Volatility (annualized assuming 365 trading days) # 2 week - historical_data['returns'] = np.around(historical_data['close'].pct_change().dropna(), 3) - sma_rolling = historical_data['returns'].rolling(rolling_number) + historical_data["returns"] = np.around( + historical_data["close"].pct_change().dropna(), 3 + ) + sma_rolling = historical_data["returns"].rolling(rolling_number) vol = sma_rolling.std() - historical_data['vol_sma_of_returns'] = vol + historical_data["vol_sma_of_returns"] = vol vol_annualized = vol * np.sqrt(365) - historical_data['vol_sma_of_returns_annualized'] = vol_annualized - return {'vol_sma_of_returns_respect_to_periods': vol, - 'vol_sma_of_returns_annualized': vol_annualized} + historical_data["vol_sma_of_returns_annualized"] = vol_annualized + return { + "vol_sma_of_returns_respect_to_periods": vol, + "vol_sma_of_returns_annualized": vol_annualized, + } @staticmethod def get_ema_std_vol_of_returns(hist_data, alpha, min_periods): @@ -51,57 +60,74 @@ def get_ema_std_vol_of_returns(hist_data, alpha, min_periods): # 2 week # historical_data = hist_data[-2*30*24:].copy() historical_data = hist_data.copy() - historical_data['returns'] = np.around(historical_data['close'].pct_change().dropna(), 3) - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + historical_data["returns"] = np.around( + historical_data["close"].pct_change().dropna(), 3 + ) + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = abs(log_returns.dropna()) ema_of_com_in_periods = log_returns.ewm(alpha=alpha, min_periods=min_periods) vol = ema_of_com_in_periods.std() - historical_data['vol_ema_of_returns'] = vol + historical_data["vol_ema_of_returns"] = vol vol_annualized = vol * np.sqrt(365) - historical_data['vol_ema_of_returns_annualized'] = vol_annualized - return {'vol_ema_of_returns_respect_to_periods': vol, - 'vol_ema_of_returns_annualized': vol_annualized} + historical_data["vol_ema_of_returns_annualized"] = vol_annualized + return { + "vol_ema_of_returns_respect_to_periods": vol, + "vol_ema_of_returns_annualized": vol_annualized, + } @staticmethod def get_sma_std_vol_of_prices(historical_data, rolling_number=14): # Rolling Volatility (annualized assuming 365 trading days) # 2 week # historical_data['returns'] = np.around(historical_data['close'].pct_change().dropna(), 3) - sma_rolling = historical_data['close'].rolling(rolling_number) + sma_rolling = historical_data["close"].rolling(rolling_number) vol = sma_rolling.std() - historical_data['vol_sma_of_prices'] = vol + historical_data["vol_sma_of_prices"] = vol vol_annualized = vol * np.sqrt(365) - historical_data['vol_sma_prices_annualized'] = vol_annualized - return {'vol_sma_of_prices_respect_to_periods': vol, - 'vol_sma_of_prices_annualized': vol_annualized} + historical_data["vol_sma_prices_annualized"] = vol_annualized + return { + "vol_sma_of_prices_respect_to_periods": vol, + "vol_sma_of_prices_annualized": vol_annualized, + } @staticmethod def get_ema_std_vol_of_prices(historical_data, alpha, min_periods): # Rolling Volatility (annualized assuming 365 trading days) # 2 week # historical_data['returns'] = np.around(historical_data['close'].pct_change().dropna(), 3) - ema_of_com_in_periods = historical_data['close'].ewm(alpha=alpha, min_periods=min_periods) + ema_of_com_in_periods = historical_data["close"].ewm( + alpha=alpha, min_periods=min_periods + ) ema = ema_of_com_in_periods.mean() vol = ema_of_com_in_periods.std() - historical_data['vol_ema_of_prices'] = vol + historical_data["vol_ema_of_prices"] = vol vol_annualized = vol * np.sqrt(365) - historical_data['vol_ema_of_prices_annualized'] = vol_annualized - return {'vol_ema_of_prices_respect_to_periods': vol, - 'vol_ema_of_prices_annualized': vol_annualized, - 'ema':ema} + historical_data["vol_ema_of_prices_annualized"] = vol_annualized + return { + "vol_ema_of_prices_respect_to_periods": vol, + "vol_ema_of_prices_annualized": vol_annualized, + "ema": ema, + } @staticmethod def get_bollinger_bands(historical_data, sma_length=20): - historical_data['returns'] = np.around(historical_data['close'].pct_change().dropna(), 3) - historical_data['sma'] = historical_data['returns'].rolling(sma_length).mean() + historical_data["returns"] = np.around( + historical_data["close"].pct_change().dropna(), 3 + ) + historical_data["sma"] = historical_data["returns"].rolling(sma_length).mean() # Upper band - historical_data['b_upper'] = historical_data['sma20'] + 2 * historical_data['sma20'].rolling(20).std() + historical_data["b_upper"] = ( + historical_data["sma20"] + 2 * historical_data["sma20"].rolling(20).std() + ) # Lower band - historical_data['b_lower'] = historical_data['sma20'] - 2 * historical_data['sma20'].rolling(20).std() + historical_data["b_lower"] = ( + historical_data["sma20"] - 2 * historical_data["sma20"].rolling(20).std() + ) return historical_data - # ARCH @staticmethod def get_arch(historical_data, p, o, q): @@ -109,15 +135,23 @@ def get_arch(historical_data, p, o, q): # ARCH the baseline volality of the Bitcoin log returns #################################################################### from arch import arch_model - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = abs(log_returns.dropna()) am = arch_model(log_returns, p=p, o=o, q=q) res = am.fit(update_freq=5) # print(res.summary()) # fig = res.plot(annualize="D") - df = pd.DataFrame({'Vol: abs(log_returns)': log_returns[10:], 'ARCH(1)': res.conditional_volatility[10:]}) + df = pd.DataFrame( + { + "Vol: abs(log_returns)": log_returns[10:], + "ARCH(1)": res.conditional_volatility[10:], + } + ) # df = pd.DataFrame({'Vol: log_returns': log_returns[10:], 'ARCH(1)': res.conditional_volatility[10:]}) - subplot = df.plot(title='ARCH(1) Model Applied to Vol') + subplot = df.plot(title="ARCH(1) Model Applied to Vol") plt.show() return list(res.conditional_volatility)[-1] @@ -128,37 +162,54 @@ def get_garch(historical_data): # GARCH the baseline volality of the Bitcoin log returns #################################################################### from arch import arch_model - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = abs(log_returns.dropna()) am = arch_model(log_returns) # GARCH MODEL p=1 , q=1 res = am.fit(update_freq=5) # print(res.summary()) # fig = res.plot(annualize="D") - df = pd.DataFrame({'Vol: abs(log_returns)': log_returns[10:], 'GARCH(1,1)': res.conditional_volatility[10:]}) + df = pd.DataFrame( + { + "Vol: abs(log_returns)": log_returns[10:], + "GARCH(1,1)": res.conditional_volatility[10:], + } + ) # df = pd.DataFrame({'Vol: log_returns': log_returns[10:], 'GARCH(1,1)': res.conditional_volatility[10:]}) - subplot = df.plot(title='GARCH(1,1) Model Applied to Vol') + subplot = df.plot(title="GARCH(1,1) Model Applied to Vol") plt.show() # EMWA @staticmethod def rho_cal(historical_data): import scipy - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = abs(log_returns.dropna()) - rho_hat = scipy.stats.pearsonr(log_returns - np.mean(log_returns), np.sign( - log_returns - np.mean(log_returns))) # rho_hat[0]:Pearson correlation , rho_hat[1]:two-tailed p-value + rho_hat = scipy.stats.pearsonr( + log_returns - np.mean(log_returns), + np.sign(log_returns - np.mean(log_returns)), + ) # rho_hat[0]:Pearson correlation , rho_hat[1]:two-tailed p-value return rho_hat[0] def get_emwa(self, historical_data, window): cut_t = window alpha = np.arange(0.01, 0.95, 0.01) - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = abs(log_returns.dropna()) t = len(log_returns) rho = self.rho_cal(historical_data) # calculate sample sign correlation # print(rho) - vol = abs(log_returns - np.mean(log_returns)) / rho # calculate observed volatility + vol = ( + abs(log_returns - np.mean(log_returns)) / rho + ) # calculate observed volatility # print(vol) MSE_alpha = np.zeros(len(alpha)) sn = np.zeros(len(alpha)) # volatility @@ -169,9 +220,12 @@ def get_emwa(self, historical_data, window): error[i] = vol[i] - s s = alpha[a] * vol[i] + (1 - alpha[a]) * s MSE_alpha[a] = np.mean( - (error[(len(error) - cut_t):(len(error))]) ** 2) # forecast error sum of squares (FESS) + (error[(len(error) - cut_t) : (len(error))]) ** 2 + ) # forecast error sum of squares (FESS) sn[a] = s - vol_forecast = sn[[i for i, j in enumerate(MSE_alpha) if j == min(MSE_alpha)]] # which min + vol_forecast = sn[ + [i for i, j in enumerate(MSE_alpha) if j == min(MSE_alpha)] + ] # which min RMSE = np.sqrt(min(MSE_alpha)) return vol_forecast @@ -179,7 +233,10 @@ def get_emwa(self, historical_data, window): @staticmethod def get_arima(historical_data): from statsmodels.tsa.arima_model import ARIMA - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = log_returns.dropna() np.var(log_returns.iloc[1:]) # variance of SPY_vol y = abs(log_returns.iloc[1:]) @@ -194,9 +251,11 @@ def plot_log_returns(historical_data, window, bins): """ historical = historical_data.copy() - pct_change = historical['close'].pct_change(window).fillna(method='bfill') - return_usd = historical['close'] - historical['close'].shift(window) - log_returns = np.log(historical['close']) - np.log(historical['close'].shift(window)) + pct_change = historical["close"].pct_change(window).fillna(method="bfill") + return_usd = historical["close"] - historical["close"].shift(window) + log_returns = np.log(historical["close"]) - np.log( + historical["close"].shift(window) + ) # historical['pct_change'] = pct_change # historical['log_returns'] = log_returns @@ -209,11 +268,11 @@ def plot_log_returns(historical_data, window, bins): # log_returns.hist(bins=50, ax=axs) # pct_change.hist(bins=50, ax=axs) axs[0].hist(log_returns, bins=bins) - axs[0].set_ylabel('Samples') - axs[1].set_ylabel('Log Returns') - axs[0].set_title('Distribution') - axs[1].set_title('Volatility') - axs[1].plot(return_usd, color='tab:blue', label='Returns dist') + axs[0].set_ylabel("Samples") + axs[1].set_ylabel("Log Returns") + axs[0].set_title("Distribution") + axs[1].set_title("Volatility") + axs[1].plot(return_usd, color="tab:blue", label="Returns dist") # To check if its normally distributed + understate the likelihood of returns beyond -2/+2 quantiles # import scipy.stats as stats # stats.probplot(historical['returns'], dist='norm', plot=axs) @@ -226,33 +285,47 @@ def plot_ACF(historical_data): # To check whether each daily return is uncorrelated with the pervious days. import statsmodels.api as sm import statsmodels.tsa.api as smt + historical = historical_data.copy() - pct_change = historical['close'].pct_change().fillna(method='bfill') - log_returns = np.log(historical['close']) - np.log(historical['close'].shift(1)) + pct_change = historical["close"].pct_change().fillna(method="bfill") + log_returns = np.log(historical["close"]) - np.log(historical["close"].shift(1)) log_returns = log_returns.dropna() fig, ax = plt.subplots(figsize=(14, 10)) smt.graphics.plot_acf(log_returns, lags=25, alpha=0.05, ax=ax) plt.show() - + @staticmethod def find_distribution(historical_data): from distfit import distfit - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(1)) + + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(1) + ) log_returns = log_returns.dropna() - dist_names = ["weibull_min", "norm", "weibull_max", "beta", - "invgauss", "uniform", "gamma", "expon", - "lognorm", "pearson3","triang"] + dist_names = [ + "weibull_min", + "norm", + "weibull_max", + "beta", + "invgauss", + "uniform", + "gamma", + "expon", + "lognorm", + "pearson3", + "triang", + ] # Initialize distfit dist = distfit() # Determine best-fitting probability distribution for data dist.fit_transform(log_returns) - #recalling that the lowest RSS will provide the best fit - print(dist.summary[['distr', 'score']]) + # recalling that the lowest RSS will provide the best fit + print(dist.summary[["distr", "score"]]) # Plot results - fig,axs = plt.subplots(2, 1, figsize=(21, 7)) + fig, axs = plt.subplots(2, 1, figsize=(21, 7)) # fig.suptitle("Log returns analysis") dist.plot(ax=axs[0]) axs[1].plot(dist.summary.distr, dist.summary.score) @@ -262,12 +335,19 @@ def find_distribution(historical_data): def calc_var(self, historical_data): # compute returns import math - data = historical_data['close'] - returns_log = [math.log(data[i + 1] / data[i], 10) for i in range(0, len(data) - 1)] - log_returns = np.log(historical_data['close']) - np.log(historical_data['close'].shift(10)) + + data = historical_data["close"] + returns_log = [ + math.log(data[i + 1] / data[i], 10) for i in range(0, len(data) - 1) + ] + log_returns = np.log(historical_data["close"]) - np.log( + historical_data["close"].shift(10) + ) log_returns = log_returns.dropna() # calculate std_21 for returns - std_21_log = [np.std(returns_log[t - 21:t]) for t in range(21, len(returns_log))] + std_21_log = [ + np.std(returns_log[t - 21 : t]) for t in range(21, len(returns_log)) + ] # std_42=[np.std(returns[t-42:t]) for t in range(42,len(returns))] @@ -279,12 +359,15 @@ def calc_var(self, historical_data): # Confidence Confidence = 0.99 # Remember that norm.ppf(c)=\phi^{-1}(c) and norm.pdf(c)=\phi(c) - Factor = norm.ppf( - 1 - Confidence) + Factor = norm.ppf(1 - Confidence) # i.e. Factor = \phi^{-1}(0.01) i.e. The method norm.ppf() takes a percentage # and returns a standard deviation multiplier for what value that percentage occurs at. # Using the \mu_10D,t term - VaR_21_log_with_mu = [mu_10D_log + Factor * std_21_log[t] * math.sqrt(10) for t in range(0, len(std_21_log))] + VaR_21_log_with_mu = [ + mu_10D_log + Factor * std_21_log[t] * math.sqrt(10) + for t in range(0, len(std_21_log)) + ] + if __name__ == "__main__": # i=4 @@ -303,9 +386,12 @@ def calc_var(self, historical_data): # initial_date=initial_date, save=True) # historical_data = pd.DataFrame(stgy.historical_data) - historical_data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv") + historical_data = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv" + ) # historical_data.index = historical_data['timestamp'] from datetime import datetime + # historical_data['timestamp'].dt.strftime('%Y-%m-%d') # plt.plot(historical_data['timestamp'], historical_data['close']) # print(type(historical_data.index[0])) @@ -323,50 +409,57 @@ def calc_var(self, historical_data): # print(sma.iloc[[-30, -7, -1]]) # print(ema.iloc[[-30, -7, -1]]) - # ema vs sma + comparison with messari and t3 - volatility_calc.historical_data = pd.DataFrame(historical_data["close"], columns=['close']) - timestamp = pd.to_datetime(historical_data['timestamp']) + volatility_calc.historical_data = pd.DataFrame( + historical_data["close"], columns=["close"] + ) + timestamp = pd.to_datetime(historical_data["timestamp"]) # stgy.historical_data.column = ['close'] # ewm = historical_data['close'].ewm(alpha=alpha, min_periods=min_periods) # EMA and SMA of prices - ewm_prices = volatility_calc.historical_data['close'].ewm(span=30) - rolling_prices = volatility_calc.historical_data['close'].rolling(30) + ewm_prices = volatility_calc.historical_data["close"].ewm(span=30) + rolling_prices = volatility_calc.historical_data["close"].rolling(30) std_prices = ewm_prices.std() ema_prices = ewm_prices.mean() sma_prices = rolling_prices.mean() - volatility_calc.historical_data['std_prices'] = std_prices - volatility_calc.historical_data['ema_prices'] = ema_prices - volatility_calc.historical_data['sma_prices'] = sma_prices + volatility_calc.historical_data["std_prices"] = std_prices + volatility_calc.historical_data["ema_prices"] = ema_prices + volatility_calc.historical_data["sma_prices"] = sma_prices # EMA and SMA of returns import numpy as np - returns = np.around(volatility_calc.historical_data['close'].pct_change().dropna(), 3) - volatility_calc.historical_data['returns'] = returns - ewm_returns = volatility_calc.historical_data['returns'].ewm(span=365) + returns = np.around( + volatility_calc.historical_data["close"].pct_change().dropna(), 3 + ) + volatility_calc.historical_data["returns"] = returns + ewm_returns = volatility_calc.historical_data["returns"].ewm(span=365) # ewm_returns = volatility_calc.historical_data['returns'].ewm(alpha=0.5) - rolling_returns = volatility_calc.historical_data['returns'].rolling(365) + rolling_returns = volatility_calc.historical_data["returns"].rolling(365) std_returns = ewm_returns.std() ema_returns = ewm_returns.mean() sma_returns = rolling_returns.mean() - volatility_calc.historical_data['std_returns'] = std_returns - volatility_calc.historical_data['ema_returns'] = ema_returns - volatility_calc.historical_data['sma_returns'] = ema_returns + volatility_calc.historical_data["std_returns"] = std_returns + volatility_calc.historical_data["ema_returns"] = ema_returns + volatility_calc.historical_data["sma_returns"] = ema_returns # EMA and SMA of log returns - log_returns = np.log(volatility_calc.historical_data['close']) - np.log(volatility_calc.historical_data['close'].shift(1)) - volatility_calc.historical_data['log_returns'] = log_returns + log_returns = np.log(volatility_calc.historical_data["close"]) - np.log( + volatility_calc.historical_data["close"].shift(1) + ) + volatility_calc.historical_data["log_returns"] = log_returns # ewm_log_returns = volatility_calc.historical_data['log_returns'].ewm(span=15) - ewm_log_returns = volatility_calc.historical_data['log_returns'][-30:].ewm(alpha=0.8, adjust=False) - rolling_log_returns = volatility_calc.historical_data['log_returns'].rolling(365) + ewm_log_returns = volatility_calc.historical_data["log_returns"][-30:].ewm( + alpha=0.8, adjust=False + ) + rolling_log_returns = volatility_calc.historical_data["log_returns"].rolling(365) std_log_returns = ewm_log_returns.std() ema_log_returns = ewm_log_returns.mean() sma_log_returns = rolling_log_returns.mean() - volatility_calc.historical_data['std_log_returns'] = std_log_returns - volatility_calc.historical_data['ema_log_returns'] = ema_log_returns - volatility_calc.historical_data['sma_log_returns'] = sma_log_returns + volatility_calc.historical_data["std_log_returns"] = std_log_returns + volatility_calc.historical_data["ema_log_returns"] = ema_log_returns + volatility_calc.historical_data["sma_log_returns"] = sma_log_returns volatility_calc.historical_data.index = timestamp # N = 3*12*30 @@ -401,4 +494,4 @@ def calc_var(self, historical_data): # plt.plot(volatility_calc.historical_data['sma_log_returns'], label='sma_30_log_returns') # plt.legend() # # plt.plot(volatility_calc.historical_data['std_log_returns']) - # plt.show() \ No newline at end of file + # plt.show() diff --git a/jupyter-lab/Simulations_lab.ipynb b/jupyter-lab/Simulations_lab.ipynb new file mode 100644 index 0000000..be1cb5a --- /dev/null +++ b/jupyter-lab/Simulations_lab.ipynb @@ -0,0 +1,1863 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /home/ubuntu/jupyter_notebook/lib/python3.10/site-packages (1.4.4)\n", + "Requirement already satisfied: scipy in /home/ubuntu/jupyter_notebook/lib/python3.10/site-packages (1.9.1)\n", + "Requirement already satisfied: pygsheets in /home/ubuntu/jupyter_notebook/lib/python3.10/site-packages (2.0.5)\n", + "Requirement already satisfied: matplotlib in 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"source": [ + "!pip install pandas scipy pygsheets matplotlib\n", + "\n", + "import os\n", + "import pygsheets\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import csv\n", + "import pandas as pd\n", + "import numpy as np\n", + "import json\n", + "import math\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## StgyApp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class StgyApp(object):\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.stk = config[\"stk\"]\n", + " self.total_costs_from_aave_n_dydx = 0\n", + " self.total_pnl = 0\n", + " self.gas_fees = 0\n", + "\n", + " # prices and intervals\n", + " self.trigger_prices = {}\n", + " self.intervals = {}\n", + "\n", + " # clients for data\n", + " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", + " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", + " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", + " # self.historical_data =\n", + "\n", + " # We create attributes to fill later\n", + " self.aave = None\n", + " self.aave_features = None\n", + " self.aave_rates = None\n", + "\n", + " self.dydx = None\n", + " self.dydx_features = None\n", + "\n", + " # self.volatility_calculator = None\n", + "\n", + " self.parameter_manager = ParameterManager()\n", + "\n", + " self.historical_data = None\n", + "\n", + " self.data_dumper = DataDamperNPlotter()\n", + "\n", + " def launch(self, config):\n", + " # self.call_binance_data_loader()\n", + " self.initialize_aave(config['initial_parameters']['aave'])\n", + " self.initialize_dydx(config['initial_parameters']['dydx'])\n", + "\n", + " # call clients functions\n", + " def get_historical_data(self, symbol, freq,\n", + " initial_date, save):\n", + " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=save)\n", + " # self.historical_data = eth_historical\n", + " self.historical_data = eth_historical[\"close\"]\n", + " for i in range(len(self.historical_data)):\n", + " self.historical_data[i] = float(self.historical_data[i])\n", + " # self.load_intervals()\n", + "\n", + " # initialize classes\n", + " def initialize_aave(self, config):\n", + " # We initialize aave and dydx classes instances\n", + " self.aave = Aave(config)\n", + " # We load methods and attributes for aave and dydx to use later\n", + " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", + " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", + " \"keys\": list(self.aave.__dict__.keys())}}\n", + " # We create an attribute for historical data\n", + " self.aave_historical_data = []\n", + "\n", + " def initialize_dydx(self, config):\n", + " self.dydx = Dydx(config)\n", + " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", + " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", + " \"keys\": list(self.dydx.__dict__.keys())}}\n", + " self.dydx_historical_data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Interval class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This class represents an actual mathematical interval [left_border, right_border] and is used to be aware in which interval every price is and therefore being able to identify price movement direction by comparing intervals between a new given price read by the bot and the last interval in which the price was." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class Interval(object):\n", + "\n", + " def __init__(self,\n", + " left_border,\n", + " right_border,\n", + " name,\n", + " position_order):\n", + " self.left_border = left_border\n", + " self.right_border = right_border\n", + " self.name = name\n", + " self.position_order = position_order\n", + "\n", + " def is_lower(self, another_interval):\n", + " if self.right_border <= another_interval.left_border:\n", + " return True\n", + " else:\n", + " return False\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aave and DyDx modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Aave" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class Aave(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert self.dydx_class_instance == isinstance(dydx)\n", + " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", + " self.market_price = config['market_price']\n", + " self.interval_current = config['interval_current']\n", + "\n", + " self.entry_price = config['entry_price']\n", + "\n", + " self.collateral_eth_initial = config['collateral_eth']\n", + " self.collateral_eth = config['collateral_eth']\n", + " self.collateral_usdc = config['collateral_usdc']\n", + "\n", + " self.reserve_margin_eth = 0\n", + " self.reserve_margin_usdc = 0\n", + "\n", + " self.borrowed_percentage = config['borrowed_pcg']\n", + " self.usdc_status = config['usdc_status']\n", + "\n", + " self.debt = config['debt']\n", + " self.debt_initial = config['debt']\n", + "\n", + " self.ltv = config['ltv']\n", + " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", + "\n", + " self.lending_rate = 0\n", + " self.lending_rate_hourly = 0\n", + " self.interest_on_lending_eth = 0 # aggregated fees\n", + " self.interest_on_lending_usd = 0\n", + " self.lending_fees_eth = 0 # fees between last 2 prices\n", + " self.lending_fees_usd = 0\n", + "\n", + " self.borrowing_rate = 0\n", + " self.borrowing_rate_hourly = 0\n", + " self.interest_on_borrowing = 0 # aggregated fees\n", + " self.borrowing_fees = 0 # fees between last 2 prices\n", + "\n", + " self.lend_minus_borrow_interest = 0\n", + "\n", + " self.costs = 0\n", + " # self.historical = pd.DataFrame()\n", + " # self.dydx_class_instance = dydx_class_instance\n", + " # self.staked_in_protocol = stk\n", + "\n", + " # def update_costs(self):\n", + " # \"\"\"\n", + " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " # \"\"\"\n", + " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", + " # self.costs = self.costs - self.lend_minus_borrow_interest\n", + "\n", + " def collateral_usd(self):\n", + " return self.collateral_eth * self.market_price\n", + "\n", + " def update_debt(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.debt = self.debt + self.borrowing_fees\n", + "\n", + " def update_collateral(self):\n", + " \"\"\"\n", + " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", + " self.collateral_usdc = self.collateral_usd()\n", + "\n", + " def track_lend_borrow_interest(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", + " in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", + "\n", + " def lending_fees_calc(self, freq):\n", + " self.simulate_lending_rate()\n", + " self.lending_rate_freq = self.lending_rate / freq\n", + "\n", + " # fees from lending are added to collateral? YES\n", + " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", + " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", + " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", + " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", + " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", + "\n", + " def borrowing_fees_calc(self, freq):\n", + " self.simulate_borrowing_rate()\n", + " self.borrowing_rate_freq = self.borrowing_rate / freq\n", + "\n", + " # fees from borrow are added to debt? YES\n", + " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", + " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", + " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", + "\n", + " def simulate_lending_rate(self):\n", + " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", + "\n", + " # best case\n", + " # self.lending_rate = 1.5 / 100\n", + "\n", + " # worst case\n", + " self.lending_rate = 0.5 / 100\n", + "\n", + " def simulate_borrowing_rate(self):\n", + " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", + "\n", + " # best case\n", + " # self.borrowing_rate = 1.5/100\n", + "\n", + " # worst case\n", + " self.borrowing_rate = 2.5/100\n", + "\n", + " def ltv_calc(self):\n", + " if self.collateral_usd() == 0:\n", + " return 0\n", + " else:\n", + " return self.debt / self.collateral_usd()\n", + "\n", + " def price_to_liquidation(self, dydx_class_instance):\n", + " return self.entry_price - (dydx_class_instance.pnl()\n", + " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", + "\n", + " def price_to_ltv_limit_calc(self):\n", + " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", + "\n", + " def buffer_for_repay(self):\n", + " return 0.01\n", + "\n", + " def ltv_limit(self):\n", + " return 0.5\n", + "\n", + " # Actions to take\n", + " def return_usdc(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " time = 0\n", + " if self.usdc_status:\n", + " # simulate 2min delay for tx\n", + " # update parameters\n", + " # AAVE parameters\n", + " self.usdc_status = False\n", + " # self.collateral_eth = 0\n", + " # self.collateral_usdc = 0\n", + " self.debt = 0\n", + " self.ltv = 0\n", + " self.price_to_ltv_limit = 0\n", + " # self.lending_rate = 0\n", + " # self.borrowing_rate = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def repay_aave(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " dydx_class_instance = stgy_instance.dydx\n", + " # aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " #\n", + " time = 0\n", + " if self.usdc_status:\n", + " # update parameters\n", + " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price)\n", + " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", + "\n", + " # pnl_for_debt = dydx_class_instance.pnl()\n", + " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", + " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price)\n", + " self.debt = self.debt - pnl_for_debt\n", + " self.ltv = self.ltv_calc()\n", + "\n", + " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " dydx_class_instance.short_size = new_short_size\n", + " dydx_class_instance.notional = dydx_class_instance.notional_calc()\n", + " dydx_class_instance.equity = dydx_class_instance.equity_calc()\n", + " dydx_class_instance.leverage = dydx_class_instance.leverage_calc()\n", + " dydx_class_instance.pnl = dydx_class_instance.pnl_calc()\n", + " # dydx_class_instance.price_to_liquidation = \\\n", + " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", + "\n", + " # fees\n", + " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + " dydx_class_instance.simulate_maker_taker_fees()\n", + " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", + " dydx_class_instance.costs = dydx_class_instance.costs \\\n", + " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", + " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + "\n", + " # Note that a negative self.debt is actually a profit\n", + " # We update the parameters\n", + " if self.debt > 0:\n", + " self.usdc_status = True\n", + " else:\n", + " self.usdc_status = False\n", + " # simulate 2min delay for tx\n", + " time = 1\n", + " return time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### DyDx" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "class Dydx(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert aave_class == isinstance(aave)\n", + " self.market_price = config['market_price']\n", + " self.interval_current = config['interval_current']\n", + " self.entry_price = config['entry_price']\n", + " self.short_size = config['short_size']\n", + " self.collateral = config['collateral']\n", + " self.notional = config['notional']\n", + " self.equity = config['equity']\n", + " self.leverage = config['leverage']\n", + " self.pnl = config['pnl']\n", + " # self.price_to_liquidation = config['price_to_liquidation']\n", + " self.collateral_status = config['collateral_status']\n", + " self.short_status = config['short_status']\n", + " self.order_status = True\n", + " self.withdrawal_fees = 0.01/100\n", + " self.funding_rates = 0\n", + " self.maker_taker_fees = 0\n", + " self.maker_fees_counter = 0\n", + " self.costs = 0\n", + "\n", + " # auxiliary functions\n", + " def pnl_calc(self):\n", + " return self.short_size * (self.market_price-self.entry_price)\n", + "\n", + " def notional_calc(self):\n", + " return abs(self.short_size)*self.market_price\n", + "\n", + " def equity_calc(self):\n", + " return self.collateral + self.pnl_calc()\n", + "\n", + " def leverage_calc(self):\n", + " if self.equity_calc() == 0:\n", + " return 0\n", + " else:\n", + " return self.notional_calc() / self.equity_calc()\n", + "\n", + " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", + " return self.entry_price \\\n", + " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", + "\n", + " @staticmethod\n", + " def price_to_liquidation_calc(dydx_client_class_instance):\n", + " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", + "\n", + " def add_funding_rates(self):\n", + " self.simulate_funding_rates()\n", + " self.costs = self.costs - self.funding_rates * self.notional\n", + "\n", + " def simulate_funding_rates(self):\n", + " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", + "\n", + " # best case\n", + " # self.funding_rates = 0.0075 / 100\n", + "\n", + " # average -0.00443%\n", + "\n", + " # worst case\n", + " self.funding_rates = -0.0075 / 100\n", + "\n", + " def simulate_maker_taker_fees(self):\n", + " # We add a counter for how many times we call this function\n", + " # i.e. how many times we open and close the short\n", + " self.maker_fees_counter += 1\n", + " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", + " \n", + " # maker fees\n", + " self.maker_taker_fees = 0.05 / 100 # <1M\n", + " # self.maker_taker_fees = 0.04 / 100 # <5M\n", + " # self.maker_taker_fees = 0.035 / 100 # <10M\n", + " # self.maker_taker_fees = 0.03 / 100 # <50M\n", + " # self.maker_taker_fees = 0.025 / 100 # <200M\n", + " # self.maker_taker_fees = 0.02 / 100 # >200M\n", + "\n", + " # Actions to take\n", + " def remove_collateral(self, stgy_instance):\n", + " self.cancel_order()\n", + " time = 0\n", + " if self.collateral_status:\n", + " self.collateral_status = False\n", + " withdrawal_fees = self.collateral * self.withdrawal_fees\n", + " self.collateral = 0\n", + " # self.price_to_liquidation = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + withdrawal_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + "\n", + " def open_short(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " intervals = stgy_instance.intervals\n", + " if (not self.short_status) and self.order_status:\n", + " self.short_status = True\n", + " # dydx parameters\n", + " # if self.market_price <= stgy_instance.trigger_prices['floor']:\n", + " # print(\"CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['floor'] - self.market_price)\n", + "\n", + " # if self.market_price <= stgy_instance.trigger_prices['open_close']:\n", + " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", + " self.entry_price = self.market_price\n", + " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + "\n", + "\n", + " price_floor = stgy_instance.trigger_prices['floor']\n", + " floor_position = intervals['floor'].position_order\n", + "\n", + " price_to_repay_debt = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", + " aave_class_instance)\n", + " price_to_ltv_limit = stgy_instance.trigger_prices['ltv_limit']\n", + " stgy_instance.trigger_prices['repay_aave'] = price_to_repay_debt\n", + " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", + " if price_to_ltv_limit < price_to_repay_debt:\n", + " intervals['floor'] = Interval(price_to_repay_debt, price_floor,\n", + " 'floor', floor_position)\n", + " intervals['repay_aave'] = Interval(price_to_ltv_limit, price_to_repay_debt,\n", + " 'repay_aave', floor_position + 1)\n", + " intervals['minus_infty'] = Interval(-math.inf, price_to_ltv_limit,\n", + " 'minus_infty', floor_position + 2)\n", + " else:\n", + " print(\"CAUTION: P_ltv > P_repay\")\n", + " print(\"Difference of: \", price_to_ltv_limit - price_to_repay_debt)\n", + " price_to_repay_debt = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", + " intervals['floor'] = Interval(price_to_ltv_limit, price_floor,\n", + " 'floor', floor_position)\n", + " intervals['ltv_limit'] = Interval(price_to_repay_debt, price_to_ltv_limit,\n", + " 'repay_aave', floor_position + 1)\n", + " intervals['minus_infty'] = Interval(-math.inf, price_to_repay_debt,\n", + " 'minus_infty', floor_position + 2)\n", + " self.order_status = False\n", + " return 0\n", + "\n", + " def close_short(self, stgy_instance):\n", + " if self.short_status:\n", + " # Next if is to move up the threshold if we didnt execute at exactly open_close\n", + " # if self.market_price >= stgy_instance.trigger_prices['open_close']:\n", + " # # new_open_close = self.market_price\n", + " # print(\"CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!\")\n", + " # print(\"Difference of: \", self.market_price - stgy_instance.trigger_prices['open_close'])\n", + " # stgy_instance.target_prices['open_close'] = self.market_price\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " self.pnl = self.pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.entry_price = 0\n", + " self.short_status = False\n", + " self.short_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + " self.place_order(stgy_instance.trigger_prices['open_close'])\n", + " return 0\n", + "\n", + " def place_order(self, price):\n", + " self.order_status = True\n", + " # self.\n", + "\n", + " def cancel_order(self):\n", + " self.order_status = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## ParameterManager Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterManager(object):\n", + " # auxiliary functions\n", + " @staticmethod\n", + " def define_target_prices(stgy_instance, slippage, K, vol, floor):\n", + " p_open_close = floor * (1+slippage) * (1+K*vol)\n", + "# maker_fee = 0.05 / 100\n", + "# p_open_close_2 = p_open_close_1*(1-2*maker_fee)\n", + "# if p_open_close_2 < floor:\n", + "# print(\"open_close_2 < floor!\")\n", + "# print(\"(floor-open_close_2)/floor=\",(floor-p_open_close_2)/floor)\n", + " ##########################################################\n", + " # We define the intervals\n", + " list_of_intervals = [\"open_close\",\n", + "# \"open_close_2\",\n", + " \"floor\"]\n", + " list_of_trigger_prices = [p_open_close,\n", + "# p_open_close_2,\n", + " floor]\n", + " # We define/update trigger prices\n", + " for i in range(len(list_of_intervals)):\n", + " interval_name = list_of_intervals[i]\n", + " trigger_price = list_of_trigger_prices[i]\n", + " stgy_instance.trigger_prices[interval_name] = trigger_price\n", + "\n", + " @staticmethod\n", + " def define_intervals(stgy_instance):\n", + " stgy_instance.intervals = {\"infty\": Interval(stgy_instance.trigger_prices['open_close'],\n", + " math.inf,\n", + " \"infty\", 0),\n", + " \"open_close\": Interval(stgy_instance.trigger_prices['floor'],\n", + " stgy_instance.trigger_prices['open_close'],\n", + " \"open_close\", 1),\n", + " \"minus_infty\": Interval(-math.inf,\n", + " stgy_instance.trigger_prices['floor'],\n", + " \"minus_infty\", 2)}\n", + "\n", + " # function to assign interval_current to each market_price in historical data\n", + " @staticmethod\n", + " def load_intervals(stgy_instance):\n", + " stgy_instance.historical_data[\"interval\"] = [[0, 0]] * len(stgy_instance.historical_data[\"close\"])\n", + " stgy_instance.historical_data[\"interval_name\"] = ['nan'] * len(stgy_instance.historical_data[\"close\"])\n", + " for loc in range(len(stgy_instance.historical_data[\"close\"])):\n", + " market_price = stgy_instance.historical_data[\"close\"][loc]\n", + " for i in list(stgy_instance.intervals.values()):\n", + " if i.left_border < market_price <= i.right_border:\n", + " stgy_instance.historical_data[\"interval\"][loc] = i\n", + " stgy_instance.historical_data[\"interval_name\"][loc] = i.name\n", + " @staticmethod\n", + " # Checking and updating data\n", + " def update_parameters(stgy_instance, new_market_price, new_interval_current):\n", + " # AAVE\n", + " stgy_instance.aave.market_price = new_market_price\n", + " stgy_instance.aave.interval_current = new_interval_current\n", + " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", + " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", + " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", + " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", + " # We have to execute track_ first because we need the fees for current collateral and debt values\n", + " stgy_instance.aave.track_lend_borrow_interest()\n", + " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", + " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", + " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", + " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", + "\n", + " # DYDX\n", + " stgy_instance.dydx.market_price = new_market_price\n", + " stgy_instance.dydx.interval_current = new_interval_current\n", + " stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc()\n", + " stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc()\n", + " stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc()\n", + " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", + " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", + "\n", + " def reset_costs(stgy_instance):\n", + " # We reset the costs in order to always start in 0\n", + " stgy_instance.aave.costs = 0\n", + " stgy_instance.dydx.costs = 0\n", + " \n", + " \n", + " def find_scenario(self, stgy_instance, new_market_price, new_interval_current, interval_old, index):\n", + " actions = self.actions_to_take(stgy_instance, new_interval_current, interval_old)\n", + " self.simulate_fees(stgy_instance)\n", + " time = 0\n", + " time_aave = 0\n", + " time_dydx = 0\n", + " for action in actions:\n", + " # if action == \"rtrn_usdc_n_rmv_coll_dydx\":\n", + " # time = stgy_instance.dydx.remove_collateral_dydx(new_market_price, new_interval_current, stgy_instance)\n", + " # stgy_instance.aave.return_usdc(new_market_price, new_interval_current, stgy_instance)\n", + " if action == \"borrow_usdc_n_add_coll\":\n", + " time_aave = stgy_instance.aave.borrow_usdc(stgy_instance)\n", + " market_price = stgy_instance.historical_data[\"close\"][index + time_aave]\n", + " interval_current = stgy_instance.historical_data[\"interval\"][index + time_aave]\n", + " time_dydx = stgy_instance.dydx.add_collateral(stgy_instance)\n", + " time_aave = 0\n", + " elif action in stgy_instance.aave_features[\"methods\"]:\n", + " time_aave = getattr(stgy_instance.aave, action)(stgy_instance)\n", + " elif action in stgy_instance.dydx_features[\"methods\"]:\n", + " time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance)\n", + " time += time_aave + time_dydx\n", + " # print(stgy_instance.aave_features[\"methods\"])\n", + " # print(stgy_instance.dydx_features[\"methods\"])\n", + " return time\n", + " # stgy_instance.append(action)\n", + "\n", + " @staticmethod\n", + " def actions_to_take(stgy_instance, new_interval_current, interval_old):\n", + " actions = []\n", + "\n", + " # Case P increasing\n", + " if interval_old.is_lower(new_interval_current):\n", + " for i in reversed(range(new_interval_current.position_order, interval_old.position_order)):\n", + " \n", + " # CASE: open_close_1 APPROACH\n", + " if list(stgy_instance.intervals.keys())[i+1] == 'open_close':\n", + " actions.append('close_short')\n", + " \n", + " # CASE: TOO MANY FEES FOR open_close_1 APPROACH\n", + "# if list(stgy_instance.intervals.keys())[i+1] == 'open_close_2':\n", + "# actions.append('close_short')\n", + " \n", + " else:\n", + " actions.append(list(stgy_instance.intervals.keys())[i+1]) # when P goes up we execute the name of previous intervals\n", + " # print(list(stgy_instance.intervals.keys())[i+1])\n", + "\n", + " # Case P decreasing\n", + " else:\n", + " for i in range(interval_old.position_order + 1, new_interval_current.position_order + 1):\n", + " \n", + " # In both cases we open at open_close_1 bc for open_close_2 case we manage the opening \n", + " # from inside the for loop of the run_sims\n", + " if list(stgy_instance.intervals.keys())[i] == 'open_close':\n", + " actions.append('open_short')\n", + " else:\n", + " actions.append(list(stgy_instance.intervals.keys())[i])\n", + " # print(actions)\n", + " return actions\n", + "\n", + " @staticmethod\n", + " def simulate_fees(stgy_instance):\n", + " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", + "\n", + " # best case\n", + " # stgy_instance.gas_fees = 1\n", + "\n", + " # stgy_instance.gas_fees = 3\n", + "\n", + " # stgy_instance.gas_fees = 6\n", + "\n", + " # worst case\n", + " stgy_instance.gas_fees = 10\n", + "\n", + " @staticmethod\n", + " def update_pnl(stgy_instance):\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", + "\n", + " @staticmethod\n", + " def add_costs(stgy_instance):\n", + " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", + " + stgy_instance.aave.costs + stgy_instance.dydx.costs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## DataDamperNPlotter Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module will write the results and is also used for plotting (for analysis porpuses)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class DataDamperNPlotter:\n", + " def __init__(self):\n", + " self.historical_data = None\n", + "\n", + " @staticmethod\n", + " def write_data(stgy_instance,\n", + " new_interval_previous, interval_old, mkt_price_index, period,\n", + " sheet=False):\n", + " aave_instance = stgy_instance.aave\n", + " dydx_instance = stgy_instance.dydx\n", + " data_aave = []\n", + " data_dydx = []\n", + " aave_wanted_keys = [\n", + " \"market_price\",\n", + " \"interval_current\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\"]\n", + "\n", + " for i in range(len(aave_instance.__dict__.values())):\n", + " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", + " # print(list(aave_instance.__dict__.keys())[i])\n", + " if isinstance(list(aave_instance.__dict__.values())[i], Interval):\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i].name))\n", + " # data_aave.append(new_interval_previous.name)\n", + " data_aave.append(interval_old.name)\n", + " else:\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " for i in range(len(dydx_instance.__dict__.values())):\n", + " if isinstance(list(dydx_instance.__dict__.values())[i], Interval):\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i].name))\n", + " # data_dydx.append(new_interval_previous.name)\n", + " data_dydx.append(interval_old.name)\n", + " else:\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", + " data_aave.append(stgy_instance.gas_fees)\n", + " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_aave.append(stgy_instance.total_pnl)\n", + " data_aave.append(mkt_price_index)\n", + "\n", + "\n", + " data_dydx.append(stgy_instance.gas_fees)\n", + " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_dydx.append(stgy_instance.total_pnl)\n", + " data_dydx.append(mkt_price_index)\n", + " # print(interval_old.name)\n", + " # print(data_dydx, list(dydx_instance.__dict__.keys()))\n", + " if sheet == True:\n", + " gc = pygsheets.authorize(service_file=\n", + " 'stgy-1-simulations-e0ee0453ddf8.json')\n", + " sh = gc.open('aave/dydx simulations')\n", + " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", + " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", + " else:\n", + " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_aave)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_dydx)\n", + "\n", + " @staticmethod\n", + " def delete_results(stgy_instance, period):\n", + " file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", + " os.remove(file_aave)\n", + " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", + " os.remove(file_dydx)\n", + "\n", + " @staticmethod\n", + " def add_header(stgy_instance, period):\n", + " aave_headers = [\n", + " \"market_price\",\n", + " \"I_current\",\n", + " # \"I_previous\",\n", + " \"I_old\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\",\n", + " \"index_of_mkt_price\"]\n", + " dydx_headers = [\n", + " \"market_price\",\n", + " \"I_current\",\n", + " # \"I_previous\",\n", + " \"I_old\",\n", + " \"entry_price\",\n", + " \"short_size\",\n", + " \"collateral\",\n", + " \"notional\",\n", + " \"equity\",\n", + " \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " \"collateral_status\",\n", + " \"short_status\",\n", + " \"order_status\",\n", + " \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\",\n", + " \"index_of_mkt_price\"]\n", + " \n", + " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(aave_headers)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(dydx_headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Track historical data\n", + "# symbol = 'ETHUSDC'\n", + "# freq = '1m'\n", + "# initial_date = \"1 Jan 2019\"\n", + "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", + "# initial_date=initial_date, save=True)\n", + "\n", + "# Load historical data if previously tracked and saved\n", + "\n", + "historical_data = pd.read_csv(\"~/Cruize Simulations/Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "# # assign data to stgy instance + define index as dates\n", + "timestamp = pd.to_datetime(historical_data['timestamp'])\n", + "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", + "historical_data.index = timestamp\n", + "#\n", + "# #######################################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-06-01\",\"2020-06-15\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.5602278826280307e-06, 0.0005989101310066664)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# log_returns = np.log(data['close']) - np.log(\n", + "# data['close'].shift(1))\n", + "# ewm_log_returns = log_returns.ewm(alpha=0.8, adjust=False)\n", + "# mean = ewm_log_returns.mean().mean()\n", + "# std = ewm_log_returns.std().mean()\n", + "# mean, std" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0005989101310066664,\n", + " 0.0011978202620133327,\n", + " 0.0023956405240266655,\n", + " 0.0035934607860399984)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normal_std = std\n", + "# medium_std = 2*std\n", + "# high_std = 4*std\n", + "# extreme_std = 6*std\n", + "# normal_std, medium_std, high_std, extreme_std" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find such a relevant price manually by taking a look at the price plot." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=243, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define a function that will\n", + "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices and the intervals\n", + "- Create a new directory \"Files/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit + defining the first interval_old to be the first interval in the dataset stgy.historical_data\n", + "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", + "- Run through the code executing everything as discussed in the dev doc.\n", + "\n", + "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(period, open_close, slippage, K_1, K_2, hat_L, L):\n", + " # Initialize everything\n", + " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / (1+slippage)\n", + " # Load target_prices + intervals in stgy.historical_data\n", + " # First we calculate weighted vol\n", + " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(stgy.historical_data[-N:]['close']) \\\n", + " - np.log(stgy.historical_data[-N:]['close'].shift(1))\n", + " global()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " K = K_1\n", + " # Now we define prices and intervals given K and vol\n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, K_1, vol, floor)\n", + " stgy.parameter_manager.define_intervals(stgy)\n", + " stgy.parameter_manager.load_intervals(stgy)\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder \n", + " # exist or not.\n", + " if not os.path.exists(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " # if the demo_folder directory is not present \n", + " # then create it.\n", + " os.makedirs(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " stgy.historical_data.to_csv(\"~/Cruize Simulations/Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data_.csv\" \n", + " % (period[0], period[1], open_close))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + " stgy.launch(config)\n", + "\n", + " # Stk eth\n", + " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.aave.interval_current = stgy.historical_data['interval'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index]\n", + " stgy.dydx.collateral = stgy.aave.debt\n", + " stgy.dydx.equity = stgy.dydx.equity_calc()\n", + " stgy.dydx.collateral_status = True\n", + " #########################\n", + " # Change or define prices that aren't defined yet if the period of simulations involves those prices\n", + " # For ex if we are executing periods of time in which ltv_limit or repay_aave are already defined\n", + "\n", + " # price_floor = stgy.intervals['open_close_1'].left_border\n", + " stgy.trigger_prices['ltv_limit'] = stgy.aave.price_to_ltv_limit\n", + " previous_position_order = stgy.intervals['open_close'].position_order\n", + " stgy.intervals['floor'] = Interval(stgy.aave.price_to_ltv_limit, floor,\n", + " 'floor', previous_position_order + 1)\n", + " stgy.intervals['minus_infty'] = Interval(-math.inf, stgy.aave.price_to_ltv_limit,\n", + " 'minus_infty', previous_position_order + 2)\n", + "\n", + " #########################\n", + " # Load interval_old\n", + " interval_old = stgy.historical_data['interval'][initial_index]\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(stgy, period)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(stgy, period)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + "\n", + " while(i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + " \n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " new_interval_previous = stgy.historical_data[\"interval\"][i-1]\n", + " new_interval_current = stgy.historical_data[\"interval\"][i]\n", + " new_market_price = stgy.historical_data[\"close\"][i]\n", + " #########################\n", + " # This case is when P crossed open_close_2 while increasing (therefore we had to close short), I_old = I_open_close_2, \n", + " # but then it goes below open_close_2 again. \n", + " # So before updating I_old the bot will read I_current = I_open_close_2 and I_old = I_open_close_2.\n", + " # So in order to be protected we manage this case as it names indicates open_close_2:\n", + " # we open and close at this price.\n", + " # Note that this also includes a situation in which price crossed floor while decreasing and the it crosses it again going up\n", + " # I_old = I_open_close_2 and before updating new I_old we have I_current= I_open_close_2.\n", + " # But here we do nothing because short is still open.\n", + "# if (new_interval_current == stgy.intervals[\"open_close_2\"]) & (interval_old == stgy.intervals[\"open_close_2\"]):\n", + "# time_dydx = stgy_instance.dydx.open_short(new_market_price, new_interval_current, stgy)\n", + " # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late\n", + " # therefore not taking the actions needed as soon as they are needed\n", + " if new_interval_previous != new_interval_current:\n", + " interval_old = new_interval_previous\n", + " # print(interval_old.name)\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, interval_current, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, interval_current, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, new_market_price, new_interval_current)\n", + " # Here we identify price movent direction by comparing current interval and old interval\n", + " # and we also execute all the actions involved since last price was read\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, new_market_price, new_interval_current, interval_old, i)\n", + " #########################\n", + " # If we executed more txs than hat_L*20 then we change to K_2\n", + " if (stgy.dydx.maker_fees_counter > hat_L * 20) and (stgy.dydx.short_status):\n", + " K = K_2\n", + " ########################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if i % (8 * 60) == 0:\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " stgy.data_dumper.write_data(stgy,\n", + " new_interval_previous, interval_old, i, period,\n", + " sheet=False)\n", + " #########################\n", + " # Update trigger prices and thresholds\n", + " # We update trigger prices and thresholds every day\n", + " # if (i+time_used - initial_index) % (1*24*60) == 0:\n", + " # # We call the paramater_manager instance with updated data\n", + " # stgy.parameter_manager.define_target_prices(stgy, N_week, data_for_thresholds, floor)\n", + " # stgy.parameter_manager.define_intervals(stgy)\n", + " # stgy.parameter_manager.load_intervals(stgy)\n", + " # save = True\n", + " # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period)\n", + "\n", + " # we increment index by the time consumed in executing actions\n", + " # i += time_used\n", + " i += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + "# [[\"2020-01-01\",\"2020-05-01\"],135], [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "open_close_2 < floor!\n", + "(floor-open_close_2)/floor= 1.0000000001384752e-06\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1664/2932766787.py:43: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " stgy_instance.historical_data[\"interval\"] = [[0, 0]] * len(stgy_instance.historical_data[\"close\"])\n", + "/tmp/ipykernel_1664/2932766787.py:44: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " stgy_instance.historical_data[\"interval_name\"] = ['nan'] * len(stgy_instance.historical_data[\"close\"])\n", + "/tmp/ipykernel_1664/2932766787.py:49: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " stgy_instance.historical_data[\"interval\"][loc] = i\n", + "/tmp/ipykernel_1664/2932766787.py:50: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " stgy_instance.historical_data[\"interval_name\"][loc] = i.name\n" + ] + } + ], + "source": [ + "for period_n_open_close in periods_n_open_close:\n", + " period = period_n_open_close[0]\n", + " open_close = period_n_open_close[1]\n", + " slippage = 0.001\n", + " run_sim(period, open_close, slippage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count how many times a given price is cross given a dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_counter(data_set, price):\n", + " crossed_down = 0\n", + " crossed_up = 0\n", + " index_up = []\n", + " index_down = []\n", + " for index in range(1,len(data_set)):\n", + " previous_price = data_set['close'][index-1]\n", + " current_price = data_set['close'][index]\n", + " if previous_price <= price < current_price:\n", + " crossed_up += 1\n", + " index_up.append(index-1)\n", + " elif previous_price >= price > current_price:\n", + " crossed_down += 1\n", + " index_down.append(index-1)\n", + " return {'down':\n", + " {'crossed_down': crossed_down,\n", + " 'index_down': index_down},\n", + " 'up':\n", + " {'crossed_up': crossed_up,\n", + " 'index_up': index_up}}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "price = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosses = cross_counter(data_set, 240)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"~/Cruize Simulations/Files/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "market_price 176910\n", + "I_current 176910\n", + "I_old 176910\n", + "entry_price 53220\n", + "short_size 53220\n", + "collateral 176910\n", + "notional 53375\n", + "equity 176910\n", + "leverage 53375\n", + "pnl 53066\n", + "collateral_status 176910\n", + "short_status 53220\n", + "order_status 123690\n", + "withdrawal_fees 176910\n", + "funding_rates 176910\n", + "maker_taker_fees 133516\n", + "maker_fees_counter 133516\n", + "costs 421\n", + "gas_fees 176910\n", + "total_costs_from_aave_n_dydx 133516\n", + "total_stgy_pnl 176910\n", + "index_of_mkt_price 176910\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dydx_results.astype(bool).sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def count_maker_fees_increment(data_set):\n", + " index_of_maker_fee = []\n", + " for index in range(1,len(data_set)):\n", + " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", + " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", + " if previous_maker_fee_counter < current_maker_fee_counter:\n", + " index_of_maker_fee.append(index)\n", + " return {'indexes': index_of_maker_fee}" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "matches_up = 0\n", + "matches_down = 0\n", + "for index_up in crosses['up']['index_up']:\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " matches_up += 1\n", + "for index_down in crosses['down']['index_down']:\n", + " if index_down in results_maker_fee_counter['indexes']:\n", + " matches_down += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(155, 136, 291)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches_up, matches_down, matches_up + matches_down" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 156)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", + "- entry_price and short_size can be found at index_up -1\n", + "- close_price is market_price in index = index_up" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " market_price I_current I_old short_size entry_price \\\n", + "43393 240.70 infty minus_infty 0.000 0.00 \n", + "43394 239.74 minus_infty infty -4334.634 239.74 \n", + "43395 240.94 infty minus_infty 0.000 0.00 \n", + "43396 240.86 infty minus_infty 0.000 0.00 \n", + "\n", + " pnl maker_fees_counter total_stgy_pnl \n", + "43393 0.0000 0 -2.879624 \n", + "43394 0.0000 1 -522.470891 \n", + "43395 -5201.5608 2 -6246.223689 \n", + "43396 0.0000 2 -6246.222332 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 1\n", + "index = crosses['up']['index_up'][i]\n", + "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "diff = []\n", + "cost = []\n", + "# we dont start the loop at i = 0 because the data_set started below open_close\n", + "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", + "for i in range(1,len(crosses['up']['index_up'])):\n", + " index_up = crosses['up']['index_up'][i]\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " entry_price = dydx_results.iloc[index-1]['entry_price']\n", + " close_price = dydx_results.iloc[index]['market_price']\n", + " short_size = dydx_results.iloc[index-1]['short_size']\n", + " diff.append(close_price-entry_price)\n", + " cost.append(short_size * (close_price-entry_price))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1999999999999886, -5201.560799999951)" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(diff), np.mean(cost)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/jupyter-lab/strategy_dydx_aave.ipynb b/jupyter-lab/strategy_dydx_aave.ipynb deleted file mode 100644 index 14ba4e6..0000000 --- a/jupyter-lab/strategy_dydx_aave.ipynb +++ /dev/null @@ -1,1358 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Strategy 1.ipynb", - "provenance": [], - "collapsed_sections": [ - "dqJhPe6PTc4N", - "ufZfhFUEtiFm" - ] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "!pip install web3" - ], - "metadata": { - "id": "rkBk72gV7Ko0" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oKLbI_uWww5E" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Historical data from binance" - ], - "metadata": { - "id": "dqJhPe6PTc4N" - } - }, - { - "cell_type": "markdown", - "source": [ - "In this section we define a function to request historical data from Binance for a variety of frequencies (\"1m\", \"5m\", \"10m\", \"15m\", \"1h\", \"6h\", \"12h\", \"1d\"). We set initial_date = '31 Mar 2021' but we can change it.\n", - "[I use my private api keys]" - ], - "metadata": { - "id": "rD0zjDFXnlGN" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install python-binance" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RC3BvJrzp7ab", - "outputId": "763a2f02-bada-46a9-d6e3-415bfd9ed4e8" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting python-binance\n", - " Downloading python_binance-1.0.16-py2.py3-none-any.whl (65 kB)\n", - "\u001b[K |████████████████████████████████| 65 kB 2.1 MB/s \n", - "\u001b[?25hCollecting ujson\n", - " Downloading ujson-5.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (45 kB)\n", - "\u001b[K |████████████████████████████████| 45 kB 2.2 MB/s \n", - "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from python-binance) (3.8.1)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from python-binance) (2.23.0)\n", - "Requirement already satisfied: websockets in /usr/local/lib/python3.7/dist-packages (from python-binance) (9.1)\n", - "Collecting dateparser\n", - " Downloading dateparser-1.1.1-py2.py3-none-any.whl (288 kB)\n", - "\u001b[K |████████████████████████████████| 288 kB 17.1 MB/s \n", - "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from python-binance) (1.15.0)\n", - "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (2.0.12)\n", - "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (6.0.2)\n", - "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (4.1.1)\n", - "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.2.0)\n", - 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"Collecting regex!=2019.02.19,!=2021.8.27,<2022.3.15\n", - " Downloading regex-2022.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)\n", - "\u001b[K |████████████████████████████████| 749 kB 8.1 MB/s \n", - "\u001b[?25hRequirement already satisfied: tzlocal in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (1.5.1)\n", - "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (1.24.3)\n", - "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (3.0.4)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (2022.6.15)\n", - "Installing collected packages: regex, ujson, dateparser, python-binance\n", - " Attempting uninstall: regex\n", - " Found existing installation: regex 2022.6.2\n", - " Uninstalling regex-2022.6.2:\n", - " Successfully uninstalled regex-2022.6.2\n", - "Successfully installed dateparser-1.1.1 python-binance-1.0.16 regex-2022.3.2 ujson-5.3.0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import math\n", - "import os.path\n", - "import time\n", - "from binance.client import Client\n", - "from datetime import timedelta, datetime\n", - "from dateutil import parser\n", - "from tqdm import tqdm_notebook #(Optional, used for progress-bars)\n", - "\n", - "### API\n", - "binance_api_key = '9zFIgetckRO80d4RciWs8jc4XSwAMEnFIuwVhAoaYIstQ9RWlfqiV6zcAGu0Ta8R' #Enter your own API-key here\n", - "binance_api_secret = 'Uu8vtNEmH1PPDPDchX51jivxBENEbNwDmDcQrnhPJBJwFcbqjnvxL2MeQJuTc4Kg' #Enter your own API-secret here\n", - "\n", - "### CONSTANTS\n", - "binsizes = {\"1m\": 1, \"5m\": 5, \"10m\": 10, \"15m\": 15, \"1h\": 60, \"6h\": 360, \"12h\": 720, \"1d\": 1440}\n", - "\n", - "batch_size = 750\n", - "binance_client = Client(api_key=binance_api_key, api_secret=binance_api_secret)\n", - "\n", - "# initial_date = '1 Jan 2017'\n", - "initial_date = '31 Mar 2021'\n", - "### FUNCTIONS\n", - "def minutes_of_new_data(symbol, kline_size, data, source):\n", - " if len(data) > 0: old = parser.parse(data[\"timestamp\"].iloc[-1])\n", - " elif source == \"binance\": old = datetime.strptime(initial_date, '%d %b %Y')\n", - " if source == \"binance\": new = pd.to_datetime(binance_client.get_klines(symbol=symbol, interval=kline_size)[-1][0], unit='ms')\n", - " return old, new\n", - "\n", - "def get_all_binance(symbol, kline_size, save = False):\n", - " filename = '%s-%s-data.csv' % (symbol, kline_size)\n", - " if os.path.isfile(filename): data_df = pd.read_csv(filename)\n", - " else: data_df = pd.DataFrame()\n", - " oldest_point, newest_point = minutes_of_new_data(symbol, kline_size, data_df, source = \"binance\")\n", - " delta_min = (newest_point - oldest_point).total_seconds()/60\n", - " available_data = math.ceil(delta_min/binsizes[kline_size])\n", - " if oldest_point == datetime.strptime(initial_date, '%d %b %Y'): print('Downloading all available %s data for %s. Be patient..!' % (kline_size, symbol))\n", - " else: print('Downloading %d minutes of new data available for %s, i.e. %d instances of %s data.' % (delta_min, symbol, available_data, kline_size))\n", - " klines = binance_client.get_historical_klines(symbol, kline_size, oldest_point.strftime(\"%d %b %Y %H:%M:%S\"), newest_point.strftime(\"%d %b %Y %H:%M:%S\"))\n", - " data = pd.DataFrame(klines, columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore' ])\n", - " data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms')\n", - " if len(data_df) > 0:\n", - " temp_df = pd.DataFrame(data)\n", - " data_df = data_df.append(temp_df)\n", - " else: data_df = data\n", - " data_df.set_index('timestamp', inplace=True)\n", - " if save: data_df.to_csv(filename)\n", - " print('All caught up..!')\n", - " return data_df\n" - ], - "metadata": { - "id": "u2Z1IoPsU8zm" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We request historical data for ETHUSDC with 5m frequency." - ], - "metadata": { - "id": "LNCkEKyanznF" - } - }, - { - "cell_type": "code", - "source": [ - "# lista = [\"ETHUSDC\"]\n", - "\n", - "# BTC ETH MATIC UNI COMP AAVE YFI CRV MKR LINK KP3R SFI SUSHI 1INCH CAKE \n", - "symbol = \"ETHUSDC\"\n", - "# Frecuencia: 1m, 5m, 10m, 15m, 1h, 1d, 1m\n", - "freq = \"5m\"\n", - "\n", - "# Descarga de datos\n", - "ETH_historical = get_all_binance(symbol, freq, save = True)" - ], - "metadata": { - "id": "TzBMvBB_VD2B", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "f391ce11-5080-4c8c-af46-347e85894d5d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Downloading all available 5m data for ETHUSDC. Be patient..!\n", - "All caught up..!\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "len(ETH_historical)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CDEicxyQoLGD", - "outputId": "dcf2438f-6147-4f2b-86be-bbfe239bd341" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "131277" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ] - }, - { - "cell_type": "code", - "source": [ - "ETH_historical" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xdVZi1ZtwTz4", - "outputId": "ce14458d-c4a9-422c-bfe1-db07c5de1d80" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " open high low \\\n", - "timestamp \n", - "2021-03-31 00:00:00 1841.29000000 1845.35000000 1841.12000000 \n", - "2021-03-31 00:05:00 1844.39000000 1847.08000000 1843.99000000 \n", - "2021-03-31 00:10:00 1845.54000000 1849.53000000 1845.54000000 \n", - "2021-03-31 00:15:00 1848.96000000 1850.35000000 1847.50000000 \n", - "2021-03-31 00:20:00 1848.12000000 1849.12000000 1846.00000000 \n", - "... ... ... ... \n", - "2022-06-30 09:00:00 1047.77000000 1049.73000000 1043.93000000 \n", - "2022-06-30 09:05:00 1044.86000000 1045.89000000 1027.01000000 \n", - "2022-06-30 09:10:00 1031.04000000 1034.60000000 1027.18000000 \n", - "2022-06-30 09:15:00 1031.38000000 1033.44000000 1022.00000000 \n", - "2022-06-30 09:20:00 1025.60000000 1026.59000000 1022.82000000 \n", - "\n", - " close volume close_time \\\n", - "timestamp \n", - "2021-03-31 00:00:00 1845.35000000 13.45650000 1617149099999 \n", - "2021-03-31 00:05:00 1846.29000000 54.69818000 1617149399999 \n", - "2021-03-31 00:10:00 1848.96000000 10.56434000 1617149699999 \n", - "2021-03-31 00:15:00 1847.50000000 126.93107000 1617149999999 \n", - "2021-03-31 00:20:00 1846.00000000 16.03979000 1617150299999 \n", - "... ... ... ... \n", - "2022-06-30 09:00:00 1044.98000000 89.34740000 1656579899999 \n", - "2022-06-30 09:05:00 1031.05000000 1255.30980000 1656580199999 \n", - "2022-06-30 09:10:00 1031.57000000 657.32350000 1656580499999 \n", - "2022-06-30 09:15:00 1025.86000000 412.19010000 1656580799999 \n", - "2022-06-30 09:20:00 1024.52000000 209.62910000 1656581099999 \n", - "\n", - " quote_av trades tb_base_av tb_quote_av \\\n", - "timestamp \n", - "2021-03-31 00:00:00 24811.47621960 39 4.46207000 8228.10868710 \n", - "2021-03-31 00:05:00 100898.41600010 50 18.71127000 34521.30106140 \n", - "2021-03-31 00:10:00 19523.84183440 31 6.35202000 11739.72432620 \n", - "2021-03-31 00:15:00 234808.65135190 117 70.35710000 130155.06817220 \n", - "2021-03-31 00:20:00 29631.68322550 61 3.04496000 5630.11817930 \n", - "... ... ... ... ... \n", - "2022-06-30 09:00:00 93600.59516200 286 41.33610000 43316.53086700 \n", - "2022-06-30 09:05:00 1301546.44966900 1255 531.65880000 552359.80033400 \n", - "2022-06-30 09:10:00 677439.13450400 867 226.92000000 234032.62150300 \n", - "2022-06-30 09:15:00 423044.39174000 903 164.64950000 169092.17367600 \n", - "2022-06-30 09:20:00 214843.13485000 478 96.15590000 98534.17775800 \n", - "\n", - " ignore \n", - "timestamp \n", - "2021-03-31 00:00:00 0 \n", - "2021-03-31 00:05:00 0 \n", - "2021-03-31 00:10:00 0 \n", - "2021-03-31 00:15:00 0 \n", - "2021-03-31 00:20:00 0 \n", - "... ... \n", - "2022-06-30 09:00:00 0 \n", - "2022-06-30 09:05:00 0 \n", - "2022-06-30 09:10:00 0 \n", - "2022-06-30 09:15:00 0 \n", - "2022-06-30 09:20:00 0 \n", - "\n", - "[131277 rows x 11 columns]" - ], - "text/html": [ - "\n", - "
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\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 7 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Given that we have too much data, we will stay with less data (say 2000 prices)." - ], - "metadata": { - "id": "Rax3lp6o3JpZ" - } - }, - { - "cell_type": "code", - "source": [ - "P_ETH = ETH_historical[-2000:]['close']\n", - "for i in range(len(P_ETH)):\n", - " P_ETH[i] = float(P_ETH[i])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Drdsv1E-3IBM", - "outputId": "fee2df08-e047-4e40-a45c-ae60153c9f7e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n", - "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:1056: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " cacher_needs_updating = self._check_is_chained_assignment_possible()\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## LTV calculator" - ], - "metadata": { - "id": "eUsd347622oG" - } - }, - { - "cell_type": "markdown", - "source": [ - "Given\n", - "\n", - "- $A=collateral$\n", - "- $D=borrowed\\_capital / debt$\n", - "- $LTV=Loan\\ to\\ value$\n", - "- $LT$ liquidation threshold\n", - "\n", - "then $LTV = \\frac{D}{A}$ and we will be liquidated if $LTV > LT$." - ], - "metadata": { - "id": "r974k18tw_I1" - } - }, - { - "cell_type": "code", - "source": [ - "def liquidation(collateral_ETH, P_ETH, Debt_USDC, LT):\n", - " '''\n", - " LT = liquidation threshold\n", - " debt_value = Debt_USDC (in USDC)\n", - " collateral_USDC = colateral_ETH * P_ETH\n", - " liquidation if debt_value > collateral_USDC * LT iff debt_value / collateral_USDC = new_LTV > LT\n", - " '''\n", - " collateral_USDC = collateral_ETH * P_ETH\n", - " new_LTV = Debt_USDC / collateral_USDC\n", - " return new_LTV > LT\n", - "\n", - "def LTV_(collateral_ETH, P_ETH, Debt_USDC):\n", - " collateral_USDC = collateral_ETH * P_ETH\n", - " LTV = Debt_USDC / collateral_USDC\n", - " # P_ETH = Debt_USDC / (collateral_ETH * LT)\n", - " return LTV" - ], - "metadata": { - "id": "dHMnN9lRw6Et" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## PyL DyDx" - ], - "metadata": { - "id": "S1hCnpbnb-PS" - } - }, - { - "cell_type": "code", - "source": [ - "def short_pyl(size, P):\n", - " '''\n", - " P_entry, leverage are given and fixed\n", - " '''\n", - " return size * (short_entry_price - P) * short_leverage" - ], - "metadata": { - "id": "FnjkJ0adcBFe" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## AAVE yields" - ], - "metadata": { - "id": "UL8cWr7wg3ZL" - } - }, - { - "cell_type": "code", - "source": [ - "def AAVE_profits(collateral_USDC, pcg_collateral_to_borrow,\n", - " supply_rate_ETH, supply_rewards_rate_ETH,\n", - " borrow_rate_USDC, borrow_rewards_rate_USDC):\n", - " debt = collateral_USDC * pcg_collateral_to_borrow\n", - " return collateral_USDC * (supply_rate_ETH + supply_rewards_rate_ETH) - debt * (borrow_rate_USDC - borrow_rewards_rate_USDC)" - ], - "metadata": { - "id": "FthwXfysg4co" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Request borrow_rate + supply_rate" - ], - "metadata": { - "id": "ufZfhFUEtiFm" - } - }, - { - "cell_type": "code", - "source": [ - "import json\n", - "#from google.colab import files\n", - "#uploaded = files.upload()\n", - "from binance.client import Client\n", - "import pandas as pd\n", - "from datetime import datetime\n", - "from web3 import Web3\n", - "#import pandas as pd\n" - ], - "metadata": { - "id": "wvod8AfzsUNF" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Conectamos con el nodo\n", - "w3 = Web3(Web3.HTTPProvider(\"https://mainnet.infura.io/v3/da48e8cbe7ed479688437e27dd07fe8b\"))\n", - "# Cargamos las address del contrato del tricrypto y del LP token\n", - "lending_pool_address = '0x7d2768dE32b0b80b7a3454c06BdAc94A69DDc7A9'\n", - "wETH_address = '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2'\n", - "USDC_address = '0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48'\n", - "# Cargamos las abis de cada uno\n", - "pool_abi = '''\n", - "[{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"onBehalfOf\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"borrowRateMode\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"borrowRate\",\"type\":\"uint256\"},{\"indexed\":true,\"internalType\":\"uint16\",\"name\":\"referral\",\"type\":\"uint16\"}],\"name\":\"Borrow\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"onBehalfOf\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"indexed\":true,\"internalType\":\"uint16\",\"name\":\"referral\",\"type\":\"uint16\"}],\"name\":\"Deposit\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"target\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"initiator\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"premium\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint16\",\"name\":\"referralCode\",\"type\":\"uint16\"}],\"name\":\"FlashLoan\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"collateralAsset\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"debtAsset\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"debtToCover\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"liquidatedCollateralAmount\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"address\",\"name\":\"liquidator\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"bool\",\"name\":\"receiveAToken\",\"type\":\"bool\"}],\"name\":\"LiquidationCall\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[],\"name\":\"Paused\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"}],\"name\":\"RebalanceStableBorrowRate\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"repayer\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"}],\"name\":\"Repay\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"liquidityRate\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"stableBorrowRate\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"variableBorrowRate\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"liquidityIndex\",\"type\":\"uint256\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"variableBorrowIndex\",\"type\":\"uint256\"}],\"name\":\"ReserveDataUpdated\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"}],\"name\":\"ReserveUsedAsCollateralDisabled\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"}],\"name\":\"ReserveUsedAsCollateralEnabled\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"rateMode\",\"type\":\"uint256\"}],\"name\":\"Swap\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[],\"name\":\"Unpaused\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"internalType\":\"address\",\"name\":\"reserve\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"indexed\":true,\"internalType\":\"address\",\"name\":\"to\",\"type\":\"address\"},{\"indexed\":false,\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"}],\"name\":\"Withdraw\",\"type\":\"event\"},{\"inputs\":[],\"name\":\"FLASHLOAN_PREMIUM_TOTAL\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"LENDINGPOOL_REVISION\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"MAX_NUMBER_RESERVES\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"MAX_STABLE_RATE_BORROW_SIZE_PERCENT\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"interestRateMode\",\"type\":\"uint256\"},{\"internalType\":\"uint16\",\"name\":\"referralCode\",\"type\":\"uint16\"},{\"internalType\":\"address\",\"name\":\"onBehalfOf\",\"type\":\"address\"}],\"name\":\"borrow\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"internalType\":\"address\",\"name\":\"onBehalfOf\",\"type\":\"address\"},{\"internalType\":\"uint16\",\"name\":\"referralCode\",\"type\":\"uint16\"}],\"name\":\"deposit\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"from\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"to\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"balanceFromBefore\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"balanceToBefore\",\"type\":\"uint256\"}],\"name\":\"finalizeTransfer\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"receiverAddress\",\"type\":\"address\"},{\"internalType\":\"address[]\",\"name\":\"assets\",\"type\":\"address[]\"},{\"internalType\":\"uint256[]\",\"name\":\"amounts\",\"type\":\"uint256[]\"},{\"internalType\":\"uint256[]\",\"name\":\"modes\",\"type\":\"uint256[]\"},{\"internalType\":\"address\",\"name\":\"onBehalfOf\",\"type\":\"address\"},{\"internalType\":\"bytes\",\"name\":\"params\",\"type\":\"bytes\"},{\"internalType\":\"uint16\",\"name\":\"referralCode\",\"type\":\"uint16\"}],\"name\":\"flashLoan\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"getAddressesProvider\",\"outputs\":[{\"internalType\":\"contract ILendingPoolAddressesProvider\",\"name\":\"\",\"type\":\"address\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"}],\"name\":\"getConfiguration\",\"outputs\":[{\"components\":[{\"internalType\":\"uint256\",\"name\":\"data\",\"type\":\"uint256\"}],\"internalType\":\"struct DataTypes.ReserveConfigurationMap\",\"name\":\"\",\"type\":\"tuple\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"}],\"name\":\"getReserveData\",\"outputs\":[{\"components\":[{\"components\":[{\"internalType\":\"uint256\",\"name\":\"data\",\"type\":\"uint256\"}],\"internalType\":\"struct DataTypes.ReserveConfigurationMap\",\"name\":\"configuration\",\"type\":\"tuple\"},{\"internalType\":\"uint128\",\"name\":\"liquidityIndex\",\"type\":\"uint128\"},{\"internalType\":\"uint128\",\"name\":\"variableBorrowIndex\",\"type\":\"uint128\"},{\"internalType\":\"uint128\",\"name\":\"currentLiquidityRate\",\"type\":\"uint128\"},{\"internalType\":\"uint128\",\"name\":\"currentVariableBorrowRate\",\"type\":\"uint128\"},{\"internalType\":\"uint128\",\"name\":\"currentStableBorrowRate\",\"type\":\"uint128\"},{\"internalType\":\"uint40\",\"name\":\"lastUpdateTimestamp\",\"type\":\"uint40\"},{\"internalType\":\"address\",\"name\":\"aTokenAddress\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"stableDebtTokenAddress\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"variableDebtTokenAddress\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"interestRateStrategyAddress\",\"type\":\"address\"},{\"internalType\":\"uint8\",\"name\":\"id\",\"type\":\"uint8\"}],\"internalType\":\"struct DataTypes.ReserveData\",\"name\":\"\",\"type\":\"tuple\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"}],\"name\":\"getReserveNormalizedIncome\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"}],\"name\":\"getReserveNormalizedVariableDebt\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"getReservesList\",\"outputs\":[{\"internalType\":\"address[]\",\"name\":\"\",\"type\":\"address[]\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"}],\"name\":\"getUserAccountData\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"totalCollateralETH\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"totalDebtETH\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"availableBorrowsETH\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"currentLiquidationThreshold\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"ltv\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"healthFactor\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"}],\"name\":\"getUserConfiguration\",\"outputs\":[{\"components\":[{\"internalType\":\"uint256\",\"name\":\"data\",\"type\":\"uint256\"}],\"internalType\":\"struct DataTypes.UserConfigurationMap\",\"name\":\"\",\"type\":\"tuple\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"aTokenAddress\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"stableDebtAddress\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"variableDebtAddress\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"interestRateStrategyAddress\",\"type\":\"address\"}],\"name\":\"initReserve\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"contract ILendingPoolAddressesProvider\",\"name\":\"provider\",\"type\":\"address\"}],\"name\":\"initialize\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"collateralAsset\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"debtAsset\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"debtToCover\",\"type\":\"uint256\"},{\"internalType\":\"bool\",\"name\":\"receiveAToken\",\"type\":\"bool\"}],\"name\":\"liquidationCall\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"paused\",\"outputs\":[{\"internalType\":\"bool\",\"name\":\"\",\"type\":\"bool\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"user\",\"type\":\"address\"}],\"name\":\"rebalanceStableBorrowRate\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"internalType\":\"uint256\",\"name\":\"rateMode\",\"type\":\"uint256\"},{\"internalType\":\"address\",\"name\":\"onBehalfOf\",\"type\":\"address\"}],\"name\":\"repay\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"configuration\",\"type\":\"uint256\"}],\"name\":\"setConfiguration\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"bool\",\"name\":\"val\",\"type\":\"bool\"}],\"name\":\"setPause\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"address\",\"name\":\"rateStrategyAddress\",\"type\":\"address\"}],\"name\":\"setReserveInterestRateStrategyAddress\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"bool\",\"name\":\"useAsCollateral\",\"type\":\"bool\"}],\"name\":\"setUserUseReserveAsCollateral\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"rateMode\",\"type\":\"uint256\"}],\"name\":\"swapBorrowRateMode\",\"outputs\":[],\"stateMutability\":\"nonpayable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"address\",\"name\":\"asset\",\"type\":\"address\"},{\"internalType\":\"uint256\",\"name\":\"amount\",\"type\":\"uint256\"},{\"internalType\":\"address\",\"name\":\"to\",\"type\":\"address\"}],\"name\":\"withdraw\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"nonpayable\",\"type\":\"function\"}]\n", - "'''" - ], - "metadata": { - "id": "GXOotHCTthRL" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Info del pool\n", - "# Conectamos con los contratos\n", - "pool_contract = w3.eth.contract(address=lending_pool_address, abi=pool_abi)\n", - "\n", - "USDC_reserve_data=pool_contract.functions['getReserveData'](USDC_address).call()\n", - "USDC_liquidity_index = USDC_reserve_data[1] / 10**18\n", - "USDC_variable_borrow_index = USDC_reserve_data[2] / 10**18\n", - "USDC_liquidity_rate = USDC_reserve_data[3] / 10**27\n", - "USDC_variable_borrow_rate = USDC_reserve_data[4] / 10**27\n", - "USDC_stable_borrow_rate = USDC_reserve_data[5] / 10**27\n", - "wETH_reserve_data=pool_contract.functions['getReserveData'](wETH_address).call()\n", - "wETH_liquidity_index = wETH_reserve_data[1] / 10**18\n", - "wETH_variable_borrow_index = wETH_reserve_data[2] / 10**18\n", - "wETH_liquidity_rate = wETH_reserve_data[3] / 10**27\n", - "wETH_variable_borrow_rate = wETH_reserve_data[4] / 10**27\n", - "wETH_stable_borrow_rate = wETH_reserve_data[5] / 10**27" - ], - "metadata": { - "id": "IUhEX-KvtIXd" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Strategy\n" - ], - "metadata": { - "id": "bvZFGxrg3qjZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Use the staked ETH to borrow 10% USDC from Aave (For example, $1000 worth of ETH staked to take out 100 USDC)\n", - "\n" - ], - "metadata": { - "id": "dpXBxTzV3tMf" - } - }, - { - "cell_type": "markdown", - "source": [ - "2. Use the USDC to open a short position (preferably stop limit) on DyDx with 10x leverage (10 x 10 = 100% of staked ETH amount)\n" - ], - "metadata": { - "id": "gWbnXhvi6Z9x" - } - }, - { - "cell_type": "markdown", - "source": [ - "3. Find a threshold to keep cashing out the short profit and adding it back to the collateral to prevent liquidation (Max borrow amount on Aave is 80%)" - ], - "metadata": { - "id": "EW8hCfAq6az-" - } - }, - { - "cell_type": "code", - "source": [ - "P_ETH[0]" - ], - "metadata": { - "id": "Pd3VIlFV5zri", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e553fcb7-ff26-49e1-88e4-aab810c589f9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "1109.81" - ] - }, - "metadata": {}, - "execution_count": 15 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# we set initial values\n", - "\n", - "# ETH price at time of borrowing\n", - "P_ETH_0 = P_ETH[0]\n", - "# \n", - "staked_ETH = 100\n", - "collateral_ETH_0 = staked_ETH \n", - "pcg_collateral_to_borrow = 0.1\n", - "USDC_borrowed = (collateral_ETH_0 * P_ETH_0 ) * pcg_collateral_to_borrow\n", - "\n", - "\n", - "# DyDx parameters\n", - "# floor_price = max(list(P_ETH))*0.85\n", - "floor_price = 1000\n", - "# We set the short_entry_price 1% above floor_price (CHANGEABLE)\n", - "short_entry_price = floor_price * 1.01\n", - "# We set the stop limit 3% above (and higher than the entry_price) floor_price (CHANGEABLE)\n", - "stop_limit = floor_price * 1.03\n", - "#\n", - "short_size_0 = USDC_borrowed\n", - "short_leverage = 10\n", - "\n", - "\n", - "# AAVE parameters \n", - "# Rates (We should bring these values from the smart contract)\n", - "USDC_reserve_data=pool_contract.functions['getReserveData'](USDC_address).call()\n", - "wETH_reserve_data=pool_contract.functions['getReserveData'](wETH_address).call()\n", - "supply_rate_ETH = wETH_reserve_data[3] / 10**27\n", - "borrow_rate_USDC = USDC_reserve_data[4] / 10**27\n", - "borrow_rate_USDC += USDC_reserve_data[5] / 10**27\n", - "supply_rewards_rate_ETH = 0\n", - "borrow_rewards_rate_USDC = 0\n", - "# we should bring these values from the smart contract\n", - "max_LTV = 0.85\n", - "# LT = 0.88\n", - "# liquidation_penalty = 0.045\n", - "\n", - "# liquidation_price_0 = USDC_borrowed / (collateral_ETH_0 * LT)\n", - "price_for_LTV_at_half_0 = P_ETH_0 / 5\n", - "take_profit_0 = price_for_LTV_at_half_0 * 1.05\n", - "\n", - "# We set the liquidation threshold as +-10% from the liquidation_price (CHANGEABLE)\n", - "# LTV_threshold = [price_for_LTV_at_half * 0.95, price_for_LTV_at_half * 1.05]\n", - "\n", - "# initial values\n", - "collateral_USDC_0 = collateral_ETH_0 * P_ETH_0\n", - "AAVE_LTV_0 = LTV_(collateral_ETH_0, P_ETH_0, USDC_borrowed)\n", - "data = {'market_price': P_ETH, \n", - " 'PyL DyDx': 0*P_ETH, \n", - " 'LTV': 0*P_ETH}\n", - "data['LTV'][0] = AAVE_LTV_0" - ], - "metadata": { - "id": "mZv_rhhEA6MM" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "collateral_ETH_historical = 0*P_ETH\n", - "collateral_USDC_historical = 0*P_ETH\n", - "price_LTV_at_half_historical = 0*P_ETH\n", - "take_profit = 0*P_ETH\n", - "short_size_historical = 0*P_ETH\n", - "pyl_historical = 0*P_ETH\n", - "DyDx_pyl_historical = 0*P_ETH\n", - "AAVE_earnings_historical = 0*P_ETH\n", - "AAVE_LTV_historical = 0*P_ETH\n", - "is_short_open = False * P_ETH\n", - "\n", - "collateral_ETH_historical[0] = collateral_ETH_0\n", - "collateral_USDC_historical[0] = collateral_USDC_0\n", - "price_LTV_at_half_historical[0] = price_for_LTV_at_half_0\n", - "take_profit[0] = take_profit_0\n", - "short_size_historical[0] = short_size_0\n", - "AAVE_LTV_historical[0] = AAVE_LTV_0\n", - "\n", - "for i in range(1,len(P_ETH)):\n", - " market_price = P_ETH[i]\n", - " \n", - " if (market_price >= stop_limit) & (is_short_open[i-1] == True):\n", - " # we close the short position\n", - " DyDx_pyl_historical[i] = short_pyl(short_size_historical[i-1], market_price)\n", - " is_short_open[i] = False\n", - " collateral_USDC_historical[i] = collateral_USDC_0\n", - " AAVE_earnings_historical[i] = AAVE_profits(collateral_USDC_historical[i], pcg_collateral_to_borrow,\n", - " supply_rate_ETH, supply_rewards_rate_ETH,\n", - " borrow_rate_USDC, borrow_rewards_rate_USDC)\n", - " pyl_historical[i] = DyDx_pyl_historical[i] + AAVE_earnings_historical[i]\n", - "\n", - " elif market_price < stop_limit: # we keep the position open\n", - " is_short_open[i] = True\n", - " # market_price near (+-5%) price_for_LTC_at_half\n", - " if market_price < price_LTV_at_half_historical[i-1]: # we have to configure this part to contemplate all the possible LTV_thresholds and not only the previous one (ex what happens if market_price is between 2 thresholds? we have to config that situation)\n", - " # AAVE_earnings\n", - " # if market_price <= take_profit[i-1]:\n", - " short_size_historical[i] = USDC_borrowed / (10*(P_ETH_0 - market_price))\n", - " partial_pyl = short_pyl(short_size_historical[i-1] - short_size_historical[i], market_price) # changeable\n", - " \n", - " collateral_USDC_historical[i] = collateral_USDC_historical[i-1] + partial_pyl\n", - " collateral_ETH_historical[i] = collateral_USDC_historical[i] / market_price\n", - " \n", - " AAVE_LTV_historical[i] = LTV_(collateral_ETH_historical[i], market_price, USDC_borrowed)\n", - " price_LTV_at_half_historical[i] = market_price / 5\n", - " take_profit[i] = price_LTV_at_half_historical[i] * 1.05\n", - " # LTV_threshold = [new_price_for_LTV_at_half * 0.95, new_price_for_LTV_at_half * 1.05]\n", - " else:\n", - " short_size_historical[i] = short_size_historical[i-1] # I have to think if using the last size is correct, because if your price is higger than a LTV limit price then the size shouldn't be the last one (for ex if market price is higger than the firt LTV limit price, then size should be the inital one) (this is related with the way we config the different thresholds)\n", - " DyDx_pyl_historical[i] = short_pyl(short_size_historical[i], market_price)\n", - " collateral_USDC_historical[i] = collateral_USDC_historical[i-1]\n", - " AAVE_earnings_historical[i] = AAVE_profits(collateral_USDC_historical[i], pcg_collateral_to_borrow,\n", - " supply_rate_ETH, supply_rewards_rate_ETH,\n", - " borrow_rate_USDC, borrow_rewards_rate_USDC)\n", - " pyl_historical[i] = DyDx_pyl_historical[i] + AAVE_earnings_historical[i]\n", - " # market_price above stop_limit --> close position\n", - "\n", - " data['market_price'][i] = market_price\n", - " data['PyL DyDx'][i] = pyl_historical[i]\n", - " data['LTV'][i] = AAVE_LTV_historical[i]" - ], - "metadata": { - "id": "pf8H55dy4Ulj", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8c0b5ce3-ebc5-4687-807d-dd8516eca4ac" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:1056: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " cacher_needs_updating = self._check_is_chained_assignment_possible()\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "summary = pd.DataFrame.from_dict(data)\n", - "# summary.columns = ['market_price', 'PyL DyDx', 'LTV']\n", - "summary" - ], - "metadata": { - "id": "8RWk0ND1K0LN", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 455 - }, - "outputId": "cba19bf1-335c-461c-e38c-977c0e9ccbd2" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " market_price PyL DyDx LTV\n", - "timestamp \n", - "2022-06-23 10:45:00 1109.81 0.0 0.1\n", - "2022-06-23 10:50:00 1108.12 0.0 0.0\n", - "2022-06-23 10:55:00 1109.12 0.0 0.0\n", - "2022-06-23 11:00:00 1106.88 0.0 0.0\n", - "2022-06-23 11:05:00 1106.51 0.0 0.0\n", - "... ... ... ...\n", - "2022-06-30 09:00:00 1044.98 0.0 0.0\n", - "2022-06-30 09:05:00 1031.05 0.0 0.0\n", - "2022-06-30 09:10:00 1031.57 0.0 0.0\n", - "2022-06-30 09:15:00 1025.86 0.0 0.0\n", - "2022-06-30 09:20:00 1024.52 0.0 0.0\n", - "\n", - "[2000 rows x 3 columns]" - ], - "text/html": [ - "\n", - "
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market_pricePyL DyDxLTV
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 18 - } - ] - }, - { - "cell_type": "code", - "source": [ - "fig, axs = plt.subplots(1,1, figsize = (21, 7))\n", - "axs.plot(list(summary['market_price']), label = 'P_ETH', c = 'blue')\n", - "axs.axhline(y = floor_price, color = 'r', linestyle = '--', label = 'floor price')\n", - "axs.axhline(y = stop_limit, color = 'magenta', linestyle = '--', label = 'stop limit')\n", - "axs.axhline(y = short_entry_price, color = 'green', linestyle = '--', label = 'short entry price')\n", - "axs.grid()\n", - "axs.legend(loc = 'upper left')\n", - "axs_ = axs.twinx()\n", - "axs_.plot(list(summary['PyL DyDx']), label = 'PyL DyDx', c = 'orange')\n", - "axs_.legend(loc = 'upper right')" - ], - "metadata": { - "id": "WK3_TVl-6_65", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 445 - }, - "outputId": "969d79b5-e810-48e6-e2fb-cffaf9c207b1" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 19 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "P = np.linspace(0.01, 5, 10)\n", - "collateral_usd = 1500\n", - "USDC_borrowed = collateral_usd * 0.1\n", - "plt.plot(P, LTV_(collateral*P, P, USDC_borrowed))" - ], - "metadata": { - "id": "107gAtXuK1RU", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 200 - }, - "outputId": "577a7678-8a82-443b-e464-9caab760d82e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "error", - "ename": "NameError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mcollateral_usd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1500\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mUSDC_borrowed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollateral_usd\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLTV_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollateral\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSDC_borrowed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'collateral' is not defined" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "" - ], - "metadata": { - "id": "WgTETo9A6bGD" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 7e9caf9..0b91bc7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,36 +1,74 @@ -black==21.9b0 -brotlipy==0.7.0 -certifi==2021.5.30 -cffi -charset-normalizer -click==8.0.2 -cryptography -Django==2.2.24 -djangorestframework==3.9.4 -idna -importlib-metadata==4.8.1 +aiohttp==3.8.1 +aiosignal==1.2.0 +async-timeout==4.0.2 +atomicwrites==1.4.1 +attrs==22.1.0 +base58==2.1.1 +bitarray==2.6.0 +black==22.8.0 +certifi @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_83242e7e-f82d-4a71-8ef2-9d71d212d249gu_wxmeq/croots/recipe/certifi_1655968827803/work/certifi +charset-normalizer==2.1.1 +click==8.1.3 +cytoolz==0.11.2 +dateparser==1.0.0 +distlib==0.3.6 +dydx-v3-python==1.7.0 +ecdsa==0.16.0 +eth-abi==2.2.0 +eth-account==0.5.9 +eth-hash==0.5.0 +eth-keyfile==0.5.1 +eth-keys==0.3.4 +eth-rlp==0.2.1 +eth-typing==2.3.0 +eth-utils==1.9.5 +filelock==3.8.0 +frozenlist==1.3.1 +hexbytes==0.3.0 +idna==3.4 +importlib-metadata==0.23 +ipfshttpclient==0.8.0a2 +jsonschema==4.16.0 +lru-dict==1.1.8 +more-itertools==8.14.0 +mpmath==1.0.0 +multiaddr==0.0.9 +multidict==6.0.2 mypy-extensions==0.4.3 -pathspec==0.9.0 -platformdirs==2.4.0 -pycparser -pyOpenSSL -PySocks -pytz==2021.3 -regex==2021.10.8 -requests~=2.28.1 -six -sqlparse==0.4.2 -tomli==1.2.1 -typed-ast==1.4.3 -typing-extensions==3.10.0.2 -urllib3==1.26.7 -zipp==3.6.0 -pandas==1.3.5 -pendulum -python-dateutil~=2.8.2 -beautifulsoup4==4.10.0 -selenium==4.1.3 - -numpy~=1.23.1 -web3~=5.30.0 -binance~=0.3 \ No newline at end of file +netaddr==0.8.0 +packaging==21.3 +parsimonious==0.8.1 +pathspec==0.10.1 +platformdirs==2.5.2 +pluggy==0.13.1 +protobuf==3.20.2 +py==1.11.0 +pycryptodome==3.15.0 +pyparsing==3.0.9 +pyrsistent==0.18.1 +pytest==4.6.11 +python-dateutil==2.8.2 +python-decouple==3.6 +pytz==2022.2.1 +pytz-deprecation-shim==0.1.0.post0 +regex==2022.9.13 +requests==2.28.1 +requests-mock==1.6.0 +rlp==2.0.1 +six==1.16.0 +sympy==1.6 +toml==0.10.2 +tomli==2.0.1 +toolz==0.12.0 +tox==3.13.2 +typing_extensions==4.3.0 +tzdata==2022.2 +tzlocal==4.2 +urllib3==1.26.12 +varint==1.0.2 +virtualenv==20.16.5 +wcwidth==0.2.5 +web3==5.30.0 +websockets==9.1 +yarl==1.8.1 +zipp==3.8.1 diff --git a/services/dydx_p_client.py b/services/dydx_p_client.py new file mode 100644 index 0000000..2171074 --- /dev/null +++ b/services/dydx_p_client.py @@ -0,0 +1,54 @@ +from datetime import datetime + +from decouple import config +from dydx3 import Client +from web3 import Web3 +from dydx3 import constants + +""" +This class DydxPClient is responsible for initializing the DydxClient instance. +It has a function __create_dydx_Instance() that is responsible for initializing the dydx instance and returning it. +""" + + +class DydxPClient(object): + def __init__(self): + self.client = None + + def create_dydx_Instance(self): + self.client = Client( + host=constants.API_HOST_MAINNET, + network_id=constants.NETWORK_ID_MAINNET, + web3=Web3(Web3.HTTPProvider(config("PROVIDER"))), + ) + return self.client + + @property + def get_dydx_instance(self): + if self.client is not None: + return self.client + return self.create_dydx_Instance() + + def get_order_book(self, market="ETH-USD"): + client = self.get_dydx_instance + order_book = client.public.get_orderbook( + market=market, + ) + return order_book.data + + def get_historical_data(self, market="ETH-USD"): + # effective_before_or_at (Optional): Set a date by which the historical funding rates had to be created. + effective_before_or_at = str( + datetime.utcnow().replace(hour=12, day=1, month=1, year=2022) + ) + client = self.get_dydx_instance + historical_funding = client.public.get_historical_funding( + market=market, effective_before_or_at=effective_before_or_at + ) + historical_funding = vars(historical_funding) + return historical_funding["data"] + + +if __name__ == "__main__": + d = DydxPClient() + print(d.get_historical_data(market="BTC-USD")) From e636eb294b10caa78d0d3efed533bf807ad7e330 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Thu, 6 Oct 2022 06:15:38 -0300 Subject: [PATCH 02/16] updated files --- hedge_scripts/aave.py | 301 +++++++++++++++++ hedge_scripts/binance_client_.py | 152 +++++++++ hedge_scripts/data_dumper.py | 394 ++++++++++++++++++++++ hedge_scripts/dydx.py | 255 ++++++++++++++ hedge_scripts/parameter_manager.py | 522 +++++++++++++++++++++++++++++ hedge_scripts/stgyapp.py | 399 ++++++++++++++++++++++ 6 files changed, 2023 insertions(+) create mode 100644 hedge_scripts/aave.py create mode 100644 hedge_scripts/binance_client_.py create mode 100644 hedge_scripts/data_dumper.py create mode 100644 hedge_scripts/dydx.py create mode 100644 hedge_scripts/parameter_manager.py create mode 100644 hedge_scripts/stgyapp.py diff --git a/hedge_scripts/aave.py b/hedge_scripts/aave.py new file mode 100644 index 0000000..d1b4f1d --- /dev/null +++ b/hedge_scripts/aave.py @@ -0,0 +1,301 @@ +import math +import random +import numpy as np +from hedge_scripts import interval + +# import time + + +class Aave(object): + def __init__(self, config): + # assert self.dydx_class_instance == isinstance(dydx) + # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg'] + self.market_price = config["market_price"] + self.interval_current = config["interval_current"] + + self.entry_price = config["entry_price"] + + self.collateral_eth_initial = config["collateral_eth"] + self.collateral_eth = config["collateral_eth"] + self.collateral_usdc = config["collateral_usdc"] + + self.reserve_margin_eth = 0 + self.reserve_margin_usdc = 0 + + self.borrowed_percentage = config["borrowed_pcg"] + self.usdc_status = config["usdc_status"] + + self.debt = config["debt"] + self.debt_initial = config["debt"] + + self.ltv = config["ltv"] + self.price_to_ltv_limit = config["price_to_ltv_limit"] + + self.lending_rate = 0 + self.lending_rate_hourly = 0 + self.interest_on_lending_eth = 0 # aggregated fees + self.interest_on_lending_usd = 0 + self.lending_fees_eth = 0 # fees between last 2 prices + self.lending_fees_usd = 0 + + self.borrowing_rate = 0 + self.borrowing_rate_hourly = 0 + self.interest_on_borrowing = 0 # aggregated fees + self.borrowing_fees = 0 # fees between last 2 prices + + self.lend_minus_borrow_interest = 0 + + self.costs = 0 + # self.historical = pd.DataFrame() + # self.dydx_class_instance = dydx_class_instance + # self.staked_in_protocol = stk + + # def update_costs(self): + # """ + # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees + # """ + # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit) + # self.costs = self.costs - self.lend_minus_borrow_interest + + def collateral_usd(self): + return self.collateral_eth * self.market_price + + def update_debt(self): + """ + it requires having called borrowing_fees_calc() in order to use updated values of last earned fees + """ + self.debt = self.debt + self.borrowing_fees + + def update_collateral(self): + """ + it requires having called lending_fees_calc() in order to use updated values of last earned fees + """ + self.collateral_eth = self.collateral_eth + self.lending_fees_eth + self.collateral_usdc = self.collateral_usd() + + def track_lend_borrow_interest(self): + """ + it requires having called borrowing_fees_calc() and lending_fees_calc() + in order to use updated values of last earned fees + """ + self.lend_minus_borrow_interest = ( + self.interest_on_lending_usd - self.interest_on_borrowing + ) + + def lending_fees_calc(self, freq): + self.simulate_lending_rate() + self.lending_rate_freq = self.lending_rate / freq + + # fees from lending are added to collateral? YES + # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE + self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq + self.lending_fees_usd = self.lending_fees_eth * self.market_price + self.interest_on_lending_eth = ( + self.interest_on_lending_eth + self.lending_fees_eth + ) + self.interest_on_lending_usd = ( + self.interest_on_lending_usd + self.lending_fees_usd + ) + + def borrowing_fees_calc(self, freq): + self.simulate_borrowing_rate() +<<<<<<< HEAD + self.borrowing_rate_freq = self.borrowing_rate / freq + + # fees from borrow are added to debt? YES + # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE + self.borrowing_fees = self.debt * self.borrowing_rate_freq +======= + self.borrowing_rate_hourly = self.borrowing_rate / freq + self.borrowing_fees = ( + self.collateral_eth + * self.entry_price + * self.borrowed_percentage + * self.borrowing_rate_hourly + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees + + def simulate_lending_rate(self): + # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate'] + + # best case + # self.lending_rate = 1.5 / 100 + + # worst case + self.lending_rate = 0.5 / 100 + + def simulate_borrowing_rate(self): + # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate'] + + # best case + # self.borrowing_rate = 1.5/100 + + # worst case + self.borrowing_rate = 2.5 / 100 + + def ltv_calc(self): + if self.collateral_usd() == 0: + return 0 + else: + return self.debt / self.collateral_usd() + + def price_to_liquidation(self, dydx_class_instance): + return ( + self.entry_price + - (dydx_class_instance.pnl() + self.debt - self.lend_minus_borrow_interest) + / self.collateral_eth + ) + + def price_to_ltv_limit_calc(self): + return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3) + + def buffer_for_repay(self): + return 0.01 + + def ltv_limit(self): + return 0.5 + + # Actions to take + def return_usdc(self, new_market_price, new_interval_current, stgy_instance): + gas_fees = stgy_instance.gas_fees + time = 0 + if self.usdc_status: + # simulate 2min delay for tx + # update parameters + # AAVE parameters + self.usdc_status = False + # self.collateral_eth = 0 + # self.collateral_usdc = 0 + self.debt = 0 + self.ltv = 0 + self.price_to_ltv_limit = 0 + # self.lending_rate = 0 + # self.borrowing_rate = 0 + + # fees + self.costs = self.costs + gas_fees + + time = 1 + return time + + def borrow_usdc(self, new_market_price, new_interval_current, stgy_instance): + gas_fees = stgy_instance.gas_fees + intervals = stgy_instance.intervals + time = 0 + if not self.usdc_status: + # AAVE parameters + # update parameters + self.usdc_status = True + self.entry_price = self.market_price +<<<<<<< HEAD + self.debt = self.collateral_eth_initial * self.borrowed_percentage * stgy_instance.trigger_prices['open_close'] +======= + self.debt = ( + self.collateral_eth_initial + * self.borrowed_percentage + * stgy_instance.target_prices["open_close"] + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + self.debt_initial = self.debt + self.ltv = self.ltv_calc() + + # ltv_limit = 0.85 + # vol = stgy_instance.historical_data['vol'] + # benchmark_vol = 0.05 + # for i in range(5): + # if i*benchmark_vol < vol <= (i+1)*benchmark_vol: + # ltv_limit = 0.85 * 1/(i+1) = debt / coll(t) = debt / p_eth*coll = debt/p_eth_-1 * vol * coll + self.price_to_ltv_limit = ( + self.price_to_ltv_limit_calc() + ) # We have to define the criteria for this price + # self.lending_rate = 0 + # self.borrowing_rate = 0 + + # fees + self.costs = self.costs + gas_fees + + price_floor = intervals["open_close"].left_border + previous_position_order = intervals["open_close"].position_order + intervals["floor"] = interval.Interval( + self.price_to_ltv_limit, + price_floor, + "floor", + previous_position_order + 1, + ) + intervals["minus_infty"] = interval.Interval( + -math.inf, + self.price_to_ltv_limit, + "minus_infty", + previous_position_order + 2, + ) + # simulate 2min delay for tx + time = 1 + return time + +<<<<<<< HEAD + def repay_aave(self, + stgy_instance): +======= + def repay_aave(self, new_market_price, new_interval_current, stgy_instance): +>>>>>>> cd6cfcb... write function for getting order book and historical data + gas_fees = stgy_instance.gas_fees + dydx_class_instance = stgy_instance.dydx + # aave_class_instance = stgy_instance.aave + # dydx_client_class_instance = stgy_instance.dydx_client + # + time = 0 + if self.usdc_status: + # update parameters + short_size_for_debt = self.debt / ( + self.market_price - dydx_class_instance.entry_price + ) + new_short_size = dydx_class_instance.short_size - short_size_for_debt + + # pnl_for_debt = dydx_class_instance.pnl() + # We have to repeat the calculations for pnl and notional methods, but using different size_eth +<<<<<<< HEAD + pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price) +======= + pnl_for_debt = short_size_for_debt * ( + new_market_price - dydx_class_instance.entry_price + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + self.debt = self.debt - pnl_for_debt + self.ltv = self.ltv_calc() + + self.price_to_ltv_limit = round( + self.entry_price + * (self.debt / self.collateral_usdc) + / self.ltv_limit(), + 3, + ) + self.costs = self.costs + gas_fees + + dydx_class_instance.short_size = new_short_size + dydx_class_instance.notional = dydx_class_instance.notional_calc() + dydx_class_instance.equity = dydx_class_instance.equity_calc() + dydx_class_instance.leverage = dydx_class_instance.leverage_calc() + dydx_class_instance.pnl = dydx_class_instance.pnl_calc() + # dydx_class_instance.price_to_liquidation = \ + # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance) + + # fees + # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees + dydx_class_instance.simulate_maker_taker_fees() + notional_for_fees = abs(short_size_for_debt) * self.market_price + dydx_class_instance.costs = ( + dydx_class_instance.costs + + dydx_class_instance.maker_taker_fees * notional_for_fees + + pnl_for_debt * dydx_class_instance.withdrawal_fees + ) + + # Note that a negative self.debt is actually a profit + # We update the parameters + if self.debt > 0: + self.usdc_status = True + else: + self.usdc_status = False + # simulate 2min delay for tx + time = 1 + return time diff --git a/hedge_scripts/binance_client_.py b/hedge_scripts/binance_client_.py new file mode 100644 index 0000000..6f934f1 --- /dev/null +++ b/hedge_scripts/binance_client_.py @@ -0,0 +1,152 @@ +import math +import pandas as pd +import os.path +from datetime import timedelta, datetime +from dateutil import parser +from binance.client import Client as Client_binance + + +class BinanceClient(object): + def __init__(self, config): + self.binance_api_key = config["binance_api_key"] + self.binance_api_secret = config["binance_api_secret"] + + self.client = Client_binance( + api_key=self.binance_api_key, api_secret=self.binance_api_secret + ) + # self.initial_date = config['initial_date'] + # self.symbol = config['symbol'] + # self.freq = config['freq'] + + ### FUNCTIONS + def minutes_of_new_data(self, symbol, kline_size, initial_date, data, source): + if len(data) > 0: + old = parser.parse(data["timestamp"].iloc[-1]) + elif source == "binance": + old = datetime.strptime(initial_date, "%d %b %Y") + if source == "binance": + new = pd.to_datetime( + self.client.get_klines(symbol=symbol, interval=kline_size)[-1][0], + unit="ms", + ) + return old, new + + def get_all_binance(self, symbol, freq, initial_date, save=False): + binsizes = { + "1m": 1, + "5m": 5, + "10m": 10, + "15m": 15, + "1h": 60, + "6h": 360, + "12h": 720, + "1d": 1440, + } + filename = ( + "/home/agustin/Git-Repos/HedgingScripts/files/%s-%s-data_since_%s.csv" + % (symbol, freq, initial_date) + ) + data_df = pd.DataFrame() + oldest_point, newest_point = self.minutes_of_new_data( + symbol, freq, initial_date, data_df, source="binance" + ) + delta_min = (newest_point - oldest_point).total_seconds() / 60 + available_data = math.ceil(delta_min / binsizes[freq]) + if oldest_point == datetime.strptime(initial_date, "%d %b %Y"): + print( + "Downloading all available %s data for %s. Be patient..!" + % (freq, symbol) + ) + else: + print( + "Downloading %d minutes of new data available for %s, i.e. %d instances of %s data." + % (delta_min, symbol, available_data, freq) + ) + klines = self.client.get_historical_klines( + symbol, + freq, + oldest_point.strftime("%d %b %Y %H:%M:%S"), + newest_point.strftime("%d %b %Y %H:%M:%S"), + ) + data = pd.DataFrame( + klines, + columns=[ + "timestamp", + "open", + "high", + "low", + "close", + "volume", + "close_time", + "quote_av", + "trades", + "tb_base_av", + "tb_quote_av", + "ignore", + ], + ) + data["timestamp"] = pd.to_datetime(data["timestamp"], unit="ms") + # data.index = pd.to_datetime(data['timestamp'], unit='ms') + if len(data_df) > 0: + temp_df = pd.DataFrame(data) + data_df = data_df.append(temp_df) + else: + data_df = data + data_df.set_index("timestamp", inplace=True) + if save: + data_df.to_csv(filename) + print("All caught up..!") + print(initial_date) + return data_df + +<<<<<<< HEAD +import json + +with open('/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json') as json_file: + config = json.load(json_file) +======= + +# import json +# +# with open('/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json') as json_file: +# config = json.load(json_file) +>>>>>>> cd6cfcb... write function for getting order book and historical data +# _binance_client_ = BinanceClient(config['binance_client']) +# eth_historical = _binance_client_.get_all_binance(save=True) +# +# +# eth_prices = eth_historical[-2000:]['close'] +# for i in range(len(eth_prices)): +# eth_prices[i] = float(eth_prices[i]) +# historical_data = eth_prices +# +# Track historical data +# symbol = 'ETHUSDC' +# freq = '1m' +# initial_date = "1 Sep 2019" +# _binance_client_ = BinanceClient(config['binance_client']) +# eth_historical = _binance_client_.get_all_binance(symbol=symbol, freq=freq, +# initial_date=initial_date, save=True) +# eth_prices = eth_historical['close'] +# for i in range(len(eth_prices)): +# eth_prices[i] = float(eth_prices[i]) +# historical_data = eth_prices +<<<<<<< HEAD +# print(historical_data) + +# initial_dates = ["1 Jan 2022", "1 Jan 2021", "1 Jan 2020", "1 Jan 2019", "1 Jan 2018", "1 Jan 2017", "1 Jan 2016", +# "1 Jan 2015", "1 Jan 2014"] +# end_dates = [-1, 232, 963, 1328, 1693, 2058, 2424, 2789, 3154] +# +# # eth_historical_prices_year_wise = [] +# parallel_pool = Parallel(n_jobs=9) +# delayed_function = [delayed(_binance_client_.get_all_binance)(symbol=symbol, freq=freq, +# initial_date=initial_date, save=True, +# end_date=end_date) for initial_date, end_date in +# zip(initial_dates, end_dates)] +# +# eth_historical_prices_year_wise = parallel_pool(delayed_function) +# print('eth_historical_prices_year_wise', eth_historical_prices_year_wise) +======= +# # print(historical_data) +>>>>>>> cd6cfcb... write function for getting order book and historical data diff --git a/hedge_scripts/data_dumper.py b/hedge_scripts/data_dumper.py new file mode 100644 index 0000000..c022a6d --- /dev/null +++ b/hedge_scripts/data_dumper.py @@ -0,0 +1,394 @@ +import os +import pygsheets +import matplotlib.pyplot as plt +from scipy.stats import norm +import csv +import pandas as pd +import numpy as np + +import interval + + +class DataDamperNPlotter: + def __init__(self): + self.historical_data = None + + @staticmethod + def write_data( + stgy_instance, new_interval_previous, interval_old, mkt_price_index, sheet=False + ): + aave_instance = stgy_instance.aave + dydx_instance = stgy_instance.dydx + data_aave = [] + data_dydx = [] + aave_wanted_keys = [ + "market_price", + "interval_current", + "entry_price", + "collateral_eth", + "usdc_status", + "debt", + "ltv", + "lending_rate", + "interest_on_lending_usd", + "borrowing_rate", + "interest_on_borrowing", + "lend_minus_borrow_interest", + "costs", + ] + + for i in range(len(aave_instance.__dict__.values())): + if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys: + # print(list(aave_instance.__dict__.keys())[i]) + if isinstance( + list(aave_instance.__dict__.values())[i], interval.Interval + ): + data_aave.append(str(list(aave_instance.__dict__.values())[i].name)) + # data_aave.append(new_interval_previous.name) + data_aave.append(interval_old.name) + else: + data_aave.append(str(list(aave_instance.__dict__.values())[i])) + for i in range(len(dydx_instance.__dict__.values())): + if isinstance(list(dydx_instance.__dict__.values())[i], interval.Interval): + data_dydx.append(str(list(dydx_instance.__dict__.values())[i].name)) + # data_dydx.append(new_interval_previous.name) + data_dydx.append(interval_old.name) + else: + data_dydx.append(str(list(dydx_instance.__dict__.values())[i])) + # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker + data_aave.append(stgy_instance.gas_fees) + data_aave.append(stgy_instance.total_costs_from_aave_n_dydx) + data_aave.append(stgy_instance.total_pnl) + data_aave.append(mkt_price_index) + + + data_dydx.append(stgy_instance.gas_fees) + data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx) + data_dydx.append(stgy_instance.total_pnl) + data_dydx.append(mkt_price_index) + + # print(data_dydx, list(dydx_instance.__dict__.keys())) + if sheet == True: + gc = pygsheets.authorize( + service_file="/home/agustin/Git-Repos/HedgingScripts/files/stgy-1-simulations-e0ee0453ddf8.json" + ) + sh = gc.open("aave/dydx simulations") + sh[0].append_table(data_aave, end=None, dimension="ROWS", overwrite=False) + sh[1].append_table(data_dydx, end=None, dimension="ROWS", overwrite=False) + else: + with open( + "/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv", "a" + ) as file: + writer = csv.writer(file, lineterminator="\n") + writer.writerow(data_aave) + with open( + "/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv", + "a", + newline="", + encoding="utf-8", + ) as file: + writer = csv.writer(file, lineterminator="\n") + writer.writerow(data_dydx) + + @staticmethod + def delete_results(): + file_aave = "/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv" + file_dydx = "/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv" + if os.path.exists(file_aave) and os.path.isfile(file_aave): + os.remove(file_aave) + if os.path.exists(file_dydx) and os.path.isfile(file_dydx): + os.remove(file_dydx) + + @staticmethod + def add_header(): + aave_headers = [ + "market_price", + "I_current", + # "I_previous", + "I_old", + "entry_price", + "collateral_eth", + "usdc_status", + "debt", + "ltv", + "lending_rate", + "interest_on_lending_usd", + "borrowing_rate", + "interest_on_borrowing", + "lend_minus_borrow_interest", + "costs", + "gas_fees", +<<<<<<< HEAD + "total_costs_from_aave_n_dydx", + "total_stgy_pnl", + "index_of_mkt_price"] +======= + "total_costs", + "index_of_mkt_price", + ] +>>>>>>> cd6cfcb... write function for getting order book and historical data + dydx_headers = [ + "market_price", + "I_current", + # "I_previous", + "I_old", + "entry_price", + "short_size", + "collateral", + "notional", + "equity", + "leverage", + "pnl", + # "price_to_liquidation", + "collateral_status", + "short_status", + "order_status", + "withdrawal_fees", + "funding_rates", + "maker_taker_fees", + "costs", + "gas_fees", +<<<<<<< HEAD + "total_costs_from_aave_n_dydx", + "total_stgy_pnl", + "index_of_mkt_price"] + with open('/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv', 'a') as file: + writer = csv.writer(file, lineterminator='\n') +======= + "total_costs", + "index_of_mkt_price", + ] + with open( + "/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv", "a" + ) as file: + writer = csv.writer(file, lineterminator="\n") +>>>>>>> cd6cfcb... write function for getting order book and historical data + writer.writerow(aave_headers) + with open( + "/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv", + "a", + newline="", + encoding="utf-8", + ) as file: + writer = csv.writer(file, lineterminator="\n") + writer.writerow(dydx_headers) + + @staticmethod + def historical_parameters_data(aave_instance, dydx_instance): + aave_df = pd.DataFrame( + aave_instance.historical_data, columns=list(aave_instance.__dict__.keys()) + ) + dydx_df = pd.DataFrame( + dydx_instance.historical_data, columns=list(dydx_instance.__dict__.keys()) + ) + return {"aave_df": aave_df, "dydx_df": dydx_df} + + @staticmethod + def plot_data(stgy_instance): # , + # save, + # factors, vol, period): + # colors https://datascientyst.com/full-list-named-colors-pandas-python-matplotlib/ + fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + # fig.suptitle("Factors = (%s, %s, %s), Vol=%s, Period=%s to %s" % (factors[0], factors[1], factors[2], + # vol, period[0], period[1])) + axs.plot( + stgy_instance.historical_data["close"], + color="tab:blue", + label="market price", + ) + # axs.plot(list(pnl_), label='DyDx pnl') + # p_rtrn_usdc_n_rmv_coll_dydx = stgy_instance.target_prices['rtrn_usdc_n_rmv_coll_dydx'] +<<<<<<< HEAD + p_borrow_usdc_n_add_coll = stgy_instance.trigger_prices['borrow_usdc_n_add_coll'] + # p_add_collateral_dydx = stgy_instance.target_prices['p_borrow_usdc_n_add_coll'] + # p_close_short = stgy_instance.target_prices['close_short'] + p_open_close = stgy_instance.trigger_prices['open_close'] + floor = min(list(stgy_instance.trigger_prices.values())) +======= + p_borrow_usdc_n_add_coll = stgy_instance.target_prices["borrow_usdc_n_add_coll"] + # p_add_collateral_dydx = stgy_instance.target_prices['p_borrow_usdc_n_add_coll'] + # p_close_short = stgy_instance.target_prices['close_short'] + p_open_close = stgy_instance.target_prices["open_close"] + floor = min(list(stgy_instance.target_prices.values())) +>>>>>>> cd6cfcb... write function for getting order book and historical data + # axs.axhline(y=p_rtrn_usdc_n_rmv_coll_dydx, color='black', linestyle='--', + # label='rtrn_usdc_n_rmv_coll_dydx') + axs.axhline( + y=p_borrow_usdc_n_add_coll, + color="darkgoldenrod", + linestyle="--", + label="borrow_usdc_n_add_coll", + ) + # axs.axhline(y=p_add_collateral_dydx, color='tab:orange', linestyle='--', label='add_collateral_dydx') + # axs.axhline(y=p_close_short, color='olive', linestyle='--', label='close_short') +<<<<<<< HEAD + axs.axhline(y=p_open_close, color='darkred', linestyle='--', label='open_close') + axs.axhline(y=floor, color='red', linestyle='--', label='floor') + if 'repay_aave' in list(stgy_instance.trigger_prices.keys()): + p_repay_aave = stgy_instance.trigger_prices['repay_aave'] + axs.axhline(y=p_repay_aave, color='magenta', linestyle='--', label='repay_aave') + if 'ltv_limit' in list(stgy_instance.trigger_prices.keys()): + p_ltv_limit = stgy_instance.trigger_prices['ltv_limit'] + axs.axhline(y=p_ltv_limit, color='purple', linestyle='--', label='ltv_limit') +======= + axs.axhline(y=p_open_close, color="darkred", linestyle="--", label="open_close") + axs.axhline(y=floor, color="red", linestyle="--", label="floor") + if "repay_aave" in list(stgy_instance.target_prices.keys()): + p_repay_aave = stgy_instance.target_prices["repay_aave"] + axs.axhline( + y=p_repay_aave, color="magenta", linestyle="--", label="repay_aave" + ) + if "ltv_limit" in list(stgy_instance.target_prices.keys()): + p_ltv_limit = stgy_instance.target_prices["ltv_limit"] + axs.axhline( + y=p_ltv_limit, color="purple", linestyle="--", label="ltv_limit" + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + # print(list(stgy_instance.target_prices.keys())) + axs.grid() + axs.legend(loc="lower left") + # if save: + # plt.savefig('/home/agustin/Git-Repos/HedgingScripts/files/simulated_plot_index_%s_to_%s.png' + # % (period[0], period[1])) + # else: + plt.show() + + def get_gif(self): + import numpy as np + from matplotlib.animation import FuncAnimation + from IPython import display + import matplotlib.pyplot as plt + + Figure = plt.figure() + lines_plotted = plt.plot([]) + self.line_plotted = lines_plotted[0] + anim_created = FuncAnimation( + Figure, self.AnimationFunction, frames=100, interval=25 + ) + video = anim_created.to_html5_video() + plot = display.HTML(video) + # plot.save() + display.display(plot) + # with open('plot.html', 'w') as f: + # f.write(plot.text) + # with open("plot.html", "w") as file: + # file.write(plot) + + # function takes frame as an input + def AnimationFunction(self, frame): + + # setting y according to frame + # number and + x. It's logic + y = self.historical_data["close"][frame] + x = self.historical_data.index[frame] + + # line is set with new values of x and y + self.line_plotted.set_data((x, y)) + + @staticmethod + def plot_price_distribution(stgy_instance): + # fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + # from https://stackoverflow.com/questions/6855710/how-to-have-logarithmic-bins-in-a-python-histogram + data = np.log(stgy_instance.historical_data["close"]) + MIN, MAX = data.min(), data.max() + data.hist(bins=np.linspace(MIN, MAX, 50)) + plt.gca().set_xscale("log") + plt.show() + # print(np.log(historical_data['close'])) + + # @staticmethod + def plot_returns_distribution(self): # stgy_instance): + """ + We assume returns are normally distributed + """ + + historical = self.historical_data # stgy_instance.historical_data.copy() + pct_change = historical["close"].pct_change().fillna(method="bfill") + log_returns = np.log(historical["close"]) - np.log( + historical["close"].shift(60) + ) + historical["pct_change"] = pct_change + historical["log_returns"] = log_returns + + x = np.linspace(pct_change.min(), 1, 100) + mean = np.mean(pct_change) + std = np.std(pct_change) + norm_dist = norm.pdf(x, mean, std) + fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + log_returns.hist(bins=50, ax=axs) + # pct_change.hist(bins=50, ax=axs) + # axs.set_xlabel('Return') + # axs.set_ylabel('Sample') + # axs.set_title('Return distribution') + # axs.plot(x, norm_dist, color='tab:blue', label='Returns dist') + + # To check if its normally distributed + understate the likelihood of returns beyond -2/+2 quantiles + # import scipy.stats as stats + # stats.probplot(historical['returns'], dist='norm', plot=axs) + # axs.grid() + plt.show() + # print(historical.describe()) + + @staticmethod + def prob_return_in_range(stgy_instance, range): + """ + range = [a, b] with a < b + Recall: + cumulative distribution function of a random variable X is F_X(x) := P(X <= x) + So the probability of returns (R) falling in range is P(a <= R <= b) = P(R <= b) - P(R < a) = F_R(b) - F_R(a) + If we assume returns are normally distributed then F could be estimated using norm(mean, std).cdf function + """ + returns = stgy_instance.historical_data["returns"] + mean = np.mean(returns) + std = np.std(returns) + norm_cdf = norm(mean, std).cdf + return norm_cdf(range[1]) - norm_cdf(range[0]) + + @staticmethod + def plot_volatility(stgy_instance, method): + """ + We assume returns are normally distributed + """ + if method == "arch": + vol = stgy_instance.volatility_calculator.get_arch( + stgy_instance.historical_data, 1, 0, 0 + ) + elif method == "garch": + vol = stgy_instance.volatility_calculator.get_garch( + stgy_instance.historical_data + ) + elif method == "emwa": + vol = stgy_instance.volatility_calculator.get_emwa( + stgy_instance.historical_data, 1, 0, 0 + ) + historical = stgy_instance.historical_data.copy() + pct_change = historical["close"].pct_change().fillna(method="bfill") + log_returns = np.log(historical["close"]) - np.log(historical["close"].shift(1)) + historical["pct_change"] = pct_change + historical["log_returns"] = log_returns + + x = np.linspace(pct_change.min(), 1, 100) + mean = np.mean(pct_change) + std = np.std(pct_change) + norm_dist = norm.pdf(x, mean, std) + fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + log_returns.hist(bins=50, ax=axs) + + +if __name__ == "__main__": + data_dumper = DataDamperNPlotter() + historical_daily = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv" + ) + historical_hourly = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1h-data.csv" + ) + historical_minutes = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv" + ) + # assign data to stgy instance + define index as dates + data_dumper.historical_data = pd.DataFrame( + historical_minutes["close"], columns=["close"] + ) + # data_dumper.historical_data = pd.DataFrame(historical_hourly["close"], columns=['close']) + data_dumper.plot_returns_distribution() diff --git a/hedge_scripts/dydx.py b/hedge_scripts/dydx.py new file mode 100644 index 0000000..f55985a --- /dev/null +++ b/hedge_scripts/dydx.py @@ -0,0 +1,255 @@ +import math +import random +import numpy as np +import interval + + +class Dydx(object): + def __init__(self, config): + # assert aave_class == isinstance(aave) + self.market_price = config["market_price"] + self.interval_current = config["interval_current"] + self.entry_price = config["entry_price"] + self.short_size = config["short_size"] + self.collateral = config["collateral"] + self.notional = config["notional"] + self.equity = config["equity"] + self.leverage = config["leverage"] + self.pnl = config["pnl"] + # self.price_to_liquidation = config['price_to_liquidation'] + self.collateral_status = config["collateral_status"] + self.short_status = config["short_status"] + self.order_status = True + self.withdrawal_fees = 0.01 / 100 + self.funding_rates = 0 + self.maker_taker_fees = 0 + self.costs = 0 + # self.historical = pd.DataFrame() + # self.aave_class_instance = aave_class_instance + # self.staked_in_protocol = stk + + # auxiliary functions + def pnl_calc(self): + return self.short_size * (self.market_price - self.entry_price) + + def notional_calc(self): + return abs(self.short_size) * self.market_price + + def equity_calc(self): + return self.collateral + self.pnl_calc() + + def leverage_calc(self): + if self.equity_calc() == 0: + return 0 + else: + return self.notional_calc() / self.equity_calc() + + def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance): + return ( + self.entry_price + + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size + ) + + @staticmethod + def price_to_liquidation_calc(dydx_client_class_instance): + return dydx_client_class_instance.dydx_margin_parameters["liquidation_price"] + + def add_funding_rates(self): + self.simulate_funding_rates() + self.costs = self.costs - self.funding_rates + + def simulate_funding_rates(self): + # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6) + + # best case + # self.funding_rates = 0.0075 / 100 + + # average -0.00443% + + # worst case + self.funding_rates = -0.0075 / 100 + + def simulate_maker_taker_fees(self): + # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6) + + # maker fees + self.maker_taker_fees = 0.05 / 100 # <1M + # self.maker_taker_fees = 0.04 / 100 # <5M + # self.maker_taker_fees = 0.035 / 100 # <10M + # self.maker_taker_fees = 0.03 / 100 # <50M + # self.maker_taker_fees = 0.025 / 100 # <200M + # self.maker_taker_fees = 0.02 / 100 # >200M + + # Actions to take + def remove_collateral(self, new_market_price, new_interval_current, stgy_instance): + self.cancel_order() + time = 0 + if self.collateral_status: + self.collateral_status = False + withdrawal_fees = self.collateral * self.withdrawal_fees + self.collateral = 0 + # self.price_to_liquidation = 0 + + # fees + self.costs = self.costs + withdrawal_fees + + time = 1 + return time + + def add_collateral(self, new_market_price, new_interval_current, stgy_instance): + gas_fees = stgy_instance.gas_fees + aave_class_instance = stgy_instance.aave + time = 0 + if not self.collateral_status: + self.collateral_status = True + self.collateral = aave_class_instance.debt_initial + # fees + self.costs = self.costs + gas_fees + # We place an order in open_close +<<<<<<< HEAD + self.place_order(stgy_instance.trigger_prices['open_close']) +======= + self.place_order(stgy_instance.target_prices["open_close"]) +>>>>>>> cd6cfcb... write function for getting order book and historical data + # add time + time = 10 + return time + + def open_short(self, new_market_price, new_interval_current, stgy_instance): + aave_class_instance = stgy_instance.aave + # dydx_client_class_instance = stgy_instance.dydx_client + intervals = stgy_instance.intervals + if (not self.short_status) and self.order_status: + self.short_status = True + # dydx parameters +<<<<<<< HEAD + if self.market_price <= stgy_instance.trigger_prices['floor']: + print("CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!") + print("Difference of: ", stgy_instance.trigger_prices['floor'] - self.market_price) + + # if self.market_price <= stgy_instance.trigger_prices['open_close']: + # print("CAUTION: OPEN PRICE LOWER THAN open_close!") + # print("Difference of: ", stgy_instance.trigger_prices['open_close'] - self.market_price) +======= + if self.market_price <= stgy_instance.target_prices["floor"]: + print("CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!") + print( + "Difference of: ", + stgy_instance.target_prices["floor"] - self.market_price, + ) + + if self.market_price <= stgy_instance.target_prices["open_close"]: + print("CAUTION: OPEN PRICE LOWER THAN open_close!") + print( + "Difference of: ", + stgy_instance.target_prices["open_close"] - self.market_price, + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + self.entry_price = self.market_price + self.short_size = -aave_class_instance.collateral_eth_initial + # self.collateral = aave_class_instance.debt_initial + self.notional = self.notional_calc() + self.equity = self.equity_calc() + self.leverage = self.leverage_calc() + # Simulate maker taker fees + self.simulate_maker_taker_fees() + # Add costs + self.costs = self.costs + self.maker_taker_fees * self.notional + + price_floor = intervals["open_close"].left_border + floor_position = intervals["floor"].position_order + +<<<<<<< HEAD + price_floor = intervals['open_close'].left_border + floor_position = intervals['floor'].position_order + + price_to_repay_debt = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(), + aave_class_instance) + price_to_ltv_limit = intervals['floor'].left_border + stgy_instance.trigger_prices['repay_aave'] = price_to_repay_debt + stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit +======= + price_to_repay_debt = self.price_to_repay_aave_debt_calc( + 1 + aave_class_instance.buffer_for_repay(), aave_class_instance + ) + price_to_ltv_limit = intervals["floor"].left_border + stgy_instance.target_prices["repay_aave"] = price_to_repay_debt + stgy_instance.target_prices["ltv_limit"] = price_to_ltv_limit +>>>>>>> cd6cfcb... write function for getting order book and historical data + if price_to_ltv_limit < price_to_repay_debt: + intervals["floor"] = interval.Interval( + price_to_repay_debt, price_floor, "floor", floor_position + ) + intervals["repay_aave"] = interval.Interval( + price_to_ltv_limit, + price_to_repay_debt, + "repay_aave", + floor_position + 1, + ) + intervals["minus_infty"] = interval.Interval( + -math.inf, price_to_ltv_limit, "minus_infty", floor_position + 2 + ) + else: + print("CAUTION: P_ltv > P_repay") + print("Difference of: ", price_to_ltv_limit - price_to_repay_debt) + price_to_repay_debt = self.price_to_repay_aave_debt_calc( + 0.5, aave_class_instance + ) + intervals["floor"] = interval.Interval( + price_to_ltv_limit, price_floor, "floor", floor_position + ) + intervals["ltv_limit"] = interval.Interval( + price_to_repay_debt, + price_to_ltv_limit, + "repay_aave", + floor_position + 1, + ) + intervals["minus_infty"] = interval.Interval( + -math.inf, price_to_repay_debt, "minus_infty", floor_position + 2 + ) + self.order_status = False + return 0 + + def close_short(self, new_market_price, new_interval_current, stgy_instance): + if self.short_status: + # Next if is to move up the threshold if we didnt execute at exactly open_close +<<<<<<< HEAD + if self.market_price >= stgy_instance.trigger_prices['open_close']: + # new_open_close = self.market_price + print("CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!") + print("Difference of: ", self.market_price - stgy_instance.trigger_prices['open_close']) +======= + if self.market_price >= stgy_instance.target_prices["open_close"]: + # new_open_close = self.market_price + print( + "CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!" + ) + print( + "Difference of: ", + self.market_price - stgy_instance.target_prices["open_close"], + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + # stgy_instance.target_prices['open_close'] = self.market_price + self.notional = self.notional_calc() + self.equity = self.equity_calc() + self.leverage = self.leverage_calc() + self.pnl = self.pnl_calc() + # We update short parameters after the calculation of pnl + self.entry_price = 0 + self.short_status = False + self.short_size = 0 + self.simulate_maker_taker_fees() + self.costs = self.costs + self.maker_taker_fees * self.notional +<<<<<<< HEAD + self.place_order(stgy_instance.trigger_prices['open_close']) + return 0 +======= + self.place_order(stgy_instance.target_prices["open_close"]) +>>>>>>> cd6cfcb... write function for getting order book and historical data + + def place_order(self, price): + self.order_status = True + # self. + + def cancel_order(self): + self.order_status = False diff --git a/hedge_scripts/parameter_manager.py b/hedge_scripts/parameter_manager.py new file mode 100644 index 0000000..4ca5b72 --- /dev/null +++ b/hedge_scripts/parameter_manager.py @@ -0,0 +1,522 @@ +import math +import random +import numpy as np +from scipy.stats import norm +import pandas as pd +import matplotlib.pyplot as plt + +<<<<<<< HEAD +import interval + +======= +>>>>>>> cd6cfcb... write function for getting order book and historical data + +class ParameterManager(object): + # auxiliary functions + @staticmethod +<<<<<<< HEAD + def define_target_prices(stgy_instance, slippage, floor): + p_open_close = floor * (1+slippage) + ########################################################## + # We define the intervals + list_of_intervals = ["open_close", + "floor"] + list_of_trigger_prices = [p_open_close, + floor] +======= + def define_target_prices(stgy_instance, N_week, data_for_thresholds, floor): + # P_open_close to be P_floor * e^(mu + factor * sigma) where mu, sigma are calculated + # based on last 3 month of data. Factor is calculated using the VaR approach in which we choose a confidence + # level X (a probability of ensurance) and we calculate the maximum loss we are X % sure we wont lose more than + # that. + log_returns_1_week = np.log(data_for_thresholds["close"]) - np.log( + data_for_thresholds["close"].shift(1) + ) + ewm_log_returns = log_returns_1_week[-N_week:].ewm(alpha=0.8, adjust=False) + mean_ema_log_returns = round(ewm_log_returns.mean().mean() * 365, 3) + std_ema_log_returns = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) + + mu = mean_ema_log_returns / 365 * 24 * 60 + sigma = (std_ema_log_returns / np.sqrt(365)) * np.sqrt(24 * 60) + + factor_open_close = round(norm.ppf(0.90), 3) + p_open_close = floor * math.e ** (mu + factor_open_close * sigma) + ########################################################## + # P_borrow_usdc_n_add_coll to be P_open_close * e^(mu + factor * sigma) where mu, sigma are calculated + # based on last 3 month of data. Factor is calculated using the VaR approach in which we choose a confidence + # level X (a probability of ensurance) and we calculate the maximum loss we are X % sure we wont lose more than + # that. + log_returns_10min_last_3_months = np.log( + stgy_instance.historical_data[-3 * 30 * 24 * 60 :]["close"] + ) - np.log(data_for_thresholds[-3 * 30 * 24 * 60 :]["close"].shift(10)) + + # vol benchmark: daily version of last 3month 2min vol (mean std) + ewm_log_returns = log_returns_10min_last_3_months.ewm(alpha=0.8, adjust=False) + std_10min_ema_mean_value = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) + mean_10min_ema = round(ewm_log_returns.mean().mean() * 365, 3) + mu_10min_mean_daily = mean_10min_ema / 365 * 24 * 6 + sigma_10min_mean_daily = round( + (std_10min_ema_mean_value / np.sqrt(365) * np.sqrt(24 * 6)), 3 + ) + + factor_add = round(norm.ppf(0.90), 3) + + p_borrow_usdc_n_add_coll = p_open_close * math.e ** ( + mu_10min_mean_daily + factor_add * sigma_10min_mean_daily + ) + + stgy_instance.target_prices_copy = stgy_instance.target_prices + list_of_intervals = [ # "rtrn_usdc_n_rmv_coll_dydx", + "borrow_usdc_n_add_coll", + "open_close", + # "open_short", + "floor", + ] + list_of_trigger_prices = [ # p_rtrn_usdc_n_rmv_coll_dydx, + p_borrow_usdc_n_add_coll, + p_open_close, + # p_open_short, + floor, + ] +>>>>>>> cd6cfcb... write function for getting order book and historical data + # We define/update trigger prices + for i in range(len(list_of_intervals)): + interval_name = list_of_intervals[i] + trigger_price = list_of_trigger_prices[i] + stgy_instance.trigger_prices[interval_name] = trigger_price + + @staticmethod + def define_intervals(stgy_instance): +<<<<<<< HEAD + stgy_instance.intervals = {"infty": interval.Interval(stgy_instance.trigger_prices['open_close'], + math.inf, + "infty", 0), + "open_close": interval.Interval(stgy_instance.trigger_prices['floor'], + stgy_instance.trigger_prices['open_close'], + "open_close", 1), + "minus_infty": interval.Interval(-math.inf, + stgy_instance.trigger_prices['floor'], + "minus_infty", 2)} +======= + stgy_instance.intervals = { + "infty": interval.Interval( + stgy_instance.target_prices["borrow_usdc_n_add_coll"], + math.inf, + "infty", + 0, + ), + } + # By reading current names and values (instead of defining the list of names and values at hand) we can + # use this method both for defining the thresholds the first time and for updating them every day + names = list(stgy_instance.target_prices.keys()) + values = list(stgy_instance.target_prices.values()) + + # We define/update thresholds + for i in range(len(stgy_instance.target_prices) - 1): + stgy_instance.intervals[names[i]] = interval.Interval( + values[i + 1], values[i], names[i], i + 1 + ) + stgy_instance.intervals["minus_infty"] = interval.Interval( + -math.inf, values[-1], "minus_infty", len(values) + ) + # print(stgy_instance.intervals.keys()) +>>>>>>> cd6cfcb... write function for getting order book and historical data + + # function to assign interval_current to each market_price in historical data + @staticmethod + def load_intervals(stgy_instance): + stgy_instance.historical_data["interval"] = [[0, 0]] * len( + stgy_instance.historical_data["close"] + ) + stgy_instance.historical_data["interval_name"] = ["nan"] * len( + stgy_instance.historical_data["close"] + ) + for loc in range(len(stgy_instance.historical_data["close"])): + market_price = stgy_instance.historical_data["close"][loc] + for i in list(stgy_instance.intervals.values()): + if i.left_border < market_price <= i.right_border: + stgy_instance.historical_data["interval"][loc] = i + stgy_instance.historical_data["interval_name"][loc] = i.name + + @staticmethod + # Checking and updating data + def update_parameters(stgy_instance, new_market_price, new_interval_current): + # AAVE + stgy_instance.aave.market_price = new_market_price + stgy_instance.aave.interval_current = new_interval_current + # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now + # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24 + stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60) + stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60) + # We have to execute track_ first because we need the fees for current collateral and debt values + stgy_instance.aave.track_lend_borrow_interest() + # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs + stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt + stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values + stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc() + + # DYDX + stgy_instance.dydx.market_price = new_market_price + stgy_instance.dydx.interval_current = new_interval_current + stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc() + stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc() + stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc() + stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc() + # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client) + + def find_scenario( + self, stgy_instance, new_market_price, new_interval_current, interval_old, index + ): + actions = self.actions_to_take( + stgy_instance, new_interval_current, interval_old + ) + self.simulate_fees(stgy_instance) + # We reset the costs in order to always start in 0 + stgy_instance.aave.costs = 0 + stgy_instance.dydx.costs = 0 + time = 0 + time_aave = 0 + time_dydx = 0 + for action in actions: + # if action == "rtrn_usdc_n_rmv_coll_dydx": + # time = stgy_instance.dydx.remove_collateral_dydx(new_market_price, new_interval_current, stgy_instance) + # stgy_instance.aave.return_usdc(new_market_price, new_interval_current, stgy_instance) + if action == "borrow_usdc_n_add_coll": + time_aave = stgy_instance.aave.borrow_usdc( + new_market_price, new_interval_current, stgy_instance + ) + market_price = stgy_instance.historical_data["close"][index + time_aave] + interval_current = stgy_instance.historical_data["interval"][ + index + time_aave + ] + time_dydx = stgy_instance.dydx.add_collateral( + market_price, interval_current, stgy_instance + ) + time_aave = 0 + elif action in stgy_instance.aave_features["methods"]: + time_aave = getattr(stgy_instance.aave, action)( + new_market_price, new_interval_current, stgy_instance + ) + elif action in stgy_instance.dydx_features["methods"]: + time_dydx = getattr(stgy_instance.dydx, action)( + new_market_price, new_interval_current, stgy_instance + ) + time += time_aave + time_dydx + # print(stgy_instance.aave_features["methods"]) + # print(stgy_instance.dydx_features["methods"]) + return time + # stgy_instance.append(action) + + @staticmethod + def actions_to_take(stgy_instance, new_interval_current, interval_old): + actions = [] + + # Case P increasing + if interval_old.is_lower(new_interval_current): +<<<<<<< HEAD + for i in reversed(range(new_interval_current.position_order, interval_old.position_order)): + if list(stgy_instance.intervals.keys())[i+1] == 'open_close': + actions.append('close_short') + else: + actions.append(list(stgy_instance.intervals.keys())[i+1]) # when P goes up we execute the name of previous intervals +======= + for i in reversed( + range(new_interval_current.position_order, interval_old.position_order) + ): + actions.append( + list(stgy_instance.intervals.keys())[i + 1] + ) # when P goes up we execute the name of previous intervals +>>>>>>> cd6cfcb... write function for getting order book and historical data + # print(list(stgy_instance.intervals.keys())[i+1]) + + # Case P decreasing + else: +<<<<<<< HEAD + for i in range(interval_old.position_order + 1, new_interval_current.position_order + 1): + if list(stgy_instance.intervals.keys())[i] == 'open_close': + actions.append('open_short') + else: + actions.append(list(stgy_instance.intervals.keys())[i]) + # print(actions) +======= + for i in range( + interval_old.position_order + 1, new_interval_current.position_order + 1 + ): + actions.append(list(stgy_instance.intervals.keys())[i]) + print(actions) +>>>>>>> cd6cfcb... write function for getting order book and historical data + return actions + + @staticmethod + def simulate_fees(stgy_instance): + # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6) + + # best case + # stgy_instance.gas_fees = 1 + + # stgy_instance.gas_fees = 3 + + # stgy_instance.gas_fees = 6 + + # worst case + stgy_instance.gas_fees = 10 + + @staticmethod + def update_pnl(stgy_instance): + stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs \ + + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees + + @staticmethod + def add_costs(stgy_instance): +<<<<<<< HEAD + stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \ + + stgy_instance.aave.costs + stgy_instance.dydx.costs +======= + stgy_instance.total_costs = ( + stgy_instance.total_costs + + stgy_instance.aave.costs + + stgy_instance.dydx.costs + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + + @staticmethod + def value_at_risk(data, method, X): # T, + # exposure = abs(stgy_instance.dydx.short_size) # we are exposed to an amount equal to the size + # window_to_use = 3 * 30 * 24 * 60 # 3 months of data + # data = stgy_instance.historical_data[-window_to_use:]['close'] + # vol benchmark: daily version of last 3month 2min vol (mean std) + if method == "parametric": + """ +<<<<<<< HEAD + 1) Normal returns assumption (deprecated): + We assume portfolio value is normally distributed. Let's mu and sigma be the drift (SMA, EMA) and std of + returns V_T/V_0. + V_T / V_0 ~ N(mu*T, sigma^2*T) --> V_T ~ V_0 * N(mu*T, sigma^2*T) = N(V_0 * mu*T, V_0^2 * sigma^2*T) + (mu*T = mu_T, sigma*T^1/2 = sigma_T, ie the value of mu and sigma expresses in the freq T) + Then, using that 95% of values under normal dist falls between 1.96 sigmas, + we can say that with a 95% confidence + |V_T| < V_0 * mu*T +- 1.96 * V_0 * sigma * T^1/2 + = V_0 * (mu*T +- 1.96 * sigma * T^1/2) + 2) Log-normal returns assumption: + We assume portfolio value is log-normally distributed. Let's mu and sigma be the drift (SMA, EMA) and std of + returns V_T/V_0. + mu*T = mu_T, sigma*T^1/2 = sigma_T + ln(V_T / V_0) ~ N((mu-sigma^2/2)*T, sigma^2*T) + --> ln V_T ~ ln V_0 + N((mu-sigma^2/2)*T, sigma^2*T) + = N(ln V_0 + (mu-sigma^2/2)*T, sigma^2*T) + Then, using that 95% of values under normal dist falls between 1.96 sigmas, + we can say that with a 95% confidence + |ln V_T| < ln V_0 +(mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2 + |V_T| < e^{ln V_0 +(mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2} + = V_0 * e^{(mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2} + ~ V_0 * (1 + (mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2) +======= + We assume portfolio value is log-normally distributed + ln(V_T / V_0) ~ N((mu-sigma^2/2)*T, sigma^2*T) --> ln V_T ~ N(ln V_0 +(mu-sigma^2/2)*T, sigma^2*T) + Then, using that 95% of values under normal dist falls between 1.96 sigmas, + we can say that with a 95% confidence + |ln V_T| < [ln V_0 +(mu-sigma^2/2)*T] +- 1.96 * sigma * T^1/2 + V_T < e^{[ln V_0 +(mu-sigma^2/2)*T] +- 1.96 * sigma * T^1/2} + +>>>>>>> cd6cfcb... write function for getting order book and historical data + In general, given a c-level X we can say the same using factor = F^-1(X) = norm.ppf(X) + """ + # 2nd case + log_returns = np.log(data) - np.log(data.shift(1)) + sigma = round(log_returns.ewm(alpha=0.8, adjust=False).std().mean(), 3) + mu = round(log_returns.ewm(alpha=0.8, adjust=False).mean().mean(), 3) + factor = round(norm.ppf(X), 3) +<<<<<<< HEAD + var = (mu-sigma**2/2) + sigma * factor + return var['close'] +======= + var = mu + sigma * factor + return var["close"] +>>>>>>> cd6cfcb... write function for getting order book and historical data + elif method == "non_parametric": + """ + We dont assume anything here. The idea will be to use past data for simulating different + today's portfolio's value by taking + change_i = price_i / price_{i-1} --> change on i-th day + simulated_price_i = today_price * change_i + --> simulated a new price assuming yesterday/today's change is equal to i-th/i-1-th's change + portf_value_i = exposure * simulated_price_i / today_price + [ = exposure * change_i ] + Then, we calculate our potential profits/losses taking +<<<<<<< HEAD + loss_i = exposure - portf_value_i + [ = exposure * (1 - simulated_price_i / today_price) + = exposure * (1 - today_price * change_i / today_price + = exposure * (1 - change_i) ] +======= + loss_i = exposure - portf_value_i + [ = exposure * (1 - simulated_price_i / today_price) + = exposure * (1 - today_price * change_i / today_price + = exposure * (1 - change_i ] +>>>>>>> cd6cfcb... write function for getting order book and historical data + i.e. we calculate the potential loss by comparing a portf value with actual exposure against + portf value with a different exposure (exposure * change_i) + That will give us a dataset of daily losses and therefore a distribution for daily losses in the value of + our portf. + We take the VaR as the X-th percentile of this dist. That will be our 1-day VaR. In order to + calculate N-day potential loss we take 1-day VaR * N^1/2. +<<<<<<< HEAD + So we will be X% confident that we will not take a loss greater than this VaR estimate if market behaviour +======= + So we will be X% confident that we wil not take a loss greater than this VaR estimate if market behaviour +>>>>>>> cd6cfcb... write function for getting order book and historical data + is according to last data. + Everywhere day can be changed by any other time freq, in our case by minutes. + We repeat this for every new price, ie for every new data-set of last data to keep an + up to date VaR estimation. + The estimate of VaR is the loss when we are at this 99th percentile point. When there are n observations + and k is an integer, the k/(n-1)-percentile is the observation ranked k + 1 of the list of losses ordered + from lowest to highest losses. + (Ex. n=501, X=99% --> 99th percentile --> k = (n-1)*0.99 = 495 --> The fifth-highest loss) + """ + changes = list(round(data.pct_change().dropna()["close"], 3)) # returns + today = data.iloc[-1]["close"] + # print(today, changes) + scenarios = [] + portf_value = [] + difference_in_portf_value = [] + difference_in_portf_value_pcg = [] + for i in range(len(changes)): + scenarios.append(today * changes[i]) + # portf_value.append(exposure*scenarios[i]/today) + # difference_in_portf_value.append(exposure - portf_value[i]) + difference_in_portf_value_pcg.append([changes[i], i]) + difference_in_portf_value_pcg.sort() + plt.hist(changes) + return difference_in_portf_value_pcg[-10:] + +<<<<<<< HEAD +if __name__ == '__main__': + pass +======= + +if __name__ == "__main__": + #######################################3 + # get historical data in seconds + import requests + from requests import Request + from datetime import datetime + import pandas as pd + import numpy as np + + # import json + # url = 'https://api.coinbase.com/v2/prices/BTC-USD/historic?2018-07-15T00:00:00-04:00' + # request = Request('GET', url) + # s = requests.Session() + # prepared = request.prepare() + # response = s.send(prepared).json()['data']['prices'] + # historical_seconds = {'prices': [], 'date': []} + # for i in range(len(response)): + # item = response[i] + # historical_seconds['prices'].append(float(item['price'])) + # historical_seconds['date'].append(datetime.strptime(item['time'], '%Y-%m-%dT%H:%M:%SZ')) + # historical_seconds = pd.DataFrame(historical_seconds['prices'], + # index=historical_seconds['date'], + # columns=['close']).iloc[::-1] + historical_daily = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv" + ) + historical_hourly = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1h-data.csv" + ) + historical_minutes = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv" + ) + # assign data to stgy instance + define index as dates + historical_data_daily = pd.DataFrame(historical_daily["close"], columns=["close"]) + historical_data_hourly = pd.DataFrame(historical_hourly["close"], columns=["close"]) + historical_data_minutes = pd.DataFrame( + historical_minutes["close"], columns=["close"] + ) + + ######################################################3 + # check historical 2min vol as benchmark to define add threshold + # manager = ParameterManager() + # N_week = 1 * 1 * 7 * 24 * 60 # 7 days + # data_for_thresholds = historical_data_minutes[:N_week].copy() # First week of data + + # log_returns_10_minutes = np.log(historical_minutes['close']) - np.log( + # historical_minutes['close'].shift(10)) + # log_returns = np.log(historical_minutes['close']) - np.log( + # historical_minutes['close'].shift(1)) + # + # # ema log returns + # ewm_log_returns = log_returns_10_minutes.ewm(alpha=0.8, adjust=False) + # + # mean_ema_log_returns_mean_value = round(ewm_log_returns.mean().mean() * 365, 3) + # mean_ema_log_returns_max_value = round(ewm_log_returns.mean().max() * 365, 3) + # mean_ema_log_returns_min_value = round(ewm_log_returns.mean().min() * 365, 3) + # std_ema_log_returns_mean_value = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) + # std_ema_log_returns_max_value = round(ewm_log_returns.std().max() * np.sqrt(365), 3) + # std_ema_log_returns_min_value = round(ewm_log_returns.std().min() * np.sqrt(365), 3) + # mu_2min_mean = round(mean_ema_log_returns_mean_value / 365 * 24 * 30, 3) + # mu_2min_max = round(mean_ema_log_returns_max_value / 365 * 24 * 30, 3) + # mu_2min_min = round(mean_ema_log_returns_min_value / 365 * 24 * 30, 3) + # sigma_2min_mean = round((std_ema_log_returns_mean_value / np.sqrt(365)), 3) + # sigma_2min_max = round((std_ema_log_returns_max_value / np.sqrt(365)), 3) + # sigma_2min_min = round((std_ema_log_returns_min_value / np.sqrt(365)), 3) + # std = ewm_log_returns.std() + # # print(std[std==std.max()]) + # # print(historical_minutes['close'][9413-10:9413+10]) + # + # print('Hist_2min_mean_vol_last_3_month + daily v:', [sigma_2min_mean, sigma_2min_mean * np.sqrt(24*30)]) + # print('Hist_2min_max_vol_last_3_month + daily v:', [sigma_2min_max, sigma_2min_max * np.sqrt(24*30)]) + # print('Hist_2min_min_vol_last_3_month + daily v:', [sigma_2min_min, sigma_2min_min * np.sqrt(24*30)]) + + ###################################################### + # check P_open / P_borrow to define ltv_0 + # N_week = 1 * 1 * 7 * 24 * 60 # 7 days + # data_for_thresholds = historical_data_minutes[:N_week].copy() # First week of data + # log_returns = np.log(data_for_thresholds['close']) - np.log( + # data_for_thresholds['close'].shift(1)) + # # ema log returns + # ewm_log_returns = log_returns.ewm(alpha=0.8, adjust=False) + # mean_ema_log_returns = round(ewm_log_returns.mean().mean() * 365, 3) + # std_ema_log_returns = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) + # + # mu = mean_ema_log_returns / 365 * 24 * 60 + # sigma = (std_ema_log_returns / np.sqrt(365)) * np.sqrt(24 * 60) + # + # factor_close_open = round(norm.ppf(0.99), 3) + # print('1+mu+factor_99 * sigma:', 1+mu+factor_close_open*sigma) + # + # top_pcg_open = 0.02 + # number_of_sigmas_open = (top_pcg_open - mu) / sigma + # confidence_for_close = norm.cdf(number_of_sigmas_open) + # + # print('f_confidence:', number_of_sigmas_open) + # print('confidence:', confidence_for_close) + + ################################################### + # Check VaR results + manager = ParameterManager() + historical_daily = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/BTCUSDC-1d-data_since_1 Jan 2021.csv" + )[-500:] + # assign data to stgy instance + define index as dates + historical_data_daily = pd.DataFrame(historical_daily["close"], columns=["close"]) + data = historical_data_daily + print("VaR_99 Parametric:", manager.value_at_risk(data, "parametric", 0.99)) + print("VaR_99 historical:", manager.value_at_risk(data, "non_parametric", 0.99)) + print(historical_daily["timestamp"][319]) + plt.show() + + ################################################## + # Plot + # axs.axhline(y=p_rtrn_usdc_n_rmv_coll_dydx, color='black', linestyle='--', + # label='rtrn_usdc_n_rmv_coll_dydx') + # axs.axhline(y=p_borrow_usdc_n_add_coll, color='darkgoldenrod', linestyle='--', label='borrow_usdc_n_add_coll') + # axs.axhline(y=p_close_short, color='olive', linestyle='--', label='close_short') + # axs.axhline(y=p_close_short_pcg, color='darkgoldenrod', linestyle='--', label='close_short_pcg') + # axs.axhline(y=p_open_short, color='darkred', linestyle='--', label='open_short') + # axs.axhline(y=p_open_short_pcg, color='black', linestyle='--', label='open_short_pcg') + # axs.axhline(y=floor, color='red', linestyle='--', label='floor') + # axs.grid() + # axs.legend(loc='lower left') + # plt.show() +>>>>>>> cd6cfcb... write function for getting order book and historical data diff --git a/hedge_scripts/stgyapp.py b/hedge_scripts/stgyapp.py new file mode 100644 index 0000000..1a57417 --- /dev/null +++ b/hedge_scripts/stgyapp.py @@ -0,0 +1,399 @@ +import json +import pandas as pd +import math + +import aave +import dydx +import binance_client_ +import dydx_client +import sm_interactor +import volatility_calculator +import data_dumper +import parameter_manager +import interval + + +class StgyApp(object): + def __init__(self, config): + + self.stk = config["stk"] + self.total_costs_from_aave_n_dydx = 0 + self.total_pnl = 0 + self.gas_fees = 0 + + # prices and intervals + self.trigger_prices = {} + self.intervals = {} + + # clients for data + self.binance_client = binance_client_.BinanceClient(config["binance_client"]) + self.dydx_client = dydx_client.DydxClient(config["dydx_client"]) + self.sm_interactor = sm_interactor.SmInteractor(config["sm_interactor"]) + # self.historical_data = + + # We create attributes to fill later + self.aave = None + self.aave_features = None + self.aave_historical_data = None + self.aave_rates = None + self.aave_df = None + + self.dydx = None + self.dydx_features = None + self.dydx_historical_data = None + self.dydx_df = None + + self.volatility_calculator = None + + self.parameter_manager = parameter_manager.ParameterManager() + + self.historical_data = None + + self.data_dumper = data_dumper.DataDamperNPlotter() + + def launch(self, config): + # self.call_binance_data_loader() + self.initialize_aave(config["initial_parameters"]["aave"]) + self.initialize_dydx(config["initial_parameters"]["dydx"]) + self.call_dydx_client() + self.call_sm_interactor() + # self.initialize_volatility_calculator() + # floor = 1300 + # self.define_target_prices(floor) + # self.define_intervals() + + # def run_simulations(self): + # interval_old = self.intervals["infty"] + # for i in range(1, len(self.historical_data["close"]) - 1): + # new_interval_previous = self.historical_data["interval"][i - 1] + # new_interval_current = self.historical_data["interval"][i] + # new_market_price = self.historical_data["close"][i] + # # We could pass the whole AAVE_historical_df, DyDx_historical_df as parameters for scenarios if necessary + # self.find_scenario(new_market_price, new_interval_current, interval_old) + # if new_interval_previous != new_interval_current: + # interval_old = new_interval_previous + + # call clients functions +<<<<<<< HEAD + def get_historical_data(self, symbol, freq, + initial_date, save): + eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq, + initial_date=initial_date, save=save) +======= + def call_binance_data_loader(self, symbol, freq, initial_date, save): + eth_historical = self.binance_client.get_all_binance( + symbol=symbol, freq=freq, initial_date=initial_date, save=save + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + # self.historical_data = eth_historical + self.historical_data = eth_historical["close"] + for i in range(len(self.historical_data)): + self.historical_data[i] = float(self.historical_data[i]) + # self.load_intervals() + + def call_dydx_client(self): + self.dydx_client.get_dydx_parameters(self.dydx) + + def call_sm_interactor(self): + self.aave_rates = self.sm_interactor.get_rates() + + # initialize classes + def initialize_aave(self, config): + # We initialize aave and dydx classes instances + self.aave = aave.Aave(config) + # We load methods and attributes for aave and dydx to use later + self.aave_features = { + "methods": [ + func + for func in dir(self.aave) + if (callable(getattr(self.aave, func))) & (not func.startswith("__")) + ], + "attributes": { + "values": list(self.aave.__dict__.values()), + "keys": list(self.aave.__dict__.keys()), + }, + } + # We create an attribute for historical data + self.aave_historical_data = [] + + def initialize_dydx(self, config): + self.dydx = dydx.Dydx(config) + self.dydx_features = { + "methods": [ + func + for func in dir(self.dydx) + if (callable(getattr(self.dydx, func))) & (not func.startswith("__")) + ], + "attributes": { + "values": list(self.dydx.__dict__.values()), + "keys": list(self.dydx.__dict__.keys()), + }, + } + self.dydx_historical_data = [] + + def initialize_volatility_calculator(self): + self.volatility_calculator = volatility_calculator.VolatilityCalculator() + + +if __name__ == "__main__": + # load configurations + with open( + "/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json" + ) as json_file: + config = json.load(json_file) + + # Initialize stgyApp + stgy = StgyApp(config) + + # Track historical data + # symbol = 'ETHUSDC' + # freq = '1m' + # initial_date = "1 Jan 2019" + # stgy.get_historical_data(symbol=symbol, freq=freq, + # initial_date=initial_date, save=True) + + # Load historical data if previously tracked and saved +<<<<<<< HEAD + historical_data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv")[-1000:] +======= + historical_data = pd.read_csv( + "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv" + )[-30000:] +>>>>>>> cd6cfcb... write function for getting order book and historical data + # # assign data to stgy instance + define index as dates + stgy.historical_data = pd.DataFrame(historical_data["close"], columns=["close"]) + timestamp = pd.to_datetime(historical_data["timestamp"]) + stgy.historical_data.index = timestamp + # + # ####################################################### + # # Simulations + + # Define trigger prices and thresholds + slippage = max(stgy.historical_data.pct_change().dropna()['close']) + # Define floor +<<<<<<< HEAD + floor = 1558 / (1+slippage) + print([round(slippage, 3), round(1+slippage, 3), floor]) + ######################### + stgy.parameter_manager.define_target_prices(stgy, slippage, floor) +======= + floor = stgy.historical_data["close"].max() * 0.8 + ######################### + # Define trigger prices and thresholds + N_week = 1 * 1 * 7 * 24 * 60 # 7 days + data_for_thresholds = stgy.historical_data[:N_week].copy() # First week of data + stgy.parameter_manager.define_target_prices( + stgy, N_week, data_for_thresholds, floor + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + stgy.parameter_manager.define_intervals(stgy) + stgy.parameter_manager.load_intervals(stgy) + ######################### + # Save historical data with trigger prices and thresholds loaded + stgy.historical_data.to_csv("/home/agustin/Git-Repos/HedgingScripts/files/stgy.historical_data.csv") + ######################### + # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations + + # Define initial and final index if needed in order to only run simulations in periods of several trigger prices + # As we calculate vol using first week of data, we initialize simulations from that week on + initial_index = 28 + stgy.launch(config) + + # Stk eth + stgy.stk = 500000/stgy.historical_data['close'][initial_index] + + # AAVE +<<<<<<< HEAD + stgy.aave.market_price = stgy.historical_data['close'][initial_index] + stgy.aave.interval_current = stgy.historical_data['interval'][initial_index] + + # What is the price at which we place the collateral in AAVE given our initial_index? + stgy.aave.entry_price = stgy.aave.market_price + # We place 90% of staked as collateral and save 10% as a reserve margin +======= + stgy.aave.market_price = stgy.historical_data["close"][initial_index] + stgy.aave.interval_current = stgy.historical_data["interval"][initial_index] + stgy.aave.entry_price = stgy.target_prices["open_close"] +>>>>>>> cd6cfcb... write function for getting order book and historical data + stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3) + stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3) + stgy.reserve_margin_eth = stgy.stk * 0.1 + # We calculate collateral and reserve current value + stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price + stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price + + # What is the usdc_status for our initial_index? + stgy.aave.usdc_status = True +<<<<<<< HEAD + stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage + stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage + # debt_initial + stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3) +======= + stgy.aave.debt = ( + stgy.aave.collateral_eth_initial + * stgy.target_prices["open_close"] + * stgy.aave.borrowed_percentage + ) + # debt_initial + stgy.aave.price_to_ltv_limit = round( + stgy.aave.entry_price * stgy.aave.borrowed_percentage / 0.5, 3 + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + # stgy.total_costs = 104 + + # DyDx + stgy.dydx.market_price = stgy.historical_data["close"][initial_index] + stgy.dydx.interval_current = stgy.historical_data["interval"][initial_index] + stgy.dydx.collateral = stgy.aave.debt + stgy.dydx.equity = stgy.dydx.equity_calc() + stgy.dydx.collateral_status = True + ######################### + # Change or define prices that aren't defined yet if the period of simulations involves those prices + # For ex if we are executing periods of time in which ltv_limit or repay_aave are already defined + + # price_floor = stgy.intervals['open_close'].left_border + previous_position_order = stgy.intervals["open_close"].position_order + stgy.intervals["floor"] = interval.Interval( + stgy.aave.price_to_ltv_limit, floor, "floor", previous_position_order + 1 + ) + stgy.intervals["minus_infty"] = interval.Interval( + -math.inf, + stgy.aave.price_to_ltv_limit, + "minus_infty", + previous_position_order + 2, + ) + + ######################### + # Load interval_old + interval_old = stgy.intervals["infty"] + ######################### + # Clear previous csv data for aave and dydx + stgy.data_dumper.delete_results() + ######################### + # add header to csv of aave and dydx + stgy.data_dumper.add_header() + ######################### +<<<<<<< HEAD + # import time + # # run simulations + # starttime = time.time() + # print('starttime:', starttime) + # for i in range(initial_index, len(stgy.historical_data)): + i = initial_index + + + while(i < len(stgy.historical_data)): + # for i in range(initial_index, len(stgy.historical_data)): +======= + import time + + # run simulations + starttime = time.time() + print("starttime:", starttime) + # for i in range(initial_index, len(stgy.historical_data)): + i = initial_index + while i < len(stgy.historical_data): + # for i in range(initial_index, len(stgy.historical_data)): +>>>>>>> cd6cfcb... write function for getting order book and historical data + # pass + new_interval_previous = stgy.historical_data["interval"][i - 1] + new_interval_current = stgy.historical_data["interval"][i] + new_market_price = stgy.historical_data["close"][i] + ######################### + # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late + # therefore not taking the actions needed as soon as they are needed + if new_interval_previous != new_interval_current: + interval_old = new_interval_previous + ######################### + # Update parameters + # First we update everything in order to execute scenarios with updated values +<<<<<<< HEAD + # We have to update + # AAVE: market_price, interval_current, lending and borrowing fees (and the diference), + # debt value, collateral value and ltv value + # DyDx: market_price, interval_current, notional, equity, leverage and pnl + stgy.parameter_manager.update_parameters(stgy, new_market_price, new_interval_current) + + # Here we identify price movent direction by comparing current interval and old interval + # and we also execute all the actions involved since last price was read + time_used = stgy.parameter_manager.find_scenario(stgy, new_market_price, new_interval_current, interval_old, i) +======= + stgy.parameter_manager.update_parameters( + stgy, new_market_price, new_interval_current + ) + time_used = stgy.parameter_manager.find_scenario( + stgy, new_market_price, new_interval_current, interval_old, i + ) +>>>>>>> cd6cfcb... write function for getting order book and historical data + ######################### + # Funding rates + # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency) + # Moreover, we need to call this method after find_scenarios in order to have all costs updated. + # Calling it before find_scenarios will overwrite the funding by 0 + # We have to check all the indexes between old index i and next index i+time_used +<<<<<<< HEAD + # for index in range(i, i+time_used): + if (i - initial_index) % (8 * 60) == 0: + stgy.dydx.add_funding_rates() + # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates +======= + for index in range(i, i + time_used): + if (index - initial_index) % (8 * 60) == 0: + stgy.dydx.add_funding_rates() + # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates +>>>>>>> cd6cfcb... write function for getting order book and historical data + ######################### + # Add costs + stgy.parameter_manager.add_costs(stgy) + stgy.parameter_manager.update_pnl(stgy) + ######################### + # Write data + # We write the data into the google sheet or csv file acording to sheet value + # (sheet = True --> sheet, sheet = False --> csv) + stgy.data_dumper.write_data( + stgy, new_interval_previous, interval_old, i, sheet=False + ) + ######################### + # Update trigger prices and thresholds + # We update trigger prices and thresholds every day +<<<<<<< HEAD + # if (i+time_used - initial_index) % (1*24*60) == 0: + # # We call the paramater_manager instance with updated data + # stgy.parameter_manager.define_target_prices(stgy, N_week, data_for_thresholds, floor) + # stgy.parameter_manager.define_intervals(stgy) + # stgy.parameter_manager.load_intervals(stgy) + # save = True + # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period) + + # we increment index by the time consumed in executing actions + # i += time_used + i += 1 + # endtime = time.time() + # print('endtime:', endtime) + import matplotlib.pyplot as plt + fig, axs = plt.subplots(1, 1, figsize=(21, 7)) + axs.plot(stgy.historical_data['close'], color='tab:blue', label='market price') + axs.axhline(y=stgy.trigger_prices['floor'], color='darkgoldenrod', linestyle='--', label='floor') + axs.axhline(y=stgy.trigger_prices['open_close'], color='red', linestyle='--', label='open_close') + axs.grid() + axs.legend(loc='lower left') + plt.show() +======= + if (i + time_used - initial_index) % (1 * 24 * 60) == 0: + # We call the paramater_manager instance with updated data + data_for_thresholds = stgy.historical_data[:i].copy() + stgy.parameter_manager.define_target_prices( + stgy, N_week, data_for_thresholds, floor + ) + stgy.parameter_manager.define_intervals(stgy) + stgy.parameter_manager.load_intervals(stgy) + save = True + # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period) + + # we increment index by the time consumed in executing actions + i += time_used + + endtime = time.time() + print("endtime:", endtime) +>>>>>>> cd6cfcb... write function for getting order book and historical data From c18d748eb5b2d10da53cf58ee5d6d9765f21fa4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Thu, 6 Oct 2022 10:38:13 -0300 Subject: [PATCH 03/16] long_short strategy + up to date jupyters --- hedge_scripts/{ => Long_short}/aave.py | 169 +- hedge_scripts/Long_short/command_center.py | 137 + hedge_scripts/Long_short/data_dumper.py | 134 + hedge_scripts/Long_short/dydx.py | 178 ++ hedge_scripts/Long_short/parameter_manager.py | 117 + hedge_scripts/Long_short/stgyapp.py | 76 + hedge_scripts/Short_only/aave.py | 201 ++ .../{ => Short_only}/binance_client_.py | 109 +- .../{ => Short_only}/checking_var.py | 2 +- hedge_scripts/Short_only/command_center.py | 235 ++ hedge_scripts/Short_only/data_dumper.py | 152 ++ hedge_scripts/Short_only/dydx.py | 173 ++ hedge_scripts/{ => Short_only}/dydx_client.py | 2 +- hedge_scripts/{ => Short_only}/interval.py | 0 .../{ => Short_only}/metrics_calculator.py | 3 +- hedge_scripts/Short_only/parameter_manager.py | 220 ++ hedge_scripts/{ => Short_only}/plot.html | 0 .../{ => Short_only}/sm_interactor.py | 0 hedge_scripts/Short_only/stgyapp.py | 76 + .../{ => Short_only}/volatility_calculator.py | 0 hedge_scripts/data_dumper.py | 394 --- hedge_scripts/dydx.py | 255 -- hedge_scripts/parameter_manager.py | 522 ---- hedge_scripts/stgyapp.py | 399 --- jupyter-lab/Long_Short_Simulations.ipynb | 2199 +++++++++++++++++ jupyter-lab/Simulations_lab.ipynb | 886 +++++-- 26 files changed, 4664 insertions(+), 1975 deletions(-) rename hedge_scripts/{ => Long_short}/aave.py (52%) create mode 100644 hedge_scripts/Long_short/command_center.py create mode 100644 hedge_scripts/Long_short/data_dumper.py create mode 100644 hedge_scripts/Long_short/dydx.py create mode 100644 hedge_scripts/Long_short/parameter_manager.py create mode 100644 hedge_scripts/Long_short/stgyapp.py create mode 100644 hedge_scripts/Short_only/aave.py rename hedge_scripts/{ => Short_only}/binance_client_.py (50%) rename hedge_scripts/{ => Short_only}/checking_var.py (98%) create mode 100644 hedge_scripts/Short_only/command_center.py create mode 100644 hedge_scripts/Short_only/data_dumper.py create mode 100644 hedge_scripts/Short_only/dydx.py rename hedge_scripts/{ => Short_only}/dydx_client.py (97%) rename hedge_scripts/{ => Short_only}/interval.py (100%) rename hedge_scripts/{ => Short_only}/metrics_calculator.py (94%) create mode 100644 hedge_scripts/Short_only/parameter_manager.py rename hedge_scripts/{ => Short_only}/plot.html (100%) rename hedge_scripts/{ => Short_only}/sm_interactor.py (100%) create mode 100644 hedge_scripts/Short_only/stgyapp.py rename hedge_scripts/{ => Short_only}/volatility_calculator.py (100%) delete mode 100644 hedge_scripts/data_dumper.py delete mode 100644 hedge_scripts/dydx.py delete mode 100644 hedge_scripts/parameter_manager.py delete mode 100644 hedge_scripts/stgyapp.py create mode 100644 jupyter-lab/Long_Short_Simulations.ipynb diff --git a/hedge_scripts/aave.py b/hedge_scripts/Long_short/aave.py similarity index 52% rename from hedge_scripts/aave.py rename to hedge_scripts/Long_short/aave.py index d1b4f1d..d501aa5 100644 --- a/hedge_scripts/aave.py +++ b/hedge_scripts/Long_short/aave.py @@ -1,47 +1,39 @@ -import math -import random -import numpy as np -from hedge_scripts import interval - -# import time - - class Aave(object): + def __init__(self, config): # assert self.dydx_class_instance == isinstance(dydx) # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg'] - self.market_price = config["market_price"] - self.interval_current = config["interval_current"] + self.market_price = config['market_price'] - self.entry_price = config["entry_price"] + self.entry_price = config['entry_price'] - self.collateral_eth_initial = config["collateral_eth"] - self.collateral_eth = config["collateral_eth"] - self.collateral_usdc = config["collateral_usdc"] + self.collateral_eth_initial = config['collateral_eth'] + self.collateral_eth = config['collateral_eth'] + self.collateral_usdc = config['collateral_usdc'] self.reserve_margin_eth = 0 self.reserve_margin_usdc = 0 - self.borrowed_percentage = config["borrowed_pcg"] - self.usdc_status = config["usdc_status"] + self.borrowed_percentage = config['borrowed_pcg'] + self.usdc_status = config['usdc_status'] - self.debt = config["debt"] - self.debt_initial = config["debt"] + self.debt = config['debt'] + self.debt_initial = config['debt'] - self.ltv = config["ltv"] - self.price_to_ltv_limit = config["price_to_ltv_limit"] + self.ltv = config['ltv'] + self.price_to_ltv_limit = config['price_to_ltv_limit'] self.lending_rate = 0 self.lending_rate_hourly = 0 self.interest_on_lending_eth = 0 # aggregated fees self.interest_on_lending_usd = 0 - self.lending_fees_eth = 0 # fees between last 2 prices + self.lending_fees_eth = 0 # fees between last 2 prices self.lending_fees_usd = 0 self.borrowing_rate = 0 self.borrowing_rate_hourly = 0 - self.interest_on_borrowing = 0 # aggregated fees - self.borrowing_fees = 0 # fees between last 2 prices + self.interest_on_borrowing = 0 # aggregated fees + self.borrowing_fees = 0 # fees between last 2 prices self.lend_minus_borrow_interest = 0 @@ -78,9 +70,7 @@ def track_lend_borrow_interest(self): it requires having called borrowing_fees_calc() and lending_fees_calc() in order to use updated values of last earned fees """ - self.lend_minus_borrow_interest = ( - self.interest_on_lending_usd - self.interest_on_borrowing - ) + self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing def lending_fees_calc(self, freq): self.simulate_lending_rate() @@ -90,30 +80,16 @@ def lending_fees_calc(self, freq): # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq self.lending_fees_usd = self.lending_fees_eth * self.market_price - self.interest_on_lending_eth = ( - self.interest_on_lending_eth + self.lending_fees_eth - ) - self.interest_on_lending_usd = ( - self.interest_on_lending_usd + self.lending_fees_usd - ) + self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth + self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd def borrowing_fees_calc(self, freq): self.simulate_borrowing_rate() -<<<<<<< HEAD self.borrowing_rate_freq = self.borrowing_rate / freq # fees from borrow are added to debt? YES # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE self.borrowing_fees = self.debt * self.borrowing_rate_freq -======= - self.borrowing_rate_hourly = self.borrowing_rate / freq - self.borrowing_fees = ( - self.collateral_eth - * self.entry_price - * self.borrowed_percentage - * self.borrowing_rate_hourly - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees def simulate_lending_rate(self): @@ -132,7 +108,7 @@ def simulate_borrowing_rate(self): # self.borrowing_rate = 1.5/100 # worst case - self.borrowing_rate = 2.5 / 100 + self.borrowing_rate = 2.5/100 def ltv_calc(self): if self.collateral_usd() == 0: @@ -141,11 +117,8 @@ def ltv_calc(self): return self.debt / self.collateral_usd() def price_to_liquidation(self, dydx_class_instance): - return ( - self.entry_price - - (dydx_class_instance.pnl() + self.debt - self.lend_minus_borrow_interest) - / self.collateral_eth - ) + return self.entry_price - (dydx_class_instance.short_pnl() + + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth def price_to_ltv_limit_calc(self): return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3) @@ -157,7 +130,7 @@ def ltv_limit(self): return 0.5 # Actions to take - def return_usdc(self, new_market_price, new_interval_current, stgy_instance): + def return_usdc(self, stgy_instance): gas_fees = stgy_instance.gas_fees time = 0 if self.usdc_status: @@ -179,66 +152,7 @@ def return_usdc(self, new_market_price, new_interval_current, stgy_instance): time = 1 return time - def borrow_usdc(self, new_market_price, new_interval_current, stgy_instance): - gas_fees = stgy_instance.gas_fees - intervals = stgy_instance.intervals - time = 0 - if not self.usdc_status: - # AAVE parameters - # update parameters - self.usdc_status = True - self.entry_price = self.market_price -<<<<<<< HEAD - self.debt = self.collateral_eth_initial * self.borrowed_percentage * stgy_instance.trigger_prices['open_close'] -======= - self.debt = ( - self.collateral_eth_initial - * self.borrowed_percentage - * stgy_instance.target_prices["open_close"] - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - self.debt_initial = self.debt - self.ltv = self.ltv_calc() - - # ltv_limit = 0.85 - # vol = stgy_instance.historical_data['vol'] - # benchmark_vol = 0.05 - # for i in range(5): - # if i*benchmark_vol < vol <= (i+1)*benchmark_vol: - # ltv_limit = 0.85 * 1/(i+1) = debt / coll(t) = debt / p_eth*coll = debt/p_eth_-1 * vol * coll - self.price_to_ltv_limit = ( - self.price_to_ltv_limit_calc() - ) # We have to define the criteria for this price - # self.lending_rate = 0 - # self.borrowing_rate = 0 - - # fees - self.costs = self.costs + gas_fees - - price_floor = intervals["open_close"].left_border - previous_position_order = intervals["open_close"].position_order - intervals["floor"] = interval.Interval( - self.price_to_ltv_limit, - price_floor, - "floor", - previous_position_order + 1, - ) - intervals["minus_infty"] = interval.Interval( - -math.inf, - self.price_to_ltv_limit, - "minus_infty", - previous_position_order + 2, - ) - # simulate 2min delay for tx - time = 1 - return time - -<<<<<<< HEAD - def repay_aave(self, - stgy_instance): -======= - def repay_aave(self, new_market_price, new_interval_current, stgy_instance): ->>>>>>> cd6cfcb... write function for getting order book and historical data + def repay_aave(self, stgy_instance): gas_fees = stgy_instance.gas_fees dydx_class_instance = stgy_instance.dydx # aave_class_instance = stgy_instance.aave @@ -247,36 +161,23 @@ def repay_aave(self, new_market_price, new_interval_current, stgy_instance): time = 0 if self.usdc_status: # update parameters - short_size_for_debt = self.debt / ( - self.market_price - dydx_class_instance.entry_price - ) + short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.short_entry_price) new_short_size = dydx_class_instance.short_size - short_size_for_debt # pnl_for_debt = dydx_class_instance.pnl() # We have to repeat the calculations for pnl and notional methods, but using different size_eth -<<<<<<< HEAD - pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price) -======= - pnl_for_debt = short_size_for_debt * ( - new_market_price - dydx_class_instance.entry_price - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data + pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.short_entry_price) self.debt = self.debt - pnl_for_debt self.ltv = self.ltv_calc() - self.price_to_ltv_limit = round( - self.entry_price - * (self.debt / self.collateral_usdc) - / self.ltv_limit(), - 3, - ) + self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3) self.costs = self.costs + gas_fees dydx_class_instance.short_size = new_short_size - dydx_class_instance.notional = dydx_class_instance.notional_calc() - dydx_class_instance.equity = dydx_class_instance.equity_calc() - dydx_class_instance.leverage = dydx_class_instance.leverage_calc() - dydx_class_instance.pnl = dydx_class_instance.pnl_calc() + dydx_class_instance.short_notional = dydx_class_instance.short_notional_calc() + dydx_class_instance.short_equity = dydx_class_instance.short_equity_calc() + dydx_class_instance.short_leverage = dydx_class_instance.short_leverage_calc() + dydx_class_instance.short_pnl = dydx_class_instance.short_pnl_calc() # dydx_class_instance.price_to_liquidation = \ # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance) @@ -284,11 +185,9 @@ def repay_aave(self, new_market_price, new_interval_current, stgy_instance): # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees dydx_class_instance.simulate_maker_taker_fees() notional_for_fees = abs(short_size_for_debt) * self.market_price - dydx_class_instance.costs = ( - dydx_class_instance.costs - + dydx_class_instance.maker_taker_fees * notional_for_fees - + pnl_for_debt * dydx_class_instance.withdrawal_fees - ) + dydx_class_instance.short_costs = dydx_class_instance.short_costs \ + + dydx_class_instance.maker_taker_fees * notional_for_fees \ + + pnl_for_debt * dydx_class_instance.withdrawal_fees # Note that a negative self.debt is actually a profit # We update the parameters @@ -298,4 +197,4 @@ def repay_aave(self, new_market_price, new_interval_current, stgy_instance): self.usdc_status = False # simulate 2min delay for tx time = 1 - return time + return time \ No newline at end of file diff --git a/hedge_scripts/Long_short/command_center.py b/hedge_scripts/Long_short/command_center.py new file mode 100644 index 0000000..6617b83 --- /dev/null +++ b/hedge_scripts/Long_short/command_center.py @@ -0,0 +1,137 @@ +import os +import json + + +from hedge_scripts.Short_only.stgyapp import StgyApp + + +def run_sim(period, slippage, floor, pcg): + global ocs + # Initialize everything + with open("Files/StgyApp_config.json") as json_file: + config = json.load(json_file) + + # Initialize stgyApp + stgy = StgyApp(config) + # Period of Simulations + # period = ["2019-09-01","2019-12-31"] + stgy.historical_data = historical_data.loc[period[0] + ' 00:00:00':period[1] + ' 00:00:00'] + # For vol updates we take all data up to the last date + stgy.launch(config) + # Load target_prices + intervals in stgy.historical_data + # First we calculate weighted vol + last_date = period[1] + ' 00:00:00' + vol = stgy.parameter_manager.calc_vol(last_date, historical_data) + mu, sigma = vol + # floor just in order to get triger_price['open_close_1'] = open_close_1 + # Now we define prices and intervals given K and vol + stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, pcg) + ######################### + # Save historical data with trigger prices and thresholds loaded + # checking if the directory demo_folder + # exist or not. + if not os.path.exists("Files/From_%s_to_%s_open_close_at_%s" % (period[0], period[1], floor)): + # if the demo_folder directory is not present + # then create it. + os.makedirs("Files/From_%s_to_%s_open_close_at_%s" % (period[0], period[1], floor)) + stgy.historical_data.to_csv("Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv" + % (period[0], period[1], floor)) + ######################### + # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations + + # Define initial and final index if needed in order to only run simulations in periods of several trigger prices + # As we calculate vol using first week of data, we initialize simulations from that week on + initial_index = 1 + + # Stk eth + stgy.stk = 1000000 / stgy.historical_data['close'][initial_index] + + # AAVE + stgy.aave.market_price = stgy.historical_data['close'][initial_index] + + # What is the price at which we place the collateral in AAVE given our initial_index? + stgy.aave.entry_price = stgy.aave.market_price + # We place 90% of staked as collateral and save 10% as a reserve margin + stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3) + stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3) + stgy.reserve_margin_eth = stgy.stk * 0.1 + # We calculate collateral and reserve current value + stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price + stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price + + # What is the usdc_status for our initial_index? + stgy.aave.usdc_status = True + stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage + stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage + # debt_initial + stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), + 3) + # stgy.total_costs = 104 + + # DyDx + stgy.dydx.market_price = stgy.historical_data['close'][initial_index] + # stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index] + stgy.dydx.short_collateral = stgy.aave.debt + stgy.dydx.short_equity = stgy.dydx.short_equity_calc() + stgy.dydx.short_collateral_status = True + ######################### + # Clear previous csv data for aave and dydx + stgy.data_dumper.delete_results(period, floor) + ######################### + # add header to csv of aave and dydx + stgy.data_dumper.add_header(period, floor) + ################################## + # Run through dataset + ######################### + # import time + # # run simulations + # starttime = time.time() + # print('starttime:', starttime) + # for i in range(initial_index, len(stgy.historical_data)): + i = initial_index + + maker_fees_counter = [] + while (i < len(stgy.historical_data)): + # for i in range(initial_index, len(stgy.historical_data)): + # pass + + # We reset costs in every instance + stgy.parameter_manager.reset_costs(stgy) + previous_market_price = stgy.historical_data["close"][i-1] + market_price = stgy.historical_data["close"][i] + ######################### + # Update parameters + # First we update everything in order to execute scenarios with updated values + # We have to update + # AAVE: market_price, interval_current, lending and borrowing fees (and the diference), + # debt value, collateral value and ltv value + # DyDx: market_price, interval_current, notional, equity, leverage and pnl + stgy.parameter_manager.update_parameters(stgy, market_price) + ############################## + stgy.parameter_manager.find_scenario(stgy, market_price, previous_market_price) + ############################## + # Funding rates + # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency) + # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated. + # Calling it before find_scenarios will overwrite the funding by 0 + # We have to check all the indexes between old index i and next index i+time_used + # for index in range(i, i+time_used): + if (i % (8 * 60) == 0) and (stgy.dydx.short_status): + stgy.dydx.add_funding_rates() + # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates + ######################### + # Add costs + stgy.parameter_manager.add_costs(stgy) + stgy.parameter_manager.update_pnl(stgy) + ######################### + # Write data + # We write the data into the google sheet or csv file acording to sheet value + # (sheet = True --> sheet, sheet = False --> csv) + stgy.data_dumper.write_data(stgy, + period, floor, + sheet=False) + ######################### + # we increment index by the time consumed in executing actions + # i += time_used + i += 1 + return maker_fees_counter \ No newline at end of file diff --git a/hedge_scripts/Long_short/data_dumper.py b/hedge_scripts/Long_short/data_dumper.py new file mode 100644 index 0000000..2ee56d5 --- /dev/null +++ b/hedge_scripts/Long_short/data_dumper.py @@ -0,0 +1,134 @@ +import csv +import os + +import pygsheets + +from hedge_scripts.Short_only.interval import Interval + + +class DataDamperNPlotter: + def __init__(self): + self.historical_data = None + + @staticmethod + def write_data(stgy_instance, + period, floor, + sheet=False): + aave_instance = stgy_instance.aave + dydx_instance = stgy_instance.dydx + data_aave = [] + data_dydx = [] + aave_wanted_keys = [ + "market_price", + # "interval_current", + "entry_price", + "collateral_eth", + "usdc_status", + "debt", + "ltv", + "lending_rate", + "interest_on_lending_usd", + "borrowing_rate", + "interest_on_borrowing", + "lend_minus_borrow_interest", + "costs"] + + for i in range(len(aave_instance.__dict__.values())): + if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys: + data_aave.append(str(list(aave_instance.__dict__.values())[i])) + for i in range(len(dydx_instance.__dict__.values())): + data_dydx.append(str(list(dydx_instance.__dict__.values())[i])) + # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker + data_aave.append(stgy_instance.gas_fees) + data_aave.append(stgy_instance.total_costs_from_aave_n_dydx) + data_aave.append(stgy_instance.total_pnl) + + data_dydx.append(stgy_instance.gas_fees) + data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx) + data_dydx.append(stgy_instance.total_pnl) + if sheet == True: + gc = pygsheets.authorize(service_file= + 'stgy-1-simulations-e0ee0453ddf8.json') + sh = gc.open('aave/dydx simulations') + sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False) + sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False) + else: + path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % ( + period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close'])) + path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % ( + period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close'])) + with open(path_to_aave, 'a') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(data_aave) + with open(path_to_dydx, 'a', + newline='', encoding='utf-8') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(data_dydx) + + @staticmethod + def delete_results(period, floor): + file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % ( + period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close'])) + file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % ( + period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close'])) + if (os.path.exists(file_aave) and os.path.isfile(file_aave)): + os.remove(file_aave) + if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)): + os.remove(file_dydx) + + @staticmethod + def add_header(period, floor): + aave_headers = [ + "market_price", + "entry_price", + "collateral_eth", + "usdc_status", + "debt", + "ltv", + "lending_rate", + "interest_on_lending_usd", + "borrowing_rate", + "interest_on_borrowing", + "lend_minus_borrow_interest", + "costs", + "gas_fees", + "total_costs_from_aave_n_dydx", + "total_stgy_pnl"] + dydx_headers = [ + "market_price", + "short_entry_price", + "short_size", + "short_collateral", + "short_notional", + "short_equity", + "short_leverage", + "short_pnl", + "short_collateral_status", + "short_status", + "short_costs", + "long_entry_price", + "long_size", + "long_notional", + "long_pnl", + "long_status", + "long_costs", + "order_status", + "withdrawal_fees", + "funding_rates", + "maker_taker_fees", + "maker_fees_counter", + "gas_fees", + "total_costs_from_aave_n_dydx", + "total_stgy_pnl"] + + path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % ( + period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close'])) + path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % ( + period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close'])) + with open(path_to_aave, 'a') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(aave_headers) + with open(path_to_dydx, 'a', + newline='', encoding='utf-8') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(dydx_headers) \ No newline at end of file diff --git a/hedge_scripts/Long_short/dydx.py b/hedge_scripts/Long_short/dydx.py new file mode 100644 index 0000000..2cdf4ad --- /dev/null +++ b/hedge_scripts/Long_short/dydx.py @@ -0,0 +1,178 @@ +class Dydx(object): + + def __init__(self, config): + # assert aave_class == isinstance(aave) + self.market_price = config['market_price'] + + # Short attributes + self.short_entry_price = config['entry_price'] + self.short_size = config['short_size'] + self.short_collateral = config['collateral'] + self.short_notional = config['notional'] + self.short_equity = config['equity'] + self.short_leverage = config['leverage'] + self.short_pnl = config['pnl'] + self.short_collateral_status = config['collateral_status'] + self.short_status = config['short_status'] + self.short_costs = 0 + + # Long attributes + self.long_entry_price = config['entry_price'] + self.long_size = config['short_size'] + self.long_notional = config['notional'] + # self.long_equity = config['equity'] + # self.long_leverage = config['leverage'] + self.long_pnl = config['pnl'] + self.long_status = config['short_status'] + self.long_costs = 0 + + self.order_status = True + self.withdrawal_fees = 0.01 / 100 + self.funding_rates = 0 + self.maker_taker_fees = 0 + self.maker_fees_counter = 0 + + + # auxiliary functions + # Short methods + def short_pnl_calc(self): + return self.short_size * (self.market_price - self.short_entry_price) + + def short_notional_calc(self): + return abs(self.short_size) * self.market_price + + def short_equity_calc(self): + return self.short_collateral + self.short_pnl_calc() + + def short_leverage_calc(self): + if self.short_equity_calc() == 0: + return 0 + else: + return self.short_notional_calc() / self.short_equity_calc() + + # Long methods + def long_pnl_calc(self): + return self.long_size * (self.market_price - self.long_entry_price) + + def long_notional_calc(self): + return abs(self.long_size) * self.market_price + + def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance): + return self.short_entry_price \ + + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size + + @staticmethod + def price_to_liquidation_calc(dydx_client_class_instance): + return dydx_client_class_instance.dydx_margin_parameters["liquidation_price"] + + def add_funding_rates(self): + self.simulate_funding_rates() + self.short_costs = self.short_costs - self.funding_rates * self.short_notional + + def simulate_funding_rates(self): + # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6) + + # best case + # self.funding_rates = 0.0075 / 100 + + # average -0.00443% + + # worst case + self.funding_rates = -0.0075 / 100 + + def simulate_maker_taker_fees(self): + # We add a counter for how many times we call this function + # i.e. how many times we open and close the short + self.maker_fees_counter += 1 + # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6) + + # maker fees + self.maker_taker_fees = 0.05 / 100 # <1M + # self.maker_taker_fees = 0.04 / 100 # <5M + # self.maker_taker_fees = 0.035 / 100 # <10M + # self.maker_taker_fees = 0.03 / 100 # <50M + # self.maker_taker_fees = 0.025 / 100 # <200M + # self.maker_taker_fees = 0.02 / 100 # >200M + + # Actions to take + def remove_collateral(self, stgy_instance): + self.cancel_order() + time = 0 + if self.short_collateral_status: + self.short_collateral_status = False + withdrawal_fees = self.short_collateral * self.withdrawal_fees + self.short_collateral = 0 + # self.price_to_liquidation = 0 + + # fees + self.short_costs = self.short_costs + withdrawal_fees + + time = 1 + return time + + def open_short(self, stgy_instance): + aave_class_instance = stgy_instance.aave + # dydx_client_class_instance = stgy_instance.dydx_client + if (not self.short_status) and self.order_status: + self.short_status = True + self.short_entry_price = self.market_price + self.short_size = -aave_class_instance.collateral_eth_initial + # self.collateral = aave_class_instance.debt_initial + self.short_notional = self.short_notional_calc() + self.short_equity = self.short_equity_calc() + self.short_leverage = self.short_leverage_calc() + # Simulate maker taker fees + self.simulate_maker_taker_fees() + # Add costs + self.short_costs = self.short_costs + self.maker_taker_fees * self.short_notional + return 0 + + def close_short(self, stgy_instance): + if self.short_status: + self.short_notional = self.short_notional_calc() + self.short_equity = self.short_equity_calc() + self.short_leverage = self.short_leverage_calc() + self.short_pnl = self.short_pnl_calc() + stgy_instance.total_pnl = stgy_instance.total_pnl + self.short_pnl + # We update short parameters after the calculation of pnl + self.short_entry_price = 0 + self.short_status = False + self.short_size = 0 + self.simulate_maker_taker_fees() + self.short_costs = self.short_costs + self.maker_taker_fees * self.short_notional + return 0 + + def open_long(self, stgy_instance): + aave_class_instance = stgy_instance.aave + # dydx_client_class_instance = stgy_instance.dydx_client + if not self.long_status: + self.long_status = True + self.long_entry_price = self.market_price + self.long_size = aave_class_instance.collateral_eth_initial + # self.collateral = aave_class_instance.debt_initial + self.long_notional = self.long_notional_calc() + # Simulate maker taker fees + self.simulate_maker_taker_fees() + # Add costs + self.long_costs = self.long_costs + self.maker_taker_fees * self.long_notional + return 0 + + def close_long(self, stgy_instance): + if self.long_status: + self.long_notional = self.long_notional_calc() + self.long_pnl = self.long_pnl_calc() + stgy_instance.total_pnl = stgy_instance.total_pnl + self.long_pnl + # We update short parameters after the calculation of pnl + self.long_entry_price = 0 + self.long_status = False + self.long_size = 0 + self.simulate_maker_taker_fees() + self.long_costs = self.long_costs + self.maker_taker_fees * self.long_notional + return 0 + + def place_order(self, price): + self.order_status = True + # self. + + def cancel_order(self): + self.order_status = False \ No newline at end of file diff --git a/hedge_scripts/Long_short/parameter_manager.py b/hedge_scripts/Long_short/parameter_manager.py new file mode 100644 index 0000000..27e8c28 --- /dev/null +++ b/hedge_scripts/Long_short/parameter_manager.py @@ -0,0 +1,117 @@ +import math + +import numpy as np + +from hedge_scripts.Short_only.interval import Interval + + +class ParameterManager(object): + # auxiliary functions + @staticmethod + def define_target_prices(stgy_instance, slippage, vol, floor, pcg): + mu = vol[0] + sigma = vol[1] + roof = floor * (1+pcg) + start = (roof+floor)/2 # = floor (2+pcg)/2 + ########################################################## + # We define the intervals + list_of_intervals = ["roof", + "start", + "floor"] + list_of_trigger_prices = [roof, + start, + floor] + # We define/update trigger prices + for i in range(len(list_of_intervals)): + interval_name = list_of_intervals[i] + trigger_price = list_of_trigger_prices[i] + stgy_instance.trigger_prices[interval_name] = trigger_price + + @staticmethod + def calc_vol(last_date, data): + periods_for_vol = [6 * 30 * 24 * 60, 3 * 30 * 24 * 60, 1 * 30 * 24 * 60] + last_six_months = data.loc[:last_date][-periods_for_vol[0]:] + for i in range(len(periods_for_vol)): + N = periods_for_vol[i] + log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1)) + globals()['sigma_' + str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean() + globals()['mu_' + str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean() + mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6 + sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6 + vol = [mu, sigma] + return vol + + @staticmethod + # Checking and updating data + def update_parameters(stgy_instance, new_market_price): + # AAVE + stgy_instance.aave.market_price = new_market_price + # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now + # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24 + stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60) + stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60) + # We have to execute track_ first because we need the fees for current collateral and debt values + stgy_instance.aave.track_lend_borrow_interest() + # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs + stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt + stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values + stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc() + + # DYDX + stgy_instance.dydx.market_price = new_market_price + # Short updates + stgy_instance.dydx.short_notional = stgy_instance.dydx.short_notional_calc() + stgy_instance.dydx.short_equity = stgy_instance.dydx.short_equity_calc() + stgy_instance.dydx.short_leverage = stgy_instance.dydx.short_leverage_calc() + stgy_instance.dydx.short_pnl = stgy_instance.dydx.short_pnl_calc() + # Long updates + stgy_instance.dydx.long_notional = stgy_instance.dydx.long_notional_calc() + stgy_instance.dydx.long_pnl = stgy_instance.dydx.long_pnl_calc() + + @staticmethod + def reset_costs(stgy_instance): + # We reset the costs in order to always start in 0 + stgy_instance.aave.costs = 0 + stgy_instance.dydx.short_costs = 0 + stgy_instance.dydx.long_costs = 0 + + def find_scenario(self, stgy_instance, market_price, previous_market_price): + self.simulate_fees(stgy_instance) + roof = stgy_instance.trigger_prices['roof'] + start = stgy_instance.trigger_prices['start'] + floor = stgy_instance.trigger_prices['floor'] + # Case P crossing roof upwards: Close short + if (previous_market_price <= roof) and (market_price >= roof): + if stgy_instance.dydx.short_status: + stgy_instance.dydx.close_short(stgy_instance) + # Case P crossing start in any direction: Start both + elif ((previous_market_price <= start) and (market_price >= start)) or ((previous_market_price >= start) and (market_price <= start)): + stgy_instance.dydx.open_long(stgy_instance) + stgy_instance.dydx.open_short(stgy_instance) + # Case P crossing floor downwards: Close Long + elif (previous_market_price >= floor) and (market_price <= floor): + if stgy_instance.dydx.long_status: + stgy_instance.dydx.close_long(stgy_instance) + + @staticmethod + def simulate_fees(stgy_instance): + # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6) + + # best case + # stgy_instance.gas_fees = 1 + + # stgy_instance.gas_fees = 3 + + # stgy_instance.gas_fees = 6 + + # worst case + stgy_instance.gas_fees = 10 + + @staticmethod + def update_pnl(stgy_instance): + stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.short_costs - stgy_instance.dydx.long_costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees + + @staticmethod + def add_costs(stgy_instance): + stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \ + + stgy_instance.aave.costs + stgy_instance.dydx.short_costs +stgy_instance.dydx.long_costs \ No newline at end of file diff --git a/hedge_scripts/Long_short/stgyapp.py b/hedge_scripts/Long_short/stgyapp.py new file mode 100644 index 0000000..03711ec --- /dev/null +++ b/hedge_scripts/Long_short/stgyapp.py @@ -0,0 +1,76 @@ +from hedge_scripts.Short_only.aave import Aave +from hedge_scripts.Short_only.dydx import Dydx +from hedge_scripts.Short_only.parameter_manager import ParameterManager +from hedge_scripts.Short_only.data_dumper import DataDamperNPlotter + +class StgyApp(object): + + def __init__(self, config): + + self.stk = config["stk"] + self.total_costs_from_aave_n_dydx = 0 + self.total_pnl = 0 + self.gas_fees = 0 + + # prices and intervals + self.trigger_prices = {} + self.intervals = {} + + # clients for data + # self.binance_client = binance_client_.BinanceClient(config["binance_client"]) + # self.dydx_client = dydx_client.DydxClient(config["dydx_client"]) + # self.sm_interactor = sm_interactor.SmInteractor(config["sm_interactor"]) + # self.historical_data = + + # We create attributes to fill later + self.aave = None + self.aave_features = None + self.aave_rates = None + + self.dydx = None + self.dydx_features = None + + # self.volatility_calculator = None + + self.parameter_manager = ParameterManager() + + self.historical_data = None + + + self.data_dumper = DataDamperNPlotter() + + def launch(self, config): + # self.call_binance_data_loader() + self.initialize_aave(config['initial_parameters']['aave']) + self.initialize_dydx(config['initial_parameters']['dydx']) + + # call clients functions + def get_historical_data(self, symbol, freq, + initial_date, save): + eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq, + initial_date=initial_date, save=save) + # self.historical_data = eth_historical + self.historical_data = eth_historical["close"] + for i in range(len(self.historical_data)): + self.historical_data[i] = float(self.historical_data[i]) + # self.load_intervals() + + # initialize classes + def initialize_aave(self, config): + # We initialize aave and dydx classes instances + self.aave = Aave(config) + # We load methods and attributes for aave and dydx to use later + self.aave_features = {"methods": [func for func in dir(self.aave) + if (callable(getattr(self.aave, func))) & (not func.startswith('__'))], + "attributes": {"values": list(self.aave.__dict__.values()), + "keys": list(self.aave.__dict__.keys())}} + # We create an attribute for historical data + self.aave_historical_data = [] + + def initialize_dydx(self, config): + self.dydx = Dydx(config) + self.dydx_features = {"methods": [func for func in dir(self.dydx) + if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))], + "attributes": {"values": list(self.dydx.__dict__.values()), + "keys": list(self.dydx.__dict__.keys())}} + self.dydx_historical_data = [] \ No newline at end of file diff --git a/hedge_scripts/Short_only/aave.py b/hedge_scripts/Short_only/aave.py new file mode 100644 index 0000000..0edb6e0 --- /dev/null +++ b/hedge_scripts/Short_only/aave.py @@ -0,0 +1,201 @@ +class Aave(object): + + def __init__(self, config): + # assert self.dydx_class_instance == isinstance(dydx) + # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg'] + self.market_price = config['market_price'] + self.interval_current = config['interval_current'] + + self.entry_price = config['entry_price'] + + self.collateral_eth_initial = config['collateral_eth'] + self.collateral_eth = config['collateral_eth'] + self.collateral_usdc = config['collateral_usdc'] + + self.reserve_margin_eth = 0 + self.reserve_margin_usdc = 0 + + self.borrowed_percentage = config['borrowed_pcg'] + self.usdc_status = config['usdc_status'] + + self.debt = config['debt'] + self.debt_initial = config['debt'] + + self.ltv = config['ltv'] + self.price_to_ltv_limit = config['price_to_ltv_limit'] + + self.lending_rate = 0 + self.lending_rate_hourly = 0 + self.interest_on_lending_eth = 0 # aggregated fees + self.interest_on_lending_usd = 0 + self.lending_fees_eth = 0 # fees between last 2 prices + self.lending_fees_usd = 0 + + self.borrowing_rate = 0 + self.borrowing_rate_hourly = 0 + self.interest_on_borrowing = 0 # aggregated fees + self.borrowing_fees = 0 # fees between last 2 prices + + self.lend_minus_borrow_interest = 0 + + self.costs = 0 + # self.historical = pd.DataFrame() + # self.dydx_class_instance = dydx_class_instance + # self.staked_in_protocol = stk + + # def update_costs(self): + # """ + # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees + # """ + # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit) + # self.costs = self.costs - self.lend_minus_borrow_interest + + def collateral_usd(self): + return self.collateral_eth * self.market_price + + def update_debt(self): + """ + it requires having called borrowing_fees_calc() in order to use updated values of last earned fees + """ + self.debt = self.debt + self.borrowing_fees + + def update_collateral(self): + """ + it requires having called lending_fees_calc() in order to use updated values of last earned fees + """ + self.collateral_eth = self.collateral_eth + self.lending_fees_eth + self.collateral_usdc = self.collateral_usd() + + def track_lend_borrow_interest(self): + """ + it requires having called borrowing_fees_calc() and lending_fees_calc() + in order to use updated values of last earned fees + """ + self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing + + def lending_fees_calc(self, freq): + self.simulate_lending_rate() + self.lending_rate_freq = self.lending_rate / freq + + # fees from lending are added to collateral? YES + # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE + self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq + self.lending_fees_usd = self.lending_fees_eth * self.market_price + self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth + self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd + + def borrowing_fees_calc(self, freq): + self.simulate_borrowing_rate() + self.borrowing_rate_freq = self.borrowing_rate / freq + + # fees from borrow are added to debt? YES + # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE + self.borrowing_fees = self.debt * self.borrowing_rate_freq + self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees + + def simulate_lending_rate(self): + # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate'] + + # best case + # self.lending_rate = 1.5 / 100 + + # worst case + self.lending_rate = 0.5 / 100 + + def simulate_borrowing_rate(self): + # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate'] + + # best case + # self.borrowing_rate = 1.5/100 + + # worst case + self.borrowing_rate = 2.5/100 + + def ltv_calc(self): + if self.collateral_usd() == 0: + return 0 + else: + return self.debt / self.collateral_usd() + + def price_to_liquidation(self, dydx_class_instance): + return self.entry_price - (dydx_class_instance.short_pnl() + + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth + + def price_to_ltv_limit_calc(self): + return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3) + + def buffer_for_repay(self): + return 0.01 + + def ltv_limit(self): + return 0.5 + + # Actions to take + def return_usdc(self, stgy_instance): + gas_fees = stgy_instance.gas_fees + time = 0 + if self.usdc_status: + # simulate 2min delay for tx + # update parameters + # AAVE parameters + self.usdc_status = False + # self.collateral_eth = 0 + # self.collateral_usdc = 0 + self.debt = 0 + self.ltv = 0 + self.price_to_ltv_limit = 0 + # self.lending_rate = 0 + # self.borrowing_rate = 0 + + # fees + self.costs = self.costs + gas_fees + + time = 1 + return time + + def repay_aave(self, stgy_instance): + gas_fees = stgy_instance.gas_fees + dydx_class_instance = stgy_instance.dydx + # aave_class_instance = stgy_instance.aave + # dydx_client_class_instance = stgy_instance.dydx_client + # + time = 0 + if self.usdc_status: + # update parameters + short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.short_entry_price) + new_short_size = dydx_class_instance.short_size - short_size_for_debt + + # pnl_for_debt = dydx_class_instance.pnl() + # We have to repeat the calculations for pnl and notional methods, but using different size_eth + pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.short_entry_price) + self.debt = self.debt - pnl_for_debt + self.ltv = self.ltv_calc() + + self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3) + self.costs = self.costs + gas_fees + + dydx_class_instance.short_size = new_short_size + dydx_class_instance.short_notional = dydx_class_instance.short_notional_calc() + dydx_class_instance.short_equity = dydx_class_instance.short_equity_calc() + dydx_class_instance.short_leverage = dydx_class_instance.short_leverage_calc() + dydx_class_instance.short_pnl = dydx_class_instance.short_pnl_calc() + # dydx_class_instance.price_to_liquidation = \ + # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance) + + # fees + # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees + dydx_class_instance.simulate_maker_taker_fees() + notional_for_fees = abs(short_size_for_debt) * self.market_price + dydx_class_instance.short_costs = dydx_class_instance.short_costs \ + + dydx_class_instance.maker_taker_fees * notional_for_fees \ + + pnl_for_debt * dydx_class_instance.withdrawal_fees + + # Note that a negative self.debt is actually a profit + # We update the parameters + if self.debt > 0: + self.usdc_status = True + else: + self.usdc_status = False + # simulate 2min delay for tx + time = 1 + return time \ No newline at end of file diff --git a/hedge_scripts/binance_client_.py b/hedge_scripts/Short_only/binance_client_.py similarity index 50% rename from hedge_scripts/binance_client_.py rename to hedge_scripts/Short_only/binance_client_.py index 6f934f1..c57ad51 100644 --- a/hedge_scripts/binance_client_.py +++ b/hedge_scripts/Short_only/binance_client_.py @@ -7,110 +7,67 @@ class BinanceClient(object): - def __init__(self, config): - self.binance_api_key = config["binance_api_key"] - self.binance_api_secret = config["binance_api_secret"] - self.client = Client_binance( - api_key=self.binance_api_key, api_secret=self.binance_api_secret - ) + def __init__(self, + config): + self.binance_api_key = config['binance_api_key'] + self.binance_api_secret = config['binance_api_secret'] + + self.client = Client_binance(api_key=self.binance_api_key, api_secret=self.binance_api_secret) # self.initial_date = config['initial_date'] # self.symbol = config['symbol'] # self.freq = config['freq'] ### FUNCTIONS - def minutes_of_new_data(self, symbol, kline_size, initial_date, data, source): + def minutes_of_new_data(self, symbol, kline_size, + initial_date, data, source): if len(data) > 0: old = parser.parse(data["timestamp"].iloc[-1]) elif source == "binance": - old = datetime.strptime(initial_date, "%d %b %Y") + old = datetime.strptime(initial_date, '%d %b %Y') if source == "binance": - new = pd.to_datetime( - self.client.get_klines(symbol=symbol, interval=kline_size)[-1][0], - unit="ms", - ) + new = pd.to_datetime(self.client.get_klines(symbol=symbol, interval=kline_size)[-1][0], unit='ms') return old, new - def get_all_binance(self, symbol, freq, initial_date, save=False): - binsizes = { - "1m": 1, - "5m": 5, - "10m": 10, - "15m": 15, - "1h": 60, - "6h": 360, - "12h": 720, - "1d": 1440, - } - filename = ( - "/home/agustin/Git-Repos/HedgingScripts/files/%s-%s-data_since_%s.csv" - % (symbol, freq, initial_date) - ) + def get_all_binance(self, symbol, freq, + initial_date, save=False): + binsizes = {"1m": 1, "5m": 5, "10m": 10, "15m": 15, "1h": 60, "6h": 360, "12h": 720, "1d": 1440} + filename = '/home/agustin/Git-Repos/HedgingScripts/files/%s-%s-data_since_%s.csv' % (symbol, freq, initial_date) data_df = pd.DataFrame() - oldest_point, newest_point = self.minutes_of_new_data( - symbol, freq, initial_date, data_df, source="binance" - ) + oldest_point, newest_point = self.minutes_of_new_data(symbol, freq, + initial_date, data_df, source="binance") delta_min = (newest_point - oldest_point).total_seconds() / 60 available_data = math.ceil(delta_min / binsizes[freq]) - if oldest_point == datetime.strptime(initial_date, "%d %b %Y"): - print( - "Downloading all available %s data for %s. Be patient..!" - % (freq, symbol) - ) + if oldest_point == datetime.strptime(initial_date, '%d %b %Y'): + print('Downloading all available %s data for %s. Be patient..!' % (freq, symbol)) else: - print( - "Downloading %d minutes of new data available for %s, i.e. %d instances of %s data." - % (delta_min, symbol, available_data, freq) - ) - klines = self.client.get_historical_klines( - symbol, - freq, - oldest_point.strftime("%d %b %Y %H:%M:%S"), - newest_point.strftime("%d %b %Y %H:%M:%S"), - ) - data = pd.DataFrame( - klines, - columns=[ - "timestamp", - "open", - "high", - "low", - "close", - "volume", - "close_time", - "quote_av", - "trades", - "tb_base_av", - "tb_quote_av", - "ignore", - ], - ) - data["timestamp"] = pd.to_datetime(data["timestamp"], unit="ms") + print('Downloading %d minutes of new data available for %s, i.e. %d instances of %s data.' + % (delta_min, symbol, available_data, freq)) + klines = self.client.get_historical_klines(symbol, freq, + oldest_point.strftime("%d %b %Y %H:%M:%S"), + newest_point.strftime("%d %b %Y %H:%M:%S")) + data = pd.DataFrame(klines, + columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', + 'trades', 'tb_base_av', 'tb_quote_av', 'ignore']) + data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms') # data.index = pd.to_datetime(data['timestamp'], unit='ms') if len(data_df) > 0: temp_df = pd.DataFrame(data) data_df = data_df.append(temp_df) else: data_df = data - data_df.set_index("timestamp", inplace=True) + data_df.set_index('timestamp', inplace=True) if save: data_df.to_csv(filename) - print("All caught up..!") + print('All caught up..!') print(initial_date) return data_df -<<<<<<< HEAD + import json with open('/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json') as json_file: config = json.load(json_file) -======= - -# import json -# -# with open('/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json') as json_file: -# config = json.load(json_file) ->>>>>>> cd6cfcb... write function for getting order book and historical data # _binance_client_ = BinanceClient(config['binance_client']) # eth_historical = _binance_client_.get_all_binance(save=True) # @@ -131,7 +88,6 @@ def get_all_binance(self, symbol, freq, initial_date, save=False): # for i in range(len(eth_prices)): # eth_prices[i] = float(eth_prices[i]) # historical_data = eth_prices -<<<<<<< HEAD # print(historical_data) # initial_dates = ["1 Jan 2022", "1 Jan 2021", "1 Jan 2020", "1 Jan 2019", "1 Jan 2018", "1 Jan 2017", "1 Jan 2016", @@ -146,7 +102,4 @@ def get_all_binance(self, symbol, freq, initial_date, save=False): # zip(initial_dates, end_dates)] # # eth_historical_prices_year_wise = parallel_pool(delayed_function) -# print('eth_historical_prices_year_wise', eth_historical_prices_year_wise) -======= -# # print(historical_data) ->>>>>>> cd6cfcb... write function for getting order book and historical data +# print('eth_historical_prices_year_wise', eth_historical_prices_year_wise) \ No newline at end of file diff --git a/hedge_scripts/checking_var.py b/hedge_scripts/Short_only/checking_var.py similarity index 98% rename from hedge_scripts/checking_var.py rename to hedge_scripts/Short_only/checking_var.py index 6b819f1..df37f84 100644 --- a/hedge_scripts/checking_var.py +++ b/hedge_scripts/Short_only/checking_var.py @@ -121,7 +121,7 @@ def run_through_dataset(data_set, historical_dataset): "Index at which P_current reached P_add": i} if __name__ == '__main__': - data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv") + data = pd.read_csv("/files/ETHUSDC-1m-data_since_1 Sep 2019.csv") historical_data = pd.DataFrame(data["close"], columns=['close']) timestamp = pd.to_datetime(data['timestamp']) historical_data.index = timestamp diff --git a/hedge_scripts/Short_only/command_center.py b/hedge_scripts/Short_only/command_center.py new file mode 100644 index 0000000..f1bc180 --- /dev/null +++ b/hedge_scripts/Short_only/command_center.py @@ -0,0 +1,235 @@ +import os +import pygsheets +import matplotlib.pyplot as plt +from scipy.stats import norm +import csv +import pandas as pd +import numpy as np +import json +import math +import random + +from hedge_scripts.Short_only.stgyapp import StgyApp + + +def run_sim(period, open_close, slippage, max_txs, L): + global ocs + # Initialize everything + with open("Files/StgyApp_config.json") as json_file: + config = json.load(json_file) + + # Initialize stgyApp + stgy = StgyApp(config) + # Period of Simulations + # period = ["2019-09-01","2019-12-31"] + stgy.historical_data = historical_data.loc[period[0] + ' 00:00:00':period[1] + ' 00:00:00'] + # For vol updates we take all data up to the last date + stgy.launch(config) + # Load target_prices + intervals in stgy.historical_data + # First we calculate weighted vol + last_date = period[1] + ' 00:00:00' + vol = stgy.parameter_manager.calc_vol(last_date, historical_data) + mu, sigma = vol + # floor just in order to get triger_price['open_close_1'] = open_close_1 + floor = open_close / ((1 + slippage) * (1 + mu + 2 * sigma)) + # Now we define prices and intervals given K and vol + stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor) + # We create five equidistant OCs + oc1 = stgy.trigger_prices['open_close'] + # oc2 = oc1 * (1+6/2/100) + ocs = [oc1] + for i in range(1, 5): + globals()["oc" + str(i + 1)] = oc1 * (1 + 0.03 / 5) ** i # We define 5 OCs based on a top width of 3% + ocs.append(globals()["oc" + str(i + 1)]) + # But we start with the first oc1 + stgy.trigger_prices['open_close'] = oc1 + stgy.parameter_manager.define_intervals(stgy) + + # print("Volatility:", vol) + # print("Floor:", stgy.trigger_prices['floor']) + # print("Open_close1:", oc1) + # print("Open_close2:", oc2) + # print("1-OC2/OC1 - 1:", 1-oc2/oc1) + ######################### + # Save historical data with trigger prices and thresholds loaded + # checking if the directory demo_folder + # exist or not. + if not os.path.exists("Files/From_%s_to_%s_open_close_at_%s" % (period[0], period[1], open_close)): + # if the demo_folder directory is not present + # then create it. + os.makedirs("Files/From_%s_to_%s_open_close_at_%s" % (period[0], period[1], open_close)) + stgy.historical_data.to_csv("Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv" + % (period[0], period[1], open_close)) + ######################### + # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations + + # Define initial and final index if needed in order to only run simulations in periods of several trigger prices + # As we calculate vol using first week of data, we initialize simulations from that week on + initial_index = 1 + + # Stk eth + stgy.stk = 1000000 / stgy.historical_data['close'][initial_index] + + # AAVE + stgy.aave.market_price = stgy.historical_data['close'][initial_index] + # stgy.aave.interval_current = stgy.historical_data['interval'][initial_index] + stgy.aave.interval_current = stgy.parameter_manager.find_interval(stgy, stgy.aave.market_price)['interval'] + + # What is the price at which we place the collateral in AAVE given our initial_index? + stgy.aave.short_entry_price = stgy.aave.market_price + # We place 90% of staked as collateral and save 10% as a reserve margin + stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3) + stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3) + stgy.reserve_margin_eth = stgy.stk * 0.1 + # We calculate collateral and reserve current value + stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price + stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price + + # What is the usdc_status for our initial_index? + stgy.aave.usdc_status = True + stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.short_entry_price) * stgy.aave.borrowed_percentage + stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.short_entry_price) * stgy.aave.borrowed_percentage + # debt_initial + stgy.aave.price_to_ltv_limit = round(stgy.aave.short_entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), + 3) + # stgy.total_costs = 104 + + # DyDx + stgy.dydx.market_price = stgy.historical_data['close'][initial_index] + # stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index] + stgy.dydx.interval_current = stgy.parameter_manager.find_interval(stgy, stgy.dydx.market_price)['interval'] + stgy.dydx.short_collateral = stgy.aave.debt + stgy.dydx.short_equity = stgy.dydx.short_equity_calc() + stgy.dydx.short_collateral_status = True + ######################### + # Load interval_old + # interval_old = stgy.historical_data['interval'][initial_index] + interval_old = stgy.aave.interval_current + ######################### + # Clear previous csv data for aave and dydx + stgy.data_dumper.delete_results(stgy, period, open_close) + ######################### + # add header to csv of aave and dydx + stgy.data_dumper.add_header(stgy, period, open_close) + ################################## + # Run through dataset + ######################### + # import time + # # run simulations + # starttime = time.time() + # print('starttime:', starttime) + # for i in range(initial_index, len(stgy.historical_data)): + i = initial_index + + maker_fees_counter = [] + while (i < len(stgy.historical_data)): + # for i in range(initial_index, len(stgy.historical_data)): + # pass + + # We reset costs in every instance + stgy.parameter_manager.reset_costs(stgy) + # new_interval_previous = stgy.historical_data["interval"][i-1] + interval_previous = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i - 1])['interval'] + # new_interval_current = stgy.historical_data["interval"][i] + interval_current = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i])['interval'] + market_price = stgy.historical_data["close"][i] + ######################### + # This case is when P crossed open_close_2 while increasing (therefore we had to close short), I_old = I_open_close_2, + # but then it goes below open_close_2 again. + # So before updating I_old the bot will read I_current = I_open_close_2 and I_old = I_open_close_2. + # So in order to be protected we manage this case as it names indicates open_close_2: + # we open and close at this price. + # Note that this also includes a situation in which price crossed floor while decreasing and the it crosses it again going up + # I_old = I_open_close_2 and before updating new I_old we have I_current= I_open_close_2. + # But here we do nothing because short is still open. + # if (new_interval_current == stgy.intervals["open_close_2"]) & (interval_old == stgy.intervals["open_close_2"]): + # time_dydx = stgy_instance.dydx.open_short(new_market_price, new_interval_current, stgy) + # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late + # therefore not taking the actions needed as soon as they are needed + if interval_previous != interval_current: + interval_old = interval_previous + # print(interval_old.name) + ######################### + # Update parameters + # First we update everything in order to execute scenarios with updated values + # We have to update + # AAVE: market_price, interval_current, lending and borrowing fees (and the diference), + # debt value, collateral value and ltv value + # DyDx: market_price, interval_current, notional, equity, leverage and pnl + stgy.parameter_manager.update_parameters(stgy, market_price, interval_current) + # Here we identify price movent direction by comparing current interval and old interval + # and we also execute all the actions involved since last price was read + time_used = stgy.parameter_manager.find_scenario(stgy, market_price, interval_current, interval_old, i) + ############################## + # We update vol and ocs if short_status = False + # if not stgy.dydx.short_status: + # current_date = list(stgy.historical_data.index)[i] + # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol) + # mu, sigma = vol + # oc1 = floor * (1+slippage) * (1+mu+2*sigma) + # ocs = [oc1] + # for i in range(1,5): + # globals()["oc"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3% + # ocs.append(globals()["oc"+str(i+1)]) + ######################### + # If we executed more txs than hat_L*20 then we change to K_2 + if (stgy.dydx.maker_fees_counter >= max_txs): + # stgy.historical_data = stgy.historical_data_OC2 + # print(stgy.dydx.maker_fees_counter) + current_date = list(stgy.historical_data.index)[i] + current_oc = stgy.trigger_prices['open_close'] + vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data) + ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol) + # if short = open and if there are up_choices available, we take the last option (the furthest) + # if there isn't options we take max_distance + # random.seed(4) + if stgy.dydx.short_status: + if len(ocs_choices['up_choices']) != 0: + stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0] + # oc_choice_up = random.choice(range(len(ocs_choices['up_choices']))) + # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] + else: + pass + # if short = close and if there are down_choices available, we take the first option (the furthest) + # if there isn't options we take max_distance + else: + if len(ocs_choices['down_choices']) != 0: + stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][-1] + # oc_choice_down = random.choice(range(len(ocs_choices['down_choices']))) + # stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][oc_choice_down] + else: + pass + # If we didnt change oc we dont clean maker_fees_counter + if current_oc != stgy.trigger_prices['open_close']: + maker_fees_counter.append({'oc': stgy.trigger_prices['open_close'], + 'txs': stgy.dydx.maker_fees_counter, + # 'index': i, + 'date': str(stgy.historical_data.index[i])}) + stgy.dydx.maker_fees_counter = 0 + stgy.parameter_manager.define_intervals(stgy) + ######################## + # Funding rates + # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency) + # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated. + # Calling it before find_scenarios will overwrite the funding by 0 + # We have to check all the indexes between old index i and next index i+time_used + # for index in range(i, i+time_used): + if (i % (8 * 60) == 0) and (stgy.dydx.short_status): + stgy.dydx.add_funding_rates() + # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates + ######################### + # Add costs + stgy.parameter_manager.add_costs(stgy) + stgy.parameter_manager.update_pnl(stgy) + ######################### + # Write data + # We write the data into the google sheet or csv file acording to sheet value + # (sheet = True --> sheet, sheet = False --> csv) + stgy.data_dumper.write_data(stgy, + interval_previous, interval_old, i, period, open_close, + sheet=False) + ######################### + # we increment index by the time consumed in executing actions + # i += time_used + i += 1 + return maker_fees_counter \ No newline at end of file diff --git a/hedge_scripts/Short_only/data_dumper.py b/hedge_scripts/Short_only/data_dumper.py new file mode 100644 index 0000000..3b5657b --- /dev/null +++ b/hedge_scripts/Short_only/data_dumper.py @@ -0,0 +1,152 @@ +import csv +import os + +import pygsheets + +from hedge_scripts.Short_only.interval import Interval + + +class DataDamperNPlotter: + def __init__(self): + self.historical_data = None + + @staticmethod + def write_data(stgy_instance, + new_interval_previous, interval_old, mkt_price_index, period, oc1, + sheet=False): + aave_instance = stgy_instance.aave + dydx_instance = stgy_instance.dydx + data_aave = [] + data_dydx = [] + aave_wanted_keys = [ + "market_price", + "interval_current", + "entry_price", + "collateral_eth", + "usdc_status", + "debt", + "ltv", + "lending_rate", + "interest_on_lending_usd", + "borrowing_rate", + "interest_on_borrowing", + "lend_minus_borrow_interest", + "costs"] + + for i in range(len(aave_instance.__dict__.values())): + if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys: + # print(list(aave_instance.__dict__.keys())[i]) + if isinstance(list(aave_instance.__dict__.values())[i], Interval): + data_aave.append(str(list(aave_instance.__dict__.values())[i].name)) + # data_aave.append(new_interval_previous.name) + data_aave.append(interval_old.name) + else: + data_aave.append(str(list(aave_instance.__dict__.values())[i])) + for i in range(len(dydx_instance.__dict__.values())): + if isinstance(list(dydx_instance.__dict__.values())[i], Interval): + data_dydx.append(str(list(dydx_instance.__dict__.values())[i].name)) + # data_dydx.append(new_interval_previous.name) + data_dydx.append(interval_old.name) + else: + data_dydx.append(str(list(dydx_instance.__dict__.values())[i])) + # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker + data_aave.append(stgy_instance.gas_fees) + data_aave.append(stgy_instance.total_costs_from_aave_n_dydx) + data_aave.append(stgy_instance.total_pnl) + data_aave.append(mkt_price_index) + + data_dydx.append(stgy_instance.gas_fees) + data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx) + data_dydx.append(stgy_instance.total_pnl) + data_dydx.append(mkt_price_index) + # print(interval_old.name) + # print(data_dydx, list(dydx_instance.__dict__.keys())) + if sheet == True: + gc = pygsheets.authorize(service_file= + 'stgy-1-simulations-e0ee0453ddf8.json') + sh = gc.open('aave/dydx simulations') + sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False) + sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False) + else: + path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % ( + period[0], period[1], int(oc1)) # int(stgy_instance.trigger_prices['open_close'])) + path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % ( + period[0], period[1], int(oc1)) # int(stgy_instance.trigger_prices['open_close'])) + with open(path_to_aave, 'a') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(data_aave) + with open(path_to_dydx, 'a', + newline='', encoding='utf-8') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(data_dydx) + + @staticmethod + def delete_results(stgy_instance, period, oc1): + file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % ( + period[0], period[1], int(oc1)) # int(stgy_instance.trigger_prices['open_close'])) + file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % ( + period[0], period[1], int(oc1)) # int(stgy_instance.trigger_prices['open_close'])) + if (os.path.exists(file_aave) and os.path.isfile(file_aave)): + os.remove(file_aave) + if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)): + os.remove(file_dydx) + + @staticmethod + def add_header(stgy_instance, period, oc1): + aave_headers = [ + "market_price", + "I_current", + # "I_previous", + "I_old", + "entry_price", + "collateral_eth", + "usdc_status", + "debt", + "ltv", + "lending_rate", + "interest_on_lending_usd", + "borrowing_rate", + "interest_on_borrowing", + "lend_minus_borrow_interest", + "costs", + "gas_fees", + "total_costs_from_aave_n_dydx", + "total_stgy_pnl", + "index_of_mkt_price"] + dydx_headers = [ + "market_price", + "I_current", + # "I_previous", + "I_old", + "entry_price", + "short_size", + "collateral", + "notional", + "equity", + "leverage", + "pnl", + # "price_to_liquidation", + "collateral_status", + "short_status", + "order_status", + "withdrawal_fees", + "funding_rates", + "maker_taker_fees", + "maker_fees_counter", + "costs", + "gas_fees", + "total_costs_from_aave_n_dydx", + "total_stgy_pnl", + "index_of_mkt_price"] + + path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % ( + period[0], period[1], int(oc1)) # int(stgy_instance.trigger_prices['open_close'])) + path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % ( + period[0], period[1], int(oc1)) # int(stgy_instance.trigger_prices['open_close'])) + with open(path_to_aave, 'a') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(aave_headers) + with open(path_to_dydx, 'a', + newline='', encoding='utf-8') as file: + writer = csv.writer(file, lineterminator='\n') + writer.writerow(dydx_headers) \ No newline at end of file diff --git a/hedge_scripts/Short_only/dydx.py b/hedge_scripts/Short_only/dydx.py new file mode 100644 index 0000000..b9045c5 --- /dev/null +++ b/hedge_scripts/Short_only/dydx.py @@ -0,0 +1,173 @@ +class Dydx(object): + + def __init__(self, config): + # assert aave_class == isinstance(aave) + self.market_price = config['market_price'] + self.interval_current = config['interval_current'] + self.entry_price = config['entry_price'] + self.short_size = config['short_size'] + self.collateral = config['collateral'] + self.notional = config['notional'] + self.equity = config['equity'] + self.leverage = config['leverage'] + self.pnl = config['pnl'] + # self.price_to_liquidation = config['price_to_liquidation'] + self.collateral_status = config['collateral_status'] + self.short_status = config['short_status'] + self.order_status = True + self.withdrawal_fees = 0.01 / 100 + self.funding_rates = 0 + self.maker_taker_fees = 0 + self.maker_fees_counter = 0 + self.costs = 0 + + # auxiliary functions + def pnl_calc(self): + return self.short_size * (self.market_price - self.entry_price) + + def notional_calc(self): + return abs(self.short_size) * self.market_price + + def equity_calc(self): + return self.collateral + self.pnl_calc() + + def leverage_calc(self): + if self.equity_calc() == 0: + return 0 + else: + return self.notional_calc() / self.equity_calc() + + def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance): + return self.entry_price \ + + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size + + @staticmethod + def price_to_liquidation_calc(dydx_client_class_instance): + return dydx_client_class_instance.dydx_margin_parameters["liquidation_price"] + + def add_funding_rates(self): + self.simulate_funding_rates() + self.costs = self.costs - self.funding_rates * self.notional + + def simulate_funding_rates(self): + # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6) + + # best case + # self.funding_rates = 0.0075 / 100 + + # average -0.00443% + + # worst case + self.funding_rates = -0.0075 / 100 + + def simulate_maker_taker_fees(self): + # We add a counter for how many times we call this function + # i.e. how many times we open and close the short + self.maker_fees_counter += 1 + # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6) + + # maker fees + self.maker_taker_fees = 0.05 / 100 # <1M + # self.maker_taker_fees = 0.04 / 100 # <5M + # self.maker_taker_fees = 0.035 / 100 # <10M + # self.maker_taker_fees = 0.03 / 100 # <50M + # self.maker_taker_fees = 0.025 / 100 # <200M + # self.maker_taker_fees = 0.02 / 100 # >200M + + # Actions to take + def remove_collateral(self, stgy_instance): + self.cancel_order() + time = 0 + if self.collateral_status: + self.collateral_status = False + withdrawal_fees = self.collateral * self.withdrawal_fees + self.collateral = 0 + # self.price_to_liquidation = 0 + + # fees + self.costs = self.costs + withdrawal_fees + + time = 1 + return time + + def open_short(self, stgy_instance): + aave_class_instance = stgy_instance.aave + # dydx_client_class_instance = stgy_instance.dydx_client + intervals = stgy_instance.intervals + if (not self.short_status) and self.order_status: + self.short_status = True + # dydx parameters + # if self.market_price <= stgy_instance.trigger_prices['floor']: + # print("CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!") + # print("Difference of: ", stgy_instance.trigger_prices['floor'] - self.market_price) + + # if self.market_price <= stgy_instance.trigger_prices['open_close']: + # print("CAUTION: OPEN PRICE LOWER THAN open_close!") + # print("Difference of: ", stgy_instance.trigger_prices['open_close'] - self.market_price) + self.entry_price = self.market_price + self.short_size = -aave_class_instance.collateral_eth_initial + # self.collateral = aave_class_instance.debt_initial + self.notional = self.notional_calc() + self.equity = self.equity_calc() + self.leverage = self.leverage_calc() + # Simulate maker taker fees + self.simulate_maker_taker_fees() + # Add costs + self.costs = self.costs + self.maker_taker_fees * self.notional + + price_floor = stgy_instance.trigger_prices['floor'] + floor_position = intervals['floor'].position_order + + price_to_repay_debt = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(), + aave_class_instance) + price_to_ltv_limit = stgy_instance.trigger_prices['ltv_limit'] + stgy_instance.trigger_prices['repay_aave'] = price_to_repay_debt + # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit + if price_to_ltv_limit < price_to_repay_debt: + intervals['floor'] = Interval(price_to_repay_debt, price_floor, + 'floor', floor_position) + intervals['repay_aave'] = Interval(price_to_ltv_limit, price_to_repay_debt, + 'repay_aave', floor_position + 1) + intervals['minus_infty'] = Interval(-math.inf, price_to_ltv_limit, + 'minus_infty', floor_position + 2) + else: + print("CAUTION: P_ltv > P_repay") + print("Difference of: ", price_to_ltv_limit - price_to_repay_debt) + price_to_repay_debt = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance) + intervals['floor'] = Interval(price_to_ltv_limit, price_floor, + 'floor', floor_position) + intervals['ltv_limit'] = Interval(price_to_repay_debt, price_to_ltv_limit, + 'repay_aave', floor_position + 1) + intervals['minus_infty'] = Interval(-math.inf, price_to_repay_debt, + 'minus_infty', floor_position + 2) + self.order_status = False + return 0 + + def close_short(self, stgy_instance): + if self.short_status: + # Next if is to move up the threshold if we didnt execute at exactly open_close + # if self.market_price >= stgy_instance.trigger_prices['open_close']: + # # new_open_close = self.market_price + # print("CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!") + # print("Difference of: ", self.market_price - stgy_instance.trigger_prices['open_close']) + # stgy_instance.target_prices['open_close'] = self.market_price + self.notional = self.notional_calc() + self.equity = self.equity_calc() + self.leverage = self.leverage_calc() + self.pnl = self.pnl_calc() + stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl + # We update short parameters after the calculation of pnl + self.entry_price = 0 + self.short_status = False + self.short_size = 0 + self.simulate_maker_taker_fees() + self.costs = self.costs + self.maker_taker_fees * self.notional + self.place_order(stgy_instance.trigger_prices['open_close']) + return 0 + + def place_order(self, price): + self.order_status = True + # self. + + def cancel_order(self): + self.order_status = False \ No newline at end of file diff --git a/hedge_scripts/dydx_client.py b/hedge_scripts/Short_only/dydx_client.py similarity index 97% rename from hedge_scripts/dydx_client.py rename to hedge_scripts/Short_only/dydx_client.py index df24715..ece5f4b 100644 --- a/hedge_scripts/dydx_client.py +++ b/hedge_scripts/Short_only/dydx_client.py @@ -56,7 +56,7 @@ def get_dydx_parameters(self, dydx_class_instance): # total_account_value self.dydx_margin_parameters["total_account_value"] = ( - dydx_class_instance.collateral + dydx_class_instance.notional + dydx_class_instance.short_collateral + dydx_class_instance.short_notional ) self.dydx_margin_parameters["Free_collateral"] = ( self.dydx_margin_parameters["total_account_value"] diff --git a/hedge_scripts/interval.py b/hedge_scripts/Short_only/interval.py similarity index 100% rename from hedge_scripts/interval.py rename to hedge_scripts/Short_only/interval.py diff --git a/hedge_scripts/metrics_calculator.py b/hedge_scripts/Short_only/metrics_calculator.py similarity index 94% rename from hedge_scripts/metrics_calculator.py rename to hedge_scripts/Short_only/metrics_calculator.py index 109ade0..991de7f 100644 --- a/hedge_scripts/metrics_calculator.py +++ b/hedge_scripts/Short_only/metrics_calculator.py @@ -54,8 +54,7 @@ def CES_test(self, df_with_ces, n, m): if __name__ == '__main__': metric_calculator = MetricsCalculator() - metric_calculator.df = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/" - "ETHUSDC-1m-data_since_1 Sep 2019.csv")[-1000:] + metric_calculator.df = pd.read_csv("/files/ETHUSDC-1m-data_since_1 Sep 2019.csv")[-1000:] # # assign data to stgy instance + define index as dates # df = pd.DataFrame(historical_data["close"], columns=['close']) timestamp = pd.to_datetime(metric_calculator.df['timestamp']) diff --git a/hedge_scripts/Short_only/parameter_manager.py b/hedge_scripts/Short_only/parameter_manager.py new file mode 100644 index 0000000..fb79023 --- /dev/null +++ b/hedge_scripts/Short_only/parameter_manager.py @@ -0,0 +1,220 @@ +import math + +import numpy as np + +from hedge_scripts.Short_only.interval import Interval + + +class ParameterManager(object): + # auxiliary functions + @staticmethod + def define_target_prices(stgy_instance, slippage, vol, floor): + mu = vol[0] + sigma = vol[1] + p_open_close = floor * (1 + slippage) * (1 + mu + 2 * sigma) + ########################################################## + # We define the intervals + list_of_intervals = ["open_close", + "floor", + "ltv_limit"] + list_of_trigger_prices = [p_open_close, + floor, + stgy_instance.aave.price_to_ltv_limit] + # We define/update trigger prices + for i in range(len(list_of_intervals)): + interval_name = list_of_intervals[i] + trigger_price = list_of_trigger_prices[i] + stgy_instance.trigger_prices[interval_name] = trigger_price + + @staticmethod + def define_intervals(stgy_instance): + stgy_instance.intervals = {"infty": Interval(stgy_instance.trigger_prices['open_close'], + math.inf, + "infty", 0), + "open_close": Interval(stgy_instance.trigger_prices['floor'], + stgy_instance.trigger_prices['open_close'], + "open_close", 1), + "floor": Interval(stgy_instance.trigger_prices['ltv_limit'], + stgy_instance.trigger_prices['floor'], + "floor", 2), + "minus_infty": Interval(-math.inf, + stgy_instance.trigger_prices['ltv_limit'], + "minus_infty", 3)} + + # function to assign interval_current to each market_price in historical data + @staticmethod + def find_interval(stgy_instance, market_price): + for i in list(stgy_instance.intervals.values()): + if i.left_border < market_price <= i.right_border: + return {"interval": i, "interval_name": i.name} + + @staticmethod + def find_oc(current_oc, ocs, vol): + mu, sigma = vol + oc_up = current_oc * (1 + slippage) * (1 + mu + 2 * sigma) + oc_down = current_oc * (1 + slippage) * (1 + mu - 2 * sigma) + distances = [] + next_oc_up = [] + next_oc_down = [] + for i in range(len(ocs)): + oci = ocs[i] + if oc_up < oci: + next_oc_up.append(oci) + # ocs['up'].append(oci) + elif oc_down > oci: + next_oc_down.append(oci) + # ocs['down'].append(oci) + distances.append(current_oc - oci) + # If we get here then we didnt return anything, so we return the farthest oc + # Furthest down (positive distance current_oc > oci) + max_value = max(distances) + max_index = distances.index(max_value) + # Furthest up (negative distance current_oc < oci) + min_value = min(distances) + min_index = distances.index(min_value) + # print(next_oc_up) + # print(next_oc_down) + return {'up_choices': next_oc_up, + 'down_choices': next_oc_down, + 'max_distance_up': ocs[min_index], + 'max_distance_down': ocs[max_index]} + + @staticmethod + def load_intervals(stgy_instance): + stgy_instance.historical_data["interval"] = [[0, 0]] * len(stgy_instance.historical_data["close"]) + stgy_instance.historical_data["interval_name"] = ['nan'] * len(stgy_instance.historical_data["close"]) + for loc in range(len(stgy_instance.historical_data["close"])): + market_price = stgy_instance.historical_data["close"][loc] + for i in list(stgy_instance.intervals.values()): + if i.left_border < market_price <= i.right_border: + stgy_instance.historical_data["interval"][loc] = i + stgy_instance.historical_data["interval_name"][loc] = i.name + + @staticmethod + def calc_vol(last_date, data): + periods_for_vol = [6 * 30 * 24 * 60, 3 * 30 * 24 * 60, 1 * 30 * 24 * 60] + last_six_months = data.loc[:last_date][-periods_for_vol[0]:] + for i in range(len(periods_for_vol)): + N = periods_for_vol[i] + log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1)) + globals()['sigma_' + str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean() + globals()['mu_' + str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean() + mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6 + sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6 + vol = [mu, sigma] + return vol + + @staticmethod + # Checking and updating data + def update_parameters(stgy_instance, new_market_price, new_interval_current): + # AAVE + stgy_instance.aave.market_price = new_market_price + stgy_instance.aave.interval_current = new_interval_current + # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now + # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24 + stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60) + stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60) + # We have to execute track_ first because we need the fees for current collateral and debt values + stgy_instance.aave.track_lend_borrow_interest() + # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs + stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt + stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values + stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc() + + # DYDX + stgy_instance.dydx.market_price = new_market_price + stgy_instance.dydx.interval_current = new_interval_current + stgy_instance.dydx.short_notional = stgy_instance.dydx.short_notional_calc() + stgy_instance.dydx.short_equity = stgy_instance.dydx.short_equity_calc() + stgy_instance.dydx.short_leverage = stgy_instance.dydx.short_leverage_calc() + stgy_instance.dydx.short_pnl = stgy_instance.dydx.short_pnl_calc() + # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client) + + @staticmethod + def reset_costs(stgy_instance): + # We reset the costs in order to always start in 0 + stgy_instance.aave.short_costs = 0 + stgy_instance.dydx.short_costs = 0 + + def find_scenario(self, stgy_instance, new_market_price, new_interval_current, interval_old, index): + actions = self.actions_to_take(stgy_instance, new_interval_current, interval_old) + self.simulate_fees(stgy_instance) + time = 0 + time_aave = 0 + time_dydx = 0 + for action in actions: + # if action == "rtrn_usdc_n_rmv_coll_dydx": + # time = stgy_instance.dydx.remove_collateral_dydx(new_market_price, new_interval_current, stgy_instance) + # stgy_instance.aave.return_usdc(new_market_price, new_interval_current, stgy_instance) + if action == "borrow_usdc_n_add_coll": + time_aave = stgy_instance.aave.borrow_usdc(stgy_instance) + market_price = stgy_instance.historical_data["close"][index + time_aave] + interval_current = stgy_instance.historical_data["interval"][index + time_aave] + time_dydx = stgy_instance.dydx.add_collateral(stgy_instance) + time_aave = 0 + elif action in stgy_instance.aave_features["methods"]: + time_aave = getattr(stgy_instance.aave, action)(stgy_instance) + elif action in stgy_instance.dydx_features["methods"]: + time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance) + time += time_aave + time_dydx + # print(stgy_instance.aave_features["methods"]) + # print(stgy_instance.dydx_features["methods"]) + return time + # stgy_instance.append(action) + + @staticmethod + def actions_to_take(stgy_instance, new_interval_current, interval_old): + actions = [] + + # Case P increasing + if interval_old.is_lower(new_interval_current): + for i in reversed(range(new_interval_current.position_order, interval_old.position_order)): + + # CASE: open_close_1 APPROACH + if list(stgy_instance.intervals.keys())[i + 1] == 'open_close': + actions.append('close_short') + + # CASE: TOO MANY FEES FOR open_close_1 APPROACH + # if list(stgy_instance.intervals.keys())[i+1] == 'open_close_2': + # actions.append('close_short') + + else: + actions.append(list(stgy_instance.intervals.keys())[ + i + 1]) # when P goes up we execute the name of previous intervals + # print(list(stgy_instance.intervals.keys())[i+1]) + + # Case P decreasing + else: + for i in range(interval_old.position_order + 1, new_interval_current.position_order + 1): + + # In both cases we open at open_close_1 bc for open_close_2 case we manage the opening + # from inside the for loop of the run_sims + if list(stgy_instance.intervals.keys())[i] == 'open_close': + actions.append('open_short') + else: + actions.append(list(stgy_instance.intervals.keys())[i]) + # print(actions) + return actions + + @staticmethod + def simulate_fees(stgy_instance): + # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6) + + # best case + # stgy_instance.gas_fees = 1 + + # stgy_instance.gas_fees = 3 + + # stgy_instance.gas_fees = 6 + + # worst case + stgy_instance.gas_fees = 10 + + @staticmethod + def update_pnl(stgy_instance): + stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.short_costs - stgy_instance.dydx.short_costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees + + @staticmethod + def add_costs(stgy_instance): + stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \ + + stgy_instance.aave.short_costs + stgy_instance.dydx.short_costs \ No newline at end of file diff --git a/hedge_scripts/plot.html b/hedge_scripts/Short_only/plot.html similarity index 100% rename from hedge_scripts/plot.html rename to hedge_scripts/Short_only/plot.html diff --git a/hedge_scripts/sm_interactor.py b/hedge_scripts/Short_only/sm_interactor.py similarity index 100% rename from hedge_scripts/sm_interactor.py rename to hedge_scripts/Short_only/sm_interactor.py diff --git a/hedge_scripts/Short_only/stgyapp.py b/hedge_scripts/Short_only/stgyapp.py new file mode 100644 index 0000000..03711ec --- /dev/null +++ b/hedge_scripts/Short_only/stgyapp.py @@ -0,0 +1,76 @@ +from hedge_scripts.Short_only.aave import Aave +from hedge_scripts.Short_only.dydx import Dydx +from hedge_scripts.Short_only.parameter_manager import ParameterManager +from hedge_scripts.Short_only.data_dumper import DataDamperNPlotter + +class StgyApp(object): + + def __init__(self, config): + + self.stk = config["stk"] + self.total_costs_from_aave_n_dydx = 0 + self.total_pnl = 0 + self.gas_fees = 0 + + # prices and intervals + self.trigger_prices = {} + self.intervals = {} + + # clients for data + # self.binance_client = binance_client_.BinanceClient(config["binance_client"]) + # self.dydx_client = dydx_client.DydxClient(config["dydx_client"]) + # self.sm_interactor = sm_interactor.SmInteractor(config["sm_interactor"]) + # self.historical_data = + + # We create attributes to fill later + self.aave = None + self.aave_features = None + self.aave_rates = None + + self.dydx = None + self.dydx_features = None + + # self.volatility_calculator = None + + self.parameter_manager = ParameterManager() + + self.historical_data = None + + + self.data_dumper = DataDamperNPlotter() + + def launch(self, config): + # self.call_binance_data_loader() + self.initialize_aave(config['initial_parameters']['aave']) + self.initialize_dydx(config['initial_parameters']['dydx']) + + # call clients functions + def get_historical_data(self, symbol, freq, + initial_date, save): + eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq, + initial_date=initial_date, save=save) + # self.historical_data = eth_historical + self.historical_data = eth_historical["close"] + for i in range(len(self.historical_data)): + self.historical_data[i] = float(self.historical_data[i]) + # self.load_intervals() + + # initialize classes + def initialize_aave(self, config): + # We initialize aave and dydx classes instances + self.aave = Aave(config) + # We load methods and attributes for aave and dydx to use later + self.aave_features = {"methods": [func for func in dir(self.aave) + if (callable(getattr(self.aave, func))) & (not func.startswith('__'))], + "attributes": {"values": list(self.aave.__dict__.values()), + "keys": list(self.aave.__dict__.keys())}} + # We create an attribute for historical data + self.aave_historical_data = [] + + def initialize_dydx(self, config): + self.dydx = Dydx(config) + self.dydx_features = {"methods": [func for func in dir(self.dydx) + if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))], + "attributes": {"values": list(self.dydx.__dict__.values()), + "keys": list(self.dydx.__dict__.keys())}} + self.dydx_historical_data = [] \ No newline at end of file diff --git a/hedge_scripts/volatility_calculator.py b/hedge_scripts/Short_only/volatility_calculator.py similarity index 100% rename from hedge_scripts/volatility_calculator.py rename to hedge_scripts/Short_only/volatility_calculator.py diff --git a/hedge_scripts/data_dumper.py b/hedge_scripts/data_dumper.py deleted file mode 100644 index c022a6d..0000000 --- a/hedge_scripts/data_dumper.py +++ /dev/null @@ -1,394 +0,0 @@ -import os -import pygsheets -import matplotlib.pyplot as plt -from scipy.stats import norm -import csv -import pandas as pd -import numpy as np - -import interval - - -class DataDamperNPlotter: - def __init__(self): - self.historical_data = None - - @staticmethod - def write_data( - stgy_instance, new_interval_previous, interval_old, mkt_price_index, sheet=False - ): - aave_instance = stgy_instance.aave - dydx_instance = stgy_instance.dydx - data_aave = [] - data_dydx = [] - aave_wanted_keys = [ - "market_price", - "interval_current", - "entry_price", - "collateral_eth", - "usdc_status", - "debt", - "ltv", - "lending_rate", - "interest_on_lending_usd", - "borrowing_rate", - "interest_on_borrowing", - "lend_minus_borrow_interest", - "costs", - ] - - for i in range(len(aave_instance.__dict__.values())): - if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys: - # print(list(aave_instance.__dict__.keys())[i]) - if isinstance( - list(aave_instance.__dict__.values())[i], interval.Interval - ): - data_aave.append(str(list(aave_instance.__dict__.values())[i].name)) - # data_aave.append(new_interval_previous.name) - data_aave.append(interval_old.name) - else: - data_aave.append(str(list(aave_instance.__dict__.values())[i])) - for i in range(len(dydx_instance.__dict__.values())): - if isinstance(list(dydx_instance.__dict__.values())[i], interval.Interval): - data_dydx.append(str(list(dydx_instance.__dict__.values())[i].name)) - # data_dydx.append(new_interval_previous.name) - data_dydx.append(interval_old.name) - else: - data_dydx.append(str(list(dydx_instance.__dict__.values())[i])) - # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker - data_aave.append(stgy_instance.gas_fees) - data_aave.append(stgy_instance.total_costs_from_aave_n_dydx) - data_aave.append(stgy_instance.total_pnl) - data_aave.append(mkt_price_index) - - - data_dydx.append(stgy_instance.gas_fees) - data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx) - data_dydx.append(stgy_instance.total_pnl) - data_dydx.append(mkt_price_index) - - # print(data_dydx, list(dydx_instance.__dict__.keys())) - if sheet == True: - gc = pygsheets.authorize( - service_file="/home/agustin/Git-Repos/HedgingScripts/files/stgy-1-simulations-e0ee0453ddf8.json" - ) - sh = gc.open("aave/dydx simulations") - sh[0].append_table(data_aave, end=None, dimension="ROWS", overwrite=False) - sh[1].append_table(data_dydx, end=None, dimension="ROWS", overwrite=False) - else: - with open( - "/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv", "a" - ) as file: - writer = csv.writer(file, lineterminator="\n") - writer.writerow(data_aave) - with open( - "/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv", - "a", - newline="", - encoding="utf-8", - ) as file: - writer = csv.writer(file, lineterminator="\n") - writer.writerow(data_dydx) - - @staticmethod - def delete_results(): - file_aave = "/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv" - file_dydx = "/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv" - if os.path.exists(file_aave) and os.path.isfile(file_aave): - os.remove(file_aave) - if os.path.exists(file_dydx) and os.path.isfile(file_dydx): - os.remove(file_dydx) - - @staticmethod - def add_header(): - aave_headers = [ - "market_price", - "I_current", - # "I_previous", - "I_old", - "entry_price", - "collateral_eth", - "usdc_status", - "debt", - "ltv", - "lending_rate", - "interest_on_lending_usd", - "borrowing_rate", - "interest_on_borrowing", - "lend_minus_borrow_interest", - "costs", - "gas_fees", -<<<<<<< HEAD - "total_costs_from_aave_n_dydx", - "total_stgy_pnl", - "index_of_mkt_price"] -======= - "total_costs", - "index_of_mkt_price", - ] ->>>>>>> cd6cfcb... write function for getting order book and historical data - dydx_headers = [ - "market_price", - "I_current", - # "I_previous", - "I_old", - "entry_price", - "short_size", - "collateral", - "notional", - "equity", - "leverage", - "pnl", - # "price_to_liquidation", - "collateral_status", - "short_status", - "order_status", - "withdrawal_fees", - "funding_rates", - "maker_taker_fees", - "costs", - "gas_fees", -<<<<<<< HEAD - "total_costs_from_aave_n_dydx", - "total_stgy_pnl", - "index_of_mkt_price"] - with open('/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv', 'a') as file: - writer = csv.writer(file, lineterminator='\n') -======= - "total_costs", - "index_of_mkt_price", - ] - with open( - "/home/agustin/Git-Repos/HedgingScripts/files/aave_results.csv", "a" - ) as file: - writer = csv.writer(file, lineterminator="\n") ->>>>>>> cd6cfcb... write function for getting order book and historical data - writer.writerow(aave_headers) - with open( - "/home/agustin/Git-Repos/HedgingScripts/files/dydx_results.csv", - "a", - newline="", - encoding="utf-8", - ) as file: - writer = csv.writer(file, lineterminator="\n") - writer.writerow(dydx_headers) - - @staticmethod - def historical_parameters_data(aave_instance, dydx_instance): - aave_df = pd.DataFrame( - aave_instance.historical_data, columns=list(aave_instance.__dict__.keys()) - ) - dydx_df = pd.DataFrame( - dydx_instance.historical_data, columns=list(dydx_instance.__dict__.keys()) - ) - return {"aave_df": aave_df, "dydx_df": dydx_df} - - @staticmethod - def plot_data(stgy_instance): # , - # save, - # factors, vol, period): - # colors https://datascientyst.com/full-list-named-colors-pandas-python-matplotlib/ - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - # fig.suptitle("Factors = (%s, %s, %s), Vol=%s, Period=%s to %s" % (factors[0], factors[1], factors[2], - # vol, period[0], period[1])) - axs.plot( - stgy_instance.historical_data["close"], - color="tab:blue", - label="market price", - ) - # axs.plot(list(pnl_), label='DyDx pnl') - # p_rtrn_usdc_n_rmv_coll_dydx = stgy_instance.target_prices['rtrn_usdc_n_rmv_coll_dydx'] -<<<<<<< HEAD - p_borrow_usdc_n_add_coll = stgy_instance.trigger_prices['borrow_usdc_n_add_coll'] - # p_add_collateral_dydx = stgy_instance.target_prices['p_borrow_usdc_n_add_coll'] - # p_close_short = stgy_instance.target_prices['close_short'] - p_open_close = stgy_instance.trigger_prices['open_close'] - floor = min(list(stgy_instance.trigger_prices.values())) -======= - p_borrow_usdc_n_add_coll = stgy_instance.target_prices["borrow_usdc_n_add_coll"] - # p_add_collateral_dydx = stgy_instance.target_prices['p_borrow_usdc_n_add_coll'] - # p_close_short = stgy_instance.target_prices['close_short'] - p_open_close = stgy_instance.target_prices["open_close"] - floor = min(list(stgy_instance.target_prices.values())) ->>>>>>> cd6cfcb... write function for getting order book and historical data - # axs.axhline(y=p_rtrn_usdc_n_rmv_coll_dydx, color='black', linestyle='--', - # label='rtrn_usdc_n_rmv_coll_dydx') - axs.axhline( - y=p_borrow_usdc_n_add_coll, - color="darkgoldenrod", - linestyle="--", - label="borrow_usdc_n_add_coll", - ) - # axs.axhline(y=p_add_collateral_dydx, color='tab:orange', linestyle='--', label='add_collateral_dydx') - # axs.axhline(y=p_close_short, color='olive', linestyle='--', label='close_short') -<<<<<<< HEAD - axs.axhline(y=p_open_close, color='darkred', linestyle='--', label='open_close') - axs.axhline(y=floor, color='red', linestyle='--', label='floor') - if 'repay_aave' in list(stgy_instance.trigger_prices.keys()): - p_repay_aave = stgy_instance.trigger_prices['repay_aave'] - axs.axhline(y=p_repay_aave, color='magenta', linestyle='--', label='repay_aave') - if 'ltv_limit' in list(stgy_instance.trigger_prices.keys()): - p_ltv_limit = stgy_instance.trigger_prices['ltv_limit'] - axs.axhline(y=p_ltv_limit, color='purple', linestyle='--', label='ltv_limit') -======= - axs.axhline(y=p_open_close, color="darkred", linestyle="--", label="open_close") - axs.axhline(y=floor, color="red", linestyle="--", label="floor") - if "repay_aave" in list(stgy_instance.target_prices.keys()): - p_repay_aave = stgy_instance.target_prices["repay_aave"] - axs.axhline( - y=p_repay_aave, color="magenta", linestyle="--", label="repay_aave" - ) - if "ltv_limit" in list(stgy_instance.target_prices.keys()): - p_ltv_limit = stgy_instance.target_prices["ltv_limit"] - axs.axhline( - y=p_ltv_limit, color="purple", linestyle="--", label="ltv_limit" - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - # print(list(stgy_instance.target_prices.keys())) - axs.grid() - axs.legend(loc="lower left") - # if save: - # plt.savefig('/home/agustin/Git-Repos/HedgingScripts/files/simulated_plot_index_%s_to_%s.png' - # % (period[0], period[1])) - # else: - plt.show() - - def get_gif(self): - import numpy as np - from matplotlib.animation import FuncAnimation - from IPython import display - import matplotlib.pyplot as plt - - Figure = plt.figure() - lines_plotted = plt.plot([]) - self.line_plotted = lines_plotted[0] - anim_created = FuncAnimation( - Figure, self.AnimationFunction, frames=100, interval=25 - ) - video = anim_created.to_html5_video() - plot = display.HTML(video) - # plot.save() - display.display(plot) - # with open('plot.html', 'w') as f: - # f.write(plot.text) - # with open("plot.html", "w") as file: - # file.write(plot) - - # function takes frame as an input - def AnimationFunction(self, frame): - - # setting y according to frame - # number and + x. It's logic - y = self.historical_data["close"][frame] - x = self.historical_data.index[frame] - - # line is set with new values of x and y - self.line_plotted.set_data((x, y)) - - @staticmethod - def plot_price_distribution(stgy_instance): - # fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - # from https://stackoverflow.com/questions/6855710/how-to-have-logarithmic-bins-in-a-python-histogram - data = np.log(stgy_instance.historical_data["close"]) - MIN, MAX = data.min(), data.max() - data.hist(bins=np.linspace(MIN, MAX, 50)) - plt.gca().set_xscale("log") - plt.show() - # print(np.log(historical_data['close'])) - - # @staticmethod - def plot_returns_distribution(self): # stgy_instance): - """ - We assume returns are normally distributed - """ - - historical = self.historical_data # stgy_instance.historical_data.copy() - pct_change = historical["close"].pct_change().fillna(method="bfill") - log_returns = np.log(historical["close"]) - np.log( - historical["close"].shift(60) - ) - historical["pct_change"] = pct_change - historical["log_returns"] = log_returns - - x = np.linspace(pct_change.min(), 1, 100) - mean = np.mean(pct_change) - std = np.std(pct_change) - norm_dist = norm.pdf(x, mean, std) - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - log_returns.hist(bins=50, ax=axs) - # pct_change.hist(bins=50, ax=axs) - # axs.set_xlabel('Return') - # axs.set_ylabel('Sample') - # axs.set_title('Return distribution') - # axs.plot(x, norm_dist, color='tab:blue', label='Returns dist') - - # To check if its normally distributed + understate the likelihood of returns beyond -2/+2 quantiles - # import scipy.stats as stats - # stats.probplot(historical['returns'], dist='norm', plot=axs) - # axs.grid() - plt.show() - # print(historical.describe()) - - @staticmethod - def prob_return_in_range(stgy_instance, range): - """ - range = [a, b] with a < b - Recall: - cumulative distribution function of a random variable X is F_X(x) := P(X <= x) - So the probability of returns (R) falling in range is P(a <= R <= b) = P(R <= b) - P(R < a) = F_R(b) - F_R(a) - If we assume returns are normally distributed then F could be estimated using norm(mean, std).cdf function - """ - returns = stgy_instance.historical_data["returns"] - mean = np.mean(returns) - std = np.std(returns) - norm_cdf = norm(mean, std).cdf - return norm_cdf(range[1]) - norm_cdf(range[0]) - - @staticmethod - def plot_volatility(stgy_instance, method): - """ - We assume returns are normally distributed - """ - if method == "arch": - vol = stgy_instance.volatility_calculator.get_arch( - stgy_instance.historical_data, 1, 0, 0 - ) - elif method == "garch": - vol = stgy_instance.volatility_calculator.get_garch( - stgy_instance.historical_data - ) - elif method == "emwa": - vol = stgy_instance.volatility_calculator.get_emwa( - stgy_instance.historical_data, 1, 0, 0 - ) - historical = stgy_instance.historical_data.copy() - pct_change = historical["close"].pct_change().fillna(method="bfill") - log_returns = np.log(historical["close"]) - np.log(historical["close"].shift(1)) - historical["pct_change"] = pct_change - historical["log_returns"] = log_returns - - x = np.linspace(pct_change.min(), 1, 100) - mean = np.mean(pct_change) - std = np.std(pct_change) - norm_dist = norm.pdf(x, mean, std) - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - log_returns.hist(bins=50, ax=axs) - - -if __name__ == "__main__": - data_dumper = DataDamperNPlotter() - historical_daily = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv" - ) - historical_hourly = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1h-data.csv" - ) - historical_minutes = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv" - ) - # assign data to stgy instance + define index as dates - data_dumper.historical_data = pd.DataFrame( - historical_minutes["close"], columns=["close"] - ) - # data_dumper.historical_data = pd.DataFrame(historical_hourly["close"], columns=['close']) - data_dumper.plot_returns_distribution() diff --git a/hedge_scripts/dydx.py b/hedge_scripts/dydx.py deleted file mode 100644 index f55985a..0000000 --- a/hedge_scripts/dydx.py +++ /dev/null @@ -1,255 +0,0 @@ -import math -import random -import numpy as np -import interval - - -class Dydx(object): - def __init__(self, config): - # assert aave_class == isinstance(aave) - self.market_price = config["market_price"] - self.interval_current = config["interval_current"] - self.entry_price = config["entry_price"] - self.short_size = config["short_size"] - self.collateral = config["collateral"] - self.notional = config["notional"] - self.equity = config["equity"] - self.leverage = config["leverage"] - self.pnl = config["pnl"] - # self.price_to_liquidation = config['price_to_liquidation'] - self.collateral_status = config["collateral_status"] - self.short_status = config["short_status"] - self.order_status = True - self.withdrawal_fees = 0.01 / 100 - self.funding_rates = 0 - self.maker_taker_fees = 0 - self.costs = 0 - # self.historical = pd.DataFrame() - # self.aave_class_instance = aave_class_instance - # self.staked_in_protocol = stk - - # auxiliary functions - def pnl_calc(self): - return self.short_size * (self.market_price - self.entry_price) - - def notional_calc(self): - return abs(self.short_size) * self.market_price - - def equity_calc(self): - return self.collateral + self.pnl_calc() - - def leverage_calc(self): - if self.equity_calc() == 0: - return 0 - else: - return self.notional_calc() / self.equity_calc() - - def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance): - return ( - self.entry_price - + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size - ) - - @staticmethod - def price_to_liquidation_calc(dydx_client_class_instance): - return dydx_client_class_instance.dydx_margin_parameters["liquidation_price"] - - def add_funding_rates(self): - self.simulate_funding_rates() - self.costs = self.costs - self.funding_rates - - def simulate_funding_rates(self): - # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6) - - # best case - # self.funding_rates = 0.0075 / 100 - - # average -0.00443% - - # worst case - self.funding_rates = -0.0075 / 100 - - def simulate_maker_taker_fees(self): - # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6) - - # maker fees - self.maker_taker_fees = 0.05 / 100 # <1M - # self.maker_taker_fees = 0.04 / 100 # <5M - # self.maker_taker_fees = 0.035 / 100 # <10M - # self.maker_taker_fees = 0.03 / 100 # <50M - # self.maker_taker_fees = 0.025 / 100 # <200M - # self.maker_taker_fees = 0.02 / 100 # >200M - - # Actions to take - def remove_collateral(self, new_market_price, new_interval_current, stgy_instance): - self.cancel_order() - time = 0 - if self.collateral_status: - self.collateral_status = False - withdrawal_fees = self.collateral * self.withdrawal_fees - self.collateral = 0 - # self.price_to_liquidation = 0 - - # fees - self.costs = self.costs + withdrawal_fees - - time = 1 - return time - - def add_collateral(self, new_market_price, new_interval_current, stgy_instance): - gas_fees = stgy_instance.gas_fees - aave_class_instance = stgy_instance.aave - time = 0 - if not self.collateral_status: - self.collateral_status = True - self.collateral = aave_class_instance.debt_initial - # fees - self.costs = self.costs + gas_fees - # We place an order in open_close -<<<<<<< HEAD - self.place_order(stgy_instance.trigger_prices['open_close']) -======= - self.place_order(stgy_instance.target_prices["open_close"]) ->>>>>>> cd6cfcb... write function for getting order book and historical data - # add time - time = 10 - return time - - def open_short(self, new_market_price, new_interval_current, stgy_instance): - aave_class_instance = stgy_instance.aave - # dydx_client_class_instance = stgy_instance.dydx_client - intervals = stgy_instance.intervals - if (not self.short_status) and self.order_status: - self.short_status = True - # dydx parameters -<<<<<<< HEAD - if self.market_price <= stgy_instance.trigger_prices['floor']: - print("CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!") - print("Difference of: ", stgy_instance.trigger_prices['floor'] - self.market_price) - - # if self.market_price <= stgy_instance.trigger_prices['open_close']: - # print("CAUTION: OPEN PRICE LOWER THAN open_close!") - # print("Difference of: ", stgy_instance.trigger_prices['open_close'] - self.market_price) -======= - if self.market_price <= stgy_instance.target_prices["floor"]: - print("CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!") - print( - "Difference of: ", - stgy_instance.target_prices["floor"] - self.market_price, - ) - - if self.market_price <= stgy_instance.target_prices["open_close"]: - print("CAUTION: OPEN PRICE LOWER THAN open_close!") - print( - "Difference of: ", - stgy_instance.target_prices["open_close"] - self.market_price, - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - self.entry_price = self.market_price - self.short_size = -aave_class_instance.collateral_eth_initial - # self.collateral = aave_class_instance.debt_initial - self.notional = self.notional_calc() - self.equity = self.equity_calc() - self.leverage = self.leverage_calc() - # Simulate maker taker fees - self.simulate_maker_taker_fees() - # Add costs - self.costs = self.costs + self.maker_taker_fees * self.notional - - price_floor = intervals["open_close"].left_border - floor_position = intervals["floor"].position_order - -<<<<<<< HEAD - price_floor = intervals['open_close'].left_border - floor_position = intervals['floor'].position_order - - price_to_repay_debt = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(), - aave_class_instance) - price_to_ltv_limit = intervals['floor'].left_border - stgy_instance.trigger_prices['repay_aave'] = price_to_repay_debt - stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit -======= - price_to_repay_debt = self.price_to_repay_aave_debt_calc( - 1 + aave_class_instance.buffer_for_repay(), aave_class_instance - ) - price_to_ltv_limit = intervals["floor"].left_border - stgy_instance.target_prices["repay_aave"] = price_to_repay_debt - stgy_instance.target_prices["ltv_limit"] = price_to_ltv_limit ->>>>>>> cd6cfcb... write function for getting order book and historical data - if price_to_ltv_limit < price_to_repay_debt: - intervals["floor"] = interval.Interval( - price_to_repay_debt, price_floor, "floor", floor_position - ) - intervals["repay_aave"] = interval.Interval( - price_to_ltv_limit, - price_to_repay_debt, - "repay_aave", - floor_position + 1, - ) - intervals["minus_infty"] = interval.Interval( - -math.inf, price_to_ltv_limit, "minus_infty", floor_position + 2 - ) - else: - print("CAUTION: P_ltv > P_repay") - print("Difference of: ", price_to_ltv_limit - price_to_repay_debt) - price_to_repay_debt = self.price_to_repay_aave_debt_calc( - 0.5, aave_class_instance - ) - intervals["floor"] = interval.Interval( - price_to_ltv_limit, price_floor, "floor", floor_position - ) - intervals["ltv_limit"] = interval.Interval( - price_to_repay_debt, - price_to_ltv_limit, - "repay_aave", - floor_position + 1, - ) - intervals["minus_infty"] = interval.Interval( - -math.inf, price_to_repay_debt, "minus_infty", floor_position + 2 - ) - self.order_status = False - return 0 - - def close_short(self, new_market_price, new_interval_current, stgy_instance): - if self.short_status: - # Next if is to move up the threshold if we didnt execute at exactly open_close -<<<<<<< HEAD - if self.market_price >= stgy_instance.trigger_prices['open_close']: - # new_open_close = self.market_price - print("CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!") - print("Difference of: ", self.market_price - stgy_instance.trigger_prices['open_close']) -======= - if self.market_price >= stgy_instance.target_prices["open_close"]: - # new_open_close = self.market_price - print( - "CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!" - ) - print( - "Difference of: ", - self.market_price - stgy_instance.target_prices["open_close"], - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - # stgy_instance.target_prices['open_close'] = self.market_price - self.notional = self.notional_calc() - self.equity = self.equity_calc() - self.leverage = self.leverage_calc() - self.pnl = self.pnl_calc() - # We update short parameters after the calculation of pnl - self.entry_price = 0 - self.short_status = False - self.short_size = 0 - self.simulate_maker_taker_fees() - self.costs = self.costs + self.maker_taker_fees * self.notional -<<<<<<< HEAD - self.place_order(stgy_instance.trigger_prices['open_close']) - return 0 -======= - self.place_order(stgy_instance.target_prices["open_close"]) ->>>>>>> cd6cfcb... write function for getting order book and historical data - - def place_order(self, price): - self.order_status = True - # self. - - def cancel_order(self): - self.order_status = False diff --git a/hedge_scripts/parameter_manager.py b/hedge_scripts/parameter_manager.py deleted file mode 100644 index 4ca5b72..0000000 --- a/hedge_scripts/parameter_manager.py +++ /dev/null @@ -1,522 +0,0 @@ -import math -import random -import numpy as np -from scipy.stats import norm -import pandas as pd -import matplotlib.pyplot as plt - -<<<<<<< HEAD -import interval - -======= ->>>>>>> cd6cfcb... write function for getting order book and historical data - -class ParameterManager(object): - # auxiliary functions - @staticmethod -<<<<<<< HEAD - def define_target_prices(stgy_instance, slippage, floor): - p_open_close = floor * (1+slippage) - ########################################################## - # We define the intervals - list_of_intervals = ["open_close", - "floor"] - list_of_trigger_prices = [p_open_close, - floor] -======= - def define_target_prices(stgy_instance, N_week, data_for_thresholds, floor): - # P_open_close to be P_floor * e^(mu + factor * sigma) where mu, sigma are calculated - # based on last 3 month of data. Factor is calculated using the VaR approach in which we choose a confidence - # level X (a probability of ensurance) and we calculate the maximum loss we are X % sure we wont lose more than - # that. - log_returns_1_week = np.log(data_for_thresholds["close"]) - np.log( - data_for_thresholds["close"].shift(1) - ) - ewm_log_returns = log_returns_1_week[-N_week:].ewm(alpha=0.8, adjust=False) - mean_ema_log_returns = round(ewm_log_returns.mean().mean() * 365, 3) - std_ema_log_returns = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - - mu = mean_ema_log_returns / 365 * 24 * 60 - sigma = (std_ema_log_returns / np.sqrt(365)) * np.sqrt(24 * 60) - - factor_open_close = round(norm.ppf(0.90), 3) - p_open_close = floor * math.e ** (mu + factor_open_close * sigma) - ########################################################## - # P_borrow_usdc_n_add_coll to be P_open_close * e^(mu + factor * sigma) where mu, sigma are calculated - # based on last 3 month of data. Factor is calculated using the VaR approach in which we choose a confidence - # level X (a probability of ensurance) and we calculate the maximum loss we are X % sure we wont lose more than - # that. - log_returns_10min_last_3_months = np.log( - stgy_instance.historical_data[-3 * 30 * 24 * 60 :]["close"] - ) - np.log(data_for_thresholds[-3 * 30 * 24 * 60 :]["close"].shift(10)) - - # vol benchmark: daily version of last 3month 2min vol (mean std) - ewm_log_returns = log_returns_10min_last_3_months.ewm(alpha=0.8, adjust=False) - std_10min_ema_mean_value = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - mean_10min_ema = round(ewm_log_returns.mean().mean() * 365, 3) - mu_10min_mean_daily = mean_10min_ema / 365 * 24 * 6 - sigma_10min_mean_daily = round( - (std_10min_ema_mean_value / np.sqrt(365) * np.sqrt(24 * 6)), 3 - ) - - factor_add = round(norm.ppf(0.90), 3) - - p_borrow_usdc_n_add_coll = p_open_close * math.e ** ( - mu_10min_mean_daily + factor_add * sigma_10min_mean_daily - ) - - stgy_instance.target_prices_copy = stgy_instance.target_prices - list_of_intervals = [ # "rtrn_usdc_n_rmv_coll_dydx", - "borrow_usdc_n_add_coll", - "open_close", - # "open_short", - "floor", - ] - list_of_trigger_prices = [ # p_rtrn_usdc_n_rmv_coll_dydx, - p_borrow_usdc_n_add_coll, - p_open_close, - # p_open_short, - floor, - ] ->>>>>>> cd6cfcb... write function for getting order book and historical data - # We define/update trigger prices - for i in range(len(list_of_intervals)): - interval_name = list_of_intervals[i] - trigger_price = list_of_trigger_prices[i] - stgy_instance.trigger_prices[interval_name] = trigger_price - - @staticmethod - def define_intervals(stgy_instance): -<<<<<<< HEAD - stgy_instance.intervals = {"infty": interval.Interval(stgy_instance.trigger_prices['open_close'], - math.inf, - "infty", 0), - "open_close": interval.Interval(stgy_instance.trigger_prices['floor'], - stgy_instance.trigger_prices['open_close'], - "open_close", 1), - "minus_infty": interval.Interval(-math.inf, - stgy_instance.trigger_prices['floor'], - "minus_infty", 2)} -======= - stgy_instance.intervals = { - "infty": interval.Interval( - stgy_instance.target_prices["borrow_usdc_n_add_coll"], - math.inf, - "infty", - 0, - ), - } - # By reading current names and values (instead of defining the list of names and values at hand) we can - # use this method both for defining the thresholds the first time and for updating them every day - names = list(stgy_instance.target_prices.keys()) - values = list(stgy_instance.target_prices.values()) - - # We define/update thresholds - for i in range(len(stgy_instance.target_prices) - 1): - stgy_instance.intervals[names[i]] = interval.Interval( - values[i + 1], values[i], names[i], i + 1 - ) - stgy_instance.intervals["minus_infty"] = interval.Interval( - -math.inf, values[-1], "minus_infty", len(values) - ) - # print(stgy_instance.intervals.keys()) ->>>>>>> cd6cfcb... write function for getting order book and historical data - - # function to assign interval_current to each market_price in historical data - @staticmethod - def load_intervals(stgy_instance): - stgy_instance.historical_data["interval"] = [[0, 0]] * len( - stgy_instance.historical_data["close"] - ) - stgy_instance.historical_data["interval_name"] = ["nan"] * len( - stgy_instance.historical_data["close"] - ) - for loc in range(len(stgy_instance.historical_data["close"])): - market_price = stgy_instance.historical_data["close"][loc] - for i in list(stgy_instance.intervals.values()): - if i.left_border < market_price <= i.right_border: - stgy_instance.historical_data["interval"][loc] = i - stgy_instance.historical_data["interval_name"][loc] = i.name - - @staticmethod - # Checking and updating data - def update_parameters(stgy_instance, new_market_price, new_interval_current): - # AAVE - stgy_instance.aave.market_price = new_market_price - stgy_instance.aave.interval_current = new_interval_current - # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now - # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24 - stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60) - stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60) - # We have to execute track_ first because we need the fees for current collateral and debt values - stgy_instance.aave.track_lend_borrow_interest() - # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs - stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt - stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values - stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc() - - # DYDX - stgy_instance.dydx.market_price = new_market_price - stgy_instance.dydx.interval_current = new_interval_current - stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc() - stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc() - stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc() - stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc() - # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client) - - def find_scenario( - self, stgy_instance, new_market_price, new_interval_current, interval_old, index - ): - actions = self.actions_to_take( - stgy_instance, new_interval_current, interval_old - ) - self.simulate_fees(stgy_instance) - # We reset the costs in order to always start in 0 - stgy_instance.aave.costs = 0 - stgy_instance.dydx.costs = 0 - time = 0 - time_aave = 0 - time_dydx = 0 - for action in actions: - # if action == "rtrn_usdc_n_rmv_coll_dydx": - # time = stgy_instance.dydx.remove_collateral_dydx(new_market_price, new_interval_current, stgy_instance) - # stgy_instance.aave.return_usdc(new_market_price, new_interval_current, stgy_instance) - if action == "borrow_usdc_n_add_coll": - time_aave = stgy_instance.aave.borrow_usdc( - new_market_price, new_interval_current, stgy_instance - ) - market_price = stgy_instance.historical_data["close"][index + time_aave] - interval_current = stgy_instance.historical_data["interval"][ - index + time_aave - ] - time_dydx = stgy_instance.dydx.add_collateral( - market_price, interval_current, stgy_instance - ) - time_aave = 0 - elif action in stgy_instance.aave_features["methods"]: - time_aave = getattr(stgy_instance.aave, action)( - new_market_price, new_interval_current, stgy_instance - ) - elif action in stgy_instance.dydx_features["methods"]: - time_dydx = getattr(stgy_instance.dydx, action)( - new_market_price, new_interval_current, stgy_instance - ) - time += time_aave + time_dydx - # print(stgy_instance.aave_features["methods"]) - # print(stgy_instance.dydx_features["methods"]) - return time - # stgy_instance.append(action) - - @staticmethod - def actions_to_take(stgy_instance, new_interval_current, interval_old): - actions = [] - - # Case P increasing - if interval_old.is_lower(new_interval_current): -<<<<<<< HEAD - for i in reversed(range(new_interval_current.position_order, interval_old.position_order)): - if list(stgy_instance.intervals.keys())[i+1] == 'open_close': - actions.append('close_short') - else: - actions.append(list(stgy_instance.intervals.keys())[i+1]) # when P goes up we execute the name of previous intervals -======= - for i in reversed( - range(new_interval_current.position_order, interval_old.position_order) - ): - actions.append( - list(stgy_instance.intervals.keys())[i + 1] - ) # when P goes up we execute the name of previous intervals ->>>>>>> cd6cfcb... write function for getting order book and historical data - # print(list(stgy_instance.intervals.keys())[i+1]) - - # Case P decreasing - else: -<<<<<<< HEAD - for i in range(interval_old.position_order + 1, new_interval_current.position_order + 1): - if list(stgy_instance.intervals.keys())[i] == 'open_close': - actions.append('open_short') - else: - actions.append(list(stgy_instance.intervals.keys())[i]) - # print(actions) -======= - for i in range( - interval_old.position_order + 1, new_interval_current.position_order + 1 - ): - actions.append(list(stgy_instance.intervals.keys())[i]) - print(actions) ->>>>>>> cd6cfcb... write function for getting order book and historical data - return actions - - @staticmethod - def simulate_fees(stgy_instance): - # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6) - - # best case - # stgy_instance.gas_fees = 1 - - # stgy_instance.gas_fees = 3 - - # stgy_instance.gas_fees = 6 - - # worst case - stgy_instance.gas_fees = 10 - - @staticmethod - def update_pnl(stgy_instance): - stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs \ - + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees - - @staticmethod - def add_costs(stgy_instance): -<<<<<<< HEAD - stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \ - + stgy_instance.aave.costs + stgy_instance.dydx.costs -======= - stgy_instance.total_costs = ( - stgy_instance.total_costs - + stgy_instance.aave.costs - + stgy_instance.dydx.costs - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - - @staticmethod - def value_at_risk(data, method, X): # T, - # exposure = abs(stgy_instance.dydx.short_size) # we are exposed to an amount equal to the size - # window_to_use = 3 * 30 * 24 * 60 # 3 months of data - # data = stgy_instance.historical_data[-window_to_use:]['close'] - # vol benchmark: daily version of last 3month 2min vol (mean std) - if method == "parametric": - """ -<<<<<<< HEAD - 1) Normal returns assumption (deprecated): - We assume portfolio value is normally distributed. Let's mu and sigma be the drift (SMA, EMA) and std of - returns V_T/V_0. - V_T / V_0 ~ N(mu*T, sigma^2*T) --> V_T ~ V_0 * N(mu*T, sigma^2*T) = N(V_0 * mu*T, V_0^2 * sigma^2*T) - (mu*T = mu_T, sigma*T^1/2 = sigma_T, ie the value of mu and sigma expresses in the freq T) - Then, using that 95% of values under normal dist falls between 1.96 sigmas, - we can say that with a 95% confidence - |V_T| < V_0 * mu*T +- 1.96 * V_0 * sigma * T^1/2 - = V_0 * (mu*T +- 1.96 * sigma * T^1/2) - 2) Log-normal returns assumption: - We assume portfolio value is log-normally distributed. Let's mu and sigma be the drift (SMA, EMA) and std of - returns V_T/V_0. - mu*T = mu_T, sigma*T^1/2 = sigma_T - ln(V_T / V_0) ~ N((mu-sigma^2/2)*T, sigma^2*T) - --> ln V_T ~ ln V_0 + N((mu-sigma^2/2)*T, sigma^2*T) - = N(ln V_0 + (mu-sigma^2/2)*T, sigma^2*T) - Then, using that 95% of values under normal dist falls between 1.96 sigmas, - we can say that with a 95% confidence - |ln V_T| < ln V_0 +(mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2 - |V_T| < e^{ln V_0 +(mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2} - = V_0 * e^{(mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2} - ~ V_0 * (1 + (mu-sigma^2/2)*T +- 1.96 * sigma * T^1/2) -======= - We assume portfolio value is log-normally distributed - ln(V_T / V_0) ~ N((mu-sigma^2/2)*T, sigma^2*T) --> ln V_T ~ N(ln V_0 +(mu-sigma^2/2)*T, sigma^2*T) - Then, using that 95% of values under normal dist falls between 1.96 sigmas, - we can say that with a 95% confidence - |ln V_T| < [ln V_0 +(mu-sigma^2/2)*T] +- 1.96 * sigma * T^1/2 - V_T < e^{[ln V_0 +(mu-sigma^2/2)*T] +- 1.96 * sigma * T^1/2} - ->>>>>>> cd6cfcb... write function for getting order book and historical data - In general, given a c-level X we can say the same using factor = F^-1(X) = norm.ppf(X) - """ - # 2nd case - log_returns = np.log(data) - np.log(data.shift(1)) - sigma = round(log_returns.ewm(alpha=0.8, adjust=False).std().mean(), 3) - mu = round(log_returns.ewm(alpha=0.8, adjust=False).mean().mean(), 3) - factor = round(norm.ppf(X), 3) -<<<<<<< HEAD - var = (mu-sigma**2/2) + sigma * factor - return var['close'] -======= - var = mu + sigma * factor - return var["close"] ->>>>>>> cd6cfcb... write function for getting order book and historical data - elif method == "non_parametric": - """ - We dont assume anything here. The idea will be to use past data for simulating different - today's portfolio's value by taking - change_i = price_i / price_{i-1} --> change on i-th day - simulated_price_i = today_price * change_i - --> simulated a new price assuming yesterday/today's change is equal to i-th/i-1-th's change - portf_value_i = exposure * simulated_price_i / today_price - [ = exposure * change_i ] - Then, we calculate our potential profits/losses taking -<<<<<<< HEAD - loss_i = exposure - portf_value_i - [ = exposure * (1 - simulated_price_i / today_price) - = exposure * (1 - today_price * change_i / today_price - = exposure * (1 - change_i) ] -======= - loss_i = exposure - portf_value_i - [ = exposure * (1 - simulated_price_i / today_price) - = exposure * (1 - today_price * change_i / today_price - = exposure * (1 - change_i ] ->>>>>>> cd6cfcb... write function for getting order book and historical data - i.e. we calculate the potential loss by comparing a portf value with actual exposure against - portf value with a different exposure (exposure * change_i) - That will give us a dataset of daily losses and therefore a distribution for daily losses in the value of - our portf. - We take the VaR as the X-th percentile of this dist. That will be our 1-day VaR. In order to - calculate N-day potential loss we take 1-day VaR * N^1/2. -<<<<<<< HEAD - So we will be X% confident that we will not take a loss greater than this VaR estimate if market behaviour -======= - So we will be X% confident that we wil not take a loss greater than this VaR estimate if market behaviour ->>>>>>> cd6cfcb... write function for getting order book and historical data - is according to last data. - Everywhere day can be changed by any other time freq, in our case by minutes. - We repeat this for every new price, ie for every new data-set of last data to keep an - up to date VaR estimation. - The estimate of VaR is the loss when we are at this 99th percentile point. When there are n observations - and k is an integer, the k/(n-1)-percentile is the observation ranked k + 1 of the list of losses ordered - from lowest to highest losses. - (Ex. n=501, X=99% --> 99th percentile --> k = (n-1)*0.99 = 495 --> The fifth-highest loss) - """ - changes = list(round(data.pct_change().dropna()["close"], 3)) # returns - today = data.iloc[-1]["close"] - # print(today, changes) - scenarios = [] - portf_value = [] - difference_in_portf_value = [] - difference_in_portf_value_pcg = [] - for i in range(len(changes)): - scenarios.append(today * changes[i]) - # portf_value.append(exposure*scenarios[i]/today) - # difference_in_portf_value.append(exposure - portf_value[i]) - difference_in_portf_value_pcg.append([changes[i], i]) - difference_in_portf_value_pcg.sort() - plt.hist(changes) - return difference_in_portf_value_pcg[-10:] - -<<<<<<< HEAD -if __name__ == '__main__': - pass -======= - -if __name__ == "__main__": - #######################################3 - # get historical data in seconds - import requests - from requests import Request - from datetime import datetime - import pandas as pd - import numpy as np - - # import json - # url = 'https://api.coinbase.com/v2/prices/BTC-USD/historic?2018-07-15T00:00:00-04:00' - # request = Request('GET', url) - # s = requests.Session() - # prepared = request.prepare() - # response = s.send(prepared).json()['data']['prices'] - # historical_seconds = {'prices': [], 'date': []} - # for i in range(len(response)): - # item = response[i] - # historical_seconds['prices'].append(float(item['price'])) - # historical_seconds['date'].append(datetime.strptime(item['time'], '%Y-%m-%dT%H:%M:%SZ')) - # historical_seconds = pd.DataFrame(historical_seconds['prices'], - # index=historical_seconds['date'], - # columns=['close']).iloc[::-1] - historical_daily = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1d-data.csv" - ) - historical_hourly = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1h-data.csv" - ) - historical_minutes = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv" - ) - # assign data to stgy instance + define index as dates - historical_data_daily = pd.DataFrame(historical_daily["close"], columns=["close"]) - historical_data_hourly = pd.DataFrame(historical_hourly["close"], columns=["close"]) - historical_data_minutes = pd.DataFrame( - historical_minutes["close"], columns=["close"] - ) - - ######################################################3 - # check historical 2min vol as benchmark to define add threshold - # manager = ParameterManager() - # N_week = 1 * 1 * 7 * 24 * 60 # 7 days - # data_for_thresholds = historical_data_minutes[:N_week].copy() # First week of data - - # log_returns_10_minutes = np.log(historical_minutes['close']) - np.log( - # historical_minutes['close'].shift(10)) - # log_returns = np.log(historical_minutes['close']) - np.log( - # historical_minutes['close'].shift(1)) - # - # # ema log returns - # ewm_log_returns = log_returns_10_minutes.ewm(alpha=0.8, adjust=False) - # - # mean_ema_log_returns_mean_value = round(ewm_log_returns.mean().mean() * 365, 3) - # mean_ema_log_returns_max_value = round(ewm_log_returns.mean().max() * 365, 3) - # mean_ema_log_returns_min_value = round(ewm_log_returns.mean().min() * 365, 3) - # std_ema_log_returns_mean_value = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - # std_ema_log_returns_max_value = round(ewm_log_returns.std().max() * np.sqrt(365), 3) - # std_ema_log_returns_min_value = round(ewm_log_returns.std().min() * np.sqrt(365), 3) - # mu_2min_mean = round(mean_ema_log_returns_mean_value / 365 * 24 * 30, 3) - # mu_2min_max = round(mean_ema_log_returns_max_value / 365 * 24 * 30, 3) - # mu_2min_min = round(mean_ema_log_returns_min_value / 365 * 24 * 30, 3) - # sigma_2min_mean = round((std_ema_log_returns_mean_value / np.sqrt(365)), 3) - # sigma_2min_max = round((std_ema_log_returns_max_value / np.sqrt(365)), 3) - # sigma_2min_min = round((std_ema_log_returns_min_value / np.sqrt(365)), 3) - # std = ewm_log_returns.std() - # # print(std[std==std.max()]) - # # print(historical_minutes['close'][9413-10:9413+10]) - # - # print('Hist_2min_mean_vol_last_3_month + daily v:', [sigma_2min_mean, sigma_2min_mean * np.sqrt(24*30)]) - # print('Hist_2min_max_vol_last_3_month + daily v:', [sigma_2min_max, sigma_2min_max * np.sqrt(24*30)]) - # print('Hist_2min_min_vol_last_3_month + daily v:', [sigma_2min_min, sigma_2min_min * np.sqrt(24*30)]) - - ###################################################### - # check P_open / P_borrow to define ltv_0 - # N_week = 1 * 1 * 7 * 24 * 60 # 7 days - # data_for_thresholds = historical_data_minutes[:N_week].copy() # First week of data - # log_returns = np.log(data_for_thresholds['close']) - np.log( - # data_for_thresholds['close'].shift(1)) - # # ema log returns - # ewm_log_returns = log_returns.ewm(alpha=0.8, adjust=False) - # mean_ema_log_returns = round(ewm_log_returns.mean().mean() * 365, 3) - # std_ema_log_returns = round(ewm_log_returns.std().mean() * np.sqrt(365), 3) - # - # mu = mean_ema_log_returns / 365 * 24 * 60 - # sigma = (std_ema_log_returns / np.sqrt(365)) * np.sqrt(24 * 60) - # - # factor_close_open = round(norm.ppf(0.99), 3) - # print('1+mu+factor_99 * sigma:', 1+mu+factor_close_open*sigma) - # - # top_pcg_open = 0.02 - # number_of_sigmas_open = (top_pcg_open - mu) / sigma - # confidence_for_close = norm.cdf(number_of_sigmas_open) - # - # print('f_confidence:', number_of_sigmas_open) - # print('confidence:', confidence_for_close) - - ################################################### - # Check VaR results - manager = ParameterManager() - historical_daily = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/BTCUSDC-1d-data_since_1 Jan 2021.csv" - )[-500:] - # assign data to stgy instance + define index as dates - historical_data_daily = pd.DataFrame(historical_daily["close"], columns=["close"]) - data = historical_data_daily - print("VaR_99 Parametric:", manager.value_at_risk(data, "parametric", 0.99)) - print("VaR_99 historical:", manager.value_at_risk(data, "non_parametric", 0.99)) - print(historical_daily["timestamp"][319]) - plt.show() - - ################################################## - # Plot - # axs.axhline(y=p_rtrn_usdc_n_rmv_coll_dydx, color='black', linestyle='--', - # label='rtrn_usdc_n_rmv_coll_dydx') - # axs.axhline(y=p_borrow_usdc_n_add_coll, color='darkgoldenrod', linestyle='--', label='borrow_usdc_n_add_coll') - # axs.axhline(y=p_close_short, color='olive', linestyle='--', label='close_short') - # axs.axhline(y=p_close_short_pcg, color='darkgoldenrod', linestyle='--', label='close_short_pcg') - # axs.axhline(y=p_open_short, color='darkred', linestyle='--', label='open_short') - # axs.axhline(y=p_open_short_pcg, color='black', linestyle='--', label='open_short_pcg') - # axs.axhline(y=floor, color='red', linestyle='--', label='floor') - # axs.grid() - # axs.legend(loc='lower left') - # plt.show() ->>>>>>> cd6cfcb... write function for getting order book and historical data diff --git a/hedge_scripts/stgyapp.py b/hedge_scripts/stgyapp.py deleted file mode 100644 index 1a57417..0000000 --- a/hedge_scripts/stgyapp.py +++ /dev/null @@ -1,399 +0,0 @@ -import json -import pandas as pd -import math - -import aave -import dydx -import binance_client_ -import dydx_client -import sm_interactor -import volatility_calculator -import data_dumper -import parameter_manager -import interval - - -class StgyApp(object): - def __init__(self, config): - - self.stk = config["stk"] - self.total_costs_from_aave_n_dydx = 0 - self.total_pnl = 0 - self.gas_fees = 0 - - # prices and intervals - self.trigger_prices = {} - self.intervals = {} - - # clients for data - self.binance_client = binance_client_.BinanceClient(config["binance_client"]) - self.dydx_client = dydx_client.DydxClient(config["dydx_client"]) - self.sm_interactor = sm_interactor.SmInteractor(config["sm_interactor"]) - # self.historical_data = - - # We create attributes to fill later - self.aave = None - self.aave_features = None - self.aave_historical_data = None - self.aave_rates = None - self.aave_df = None - - self.dydx = None - self.dydx_features = None - self.dydx_historical_data = None - self.dydx_df = None - - self.volatility_calculator = None - - self.parameter_manager = parameter_manager.ParameterManager() - - self.historical_data = None - - self.data_dumper = data_dumper.DataDamperNPlotter() - - def launch(self, config): - # self.call_binance_data_loader() - self.initialize_aave(config["initial_parameters"]["aave"]) - self.initialize_dydx(config["initial_parameters"]["dydx"]) - self.call_dydx_client() - self.call_sm_interactor() - # self.initialize_volatility_calculator() - # floor = 1300 - # self.define_target_prices(floor) - # self.define_intervals() - - # def run_simulations(self): - # interval_old = self.intervals["infty"] - # for i in range(1, len(self.historical_data["close"]) - 1): - # new_interval_previous = self.historical_data["interval"][i - 1] - # new_interval_current = self.historical_data["interval"][i] - # new_market_price = self.historical_data["close"][i] - # # We could pass the whole AAVE_historical_df, DyDx_historical_df as parameters for scenarios if necessary - # self.find_scenario(new_market_price, new_interval_current, interval_old) - # if new_interval_previous != new_interval_current: - # interval_old = new_interval_previous - - # call clients functions -<<<<<<< HEAD - def get_historical_data(self, symbol, freq, - initial_date, save): - eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq, - initial_date=initial_date, save=save) -======= - def call_binance_data_loader(self, symbol, freq, initial_date, save): - eth_historical = self.binance_client.get_all_binance( - symbol=symbol, freq=freq, initial_date=initial_date, save=save - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - # self.historical_data = eth_historical - self.historical_data = eth_historical["close"] - for i in range(len(self.historical_data)): - self.historical_data[i] = float(self.historical_data[i]) - # self.load_intervals() - - def call_dydx_client(self): - self.dydx_client.get_dydx_parameters(self.dydx) - - def call_sm_interactor(self): - self.aave_rates = self.sm_interactor.get_rates() - - # initialize classes - def initialize_aave(self, config): - # We initialize aave and dydx classes instances - self.aave = aave.Aave(config) - # We load methods and attributes for aave and dydx to use later - self.aave_features = { - "methods": [ - func - for func in dir(self.aave) - if (callable(getattr(self.aave, func))) & (not func.startswith("__")) - ], - "attributes": { - "values": list(self.aave.__dict__.values()), - "keys": list(self.aave.__dict__.keys()), - }, - } - # We create an attribute for historical data - self.aave_historical_data = [] - - def initialize_dydx(self, config): - self.dydx = dydx.Dydx(config) - self.dydx_features = { - "methods": [ - func - for func in dir(self.dydx) - if (callable(getattr(self.dydx, func))) & (not func.startswith("__")) - ], - "attributes": { - "values": list(self.dydx.__dict__.values()), - "keys": list(self.dydx.__dict__.keys()), - }, - } - self.dydx_historical_data = [] - - def initialize_volatility_calculator(self): - self.volatility_calculator = volatility_calculator.VolatilityCalculator() - - -if __name__ == "__main__": - # load configurations - with open( - "/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json" - ) as json_file: - config = json.load(json_file) - - # Initialize stgyApp - stgy = StgyApp(config) - - # Track historical data - # symbol = 'ETHUSDC' - # freq = '1m' - # initial_date = "1 Jan 2019" - # stgy.get_historical_data(symbol=symbol, freq=freq, - # initial_date=initial_date, save=True) - - # Load historical data if previously tracked and saved -<<<<<<< HEAD - historical_data = pd.read_csv("/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv")[-1000:] -======= - historical_data = pd.read_csv( - "/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data.csv" - )[-30000:] ->>>>>>> cd6cfcb... write function for getting order book and historical data - # # assign data to stgy instance + define index as dates - stgy.historical_data = pd.DataFrame(historical_data["close"], columns=["close"]) - timestamp = pd.to_datetime(historical_data["timestamp"]) - stgy.historical_data.index = timestamp - # - # ####################################################### - # # Simulations - - # Define trigger prices and thresholds - slippage = max(stgy.historical_data.pct_change().dropna()['close']) - # Define floor -<<<<<<< HEAD - floor = 1558 / (1+slippage) - print([round(slippage, 3), round(1+slippage, 3), floor]) - ######################### - stgy.parameter_manager.define_target_prices(stgy, slippage, floor) -======= - floor = stgy.historical_data["close"].max() * 0.8 - ######################### - # Define trigger prices and thresholds - N_week = 1 * 1 * 7 * 24 * 60 # 7 days - data_for_thresholds = stgy.historical_data[:N_week].copy() # First week of data - stgy.parameter_manager.define_target_prices( - stgy, N_week, data_for_thresholds, floor - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - stgy.parameter_manager.define_intervals(stgy) - stgy.parameter_manager.load_intervals(stgy) - ######################### - # Save historical data with trigger prices and thresholds loaded - stgy.historical_data.to_csv("/home/agustin/Git-Repos/HedgingScripts/files/stgy.historical_data.csv") - ######################### - # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations - - # Define initial and final index if needed in order to only run simulations in periods of several trigger prices - # As we calculate vol using first week of data, we initialize simulations from that week on - initial_index = 28 - stgy.launch(config) - - # Stk eth - stgy.stk = 500000/stgy.historical_data['close'][initial_index] - - # AAVE -<<<<<<< HEAD - stgy.aave.market_price = stgy.historical_data['close'][initial_index] - stgy.aave.interval_current = stgy.historical_data['interval'][initial_index] - - # What is the price at which we place the collateral in AAVE given our initial_index? - stgy.aave.entry_price = stgy.aave.market_price - # We place 90% of staked as collateral and save 10% as a reserve margin -======= - stgy.aave.market_price = stgy.historical_data["close"][initial_index] - stgy.aave.interval_current = stgy.historical_data["interval"][initial_index] - stgy.aave.entry_price = stgy.target_prices["open_close"] ->>>>>>> cd6cfcb... write function for getting order book and historical data - stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3) - stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3) - stgy.reserve_margin_eth = stgy.stk * 0.1 - # We calculate collateral and reserve current value - stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price - stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price - - # What is the usdc_status for our initial_index? - stgy.aave.usdc_status = True -<<<<<<< HEAD - stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage - stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage - # debt_initial - stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3) -======= - stgy.aave.debt = ( - stgy.aave.collateral_eth_initial - * stgy.target_prices["open_close"] - * stgy.aave.borrowed_percentage - ) - # debt_initial - stgy.aave.price_to_ltv_limit = round( - stgy.aave.entry_price * stgy.aave.borrowed_percentage / 0.5, 3 - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - # stgy.total_costs = 104 - - # DyDx - stgy.dydx.market_price = stgy.historical_data["close"][initial_index] - stgy.dydx.interval_current = stgy.historical_data["interval"][initial_index] - stgy.dydx.collateral = stgy.aave.debt - stgy.dydx.equity = stgy.dydx.equity_calc() - stgy.dydx.collateral_status = True - ######################### - # Change or define prices that aren't defined yet if the period of simulations involves those prices - # For ex if we are executing periods of time in which ltv_limit or repay_aave are already defined - - # price_floor = stgy.intervals['open_close'].left_border - previous_position_order = stgy.intervals["open_close"].position_order - stgy.intervals["floor"] = interval.Interval( - stgy.aave.price_to_ltv_limit, floor, "floor", previous_position_order + 1 - ) - stgy.intervals["minus_infty"] = interval.Interval( - -math.inf, - stgy.aave.price_to_ltv_limit, - "minus_infty", - previous_position_order + 2, - ) - - ######################### - # Load interval_old - interval_old = stgy.intervals["infty"] - ######################### - # Clear previous csv data for aave and dydx - stgy.data_dumper.delete_results() - ######################### - # add header to csv of aave and dydx - stgy.data_dumper.add_header() - ######################### -<<<<<<< HEAD - # import time - # # run simulations - # starttime = time.time() - # print('starttime:', starttime) - # for i in range(initial_index, len(stgy.historical_data)): - i = initial_index - - - while(i < len(stgy.historical_data)): - # for i in range(initial_index, len(stgy.historical_data)): -======= - import time - - # run simulations - starttime = time.time() - print("starttime:", starttime) - # for i in range(initial_index, len(stgy.historical_data)): - i = initial_index - while i < len(stgy.historical_data): - # for i in range(initial_index, len(stgy.historical_data)): ->>>>>>> cd6cfcb... write function for getting order book and historical data - # pass - new_interval_previous = stgy.historical_data["interval"][i - 1] - new_interval_current = stgy.historical_data["interval"][i] - new_market_price = stgy.historical_data["close"][i] - ######################### - # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late - # therefore not taking the actions needed as soon as they are needed - if new_interval_previous != new_interval_current: - interval_old = new_interval_previous - ######################### - # Update parameters - # First we update everything in order to execute scenarios with updated values -<<<<<<< HEAD - # We have to update - # AAVE: market_price, interval_current, lending and borrowing fees (and the diference), - # debt value, collateral value and ltv value - # DyDx: market_price, interval_current, notional, equity, leverage and pnl - stgy.parameter_manager.update_parameters(stgy, new_market_price, new_interval_current) - - # Here we identify price movent direction by comparing current interval and old interval - # and we also execute all the actions involved since last price was read - time_used = stgy.parameter_manager.find_scenario(stgy, new_market_price, new_interval_current, interval_old, i) -======= - stgy.parameter_manager.update_parameters( - stgy, new_market_price, new_interval_current - ) - time_used = stgy.parameter_manager.find_scenario( - stgy, new_market_price, new_interval_current, interval_old, i - ) ->>>>>>> cd6cfcb... write function for getting order book and historical data - ######################### - # Funding rates - # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency) - # Moreover, we need to call this method after find_scenarios in order to have all costs updated. - # Calling it before find_scenarios will overwrite the funding by 0 - # We have to check all the indexes between old index i and next index i+time_used -<<<<<<< HEAD - # for index in range(i, i+time_used): - if (i - initial_index) % (8 * 60) == 0: - stgy.dydx.add_funding_rates() - # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates -======= - for index in range(i, i + time_used): - if (index - initial_index) % (8 * 60) == 0: - stgy.dydx.add_funding_rates() - # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates ->>>>>>> cd6cfcb... write function for getting order book and historical data - ######################### - # Add costs - stgy.parameter_manager.add_costs(stgy) - stgy.parameter_manager.update_pnl(stgy) - ######################### - # Write data - # We write the data into the google sheet or csv file acording to sheet value - # (sheet = True --> sheet, sheet = False --> csv) - stgy.data_dumper.write_data( - stgy, new_interval_previous, interval_old, i, sheet=False - ) - ######################### - # Update trigger prices and thresholds - # We update trigger prices and thresholds every day -<<<<<<< HEAD - # if (i+time_used - initial_index) % (1*24*60) == 0: - # # We call the paramater_manager instance with updated data - # stgy.parameter_manager.define_target_prices(stgy, N_week, data_for_thresholds, floor) - # stgy.parameter_manager.define_intervals(stgy) - # stgy.parameter_manager.load_intervals(stgy) - # save = True - # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period) - - # we increment index by the time consumed in executing actions - # i += time_used - i += 1 - # endtime = time.time() - # print('endtime:', endtime) - import matplotlib.pyplot as plt - fig, axs = plt.subplots(1, 1, figsize=(21, 7)) - axs.plot(stgy.historical_data['close'], color='tab:blue', label='market price') - axs.axhline(y=stgy.trigger_prices['floor'], color='darkgoldenrod', linestyle='--', label='floor') - axs.axhline(y=stgy.trigger_prices['open_close'], color='red', linestyle='--', label='open_close') - axs.grid() - axs.legend(loc='lower left') - plt.show() -======= - if (i + time_used - initial_index) % (1 * 24 * 60) == 0: - # We call the paramater_manager instance with updated data - data_for_thresholds = stgy.historical_data[:i].copy() - stgy.parameter_manager.define_target_prices( - stgy, N_week, data_for_thresholds, floor - ) - stgy.parameter_manager.define_intervals(stgy) - stgy.parameter_manager.load_intervals(stgy) - save = True - # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period) - - # we increment index by the time consumed in executing actions - i += time_used - - endtime = time.time() - print("endtime:", endtime) ->>>>>>> cd6cfcb... write function for getting order book and historical data diff --git a/jupyter-lab/Long_Short_Simulations.ipynb b/jupyter-lab/Long_Short_Simulations.ipynb new file mode 100644 index 0000000..a0b856e --- /dev/null +++ b/jupyter-lab/Long_Short_Simulations.ipynb @@ -0,0 +1,2199 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, 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+ }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class StgyApp(object):\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.stk = config[\"stk\"]\n", + " self.total_costs_from_aave_n_dydx = 0\n", + " self.total_pnl = 0\n", + " self.gas_fees = 0\n", + "\n", + " # prices and intervals\n", + " self.trigger_prices = {}\n", + " self.intervals = {}\n", + "\n", + " # clients for data\n", + " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", + " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", + " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", + " # self.historical_data =\n", + "\n", + " # We create attributes to fill later\n", + " self.aave = None\n", + " self.aave_features = None\n", + " self.aave_rates = None\n", + "\n", + " self.dydx = None\n", + " self.dydx_features = None\n", + "\n", + " # self.volatility_calculator = None\n", + "\n", + " self.parameter_manager = ParameterManager()\n", + "\n", + " self.historical_data = None\n", + "\n", + "\n", + " self.data_dumper = DataDamperNPlotter()\n", + "\n", + " def launch(self, config):\n", + " # self.call_binance_data_loader()\n", + " self.initialize_aave(config['initial_parameters']['aave'])\n", + " self.initialize_dydx(config['initial_parameters']['dydx'])\n", + "\n", + " # call clients functions\n", + " def get_historical_data(self, symbol, freq,\n", + " initial_date, save):\n", + " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=save)\n", + " # self.historical_data = eth_historical\n", + " self.historical_data = eth_historical[\"close\"]\n", + " for i in range(len(self.historical_data)):\n", + " self.historical_data[i] = float(self.historical_data[i])\n", + " # self.load_intervals()\n", + "\n", + " # initialize classes\n", + " def initialize_aave(self, config):\n", + " # We initialize aave and dydx classes instances\n", + " self.aave = Aave(config)\n", + " # We load methods and attributes for aave and dydx to use later\n", + " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", + " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", + " \"keys\": list(self.aave.__dict__.keys())}}\n", + " # We create an attribute for historical data\n", + " self.aave_historical_data = []\n", + "\n", + " def initialize_dydx(self, config):\n", + " self.dydx = Dydx(config)\n", + " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", + " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", + " \"keys\": list(self.dydx.__dict__.keys())}}\n", + " self.dydx_historical_data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Aave and DyDx modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Aave" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class Aave(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert self.dydx_class_instance == isinstance(dydx)\n", + " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", + " self.market_price = config['market_price']\n", + "\n", + " self.entry_price = config['entry_price']\n", + "\n", + " self.collateral_eth_initial = config['collateral_eth']\n", + " self.collateral_eth = config['collateral_eth']\n", + " self.collateral_usdc = config['collateral_usdc']\n", + "\n", + " self.reserve_margin_eth = 0\n", + " self.reserve_margin_usdc = 0\n", + "\n", + " self.borrowed_percentage = config['borrowed_pcg']\n", + " self.usdc_status = config['usdc_status']\n", + "\n", + " self.debt = config['debt']\n", + " self.debt_initial = config['debt']\n", + "\n", + " self.ltv = config['ltv']\n", + " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", + "\n", + " self.lending_rate = 0\n", + " self.lending_rate_hourly = 0\n", + " self.interest_on_lending_eth = 0 # aggregated fees\n", + " self.interest_on_lending_usd = 0\n", + " self.lending_fees_eth = 0 # fees between last 2 prices\n", + " self.lending_fees_usd = 0\n", + "\n", + " self.borrowing_rate = 0\n", + " self.borrowing_rate_hourly = 0\n", + " self.interest_on_borrowing = 0 # aggregated fees\n", + " self.borrowing_fees = 0 # fees between last 2 prices\n", + "\n", + " self.lend_minus_borrow_interest = 0\n", + "\n", + " self.costs = 0\n", + " # self.historical = pd.DataFrame()\n", + " # self.dydx_class_instance = dydx_class_instance\n", + " # self.staked_in_protocol = stk\n", + "\n", + " # def update_costs(self):\n", + " # \"\"\"\n", + " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " # \"\"\"\n", + " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", + " # self.costs = self.costs - self.lend_minus_borrow_interest\n", + "\n", + " def collateral_usd(self):\n", + " return self.collateral_eth * self.market_price\n", + "\n", + " def update_debt(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.debt = self.debt + self.borrowing_fees\n", + "\n", + " def update_collateral(self):\n", + " \"\"\"\n", + " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", + " self.collateral_usdc = self.collateral_usd()\n", + "\n", + " def track_lend_borrow_interest(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", + " in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", + "\n", + " def lending_fees_calc(self, freq):\n", + " self.simulate_lending_rate()\n", + " self.lending_rate_freq = self.lending_rate / freq\n", + "\n", + " # fees from lending are added to collateral? YES\n", + " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", + " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", + " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", + " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", + " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", + "\n", + " def borrowing_fees_calc(self, freq):\n", + " self.simulate_borrowing_rate()\n", + " self.borrowing_rate_freq = self.borrowing_rate / freq\n", + "\n", + " # fees from borrow are added to debt? YES\n", + " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", + " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", + " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", + "\n", + " def simulate_lending_rate(self):\n", + " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", + "\n", + " # best case\n", + " # self.lending_rate = 1.5 / 100\n", + "\n", + " # worst case\n", + " self.lending_rate = 0.5 / 100\n", + "\n", + " def simulate_borrowing_rate(self):\n", + " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", + "\n", + " # best case\n", + " # self.borrowing_rate = 1.5/100\n", + "\n", + " # worst case\n", + " self.borrowing_rate = 2.5/100\n", + "\n", + " def ltv_calc(self):\n", + " if self.collateral_usd() == 0:\n", + " return 0\n", + " else:\n", + " return self.debt / self.collateral_usd()\n", + "\n", + " def price_to_liquidation(self, dydx_class_instance):\n", + " return self.entry_price - (dydx_class_instance.short_pnl()\n", + " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", + "\n", + " def price_to_ltv_limit_calc(self):\n", + " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", + "\n", + " def buffer_for_repay(self):\n", + " return 0.01\n", + "\n", + " def ltv_limit(self):\n", + " return 0.5\n", + "\n", + " # Actions to take\n", + " def return_usdc(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " time = 0\n", + " if self.usdc_status:\n", + " # simulate 2min delay for tx\n", + " # update parameters\n", + " # AAVE parameters\n", + " self.usdc_status = False\n", + " # self.collateral_eth = 0\n", + " # self.collateral_usdc = 0\n", + " self.debt = 0\n", + " self.ltv = 0\n", + " self.price_to_ltv_limit = 0\n", + " # self.lending_rate = 0\n", + " # self.borrowing_rate = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def repay_aave(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " dydx_class_instance = stgy_instance.dydx\n", + " # aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " #\n", + " time = 0\n", + " if self.usdc_status:\n", + " # update parameters\n", + " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.short_entry_price)\n", + " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", + "\n", + " # pnl_for_debt = dydx_class_instance.pnl()\n", + " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", + " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.short_entry_price)\n", + " self.debt = self.debt - pnl_for_debt\n", + " self.ltv = self.ltv_calc()\n", + "\n", + " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " dydx_class_instance.short_size = new_short_size\n", + " dydx_class_instance.short_notional = dydx_class_instance.short_notional_calc()\n", + " dydx_class_instance.short_equity = dydx_class_instance.short_equity_calc()\n", + " dydx_class_instance.short_leverage = dydx_class_instance.short_leverage_calc()\n", + " dydx_class_instance.short_pnl = dydx_class_instance.short_pnl_calc()\n", + " # dydx_class_instance.price_to_liquidation = \\\n", + " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", + "\n", + " # fees\n", + " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + " dydx_class_instance.simulate_maker_taker_fees()\n", + " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", + " dydx_class_instance.short_costs = dydx_class_instance.short_costs \\\n", + " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", + " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + "\n", + " # Note that a negative self.debt is actually a profit\n", + " # We update the parameters\n", + " if self.debt > 0:\n", + " self.usdc_status = True\n", + " else:\n", + " self.usdc_status = False\n", + " # simulate 2min delay for tx\n", + " time = 1\n", + " return time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### DyDx" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class Dydx(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert aave_class == isinstance(aave)\n", + " self.market_price = config['market_price']\n", + "\n", + " # Short attributes\n", + " self.short_entry_price = config['entry_price']\n", + " self.short_size = config['short_size']\n", + " self.short_collateral = config['collateral']\n", + " self.short_notional = config['notional']\n", + " self.short_equity = config['equity']\n", + " self.short_leverage = config['leverage']\n", + " self.short_pnl = config['pnl']\n", + " self.short_collateral_status = config['collateral_status']\n", + " self.short_status = config['short_status']\n", + " self.short_costs = 0\n", + "\n", + " # Long attributes\n", + " self.long_entry_price = config['entry_price']\n", + " self.long_size = config['short_size']\n", + " self.long_notional = config['notional']\n", + " # self.long_equity = config['equity']\n", + " # self.long_leverage = config['leverage']\n", + " self.long_pnl = config['pnl']\n", + " self.long_status = config['short_status']\n", + " self.long_costs = 0\n", + "\n", + " self.order_status = True\n", + " self.withdrawal_fees = 0.01 / 100\n", + " self.funding_rates = 0\n", + " self.maker_taker_fees = 0\n", + " self.maker_fees_counter = 0\n", + "\n", + "\n", + " # auxiliary functions\n", + " # Short methods\n", + " def short_pnl_calc(self):\n", + " return self.short_size * (self.market_price - self.short_entry_price)\n", + "\n", + " def short_notional_calc(self):\n", + " return abs(self.short_size) * self.market_price\n", + "\n", + " def short_equity_calc(self):\n", + " return self.short_collateral + self.short_pnl_calc()\n", + "\n", + " def short_leverage_calc(self):\n", + " if self.short_equity_calc() == 0:\n", + " return 0\n", + " else:\n", + " return self.short_notional_calc() / self.short_equity_calc()\n", + "\n", + " # Long methods\n", + " def long_pnl_calc(self):\n", + " return self.long_size * (self.market_price - self.long_entry_price)\n", + "\n", + " def long_notional_calc(self):\n", + " return abs(self.long_size) * self.market_price\n", + "\n", + " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", + " return self.short_entry_price \\\n", + " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", + "\n", + " @staticmethod\n", + " def price_to_liquidation_calc(dydx_client_class_instance):\n", + " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", + "\n", + " def add_funding_rates(self):\n", + " self.simulate_funding_rates()\n", + " self.short_costs = self.short_costs - self.funding_rates * self.short_notional\n", + "\n", + " def simulate_funding_rates(self):\n", + " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", + "\n", + " # best case\n", + " # self.funding_rates = 0.0075 / 100\n", + "\n", + " # average -0.00443%\n", + "\n", + " # worst case\n", + " self.funding_rates = -0.0075 / 100\n", + "\n", + " def simulate_maker_taker_fees(self):\n", + " # We add a counter for how many times we call this function\n", + " # i.e. how many times we open and close the short\n", + " self.maker_fees_counter += 1\n", + " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", + "\n", + " # maker fees\n", + " self.maker_taker_fees = 0.05 / 100 # <1M\n", + " # self.maker_taker_fees = 0.04 / 100 # <5M\n", + " # self.maker_taker_fees = 0.035 / 100 # <10M\n", + " # self.maker_taker_fees = 0.03 / 100 # <50M\n", + " # self.maker_taker_fees = 0.025 / 100 # <200M\n", + " # self.maker_taker_fees = 0.02 / 100 # >200M\n", + "\n", + " # Actions to take\n", + " def remove_collateral(self, stgy_instance):\n", + " self.cancel_order()\n", + " time = 0\n", + " if self.short_collateral_status:\n", + " self.short_collateral_status = False\n", + " withdrawal_fees = self.short_collateral * self.withdrawal_fees\n", + " self.short_collateral = 0\n", + " # self.price_to_liquidation = 0\n", + "\n", + " # fees\n", + " self.short_costs = self.short_costs + withdrawal_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def open_short(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " if (not self.short_status) and self.order_status:\n", + " self.short_status = True\n", + " self.short_entry_price = self.market_price\n", + " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.short_notional = self.short_notional_calc()\n", + " self.short_equity = self.short_equity_calc()\n", + " self.short_leverage = self.short_leverage_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.short_costs = self.short_costs + self.maker_taker_fees * self.short_notional\n", + " return 0\n", + "\n", + " def close_short(self, stgy_instance):\n", + " if self.short_status:\n", + " self.short_notional = self.short_notional_calc()\n", + " self.short_equity = self.short_equity_calc()\n", + " self.short_leverage = self.short_leverage_calc()\n", + " self.short_pnl = self.short_pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.short_pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.short_entry_price = 0\n", + " self.short_status = False\n", + " self.short_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.short_costs = self.short_costs + self.maker_taker_fees * self.short_notional\n", + " return 0\n", + "\n", + " def open_long(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " if not self.long_status:\n", + " self.long_status = True\n", + " self.long_entry_price = self.market_price\n", + " self.long_size = aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.long_notional = self.long_notional_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.long_costs = self.long_costs + self.maker_taker_fees * self.long_notional\n", + " return 0\n", + "\n", + " def close_long(self, stgy_instance):\n", + " if self.long_status:\n", + " self.long_notional = self.long_notional_calc()\n", + " self.long_pnl = self.long_pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.long_pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.long_entry_price = 0\n", + " self.long_status = False\n", + " self.long_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.long_costs = self.long_costs + self.maker_taker_fees * self.long_notional\n", + " return 0\n", + "\n", + " def place_order(self, price):\n", + " self.order_status = True\n", + " # self.\n", + "\n", + " def cancel_order(self):\n", + " self.order_status = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## ParameterManager Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterManager(object):\n", + " # auxiliary functions\n", + " @staticmethod\n", + " def define_target_prices(stgy_instance, slippage, vol, floor, pcg):\n", + " mu = vol[0]\n", + " sigma = vol[1]\n", + " roof = floor * (1+pcg)\n", + " start = (roof+floor)/2\n", + " ##########################################################\n", + " # We define the intervals\n", + " list_of_intervals = [\"roof\",\n", + " \"start\",\n", + " \"floor\"]\n", + " list_of_trigger_prices = [roof,\n", + " start,\n", + " floor]\n", + " # We define/update trigger prices\n", + " for i in range(len(list_of_intervals)):\n", + " interval_name = list_of_intervals[i]\n", + " trigger_price = list_of_trigger_prices[i]\n", + " stgy_instance.trigger_prices[interval_name] = trigger_price\n", + "\n", + " @staticmethod\n", + " def calc_vol(last_date, data):\n", + " periods_for_vol = [6 * 30 * 24 * 60, 3 * 30 * 24 * 60, 1 * 30 * 24 * 60]\n", + " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", + " globals()['sigma_' + str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_' + str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " vol = [mu, sigma]\n", + " return vol\n", + "\n", + " @staticmethod\n", + " # Checking and updating data\n", + " def update_parameters(stgy_instance, new_market_price):\n", + " # AAVE\n", + " stgy_instance.aave.market_price = new_market_price\n", + " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", + " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", + " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", + " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", + " # We have to execute track_ first because we need the fees for current collateral and debt values\n", + " stgy_instance.aave.track_lend_borrow_interest()\n", + " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", + " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", + " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", + " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", + "\n", + " # DYDX\n", + " stgy_instance.dydx.market_price = new_market_price\n", + " # Short updates\n", + " stgy_instance.dydx.short_notional = stgy_instance.dydx.short_notional_calc()\n", + " stgy_instance.dydx.short_equity = stgy_instance.dydx.short_equity_calc()\n", + " stgy_instance.dydx.short_leverage = stgy_instance.dydx.short_leverage_calc()\n", + " stgy_instance.dydx.short_pnl = stgy_instance.dydx.short_pnl_calc()\n", + " # Long updates\n", + " stgy_instance.dydx.long_notional = stgy_instance.dydx.long_notional_calc()\n", + " stgy_instance.dydx.long_pnl = stgy_instance.dydx.long_pnl_calc()\n", + "\n", + " @staticmethod\n", + " def reset_costs(stgy_instance):\n", + " # We reset the costs in order to always start in 0\n", + " stgy_instance.aave.costs = 0\n", + " stgy_instance.dydx.short_costs = 0\n", + " stgy_instance.dydx.long_costs = 0\n", + "\n", + " def find_scenario(self, stgy_instance, market_price, previous_market_price):\n", + " self.simulate_fees(stgy_instance)\n", + " roof = stgy_instance.trigger_prices['roof']\n", + " start = stgy_instance.trigger_prices['start']\n", + " floor = stgy_instance.trigger_prices['floor']\n", + " # Case P crossing roof upwards: Close short\n", + " if (previous_market_price <= roof) and (market_price >= roof):\n", + " if stgy_instance.dydx.short_status:\n", + " stgy_instance.dydx.close_short(stgy_instance)\n", + " # Case P crossing start in any direction: Start both\n", + " elif ((previous_market_price <= start) and (market_price >= start)) or ((previous_market_price >= start) and (market_price <= start)):\n", + " stgy_instance.dydx.open_long(stgy_instance)\n", + " stgy_instance.dydx.open_short(stgy_instance)\n", + " # Case P crossing floor downwards: Close Long\n", + " elif (previous_market_price >= floor) and (market_price <= floor):\n", + " if stgy_instance.dydx.long_status:\n", + " stgy_instance.dydx.close_long(stgy_instance)\n", + "\n", + " @staticmethod\n", + " def simulate_fees(stgy_instance):\n", + " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", + "\n", + " # best case\n", + " # stgy_instance.gas_fees = 1\n", + "\n", + " # stgy_instance.gas_fees = 3\n", + "\n", + " # stgy_instance.gas_fees = 6\n", + "\n", + " # worst case\n", + " stgy_instance.gas_fees = 10\n", + "\n", + " @staticmethod\n", + " def update_pnl(stgy_instance):\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.short_costs - stgy_instance.dydx.long_costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", + "\n", + " @staticmethod\n", + " def add_costs(stgy_instance):\n", + " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", + " + stgy_instance.aave.costs + stgy_instance.dydx.short_costs +stgy_instance.dydx.long_costs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## DataDamperNPlotter Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module will write the results and is also used for plotting (for analysis porpuses)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "class DataDamperNPlotter:\n", + " def __init__(self):\n", + " self.historical_data = None\n", + "\n", + " @staticmethod\n", + " def write_data(stgy_instance,\n", + " period, floor,\n", + " sheet=False):\n", + " aave_instance = stgy_instance.aave\n", + " dydx_instance = stgy_instance.dydx\n", + " data_aave = []\n", + " data_dydx = []\n", + " aave_wanted_keys = [\n", + " \"market_price\",\n", + " # \"interval_current\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\"]\n", + "\n", + " for i in range(len(aave_instance.__dict__.values())):\n", + " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " for i in range(len(dydx_instance.__dict__.values())):\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", + " data_aave.append(stgy_instance.gas_fees)\n", + " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_aave.append(stgy_instance.total_pnl)\n", + "\n", + " data_dydx.append(stgy_instance.gas_fees)\n", + " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_dydx.append(stgy_instance.total_pnl)\n", + " if sheet == True:\n", + " gc = pygsheets.authorize(service_file=\n", + " 'stgy-1-simulations-e0ee0453ddf8.json')\n", + " sh = gc.open('aave/dydx simulations')\n", + " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", + " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", + " else:\n", + " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (\n", + " period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (\n", + " period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_aave)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_dydx)\n", + "\n", + " @staticmethod\n", + " def delete_results(period, floor):\n", + " file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (\n", + " period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (\n", + " period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close']))\n", + " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", + " os.remove(file_aave)\n", + " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", + " os.remove(file_dydx)\n", + "\n", + " @staticmethod\n", + " def add_header(period, floor):\n", + " aave_headers = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + " dydx_headers = [\n", + " \"market_price\",\n", + " \"short_entry_price\",\n", + " \"short_size\",\n", + " \"short_collateral\",\n", + " \"short_notional\",\n", + " \"short_equity\",\n", + " \"short_leverage\",\n", + " \"short_pnl\",\n", + " \"short_collateral_status\",\n", + " \"short_status\",\n", + " \"short_costs\",\n", + " \"long_entry_price\",\n", + " \"long_size\",\n", + " \"long_notional\",\n", + " \"long_pnl\",\n", + " \"long_status\",\n", + " \"long_costs\",\n", + " \"order_status\",\n", + " \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + "\n", + " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (\n", + " period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (\n", + " period[0], period[1], int(floor)) # int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(aave_headers)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(dydx_headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given that shorting involve executing too many txs, let's try combining it with a long position to get a delta neutral final strategy and at the same time giving room to the price to move laterally." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The idea is to \n", + "- open a short and a long position (both leveraged) using the debt from AAVE at a certain price called start\n", + "- if price reaches some point start + delta we will close the short position and stay with the long only\n", + "- if price reaches floor (= start - delta) we will close the long and stay only with the short" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Track historical data\n", + "# symbol = 'ETHUSDC'\n", + "# freq = '1m'\n", + "# initial_date = \"1 Jan 2019\"\n", + "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", + "# initial_date=initial_date, save=True)\n", + "\n", + "# Load historical data if previously tracked and saved\n", + "\n", + "historical_data = pd.read_csv(\"Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "# # assign data to stgy instance + define index as dates\n", + "timestamp = pd.to_datetime(historical_data['timestamp'])\n", + "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", + "historical_data.index = timestamp\n", + "#\n", + "# #######################################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-11-01\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", + "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", + "where $\\sigma=vol$." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "weighted mu: 1.4462763455814235e-05%\n", + "weighted sigmas: 0.17999807112824498%\n", + "[min_6m_change, max_6m_change]: ['-6.786594905713236%', '9.135956592119358%']\n", + "avg movement: (1+slip)(1+mu+2vol): 1.1305428909776651%\n" + ] + } + ], + "source": [ + "# First we calculate weighted vol\n", + "last_date = \"2021-06-01\"\n", + "slippage = 0.0005\n", + "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]:]\n", + "for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", + " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", + "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + "print(\"weighted mu: \", str(mu*100)+'%')\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+6*vol)*100-100)+'%')\n", + "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0005989101310066664,\n", + " 0.0011978202620133327,\n", + " 0.0023956405240266655,\n", + " 0.0035934607860399984)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normal_std = std\n", + "# medium_std = 2*std\n", + "# high_std = 4*std\n", + "# extreme_std = 6*std\n", + "# normal_std, medium_std, high_std, extreme_std" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find such a relevant price manually by taking a look at the price plot." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.1192477876106195" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "data['close'].max()/data['close'].min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define a function that will\n", + "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices and the intervals\n", + "- Create a new directory \"Files/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit + defining the first interval_old to be the first interval in the dataset stgy.historical_data\n", + "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", + "- Run through the code executing everything as discussed in the dev doc.\n", + "\n", + "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(period, slippage, floor, pcg):\n", + " global ocs\n", + " # Initialize everything\n", + " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0] + ' 00:00:00':period[1] + ' 00:00:00']\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", + " # Load target_prices + intervals in stgy.historical_data\n", + " # First we calculate weighted vol\n", + " last_date = period[1] + ' 00:00:00'\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " # Now we define prices and intervals given K and vol\n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, pcg)\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder\n", + " # exist or not.\n", + " if not os.path.exists(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], int(floor))):\n", + " # if the demo_folder directory is not present\n", + " # then create it.\n", + " os.makedirs(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], int(floor)))\n", + " stgy.historical_data.to_csv(\"Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\"\n", + " % (period[0], period[1], int(floor)))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + "\n", + " # Stk eth\n", + " stgy.stk = 1000000 / stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(),\n", + " 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " # stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index]\n", + " stgy.dydx.short_collateral = stgy.aave.debt\n", + " stgy.dydx.short_equity = stgy.dydx.short_equity_calc()\n", + " stgy.dydx.short_collateral_status = True\n", + " \n", + " print(stgy.trigger_prices['roof'],stgy.trigger_prices['start'],stgy.trigger_prices['floor'])\n", + " print(stgy.dydx.market_price <= stgy.trigger_prices['start'])\n", + " print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= stgy.trigger_prices['start']):\n", + " stgy.dydx.open_short(stgy)\n", + " if ((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor'])):\n", + " stgy.dydx.open_long(stgy)\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(period, floor)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(period, floor)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + " maker_fees_counter = []\n", + " while (i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + "\n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " previous_market_price = stgy.historical_data[\"close\"][i-1]\n", + " market_price = stgy.historical_data[\"close\"][i]\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, interval_current, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, interval_current, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, market_price)\n", + " ##############################\n", + " stgy.parameter_manager.find_scenario(stgy, market_price, previous_market_price)\n", + " ##############################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " stgy.data_dumper.write_data(stgy,\n", + " period, floor,\n", + " sheet=False)\n", + " #########################\n", + " # we increment index by the time consumed in executing actions\n", + " # i += time_used\n", + " i += 1\n", + " return maker_fees_counter" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-01 00:00:00'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(historical_data.index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.01716814159292035, -0.034270575164515926)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu_returns_max, mu_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'mu_abs_log_returns' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m K \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[0;32m----> 2\u001b[0m condition \u001b[38;5;241m=\u001b[39m (mu_abs_log_returns\u001b[38;5;241m-\u001b[39mK\u001b[38;5;241m*\u001b[39mstd_ema_log_returns\u001b[38;5;241m<\u001b[39mlog_returns)\u001b[38;5;241m&\u001b[39m(log_returns\u001b[38;5;241m<\u001b[39mmu_abs_log_returns\u001b[38;5;241m+\u001b[39mK\u001b[38;5;241m*\u001b[39mstd_ema_log_returns)\n", + "\u001b[0;31mNameError\u001b[0m: name 'mu_abs_log_returns' is not defined" + ] + } + ], + "source": [ + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(log_returns[condition]),\u001b[38;5;28mlen\u001b[39m(log_returns),\u001b[38;5;28mlen\u001b[39m(log_returns[condition])\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(log_returns)\n", + "\u001b[0;31mNameError\u001b[0m: name 'log_returns' is not defined" + ] + } + ], + "source": [ + "len(log_returns[condition]),len(log_returns),len(log_returns[condition])/len(log_returns)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([3.800e+01, 4.800e+01, 3.500e+01, 4.000e+01, 3.700e+01, 4.000e+01,\n", + " 4.100e+01, 3.600e+01, 4.500e+01, 5.100e+01, 4.100e+01, 5.300e+01,\n", + " 5.400e+01, 2.400e+01, 5.800e+01, 3.900e+01, 5.900e+01, 7.100e+01,\n", + " 3.100e+01, 8.600e+01, 7.800e+01, 1.700e+01, 7.400e+01, 7.500e+01,\n", + " 1.300e+01, 9.000e+01, 8.600e+01, 1.800e+01, 8.500e+01, 1.500e+01,\n", + " 8.400e+01, 5.975e+03, 0.000e+00, 1.220e+02, 8.700e+01, 2.000e+00,\n", + " 8.600e+01, 9.000e+01, 6.000e+00, 8.200e+01, 6.700e+01, 2.100e+01,\n", + " 9.100e+01, 5.900e+01, 3.300e+01, 8.200e+01, 4.000e+01, 4.300e+01,\n", + " 6.600e+01, 3.700e+01, 5.700e+01, 5.100e+01, 4.100e+01, 5.000e+01,\n", + " 5.200e+01, 3.900e+01, 4.000e+01, 3.900e+01, 4.500e+01, 4.000e+01,\n", + " 3.100e+01, 4.200e+01, 3.700e+01, 3.800e+01, 3.700e+01, 3.400e+01,\n", + " 3.200e+01, 3.400e+01, 3.700e+01, 2.600e+01, 4.000e+01, 3.200e+01,\n", + " 3.100e+01, 2.300e+01, 2.100e+01, 2.300e+01, 2.500e+01, 2.000e+01,\n", + " 3.000e+01, 1.900e+01, 2.800e+01, 2.500e+01, 1.500e+01, 2.000e+01,\n", + " 2.300e+01, 2.200e+01, 2.000e+01, 1.300e+01, 1.500e+01, 2.500e+01,\n", + " 1.500e+01, 1.300e+01, 2.000e+01, 1.400e+01, 1.700e+01, 1.600e+01,\n", + " 1.500e+01, 1.800e+01, 1.200e+01, 1.000e+01]),\n", + " array([-8.50701880e-04, -8.23749587e-04, -7.96797295e-04, -7.69845002e-04,\n", + " -7.42892709e-04, -7.15940416e-04, -6.88988123e-04, -6.62035831e-04,\n", + " -6.35083538e-04, -6.08131245e-04, -5.81178952e-04, -5.54226659e-04,\n", + " -5.27274366e-04, -5.00322074e-04, -4.73369781e-04, -4.46417488e-04,\n", + " -4.19465195e-04, -3.92512902e-04, -3.65560610e-04, -3.38608317e-04,\n", + " -3.11656024e-04, -2.84703731e-04, -2.57751438e-04, -2.30799145e-04,\n", + " -2.03846853e-04, -1.76894560e-04, -1.49942267e-04, -1.22989974e-04,\n", + " -9.60376813e-05, -6.90853885e-05, -4.21330957e-05, -1.51808029e-05,\n", + " 1.17714900e-05, 3.87237828e-05, 6.56760756e-05, 9.26283684e-05,\n", + " 1.19580661e-04, 1.46532954e-04, 1.73485247e-04, 2.00437540e-04,\n", + " 2.27389833e-04, 2.54342125e-04, 2.81294418e-04, 3.08246711e-04,\n", + " 3.35199004e-04, 3.62151297e-04, 3.89103589e-04, 4.16055882e-04,\n", + " 4.43008175e-04, 4.69960468e-04, 4.96912761e-04, 5.23865054e-04,\n", + " 5.50817346e-04, 5.77769639e-04, 6.04721932e-04, 6.31674225e-04,\n", + " 6.58626518e-04, 6.85578811e-04, 7.12531103e-04, 7.39483396e-04,\n", + " 7.66435689e-04, 7.93387982e-04, 8.20340275e-04, 8.47292567e-04,\n", + " 8.74244860e-04, 9.01197153e-04, 9.28149446e-04, 9.55101739e-04,\n", + " 9.82054032e-04, 1.00900632e-03, 1.03595862e-03, 1.06291091e-03,\n", + " 1.08986320e-03, 1.11681550e-03, 1.14376779e-03, 1.17072008e-03,\n", + " 1.19767237e-03, 1.22462467e-03, 1.25157696e-03, 1.27852925e-03,\n", + " 1.30548155e-03, 1.33243384e-03, 1.35938613e-03, 1.38633842e-03,\n", + " 1.41329072e-03, 1.44024301e-03, 1.46719530e-03, 1.49414760e-03,\n", + " 1.52109989e-03, 1.54805218e-03, 1.57500447e-03, 1.60195677e-03,\n", + " 1.62890906e-03, 1.65586135e-03, 1.68281364e-03, 1.70976594e-03,\n", + " 1.73671823e-03, 1.76367052e-03, 1.79062282e-03, 1.81757511e-03,\n", + " 1.84452740e-03]),\n", + " )" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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N33Uf5FbVHmDPmR7HpCWZW+xIvFqs9jmu9vnB6p/jap/fMNO+ZfMIcMHA+sZWkyRNwbRD/zFgc5ILk5wNXAvsn/IYJKlbU728U1VvJvkE8CALt2zuraqnpzmG7yKr/hIWq3+Oq31+sPrnuNrn9x2m+kGuJOnM8s8wSFJHDH1J6oihPwZJzk1yIMmh9rxukXY7WptDSXYM1P9Bkq8kmU9ye5Is1W+SjyR5PckT7fGLE5rXtiTPtXHtHrL9nCT3tO2PJNk0sO3GVn8uydbl+mwf7j/S6ve0D/onasrz+1ySrw28ZxdPen5LjWdg+9uZ494kR5M8dUpfI/0ejNOU5/fJJEcG3sOrJjm3iakqH6f5AP4jsLst7wY+PaTNucDz7XldW17Xtj0KXA4E+H3gyqX6BT4CfGHCczoL+CrwfuBs4MvARae0+dfAr7bla4F72vJFrf05wIWtn7OW6hO4F7i2Lf8q8K9W2fw+B1wz5Z/Lsc+xbftR4BLgqZX+HrzD5/dJ4N9N8z2cxMMz/fHYDuxry/uAq4e02QocqKrjVXUCOABsS3I+8P1V9XAt/GTdNbD/KP1OyqXAfFU9X1V/BdzdxjNocHz3AVe0f6VsB+6uqjeq6mvAfOtvaJ9tn4+2PmA6c53a/CY8j6VMYo5U1R8Dx4e83rR/Xqc9v1XB0B+P9VX1Ult+GVg/pM0G4MWB9cOttqEtn1pfrt8PJ/lykt9P8sHTncAKxju0TVW9CbwOvHeJfRervxd4rfWx2GuN2zTnd9ItSZ5McluSc8YxiWVMYo5LGeX3YJymPT+AT7T3cO80Ll9NgqE/oiR/mOSpIY9vO7NoZ+tjvw/2lH7/hIW/rfEh4L8C/2vcr6exuxH4APAPWbjE93NndjiTNanfgzPsDuAHgIuBl4D/dGaH8/YY+iOqqn9aVX9/yON+4JV2mYb2fHRIF4v9CYojbfnUOov1W1XfqKq/aMsPAO9Kct7YJrv0eIe2SbIGeA/w6hL7LlZ/FVjb+ljstcZtmvOjql6qBW8Av067lDBhk5jjUkb5PRinqc6vql6pqm9V1V8Dv8Z03sOxM/THYz9w8m6cHcD9Q9o8CGxJsq79s3AL8GD75/A3klzerjVeN7D/0H6T/N3WliSXsvA+vjrmOY3yJzMGx3cN8FA7w9sPXNvunLgQ2MzCh9VD+2z7fLH18W1znaCpzQ/eCkHa+3Y18G13hkzIJOa4lFF+D8ZpqvM7+R42P8l03sPxO9OfJK+GBwvXCA8Ch4A/BM5t9VkW/newk+3+BQsfGM0D1w/UZ1n4Afoq8N/4m29KL9bvJ4CnWbj74GHgH01oXlcB/7eN6xda7WbgJ9ryu4HfavN5FHj/wL6/0PZ7jnY30mJ9tvr7Wx/zrc9zpvC+TXN+DwFfae/z/wC+b0o/m5OY42+ycHnj/7FwLXznUj+vq2h+n2/v4ZMsHDTOn8Z7OO6Hf4ZBkjri5R1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjry/wGTII8fWOfDDgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(log_returns[condition], bins=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11521" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(log_returns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "245.85365853658539 240.0 234.14634146341464\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "for period_n_open_close in periods_n_open_close:\n", + " period = period_n_open_close[0]\n", + " start = period_n_open_close[1]\n", + " pcg = 0.05\n", + " floor = start * (2/(2+pcg))\n", + " slippage = 0.0005\n", + " maker_fees_counter = run_sim(period, slippage, floor, pcg)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = periods_n_open_close[0][0]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", + "# roof, start, floor\n", + "start = periods_n_open_close[0][1]\n", + "pcg = 0.05\n", + "floor = start * (2/(2+pcg))\n", + "roof = floor * (1+pcg)\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=roof, color='green', linestyle='--', label='roof')\n", + "axs.axhline(y=start, color='green', linestyle='--', label='start')\n", + "axs.axhline(y=floor, color='green', linestyle='--', label='floor')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'oc': 186.11, 'txs': 9, 'date': '2019-09-14 16:44:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-10-11 06:36:00'},\n", + " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-10-14 18:11:00'},\n", + " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-10-26 03:02:00'},\n", + " {'oc': 188.35001996000003, 'txs': 9, 'date': '2019-10-28 01:20:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-10-29 10:52:00'},\n", + " {'oc': 186.11, 'txs': 8, 'date': '2019-10-29 12:57:00'},\n", + " {'oc': 185.0, 'txs': 8, 'date': '2019-10-29 14:51:00'},\n", + " {'oc': 186.11, 'txs': 9, 'date': '2019-11-02 17:36:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-04 16:08:00'},\n", + " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-11-04 21:02:00'},\n", + " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-11-06 01:38:00'},\n", + " {'oc': 188.35001996000003, 'txs': 9, 'date': '2019-11-06 16:31:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-07 08:56:00'},\n", + " {'oc': 186.11, 'txs': 8, 'date': '2019-11-08 01:04:00'},\n", + " {'oc': 185.0, 'txs': 8, 'date': '2019-11-10 13:46:00'},\n", + " {'oc': 186.11, 'txs': 9, 'date': '2019-11-11 23:06:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-12 07:31:00'},\n", + " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-11-13 10:47:00'},\n", + " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-11-13 17:49:00'}]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "maker_fees_counter" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(maker_fees_counter)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"Files/From_2020-05-31_to_2020-06-07_open_close_at_240/dydx_results.csv\")\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(239.4380835398584, 240.0, 247.20000000000002)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slippage = 0.0005\n", + "K_1 = 2\n", + "K_2 = 6\n", + "mu = 0.0004973569978282845\n", + "sigma = 0.0006742666391824819\n", + "floor = 240 / ((1+slippage)*(1+mu+K_1*sigma))\n", + "p_open_close_1 = floor * (1+slippage) * (1+mu+K_1*sigma)\n", + "p_open_close_2 = p_open_close_1 * (1+K_2/K_1/100)\n", + "floor, p_open_close_1, p_open_close_2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0050452283113396" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1+slippage)*(1+mu+6*sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "max_loss = 0.05\n", + "p_open_close_1 = floor * (1+slippage) * (1+mu+K_1*sigma)\n", + "oc1 = p_open_close_1\n", + "for i in range(1,5):\n", + " globals()['oc'+str(i+1)] = oc1 * 1.01**i # jumps of 1%" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.029126213592233108" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_open_close_1/p_open_close_2-1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count how many times a given price is cross given a dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_counter(data_set, price):\n", + " crossed_down = 0\n", + " crossed_up = 0\n", + " index_up = []\n", + " index_down = []\n", + " for index in range(1,len(data_set)):\n", + " previous_price = data_set['close'][index-1]\n", + " current_price = data_set['close'][index]\n", + " if previous_price <= price < current_price:\n", + " crossed_up += 1\n", + " index_up.append(index-1)\n", + " elif previous_price >= price > current_price:\n", + " crossed_down += 1\n", + " index_down.append(index-1)\n", + " return {'down':\n", + " {'crossed_down': crossed_down,\n", + " 'index_down': index_down},\n", + " 'up':\n", + " {'crossed_up': crossed_up,\n", + " 'index_up': index_up}}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "price = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosses = cross_counter(data_set, 240)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"Files/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "market_price 176910\n", + "I_current 176910\n", + "I_old 176910\n", + "entry_price 53220\n", + "short_size 53220\n", + "collateral 176910\n", + "notional 53375\n", + "equity 176910\n", + "leverage 53375\n", + "pnl 53066\n", + "collateral_status 176910\n", + "short_status 53220\n", + "order_status 123690\n", + "withdrawal_fees 176910\n", + "funding_rates 176910\n", + "maker_taker_fees 133516\n", + "maker_fees_counter 133516\n", + "costs 421\n", + "gas_fees 176910\n", + "total_costs_from_aave_n_dydx 133516\n", + "total_stgy_pnl 176910\n", + "index_of_mkt_price 176910\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dydx_results.astype(bool).sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def count_maker_fees_increment(data_set):\n", + " index_of_maker_fee = []\n", + " for index in range(1,len(data_set)):\n", + " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", + " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", + " if previous_maker_fee_counter < current_maker_fee_counter:\n", + " index_of_maker_fee.append(index)\n", + " return {'indexes': index_of_maker_fee}" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "matches_up = 0\n", + "matches_down = 0\n", + "for index_up in crosses['up']['index_up']:\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " matches_up += 1\n", + "for index_down in crosses['down']['index_down']:\n", + " if index_down in results_maker_fee_counter['indexes']:\n", + " matches_down += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(155, 136, 291)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches_up, matches_down, matches_up + matches_down" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 156)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", + "- entry_price and short_size can be found at index_up -1\n", + "- close_price is market_price in index = index_up" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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market_priceI_currentI_oldshort_sizeentry_pricepnlmaker_fees_countertotal_stgy_pnl
43393240.70inftyminus_infty0.0000.000.00000-2.879624
43394239.74minus_inftyinfty-4334.634239.740.00001-522.470891
43395240.94inftyminus_infty0.0000.00-5201.56082-6246.223689
43396240.86inftyminus_infty0.0000.000.00002-6246.222332
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" + ], + "text/plain": [ + " market_price I_current I_old short_size entry_price \\\n", + "43393 240.70 infty minus_infty 0.000 0.00 \n", + "43394 239.74 minus_infty infty -4334.634 239.74 \n", + "43395 240.94 infty minus_infty 0.000 0.00 \n", + "43396 240.86 infty minus_infty 0.000 0.00 \n", + "\n", + " pnl maker_fees_counter total_stgy_pnl \n", + "43393 0.0000 0 -2.879624 \n", + "43394 0.0000 1 -522.470891 \n", + "43395 -5201.5608 2 -6246.223689 \n", + "43396 0.0000 2 -6246.222332 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 1\n", + "index = crosses['up']['index_up'][i]\n", + "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "diff = []\n", + "cost = []\n", + "# we dont start the loop at i = 0 because the data_set started below open_close\n", + "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", + "for i in range(1,len(crosses['up']['index_up'])):\n", + " index_up = crosses['up']['index_up'][i]\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " entry_price = dydx_results.iloc[index-1]['entry_price']\n", + " close_price = dydx_results.iloc[index]['market_price']\n", + " short_size = dydx_results.iloc[index-1]['short_size']\n", + " diff.append(close_price-entry_price)\n", + " cost.append(short_size * (close_price-entry_price))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1999999999999886, -5201.560799999951)" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(diff), np.mean(cost)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/jupyter-lab/Simulations_lab.ipynb b/jupyter-lab/Simulations_lab.ipynb index be1cb5a..fe2a0ef 100644 --- a/jupyter-lab/Simulations_lab.ipynb +++ b/jupyter-lab/Simulations_lab.ipynb @@ -2,47 +2,48 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in 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"execution_count": 22, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -450,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -649,28 +650,25 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "class ParameterManager(object):\n", " # auxiliary functions\n", " @staticmethod\n", - " def define_target_prices(stgy_instance, slippage, K, vol, floor):\n", - " p_open_close = floor * (1+slippage) * (1+K*vol)\n", - "# maker_fee = 0.05 / 100\n", - "# p_open_close_2 = p_open_close_1*(1-2*maker_fee)\n", - "# if p_open_close_2 < floor:\n", - "# print(\"open_close_2 < floor!\")\n", - "# print(\"(floor-open_close_2)/floor=\",(floor-p_open_close_2)/floor)\n", + " def define_target_prices(stgy_instance, slippage, vol, floor):\n", + " mu = vol[0]\n", + " sigma = vol[1]\n", + " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", " ##########################################################\n", " # We define the intervals\n", " list_of_intervals = [\"open_close\",\n", - "# \"open_close_2\",\n", - " \"floor\"]\n", + " \"floor\",\n", + " \"ltv_limit\"]\n", " list_of_trigger_prices = [p_open_close,\n", - "# p_open_close_2,\n", - " floor]\n", + " floor,\n", + " stgy_instance.aave.price_to_ltv_limit]\n", " # We define/update trigger prices\n", " for i in range(len(list_of_intervals)):\n", " interval_name = list_of_intervals[i]\n", @@ -685,12 +683,52 @@ " \"open_close\": Interval(stgy_instance.trigger_prices['floor'],\n", " stgy_instance.trigger_prices['open_close'],\n", " \"open_close\", 1),\n", + " \"floor\": Interval(stgy_instance.trigger_prices['ltv_limit'],\n", + " stgy_instance.trigger_prices['floor'],\n", + " \"floor\", 2),\n", " \"minus_infty\": Interval(-math.inf,\n", - " stgy_instance.trigger_prices['floor'],\n", - " \"minus_infty\", 2)}\n", + " stgy_instance.trigger_prices['ltv_limit'],\n", + " \"minus_infty\", 3)}\n", "\n", " # function to assign interval_current to each market_price in historical data\n", " @staticmethod\n", + " def find_interval(stgy_instance, market_price):\n", + " for i in list(stgy_instance.intervals.values()):\n", + " if i.left_border < market_price <= i.right_border:\n", + " return {\"interval\":i, \"interval_name\":i.name}\n", + "\n", + " @staticmethod\n", + " def find_oc(current_oc, ocs, vol):\n", + " mu, sigma = vol\n", + " oc_up = current_oc * (1+slippage)*(1+mu+2*sigma)\n", + " oc_down = current_oc * (1+slippage)*(1+mu-2*sigma)\n", + " distances = []\n", + " next_oc_up = []\n", + " next_oc_down = []\n", + " for i in range(len(ocs)):\n", + " oci = ocs[i]\n", + " if oc_up < oci:\n", + " next_oc_up.append(oci)\n", + " # ocs['up'].append(oci)\n", + " elif oc_down > oci:\n", + " next_oc_down.append(oci)\n", + " # ocs['down'].append(oci)\n", + " distances.append(current_oc-oci)\n", + " # If we get here then we didnt return anything, so we return the farthest oc\n", + " # Furthest down (positive distance current_oc > oci)\n", + " max_value = max(distances)\n", + " max_index = distances.index(max_value)\n", + " # Furthest up (negative distance current_oc < oci)\n", + " min_value = min(distances)\n", + " min_index = distances.index(min_value)\n", + " # print(next_oc_up)\n", + " # print(next_oc_down)\n", + " return {'up_choices': next_oc_up,\n", + " 'down_choices': next_oc_down,\n", + " 'max_distance_up': ocs[min_index],\n", + " 'max_distance_down': ocs[max_index]}\n", + " \n", + " @staticmethod\n", " def load_intervals(stgy_instance):\n", " stgy_instance.historical_data[\"interval\"] = [[0, 0]] * len(stgy_instance.historical_data[\"close\"])\n", " stgy_instance.historical_data[\"interval_name\"] = ['nan'] * len(stgy_instance.historical_data[\"close\"])\n", @@ -700,6 +738,21 @@ " if i.left_border < market_price <= i.right_border:\n", " stgy_instance.historical_data[\"interval\"][loc] = i\n", " stgy_instance.historical_data[\"interval_name\"][loc] = i.name\n", + " \n", + " @staticmethod\n", + " def calc_vol(last_date, data):\n", + " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " vol = [mu, sigma]\n", + " return vol\n", + " \n", " @staticmethod\n", " # Checking and updating data\n", " def update_parameters(stgy_instance, new_market_price, new_interval_current):\n", @@ -726,6 +779,7 @@ " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", "\n", + " @staticmethod\n", " def reset_costs(stgy_instance):\n", " # We reset the costs in order to always start in 0\n", " stgy_instance.aave.costs = 0\n", @@ -833,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -843,7 +897,7 @@ "\n", " @staticmethod\n", " def write_data(stgy_instance,\n", - " new_interval_previous, interval_old, mkt_price_index, period,\n", + " new_interval_previous, interval_old, mkt_price_index, period,oc1,\n", " sheet=False):\n", " aave_instance = stgy_instance.aave\n", " dydx_instance = stgy_instance.dydx\n", @@ -900,8 +954,8 @@ " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", " else:\n", - " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " with open(path_to_aave, 'a') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", " writer.writerow(data_aave)\n", @@ -911,16 +965,16 @@ " writer.writerow(data_dydx)\n", "\n", " @staticmethod\n", - " def delete_results(stgy_instance, period):\n", - " file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", - " file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " def delete_results(stgy_instance, period, oc1):\n", + " file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", " os.remove(file_aave)\n", " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", " os.remove(file_dydx)\n", "\n", " @staticmethod\n", - " def add_header(stgy_instance, period):\n", + " def add_header(stgy_instance, period, oc1):\n", " aave_headers = [\n", " \"market_price\",\n", " \"I_current\",\n", @@ -967,8 +1021,8 @@ " \"total_stgy_pnl\",\n", " \"index_of_mkt_price\"]\n", " \n", - " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " with open(path_to_aave, 'a') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", " writer.writerow(aave_headers)\n", @@ -996,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1009,7 +1063,7 @@ "\n", "# Load historical data if previously tracked and saved\n", "\n", - "historical_data = pd.read_csv(\"~/Cruize Simulations/Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "historical_data = pd.read_csv(\"Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", "# # assign data to stgy instance + define index as dates\n", "timestamp = pd.to_datetime(historical_data['timestamp'])\n", "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", @@ -1027,38 +1081,67 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Period of Simulations\n", - "period = [\"2020-06-01\",\"2020-06-15\"]\n", + "period = [\"2020-05-01\",\"2020-11-01\"]\n", "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", + "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", + "where $\\sigma=vol$." + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(-1.5602278826280307e-06, 0.0005989101310066664)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "weighted mu: 1.4462763455814235e-05%\n", + "weighted sigmas: 0.17999807112824498%\n", + "[min_6m_change, max_6m_change]: ['-6.786594905713236%', '9.135956592119358%']\n", + "avg movement: (1+slip)(1+mu+2vol): 1.1305428909776651%\n" + ] } ], "source": [ - "# log_returns = np.log(data['close']) - np.log(\n", - "# data['close'].shift(1))\n", - "# ewm_log_returns = log_returns.ewm(alpha=0.8, adjust=False)\n", - "# mean = ewm_log_returns.mean().mean()\n", - "# std = ewm_log_returns.std().mean()\n", - "# mean, std" + "# First we calculate weighted vol\n", + "last_date = \"2021-06-01\"\n", + "slippage = 0.0005\n", + "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]:]\n", + "for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", + " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", + "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + "print(\"weighted mu: \", str(mu*100)+'%')\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+6*vol)*100-100)+'%')\n", + "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." ] }, { @@ -1097,12 +1180,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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PF/XvA7clouDh9kQUHNyWiIJIiM3jciJf6fUG6XIov+vK36bEBPehJ25rRN7xOI8oOLgteebPe+Neb8sHt912G2bOnImFCxeiQwfPGQmnnHIKAHtJPADIzs5GQUGBSxvxutY6S0TxwhZ4VcmoI1bI1IiP+ayxjyciIiIiCob4OVInUhepcy+TIaChJyIiIooyfv2iC4KA2267Db/++isWLFiALl26eH2MuDZS27ZtAQAjRozA1q1bceLECanN3LlzkZGRgT59+vjTHSKKIJvjbFsXpDMSnrwTERERUSTF0bwvoqhi1DOYREREFA/8+kWfPn06vv76a3zzzTdIT09Hfn4+8vPzUVtbCwDYv38/nn32Waxfvx65ubn4448/cPXVV2P06NEYMGAAAGDSpEno06cPrrrqKmzevBmzZ8/GY489hunTp6uWsiOKJ/F0gir+KY0NJekczxBP7w0RERERERHZmQzuZ43//nwt6szWCPSGiIiIAuVXMOn9999HeXk5xo4di7Zt20r/vv/+ewBAQkIC5s2bh0mTJqFXr1649957ceGFF+LPP/+UnsNgMGDmzJkwGAwYMWIErrzySlx99dV45plngvuXEUUhlrlzxzJ3RERERBQNBObKU5yL1KmXWjWL+btO4K8txyPQGyIiIgqU+yqIHgheBsJzcnKwePFir8/TqVMnzJo1y5+XJooL8RVMsv+vZzSIiIiIiOKALX4O1YmiitZ5cHmtOcw9ISIiosZg4VqiMIr2E9S8kho88fs25BZVe20rztxkLImIiIiI4kKUH6sTNVakzt2sGifCJiOHpIiIiGIJf7mJwinKT1Bv/HIdvlx5CJd/vMpr2zhKsiIiIiIiIop78nM4b5VngsmmFUzS26Nb93y/CRe9vwIWqy1sfSIiIiL/MZhEFEY1Zovbbad1bxmBnqjblV8JADheXud23z/bjuOpP7ZLB/gWxwmBUc/dCBERERHFPq6ZRPHuaFmtdLmizv3cNFTWHypVvd0mAJV1Zvyy8SjWHSrFnoKqsPWJiIiI/OfXmklE1Dg1DVa322KlTNzNX28AAHRsnoJrRnbGmoMlAIJ30s2TdyIiIiKKJGbeU7yTl5vTyhYKhVfn7lG9/UhpDV6ZvVu6Hk9rDBMREcUjBpOIwqjB4p62H47j5bKaBhgNeqQlBrbJy080npm5A6d2bSFd35NfCQwIvG8xEksjIiIiojjHYWyKd/JgkjUMJ6J1ZisqPWRAvbdov8v1A0XV6Nc+M9TdIiIiogCxPhVRGCWoLDAa6oyc2gYrBj0zF/2enO1XXeyf1h+BIAjYcLgUfZ+c7XJfQaWzDF6DlafdRERERBT7wrmGDFEkyAM14chMOuftZRj2/Dyf23MbJCIiim4MJhGF0QWD27vdFurj5aNlNdLlilrf62Lf9+Nm/LMtHxe8twK1ZtfyfNfNWCtdbpGa0PhOEhERERERUUjlNE+RLocjM2nvCf/WQGKZOyIioujGYBJRGKllJoX8NQ0G6XJxdb1fj73l/zZ4bXPViE5+94mIiIiIKNpwGJvinTzzxxrGNZNE5wxs5/F+q3tVeCIiIooiDCYRhZHaRKtQT74y25xH5MGe6ZWSYECSyeC9IRERERFRlGNSBDUlthAGbo6U1uLLlblut/fKTvf4uHCU3iMiIqLAGSPdAaKmRO3QuLSmAdX1FqQmhmZztMjWNAr2sfmyB89o9HPodI4LPG8gIiIiIiIKmXCUkSurB8a9tlT1PpNBJ12e0Ls15u084XJ/YZV/lTSIiIgovJiZRBRGasfuu/IrMfQ53xcl9ddvm45Kl4NZyqBlWgKac70kIiIiIiKimPDLBue5oRCi2XxPbtCeJGnUO4egurRMdbv/5dm7UVLdEJJ+ERERUeMxmEQURloH7LVma8he8/1F+6XLVpsAS5AKUV88NCcoz0NEREREREShtyu/UrociiQls5dzTXlm0hWndEJ6ohFpiUbkNE+Wbt+cVxb8jhEREVFQMJhEFEaRrsP+5B/b0f3Rv/Hn5mNu9x0prfHrucpqzMHqFhEREREREYVRKE5NGyyeg0kGWWZSh2bJ2Pr0ZGx9ahLySmql2/V6e8BpXW4JCitZ9o6IiCiaMJhE1ISsP1QKALj9241u9y3bW+TXc/HAnoiIiIiIiETeyqr3bJMmXTY6gkY6nc6lzWtz92DO9nxc9MFKnPbfBcHvJBEREQWMwSSiKCFEOG3poV+2+tV+dM+WQX39CCdtERERERERNRmhOP+0eAkmdWyRIl1WBpFEm/PKcNNX6wEA9RYbbvl6fcTPlYmIiMhOe2VEIgo6TwfBNgEwqB9Ph9QXK3Ixd0eBxzYju7XAiv3FLrddOiw4aybpEIE/moiIiIiIqAkLRXjGW2ZS6/Qk/PCfEUhNNPj8nH9vy8f/rT6MK0/t1NjuERERUSMxM4kojDxNqLKFcbZVdkaSdPnJP7Zj2T7PJe4uGZqDty8f7HJbotH3EwAiIiIiomhUWWeJdBeIwmbaoHYhfX5vmUkAMLxLc/Rtl+nX8z795/ZAu0RERERBxGASURh5OrT2NosrmHq1TfervV6vwzkDQ3viQUREREQUbjO3Ho90F4jCRn7GqTaXsbregks+WImPlxwI6Pk9ndNeNKRDQM8JAGYry9wRERFFAwaTiMLIU/JRKBKTtMrq1Zmtmo/59daRbrfpWYmOiIiIiOLQxsNlke4CUdi4xnrczxX/b/UhrMktwfOzdgb0/FrBpPWPTcDLFw3QfNzPt4zw+tyezmGJiIgoPBhMIooS1hBEk/JKalVvX3WgRDPQ1LttBi5WzBozOBZHvXx4RwBA+6zkIPaSiIiIiCgyEow8JaamQ34OqJ6Z1LiAjdlqU729RVoidDrtGYpDOjX3+txvzt8bcL+IiIgoOHjkTBRGgodCd6FYM6mizqx5356CKtXbk0wGvHBBf5fb9I7UpBcv6I+5d4/GvHvGBK2P4jmFVnCLiIiIiChUjHqeElPT4e2MS579c6Kyzu/nD0Xp9m6tUgEA7y/ajyV7CoP+/EREROQ7HjkThZGneIktBAfe1fXaCwp7CmyZDHp0bZkqXa+SLUzco006khMMwekgEREREVEEsZwzNSmC6kVJvcWZWVTb4H+WksVxTtsmPdHvx6q5fHhHPDCll3T96s/WBOV5iYiIKDAMJhGFkadwkdUmoLCyPqivV+UpmCQAb3soFXBGr9bSZU7YJCIiIqJ4VFjVEOkuEIWNfEKh2kRHeQCpNoA1ivadsFe/MOh1aO0IKI3o2sLv5xElGvUYd1Jr7w2JiIgoLDhETBROHlKTnv9rJ4Y9Pw+/bzoatJfzFEyy2gS8OneP5v3yetcs/0FERERE8eif7QXSZWYpUbzzVllcHkCav/OEX89tsdpw38/bAADF1Q347qZTcdPornjr8sF+91OUYNRzXTMiIqIowl9loijxy0Z7EOnZmTuD9pyegkkWL2X1zhvcXrqcyAN4IiIiIiKimCZfp1et7PmgnCzp8pzt+X499+6CSulyvcWGrq3S8MjU3mjViJJ3CQb7eajJwEgvERFRNOAIMVEY+bIqUlFV8ErdiWsm6WTH3gbHlMvyWrNb+z5tM6TLJ3dshtE9W6F32wyMZWkBIiIiIopzwV/BlCi6yDOT1LKU5IEff8vc1QVQFk/u+fP7oXlqgsttYlbSeYOcEx1351c2+rWIiIgoMMZId4CoKfFWViDYahw1r9MSjKh0BJaSjHpUN1hxjWLx0t+nj0L31mkut315/fDwdBQ8eSciIiIiIgqlOTucZR2V56YnKurw15bj0nVlYMebBovzCYd2yvK7b/86pRP+dUonDH1unjTBUgwmJZsMUrvJbyxBTvNkLLx3LIwGzo8mIiIKJwaTiMJIrZSAajtBgE7X+FR+i9X+epWycnfVDe6zuLq1SsVAWUkDIiIiIiIiajqGvzDf5fqqAyWwWG0+B2zka+4mNCLIs+zBcbjkw5UoqzFjTM9WANzPo/NKanHxhythttow49rhjSqlR0RERL7jNA6iMPI1M6lGJeATCLPN5r0RgI+vHhqU1yMiIiIiiiXnD24X6S4QRYQ8QDNj+UHVNmNfWeTz81lk554XDWnvoaVnSSYD/rjtNCx5YBx6O8qwqy33u/FwGbYdrcCagyUBvxYRERH5h8EkojDytZSbLUj18MwW357HxPIARERERNQEJRmdx8HhLklNFEny7/vTf+5QbXOktBZ3frcRj/22FYJiAzlSWoMXZ+3Eulx7MKesxrkm71n9skPWV6VgnTsTERGRdyxzRxRGvh7n+phQ5JU4Oyw90ehS6k7JaGh8ST0iIiIioljDYWgiz37fdAwAcO3ILi5r7D7z5w7M2VGAD5ccQFqiEVWy8029Prjnl8pAlst9QX0lIiIi8oTBJKIw8nXNpKBlJjnWTLp2VGfUNlgx9qTW+Gz5QSzYdcKlnSHIB/v+ENeG4oQyIiIiIgo3HoMS+abB4jrjcc6OAulylYeJi8GQnZmkeZ+nQBMREREFF2tbEYWRz5lJgoCZW45hyZ5CCIKAkuqGgF7P4lgENTnBgMfO7oPTerSUFjGVS01gXJmIiIiImh4ORFNTJX71FyomGmqxqi1cFCY3je6Kbq1SAQBPn9s3Yv0gIiJq6jiCTBRGvp6sHi+vw23fbAQA3DG+B96avxcfXz0UE/u08frYfScq0SotCZkpJpgdwSST3hk3/tcpHZGVYkLnFql4f9F+XDo8B6mJ3BUQERERUdPDUBI1VWLVjOs+X+tTe4uPtdgv7WoNuE9aUhKMmH/vWOn6k39sly4zHkxERBQ+HEEmCiPxQHdC7zZ4ZGovnPHqYtV28sVL35q/FwDw5vw9msGk6noLlu4tROuMJFzw3grp9p5t7DWtTbI1kYwGPc4b1B4A8MFVQwL/Y4iIiIiIYhwHool8U2+x4Xh5LXKLarA7v0KzXaIh9H1JTzKiss5eWi9YJeKJiIjIOwaTiMJIPMzt3joNXVulabZTW8Ooul57htfrc/fgk2UH3W7fU1AFwB5AIiIiIiIiV76uaUoU65RVMjzFYDKTTbh/8kn4c/MxrD5YAgC47KNVPr5OwF302YqHzsBF76/E7oJKBoSJiIjCiCPMRGEkzppSiRW5KK6ud7vN02PUAklyRm8vGEHR2zMiIiIiincciKamQvld9/TVr22w4spTO+H7/4yQql34ql+z0G9U6UkmZGcmAWCpSiIionBiMIkojMQDeJ2XCEptg3sWUqv0xIBfNzPZFPBjw4UnAUREREQUbjwGpabK03q+DVbn+kgGvX/DRglhKHMHOM+pfV2XmIiIiBqPwSSiCNA58nGapyao3v/3tny323plZwT8ev3aZwb8WCIiIiKiuOVW+osD0xSfAv1m+1vlIlxFMcSX4SZLREQUPgwmEYWReHIqzqKaf88Y1XYLdp1wu81stWHmlmO45/tNKK6qx7GyWp9fN6d5iv+dJSIiIiKKcxyIpqbCbc0kD23vmtBDuqyP0pLpOsdJNdc9IyIiCh9jpDtA1JTYxDJ3juvNNDKT1FisAm77ZiMA4JeNR6HTAQvvHasaKPruplPxyK9bcaCwurFdJiIiIiKKS79tPIrfNh+PdDeIIkIrkNqlZSruOMMZTDpSUhOmHvlHL5W5i2w/iIiImhJmJhGFkThrSidbNOmkNuk+PdZss7lcFwRgU14Z1h8qdWvbOj0R7/9rCHQ6oH+Ul7jztn4UEREREVEo3PX9pkh3gShs1GIuX67Mdb1+/XAsuHeMSzbSqd1a+Pwav958aoC9C4S9j39sPoaKOnMYX5eIiKjpYjCJKIzEWVPyAMrI7r4dnP+y4ajbbTZBwM/rj7jdbtTrcVJ2OlY8dAZ+vHlEQH0lIiIiImpqmOVATYeAJ37f7nJLepLRZeIjAGQlm3x6trl3j0a/9oGv8+svsZsr9hfjlq/Xh+11iYiImjIGk4jCSDw31cF5gG622tQb+8AmAN+vy3O7PTXRAABom5mMJJMh4OcPJy52TEREREREFBrK0y210y+DyvpIRh/WTJo2qB26tUoLtGsBkfdq+b5iPDdzB+rM1rD2gYiIqKnhmklEYSQGTOSTvSzWwIMoVpt6ICrTx9ljREREREREFP8ERaE7tbNQvUoNcoNefQ5y//aZmNIvGxee3AHZmUkAAGsYYznKvn6y7CD0eh0emdo7fJ0gIiJqYhhMIgojqcyd7DabbEpYTvNk5JXU+vx8D/68VfV2tRllRESkzWK1oc5iQ1oiD42IiIioaVI7jzSoxJL6t8/En7efFoYeaVNbe3fHsYrwd4SIiKgJYZk7ojAS40byBU3l5QXm3j0mKK+jrHNNRESenfnmUvR7cjaKq+oj3RUiIoogFl6meOVLmTtfM5MGdMgMVrcCpnbKa2PpdCIiopBiMIkojJSlBZSSTAZM7tsmTL2JDgx7EVE02HuiCgCwbF9RhHtCRETh0Jh1S4nigdqatWpZSGprJkXD3EWdypnkiv3F2JXvmp1kswk4951lmPLGknB1jYiIKG4xmEQURjaxzJ3suFd5CJ9gNLg9bmr/7NB1KkpwDhkRRYP5O09EugtERBQGNfVhXNyFKEaoZya536bWLty0ujDljaUu13ccr8CWI+XYlV+JFfs5aYiIiKgxGEwiCqOf1h8BAGyX1XJWTggzGdyPiqf0axvSfhERkd0fm49FugtERBQGVQ2WSHeBKKzcytyptFELHKllJkVHMEm7D/LMQ/nfvYCThoiIiBqFwSSiCPhry3HpsrL0XYJKbYH1uSUh7xMRERERUVNRy2ASNTHK805f10zSqwSTooGnXj3w0xZnO1nDBpa3JCIiahQGk4gizS0zyX2zTEk0onV6onT9ylM7urUZ3DELAHDOwHZB7R4RUVNTWt0Q6S4QEVGI1TSol7lTW0eGKB6precbW5lJ2vf9uvGodPlIaY10edHuwlB2iYiIKO4xmEQUYdWKWZFbjpS5tUlPMrocsKsFnL698VR8eNUQPHdev6D3MZQ8lScgIoqE//6zK9JdICKiENMKJhHFK7U4afusZJfrasEktdui4RTOl4BWvcWKb9bkSdcPl9TgmT93hLJbREREcY3BJKIIm729wOX65iPlbm30Oh3kx/AJRtdNNyXBgCSTAZP7ZiMzxRSSfhLFi21Hy/Hff3ahup7lbUhdQUVdpLtAREQhZrUxA4maOAGY2KeNy01qARq1zCRbFGTw+RLPmvrmUizZ45qN9Nnyg6HpEBERURNgjHQHiJq6Hq3TsPdElcc2nVukumTwHCmpdbn/9UsHhaJr4RX58xFqIm77ZgNyi2twrKwWb142ONLdoShk5f6IiCjuRcNgOFE4Kb/xaluAamaSSlWMlmmJbreFW76XyT9Dn5uHoqr6MPWGiIioaWBmElGEdW+d5nK9mUpm0eS+bWA0OA/si6udB8VPnN0Hk/tmh66DRHEmt9heN337sYoI94SiVUE5M5OIiOKdViyJISaKV2rrgSmDqgaVzKQRXVu4XH/l4oG4blTnoPYtEGU1Zo/3ewokLdtbBBuzE4mIiPzGYBJRhD1xTh+M6t4CH141BABw5/gebm10Oh0emdobAHDVqZ3w3LT+svvC00+ieMMFtklLFUsgEhHFPWYmUVMnCO5BVZ3KCJF88mOSSY+LhnRASkLki9w0Zhu+8tPVmL09P4i9ISIiahoYTCKKsLaZyfi/G06VsouUpQU6tUgBAEzum43Vj4zH0+f2dVkoNdbPgxkMo0jZX1iNia8txrK9RZHuCkWZegsXZSciincxfghN5Dfld/7KT1dDUNya5iVI1Do9Kci9Clxj1z07zkx0IiIivzGYRBRtFNGVsT1bSZfbZCRBr9fBZGAEhigY9p6own0/bo50NyjKJBoNke4CERGFGDOUqalR+8rL4zGXD8+BXmXNJAB47KzeyEw24fGz+4Sod/6z+rENt89KxrPn9XW5jdmJRERE/ot8bjJRE3T58BzN+wZ1yJIuP31uX1w8tINbG3n2Eg+BiXy370Sl2235FXVYm1uCYZ2bR6BHFI16tEnz3oiIiGKazaZ+O8eXqSlpsNg3hHsm9sQdKuXWRTec3hU3nN41XN3yiTwzadMTE1FVb8G7C/fh2zV5bm1/vXWktG6qyMI1k4iIiPzGzCSiMOrY3F6y7sKT3QNEov4dMvF/N5yCRfeNxTUjO6vWo9bFYW04ZYkFolBYc7BU9fbn/9oZ5p5QNFu0uzDSXSAiohCTH3lOzWF5U2oCVE63DhZVAwBi8exSHkzKSklAh2YpePGCAaptjQa9WzaiWpm88hpzcDtJREQUZxhMIgojMWCiVT5ANKp7S3RumerTc1bU8oCXyFcpCSxfRkRERM4SV4NzMnF6Nic1UfzzNHnP2/lpNLL5mFnUJiMR6UlGt79eGUx6f9F+DHxmDn7fdDRIPSQiIoo/DCYRhZFYTkMfxMwiDo4T+S5ZY3vR6wCL1YZPlh7AxsPq2UtEREQUP8QshXjM+CfyVSx/+30tUzf3njEwGfRuayQpH//ff3YBAD5ZejA4HSQiIopDDCYRhZF4AGsIwknra5cMxOS+bXDViE6Nfq5IiuUTGIo9SSb1YJJOp8Pvm47hub924oYv1oW5VxRpaouwc2F2IqL4Ju7m9TrX41GWXqZ4pXZoI94UizFVtTJ1Sl/9ezgykkwaj1dfOG3r0XLsO1HVqL4RERHFKwaTiMJIPODVB2HLu+DkDvjwqqGqayoRkTqjhxIem4+UAQCKqxvC1JvA2GwC5u0owImKukh3JW6oDa78uO5I+DtCRERhI45DMzOJmjJx8kwwK2eEi1Vj4s+z5/XFoJwsrH5kPE7v0cp5h6K5p8ymCa8tRkUdy8kTEREpMZhEFEZSZlIM1qQONSYBUDhozWDUATBbY+NL+POGI7jhy3UY98qiSHclbijLngDAAz9viUBPiIgoXMR9P4/KqalQO9IVg0ixuB1oHddfNaIzfps+Cm0yklxuz0pJcLnubc2lkqronmBGREQUCQwmEYWReMAbjDJ3ROS/PQWV0uUBHTKlyxabAItVvdRFtFmytwgAUN1gjXBP4ofWUML+wio8/ed2HCyqDmt/iIgo9MR9P+d4UVOhVsJ33SH7WqGxeHrqLRik1KddBh6c0gudW6QAAI6Xu2b592mb4XJdK/OJiIioKWMwiSiMnGXuYvBonSiOJJn0eOjMXtL1TXllMMdIMCnBwJ/uYFPLTAKAJ3/fjhnLc/Hor1vD3CMiIgo1eXkvHplTUxeLZe48lanTcsvYbrh1XHcAQLEi80iZ6bRw1wm/A1ZERETxjiNSRGEkHosyM0mG7wWFkXiSOLV/WyQaDS73/bbpWCS65LeMZOc6aVw3KTi0Jp4u22fPAluxvziMvSEionCQ9v06jduJ4ky8fbW1JgN508xR7q7G7Jrlb7a5Tix77q+d+HJlbkCvQUREFK8YTCIKI6nMHTOTiCJCnMFo0usxOCcLY3q2Um1XG8Ul5JJMziDY9G82RLAnREREscsmy0wiago8xV5iMYiqtWaSNykJ9mPp2gYLcouqpedRe76n/twReAeJiIjiEINJRGEk1l1mmTuiyLBYHQFdgw56vQ6fXzdMtd3Xqw6Fs1t+ke8+1uaWRq4jcUQ+s1Ue7G+flRyJ7hARURiI48Y8LCcC+rXP9N4oygRagS7ZEUzaU1CFsa8swmO/2csZi+cJREREpI3BJKIwEmsus8ydu1icDUexx+ooX2F0jBzpNLZFkyF6t1HlDOp6S/RmUcUK+f6nR+s06XKdrPxJoLNfiYgoOolrJnHFJGoqBI1Cd1ee2hEjurUIc28iR8xMEn27Jg8AYmb9VCIiokhiMIkojJyZSRHuCFETZfGx1GR6kikc3QmIMgD28M9bI9ST+CHPTGqRliBdLq52LsxssXGAgYgonoi7fp0ObusmEcUljXkx147sHNZuRFqKyeh22+78SpyorHdvqwg8ERERNXUc0iYKE0EQpJNWZiY58Z2gcBKzS4xegknrD0dv+Thl13/ZeDQyHYkj8rEVg0a0n5lJRBSIj5bsx4/r8iLdDVLBNZOI7EyG2BwW6tYqFQCQnZHk1+OSVQJEk99YIl2WnycwW4mIiMhVbB41EMUg+UCkt6wIIgoNZ2aS+8+fXgcM7pgV5h75j4NewScvczepTxvVNmbW0SciPx0qrsYLs3bh/p+2RLorpELcq/NnlZoKrSOZjCjOyPfks2uH4fLhHfHdTaf69Thv2UbyfYKFk4mIiIhcMJhEFCbyA1E9g0lEEVHrWANHbU0kmwBM6G0PJJgt0TsLkbuP4BNk0aRLhuaotmFmEhH5q7zWLF22cR8SdZiZFJ2q6i1cDzLMslJiM5jUqUUqXrygPzq3TPXrcckmz8GkU7s6148SBGDVgWJU1pkxb0cBFu8p5DEhERE1ae7FYokoJORrcngrsdUUaS0ISxRM36w+DACobVAfpEhynFzO3p6Ply8eGLZ++UO5ZhI1njwzyaDXQa+zBxfluGYSEflLvm8x22xI1HPtjWgiXzNJp3I7hV91vQX9npyNlmmJWPfYhEh3J+5ofbeb2rGlXq9D15apOFBUrXp/+6xkl+uXfbTK5fpblw/GuQPbhax/RERE0YyZSURhIp/BxBmQRJG1M79Cunz9qC4AgH+f1gUJRvvPYkWdBXklNRHpmzdq+4/fN3HdpMaQB/vVAkkAUFFrCWOPiCgW1Zmt+HjJAewtqHS7z8JSmVFHzErlUXn02HHcfnxWVFUf4Z7EJ07eczqla3PN+9p4WYOpoLwu2N0hIiKKGX4Fk1588UUMGzYM6enpaN26NaZNm4bdu3erthUEAWeeeSZ0Oh1+++03l/t0Op3bv++++y7gP4IoFsgntXPNJKLISpAtNPz42b3xz12n4+Eze7lM2SyM0oEMtd3Hnd9tCns/QmH7sXI89cd2lFQ3hPV15UMrWrNzaxoYTCIizz5ddhDPz9qJia8vcbuPi7hHH3HiACd5RQ/58Vl1PX93w6GrnyXi4oWn8/EbR3fF5L7qa2gCgJXpi0RE1IT5FUxavHgxpk+fjlWrVmHu3Lkwm82YNGkSqqvd04PfeOMNj+nSM2bMwPHjx6V/06ZN87vzRLFEftBp4EmrhG8FRcIFJ3eQLut0OvTKzoDRoIdZNnNcPqARTfwd9CqtbsA/2/JjYiDzrLeW4fMVuXj8921hfV1PYwKdW6QAABqieB0tIooOm/LKXK7Lj/3MzEyKOgLXTIo6YoY4AIx5eSF/e4NM7XinRVpC+DsSBYx653ctyaR3KW2XlmjEh1cNRev0RNXHcs0kIiJqyvwaKfvnn39w7bXXom/fvhg4cCA+//xzHD58GOvXr3dpt2nTJrz66qv47LPPNJ8rKysL2dnZ0r+kJM+pxESx7kSlMx1ez8wkoqBpsNiwcn8x6szaizWbrTYs31ckXR/cMUu1XZ1swefPlh0MWh8j6eIPV+Lmr9fjnQX7NNtYbQJWHSiOmlnAO45VeG8URM4BRff7TI6gYkMMBOOIKLKUkxDkpe247lr0kcaDFft+lgKLHJPB+WEUVTXg7QV7I9ib+KP2zU5PMoW9H9FAvobx/HvH4rTuLd3aDMrJUn2sjcEkIiJqwoyNeXB5eTkAoHlzZ73ZmpoaXHHFFXj33XeRnZ2t+djp06fjhhtuQNeuXXHzzTfjuuuu08xkqq+vR329s9xQRYV9kMlsNsNsNjfmT4hr4nvD9yg6THljqXSZn4mT1WofvLfZhKh9X7gtRbdnZ+7EV6vzcM6AbLx28QDVNjNWHMILfzvLstqsVtXPc2D7dOnyLxuP4qbTO6Nbq+gq/2GxqgfNtL6f+05UAQBmbjmG28Z2UW3z6fJcvPTPHpzcMQvf3zg8OB3V4Mv2FO79QYPjtXQ6ndvrioMNdfU85qDowt+m6KOTDdWazWbUNThLdtbUNcCc0qhTLwoy6fdUka5hNpth1nGwOBIsFtdjnF83HMEd47pGqDfxx6Lye2HSB/d3JFZ+m+T7a53NipxmzsnNYt87t0h2exwAmC3q5xFEwRQr2xJRtOO25Bt/3p+Az2hsNhvuuusujBo1Cv369ZNuv/vuuzFy5Eicd955mo995plncMYZZyAlJQVz5szBrbfeiqqqKtxxxx2q7V988UU8/fTTbrfPmTMHKSkpgf4JTcbcuXMj3QUCIN/cZs2aFcF+RJdtBToABuTn50f9+8JtKTp9tdq+bf25JR8TUo+otnlnnQHyqccLFyxAlkrlCvt4knNbfevXJZjcIboGlHYetW8zStrbj/3vqaqq0mwzY5P9/dlwuCxs26H69mTva3V1dVj3B2X1jtcWbI7XdX4HaqoqAOiwcvVaVO6Nru8CEcDfpmgyc6tz3/HLn7NwsMK5v56/cBHaqI9LUoTsOGb/fAryjwOyeMXs2XOQ6P4zS2GQXwPIf4Oramrx51+zUGUGMjWqsdVZgeX5OgxsIaAli514VCIe78gUnQjNOVi0/zYdOKSHWKhn0YL5aKUDzsrRoUu685h6xirX8wfR7j17MatOfe1womCL9m2JKFZwW/KspqbG57YBB5OmT5+Obdu2YdmyZdJtf/zxBxYsWICNGzd6fOzjjz8uXR48eDCqq6vx8ssvawaTHn74Ydxzzz3S9YqKCuTk5GDSpEnIyMgI9E+Ie2azGXPnzsXEiRNhMjXN9PVocufKOdLlqVOnRrAn0aV8bR5+OLAT2dnZmDp1UKS7o4rbUnTzZdt6ZssiwOycIX7G+DOQnaE+4nDXKufzde7aA1MndA9OR4OkcOUh/HHY/QRW628X35+UlFRMnXqaapt39i9HQW21x+cJFk/bk9jXpJQUTJ16ekj7IXe8vA5PblgCvV6PqVMnu3ynWrVohkNVZWjWuRemnqae2UUUCfxtij7yfUe7vqcis94C7NoEABh12uno2SZd45HeCYKATUfK0b1VGtKTmOEUDHc/Yf+82rVrC+CodPvkyZOQksD3OBL2FFQCm1dK16utenyb3xxrc0vx682nol9793P/x37fgT8OH8H8fAM2PT4+nN2NOUdKa/H0hqUut/Xr0RlTp/YK2mvEym/T1tl7gGO5AICpUyYhNdGICxRtHtkwH+Z694oAXbt1w9SJPULfSWrSYmVbIop23JZ8I1aB80VAR8m33XYbZs6ciSVLlqBDB+ci5gsWLMD+/fuRlZXl0v7CCy/E6aefjkWLFqk+3ymnnIJnn30W9fX1SEx0nyqemJioervJZOIXwQd8n6IPPw8no8G+G9LpdFH/vnBbin5an4+yimqCj5+l1cNzRkpGivpiwN76ebS8DlbokWRyn25tkC1CHK6/19P2JAjhfd91emeZO+Xr7i+0B9nSkhKi7rtABPC3KVplpCSissG5TpIV+kZ9TnO25+Omr9ZjQu/W+OSaYcHoYpMmCIK0ZlJucS1Ob++8z75NMZgUTu8t2oeiygZcPLSDy+1mq4C1uaUAgJ82HsPgzi3cHrv6YAkAoLrByn2hF0ajewmbeyb2Csn7Fu2/Tf07ZAEAurZKRWZqkuqSC0+f2w/3/bjZ7XZB17j9OZE/on1bIooV3JY88+e90Xtv4iQIAm677Tb8+uuvWLBgAbp0cZ2h+9BDD2HLli3YtGmT9A8AXn/9dcyYMUPzeTdt2oRmzZqpBoyIiIiCTWOJPjcWq4A/Nh/DY79tRYMlthdPb7DY0Ovxf5BfXhfprnh1tKw2rK/30C9bAED1Mx7RzT5wJbDCHRH5wWy14ZU5zizSc99Zjh/X5Wm2n7nlGFbsL9K8/8k/tgMA5u08EbxONmH1Hn7Tub8Pv//9sxufLT9oz0zSsGCX+ndfHgSobVBfV5LUjTupFTJTmubA2rkD22HePaMx+67Rmmt3t0hVr61otcX2OQEREVFj+BVMmj59Or7++mt88803SE9PR35+PvLz81Fbax/0yc7ORr9+/Vz+AUDHjh2lwNOff/6JTz75BNu2bcO+ffvw/vvv44UXXsDtt98e5D+NiIiamh88DNTJ6VTqn6sxW22449uN+HrVYfy19VhjuhY8jkGuM3q1xh3j7SU2Egy+/5xf/dlqt9v0vkbXwmjncd/TrBtr+b5izfvE74rA0UUi8uKsAW2lyw0WGw44MhtF9/+0BQUVdfhp/RHUmZ2D3rlF1bjtm4244uPV2Hi4VPW5j8smAizazYBSY8nf/wSj62/oc3/tCHd3mjSbzfn76ikYdFxjMoz8COaJ37cFq1txyWpzPZZJS2qagSTAHoTs3jodJg/H0Ikm9fusjCUREVET5lcw6f3330d5eTnGjh2Ltm3bSv++//57n5/DZDLh3XffxYgRIzBo0CB8+OGHeO211/Dkk0/63XmiWCE/YSWi0Hngpy2qtyvjAL7GTo6VOQcuNueVuwx4RIOLh9jLwTRYbVi6txBbjpR5fcyegiq32/R+HQ3470RFHd6ctxeHiqu9N3Y4Whre7CRNju9KdH3yRBSNUmRlRBs0Rhvv+3Ez7vtxM75aeUi67X+zd0mXz39vBSxeRiqvnbG2kT11VWe24uy3l6LbI7Ow/Vh5UJ87WtWZne+xckLFt2t8m5hCwWGVHaQd8yF7elNeGWYsP+ic5CH7+H5cfyTY3YsrFtlx7IAOmXh0au8I9ib6JauUhgaAI6W+L1JOREQUb/wqBh3IrFzlY6ZMmYIpU6b4/TxEsaqwsh4P/6I+wE1OHKilYNlwuBQnd2zmclttgAHdeTsLpMufr8hFdb0FL1880KVNdb0FCUa9x5mNwSQ4thYdgGay8htXfboGALD1qUlI93Omqa+ZWoF6euYO/LXlOFYdKMaX1w3x6TH7C6swAW1C2i+lri1TXa7nNE+W3hkmJhGRN/L5BlrlOpfutZey+2PzMdw4uisAYNbWfJc25bVmtEhzlv9etle7/F0w/LMtH9uO2rNBz3prGTY8PhHNNco7xQv5RC+jPvqyc4PhhVk7UVzVgFcuHqBZxisayLNl3pq/12v7ae8uBwCkJ5lw0ZAOLkcwKQnqg/9kZ3MczDRPTcAft50W4d5EP61j+zk7CrDjWAX6tMsIc4+IiIgiLzwjX0RN2L0/bmZ9ew+i+NyWYtSJinq322oUZVNsAUYGlDNey2vMGPnSAlz0/oqAnq+x0hLd54RU1ln8fp5Qj6P9teU4AGDlAe1yckqBBgAb48Ihrgt/D+yQJQ3AMZZERN7IJ9Gt3O95f2c0aO94lev5hLqsnUWRdXvys3ND+nrRoM7i/I3Rq/wIessOi3aCIOCjJQfw84Yj2HvCPSM5mii/f756f9E+2GwCcoudWSKNCYKuOViC1X4cp8QiMXAXjeWNY83+wujeroiIiEKFwSSiENt+tGmUCyGKFqmJ7rNSExXrITQmy8QsG2Bam1uC8lozNh8J33YuVXXRGAdQ1sP3RTTPWA6nM/tlu1wXAFlmEsNJROSZfKLCTEcQXcugnCzsOFaB27/d6HafMpiktlsPVtlVQRBw34+b3W4P5LcklhRVNkiXDSq/gae+uACVdeZwdimo5J/fMY0suWhhtfr2XUtVZB0lmQx46s/tLn/rkdJalFQ3KB/qVVlNAy75cCUu/WgVymrcH19YWR8XxwHiexWmZPqYpzx/AIDRPVsBsK+LR0RE1BTxMIKIogLXlaJgEeubC4KAv7YcR25RNcae1MqlTaCZSQCw4ZBzcXT5mhjbj5WHeSazffBLObgSyN8W6VhSbYMVz850XfA8nAOZYlkcg2J2+qAOWRF/byg4th8rx0t/74rpwWGKfj6OiQOwlxd99Let+HPzMbf7flyXh61H7N/ZmgaL6n5dGXAKlNa+trrB/yzXWCJfp0pt3cCiqnq8Ptd7ybVoJc/2uf0b94BlNLHYfPsuVyuyzLcfq8CXsrXHRGe9tdTvPhwucWY3ldaYsf1YubRtLNlTiGHPz8PTf+7QenjMEPclagFUcte9dRouHZrjcpsYYArWPpiIiCjWMJhEFGI8Vvds6d5Cx/+hXQ+Amg5xAGX29gJM/2YDxr6yyK1NkjHwmvryknlV9c7BtrPeWobHftsW8PP6SjnsN76367pC3oIw6UnupfE2Hi5rZK8a54d1efh02UGX295esC9sr+9cw9u+w55792g8eU4fXDuqs1QKJg4mJDdpZ721DB8s3o///bM70l2hOOZPMH/RnhOa+973Fu3HOe/Yv7N3frdJtXxpTZCCPWaNCFhtQ3xP8tkiyyjW63Sqx+ufLT/odtufm49hzvZ898ZRpqjKWfK3sj66A4Nqxy0Tequvmbhwl/eSj8fL6/zuQ4GsRPJrc/fgrLeW4b//2AOO7y60H498viLX7+eNNuIxslppR3Kn0+nw34sGYN1jE3DxkA74+ZYRSJCCSfG9jyQiItLCYBJRmN1wWpdIdyGqKBedJvJXdkaSy3VxUGLODud3a/b2Auny/y4cgGaNqKkvn4l4vMx1wOK7tXkBP6+/xIGvly7s73K7t8HMrBRTqLoUsINF1aq3hyvTS3CE6MT3tEebdFw3qgtMBr2zzB1XTYoLO49XRLoLFMf8KYN1oFB9v6e0K79CdV27QAbM1Zg1skJ251cG5fljgTIrVUtxVT1u/3YjbvpqfdSvqVRc5X+pt3B78KctuPeHzXj8d/eJOIkm9WGKVT6uaSSuM1ZW04BtPpQcn7/TeZwoZgt+tOQADhRWYfXBEum+WA6y2mwCvltzGIDv33mya5mWiJcvHoghnZrD5HjvIj0Ri4iIKFIYTCIKs1vGdot0F4jiinI2sTjr0qIy2/rz64bhkmE5brf7Qz4TsValPGOoZyoqxypTElwzjbyNb3mr8f7ZMveZ2KGmlU0lruNxpLQGY15eiDfnBV5y6Id1eRjz8kLsU1mI3OP4r86HNhQzmC1MoeRjtS6/GPV6HJKV4BKd/fYy1fbHy2sxd0eBz4Ets8ZvQkl1A6a9uxzXzVgTF2vFKI3s1kK67GvJL3mGmCXK15SK9v4dKa3B9+vy8POGIy4TfkRan4mvQZDbv92Iv7cex6Bn5uLst5dhw+FSj+3VjucA4IxXF7tcf/KP0Gegh8prc/fgh3VHALDMXWOI6+H9oVKilIiIqClgMIkoxJTnci3SEiPTEaI4pVecEL82dw9mLD+INJVybmNPau31+dTKwMnHLg4XOwf1rCojh3sL3IMVoaA1DCAGZsprzVitMoO3oKIeZlnESbmI+zMzw78mwPL96mUuf9t0DOe8vQw/rDuCQ8U1eH3eHhwvD2wh8Qd+2oJDxTV45NetbveJ74Da2IpY+i66h+XIV2tzPQ8oEjVGY9bj03KwqBprZJkRWkqrG2C1CRj3yiLc+OU6/LrxKABgxvKDuOj9FaioMyO/vA5rc12fS6vM3V3fb8KmvDIs3F2IQ8XuwaxY179DpnTZU8mvSz5YiU+WHgDg+hthjvLMpD82HZUun9a9ZQR7ok7reyfSChoZfQwmVdZZcMv/bZCuL9ipXR6vvMaM3zf5FhgQgzGxaOk+57EWM5MCF+2BWiIiolBjMIkoxKK9DAZRrOvZJs3l+ua8Mjz95w7sLXAt0TOkUzOfnm/O3aPx2iUDXW4b2a0l+re3Dzw1yLZptc071CeZauXW5BmP4mDmhe+vwKUfrVJ9jtfm7pEuN0TBPspTuaetR8uRYHAOeojlZ1bsL0L/J2dj8utLUKcxo1hNtWLtiDqzVYoU6VSiSeJNoRgkJqL4Eur9xBNn93G5LmbCHiiswuBn5+Kaz9agzmzfp893rC3z9J87sO5QKT5fnosrPlmFiz9Yibu+2yg9hy9BkaooX3MnEPKPSq/TnqCxJrcEz/210+12b8GQSJMfi7TOSITVJuC1uXuwLErWKPWWJa31nQt0rZ93Fu7DGa8uUs2E/m7t4YCeM9bIDqUYTCIiIqKAMZhEFGKcvUQUWlqb2InKepfr6w/5lpHQNjMZF5zcweW258/vh55t0h2v53xBi0pmUqgDyIIU+HDeduf4HtJlsX9q5dxEM2SLipfWhG5dhUPF1W6z4ANhMjgPV+odA6Ur9hWjst6C3QWVfs2ar5Gtd/DmvL3o9+RsKaCmNrQirZnEXTkReRHqQ77MZNc175bsKcKi3SekUlzLZJkH9WYbjpU5MzlrGqxS4P43WRaGLxMK6r0M/McieVDhdB8yd7o8/Bd+Wu/MSon2yWKuWVQCft90FG/N34srP10duU7JfLTkgMf79TqgY/MUAK7ZSG9olLs9+OJUr695oLAa3R6Z5ZaRXVRVr/GI+CI/lgrlsR8RERHFNwaTiEKsJoYXaiWKBQeK1IMmygyUxujUIhUt0xMAQJr1DQDfrHafzfrFykMoqQ7dSbra2hVJJgM6NEsGoL3+kJxYuq3eYsWIFxcEt4MyY15ehIs/WOm1ncngeYbs5ytypctWx98vn00vZmuV15ix7Wg5jpfXYv7OAtQ0uH8HDhY5s6Ben7fHJeCvWuaOk3fjzs7jFZHuAsUptcyk9ET30qmBmtIvG3/dcZp0/cYv1+HaGWtV2wqCgJEvOffvWokIvmQmecsiiUX55XUAgK6tUnHewLZe2wsC8PaCfdL1aMjq9aSm3nn+YbHaoqpUYXW9BbO2HvfY5sEpvfDtTafi2pGd8em1wzy27dYqFTqdDu2zkn16/Y+XugayqhzvVbMUk1pzN+U1Zp/aRRt5MOnSoY1bP5SIiIiaLgaTiCiiOrdIiXQXKMZpDXIFqyxPguPkO8loAOA6EN0q3X0NtD83H8PJz85FYWVoZro+/vt2AO6ZV2LJEl/KLLXLSgIA7D+hXV6usQ4U+r52lLdyQccdg36Ac40n+UCe+CdPfH0xzn57GUa8uAD//mIdnv5Dff0n5axkkU4lN0laM4mpSXHjzDeXRroLYXW0rBY3fblOdQ01Ci61XYvRS7Bc1Dw1wWub1EQj+rbL9NoOcJa5Eymzi8T9oNni7PRdE3pAjXx/e7y8Fq/N2Y2CijrVtrGirNY+6WN0j1YBlU675esNfpVYDTf5ZLZoWt+ptLoBp744H7Ue3rtLh+aga6s0tM9KxlPn9sWYnq2QobKepei6UV0AAH/dcZo0scaTF//eJZXMBYBv19gnBinX4JS78tSO0uWKutgMJsm/5+cOah/BnsQ2X/bVRERE8YzBJKIQy2nu2yy5puqeSScBANI9nCQSedIsRf2kTp5B1Bi922UAgFuWiyAIKK7SzkCSl5ILlmJZKZaNh8tc7jM4BkF8GTPKcrxnoVrfY+fxCqnskjf7TlR6byRjFQQIgoCjpc7yTYIAVNaZ3QJs+x0BLWUg6JIPV6oGh9TGkfR652sQxaL7f9yMOTsKNNdQo+BRC1T7ujbJ33eeHuzuuPh0metvUrXjN80sK9d6Upt01UF7edDkuhlr8daCfbjpy3Vu7WobrDFTMkwsGZjlYzaK0taj5fhwsedSbb4SBAHHy2u9N/RDjVkeTIqeH7DVB0tQWed5so9aALajh8lnLdPsxzRZKQlSxpk3t3+7EZNeXywdJwBA64wk1baPndUbz03rj5QE+6SiWD0eSDY5h344mS9wL17QHwCQaORQGhERNU38BSQKsZFdvddhb8pSTPYTs26t0iLcE4pVoV7w3OQYCMxx1O4XTx4rai0ey9zkB3nW9u+bjmLIc/M07xcDIZ7K3PXKtq/71C7L95J4gTjn7WU+t525xXOpG6Wle4vwxry9mLOjQLrNJgh47Ldtbm2THQM/yrXr1h0qVR1cUxvyLa22z0DeV1iFvQWVPg9UEUWLXFlpx1gZ6I9Var9HvgSTFt8/Fm00BrJFJ3fMCrRbqiocA/pmWcaSTgcsuG+sW9tDxc7v0K58+wSAzUfKcVhWOs1mEzD5jSU49YX5Lt+5aNXgyMjK9vK+e/L3Nv9+v7S8vWAfRry4IKiTUORrOi3eU4g356uvNRRuRh+2B3k5NpHKEpWSI7LJJX/efpp2Q4U9BVV4bc4eKRglL488pFMz6XKS41xFzFwSS+vGmlRHyc2JfdrAqPIek2+yHIHo9j5kwREREcUjHkUQhVhWamAzHpua2Dwto2jgazwkLcB1K8RBjZQEo8vr5coG1x6c0gt92ma4PK6Dj7X7ffXYr67BkgTFQIAvZe6uGtEJAFDvmLFsDVEgThm8kVOWJfxCth4S4H227JYj5aqDYr/LFpSX+uEIGM3enu92n2rZH5Uxrn8cj/190zFMfH0Jxr+6KKpKBhF5Uyxbw01Z6oyCS23/W1DhPYDXqUWq1zYnZTt/Y77693D/OqaixjFwLg+sZ2cmo2Wae/nWExX1qllX07/ZIF2urLPgcEkNLDZBCjhFM3EySGMG1cUgQ2O9NncPAOCV2buD8nxA8CeLCIKAo2W1jS756ktwVS3gpNy2LhrSQbosn9jTu20GNj0x0aegFQDkldagyJFlLv9tv2So8/nF0nniM245Uu7TczfWtqPl+GjJfpfAYGPsL7Qft57SpXlQnq+p0omzt3jySkRETRSDSUShxgNNj7i4PTWWr5lJb142yK/nveIUe338uyf2BACI403i68kHHW4Z2w2/Th/p8vi0IJdurFSsASVm3YjEGbPrD5Xiyd/ds3QAINGx7pM4oKw2ODisczO324LpnHdXSpcLK+tRKlvI+r1/nYwvrh+Op8/tq/l4tYFOra/AygPFKK8xY8exCrf71NbUUlszSam6weoye5kiZ9vRcpTXxubaFeEkDyBx7a/QUhu/f/GC/gGXQ7rH8fsDALef0V26fHqPVvjwqiEBPadI7Kr8t2xQThYAYOkD41zafrLsIEa8NB+l1a6lXbceLZd+R+TryPha2i9S6sxWLNlTCAAw+bimlZpg73+qG6woqdYun+sPT8Ektf2A1Sbg+7WHcVAjq+y9Rfsx6qUFeGfBPr/7IgiC9F75sj6VWoBP2eWurZwB2N6KyTxZKQn45y7fykbK3+9xvVpLl88a0A6fXTsU908+CaN7tHJ5zO3fbpT+nup6S0jWDyuvMePst5fhhVm78O3aPK/t/9mWj0s/XIljZdrlEjfnlQFwXU+L/MdzVyIiauoYTCIKsVCX4CJq6nzdxNKT/MsSfH5aP2x5ahKGO2ZwisEaZzDJ/n83x4CGGKgRhXqNAuVgkDh499rcPfhi5SHVx4gDmvUWR2aSbLDp1K72v9NTVlEwHCiqRr1jHKO42nXG/tT+bdGpRSrGndRa5ZF2GUlGnD2grcttyv3sk+f0kS4v3Veomhlw/edr3W7zdYDggZ+24NNlB7FQscA9hc/SvYU4++1lGO/j2lxkF6rSlmSnNkh/6dAcbH5yEtY8Mt7v57tjfA/kvnQWcl86SypPKmrseh1iVxc49mPyMno5zVOQ+9JZLsGsgop6zNzqXtbtpq/sayfJAytWTzXJosCO484JBkc9DL570ykE685sOVKGb9ccRs9H/8ZHS/YH/DyeMo/VMmu+X5uHB3/einGvLALgnr37siNr6lVHFpU/Hvl1K4Y8Oxeb88qk9R09UQvwKX/nEwx6zLrjdLx68UCM7dnKrX331ul44uw+OL1HS4+BV/n6mk+e0wcvXtAfSx8Yh7REI87o1QbTx3WXAmDy97Swsh7lNWac+uJ8nPLCfPy4znvAxx9L9hZKl3ccc35eP6zNww1frHVZQxMAbv56PVYfLMHIlxaoBpTUgsbUOPw1JSKiporBJKIQk5/7dGnpvYxJk8WgGwXI14Btgp8DbzqdDhmyAJQYTBIHYy2OwTJ5bf/fp4+SLstP3AVB8Hvm6oHCKpz3zjJcN2ONaukb5V/ty0xwZzDJ3jf5wEj31vZ1yxo72CxfzFpLvmOZjYpa9QwfvYePyiYIbp+5ssfTBrWXLpsMevy84QgAoGcb59ps21WylXydbDpnRwGenbkD132+1mVhegqfOdvta2b5ugaQcl2UppqhE+pgcVMn7j/bOwI/s+44HXq9DkkmA1pnJOG36aPwwvn9NR//6NTePr9Wv/aZLtff+9fJfvVVXPflq1X2yQcbDpe5talQZt6obDfzdtqDUa/Mcf5ORXslUHmZ2HJHdmwgyQYNFhvm7ijADV+sxcLdnicXrD9UgqlvLsUiRbu9Ba4lAZfvK8LDv2xFg9WGF2btCqBXdp5+yz9eesDttnWHSqTL//pkFfo9ORsfLA48mCX37Zo8WGwCPl12EL4krRlVDgLKFN/Fshoz+rTLwIVDOjjLjilcf1oXfPXvUzC5bzbuntBTtY34G5Jg0CPRaMDlwztKa2QqyfefJyrrcKikGpWOtcfUjilExVX1WJtb4tfvjvxPkk9WeuDnLZi384RbiWC5N+e5lwKuqnMeb43s1sLnfpA78aPRyuIjIiKKdwwmEYWY/FwuOUi11eMJSwVQY/kSTMpMNknBkkBJaxI5BsnE9XiMshm0A3OycO3IzgCAN+btlQYOujw8C6e8MB9frsz1+fXu/mEzNh8px8LdhXhnoXtZGeWfrfdhY0p07IOOlNrXPZBPHh/fuw2Axq8FUFTpfXC/zmbvq5ghpeQpMCbA+d6LlGXnmqUmYOxJ9pnK8gXi75l4ksd+aQ1IefLHZve1mij0/K2ilZLo+vsbrFJSseDMftnSZeW2Q541WGx+BdjFpk+d2xe7np2CPu1cy28NyslC77bpmo+/cXRX7Hp2Cm4e0w0/3zJSsx3gXvLztB4tVdv9eqv68/gyrl2tKIe16mCJRktg0W5nJoUlyjOT5JM9LpStvSPny5o7K/YX487vNmLezhO45ev1+HbNYc2JI4/+ug07jlfg7u83oc5sldbB+e8/rgGjb1Yf9vXP8MjT91YtU1u+nuHyfcWot9jw0t/qwaxAg/G1ZqtPv7NGlcyk60d1cbk+bXB7tzae3CYrE6mmwYcIqPw9veLj1Xhu5k7putbxDACMfWURLv5gJRbtKdRsoyTPmPp8RS6u/3yty2d0rNz5PTtR6fqd+35dHq75bA3W5jq3V7EMZUqCoVHrhJHrueuK/UWR6wgREVGE8EiCKMQE2Zz5JBM3OS0c3qJAeRuz+unmEVjiKFnSGOK4knLNJOUMWvmAwgeLD7jMQn7i9+0+r7dT6WUtBuVgji+D62JmUmFlPR76eauUmdS7bQaOlDrLooQ620ZMSNLKkjCpDHS8fflgAPb3X/m4t+Y7Z+H+ePMIAPYAIuA623qKbFBdTSDrfNRw/aSI8Dfwp1wfTL6OULzbe8KZLXjL1+sj2JPYUtNgwfAX5uGyj1Z6b+wg7pf1OiBJYwJRquK36ALFoHiSyYCHzuyFIZ18X79ueJfmSFKUWj1vUDu8edkgDO6o/jyC4LpdXKQSVFGWOvtri3uZO8B9+7rzu01SsCQaib8hmckm9GyjHtzr0My1rKAYlJ3aPxvvXDFYul1cf6bObMPDv2zFgz9vUX2+Xfn2DKTSGjNGvDgf3R/9G2e8ukjK7BIpA3iB8hRM+nbNYew74ZpFPFPjs33qj+1umS6LdheiuKoeP67LQ15JjerjtPok+HDEr3YMcuPpXfDkOX0w5+7ROPjiVL8nCAVjHS/le7pGFqw5Xl6nuhYjACl7acFO9+y1wsp6dH7oL4x4cb7L7bUNrs+1YNcJfLrsoHRdvm0Of971sQCweE8hLv7Aue96dY69PGFKAic2NtYJWenkZXsZTCIioqYnuKuDE5Eb+Xhv28xk7YZNlC8L3hN5osxMSjTqXQaKh3RqFlDGiZJU5k4QUFlnlgajlLX9UxKcP63KGccAUFrT4DaYqMpLl5XjRNX1ngegDHqdyxob36/LQ8/sdMd9QKpsgKGwsl6zzIs3vgSG1xY63ktZlsTn1w2TLrdMS8R1ozrjSGktJvZpg8l9snGw2F5ORBDcg1Dy0nrDOruu/XTIkZkk/u0ZSUZU1KkP+IgBKH/4spg4RZ5y/ZCGJhRMkg8aHyiqRnW9xW0fVF5jRmaK/9//eLb6YAnKasxYm1vq82PEXZOnTNEeikHw587vF1D/5AZ3zHIp5do2MwlvXuYMeLx2yUDc88Nml8cIELD1aLmjv8Bz09z7MTAnCz+tP+L19S9VCbit2F+M0Spr2UQDcSC+dXqiZhtladz3rxyCBosNCUa9ZtAAcM3Q0lLqKK13oNC9TFanFinS71ZjeMuou+GLtVh0/zivz/O5Sjm1Q8XV+GDxfqw+WIKT2qRj9t2jUVFndikNrMZstfmUEbd4TyHuneSaSWw06HGdIjvJX+N7tcb8EK11uGh3IUb/byGWPjBO8xjvq1WHcNPori7HV/f/ZN8uj5fX4ey3l+LSYR3x+G/bVB8vP6YUv8OHir2XWhMEQcqiLqpqOlm5oVIoK6/rbwltIiKieMBfP6IQk2cPyBeFJ6LgEINJX1w/HM9N6+e2HkUwAkmAc3Bw4+Ey9H9qDpbts89GVGYmectA9LXMlLeydcrZvSO81MBfdN9Yt5nyz87cAQAw6HQus3bfW+ReVs9XWxVl8i4dmuPWZlupHsVV9VLAZ2inZhh7UmuXNk+e0xcfXz0UlwzNQWaKSYqtCYL74u5qgyMnOWabi5+/mO30+fXD/f+jPPAWxKPQ2Hui0nsjGWUGoy8ljeKVMgD/15bjGPjMHNyrCDY0dTV+btvL9hY5gzMegszy3ySjXucyASFQYmB+cMcsAMClw1z3uxec3AFfXD8cX/17ONpk2AMouUU1eNOR1ZmeZFLNpLpYowScklrATSyrFY3MUpla2e+14iNTy5AVB47TEo3o6sc6qP6USlT+9gdaUk4ZQP/hPyOQkeT8ruUW1+CC95bjIY1MKqXOLZwBEINeh9WOkoe7CyqxeE8hBjw1R3UCjZzF6r7moZpbxnTzqU/+kk80evXigUF//pLqBuR6Ce68ocjyap6SIF3edrRCM5CktPVoOeZsz/dp3cCm/HsXCvLjZV/KYRIREcUbBpOIQmy2Y5HwW8d2Q2vFAuDk1ETXQqcgEMdoWqcn4spTO7nU2p/Qu7XGo/ynLJEirmugrO2faPRcQkSrtJuSt9NT5dP0aZvh1qZbq1Q8O60fPr1mKHKap7hkJsltVgSAvl2TF/AAlnLQ4qUL++Oxs9wXlS+rtUjravhSfkYcYLMJgmZATl6+RRwwFUvwiK/Rt537+9QYvgyMUfAt31fsV3vl5zRfpdxQU3HxBytdykj9b7Z9APjnDUewdK/va3rEO/l7VOjDWnC3fbtBuuzr+GJj5zqIj+/Syh7Y+Py64fj0mqGYPs59fZgxPVvh9B6tUOAo0TT9mw1Y4MjSEINQSkkmA36fPgp3T+iJ8wa186tv0bY+14HCKtz9/SZsO1ouleBTZhbLqQWTXJ6vyHtGCGAPBr0xb4/P/TyoeF5/AlFqj/v2xlOx+pHxGN6lOR48s5dLmw2Hy/Dd2jyfni9Xli1VUu0aKLzmszUAgPcX7ffaJ19+Ms/s39anPvlL/jtw4ZAOLseIY4KUReethKryt0hcr1JNaoIBn1w9FB1VMsXzSmpx01fr8dP6o177JF9/iRpPHkBidjoRETVFDCYRhVi+YyHeb9cEZ0HduMNjcAqAIAj4fdNRHCqulq1RYf8y9WufKbVTZrs0htagn3LAqV97z8EKXxcm9zrIqBiQSVKpg59gNOCqUztJgxXeBsfkxPUd/CUv4TTUUWKwVmUNCIPeOdiltti2kvh+qJW5E43u4RwMEktoiuWIxJP/RKMBE/u4D94EGmSqD/H6UtR4X6zIxfFy1wXKvc2gj2e78iux/Zg9gCwIAkpkmX2+Diw3BfK9zCuzd3ttX1bjHGD3llkaLF9ePxz/GdMVF55szyDKTDZhfO82fu3rAWcmp5qBOVm4c0IPv9cdVK63FGlP/7kDv248igd+2uLMTPIwECzPMlabrKGWdSuST8ZYvKcQby8IPNvXKgg4WFSN9YdKfZ7kUV5jlkrlJScY0MYxmc2sEeg47GdZvWNltZr3eQq8VtZb3DKmgPCVCnN/aefnH6xNVrnek/Iz+3XjUZz7zjKccJwfaq0hdfsZ3bHwvrGY0KeNx3UsPZ1fGvQ6HC2rxcCn57jcRo0jL9HOzCQiImqKGEwiCpPSmugt9xENfFmQl0j0/do83PndJox5eZFU4kw8n+vWKg1n9GqNdplJfs+k9kTrBFx5IjnOSwArWGXuEhXl9BLUSvIogjT+JNFUaqwr5I28hNM0x8LyaoNHep1Oei8Meu+HI+LT2gRBM5gkf09qFYMv8s/v46uHuj32mhGdvfZBjVjWiqKDIAhYsqfQZVH4J//YHsEeRad6sw1Wm4AuD89CpWz9F7X9SFMlzyAorvZvnZEwxZJweo9WePjM3qol6vx9Hm98yc6SMweQmbQprwzzdxbAFmA2jpqaBgvOe2cZFu+xZ93tOF6Beov998FTJnFaonP9nw7N3Nc8fdxD6eqdx52TMXJ9zGDSsmJ/Mca9sggXvr8Cs7bm+/SYHccrpMu9sp2BwsuGd1Rt/+hvW/3q0/frtIPOD/28RfPz23m8wqWc5t7nz8TjZ/fBtzee4tfrB+qakZ0BAKf3aAkASDA6N1QxY6+xSqobIAgCvll9GE/9sR2nvDDfrc2WI+X4P0d2u/ytOr1HS+S+dBZyXzoL9046SapoccUp6p+bGnmGttUmYNRLC1zuDzTTjZxSEp3vccs07XXXiIiI4hXPGIkoojifi/xV02DBQ7+4D3zIgxifXTsMKx4ej3Qvi0H7Q+sEXBlk8rZGk69l7ry5T7E4tVqsS5mNkdM8WRpEkbtrQg+326obAgsmyfshzkxXC9TY1z7yPjvc+bz2NgLc10wSaZXxU3sN+foPQOCfC4NJ0WVtbimu/mwN/vXJ6kh3JarVWaz4Z5v7wLSnbaipkWcUeMso9TcLR8yEPLNfaMp5+es0ld8FpTk7CqTLN5zWxWt7ZUDfm4+XHMC0d5fj31+sQ/+nZmPBrgLvD/LBkj1FbqVcGxwZOvIJCMpfoRfO7ydlzDx3fj+3501VyQYWJZr02J1fiRMVdWihMdic7mOm13Uz1kqXva3HIxK/rz1ap7kEGpNMBjw4pZdb+6V7i3x6Xl/M33UCXR+ZpXnMVOIIzA7MyYLJoMe/T+uCIZ2aB+31PZnSLxsL7xuLz64dBgA4b1B76b5rRnQKymvc9s1GPPLrNjzy61Z8viIXJzSCsJvyynCisg61smOtG0/vqv6cKmUrtXyiMllGrlML95J55B/5/CgG54iIqCniGSMREcUUrZm5oa40kVeqXtblEg+lbtRoBUKUtBZMHtKpGebePVqaYStSy2Q6pWsLl+s6nQ5f/fsUbHt6ssvtOc1S0KGZ6wBDdX2gmUnOy+KMzWapCW7rJllsApY41mfxpeyK+LyFlfVSRtMjU10HxTzNMjcosrQePct1VvmmPPcF5H1RVNXg0yz64+W1mLnlWNSVfoo3Gw7bP8fDJeplm7q0tK8tM6BDpur9TcWTf2xHYWWd2+3i4LkgCNhbUNmkB8rkA4aettuV+4sx/Pl5LrftLajy+NxfXD8cz03rh+dVghThphZc8Kas1nu2/bMzd2BPge/lUp+ftVO6XN1gxfWfr8PGw4Htl+XUfl/EdW20MvH6t89E64wk7HnuTOS+dBZap7uveepp4sjR0lpMfmMJhr8wH8/O3KHaxiYIuG5UZ+n6o1N74/Lhno8n/tpy3OP9IvE3UpnBDADL9wUvcOTJZ8sOSmVm1SjfvatOtQdz2mWGdn3ZLi1TpTKQk/q0wbc3noovrx/u97EcALx68UB8cf1wbH1qksvtvpQ2X7ynEMOfn48Hf7ZPjurTNgOjNdZtMhr0XrOTLh+eg5tGd8XI7i0x/94xmu04YSC4mvJvJBERNV3+Fb+ONtXVgEFl4MhgAJKSXNtp0euB5OTA2tbUaNcM0umAlJTA2tbWAp4GG1NTfWtrVpzo1dUBVg+zBOXP661tSopzZK++HrB4GHT0p21ysv19BoCGBve/IdC2SUnO74o/bc1me3stiYmA0eixbXKDfbDm7rNkAwYWi/290JKQAJhM/re1Wu2fnRaTyd7e37Y2m/27Foy2RqP9fQMAQYC+phrJDXVIqDO5b3+KtqjxUNPdn+3e332E0eh721jaRyjbxso+wsFkNcMo64Oxtgao1rm2DeI+YnLnNLzQUId6owk2vb2t0WrB8NaJbt+L368diMs+Wo0GowlWWVuT1YI9BwowpKXKTGX5/sRiQa90PY4fdd9GP7u4DzLlgy2OfYSxrlra34jmrT0AXNTPbR+RBmBEm0RsyrPP1k4212FIy2YwWc0wG+xtd+SV4uxuHgbctbb7Kmc/WunN9vfGZMJ1o7qgW6s0XD9jNZLMDTh8uBDz1x1EMoCUhjrne6ix3RtqaqTnPXS4DskAejZLcPZHEJBmcT6Pocb1/Ui2umapTeiYivennYR7HCV3khpqnX1Q7COU76ucTaeDxSYgQRywVHwXLFYbbv6/DVi+twg2nQ5Vlw51lhriPsJ+OYB9hMtnInvPjfKSqY5tWd726v6d8L/ZxWitT7X/3X7uIxosNizZdhRtkvXo3yHLY1sAQTmOUG1rsQDV1TDU1UnbmAvZsYHBZkWCxfVvK8qvQ7ahE5Ib6mA2GGEx2J+3eaIBqK7GZ0sP4JU5e3D9aZ1x/2RZsCEKjyM8Hhs05jhCtg+pLa1w3bZlbS//eBWSG+z7JJG+ptrZXuVco6UOuLJ/S8DaAMhL6IXpOCLRXA+9o+0tw7Ldj21U9hHy7SjFXId2JitKq82oTXC+Z4mWBuhl+5MPZ23Fq5cMVH9exT5CuZ+tNSXi/PdWYPZdo3Hf16tx08hOOGegRulaD/uTxPoal+euMyXg8xW59ofpbPa/3WyGoa4OyQ1Gx2MS7Ld7OY6QP6/82OCOL1Yh2bEtVxa7fjfEttUNQIvUBOnYoH8zA24c0g3LNh9CUaXzOyE/jthzpATmikrtNbEc+wiz1QaDzYpUc73bZ1tZXOa23avtI+TkbfU2KxI9tLUYDDAbTHh+1k4cL6nCExO7qf6Gmupq7O+pY7t/5pzeuGVYNtplJasfa4dgH6EDMKJDms/7iBmX9cN1322Tro/LSUXz1ATA2uD2N9r0etQbnccod49ohw8WH1B9XptOhxZpsuMZlb8/1VyH5IY62HQ6nHtqN/y4/ggAIMlch5apCXhxcjfpsdbKKiQ31EHQAXUm5/aZZK5DqlnlfAsI7nGEY3uSfpui9DhCk5ft3lDr3KccLpJNHIji8QjVthyPsF8O5XFEY8cjxG2pttb1OI/jEb615ZilHfcRgNkMnfz9jJd9hChYcQ1Pj1MSYlB5ebkAQCi3/8nu/6ZOdX1ASop6O0AQxoxxbduypXbboUNd23bqpN22Tx/Xtn36aLft1Mm17dCh2m1btnRtO2aMZltbSorw22+/CQ0NDfa2U6dqP6/yq3DRRZ7bVlU5215zjee2J0442956q+e2Bw862953n+e227Y52z75pOe2a9Y42/7vf57bLlzobPvOO57bzpzpbDtjhse2e97+1Nn2hx88P++MGc62M2d6bvvOO862Cxd6bvu//znbrlnjue2TTzrbbtvmue199znbHjzoue2ttzrbnjjhue011zjbVlV5bnvRRYILT2393Ec0NDQ4t6U42kcIKSmubWNkH/H92sNCpwdnCh8Mv8Bz2xDtIy69/AWh04MzhU4PzhQen3izx7bXXvSk1PaJ873s0374wdmHCO0jPp92q9Tf/3v7R8/PG+A+4tx7v/Lc1o99xIkLL5P62+vunzy2XTxgjOt32FMfZPuITg/OFKpNiZptV+b0E+rMFkEQBOHTpQeEuqzmmm03ZfcQrvxklbMP3EfYBXEf8eOPS6TvRCiOI75amSs8P/Y6z21DdBwR6D7iP5c97bHtYxNvlt6zSy9/wfPz8jjC/s+PfUS0nWt0enCmsCm7h3ZbP/YR1aZE6bvT6cGZwvyuHvY9gMvTlp91nse2ve7+SXreH/uN9/y8fuwjRt38qdDj0VlCpwdnCjMn/cvz8/qxjzjn6tek/nrbR1x6+QvCqS/ME75elSs85sdxxL1T7/LcX8c+YtaWY8It5z3kse29U++SnvfWy4O3j3h+7HVS2/Ovfd1zf2NwH7E7v0Lo9OBMoevDf3lsO7/rUOl9uPaz1R7PNVbm9BOu+HilT/uITdk9hCd+2yo9d1XbDpptd7fo6LJ97m7RUbvPPI5w/vNjPOLTD/5wto2B8YhoONfgcYTjnx/jEdbRo13bcjzCLgb2EbE2ZhnP+4g906Y5x8bjbB8RrHONckAAIJSXlwveMM+ZKFzCtRozUZzbX+i5fFA4qZWWU5OeaETfthkh7k1wfbdWe4HtxtAHsR6hr+8/ENpdsCAAeSU1eGbmDo9lfQDnWh0UGvJyhoIgBP35j5Z5mMUWpULxPhABQLLJgH/uOj2gxy7fXxzk3vivbYhLqnkyuW82kjyUZm0Ms5+lt2rMofldCubvfbTo2SYd25+ejG1PTfbe2GHh7kKvbZbv8317SJKt1+Vp7S4KPaOew2lERNT06IQYPMOsqKhAZmYmyo8dQ0aGyuAcy9wBAMxmM2YtXoypU6fCZDIxZTRCKaNT3liMQ8W1+PLW0zGse2v7jfGUMupvW0Ua6NLNh3DTl+vRKzsdv04f5bFtpFJGzUYjZs2aZd+WPH0fYmwf4dY2RvYRnR/5G4BrmbtnzuuLi5X17kOwj+jzxD+oM7iWudv31ATNp+329HxY9Qa0SE3ARQNa48vF+3D2gLZ4+eKB7o0VaeVP/LAOP6476tLk6xuG2xeqVtlHrNhXhH9/sc7taXf+91yf9xGXfLoWa47Ztwe9zYq9j5+hvZ6RxnY/d3sB7vhuI07umIX/u/FUt7anvzAXRUUVmNK3Df7Z7lxgfeezU+wXNLb7A4VVOOutZS5d+GH6aWjTOhPDn58PCAJuHZ6NB6bY12aqabBgyLPOdUxOap+J3+6VfVaO7b734/8AAG4d2w23j+9hv0+2j1i5vxjXv7tI8z2z6XT49ObRaJWeiMlvLEFyQx0+uWYIRnVv5Xh8Ea7/fJ3Utt5k/9sGdsjEt1cOQIpJYyCI+wjNtq//vR0fLTko3X3L2G5okZqACb3bYPGRKjzwi70M0b1jOuPmUR0x4Kk5AIDPrxuGkuoG3PPDZgzr3Axf3jbW731E54f+kvY939w4HIM7qiwcH4TjiPWHSvDj2iNomZGAwR2aYXyfNm77CHNVFWbPno3Jkyfbj/PkZPuI3o/MBOrqseDeMdhxrAK3fbvRpam3ElbStglE5XFEqEpP9L7nZ5e7Xd4HWdvOD/3lVuLKpW2UnWt0fugvlzJ3Ln0VqewjxH0lADw6tReuHNEZALC3yoaJry8BYC9zd/+E7nh1zh4AwLherfDev4aoPu9J9/0Kvc2G/100AOcMbOfy/IC9zJ243SdYzDDYrLjw5PZ47vz+7v3V2J8onxOwl7kTdPbt/oWzeuKKk9vBbDbj739m48E19u1gQIdMfP+fEV6PI/LLa3HxhytRVNngUuZOWYZX7tfpI/H33jL854weqDPbMPypv3Fy21T88J8RLu0e+mkLft98TLVcrpqdz07BzF3FaJ6VgvzyOtz/3QaM65yBT64Z5tLuso9WYnNeuWqZu9cuGYgz+7eFxWqDxSbgSEkNLv1oFcqtOtV9xMQ+bTB3h/13/N0rBmP6NxulMncAkKQXsOvRcRj50nyUVru+dx2aJ2HuAxNjeh8h35YtVhv6O35rAPcyd7mPj3W8hIA+T8x2eVqbTgdLYhL2vzDV7XlFW4+U4ZIPV8Gm0+G7O8fh/PdWIDXBgO0PjVbd7qe9uxy7CipRZ0rCfy/sj9oGK5rrLJjaLxtGtTKJQTyOMJvNrr9NUXgc0ZjxiJlbjuH+H7cAAG6Y1Af3Oo47o3k8QrUtxyPsl8O0jwikrbQtnXkmTPJxzzgas3RrGwf7iIDbch9hF4J9hNlsxt/z5uHM886z/y7FyT5CEqRzjYqKCmS2a4fy8nL1WItMbK+ZlJrqujPx1M6f5/SVfGcazLbyD7YxbZU7H/kX0Rt/2iYmOjeeYLZNSHDuGCLV1mRyX4fAz7a1CcmoTRCgk6+7YzS6rsPjiT9tDQbfv8P+tNXrQ9NWp4OQkorahCTUJyZ7fpxOF7pt2Vtb+bYUT/sIpRjbR5gNJmnAok3bFp4/myDtI+oSkiCf8GsxGD2+rjgAZBMEtG+didqEJFSbEr1/j4xG6NPSXdaiAIBundsAKQlubWE0Aqm1bu0BuO6XvGz3FqOzrU1vwNEGPTq28OG7KdvuLcnJqE1IQkOS+jatNxpRm5AEfXqq1N/2WRrbv2y719XC7e/TJyehdXoSpvTNxqa8Mtx05gDp/UlJBb68Yxwu/mAlAGBHqeI30fG84nNaklNU+zCiWwv191Xmyk9X48KTO0jPZ012Hp/srjyh+vjNR8qxobABp/Vo6fG5JdxHSG2PNBhc3tPXVtiDrguP1ODcQc41VV5dnIt/jz9Jatv/pHZYuKsQtQlJqEuQndwBfu0jxH1PbYKX3y0g4OOIt1Ztx+I9JQAA/Zp8bOjbAVnK44jUVFiTkux90HiNkuoG1Np0QEISdGlpQJpV8/v85fXDcfVna1CrmOkupKRAp5baFyXHESFpC/f9zYEaAV1bpUEQBNzwxTrsPF6BZQ+eodrW4+tEwXGEGNT2qT+OfYT8b7x4bC/AkVVTeqLE+bzGBFiSU5z71ST1/arYFgBsKfY2nvazDUYTABPWFTV4769sf+Jt352Q4tiGzWZYk5JQm2DfxlR/v1T2EdmpqZj72FT0UgSt5McncpcPz0Gvbm3Rq1tbAPafq83PnYUkkwFQTNyoMCa49D8jyYiKOkhBHaWdFVbc9uNWAMD9k0+CVW+AVeV3bVjfHKwqcB3wseoNqE0wwJiRDqSmwgj7IEH3rAw8cdkw3PndJqmtzdEWAHRpzs9tzMld8JjVgEd/da4p1CDYt7kaU5LbfsWQlu76fsbgPkLe1iAImt+3+yef5DyWATByQEfM33XCpc2L02Rr66r0oUvnRNQmJMFk0GFwx2b4647T0C4z2f2Y0KFjTitsKrEf91w6rKPvf5OoMccRju1J9bcpSo4jGjMeYZXt42zy38YoHo9QxfEIuzDtIwJqK25Lym0sCo4j4v1co6mNWaqKp32E2QxB/nfHyz4iGG3l272n4KwC83KJQkyc3MEqd57FXIokBezl2btw1aerYbEGr6xKJ18CHhEmLpjdYPHt256R5H5Apjqb1IOLh3Twq72St5JtasS/Tgf1nZ6Y6VTX4Pz8Z9892uvzqj2bWF7kg6uGYMVDZyBLMagyrLMza8RbebnG7oN+3nBEumyWfbeTtDKPAAgBvGpxVT1WHyhu0qXLtDaDeTsL3N6XT5Y6M5hMBr00XhvIe69UU+/7Abe/SqqdswZtAlBZ5/+2CABvzd8rXdbrPJedykpRP7k8Vu5hRmATcsariwEAtWYr5u86gWPldVi2ryjCvYqMRFl5tv7tMzXbGQ3eD379KVdaWqM+m9aX/eHgjll46pw+LrclmdR3Jv4cspv8+F1+bpp7VlVqolE7A9jhxQv6o8LLPuDMN5dKlzfllQFQP2aY1Cdb8znU9gHyz/qMXq1d7rtpdFcAgMmgg0Gvw79O6YTOsuMxcfKNTaXs3sNTe2v2IxapBtwdOrdwHdT5+OqhWHz/WEzp6/wsspI9D+6lJRqx8fGJ2PTEJABA33aZaJaqPdB414QemNC7NV67RCUTnoLGz4qSREREcYHBJKIQEwesPJ1kNGV8W5qedxfux9K9RRjy3LygDYgnhmjdAaVAt2MBzgEns49BNLVBIJOHgTm1NRJapfs4q8qhpsF1cDyQYNKag/ZZ6mtyS1TvF/+s6gb7c08b1A5pid5nMqkNOMoDROFYm8HXj1/+GVs9fMetAYxCTHx9CS79aBVu+mo91h8q9fvx8aDeQ2BQ+Z6+NnePdNmo10mfYTAGgP7z9frGP4kG5bYeyHcFAJbsca7VodfrYPSwnWgFXFcfiPzaNtFEvkkHso+MJref0d3ntpcNs5eSFbMwRckJBlwzopPqY2ZtzVddZ2zjYee+y5+f1ewM91nIy/YWYfCzczF7e77Hxxp0OregTXGVMzglv8ef33qDXodbx3bDNFlWpJrzB7f3GjTScuHJHTB9XDef29c6fsvVjhk8BdK7tHSfyTooJwttMhKRkWTEneN74M7xPdArOx2z7jgdfdtlYuF9Y7H2UWcJ2U+uGer+miovOaZnK1/+lJg2uGMW/jO6Kyb0cQ3C6fU6dGqRipbpzmBQokZgU65ZagJSfTheAoCurdLwyTXDcMHJjZtURJ7ZmvDEHiIiaroYTCIKMSkzKbLdIIo65bXmoC1mn2AMz89ZoAO6guAc1PGWISN/jJLJw0K/aie0/sa+duVXulyvDmCg1FtgyOD4Gxbtsc/o93UwVm0AzFOgJpLemLcXY19eiPJas+qMbFEggxBixsrcHQW48P0VTTJDKbdYuxa1p21Up9NJg8SBvm/ysWBBEEL2/lsUf4fFU615Dw4UOWtm63U6GFR2CkkmPd7718koq1Gvzb73RFVArx2v5Nutr5MDotHIbi0wfZzvwaSnz+uLb244BS9c0M/tvqfO7StdVu7Xxr28yK39rf+3Qdbefbv992ldVPsgzzaV2n6xFmU1Zvznq/Uun4dy2xzXqzWKq10zm5prZHb4e8z+wJReeOOywfj11pFu9616eDy+ueEUvHiBylpPHsizexOMer8mzYiTNYwqxwxmq/Y+q2Wa+wSU7MwkrHp4PDY/OQkDc7Jw98Se+Oeu0ejTzl5Lv0vLVJes4O6t06XL3VrZg1PR+lsdbL8oPv8LT+6Ah6f21vzsUhOcx0vhmhRFweXpGI+IiCheMZhEFGLOMncMJ3nSFAdEyfOghpZMlVIgiWEKJvlLLBnTt12GVO5s85Eyr4/bcqQMr8/b43a7p+ybUGxDlQEEk8TBuXMGqs/SVmZGzNt5QrWdktp3JagzQj08V5+29kGzCwb7NsN3V34lcotr8MTv2zwGNywBfP+VPGXpxKsEDxl69/+0xeNjxUfK3/nl+4qwcJf37+G7C/e5ZDSpDYIH6oe1eZj27nIpWKj8biiDS54cKa3BJR+sxOzt+UiXBXcNOp3bPmTXs1Ow7anJmNq/LXq3cy60empX56D9+4v2N7nfaE9/r/yjkK8lAwDnDWqHly8aEKJeBcftZ3THSW3S8eFVQzyW4VRKNBowsntL1UFv+TGusvxjg0rA7bisdOId327EyJfmS9dbpiXg8bP7uD0GAFYfLEFNg+vvkjyb5kSlcy2gEkXgSKcD9hS4TpiY2r+t6usEesg+uGMzt9uyM5MwsntLv95rwJ7xJffeon0u1y8Zqv17VOr429UyEdVK6HojD8T7Qvz8cprbS941leyNnm3SXa57mzzUQ9Y+XJOiqPEExXEAERFRU8OjFqIwYShJnda6KtQ0BLJu0qjuLQAAN8hmLif7OUgTLJcPz/F4//v/GoILTm6Pp87ti66OAa86s/d1Vi77aJXbbV//+xSPj1Ebq2ns+E1VAOu0iINGWuP9yjI/z01zn+WuRjlof1KbdI9rdQTTT7eMwG/TR2HaYGeA7JWLva9DUGe2wlO8yNsAW53ZilxZZoma7cfKvfYj3qTIZnO/cvFAXDrU83YoJw6Iil8ns9WGf32yGtd9vhZlGuuxiF6evdvttks/WoW8Eu1MKV898PMWbMorw41frgPgnonkT+Dxyd+3Y01uCf7z1XpcOsz53qQkGlwGl3+fPgpJJoNUUrN9VjLm3j0aax4dj+9uGuHyvpbXqmctxSutTbO8xuwx0PTmZYNxsR/fx0i4d9JJmH33aKQn+bgAs5+6t07z+zEFFc4g0F93nK7ZbuvRclw7Yy3u/n4TRr20AEfLajGiWwvp/q9WHpKyk5RlW4d1bu62vpFW2bmqIK2Hdq7GpApfDOnkGpiqM7vuE4Z3aQEtYvamPLgm6qEIeIRCC8ekEotVgMVqc+t7vFIG71ISPB+bytf7jNZJUeROvs9pKoFSIiIiOR61EIWYOOjAxCQid4FkJoljrJ1apuKH/4zA8ofOCMt6OUofXHkyHvGygPSIbi3w2iWD0LNNOjI1FrdXoxwEA4DTerT0+JhgzI781ykdFf3wP5gkZlAYNEryKQfvevg48CgvXzR9XDf8c9fpfi183pivSEqCEYNyslwGmH15vpX7iz2WQPEWS730w5UY+8oirNhfpNnmwvdXSutjNBXyQbeLhnTAfy8agKsVa7Yo1woRFzqXPjfHhyn/XlUGEDxdf6gUc3YU+P04T8/3+G/bsKfAtbScP5lJ8sCPONv9oiEdYDLoXTKy5AOZoh5t0tE63b4uzX8vGiA9XhlM+nFdHs59ZxkKKpwZJn9sPoZle7W/q7FEqyzX6oPFmvvaAR3CE9yOdskJBtw8xvf1fZS87dfXHCzBrxuP4mhZLV6dsxszludK932weD/+3mZfO0k5cWNY5+Ye95Xy4/Sdxyv877jDad2dv9U3je4a8PNcMjQHj07tjT9uG+V2X/fWaZjYu43X59ityMQS/fCfEejZJg3f3ngq3rh0ECb0bo0tT00KuK9KRsf+d+fxCny+IjdozxvtlMc3HVX2sXLyssAscxc72mQk4QrH8TLL3BERUVPEYBJREOzOr8Tzf+2QykrIiYeYzMBRxyBb0xbIOiCLxQXlBQHDuzRH+6zkIPfKu/QkI6b0axvQzO5QnXbKZ0cOdcxo9nfh5Qcm98L/LhqAM/vZB959yaJSskrBJPX7lYMtRg8ly+S6tbIHndITjbh/ci+/S4fqvbT35XORtzmlq/ascFFFncVtULqrrCST8j7l+735iD3r6Ie1eQCATXllqq/z4ZL9XvsSTyaoDKI+fW5fl9Jsk/tmY/dzU7D1qUnIfeksfHDVEADO3xxxLSF5kKbaQ/DUUzk7a4DrGWn5atWhRr2GfDBeLKuZ5SgPmi4rcSXP8NIilml6duZOrMstgc0mYOuRctz/0xZsOVKOU16YD5tNQF5JDe74diOu/HS1z/2MZlqzzQ16HXZpBBq+8pI9Gu8eO6s3zhnYDhN6t3Hb/x8vd10fcWr/bM3nEdf1Ot3LBAoA+GXDUbfbjpTW4I/Nx7BNlrX5H0dQ50zZ6yoDzsHyyTVD8calg/Dx1UPRrxHZswa9DjeO7ooBHbIAOLOyAeDnm0ciM8WEb288Fe0ykzSfQ2u/NbxLc8y5ewxGdGuBaYPb45NrhiEjiJlqYialTgfM2R68YHu0U65J5y0wKg8mscxdbBHXF/ti5SEGlIiIqMnhUQtREEx+Ywk+XnoQj/621e0+55pJYe4UUQyY/s0Gv7Mqah0D7n9uOR6KLnn0zY2nYECHTHxzw6l+PzbUAWV56aXv/zMCm56Y6He5ocwUEy4ZmiOt9RRI9Q6bFExS/3uVZWDUFghXk5xgwLanJ2PtYxP87xS8B5N8IX+PW2gs3K4kDuad3DELC+4dg3n3jJHW+JAPQMzbUYBej/+Dz5cfdHuOLUftg6Ir9xervsaqA+q3x6tEk/07Ix9c1el0LgOnqQlGJBoNbgFfMQhZWWdBXkkNDhc7S9Td9s1Gzde84P0VmvcFo8qNpwFhALh2xlp8t+awT881vIszqLbjmD3wIW6PvbIz8N8L+2PGtcP8Gryct7MAF32wErd/uxHnvLPM5b7txyrw9J/bpeuBBKGjjVbsbsexClzxiXrALMnUtE+rbji9K96+fDAMep1bZupb813X+8krcQ0uyYkPfeeKk6Xfoo+vHorsDM/biLhfnb0tH3d8uxF3f78ZANCtVSoedmQRnzewvdS+bWZoJqIkmQyYNrg9Jvbxnjnkj0+vGYb+7TPx2Fm9pUznEd1auGzvSr3bhr6knZpsx/5Muf+9fLg9m+OCk9u7PSYeKLPkWzkCDlrkWbYscxdjZD/8ucWeyxETERHFG/9X4CQiTbO25rvdJoQsDyG+sOR005RXUovFewoxpZ/2LGUt8kHgcBnZrSX+uO20Rj1HqL7r8uc16HXISvEt2KHOPiASSFfFbBut4I3ydq2gkxr5LF5/+Riz8kj+Hut0QN92Gdh+zHM5JDFgdFJ2Oro6sqtymqfgYFG1S/Djtm83AACe+nMHrh3VxeU5DhTa24qD9Aa9a+BEzNoqrKzHsbJaDMzJCuwPjBHSJA1FgLaNbLD5kmHqWXnyR6w5WIJ7f9wsXd93ogrltWZkJrsOgFptAjZrZIUFi0ElS6JTixT0zs7AP9vzUVlnwbuL9sEqCLh0aI60zpGaFmnObb/asfaLfJDz0mEd3R6jJdlkkAL4APDXVvcg/hWfrHIpEfjszB14/vz+Pr9GNNqVr75dL9unXcYvwY+ym/FOmaGxV1Zu7URFHbYe1V7rTfyNyEw2YdMTk2Cx2mA06FHTYMGd323SfFyGI+tOzOgUySes6PU6vHvFyfjvP7vw3r9O9vnviQZJJgP+vN39+MPTRIlHp/YJZZc0iRk5B4uqcVC27t9tZ3THiG4tMNqHrLN40FmWiaxGvl/2NUubooNZdgzmT8llIiKieMBfPqIg26OoT87MJM/4tlCghnRu5r1RFAn1PiCYVTbEvoYlMylMAyjBWABcPjlAr9Pho6uHen2MWnBNfA/kASFvn195rRl1Fvug6HUjO+OO8T2k+8QZ+1d/tgbnvbscnR/6Czd9uQ71ltjPEFEjfg7KbUoe6BjSSX22vvxzEEvAya05WOJy/aW/d+FKjUwUZ38aZ96OAtVMjTl3j8b7V56MJ8+xDwjnldTi0V+34YxXF3vuj6xDDY41oQJdM+y/Fw3w2ka51pS3AGsseOL37aq3r1Z8P+T8Lb0Zz5T79XWHSjFj+UH8sDbP6/fDvRSq/XTVW3apVtnGOyf0cLl+1oC2WPLAuEaVoIsm8u/dWQPautzXp11GuLsDQPv3v21GEs4d2K6Rk11iw70Te3ptI3+XlAFYim7y4zcGAomIqKlhZhL5pKiqHvtPVGF4l+Y8WfbieHkderZxlpUQ6+77WsqpqWIGV9OlDC746m7FAFFTl5IQvMWbxU8kkO3SW2aS20BhmPaNE3q39ni/L4EzeUBKr9MhyUtZGr1OPbgmvjfyNZNMeh3EVffWHCzB0r2FLs/16K9bcazMHnBIMhlw5/geeGv+XgDOdX/ki8bP2VGAJXuKgl5qKRpofVa+fIbyr+WXK93XJvp723HpPauut+CDxd7Xo2pstuENX65Tvd2k10On06FVumuppMMlnrMyBZUOBTpQqczS8oXa68caZbkqb0b3bBWinsQmtf3/03/uCPixAHCist7z4zR2x8rtJ549d14//BWBEsBKWpuPv9tVLOvSynNWEgA0T03A2Y4AYHMfS+dSdBDXBQMYCCQioqaHo9vkk9H/W4hLP1qFhbtPRLorUe/TZa7rXZitnmfpN3l8W5oUtUFGf4cdxU0pmItFh0Oov+qje7bCeYPa4VHH2hCNEWhm0v7CKmxxlBjSChIqA1SpicELgqkRB2puHtOt0c8lLx+n19mDOp7YBGCbYxa+fIBUrIjiOrPVeUh23Yw1eHuB6xojf2/Ll8o3ldQ0wKDX4dqRnd2eR660pkH19lin9b3s4VgjzNOgnLe1y37ZcFRam8rT+/fUOc7yUaGaDCEOvPo7UKX2dQh0EDeQQbJ4WItcPtH8mfP6em3/0VVDQtib2BPoJBFA+3hZLGOnxWxV/+IlGf3/jbluVGe/HxMp8uMqg0EnrUt0zYhOkeqSakBQ/L2Kd19cPxx3TeiBqf3aem2r0+nwzhUn450rTuZkzRhzpNQ5qSMOfvKIiIj8wswk8kmNo9744t2FOKNX/M1yDqYle1xnk4uDfCamwBOpDnr7M4vdahOkgcpYrlEuCELQBw4Meh3evGxwUJ5LF8CaSYWV9RgvK7+lNSCoHPDztqh6Y7112WA8c16/oMz6bZ+VjDl3j4ZRr4NOp/NadglwrvNUKJtVL743Nnlmkuw3orrBc3m6fu3s5ZnEAVuLxuj9oThfFFq5DX1yzVC8u3AfbhqtHTj0ZbO7/ONVWHL/OIx+eaHL7akJBlw1ojO6t07DRUM6YMfxCvyw7kijMpNsPkRe/A0EqT1joDOnj5W7l9/zxhYHmUny79bVIzrjixW52F+ovj0lGvVeA8tNTWMyULQeev7g9rj/py3S9QSDXirjCAAdmiVjzUH3xyWa/D9WiKVMA/nWZtTr8OQ5fXDOgLYRLQWs/G1sl5mEp871HpSNB2N6tsIYZirGvTk7CqTLcfCTR0RE5JfYHYmjiOCsKf+ZHSe6zEzyjAfiTYN84Efkz0dvlj0+wUuJMQqctKv3Y8PMK3UtvaU1mFhndgZKMpKMIf9d0et1PgWSfM0u6dkmHV1b2TNgfOn6H5uPAQCGyQb2pDJ38swkP8r9ifX5DQbxedTXg5q1Nd/n54wl4rumfPs7tUjF/y4aiO6ODCU1hV5KZYmWKMoMAvbv0kNn9sJFQzo4Xr9x390tR8ow8Jk5XttpZXnszq9EmcqfoxagD3TtlJNkZXt9ZbUJWH+oBNX1Fu+No5QyIObpGK7e0vj12OJNYzKTtH4TjIoJJAM6uK559MDkXqqPSwwgM8kQQxPA5Nu7XqdDksmAkd1bBvR3B0uZIqszlt5PIl+MO8kZMGSpdiIiamo4Ekd+YSzJf+JgIddMUtfYwTiKLT9vOOp2m1a8wqwSeJIHo2ItM6kxQZN+7cO7iLZzzSTfVdW5DhxrzezeebxSurz0gTP87FnsSk10JoOLA9Pi78O+E1XIr6jz+bnELCZvmUnNUmKrFKSvxMHTUB6TLNqtEkwK8gve9+NmVNZ5D7ioBWaf+mM7zn53JZ7e6Bww3nC4FJd8uBLfr81zaXvdqM4Y3zuwrPKBOVl46/LB+PdpXXx+zK78Slz4/kpc9tGqgF4zGig3qWB/9vHOl8yknObJjXoNcQ05AFj+0BnIzkzCF9cPd2uXGMDEk8YEw8JNmZkUDdo3c/1seQ5E8eaZ8/pJlzkhkoiImhoe2ZFfYqnsQzQQBEEa5DNyVp5HPA5vGjYeLlW51f3Tv+Pbjejx6N8uNckB16yCWC4d6e+JZ6cW3hdyDiYx8OVPP2saXAfFtQYT5SXcMmM82KH1k9i1pfvnlZzgHPRXBpPenL/Xr9cVB+YMjv+11kyKh7Vr1GhlJvlC63uZmuA6i3/ezgK3NsqHOtcWC84b/dblgzFIti6XSO3Y6/MVuQAAm+C877U5e7DmYAn2nqhyads6vXGlJM8d2A4ju7WQrk/o3Qa/Tx/l0iZdZT2brUfL8flylbpjscCPzCRy50tQw2wR0LONdhahN/KMMPH7l6LYjttkJKJDsxS/n9sQQ8EP+X4+Wr6nnVqk4sEpzkyxaOkXUbDkNHfuV+L0UIuIiEhT7BwpU1RoTA30pkh+ghctswWjDeOTTUed2YpffMhM+mrVIak02Gn/dV2z5LW5e6TLsVZ2szG9fXRq76D1wx+eSncs3VuIKz9ZjcPF9oBfg2ItJK3JB1nJ0RlACiQeoJVZOfeeMW639WnrzC4T3xur40Xz/VyXRvw9Ef9Xy+IDghfkiDqOPytY+4DebTN8Ws/DoviOO4NJgb2u8vszomsL3DOxJwDXxeq9DcSKg+paJfyCcfwhz8zp0jIFAxVBr+em9YOap/7cgfPeWYatR8ob3YdwUgZiORjuH4vGPkkuv6IOM65zzyTylXy/J+5Te2WnIzPZhK4tU7Hh8YlY8sA4l0C+r2Jp8px8Px9Nx0XDuzhLu/IciOKRWG5bvg3mldSgwI9McyIioljEYBL5JYrOUaLWKV2aS5ddTnR5IkVN3MJdJ1RvV47DPv7bNtV2+05U4q8tx4Pcq8jwZ+z5sbN6o11W48oB+cuXQfKrPl2DZfuKcPcPmwAAZsW6IVrZmI0tbRRNtH4T1fb3iSbngGZhlX3Q/3//7Abgfwktce0QsYzdt2vyYFNJQ4rXzCQxa6gqgDV51N6nx8/ujZZpiV4fWytb78vOkcHndy8cj1Z87K3SEzG6ZyusfXQCnjynj3S72vdDvh3tzq9EvcWK3QWVbu16ZadjXK/GLwYv78K6Q+4ZpucNao8f/jNC9bGbj5Tjzy3HGt2HcFKumcQyd/7p6eNaW+2zkrH7uSk+r4EoZiA1SzGhmWwtvCTH/jU9yYSlD47DrDtPR/PUhIDXDYqlagLRupuXbzMHiqoj2BOi0FAuL1pZZ8bp/1uIU16YH7E+ERERhQODSeQXnkx7J1/kOtDF1ZuiuJ1BT175+tFPeG1JaDsSYoHuPn/b5J7NFWo6PwbJcx2DRPf+uNnl9oo6s2r71y7uj/7NbPjpP6c0qo/RQO0jHdwxS7VtkmywVLkej7fJBjeN7upyXRzo7NbaXiJKrwO2H6twe5xW+btYN2eHPZi05mCJ349Ve0+apyb4tAbbsM7NXa439pBILfgD2INK8gwDte+HvL8P/rINM5bnurW5aEgH/HPXaHRv7dvAvie1svKUWzSyjDx9jWPtu+hLZpJyuyQneTbQsM7NPLQEEo0GXD/KtzW51j46ATOuG4ZVj4x3yR6Sfz4ZSSYpuBSoWMqk2Z2vvh+JJg0W75lqRLFGeQxwvNyZkeRLdiYREVGs4ug2+SWGzq0iRj4wLl8UnZlJ6viuNB1aZTI9lVID7CUjHv5lSyi6FDH+BE+VpbXCwZ/yXcXVDaq3N0tJUL29c4tU3NDLhoEdMgPtXkgE8i6rlRTqlW0fuP/5lpEut8sHV5sp1ory9vvwiKLMYRfHGlr929vfQ5sAfLT0gHT/x1cPddweWwP44SB/T24/oztev3QgTmqTrpoZIV/zA9D+nAJ9m319nFqcSz65Z19hNY6VuZdKDObvqzyoLQaGnjnPXhrwrgk97K/nIboWa19F5T5aWfbsgyuH4M7xPXDNiE748Koh4exaTJB/P7/6t/eJA74Gb5JMBow7qTUSjQbc7SgJeenQnMA66UEsHbPvU6yRFi34+0PxTpp45fiqy/cbDQwmERFRHGMwifzCzCTv5AMQr83ZLV2OpVmOkcBTzvgn3388N60furS0D4h7G2+494fN+HZNXii7FhZa6+t4fVwE9rtS6Q4ft0xl6bC7J/TEVad2CnKvoo/aJ5PgGPkf0qkZdjwzGQBw4ckdkJJglNq8cdlgl8f4EzBMTzSis2PbkT+nWKP/ilM6SovQcyzPnfw9uXfSSTh/cAfodDq3YNIpXZrjlrHdXG5rle5aCs/f7cS1H74/Ri0wqzykUG8TvH1Hb9maX/c6BvGvHtEZKx46A3eO76HaJ7lYG1hWdndNrjML7vnz+2FKv2ykJhrx9Hn9MLlvdph7F/3kXz1fjn8DKSt3zsB2WPnwGXjpwv5+P9abWAomRSuOpVO8E/dzdRZ75q58X8dsPCIiimcMJpFfomlh12glH3/4YuUh6bJWVkZTx+9U0yHfBBIMemRnJAHwHkiUD+KJTtYoJRYr/BlWjcQWonOOkvukqsG5do1eB9w5oQdSE40eHhEf1HZf8qBESoIRuS+dhVcvGejSpr1jDazMZHuGUnWD72v/ZCQ7s5oMeh0SHa+XV1IDABjaqZnUr1gbwA8HrfdE3B+J1IIzyuCSPxl8oqp6C9bmlqBeMdA0MCdL8zFdW6W53aaDDm9cMgAAkNMsWbWMXDDXfTm9R0vp8vRx3aXL7bKSpd9xT8GrWCtl62nbaZGqnnVJTh2yUqTLBr0O//FSEjDQCVdtM5NDchzJCWCNF2ulLYn8VeMo/3rh+yvc7tPK2iciIooHDCaRXzjLxjtx/KG8Rn29EKKmSq9Y38A5EOsccFh1oNin53r1kkHB7FpUO7lTVthfUxyc83UoqLLOGQzxdSH1aNHVkeVzVv+2fj9WbRDTl7U6lMEeMbgkJwaJTuniuk6Psq2YhSTW6k9JMErbGoNJ7to3c3+vASA7Mwl/3DYKt47thpM7ZuHJc/sAcAZRzurfFj3buK495M/aYqLbv9mAiz9YiU+XHXS5/WCh51JV/dpnuFzX6YB2mc4AmNnmfnx2MIiL3ndo5gwOaE2O8RRM+mLloZgaXPa07Ri4BqZXmSkmzLl7NBbfPxY6nQ5pXiYXTO6bDZ3OntEZDWLpM26ZFp3BTfk21LddhoeWRLFNPAb+eYOzHOyXK3Ij1BsiIqLQi/9pw9Ro8pP/Dxbvx0Nn9vLQmr5adQhXj+jkMsh47sB2EewRUXSQjzPKg0lyy/YWeX2e7q3TpBJ5MUX29/ozxv/o1D7B74sXUmKSjx3dI1sAXF4OKxb8fdfpKKysdxksb4xOLbx/N6WvguPtzc5Mcmtz9oB2+M+YrshR9Cunuev1lAQjSmWTF1ITDdKgPmNJ7k7r3hKPndVb9Xs6oEMWBnTIcrnthfP74/u1ebhSpWxjIAkRC3cXAgD+b9Uhl9tbZ7h/B+RymqVg29EKl9vEoI5NEFRLJVbV+57x5k2bjCT8ePMIpCdpnzp4ez+e+XM7Hju7D0xqi0BFGU/bTjAzvuKZPPjqLY7Yo006ltw/Di2iJDASA1/RqCc/f0z2YZIFUSwTBAFvzd8rXW+IwHqnRERE4cJDZfLKzKLXfrvxy3XSSVTz1AS8dflgL48gin/yAcR+7TPcFq4F4HGgUqRcnyfeJSdEYBBGpXxXvcXqMjjUXFbq6brP10qXnzqnb8i7F0yJRkPQAkmAb+WRlJlfagPXJoMOPdukS5//3RN6omVaAu6a0MPjc2ckmaSSkvGemRRIFoNOp8MNp3fFqO4tvTeGPXh33+STVAN+kgDe54o610DPbbLScWoemdrb5bpOp5O+a1aboJr1c/nwjn73y5NhnZujV7Z2sNjbGk1frDyEV2bv9tgmWnj6RFkCzX9WH7aRnOYpLuvARVJsrREbnX0d0CFTuvz8+cFf14oomih/g79dcxg1fpQwJiIiiiUMJpFX1UGc2RqvEhRTGHOLa6SDytg6ISUKHXFgOyvFhO6t051l7mTDduK5WNdWqZoDxbE6QB6LuwKx5nt5rRmjXlqAae8ul7KVklTK2bXLTPK49ks8M+p1SE0w4Izerb22VWZ+id/prq2cWU0Niokcd07ogbWPTnDLTDpaVutyvU1GkhSssgnA2twSvDhrJ3Yet2e12GxCzK1foyRmJj44JbKZ0n4uLeZCnjX05fXDcd4gzxnMOc1T8NW/h7u8tnh8kV9RL31frh/VBecNaodnz+sb9GCSN2qVwab0zXa5/uGSA9hypCw8HWoE5Tay8L6x0mUDg0l+k7+fSSb7F2Vy3zaR6g6FQVZKAjY+PhE7n5mCk7LTvT+AKIZZVCZ0lNey5D0REcUnBpPIq9fn7ZEuD42SWubRZlDHLLfbpr61FABnsBKJxABru0zXNUvkY3bioPqwTs0xtmcr9eeJ8YFwwDWApianufq6LuGy2FGK69eN9vrv24+Wo6iqAVuPlksLDhtV6gDFWom7YPry+uHY+MQkZCSZvLZ1BlLtxO99gWPdIwC4SrWsmvffk+QEgzTYbRMEXP3pGny45ADu+3EzCirqcMqL83Hh+ytiOsNP3JdEelBfF4RygolGPUb3bOXTZ3tKlxay1wZqzVbp+jerDwMAWqQl4M3LBuOqEZ0D71SAlJNnrh7RSbXU3rnvLMeR0ppwdSsg3Vu7Dn7L16VRWZ6KvJBPAtn4+CS8evFAPDutXwR75Jkv2yN51yw1ITLZ1URhppwABEC1/CwREVE8YDCJvFp1oES63K99poeWTZiHY8VID3YRRQtxMEncJtQGYqWMPr0OKRoLdsfCehtq/NkTjOpmL8F136SeoemMF7tkayABrutdNFjsJ8xq64bE6mcTDHq9Dgkq2VpqlCUexfe3usEZHBgUYIZXaoJBKnMnCM6Aw4HCamzOK0NhZT02HC5DaU1DQM8fDaIlmCTyFhz2pN7ie2RC/ue2zUxSzdJM09hvhoPy49DrdFi2T30dvD0Flaq3R4sxJ9knM7TPsgf2E43OAfF4mNAQbvLfkOQEAy4c0gGt0z2vExZJ0bFn8Q3jXkSRpxY44lIBREQUr5ruqA/5TF6aIlbLS4Wap4EktbIvpIJfrbgnnlOJi8arlYhyDhJDc2D+5jHdQtTD8PG2KxXvj5bZ0fITYnH2Zcfm7usMNeWF6f35y5UlHsXf2YuGdAAAnDOwnc+f/R1nONfa+d+FA2A06KUMEflvtk7nWtM/hhOTnIHpCG8fgbx8W09rL3lhNOhxx/gemNSnDd6/cojbfqRP2wyv5fJCSfmd9fT+XP/5OnR+6C+szS3RbhQF+jsmUZlk+7ZYzuqLlFg7fYiSn16f9OdEP6KI25Vf4Xab2lqGRERE8YDD3OSV/ARQrR4weR6Uyyup1b6TYuqEnRpHChQ5PnNpQF22k3G20SFRJcvllYsH4pKhOaHtaBRoTKZDsH296hAOFVdL18XMJLXBwaacmeRP4E8nyxwCnGWzurRMxfanJ+Otywb5/FyXytbFGewouSo+vzyYpNfpYJb9WMXyuknODMYId8TBn7eyseso3jOxJz66eihMBr3bfmLGdcOQlZKg8cjQUwb39Dqd25pJShd/sDKUXQqc40PVSb9XOvRonYYkkx79O3Dw3l+t0hMj3QW/xNKx6f8uGoBrR3bG7LtGR7orRE3WFR+vdrvNzDJ3REQUpyJXC4NihvwwKK8kumvcR0osD8oRhYugLHMn3i5rI5YP0ut1qqke43u1DmEPQyuQLKNoGNB67LdtuG5UZ+n64j2FSDTqUdPgvhZKU14jLtnk+7oQ4neh3mJDbYNVCvrodTqk+lmmLDPZuUaTGEhwZibJXxOwyhZ7UZsEUVRVj7u/34QxPVvhhtO7+tWPYKptsOLLlbkYc1Ir9Mp2X4dLWTIzUqRyhX48JpgzlQcqMhLaZES2bJgyUGbQ63DeoHb4Z3t+hHoUXH/cdhpqGixokRZbgZFocOWpHbEnvxLjYvg3PFq1TEvEU+f2jXQ3iEihoKIOfdo13bVEiYgofjGYRF51aZmKg0X2WelL96rXvm/qGEoi8k4cQxUH0sX/CyvrnW1kmUk9Wqe5PUez1MjNug+naItPl1Y719d57LdtLveN6dkKi/cUAgBMPq4ZFE/um9QTR8tq0a+97wMG8iH3e3/chNwi+0SNQGIjaYlGfHPjKTBbBWn2vxRMkgUudHCt6V9Vb4EgCJi55Th+WJeHNy8bjLu+24Rl+4qwdG8Rrh/VRSpJ+emyg9hxrAK3ndEdXVqm+t9JP32xMhcv/b0LP64/gnn3jHG7X57BGEnKDDNfKDO8e2WnB/z6iX4EMMNB+XHodECOSjlMpdUHitGnXQbSk0xe20ZScoIByQnR9Z7HikSjAf+9aECku+EzXUytmkRE0eiVObsZQCciorjEYBJ5Nb53ayzYdSLS3YhqrP5H5J0z+8J+fVNeGQDg5dm7MX2cfd0X55pJOjSLYLmmUJAPTXldM0l6THQMaP226ZjmfcO7NHcGk5pgZtJtZ/Tw+zHyQfdZW51ZG4GWQBvZraXL9SSTPahXZ7Y6n1uvcym5MuG1xbh8eA6+XZMHwD7ocaKyTrrfKgjQQ4eymgY8O3MHACAj2YgnzwnuDHhBEPDx0gPo2DwVU/rZS6It3Wv/Pu07UaX6GGeZu0hnJtn5U5ZSufbk+N7xM9Ck/Dz0Oh36tc/EG5cOwj/b8jUzlC79aBUAIPels0LeR19J++Cmt0sj8HMnosYrqKj33oiIiCgGMZhEXjW2vn+TEG1pBERRSF7KCwBKZNku6w+VYkinZlKZO4Ne16RngAtSFldkXt+o1/m8Rl63Vs4MsuoGq4eWJNIKEgbr8xZL7sk/Dx2AR37d6tJODCQBwDerD6NbK2fWkdUmwGQA6szO0nhVde6lDRtrbW4pXpi1CwBw8MWpOFhUjeX7ij0+RvxqRktmkid1Ziue/nMHahosuGN8D2m/99blg7HvRBVuPL1LiHsZPsrYnnh92uD22FNQGf4OERERRUhRFYNJREQUn5pePRryG+Mk3vEtIvKdWoD6wvdXAJCVudPrkCILJt0xvgfWPDI+PB0MEX/GvcVMh0gNlfu6Fo1eB4zo1kK6znX1fKP1XQjW5A21QGyND4E+efxQDP5aZOssNVhtyoc0mjyobLEJuMyRpeKJ2KdIr5kk+nDxAWlNuMPFNfhxXR7+9ckqlNeasWh3Ib5dcxi/bzqGO77dKD1mVLcWuGdiz6gv7eYPtzWTZNczkmPr75QC+lGSHUrhFcgah0RERERETQGDSeSXaBm4iTbKsjVyFw/pEMaexC4G5OKfuJ14GqOxyrKX0pNMuGdiT9w6thvumdgTrSO8uHwweS2LFeHMJJPBt8MDmwBkJpvQu619vaBTu7bw8ggCtIOECUFacyo1wT3xvN7iPRBklUWTxMw0WSwJW4+WY1d+ReM7KCM/rjBbbRh3kveyb2KfIn1MIh9wXrm/GHsKKjH65YW4/6ctWL6vGAOfniOV8wSA7cfs711O82S0SEsMd3dDzn3NJOcN7bKSw9wbosDxbIeIAnX/5JMi3QUiIqKQYjCJvJIPelptgsuC3tHu3YX78Mdm7bU+gsXTZG2jj4OyRPFOHAD2NOPXqhgkvmN8DzwwpVeouxYWgcxwj9SseH8H6T+6agheuqA/bhrdNUQ9ijMab29CkH4vAl1LSB5MEn/r5ZlJBwqrMeWNpagNYjlDeVfNVgE92jjLJrbXCEDIy2FGkvzVj5fXqa4vaVE5QBjaqXkIexU56Ymu2UfyTKVRsgzG9lnJuGdiT7fHC1GUCi84U5OoCWJiEhEFatrg9gAAk4E7EiIiik8c5Sa/ldQ0eG8UBbYeKcfLs3e7lJUJFU8DID+tz9O8jzhO05Q410xSv3/l/mJYo6x8Vah4GzON9JCqvyfAOc1TcNnwjkhN5FKMvtAKEg7qmBXejijIg0mlNWa320TlteagvaY84GC22jxm+orEPkV8TUfZy686UIyyGvf3RW3tseFd4jOYlJxgQKt0Z8aVfDcuz8R65eKBuGN8D7fHq33XiCKB5Q2JKFBpjmNhs1XAin1FEe4NERFR8DGYRF4px3XMIVgzIRSKq8O36KWnARCzlYMjRIBzX6I1ALw2t0TKTIr4IHGECT6UBAyleA/mRZrW29uzTXp4O6KQX1EnXf5g0X4A6sEQXwI+vpI/1y8bjmDmluPS9aNltXj6z+34amWulOEjz46O9Pe03uw8Hvpx/RF8sHi/W5uNh0vdbsvOjJ+SnUo9ZZllWhlyWp9bdX3wMt6ChXtCIiLyR5psYtUVn6yOYE+IiIhCg8Ek8lusTBwN5+K5Vg8Da+3ieNCIyB/eMpNem7sHm4+UAQDisTqkfJd0qLgG181Yg9UHilXbRno362us4MpTO4a2I3FK7ffpwSgr57gxzx4EUZss4cvXo8FiwzlvL8P9P2722E7+/C/M2oUtR8pd7p+xPBeP/74df26xl6yV/94aIhx0/nxFrtc2mxV/DwAkxuMOzmH1gRLpstakgNbp6utFfbEyNxRdCkik98EUWU18PgsRNUKkJ7oQERGFWvyezVLQKE+orVGSaeOttn44D+M8dWXuPWPC1xEiGUEQIAgCcouqcff3m7A7vzKi/RHHjD0FevedqAIQ/5lJ9/ywCQt3F+LSj1ap3i/48F6Fkq97+WYpCSHtR7xS+1Qn9G4d9n54Im6DasEkX44D1hwswdaj5fhx/RGP7XzNctqcV+7WH32MHsWajMHvuDFKBq/kmWzKLn1x/XC8fulAdG6ZqvrY4qrwZZT7KlL7YIosfupEREREROpi9DScImnyG0tcysxEwk1frsM57yxTXdhaVCpb2ynU/fVU5o5riFAk2GwCzn9vBc5/bwWunbEGv248igveWx7ZPnnJTJKrt8RGOc1A7fIxsBepAS1fM5PiPegXKmpvW7QNWov9WXOwxO0+s8379infzj1N/tD6Gb92ZGdkpZik6wmOAIw8+BRts3/P6OVbQDAhBJlJWiXlIkn5lR7TsxXOH9xBs/0XKw+FuEe+C2IlR4pBUbY7JqIYM7V/dqS7QEREFDIMJpHfas1WVNQFb/HtQMzZUYBtRyuw7ViFZptPlx2ULnsqQxcMXDSaok1xdQM25ZVhU14ZcotrAADVDZFdj0KQgkneR2lapjWdjBe1YHfk9yjuPVA7MV6xnwsLB0JtcfdoiwUkGOwdmukoLyfny9qJ8uwbtbUDX/p7F8a+vBCHS2pUHz99XHeXd8nk6I8litZMUlqw64Tq7beN647/XThAut61lXpmTmNE2VsBwHtAJiXBEJ6OBKCq3hLpLhARUYz692ldAACdW6REuCdERETBx2ASeacyGtDgw0BSOHia7SwfoAp1sOdoWa3q7U1pQLyxvJUtJP9EY4BT7JIymNRMln0gMsZq/SoPtGJoFrVgkmN7iNTsaLXNsV/7TLfb1uaWhqE3cUjlcw1llteLF/T3+zGn9WgJAJjQu43bfXd9twkP/rTF4+PlZdfqLO6B7A8W70ducQ3+b7V6Nkqr9ERU1zsfJ+4T5MHXSK+Z5Kt7J/XEJcNysPu5Kdj3/JlIT3Lf5wXq5Bb247Fbx3YP2nMGS5GXsnU/3TwSZ/ZzDVLXmSM76UH02tw9AICK2shOnqJIiY19CxFFJ5MjA1ltMg0REVGsi7/ROgqLhhgoQXXp0BzpstpgbbCozdC+f/JJyEw2YcF9Y0P2ukSeWHwoQxVMuUXVXjMWbRoBEoNK4Oicge2C1rdop/ZZiXusSA1ndWud5nZbaoJ7yU4xW4T8oxYDCVUwyajXYVS3lh7bXD48x+02MXtKrXzarvxKfL8uD+U12tu8PCB8vKxOs11lnXYGiHziyter7EEnaxRnJmkRSwYmGg0wBrnE3b+62/Dzf07BbeOiL5j08dKDHu/v0y4D7185xCVT6/V5e0LdLb/sLYjsWoMUGTESpyaiKCUGk6JlAi4REVEwMZhEXqmFYaIlmOQpRGSSDdj4slh4oGrq3WfRTh/XHZufnISMIM4+jlc8YQ8NSxhnwh0sqsbYVxZh3MuLPLbTykwy6nW44pSO0vX3/nWytD5KPFErbQZozFp03BSpdXTevGwQLhjcHh9fPVS6rXmqe6ZlZjKzLwOh9qmGKhlPAGCQBf1euXigW5tkk3ug0JfysMXV2pkn8rWNPGWblPuY+VFc3YCiqnqpXzpd9K0zFQlGPTCgQ2ZUrpnkq0Sjs9zdh4sPRLAn7vgda5r4qRNRYzgzk6JjzISIiCiY4m+0jsIikrNs5OXQPI11CbJQUyizNKobnLOqp4/rhn/uOj1kr0XkK2/ZeOW1ZmzOKwtKecFVB4oB2Ad7iz2UNXKumWS/frqjjNZVIzrh+Wn9pHbtspIb3adYolaSUNx/RWocs21mMl67dBCGd24u3dYsxT1wlJoYvWueRDO1LKRQZSYJguBSDi7J5H7op5ZhJpaTU1vTS5RbXK15nzwYVetD6bKc5t63++p6C8Sf81gpcSfu50ibcvLAkVL1dbTCRf6dj8OKq//P3n2HOVGubQC/U7f3wgJLWXrviHQQAcGGvdfPchTrOfbesR/LQdTjET12j11BBERAmvTe61J26bvL9rTvj+xMZiaTZJLNpt6/6/IymUwm74aUyfu8z/OQBgwiEpEvamWABWYhmBQhC3CJiIiCya+fSFOmTMHAgQORlpaG/Px8TJo0Cdu2bVPd1+FwYMKECdDpdPjhhx9ktxUXF+Pss89GcnIy8vPzcf/998NqZaPbSCXMB7XISBS3hTMzST6v5XmSS5qZoTZZG6yeMtX1zkmyjCQT7h/fBV0K0oNyXKLG8PX6nvzZapw/dTEW7TwW1Mfq/9xcj/vZxWCSc5Lm/WsG4OtbB+NvI9tDp9PhpmFFOLtnc/RS6c0Tyyw2Ox79fgM+WuxeEirc01nSYFFBRoLb7W9d3jeUw4kZoSxzp/wkSDS6BwCNKsEk4X3t7aPkhzWHPN4mnZC//P1l2HnEe7mw968ZgD6tMgF47jdod7iCVJGciVOUm4I/HxiNr245He9d0z/cw4l4CYpg0gc+yuM1NWkgdP8J9Z6YFNsi99OFiCLF+9f0x/JHx6jeZjI6P0VqGUwiIqIY5FcwacGCBZg8eTKWLVuGOXPmwGKxYNy4caiqcl+Z+sYbb6iu6rLZbDj77LNRX1+PJUuW4OOPP8ZHH32EJ554IvC/gkKib+sstM1JBhDeYJLWTApp9pQyS+N4ZR0GPj8Xj3y/odHjEbKe2DuEIomvbDwhiPTd6oONfqzHftgou+5wOFBWXe+2n/DWFb4bkswGnFaULfY9eeycbph6Vb+IniRuDE+xgqW7juOzv4rx1M+bxW1BSBgLCqNBj7VPjMWax8eqZrv1bpj8J/+olTxsqpe9wyGfHDeplJA0qqRfzN1y2Hl/L4s2KmotHjOXlAHtP3d4Dlwnmw3oUpCGd67qh+cv6IGvbh2sup/D4RAfL5Izk3oXZqBVdjIGtctBskqvsXihNSurp2IBwfEq9++PUArWYiOKXhH88UJEEUKv16lm7QOu8yqb3YGaet/Z2URERNHEr2DSrFmzcP3116N79+7o3bs3PvroIxQXF2PVqlWy/dauXYvXXnsNH374odsxZs+ejc2bN+PTTz9Fnz59MGHCBDz77LOYOnUq6uvD++OR1InBG52rFMnD3zU+CBMo6W98bxOuMzeUiJeVEwOf/VWME1X1+Pyv4kaPRzh2tDQCp/igtWfSrqOVQX/soodnos8zc7BIMXns6pkU9IeMamqBN7sr8hbi0bjLTDYjK8WMZmmJvncmTVQzk4L8xhCyPTo3S0NeqiurLDXBPbhhVHnsvcerUVZd7zUzaf62o7jhoxVu22stNizfc0K2beXek+Jl5aKQc3u1gE6nQ4vMJFw1qA3a56WqPp4D0fGdG88Nty8b0Eq8fInksjf3ju0ke0+Ee22OrzKxFPsi4KuXiKKAp4UtOZI+o/vDXLqViIgo2Bq1XLK8vBwAkJ3t6qlQXV2NK6+8ElOnTkVBQYHbfZYuXYqePXuiWTNXjdnx48fjtttuw6ZNm9C3r3vJnLq6OtTVufpwVFRUAAAsFgssFm2Nm+OR8Nw09jmyNWQ4OOx2ccJpx5FK7DxcjjbZyY0bZACkWVFWq9Xj35eVbMbhCufrpra+HhaLSbzNLsnaaOzzU1fvvL9ep+PrMQBWq3O1lt3hiNjnL1jvpVD6eZ16xpHyb6ittzXZ33XnF6ux/OHR4nVXOdPI/bduSlYPE8xfLHcFtTcfOImOzVLFfXUOe8Q8V6lm9x/MgYwtGt9PwWZV6SFks1phsQSvQcu3tw7CB4v24s4z2kPnsGH1o2dArwN2H3PPJtd5yD5aW3wCNpv3FbULth91+7ecNHUptpbKy9rN2FCCK7YfxqCibLfs5puHt9H0eqirt4gTNwZ95L6Gauo9n5sEUyS+l+4Z0w5frdwPk0GHYe2yNI0tQQ9MvbwPbv9iLQBgw8HysP5NtXXyx46k55eahvLf2GZrunMjolgXid9NoaLXuf7ugvQElFbUoaqmPi6fC2q8eH4vEQUT30va+PP8BBxMstvtuOeeezB06FD06OFqnH7vvfdiyJAhOP/881XvV1paKgskARCvl5aWqt5nypQpePrpp922z549G8nJoQ9mRJs5c+Y06v6bSnQADCgpKcGpWh2ESuK/z5uPrAQg1P3XnZnizpfukqVLcXiT+n6lJwwQxjrvjwUokLxUth9w/k0AMHPmzEaNZ+8p53jqamsafax4tKfh+auuqo7456+x76VQ+mCp+sf7zzNmNqz6dt5eVXlK9XmvtTrf275W5zoXcKs/Vn19vezYWxvedwf278fMmft8/xExxubhudp22JUddv0Hi/FoXxtKSvUA9Ni0cQNmHlkfsjH65hr/nd2sjXrPRtP7KdgsdkD5Wvh97lykmFR3D9joZGDjsv2QFqLcX+n+2Ad2bYXwnSi1dNly7DmlA6BH72w71p1QD3YpXwdbS9U/E2Yu+AvHtzhwrNY1hldOs2LTsvlQ/yqXH2fBgoWotDo/R6wWSwR8Z6j/nftLjoR0bJH2XnrjdOd3w5/zZmu+z5aTrvOyXUerwvpvW2kBhH9bk94RAa8zCrUVK1aieicz1IgaI9K+m5qO61xgQqFN/M4orXBu/2bOYhTn8/OEAhc/7yWipsX3knfV1dozaQMOJk2ePBkbN27EokWLxG0//fQT5s2bhzVr1gR6WFUPP/ww/v73v4vXKyoq0KpVK4wbNw7p6elBfaxYYrFYMGfOHIwdOxYmU+AzVEeX7sN3e7ehRfMWsJVVY3+VMzOsLKMjpizcg0cndsb1g9sEa9g+1dTbcP/y3wEAp58+GAPbZrnt879VB1BW7+o/MnTYcHQuSBOv75m/GzP37wQAdB44EjM3luKGIW1Uy/8offZXMVpmJWFUpzwAwKp9J4GNK5CWkoKJE4c16m+LR6uLy/DGxuVITknGxInDwz0cVcF6L4XS3UvVJ/HOOHMcUhKM4u2JKamYOHEoAGDv8SrkpJix/XAlrv5wJa4a1AqPTezi9XFO1VqAZX+o3jawXR4mTuwnXt/9xy5g/y60adMaEyd2C+TPimo2uwN/X+b9BOZIrQ4TJ07Et8dWAWXH0bd3b0zs2yJEI/RNeN1cPagV7jqna0DHiMb3U7BZbXbc99dc2bbx48YiPanpn48tJafw6oalsm1XTRiGL6c6t+l0QMuMRBwoq0Xffv2hO1AOHNyDPp3b4t7Oebj+o1Vux9xg6IBNhyrwwLhO6No8DVjqep13b5GGZmmJmLftKLr36ImJAwqx7kA5sOYv5KSYMenccR7H+smh5Vi5r0y8PnjoMEyatgwAUGV1vlfC6f4Vc1V7SNpMqSE5H4il91LOnhN4d+tK8Xo4/22PnqoDVi4AAFzYr1Vcfl/FG+G9JDht4ACMbDjPJyL/xNJ3kxbCufGYLnl4+bLeYlsAYftnuwx46nrP5zpEnsTbe4moqfC9pI1QBU6LgIJJd9xxB3755RcsXLgQhYWF4vZ58+Zh165dyMzMlO1/0UUXYfjw4Zg/fz4KCgqwfPly2e2HDzubPKuVxQOAhIQEJCQkuG03mUx8IWig5XlyOBxYue8kOhekIT1Rvq9e71wpqjfoYTK4Vi5PW7gHAPD8zG24eUSHII/as3q7K1XCYDC4/W2HK2rxyA+b5XfSy/czSJqNn/XWYgDA0UoLplzY0+tjbzxYjqd+2QoAWPvEWGQmm6FreH4MBh1fjwEwGp0fQzpd5D9/0fSZk2I2oEql4atdJ38v7DpaBZPJhDXFJ3HBO0uQmWzCgDbZsNod+HhpMZ4+3/t7wuSlAtbAohzZY+ka3ndGgz5qnsdg0mvsw2EymWB3OD/nzCZjRD5XBn3j/w2j6f0UbCYTMOOuYVi66ziem7EFAGA2h+b5SDC7P4bJJD8dLMhIwoGyWjh0evF9a9AbMLJzM/znugE4VWvFPV+tFff/YNFeAMDF7/+F+feNkh2rZWaymOHo0DlfN8eqnCUvm2cmev2bn53UE6/N3oa5W44AAGyQp0qG+/Xzz0v7YPLnq922pyeF9rUdC+8ls2L84fx7dAareNlkjM/vq3hnNEbmdy9RNImF7yZ/DO2Qh5Qk9zkrIPznKxTd4u29RNRU+F7yzp/nxq/i/A6HA3fccQe+//57zJs3D0VFRbLbH3roIaxfvx5r164V/wOAf/7zn5g+fToAYPDgwdiwYQOOHDki3m/OnDlIT09Ht25c+Rcu3685iEveXYrz3l7kcR8dgIMna0I3KA98Tcceq6xz22bTMIm7at8Jn/ucqKoXL7+3cLfz2A2NxNUamBOFy3l9Wqpur7OqR382HnT2wCurtmBLifYVCd7eWVab/FbhbahnZ2uvznpjodhvRh+hnys6/hs2WvcWGejTKlO8bgjRv7Xaw3TMd2XuOhyu153d4exnJ9xPp9NhTNdmaJOjXmLYZndg+MuuTMV3r+6H1y/rA2NDQEr4Lv7bp87spswks/tBJLo2T8cH1w1E24bHu/Y/y73uH2pn92qOzc+MR4pZXiIwRUOWM8mlhLpmshfS7y5+X8UnfscRkb/4sUFERPHCr1+7kydPxueff44ff/wRaWlpYo+jjIwMJCUloaCgQDW7qHXr1mLgady4cejWrRuuueYavPzyyygtLcVjjz2GyZMnq2YfUWj8tO4QAGDvcfcaidLp4NKK2hCNyDNhYgtQn8g2G9xjpFZFMEntZE9L0kCiyTXZcaLSGVgSJsc44UCRxGZ3L70EAHUWO/Ydr3Lbnmx2fR0cLHMFjX/fchhjujZz219g9/LGsTnkt321otjjvvFA6yeEEEgC1Cf+KXZIvzbC+R2iDGQJV+0Oh/hFKx2e1sDXuG4F0Ot14v5Wu0P2mTG6S76m4xgbvtel2Zbf/G2wpvs2tWSzEf/9v0FYt78M87YewaKdx3DT8CLfdySZbs3TUZiVhAMnazChh3qlglCRLkAKVZCXiIhil8PhYJCaiIhihl+ZSdOmTUN5eTlGjRqF5s2bi/999dVXmo9hMBjwyy+/wGAwYPDgwbj66qtx7bXX4plnnvF78BQ8WgIpOh1wSf9C3zs2Men8tFrGkcXmvk1LZpLd4Xsfi801Qd88M1F2bE44UKSYu/kwvl55QPW2Ua/Ox+6jrmCSQcxAUH/9vzN/l+z68co6PPXTJmw+VNFwP8/jUAa0Dlc4swZnbSz1/geQiEHq+BGqf+o6lR4/Sst2OzN1/9pzXJKZ5Bqg2usyxWzAmZLAs17nynAyGpz//3ndIdnjXzawlaYx7z8hX+jy5wOjMaBttqb7hkL/Nlm4cVgR/nP9AMz9+wic0cVzAJ7U6XQ6/N8wZxAu3OdT0gVIBn4GxyX+qxORv7x9blw0bYnXBXhERETRxK/MJIeGyXYt92nTpg1mzpzp97Go6Xj7t5Xe1rFZaiiG45V0PO/M34mhHXJlt1tVMjLUtilJJ9g9qbW4VkW3y3M+F0t2HQcAbDqkvTQYUVO66b8rvd5+w0crxMsF6c6gqKdgkrS5/HerD+DvX68DAHy0ZC92vzDRa6D29y1HMKFHc/RomSHbfuSUeynKeBDInGSkxqg5vxp8oQoc1tt8fx8KPl1WLE7wS1fUqk32V9XbUJTrKn9nlGQJ7z3m/H5du78MF7yzWNyeaNS2pkkZAEs2R05JNKkEowEdJCUDyT/Cqyrc023S7zVpRjrFD37HEVEwrS4uw8GyGrTKVi8TTEREFE38ykyi2LTj8Cn8ueOYz/0i5XeVdM578c7jbrdryUxate+k6rErai1eH7taUmJHyLp4v6F3EjVOALFq8kCt1KMnB8tqUGuxwdP8snSFthBIEkx860/8ueOox2NvLT2Fc95eJGYxUSAi5ZNXTheh44o20s+9UGVA5KeplxTu2zoTADCumzyrRhijrzJ3Z3bNh0ny2SP9HDpZ7fpuFco4GvU6WcDJG+nDZSabkJ7ExqmxSMhkm7G+BOXV3s/HmpJ0AdLNw9uFbRwUPvyOIyJ/+Spjp6UKChERUTRgMIlw95drwz0EvyhPw5Qp42qZEiVl8l5Pf2xTnwA/6iNj4sVft4qXrSpBK6JIcHav5n7t//qc7W79jQSeei8Bzknh71Yf9Hn8iW/9Kbveu1WmX+OLFVpqpXfMl2d/RmpmEgVfqFbCF2YlY/r1A3H3mI6y7a9c3AvXDW6DJ87tJtvuKnPn2qaWRZWdYpYFh6QBp3qV0nqdC7Rn8Nw9ppN4+Zc7h8mCVhQ7pK+q+75Z53G/piacR7bISERGMgOX8eTsngXoUpCGQe0ip4wmEcUGLWX3iYiIogF/jRM2l2jLGoiUppHKVT2fLS8G4CxBN2fzYZTXuK9mfeDb9TjlI+sIcA9MKaUkuMqdcHURRar0RL8qmOL9hbuxbJd7lh8gz0xSk5NqBgAMbpfjdT9pz5OHzuri1/jixfz7Rrk1no+Uz12lCB1WVAvlv/XoLvno05CJJOiQn4anz++Bwix5CZZDZTUA5Cv11YKcSSYDzAbXDdLv4r6KxwKAb28bonm80kwR5fgohkjeA3M2Hw7bMMRemAZ+0MWbNy7thV/vHs6ANRH5LT1J/vsrJ8Usu865AyIiihU8Uyafdh6pBADsO+67p1AoKM/D/t1QZu7teTtw839XYsrMLar3K1Y08FZjtTvww5qDmL/tiMfbBS/P2qZxxOQNJ6WDL5CFbzM2lKhu3320ChsPlnu8349rDwEABrTN8nr837e4Jgbb56f4P8A40DY3Bd1apMu2RVpm0qjOeQCAK05rFeaRUGN10ZgZNLthUl/vo8xdq+xkj2XrHpogDyCbDXq/etGola+l2BMpn3dCMMmo58+keBSpiziIKDI9N6kHzuvdAuf2aiHbfv2QtrLr1/xnOXYeORXCkRERETUN/koir3YeOYUvV+wH4GwcGQk1xB2KaJK1odnL1D92AQB2H1MPemlZDLS1tAL3fLUW109foZrJJE1PP15Vr3XIRCHlCHL78uunL/e5T4aPHiZP/bxZvJyfltjoMcWqBMUEe6TNaX143UCsf2ocOuRrL1FGnqkFZUKleUYSfrtnBJY9PEbbHXTSzCT3cV8yoJXH1fyFWclolZ0kXn/snK5+jdVbuU2KHZFwjgm4Fg6F8/1JRETR4erT2+CtK/q6LahRZreWlNdi0tQloRwaERFRk2Awibx6vyHrR+Brkrqsuh6XvLsEn/21r8nGpBzBofLaoDVqPl7pChCplctT9kmSlsXL89DUnCJfebUF0xfv8dkzK1oEuyT3sUrfgdNm6a4A0WNnd8VpRer9Bjg359I2x1Wu65yGPlcJRvnXcqStkNbrdUhPZA+RYOldmIlhHXJx+cDwZHp1LkhDQYa24K6vzKSMJJOszJ3StKv647ZR7fHtbUNwzelt/BonX3PxIVI+7lyZSREyICIiijpq3yGVdVa8+hurmxARUXRjMIm8qrW4VgOf3TDZ6c37C3djxd6TePT7jU02JrV6wy/O2uq2zdNktrfml3WSJuHV9TbZfTYeLEed1Sbbv9ZqQ7N0ZxDp2fN7eB84Rax//G8tnv55M278aEW4hxIUyuw9Qa/CDNn1ND97KwnuH9/ZbVtaohEPT+iCCT0KcP2Qtvj61sGq9/VUBisePSDpHdUy05m1YVY8P5zKjG16vQ6f3jQIL17UK9xDkbnnzI5u26TZSClm9c8Ob+/vHi0z8OBZXdC/TZbfQdIbhhVhVOc8vHxxZD1PFFzKebdwNStnZhIRETWWp1Kp//pjZ4hHQkREFFyc1SOvpI0kR3bK89mQ9o9tR2XXn5+xGWe9sRDHKoOX8aE2T75XpbTd8j0nVO9fVW/1eGyLzRVMWru/TLz87oJdOOftRW4ZGjX1NnHFtLLpJvkn2KXZ/DF3i7NH1gYvvYGiiaeKUMpSdI9O1FZqSphPE1bYXdivpds+aYlG3DqyPaZd3d/rhHK9leWqAGdAThrMEybXpRlegHo5MaKmphYski6wyEhWzxRqqqb1qQlGfHTDabh0AHt1xTJlmbtai83Dnk1LKKvIzCQiIgqU0Uu2NhERUTRjMInQMT9Vdl3aGLJ5hqvHQZ3Fhks8TOTY7A4s3XUcW0oqxG2HK2rx7z/3YGvpKfy49lDQxisN+AhMRvWX8i93DhMvC0Eou5eVrtKJbml2xyse0tFLK2pha9jPwElfihDS7L3eimwkqVbZyR5vk2qZlQS73SGu1k4wGpCaIJ9sTk1wn1z+9P8GuW179vzumh4z1m1UBC6Fjw/lD09+rFA4qGVktMxKUtlTzsSJE2oMxcunLkyLD4SSxsxMIiKiQPE7hIiIYhWDSeQ2Wbn+QDmOnqrD5e8vlQVRBrXLQWqCEVcNau12jOOVdfhzhzwr6d0Fu8TLwcxGOKnSH2nzIfWMkh4tM9CioR+EkPnirWyKdJwWm+9MGbtdUlufk2gUIYRX7uTR7dEuL9XjfkW5KarbUxOMmH7DQLG05dD2udh0yBUoNhv1buUmU1VK5g3rmCuWbxNccZr750c8+mvPCdnzL/zeVP7wZGYShYNaUCgv1XdfwKbKTKL4oPy8C1cmq41l7oiIqJGY3UpERLGKv/rJ7ce7xWbHS7O2YtlueZm4Ts3SADhLuyltOlQhZuio2XCwrPED9UJZfk5K2ZvBWwn+DxbtES9LM6DMkswnnQ5IMRsAAFa7XTLpwLcTRQYh0JOeaMKkvi1h0OswunMe9ijKQaoFgABg/ZPjMLpzProWpInbdh+rFC+nmA2yklcAkGwyqB7rYFmNeHlSnxbsmdSgut6KwixXZphQ3smkZ88kCj+196mWiXVpz6+XI6wPFEU+5SvM23llU2LPJCIiaixPPZOIiIiiHb/hyK1s3IPfbsA3qw7Itl3Sv1C8PKBtttsx6qw2sSyIQFr2beaGUmwrPaW8W0ACbcgslrnTODkh/XuGd8gVL3943UDkpTlXaNdZ7Thw0jlZztVHgYmkZy3ZrB4QiTbCS1yv02Fkpzysfmws/nPdQLf3eqJR/e/VN7yWhUCsw+FaIT6qc55bgBYA0pPUe6hIZSabNf8Nsa5nS/Xyg3rl50gkvUEobpyocl+goeUrblC7bAzvmIszu+bj/L4tmmBkFMuUXy1WlbLGoSCcJ3IikIiIAuWtaokjTIslCNh1tBJfrSgOW19GIqJYwF9JcW75nhPYdbTK537S1ZnZKe6Txla7A83SXSVw9Dr3SYHxbyzEB3/uDnywDbSefK187EzV7VqDSZ/+tQ9fLi/G9sOnxEnwhyZ0wegu+eKqbWnfk2bpiZqOS5FFmjnTrXl6GEcSPMJrXHgPZiSboNfroJyXMxv1eGhCF5/Hc8Ahln1UK2PVIiNR0wpurvJ2uXFokew6y9xRJDl6qs5tW63F98R+WqIJn/zfIHxw3UAkeAhWE3kSKWXu2DOJiIgay9t3CGNJ4bFw+1GMeW0BHvx2Az5asjfcwyEiiloMJsW5J37cqGk/aSaC2uSmMlsoJcHoNnENAO/M3+W+0U9aM5NSE+QlvIR7qd1frXfMvuPVeOi7DbjsvaWw2Z1/jHBOKGQhnaq1AnCW9hGylSh6/Lj2IIa+OE+8HisTR9LMJCm1QOrFkqxDAOhV6MqYEe6+/kA5TlY7MxXMKsEkabk2b2Ll+Q0GaelMAOKTbVD8m/EZo3CoU5nEd4AzH9S0lKeX/5GUHg4loUcgv7OIiChQ3rJbtS5upeC69sPl4uVV+06GcSRERNGNwaQ4p/VExlebE5vdlbkgXFc7tiUIq0y1VrkTMiiUkxPKYWUlm/D2FX09HudktQX1NiGY5DyYkLZeXe8MJqkFoyjy3f3lWtl1b+UIoonw3lPOg6mVrlL+xZ/cOEhym/PWraWn8Mpv2wAAJpXn6Onzu3scy/TrB4qXq+qsXscdT5SfSzoP293K3hGFgNr7PMAKs0QB+3LF/rA87oeLnUGshduPhuXxiYgo+nkrgc9TqvBTLuAjIiLtGEyKc1onh6QZDjUq9WU/XbYPv285LF632h2qJ1DBmBjVGgBTPpRQHk+amXTfuE74YfJQ9GiZoZpxIRACZcLzYGhYaVRSXgsAKK+xaBs8eRQJC7QW7zyOxTuPhXsYjSY8l2q9jZSk+7TKTkJGsklym/v+RsX7xKjXoauX8oAjO+WJlxfu4MScJ8JzrVwJz585FA53jO4Q7iFQHPJU1rOsuj7gfpmNYWUElYiIAuRtkSIzk0KvrFq+qJJtEYmIAseP0DinPTDjfUpzdXEZVheXidfrrXZ8oFKeJBiL7JX9YDwRJsmF/YS/VLh/eqIRd5zREW1ynFlF9V4aPZdXO4NFwkSvECj7ZX0JAKC0ota/P4LC7lStegDwjs9Xh3gkwecpM0mNdJf9J2o83iZQ9kzylc0lDSCfrGLQ1ZMuBWkAVMrccdUchUG+Sg9Ab/0KM5PdeykS+atSJXt1TfFJ9HlmDi55d0kYRkRERBQYtT6zAsaSQk9oTyBgX1oiosAxmBTntH6FSr9sG7OSJhgTo8Lq1BSz0eM+LTOTXI+p+CvFiXY/IlvL955w3kfRM4mil/KEUnCy2nfAw9ukaiRwBVw9v05fu6R3wz6ej6N2m1kRPPJWD1xwzeltAAD3nNnR577x5qc7huK5ST0wvnsBAPfPJf7OoUih/NiTBpBeuKBniEdDseizv/a5bbvgHWcQSbpgiYiIKNJJqw10U1RxiPCfkjFJGdxjX0QiosB5no2nuKA1uLO62NWgsDEnP8H4zhYevyg3Bd1bpKvW1G+Tk+zxfkLVkkDq5AoTvUI2Rm6qGccq63HbqPZ+H4vCS63BvBaTP1uNraUVmHn3cCQYDUEeVXAIr3XliqsbhxaJvSAu6l8IwPtngDIQC7ifiGck+c5IeOq87rhuSBu0z0v1uW+8EJ7bXoWZ6FWY6XE/rpqjSOFQVPhf/OAZOFldj8Is9+9bokBYbaGfXbPY7LDZHUg0Reb3ORERRSdp/8kPrhuAIS/OE68rz6mo6SkXRPM3FoVSebUF/5y7HRf0bYnerTLDPRyiRmNmUpyrl0yod2rmeaJ37f4y8XJjSsgHIzNp+pK9AIANB8vx4kW9xPJQUtI698qHFDKbAhmLcNIhZGMcq3TW3k3iJETAwlXGq1al95fgtk9XobrePXPp6Kk6zNhQgl1Hq7Bs94mmHF6jCC9/5VN715gOOL1dNl6+uJe4TbrP8I65sv3V/mmEbU+e2w1mgx53jfHdW8Wg16FDfhpLtklpfCr4jFGkUC4kSUkwMpBEQeUr870psoLHvr4AvZ6ejZp6z+cERERE/jJIqjeYjfJpN7bkCz3lU87fWPHN4XA0+ryyvNqCo6fqNO377IzN+GjJXpw/dXGjHpMoUjCYFOeKT1SLl0vKPff9SU90JbE1psydMjNp++FTmPrHTpysqle/g4qF24/KrqsNx6ra/8i5oxBM8lLGGA9N6IJ3rurntt0gBpPkf0h9gFkuFD67j1Z5vO3XjaVYsfek2/YbPl4lXo7kUocODz2TMpPN+PKWwbh0QCtxm3QXLSu0Pl1WDAC4YWgRNj0zHpcNbN3o8ZJnXDVHkYIlWaip2XzMrgV78s3hcGDv8WrUW+3YXFLhdvtLF7F8IxERBUb6O0z5u7GsWvvcBwWHXXESUWvlIpJ49uRPm9Dv2Tko9TIH6kvvZ2Zj4PNzVXt+Km0tdT/PJIpmDCaRKC81weNtj0zsKl5uTAS/haSXEQDc/tlqvPLbNrw2Z1vAx7SpjCdX8rcIp242O7DxYLmYteStzN3fRrbHxJ7N3bYLd0k0yzORurdId9uXItszv2zyervyhHPvKWDb4UrxeqIpcj8+tfRMEkj3UWZrqd2/RrKPt8ay5J3WEBFjSRQpGEuipnZ1Q389TyyqC4UCJw1eSRch5aaaAYBlSIiIKGDScuHKnqgfLd4b4tGQcsqoeUaS+o4UF/67dB9OVlvwy/pDAd1fOle095jnRcoC6SnsvuO+9yeKdJwJJJEy/VpK2l9G+kWcnyYPQE250PsqzjXFZbLI/c4jzsn5T5cVo0pDRF+N9IN8eMdcpCUY8dR53d32e3nWVpzz9iK8MHMLgMDKqwmT57kpZtn21ES2H2usUK96P1zhPSVZWcu6rF7+ern0vWWY+sfOoI8rGIS3hJasFukeQh8ltdsoPBhMokiQlmDEuO7Nwj0MinHe+scBvjOX/CUtiWyR9Gvy5zuUiIhIjfQrRLmI1RDBFS5ilbK6jre5L4of3lofeFPv5wIn6YL8ka/MD+gxiSIJP0FJlKD4Qk0yGcTGkdKMog75rt5Kd57h6pcyoE0WLh/oKp/lyeuzt6tuf+W3wLKTpOX5/nvjaVj5+Jmy8QpBo5X7nGXLlu9x9rpRnsRpmTMQesrkKLK4jHq+lWKNMrilTESy2R0Bv2abmjB0f3+mDG6XI7uu9p54ZGKXgMZEgeGPTYoEq58Yi/REU7iHQTHO13mYNcjBJOlEwPt/7hYv28VSsfz8JSKiwEgXQCirObTP99yrmpqG8gyiMa0bKHYYApjH21JSgS6PzxKva3kt8fVGsYYz4HFOCBY5L8tfDjUWG766dTCev6AHzuiSL24f0DYbb17eB5/fNAh9W2eJ2w16naZsnw8X71HdvmLvCU1jVtYcttpdkwE6nQ4JRnkJOk8jUs7R+irZ9e9rB4hBJGWJM+nzSLEpmhYviT2TNIxZelqjZd6sT6ss3ztR0Hgrx0kUKixpSaHg69Nuv6TPZzBYJdlIC7cfFZsoCxOAjOUTEVGg2uWlAHBmwCizYOrYbznk3CbzObcfsaw2O4a+OA9//3ptkxxfWtkokJ847y7YJbs+e9Nhn61Agt33kyjcODsQ5y5ryCRqnZ2MBJUeMP1aZ+GqQW3cVsef36clhnTIhVESRBFWcJoDnHRSBok8uWSAsxTXNQ219fPTEgN6PGXt4rvHdAQAXNi3pc/xKVcwMHsg9ijPB6LpX9ifVdXSEx9lMFjt3kW5KY0aGzl5C7w/PMGV/aX8nCIiilf/mhfc0rLSxUgAcKzSGUwSvhZ5bkdERIFKSzRh1WNnYs3jY91uqwuwtBYFTjnZz0yRyPXSrK04WFaD71YfRHmNBYAzwCScpzWWRXL+F0gW+o9r5X2W/vXHTqxqqILkCV9vFGsYTIpzQnm283q3CKgWvTTAsv3wKQDyH+dXn94aD56lrSyW1klT4QM/p6FB8vvX9sewDrn4cfJQ9Tt4OKzyi+O2ke3xy53D8NLFvVT3l04qbDpYLruNq7Zjn6e3R0WtJbQD0UB4C2rJFEw0uTL5spLlZazU7i80Jqem463GOhFRrPL1nbU3yA2LpZlJALB2fxkAaWYSP3+JiChwOakJSElw760caJ8WCpzyt3woM0X47+2f79ccFC9bGkoS3/H5Gpz2/FxsPlTR6OPvOFwpXvZ34dCmQ+Wq2y9+d6l4/mix2WFV9FRSxpKUtxNFG86Axzkxg0GvCyiYVGtxfQgmmQ0Nx3TdroMOt41qjz1TJvo8ltbMJFc/GOf+3Vtk4NObBqF3q0yv90tVnMgpJ2n1eh16tMyQBYYeO7ureDk7xTWJXqwotWJkmbuAReozJ7zOKuusmLXpMKqt6vudqKwP2Zi0qm44YdXy3JoMesz9+0jMvncEks3y94jaPJqWABX55u1Z1Elu5cp4IooXvj7tCrOSg/p4ymDSpkPlqLXYUNPwHcrMUCIiagrSORQKDeVk/n8WqbdeCLa3f9+BLo/PwuKdx1TG5PBZHi0eHZPMrwjnarM2lcLuAL5ZdaDRx3/0+w3iZa1zkILbPl3t8bb/Lt0Lm92BM16bj5GvzJfNryozkzo8+itOf+F3LNx+1K/HJ4oUDCbFOVc5rMAaG7fMTBIv56S4ZywIE6E6nQ6dm6UBAC7sp15GTuukqfA5rHVOW9hNWc5Ei5uGt8MXN5+O967pj+4t0l3HVDy2MYDGfRQdXvx1C+78ch2+3KX+b3y8Kjjp1sG0rmF1tdZU8A75qejU8P6UUr7Fnp3Uo5EjIy2kny+czCSieOHrvG7hjqP438r9QXs8i9191eiM9SXidX78EhFRU2CmSuiFq8zYa3O2AwCu/s9fsu12uwMXvLMEl723jAElL5RzeI5GNrs6WVWPdQdc2UX+LpRVLiqX+nVDKcqq67H/RA0OltXgRJUrKKb2+iutqJWddxJFE86Axzkhu1Kv0wW04jNLEkCqrneeFE3q00LcJi2hNapLHgBg33HXB7C0GaX2gIzzg9jf3/jKFahqPaLUDG6fg/HdC2RfNDrFo5uYmRR1fK1CEU7qPl1WDACos6vv/8GfoVnVpJVQVxgAlu463riDSV7zRbkpYp8yCh2WuaNwGdMlHwBw5aDWYR4JxQtfcyn1Vjvu/2Z90B5PmZFvsztwsKxGvM4yd0RE1BRqrQwmhdrqYveeNvYQ1rpzOICaete/+8nqeqzdX4ble0/gaJB6AcUim92BPcdcZY6V83DeVNZZ8frsbWI7jk+X7UPfZ+fI9gnmK2D53hMY+cp88Xq9pJRdAOvaiSIag0lxTpgwN+h1ePLcbjivdwvcc2ZHZKeYMf36gX4dq2/rTADACxf2FLclm13BpFO1zjphq/adRF3DCVRBeqJ4+/EqbeXC/M5MathRmXl1Sf9CbQdQoVzNamTPpKhitzsCysRTU5SbEpTjBMvMDcFb3SJ9iwWS2UeedWnungmmhkmPFC5vXdEXH1w7AE+c0y3cQ6E4EepVwxZFvfovV+zH6w0riAH/S58QERFpEUh7AQpcTb0Nj36/0W17fYj71pysVp/vOnDSuZBlw4Fy2aKWfSeqMb9EF9eZbBPe/BNjXpsvXv9w8R6UlNd4voPE8zO24K15OzHunwsBAI/94P4a8CcrrPi4PCupj0qbjco6V2+ELZL+Tp7mUqSvweLj1bj7yzVYtruRC4KJQoDTVHFO+OGu0wG5qQl464q+uOfMTlj9+FiMbliVrJWwglPad0UaTDp6yrXios7q/NCUZvSkSPb1xt+5Bk9TAY0JANVb5V8GJk44RBUtJ45aX2bJGl+3oVBnteHh71w1gO8a07FRx5MGbBlLCo5Vj52JPx8Yjfy0RN87g5lJFD4pCUac2a2ZLMOYqCmFem5NmbGupOwjSEREFAy+vn8ouE7VWlS311lD+wO3WpKZJF3YOm3+Luw5VoVz/7UIQ1+cJ26f8NZifL/XgKnzd4d0nJGkut7mdn74WUPlGF9W7D3hcx8tc4sOhwM19TYcOOkKJt06oh1+mDwUL1/Uy+P9bvrvSjEQ2LkgXXWf79ccxCPfb8A3qw5g8uer8ePaQ7j8fZY+pMjHYFKck5a5ayy1Q0gnoaQrKhwNjyv9jKzw8CXv+fEaN+bGNLZPSZBPMKQnmRo1Fgot5WrkxlAGFsPphRlbZNfbNjJrSppGnsHXeFDkpCagVbb2kqKN+ZwiIoomoc5M8pahPLFnAZIiaLEIERHFDmYmhdY3qw+obg/17/hJUxeLl6XnIFabHRsPuvr4CK8PS0PQccVe9xJ98UytZ7VayULp+2zX0UrVY2k597zzizXo9+wcVNS6so76t8kCAEzqq94PXlBSXgsA2NtQqu+O0R3c9vn8r2Lc97912CB5DYQ6a47IXwwmxTmxzF1Qgknux+jWwhWBl07gCx/a0o/uWou2D0x/m+55+tMa0+foyXPlZX+4cju6WDSsBtM6pxXqFU3erNlfJrue3MjXpfS9c+/YTo06FmknfU3pGUwiojihtXdBsFZrWhvOS9vlpeC1S3rLbntgfJegPAYREZFSsMqtkzaefhPvOHIqpOOorLNiR0P/HptkPuJEtUUWSFCWOePiQjnl/Mutn6zE6Nfmu5UDlAaTxry2QPVYWt6Kv6wvQY3Fhh/XHhS3CVWcfJVEHv3qfCzfcwLV9c5AVHaKGTufn4AXLujp9X5a50aJwoXBpDgnLXPXWNKA1I+Th+LtK/qiX+ssyWO5P650JYBVY/Td355J2w+rr0IwNqIZSfcWGQHfl8JPWIXk/cvf+UIb1TnP67HeWxg5aefSE827x3RsdCBi1xHXe6dfQ080anqzNpaKl1nmjojihda5tWAlMAmrRQ06HUYqvuszk5mNS0RETYOZScE3a2MJhr00D6uL3bN4mmcmqd6nosa/yjjBcN836wHIe+is21+G9yVzCsoqKuzhKCfNKLPY7Pht02HsO16NzSUV4ja73YETGnqyOxwOzfOQvzb8Rs9LS4CpoWWGXq/D21f09Xq/S99bimOVzrF0bZ4Oo0GPgowEr/epi+M+WRQdGEyKc8KCiGCXuevdKhPn9m4hu1264lS4JAsmaV2RKjyex25IFE3CMVcunKCZJH2zUhPUeyNoncy32uwo89BUMxwGtM3yvZMPK/a5Tsa5Iip0hJVLAJ93Ioof0oyj3/8xErmpCbhrTEdM6qM4nwxSNOmer9YCAHYcqXQ7D2bGORERNRUrm9EG3d8+XY0DJ2twx2er3W5TBmdGdHIuIJmz+UiTj+u0ttmy6+v2l6Gsul7WT1wp0WSQBxz5c1BG+u8pDQgeqahDRa0FQ16ch/OnLkZlnVXt7jLLdh9H9yd/w3+X7lW9XS0bXvnzfFC7bLd9PElLdM45jeqUj8sHtvK4nxB8IopUDCbFOeEHeTDmK30FpCySL0S7w4EVe09g/4kacZsymFRWXY//rdyPKsWXgL+ZSZ6EujY/RQ6hBq3ZqMeDZ3VBbmoCbhvVXrbPugPOVHOb4nVyZtdmqse8aNoS9HlmDvYdr2qCEbtbU3wSbR+agQe+WQcA+H3LYawpLhNvT0ts/Kpq6SooBjVCR/qKC0agn4goGki/b9vnpWLlY2fi72M74dlJPWT7SU8X/9h2BOskJV6tNju+XXUAe475912sPCc0G/gTiYiIgmdAG9dCvz+2HQ3jSGJbvUo5e6tiW03Dwr06a9Nnf5iMzt9y/5CUjO/zzBxc9v4yj/ex2x1Yf6BMvL5k14kmG180krYskPYxemf+Tmw6WIGjp+pkZQPVJDUsGpq75QjqrHY88eMmt33qrDZ8vGSv23blYmN/FiCZja6MpqsGtfG434uztgIAjlfW4fkZm8XyiESRgr+U4pzYMykIE8W+5jx7F7pKwzkcwCXvLpXdrkwvve3T1bj/m/V4+LsNsu1Cz6TGjnhkJ+/ly7TihENwBKsHghbSzKTbRrXHikfHoDBLnv4+bf4uAO5lCNKT3DOY7HaHGHz6ed2hphiym6s++AsA8PXKA7DZHfi/j1eKtw1okyV7vwVKGkxiUCM8GMQjonjhKUE9LdEky3a3Oxyw2R34aPEe3DB9Bc6XNLT+ce0h/ON/63DrJyvVDuVRdrJZdp396oiIKJi+unUwHju7KwBnma4jFbVhHlFsOlbpnvGjzEy6uH8hAKC6vumDSUISWtvcFKQnqldCUbLaHY1qyRDr6q127Dtehep6q2xB0a4jlXh+5mZNx+jSPM3r7TX1Nox4+Q889bP78ZS94tMTTXj/mv4Y1iHX5+NK5w6TzJ7/jc0N/d0f+m4D/v3nHpzz9iKfxyYKJX5CxTnhy035gRgIX5PND5zlamaslhWkzExa2tB48Cfl5HyQMpMyFRMHgWKGU/QR6uwKX9I6nU7snaCk/Pc16nV456p+OKOh6SIAvDBzi3g5VCnJ0pPf41Xyk+bnLugRlPd0fnqieDmJJX9CR/KS43wmEcULu5dyx/ee2VG87HAAMzaUyH7gCwtSFu88BsBzv0xP9HqdWwYUERFRsBj0OuSkuuYf1Hr7UNOwKDKThOwQZZCpKbgqAelwcX/PZc2kbHYHpLGkgnTv/XXizaKdxzDylfm49L2l+GHtQXF7Vb0NGw9WuO1/vqJcMgDkpLjPBUoXEW88VI7DFeqlCNXifOO6F8gWq+94foLqfU1G1529ZTQJFZxWN7QdqLOyNCZFFgaT4pxN8uXWWL4mPVMTjOIXt9p8gdYPyEjrmcRgUvQRM5MkX+YWD68/ZWZSSoIRE3s2x7Sr+4nbPli0R7xcURv6Rp7P/bJFdj09CCXuAPl7mqu0wyMYQUEiomjg7XyqhaR5tt3hwG+bSmW3L93lXICk9btKrY5+31aZAIBmnLQhIqImIJ2/+NcfO8M4kth1WpF7/xpljyqhb3K9h9//dVYbvl9zAHv9LJmrRji10eu0LxK02h2yAJgxzn6HC9k7Wcne5zQ2HqxArcV3dtnfRrZ32zZ3i3u/rMMVtbj6g7/Q95nZblWUpEZ0VK9wJD0HNep1bpVvAKBZmuscs2VmEs7r3QJndMnHjucnYO+LZ+ORic4F+NsOn4LD4XBruUAUKRhMinOuMneNP5aWSU/h81Vt9anN7nDrj6RGGHO451jzGr4I+kvqH1PgDnnIDGoK9Vbna0iaZlyvsjLJ4XBA2R912W5nzeIEowHXnO5e59YUgpR0oQSfYPkeeR3l9KTgBJOUgTQiIqKm4u07R3rOZ3c43L73vlyxH4B7HXtPLnzHVRrv/vGdAQA9Wmbg85sG4atbBmsdMhERkWbSryiWEPesut6quuhDC7W4izRo9MG1A8RgkqfMpE+W7sO9X63DDR+tCGgMUjZx7kqHg2U1PvZuuI/dLhubsoJPrBPaWkjPC1PMBjx/gXsGuZaqMJmKoFSXAvUSd6UVtVi08xhOVntfHPz8BT1Vt0uDXzqdDjPuGi67fcNT42CUzD/pdDq8dUVffHj9QPE1OagoR7z9+ukrUOZjLEThwmBSnBM+n4Ox+l3LIXydNO044rssib9fpdcO9tzYrjG++dtg3DKiHf51ZT/fO5NHvr6sm4K0Z5JgqEqN2xkbStxWMh2SnASqrV4ORqbaH1uPyJpuAs6TqY0Hy2GzO/BSQ0NGQamk5vbzF/RAaoK2esy+MOsuPNrkJId7CEREIedtrkR6/mh3AEdPyUuPFGQkotZi05yZJC2DN7yj6/t/SIdctM1N0ThiIiKiwLD6gLp6qx19npmDM16dH9DCRrWfr0Iw5qJ+hTizWzOYGkrdewrSCItH9wQhM8ku6VGuNSg0ffFenJJUOyn1UG4tVglPk/Tpyk9PVK0kozwfVJNglJeT++XOYar7HTzpPdjXIiMRt49yz3ISDGwrz4rLkCzwvXtMR6RpqB4jnZ9asP2oz/2JwiU4M44UtYQv6MasjCnKTcGeY1U4p6d7LVIl4XE8rTSRrhpJNOlRa1HLFvFvfO2aaFKgTU4KHpnYtUmOHU8OaVyhE0zC60xa5u70djn49rYhuGjaEnHbpkMVUJRYxp1ndBAvV6k07WzswqF9x6vEVVB7Xzxb3P7Mz5vw8dJ9uGVEO6/3v2pQ8IKnzEwKj9xUllgiovjTIT/V423S89SScvfzhvcX7sb7C3fLth0qq5GVx/OkV2Gm9kESEREFQYuMRN87xaE9x6pQb7XjyKk6VNZZZRPynhyrdAUU1H69WhsWkpqNznMJX2XuHEFcUCn8nNbrtM9j/bXnBP5SZGCv2ncybiriCAE46VzE+X1aYFTnfEDSLxNwZrF5c/2QtkhJcAWT0hKNsuwgqTu/WOP1WEseHuP19lbZyfjlzmGqr1mti3TVquUQRSJmJsU5exDK3P1693D8+cBo9CzM8LmvMBVw+fvLVG+3Sj48jR7KhYk9kzQGwAySVapndMkHAIzr1kzTfanphWNNlpCZZDbIH115gmax2t1KMk7q21K8rFYvt7Enn9L0968byvYAwMdL9wGA22RZU+K5THgwhEdE8ahDfio+v2kQ5tw7wu02acLRCzO3ut2uZu3+siCNjIiIKLj6ts4M9xAikrQHjpaFjadqLTj9hd9dG1TuUt+wOlSYXxL+v7X0lOpv92BW53BIepSrPdbk0a5MlzENc1VqNh0qD9qYIpnD4RCDbmbJwt/bR3VA29wUtwosFuXKX4Vm6YlIMBowoUcBWmcnY9EDZwQ0rg+vH6Bpvx4tM9Aq273KSPMM34ubAO19tYjCjcGkOOdqCBj4p1aiyaD6galGeJjyGnlpMyF6b5GcMPg6edA6YmnQ6c3L++C724fgrSv6arw3xaJ6cXWS94/Aepvd7XWYYnadwAxun6O8S6NPPqVB1Ae+Xd+oYzWWEHTNS2OmDBERNb0hHXLRsZl7LXvpudzKvSfcbldTrZI9TEREFAl8TYLHmwMnq1FZZ5WVglOWm1eqtdhw2vO/y+7jUESTjlTU4q3fdwAAjA0LSaUlxZXzUs5jBM/6A84gkE6nftwUSXDk3Wv6ezyO8vVyoqoeR06Frue0J/9buR/9np2Dn9YdQlWdFbf8dyWe+mlTwMeTTqW8eGFPpCca8eolvcV5m5WPnenX8YRF89Ou7o+FD4xGRkNfowk9CjQfo3erTJzRJbDF6B/dMBA3Dy/CJQMKNe3fs6XnBfpa+ssThQrL3MU54Qs6VDV71WrZP3VuN3y/5iDWHSgX66DuO16FGsmqFIvNLqYjO8QmhhofU7KjUa9Hv9bxkR4cLcJxGr1y70kAvgOWFpvdLTiUaPIegGrs36N8Xb/461YMbKvtNXtRP20nKVpd3L8QzTIS0aNFelCPS0REFCitQaIDJ6ubeCRERETaSedcPJVYi0fFx6sx4pU/kJlswntXuwIqVh8Btx2HK2VzRoAz20jq9Tnbxcvmhvmk5pISg2p9jKSb9hyrQpGfbRMcDgf+3HFM0cLBIJtXyEkx43hVPSb0aI6WmUlIMhlgMujRo2U6Nh6scDumtIKP3e5Av2fnAADeuqIvzuvtu91EU7n/G+fi15d+3YpNh8oxe/NhAMDk0R0CWpAqfY4Gt8/BuifHyd43iSaD2t2g16m3G6ioUQ/AnNu7BX7dWOpzPGO7NcO/r9WWlaRmVOd8Z3k+jXQ6Hd67pj9u/WSV2223fbYa/73xtIDHQhRMDCbFsT3HqsTmgtYQ1bNSy4C6fmgRfl5f4hxHQ3Brw0F5Gm+txeYKJjVs0xr+ksav2OeSNh4sxyfLnCXjhNe/J/VWh1vAyVfgtbFthrKSzbLr7y7YhXcXaLtva40Zglrp9TqM7OReyo+IiCjSlZaHf8UuERGRGvZGcVm44ygAoKzaIsvA8bbw83hlHc791yK37adqrSgtr0VBQ8Bo2e7j4m3ZKc7f2TqdTgw+qFUVkZa533200mcwyWqz49XZ23GwrAZtspPxrz92uu0zoE2WLOtm3j9G4XhVHYpyU2THTzCqB0ukQS/pa+euL9ZgaPsc5ISh5+7XK10l+Q+W1eC9Ba5y/DUBZodL/8l1Op3mRe/f3T4UNrtD1v8aAAqz1MvLGTXWk1O2PAiFUZ3V518Wbj8a4pEQecYyd3Fs9b6T4uV9x0OzelNZJ7Z3Q58lU0PKsXDyoDxxqLVITrYabtL6xaKWDUWRI9T/Ov/+U3vPIavdVeZuYJ4dn97ovirlH2M7ya43vsay9vvPuGuY7Hpjep8RERFFu4cmdBFXHH8p6TsoFY6JASIiohaSjBhmJrlIFzYfLHPNS702e5vH+3z+V7HH2x7/caNYAq60wrWw5PqhbcXLwiJntZ/u0t/zWn7ar9x3Eu8u2IWf1x1SDST1bJkBo0Ev+5WfkWxCu7xUt30TJGX4M5Jca/9zUlwLTpVjKgnT4pkHvvFckr/OGlgwSVqm0NM03oA27lVbclLM6N8mC69f2hv3j++M7i3ScdmAVjjXQ9aW0aAxmBTE/llaJRgN2PH8hJA/LpE/OPUYx2ySD8ZQfUierJbXpBV6LQl9Yn5edwiA+8mVtBGj8AUTSJm7xvSGoqYR6q9nf3oo1Fvt4vtkaDM7BhVlu+1z6cBWbvdpDK1vxdtHtXdbJdVP5cSKiIgoHkzoUYC/jWyPJ8/tJm6rVNSXr7facebrGtN9iYiIgqh/myxxEe2+41VhHk3kkC4SfvDbDeLlH9YeQnW9epmy41X1Ho83Z/NhjHx5PgD5omRp1o+Q6SPNXBJIf49r+WleodJ3SUr4N+/czBk8MntZASrt6XzDkLbok+Mcf51kjsGmmDAIR5bb4QrvASzlGLVyKDKT1Hx+8+my69OvHyjOK17YrxCTR3fAjLuG46WLe8l6UkkZ9L6nwtMSjZjUt6XGkQeXyaDH9BsGys5piSIJg0lxTLoyM1Q9k5SEx1208xgAYPbmw3A4HPh8uXyliXRlg/AFwzJ35K8jFbWY01DHFwDa5XlPWd99tAqVtc4TWE8vnWbpibLrGUmmRo1R62lXstmAZLP85MjbiSkREVGs+fymQeJlYWHUuG6upsq1il4K/126F7uPcQKPiIhCT6fTYWBb5+LEuVuOhHk0kWPb4VMeb/tp7SHV7b56Hwu9lNITvXf2uPvLtV6Prayso6bWx2JSY8Nv9L+NbI+rBrXGh9cP9HlMALj29NbihO3zM7eojg9wP9dpChZFwOr9hd6rvfj69wGAt37fgUe+3yB7jqWL3D1lJkkDbgAwuov2nkSu8Xn+NzPqdVj7xFhseGo8zu8TnmASAIzunI8bhhaF7fGJvOHMYxyTrhZo72NSPZQ+XbYPa4rLZNuk30XisLWWuZPsx1hS5GnqfxPpycnb8+Rp5y9e2MvrfbcdPiWuevJWLfGxs7uKl3NSzfhr93EcPVXncf8TVfVYuP2oaqkdrYt4hKDVhZLVMkYGk4iIKI4kmg3o1zoTAHBxf2emsF6vEyca6hQTPAdO1oR0fERERFKDinIAQCzJSt5L1m0uqVDdbtVYsrZjszQAwIsX9tS0/5FTtVi+19VXWcuj1PkI5ggLPnNSE/D8BT0xrGOux32lf1aiSY/Vx533lVY/UQZqmrplRUl5DTo++ivaPjQDh8qc51G+5nB8zWnUW+14fc52fP5XMW75ZJW4XfqnNWVVIWlvLqXXLu2NTEUf63Ca1MdZqs/QhO07Nh4sx+xNpSivtmgKoBJx5jGOSSeyz+2lXku0qXXKd68Tu/1wpds2eaqxfx9u0u8glrmLPE35VXXm6wtQ9PBMzNxQAsD9pLN7i3TNx/L23X3T8Ha4dUQ7AMCCbUdx2fvLMPzleR73v/vLNbj2w+X4pWFcUlpf32c3vGdTJauttDaSpMjG8zciIm0sVjs+v/l0/HbPCIzt1kzcnmBwn3xR8/nNg7zeTkREFEwtMp1BpE4NQQ7yLtHkLE332V/7ZEGnL5Z7DkAJHA4HVjX0CS/QGLybNn+X4hi+7+M7M0n7b/TDkv5HJsVC0ZJyZyBHGUx65ufNmo8fiPcWuLKQnvxpEwCgdU6y1/v8st59nkPqZLWrTKG0cow0M0nL1N3oznm+d1KhfA6FgA0At+ov4XZWj+YAgD6tMpvk+OXVFpz3r0W45ZNV6P3MbHR5fJb4WiPyhMGkOCZ8gJ7dqzn0YZqEPlPyw19QqlJ/1e5wYO3+Mkz+bDX2n9C2GkIgy0ziXHvcOHCyGjuPOAOTt3+2GgAgPR8b3jHXYw1dNSk+dhXeQ1tLnWn60vrMSn/ucJZ1/H71Adl2h8OBD/7c43Msr1zcC9kNTTilK1SUJ5wUncLR6JOIKBr1bZ2FRJMBnQvkk3IJJiEzyftq4SHtPa8OJiIiCjYhsMDzfW3MBj0qai149PuNeOT7DThV670/kVTRwzPFy0c8VA1JaMhkPnCyGme9sRDTF+9V7OH738lXZlKqH3MOwxVZS5e1cx1bWHStDITUNHGZO2mWtxD4qfMy1wEA7y7Y5fX2jQfLVbc7JIf1thD8pzuG4tIBhXjpYu+VZjwZ2sH1PBekJ+KNy/vilhHtMKR9DkZ0iqxzQ2G65+DJGrz1+w6c8NIvLBDHq+pkGWF1VjvWKipFESlx5jGOCZmdhjBGWNRqqSpLkgDOFSGTpi7GjA0lYqqz1mHLg0mMJkWapvoXUXttSV/r3VtkaD7W9Ov6IyvB+z6BZAX9se2o7PrcLUfwzaoDHvZ2kQaNpJf9WfVEkYs/LomItFHWzRe3a8xMIiIiCiVhbkJYXEjemY16WCTf5cNe+gPDXvJcAcSTmnp5wOXZST0AAK2ynRk2/1m0R1wU6i9fPYv86Wvcs9A5R5GZ7CxpPzjf9bvwug+XY/meE27nNgPaZGk+fiDU+gtV1Tv7Sl9xWiu/j7dy7wn838crZduEf5+yGlegxNvMRq/CTLx8cW/kpwVWLlLa51qYxnlkYld8fvPpSDAaAjpmUxHmMEsravH6nO0Y8fIfQe2TpVYysrq+6ftwUXRjMCmOCWXumrL2ps8xqEyaLtzummDPS3PO4KuV/tJpDEP0bpUBo16HNj5ScSm2PPvLFrdt0mCiSWPgpWN+KoZ1yPG5XzBKKB48qa3esTRoJMtM0vMjPRZoLAFORBTXvPX7TGgoi6NcoCRtID24ne/vdiIiomA6VOaqwuJPlk28Muh1st/Z5TUWj/0P0xI9ZwBdcVpr2fWuDRnNO49UYtr8XSj20HfIpmFNitpiaKm8dB+rUiXO7tkcUy7siU9udJbhVU4xXPreUrw+Z5tsm7Rk3J5jVZgrKRsXDGrzbkKwIS3R5HabLxe/u9Rt21vzdgAALpq2RNwWqhYVkb7gXFlyrrLOioe+Xa/pvqdqLdh/wvsck9rCq5X7TqjsSeTCmcc4ZmsI5ISzj5CvslzCPLlagzytwy7MSsaSh87AzLuG+zs8CgHpv+wl7y5BdcMql0Yd0+HA3C3uJ1HSwIvRQ+DlFUWqtNb3h1pm0suztvq8n7QET/PMJL8fS3rZZIzsEyHSxs5oEhGRm4cmdJFdP8dLv09PmUkdJb06x3V3L7VMRETUlKSLadUqaZCc3e5vx2x1ykxm6bzAS7O24vetR1Tvt+uoez9vpa9W7Pd6uz8VTIwGPa44rbWYoaTmh7WHZNfLa1zzJ6NfnY+b/rsSS3cdx5tzd6DtQzMwa2Op5scHnHMpe45VoaIh2Jme5ArSCVMjVXXOx0w2GzCik7NvUd/WmX49jtSaYmdvq2OVksykEE1tRPp63FO17vNjyteAJ0NfnIfhL//hMVgKuDKTEox69GzpfN2drHL+2ztYMYU8iPC3DTUlm5iZFJ7Hv+K0VujSsCJEOUEgEFZB7Dri/iXuz3dLfnqiX/1xKDxW7D2Jz5b5bqbpi1BPWEkWTPKQmXTJgFZ49+p+4vXjGmvSCj2TpI/xybJ9Pu/32uzt4mWrStBUjTQQpiVARtElJSGyUuuJiCJBf0UZF2+Z9Z56JiVLzgUjrYwJERHFPukEOYNJvtkd3p+nni0zMLRDDt64rI9fx9Xaa/hEVT2+XXUAVpUUJbvdgep6K5qley+11tRtJepV+kP+urEE/5zrnGf426er/DreK79tw+hX56PXU7Mx7KV5+Lekp3OHPOeinKW7jgMAUsxGfHT9QGx55ix8f/tQzP37CHFffwIRy3a7Z8KEKmPIYo3s92FjStpVNASi5qgstBYIr+2CjESc38e5UGvWplK0fWgGOj32Kz+nSBVn1+NYOMvcpSUYMeVCVwbI30a2x77j1fhiuSuQ0Ld1JkrLnWng//jfOrdjRHg2Kmmk/GesCkJmklpWEiDPMvJW5s4gCcocq1Rv1qkkrDiSftmeqrWi3mqHyaATT4aUWSfCaxwA3v9zt6bHkr72pWnnWkv3UWS7f3wX7Dpa5VaOgYgoninnJLydvwqZSfO2HsF3qw9icPscXDWojew7+JzezZtknERERJ5Iv4c4Seub3eHwGpR475r+aNFQ3ePxHzd63EfJ1xzY4HY5WLr7OD5ashcAUFFrwQ1Di2T73PjxCqzYc0K1f2OK2YCqhlJwQundplJRa4Xd7hAXtwLAf5fKF7Xa7A5N837frDqAd+bvEq8rSwruaFjkvftYFQDgYFkN9HodkszOvzFfElirrLMGVAYv1CK973S/1uo9sWotNiRqfG0d9zKnJVSBMup1bq9li82BXUcr0alZmsbRUrzgMvY4Fo4yd/+5bgDa5CTjoxtPc7stJ8Usu/7BtQNQIploV9LaM4miSzBOql/5TV5HWHiJ56a6XmMtMz330PInFV3g6eSs02O/4uJ3l4onwU/+tEl2u1XS0HLd/jJNj5UkOWno1SoDOh3QJic5Kk7WyLe8tAR8e9sQXNy/MNxDISKKGMrJJG+TIicasoo/XVaMX9aX4NHvN2LF3hOob1h9eWbXfKTzO5OIiEJM+lNXrfE9OQm/xx0Ohzhvpbqfl0CAyaDDpqfHY3z3Ao/H90QIjgj+3HHMbZ/5246iqt6Gk9Xuva8eP6cb7h7TEV0K0nDN4DZeH8uXs3u4j1+p3SMzvfbGUWZqe3KfyiJupd2S0n/KrPE0SQZ4mcrzEonl3LsUpId7CF6N6pynur3L47M0H8PbHJswH2Uy6FFncc/AC0PuAUUBZibFsZqGlRJaU3yDYUzXZhjTVb1GvTKmpTYxXpSbgj0NqyAYS4pNTbFCa1iHXABAVrIzmJRo0mOCl5OyQLL1vN1n1b6TqKixIiPZ5Fb6rr4hrVrr351o0uO0omzx+ujO+Vjx6JlISzSGJcuQiIgoFJTfkt7KxlzUv9BtYckl7y4VM3hDee5LREQksDEzSRMhgOSrzJ2nMu9vX9EXaYlGj60O9L6CSYqMD7sioOWrhNvYbs2Qk5qAe8d28rqfFgUZCarbzUa9rDfk8Jf/EC83z0iULcyutdiRLF+7HbAzXlsgXp7YU57lrdPpkJFkQnmNBcNf/gPvX9Mf4yTBPOXzKLVkp3vALhQCWUgcSoGW+5O+Rt9buBsPTegiO9bKvSfw8Hcb0K2FM5hmMuhx+Wmt8PzMLbLj1KoEmIj4SyqOCSmqHSTNiMNJ+RGpli58Ti/Xl1Vkf+RToBp7Ur3+QJnbNuEEQTh5Gdwux+sJZE0AdWl9BXLqbM5jpihWOc3dchgOh0OsOwwAt45s5/E4j53dDUbFJFhuagJ7PxARUUxTzj94+x5PT1LPOhJKeaidYxIRETW1Kwe5yljHezBpwfajeOCbdWjZUKauewtXhojwne8sc+f5GJ5+g5/buwVGdc73eD9f7XyE3osC5b/VGpWKImdKFk0rf683hqe/URpIUjpVK28doDUzyV9qY5OeY93yibxfk7cssys/+Eu83C43JQij0ybaFuSaNb62lJmP9ZK+X0//vAkXv7sUO45U4se1hwAAVQ1lCYd3zJXdb8eRU40cMcUi/pKKY0Ijt7TEyEhQs0g+7B6a0EV1nwT++I85ytOJxp5U7zhc6batYe5IPGn0tbpjgCJdWwtf5SJnbSwFALF2cutsV5m99QfKUVLuqkf80Fmu17/y3CaUZSmJiIgihfL8wFuJe5OPiQHphBUREVGoNEtPRHLD4sJ4L3N33YfL8fXKAzhY5vwdLM0iKsxyBpjUMpMKJH15Au0ZLP0trkZZ7qtU0X6hosa9hFvLzMaPS00zyd+rVWWdPJgUyuwSb8EOZSzJ0/nYjLuGB3NIXkXb9MrHDS1DfCUFWGzyf3OhMtX2w6cwffFet/2FPljK+aaTVe6vdSLOzMcx4Us5UiLxPzVExAHPqab1Nte3z66jVU0+Jgo9b6tVtJCehPYqzHAes6EOrF3sE+b9GDmprlTydnnaVsX4eh9tKakAAHHllfTL/caPVuD+b9YDAIZ3zIVOp8MFfVsi0aTHN7cNwfVD2or7RsjblYiIKKS6KSYcvH3v+vpO7tEiIyhjIiIi8pdQQi3eM5OUpP12hN/Maj2TkhNcFTkCncsyG/XY9PR4pHoog1dWUy+7vuNIJayS3+/JZvf79SzMFC97Kr8XiMsHFGJMF89ZVlrUBlB5ReofYzvhDI1j8LYAvKJWHpj46tbBqvslmkI3VR3pZe6UhMwvb5lparf/Z9EeAM6FzP4o9tKLi+IXg0lxTJhY91ZzPpSEFSmA51r20pULyi8iik7KV19jmzJKTwiF807hRF04sj91Z/PT1GsUK/l6H7XLda4cEYJIwmorADhe5TpZFW7/52V9sO7JcejXOgtjurpO3JiZRERE8Sg7RV7s39vpgq+eSCZmuhMRUZgIARCh8T05SctwHausAwCs3V+G71YfkO13UvLbuTFBm5QEI546r7vqbRar+0nGw99tEC8rM496F2bgnF7NcVG/QvxjbKegltM1G/X4z/UD8fJFvdxuu2+ctp5MdT4CD4C8x07Plq5FN5/fPAh3jumIZ85Xf66UkhRl/aXHlWZ8jeyU59abShBon6BAtM+LjLYfWgnBOl9BHmUwaXFDT6pSSUUcNa2yk2TXD5xkMInc8ZdUHBMm2H01HwwHTytM0hPVa+BT7GhsZpL0xE0IzAivda2ZSQAw/YaBOL1dNl65uLemx/X1PqpXjMVTDectJa6atEIfpJwUV0BLuhKLiIgonjTPcJV68db82tdKZV/BJiIioqYifEcxliTXMT9NvCyUZftrzwlM/WOXuP2XO4fhZLVrUbH0+/6yAa0AAGd21Z7F46kc3Znd3I/xv1WuoNbOI/LS+klmAxJNBrx2aW/cOaaj5sf3x6UDW7ltu+MMbY/lLTNp55FK3DB9OVbuOylue/6CHujTKhNvXt4HQ9o7e+gI2WKCVY+dqXo85eKfXxvK/QPyMncvXNgTBr0OpxVla/obgu3LW07HTcOKcPMIz/2qI1GGpC+ospSdVL3iNiFA56vk4e2jOmBQUTbGdXP2ACtXKelIxF9ScUxY0RkpmQ7SYQgnBUsfPkNWZiyYtWcpMnn5PtREOrkkfLmu2Os8MRJe8zq3fCh3ozvn48tbBqOVj3rKAuW8VHqiEW1yXPcVxiL8f3z3Anx20yC346h9WXdtnoanz+uO+8d3ljX2JCIiiidazwJ9nS/yfJKIiMJFmH9xuHUPjm9GyXfzoHbuAYbc1AT0kGTMpCt6f/99XCdMu6qf5sWggOfMpuskZeYFQhm+7YdPiSXqBUdP1Wl+zGA5r3cLn/sIZf8ra60e97nlvyvxx7ajuOTdpeK29nmp+GHyUJzfp6W4TZktJG0NIKUMXM3dchgHTlZjdfFJcXFvaoJRDE71aZUp2/+S/oU+/qrgOL1dDh47pxsSPWRHRaJm6QloIQnqbSs95XFfZTbaqn0ncc+Xa1Aj+fe590z3zLYWmUn46tbBuKwheHn0VB3OfXsR/v712kaOnmIJg0lxTCxzF4GvAuFrqnlGEkZ2yhO3R0p/J2o6jS1zJ727W18tITOpCV7zyqCsMujzxtwd2FpaIWYmmQw6DO2Q67bCR41Op8N1Q9pi8ugOUXWyQ0REFEzSMwRpeWQlg48vem/NoYmIiEKBLZPkhnVwZsDkpJiRlWx2u11YNDrtqn4Y2iEH8+8fLbs92WzEhJ7NkZXifl9PjB4WlyQYDZh6ZT/Ztja5zoWiczYfdtvfYgv9P+Y/VErc9WudKbsutACoqvccTNp9zL0XuacM7gFtsgDAa/+kQUU5suvfrT6IYS/9gQvfWYJbPlkJQL6QvLJOPrZXLtEeDIw3Nrt8TlSt1J3D4cDhilqMeW2B220/rD0k9k4CgJuGF4nBvFGd82T7Cq+BvcerseFgOb5bfRCn2GqEGvCXVBwTy9xFSmaS5HKCpOGeNIuEZUli35Ldxxp1f+kKr6EdXCcyNrvDlZnUBK956fuoa/N0PDyxq9s+z/y8GRa7EMR17u9tMoyIiIhcSsprxcsLt3s+X/D1LW/k+SQREYWJsN7BW7nWWKf2t1fWWbHuiXFY+vAY2YS3oF9DIGNCz+b47KbT3cqpBUJ5vnBJ/0K8fLGzN9GITrkoSE8UM5LU+igJfPWvCZZzJdlIanNjT5/XQ3ZdmHMQe0g7HOj06K9o+9AM/LzukMfH8ZTB/d41/fHkud3wmpeAz+D2OR5v237YWR5QOndSXec50EVytobamMLC5RV7T7jtU/TwTAx64Xefx7ptVHukJBjxxmV9cN+4Tnj+gp6y29X6frHkHQn4SyqOCRPrkZLtM0mSQjuuW4F4+exersuRMlZqOvtPNC64IpyXJpsNstTvOqtNzMZrileRXXJCfMfoDshLc0/7XrLruNgIkYFRIiKiwGUkB95Hk6eTREQULsJi2XjOTFILFrXMTEJGskl1EhtomvJnykWmD07ogksbei+lJZqw+KEz8J/rBwLw3p9mRKc8j7cF0yjJ4wjzCdJKPvnp8jkIIdjw+5YjAIBTdVaxl86dX6zB1tIKt8cw6nUeF9/mpCbghqFFXrO/pD19PJHO64UjqytaCf8upRXOOTMh0BmI3oWZAIC2uSm444yObhVz1N6HB086H3fh9qOYt9U9Q4/iB2cz45g9wjKTnp3UA3ed0QE/TB6KFMmHYk6K6wvRyF//5IMQ1GmTk4JJfV0Byup6mxhoaorX/KZDrhMxIU5U5WWVDQOjRERE/unWPF283NpLT0Nf0xJaeicSERE1BSGL5WRVfZhHEj7Pzdjitq17C9d3/D1ndnS7PRTl3g2KeQKDXidm6QhBmFd+2+Z2P7XxNgXp+Y1QsvdVSZaQcsFqrcU55hkbSlBrsaG2Xt7P6NZPVrk9hqfSf1qlaAhwSKdCpFWJSN0bl/VBVrIJ713THwDQv7UzS095vqssGQg4+yxlp5iRoAgOqS1+llIrCX20sg71Vjuu/XA5bvxoJcqrmakUr/iujWM2R2QFk1ISjPj7uM5uDfikKxuSJV9McZwVHtOkE0WBsIsBI2etY8Ga4jJ8tGRvo47tjTQ4JLw2j1V6/oGQYg58FQkREVE8kq78Pb2d5zIqvpiMkXHuS0RE8evFWVvDPYSIIi1B26sww+12TxlLjZFilgeokszuASshQOMtMylUvRil4xWCMDkpZhTlpqAoNwWZSSaM7qyeJfXxkr2osciDSfuOu5fnEwJQgdJSflCa+fTA+C6Nerx4MKlvS6x+fCwGts0G4Hr+lHOiVpXXaHZKAn7/+0gsf+RM2XZlfy0ltfdbvdWOWqvrNXTmPxeg2ks/LopdDCbFMbuid0ukykox49nzu+Oxs7u6pV5S9FPGBFMbkaoLuDKThCBp52ZpAIBFO46Kq8DWHShr1GOokWbN1Xs50RQIJ6q+VoQQERGR091jOmJSnxa4dWQ7XCjJPlZS9mJ4VVHbv3kGzyeJiCi8Stg716O81ES3bU0xazW4fQ56tEyH2ajHN38brJr9JASTrF7KsYWqhP3oLvm4f+DNtwAAXHFJREFUdWQ7vHxRL3Gser0Oc+4dgbl/Hwm9XoerBrUBABRmyc91vlq5H6uLT/p8jMYGxjKSTPj85kFIT/Q8ryOdgizISMT7DRk3rbJ5fuaJNAAnXLQrznetHmpnZqWYkZFswk93DMXITnmYdc9wn33E1V4HFpsde45WidePnqrDF8v3q96/vNqC/SeqVQNcFP24ND6OCR880dC65ZrBbcM9BAoR5ReivxxiMMl5fWTnPGw7fAofL90n7uPtRDBQ0i9jX3V/pQGz4R1y8d2ag0EfDxERUaxJMhvwxuV9/b7fxf0LsePIKby3YHcTjIqIiMh/niZ+CeipkpmkJePFXzqdDr/cOdzrPkKZu+NV9W6LVUIt0WTAwxO6um2XZnWN6ZqPb28bjPZ5qXjyp034ce0hAMDuo1W496t1Ph+jc0Fao8c5pH0uhnbIxa8bS1VvV1ZHGtutGb7522B0yE9t9GPHA6Fcs/LVaFP5TKmRZA71KszExzeepukxMlV6k9Zb7W6l9OZvO4L/G1Yk27bxYDkmTV0Mq92B/m2y8O1tQzQ9JkWPKAgjUFPZ25DS6isiTdSUlK8+WyNP0OwNCx+E17Va5l2TnARKjumr6aQ0ZVivGJ+0VjQREREFh7IPAhERUTipTfySy6anx+PeMzvh72M74enzuqNjs8YHOQIh7QF03EOfK7WJ93DR6XTo3yYbmclmnNOrhd/3TwxSD6MhHXI93qYMJul0Ogxo6xwz+SZMISmntdRKMaZ6yRDzJjPZjMfOlgcu66x2tyo8f+445nbfNcUnxWD5+iaoCkThx2BSHBO+JEJV35VITbDndoTMJp2HL1hAvRZyY103pK14eVRDneLHzu6KnBQzbhnRTravtCSecnLrvxpXihAREZE6k0qd90gv60xERPGlsRU5Yl1KghF3n9kRd43pKPutHWrN0l0l97aWnJLd9vnNg/DvawfI9okkCQH0mdp8qCIoj33laa3dygwLuL6ncVxzXfLPELWe3Z3yAw/C3jS8Hfa+eDYu7l8IwNnOQUuVnxNVFvEyMzBjE6MIcUx4T2c1QbowkVbKc+jGnlcIhxNWu6jVaL1rTMdGPoq7nNQE7JkyEXtfPFusmXzT8HZY+diZSFLUXj5yqk68LM1M6tEyHTmp7KFERETUGMNVVsMqV8ESERGFE+dYo8+dX6yWXR/SPhdjuzUL02h80xpM+sfYTuLlqnpbUB7boNeJQQglnpM1jlCFR/kRMmXmFvHyq5f0xoV9W+Kxc7o1+vGEyjq/bijFvuNVPvYGTla7gloOB2Dnh13MYTApjglvaCNXalIUstrsqLW4n+goeyYpv7d+mDwU5/fx3LS7MdRKRup0OszdctjjfaSJgcqVTkREROQ/o0HvVr+dmUlERETUGCerXRkX/Vpnhm8gGiWY1CuytM5Oll0/r08LFOWmAHD1iGpKauXYSDvhX0iZmXTgZI14+eL+hXj9sj5B6TUmVLPacLAcz83Y4na7cgH3CUU5SIud/96xhsGkOOVwOMR0w2hdFRClwyYfEj2c8CidP3Uxej89G1WKBoBC8EgI7CjLB/Rs6d7Ms6ndeYbnTChpmbvhHT3XFSYiIiLthrTPAQB0a+7sRdgUjbuJiIgCJUzeE3D7qPbhHoJXZ/dq7rbt39cOCMNIgkPZCycjyYSvbx2Mywe2wne3DW2SxxRaAQBASXltkzxGvHDNdcm311mDk1Wm5CvDbfGu47Lrv24skV2X9odjr7jY4FcwacqUKRg4cCDS0tKQn5+PSZMmYdu2bbJ9br31VrRv3x5JSUnIy8vD+eefj61bt8r20el0bv99+eWXjf9rSDPp+5eZSRRJpA0uvdl0qAJ1VjtWF59ERa1F/FKyKzKTrIpVEOFYmZyX5rl0nbTMnUHP+D4REVEwjOnaDJ/fNAjTru4HAJjUtyXO7tkcz5zfPcwjIyKieNa5mbOHyQV9m6ZaRrSZf98o3D++c7iH4ZWwMEUqGsrTF2YlqW5vl5cqu56ZbEZeWgJevKgXehY2zeJbE3u1B420P/jGg+U4642FuH76clTVNU0wqc7qnlnUPs8VDC+TlLXbe6wKFkVfpS+X78cPaw5i5oYStH9kJka98odqOwqKHn69mxcsWIDJkydj2bJlmDNnDiwWC8aNG4eqKlfNxP79+2P69OnYsmULfvvtNzgcDowbNw42m/xFPX36dJSUlIj/TZo0KSh/EGmzaOcx8bIhBGmsRFqlJ5r82n9rySn0emo2rvpgGQBXoFTIuPt0WbG4b7gmkLx9UUozkxjXJSIiCp4hHXLRJsf5Yzc1wYipV/XDtYPbhndQREQU1/q3zQLg3js4XrXNTVEtFx9JlJPpZ3UvCNNI/JObmoABbbJk2y7s2xLNMxKRluhcxPvp/w0KyVj0OkR0f6loIrxb7A4HZm0sxdbSU5i/7ShqVNpABINaMKm0vBYjOzmzzayS4FHxiWq3fZ/5ZTPu+Wotbv/M2XNs7/FqfLFif5OMlUJDWwpAg1mzZsmuf/TRR8jPz8eqVaswYsQIAMAtt9wi3t62bVs899xz6N27N/bu3Yv27V2pq5mZmSgoiI4P4Fh0w/Tl4mVDhH9xe8KTr9j07eoDeO3S3l73kdaGfb6hyeCy3Sdkt6m9rMM1gZSeJA+Qtcp2rRCSBnPZz4GIiIiIiCh28Rdf9DlRVSe73iYn2cOekedfV/bD6VN+F69PuagnEowG/PnAaNTb7MhPSwzJOPQ6HZJMzE4KBmmrkqp6q5c9g0NtcXRVvU2scjVn82Fc1L8QgO+SeIKf1x7CNae38brPqn0ncO9X6zCkfQ5evKiXn6OmpuRXMEmpvLwcAJCdna16e1VVFaZPn46ioiK0atVKdtvkyZNx0003oV27dvjb3/6GG264weNqhLq6OtTVuT68KyoqAAAWiwUWi0X1PgTxuVF7jqRl7hw2KyyW6IvM2O12/vvHAGXWIqD+mpXdx0Od1b99shKjOzv7DukajtMuNwW7j1VpOq4n3t5LWnTITcIjEzrjhV+dZUFNep14LIekDJ8ODr6mKeY19v1ERE58LxEFB99LRMHD95Nv9obffzabjc8TouO1kp8q77uYYNA1+biD9V7KSZb3pNbZbbBY7Egx6ZBiMjT539EuNxm7j1VjUNtMtMxKwg9rD+G0tllR8e8eqaSfIesOlqvuE8zn1+KhF5MQZJ21qRR1dfXQ63Wy+T2jXgerxx5Jvue+/rdyP4pPVKP4RDUKMxNx64iiwMbP7yVN/Hl+dA5HYPkddrsd5513HsrKyrBo0SLZbe+88w4eeOABVFVVoXPnzpgxY4YsK+nZZ5/FGWecgeTkZMyePRtPPvkkXn75Zdx1112qj/XUU0/h6aefdtv++eefIzk5elYERJK7l7riiK+fbkU0Vbp7db0B+6t0uKeHFUVp4R4NNdbSwzp8uVt+gvPmYO+rK6x24B9/eY+FN09y4KE+Niw/qsNnOw2ajtvUhPddsyQHHunj/JJ9fYMB+yqdb8CeWXbc1IW1Y4mIiIiIiGLR17v1WHxYj7MKbZjQyjkdV9Uwh5fiX8X3qPXSOgMOVeswON+Oy9tH/u/fwzXAC2td8w/XdbShX270LMiWzv+Fek6ktBrYXq7DkGYOGHTA7lNAsyQgNU5e601hZrEevx3Uo2umHVvK1DOBgvnv/N8deqw6Jn+cPtl2HKvT4UCVcy7rxYFWJBld83vpJgdqrIDFoT7Z3C/Hjus6ud77O8p1+H6vHgernfvf2c2KVcf0WHLE9bhP9LUiJzSJdHGpuroaV155JcrLy5Ge7t4nTirgYNJtt92GX3/9FYsWLUJhYaHstvLychw5cgQlJSV49dVXcfDgQSxevBiJier/6k888QSmT5+O/fvVayaqZSa1atUKx44d8/kHxjOLxYI5c+Zg7NixMJnkn9QdH58tXt7+zNiIr1ErVW+141hlHVpkqjcTpOjy9coDePTHzbJtO54d5/U+NfU29Hr2d6/7CMcpr7Hg9Bfno3dhBr68+bSAxujtveQP4X3XIS8Fv941FAAw+vU/ceBkDQBgRMcc/Ofa/gEfnygaBOv9RBTv+F4iCg6+l4iCh+8n3578eTM+X34Ad45uh7vO6ACLzY5uT80FAGx+6kx8veognv5lC1Y9MhppfvYTjhaXvP8X1u4vx7tX9sGYrvnhHo4m0jm0qVf0xrgm7v8TzPeSdOy+5loo8r01byfe/mM32uYkY+9x9x5FNw5pg4cndA7a483ffhQ3f7JGtu3P+0fgji/WYd0BZ2bUgn8MR4vMJNlrLSXBgKo69aymywYU4rnzuwEA3p63C2/9sUt2u07n3trksgEt8VwAfdD5vaRNRUUFcnNzNQWTAipzd8cdd+CXX37BwoUL3QJJAJCRkYGMjAx07NgRp59+OrKysvD999/jiiuuUD3eoEGD8Oyzz6Kurg4JCQlutyckJKhuN5lMfCFo4Ot5MpvNHm+LRCYTkJLk/nqg6GQwGNy2+Xpf12rsK2gymZBrMmHj0+NhNuihb2RPomB95hj0evE4spJ9Oj0/0yhu8DucKDj4XiIKDr6XiIKH7yfPDHrn71+93gCTyYRyycLpWpsOT/3s7Al8+osLsP35CWEZY1MTfgInmKPzdWIO4es72O+laHy+SU6nc2brqAWSACAzJSGo/85ndmuOX+5MwRtzd2DulsMw6nVolZOGyjpX9tOaA6cw+ct1svsZvCQt2B2u16IykAS4B5IA4FilpVF/F7+XvPPnufErmORwOHDnnXfi+++/x/z581FU5LteocPhgMPhkGUWKa1duxZZWVmqASMKvjpJvcsPrh0QxpEQBcbuZyZ8osk9YBVOvVtliJfrrK4/JsBEUSIiIiIiIooiwi8/u+Q34MwNJeLlepWm97HCanP+zY1d7Bku1ij7tzmzazPM3XI43MOgIPnvsn1eb6+3Bvf1qdPp0KNlBp4+vzsGt8/BFae1AgDUWlyP8/7C3dhcUiFe79s6EzuPVHo85jerD2Dp7uO45vQ2msdRY9G4qpyanHpxRQ8mT56MTz/9FJ9//jnS0tJQWlqK0tJS1NQ4SzTt3r0bU6ZMwapVq1BcXIwlS5bgkksuQVJSEiZOnAgA+Pnnn/HBBx9g48aN2LlzJ6ZNm4YXXngBd955Z/D/OlJ1qKxWvFyQwYKTFH2s/kaTIsRv94zA5NHt8dg53cRtz0jSdO0MJhEREREREcUs5WJ96U/Ax37YKF7u3Cx2G0QLv3uNURpMyk+Prnm0YR1ywj0ECqKyaovX25sqEN0yMwn/N6wIyWZnXsrNw10JJltLK2T73jqiHWq9BH8cDuDAyRpM+XWrz8dtl5sCALJMKAovv4JJ06ZNQ3l5OUaNGoXmzZuL/3311VcAgMTERPz555+YOHEiOnTogMsuuwxpaWlYsmQJ8vOddVBNJhOmTp2KwYMHo0+fPnjvvffw+uuv48knnwz+X0eqpN/XnQti9wSFYtdJH1+eAPDspB4hGIl/Ohek4f7xXZAuqX19Tq8W4mXGkoiIiIiIiOJAw48/TwsKtx0+FcrRhJSlYbLbEKXBpH6tM8M9BL9cflprXH16a7x/Dfszx6K0RHnRsboQZfBcP7QIE3oUAHCVrhQkm41IMAanQlBWirM1y/oD5fj3wt1BOSY1jt9l7rxp0aIFZs6c6XWfs846C2eddZY/D0tBJrzJk80GmAx+xROJgs7f+Mmhshq8MHOLz/1aZSUFNqAwYmYSERERERFR7FKGTzz9BMxLi902ELuOVgGA18yFSKbz0gsmEiWaDHhuUs9wD4OayMhOebh3bCeMeW0BAKBv66yQPXbzDPV5t5QEI8Z1b4bvVh9s9GP0bZWJVftOAgCen7kFN49o1+hjUuMwkhCHoj2lmGKLP6/CLSUVGPLiPMzbesTnvkZ99H28RWn1PiIiIiIiIvKDWs8kqaOn6rBy74nQDagJLNh+FPd8uQY19epBI5synYGI/DagTRba56Xij/tG4fVLe+O83i183ylIPMdVHUhN8Ct/BTPuGoZdL0zE3L+PELeZDDrcPrpD4AOkJhF9s63UaEKGWbQ2O6TYovbl4ykLcsKbf2o+bjSmzO85XhXuIRAREREREVETUWa1eCtOcfG7S8WScNHoug+X44e1h/D8zM3iNulv/Z4tM8IxrIC0zIy+yicUHxJMznJyRbkpuLBfYUjnej09VLLZiLvHdERWsgk3Dy/C9UPaAgD6eikRmZVshkGvQ3qSqy3E4+d0Q4bkOkUG/8KEFBOExR/6KEvNpdikdvJssztgNDTu9dnY+4dDi4zoauRJREREREREgfNV6bzeao/69gSfLitGdb0NeakJuG98Z3F7sHqqhMKtI9vhiR83hXsYRG4SjOH7fPA0r9ylIA06nQ5rnhgHwBlEvmRAIdrnpaLL47NU75OT6uyNZJZ83iWZDG4Lxe12B5Mjwiy6v5EoIEIaNd97FKnUst2naOiTJJVijr5YebT/SCAiIiIiIiLfhCCSr7651hgpBffd6oN4b+Fu1Ej6JCWYouf3b+/CzHAPgUhVOIOynvqHKbfrdDp0b5GBRJP6WItyU8S/IzPZjCsHtUbPlhkY163Abd9fNpQ0ctTUWNE320qNJvRlibamgRQ/1E6o31u42+f9Lu5fiG9WHQAAdG2eFvRxNTW+JYmIiIiIiOKHr1BRrPUV2nPUVdo9nBkV/urdKhOf3TQIrbKSwz0UIhlzGN9HanNY90uyD7XKSTHLrr9wQU+P+y7ecSykfaHIHYNJcUiYqDdw5poilPSE+YM/d2PDwXJN97uwX0t8s+oA8tISGCwlIiIiIiKiiORoCCP5zkyK3p5Jav7+9VrxcrT9Zh/aITfcQyByc7yyLmyPrax4tfKxM5GbmuD3cVbuO+n19kv6F+J/DQvHF2w/6vfxKbgYTIpDDrFnUnjHQeSJTXJC/dwMbeXtnjy3G4a0z8XXtw5Gq+zobI4ZbSfTREREREREpJ3yJ5/DRzAp1jKTdkkyk4gouil7JnkqY+f7ON5vf/r87mIwqbSiFrUWW8CPRY0XPTmlFDTCyhdOXFOksms8YZ5x1zD868q+2Pn8BNwwtAgAcFpRNppnRGkwKdwDICIiIiIioibn6pnkfT+rLTKDSTa7A7d+stLv3sZE1DjKEnDJCeHLE1HOKxsDzFoo9FE+MtlsRMtM1zzfh4v3BPQ4FBzMTIpDQtaHnqFEilCri0/iYFkttpee8rpfYWYyurfICNGoiIiIiIiIiAKnUywh9FXmLlIzk9YUn8Rvmw4DAB48qwv0LH1DFBJndMmXXZ/QoyBMI3HPKDIZtE80m4161FudZTyTzb6zjD64bgAmvPknAGDzoQrtg6SgYzghDglp1Mp0RKJIceNHK/H4DxvxybJ9XvdLToittFa+JYmIiIiIiGKfECLyEUuCNUKDSdIgV1W91e12T+X7uhSkNdmYiOJBokk+le9PACfYlMFxg4ag8vQbBqJdbgq+uHkQbh/VHjodcPeYjj7v17V5Ov4xthOAwMvpUXAwMykO2cWeSZy5pug0/75RSDDpw/ql2RSUX8REREREREQUO5TTMNGamSQtbzX1j114aEIX2e2e/qxTtc7A04NndVHfgYi8iqRASiAJiaM752N0Z2d2Vf822fjbqPZITzRpum9aojOM8c2qA9h1tBL3ntkJIzrl+T8IapTYmoklTYR+NIwlUSQI5HXYJic5avsiERERERERUXwTgi2Hymq97me120MwGu0cDgccDgcsNte43l2wy22/w6fU/66DZTUAgAQjpyOJAhFJ753yGkujj6E1kATI+0OtKS7DK79tE6/vP1GNdxfswqnaxo+JvGNmUhwSFrYYGE2iKKVs8hcrYvTPIiIiIiIiIkBWi+I/i/bg2V82e90/kmJJ9VY7xr+xEHuOVeGa09t43feDP/d4vb3OGkF/GFEUiaT5MGlQORS6t0iXXZeW2Dx/6mKcqKrH9sOn8PqlfUI6rngTOeFMChn2TKJI4qtGtFJGkvZVC9GGb0kiIiIiIqLY54DDZyAJAG79ZCVOVtWHYES+DXlxHvYcqwIAn/2Nq+ttXm8vyk0J2riI4s05vZqHewgAXL3fAPdAT5M8nmL+MFMyP3ii4XNyzubDTT6OeMdgUhwSMpM4cU3R6Ju/DQ73EJoMeyYRERERERHFLn/nYQ6V1+KlWVubZjB+OlZZ5/E2h2SWt7S8Fl8sLxavv3VFX/RoKZ9oNhn425coUJHSSU2amTT9hoFN/njKknhqSRJCXzZqOgwmxSFbw5e8IZBOaURh1rFZWriH0GQY4CUiIiIiIooDfswGHyr33lcpEljtrj9o4Y6j4uUHzuqM83q3QN9WWbL9jQZORxIFLEKiSVabayD5aYlN/nitc5Jl11lxKzz46R2HbA1FdxlMokgQy8EhIiIiIiIiIoG3fid9WmWqbi+rjowyd95IJ5UrGzIDdDrgtpHtAQDjujeT7W/kfBRR1JMGkcOBsaTwYDApDglZiIzgUiToUsBgkiCSGikSERERERFR01BOwbbLTcET53ZDflqC277rD5SHZlBe1Fm990Cql5S7qm3Y97zeLcTfuMpJZwaTiAKXnmQM9xAAAAu2H/W9U5A9dW438fJfe07guJfym9Q0GEyKQ0JmEr+8iYiIiIiIiIhCw9MsjE4H9GudheWPnhnS8WjhcDjw74W7ve5zqKxGvFxb7wwmZSS5+pvUWeyy/Y3smUQUsH+M64xBRdl4/dLeYR3HiarQZ01mJptl15fuPu62z6laC+xhzpqKZQwmxbitZTo88dNm1NS7VpHsP+H8ktczmERR5sJ+LcM9hCbFdyQREREREVHsczjkE53SKhWR1pJg5oZSvDp7u9d9Jrz5p3j58+XFAIAkk0HcJs1cAgCjntORRIHKTU3AV7cOxoX9CsM6jpuHF4X18QHAphI06vnUbIx/YyEsis8dCo7IyIujJjNtiwHAAdgcwMsXOyPWxxsix0cqIr+JI5FMjC8sYJU7IiIiIiKiGObhN580fmTQ61QnSMNlxd4Tfu2fYDQ0/N8VMKqzyMvkpSZyOpIo2t1zZifsPFKJCT2bh+wxHYqJQXtDYL5lZhIOSjIkdxypxKZDFehekBKyscULLgWIE1+vPIATVfXYVnoK9VZnZLZfm6wwj4qIpBhLIiIiIiIiil2Hy52LetcUl6FdrmuS02RwTc9FWkuCPq0yfe4jXRhZ2xA4OrtXC3GbIhELeSq9oYgouqQkGDH9htNw6YBWIXvMjvnyvusNnVyQkmBw27e8xhKKIcUdBpPiyL1frcX4Nxbiw8V7AADN0hPDPCIi/7JxImdtVtPQMTWJiIiIiIgoZv2w9hAAYOW+k7Lt0v5Chij8XehwuEr3CcGkRJNryvGc3vLMhWST+8QvEZEvPVpmoFdhhnjd1vC5o1NZnr2lpCJk44onDCbFkQXbj8quS1OOiaJBdb013ENoUj1bZvjeiYiIiIiIiGJKmqTsW2P6W+89VgVrkPuEWL2U3JPGveqsdjgcDtQ0BJOkPZOSzUYsf3SMeN1o4HwUEQVmVOd88bK94fNJrddcNAbmowE/veOYUMeWKFr8tulwuIfQJGbdMxwPnNUZt41qH+6hEBERERERUYgZ9a7pOemqe3/8vO4QRr06H5M/Xx2sYQGAW3DqvWv6i5elU7VP/7wJFpsDQuwpQZF9lJ+WiO9uH4LZ944I6viIKL7YJQFu4WKS2X2Ouz7IgXVyYjApjpkMjNASRYIuBem4fVQHJDLVn4iIiIiIKO5Y7a5Jz8fP6YbT22XLbhdKxynVWmywNEyY/mveTgDBX4SpzEwa371AXAj58sW9xe2HympRfKJavJ6k8vu2X+ssdGqW5radiEgru6QJm1DmbpWidCgA1FsZTGoKDCbFMbUUQKJIMbpzntu27BRzGEZCRERERERE1HSkAaBOzdLw5S2DcecZHcRtj3y3we0+J6vqMeiF3zH29QWYtbEE2w6fapKx2STBpIk9CwAAD4zvjOWPjsHF/Qvx5uV9ADgDYruPVor7cgEzETWFiT1dPdjsdgc2HSoXr0szJ21eSnRS4BhMimMMJlEkUGuSBwAD2ma7bbudZeCIiIiIiIgoDlw6oJV4+bs1B91u33m0EuU1Fuw9Xo2/fRrc0nZS0sykZ8/vAQDQ6XTIT0sE4CrRZ7E5xMnbAW2yoGO/EiJqAj1aZqB3QzlQu8OBylpXf3WzQY+bhxcB8JzRSY3DYFIMq6qzer1dzy92imAJRtfHU1ayCd/eNhg3Di0K44iIiIiIiIiIIoPFSwmnJ3/ciLGvL0B5jSXg41fUWnC4oha2hhJ8F/ZriZzUBLf9jA0ZSBU1Fvy07hAAwGTgdCMRNZ22uSkAnNlHssC1Dkg2GwEAtVYGk5qCMdwDoKaz9kC519uZmUSRTPplMKFnc/Rv456pRERERERERBSNjlfVi5dTE9yn5wqzkrzev85Lc/mPl+4DAMzZfBgX9y/0a1yHymrww9qDeHnWNgAQV/kbPcwhCeXstpaewtZSZ6k9I0vcEVETMjTMGToUlex0gNiP/GRV4MF08oxLBWKY2cdKEAaTKJKVlNWIl5VfDkRERERERETRpl3DanoAaJ6RKF42G93nb3yVifOWmSQIJEHoiR83ioEkAPj3n3sAAGuKy1T3F8rcSbESDhE1JeHz0eZwQPlxk2hyfibN2FDCvklNgMGkGKZ2MiJl4Jc7RbDZmw+jS0EaAOCCvi3DPBoiIiIiIiKixrluSFsAwMSeBchLc5WMswewgrLeS2aSIMnkf0GiuVuOqG7fcaRSdbtaFpKnLCYiomAQAuXKYJFOp8OQ9rni9ep6lroLNpa5i2PMTKJI98PkoThcUYs2OSm+dyYiIiIiIiKKYMKaXodDHkCya1g9b7M7ZPM4i3ce83kfRwjKfKj1R+J8ExE1JeEzRvkZpwPQMT9VvK4l6E7+YWZSDLP4eMPo+eVOEcBbglyiycBAEhEREREREcUE4eevwwHYJVM2WmI+tRb5Cvsvlu/3eZ/Hf9zox+gCo1ZGij2TiKgpiWXuFFPfCUY99HqdmB3pa26c/MdgUgyz2ryfjbDMHUWa3FSzeNkB1jUlIiIiIiKiGCKZh7FJM5M0RJOUwSQtjlXWY9/xKr/u42mq6LObBqlur1Pp3WRQ6aNERBQswkfmP+duR53F9RmUnmQC4Gr9Uq+htxz5h5/uMcxi9/6GCaQRI1FT8tVglIiIiIiIiCjaOeCQlbbT0iO+VjIp6k/5umOV9Zr37fjoTI9ZUkPa56hur1MJcgXSA4qISKtNh8rFy79vPSxe1jfMKwrBJIuPRAvyH8MJMcxXZpKeE/cUYaTnm2XVlvANhIiIiIiIiCjIpGXu/M1MkmYYHa6o0/yYamXo1NTU27xOvHpa/DmsY67btna5LFdPRE1HOuc9ffFe8bKQOCH0cmNmUvAxmBTDfAWT+rTODM1AiDRzvWZP1VrDOA4iIiIiIiKi4BLiMXaHPDNJSyLPlf/+Cz+vOwQAsPqoRCNl1dgzJNBG9clmo9u2fm2yAjoWEVFjNEtPBACYhWASeyYFHYNJMczXyUV+WmKIRkKkDcvcERERERERUazad7waADB3yxFZZlJGsknT/e/8Yg0A7dlGAGDVuO+WkgrNx1TKTjHLricYOd1IRKGXluj8LK2qdy5Q33vMv55x5Bs/3WMY60JStGEoiYiIiIiIiGLVN6sOiJelC+afOre7X8fRGiByPo62fafN3+XXGKTevbo/zu/TQrxuYpNuIgoxaXlNoXXGfd9uDNdwYpZ7LirFDH/SnokigTQxqUtBWvgGQkRERERERBRkDmmfpIYgzwfXDsCZ3Zr5dRxPAaK0BCOePK87dhw+hcW7jmHjwQpYAijzlGI2oKreJl6/dUQ7r/ufVpSNHi3T8ePaQ34/FhFRUHhYoV5nU99OgeFSgRjGzCSKNnpJNOlvI9uHcSREREREREREwSWdpamsc5ZhSknwf523px7Z713THxf3L8TDE7ti40Fn2bpZm0o1HbOkvEa8/Nu9I2S3PTShi8/7JxoN4mUtPaCIiIJJ76F1xvoTrIMUTAwmxbCTVfXhHgKRX6Qf/GbWWCYiIiIiIqIYIg2yHCxzBm8qai0e9/dUscPuIVpjUvkd/d3qg5rGJv09XpiVLF6+fkhbTf2N9XrXPhlJ2npAEREFi6dPqW3lDCYFE2drY9jhU3UebxvVOS+EIyHyTHpOquH8lIiIiIiIiChmbDpY7vG2qVf1U93uqWeSWq+ivq0zNY0jwcOCzq7NtZegf/WS3rhvXCd0Ztl6IgoxT5lJK44y/BFM7JkUw9IT3VeC/PnAaPy07hCuPK11GEZE5J30g79NTrKXPYmIiIiIiIiin7eKcO3zUqHXAcrYkc1Dj2yTwfWb+qpBrfHZX8UY3iFXdd8jFbX4ad0hXNK/FTKSTSirkWdI/XTHUCzfcwIX92+l6e8AgIv7F2rel4goUGpxI28L1B2svRk0DCbFMOGNct3g1hjULhcd81PRKjsZk0d3CPPIiNRJsuLRPi81fAMhIiIiIiIiCgFfv33VkpA89UwySzKTjA0/sC12BzYeLEe35umyUnTXT1+BzSUVWLb7OD64bqBYgu/uMR0BAL0KM9GrMNOPv4SIKHyk5TivOK0Vvli+X7xusTlgDsegYhDzvGKYcMKhAzCxZ3N0bMY0Y4psnlJSiYiIiIiIiKKdWm/gIR1yNN+/bUMFD5uHMndGSTBJmFidNn8Xznl7Eab+sVO27+aSCgDA3C1H8P7CXSg+UQ0AGNZRPZOJiCiSSReov3BBT/zzst7i9co6axhGFJsYTIphQkNGTtBT1GD/JCIiIiIiIopRz5zX3W2bwceP307NXJlLQjBK6JnUMjNJ1hNbWuZO6bU52z3e9sLMreJlT72TiIgihdpct7wnuw6T+rQUr68uLgvBqOIDvyFimLBOhZPyFMl0kgiSzsN2IiIiIiIiomjXNjfFbZtR731qTlrSTkhIEjKTslPMuKCva8LULMtMCmyMvsZDRBRuepXPN2WASafTIdHk/Dyrt6r3mSP/8RsihjmYmURRxiD5NuDLloiIiIiIiGKJ2u9cX7Ebq10aTHLIthn0OiQYDeLt0jJ6gS7Q9JbdREQUCfQq0SS1T65+rTMBOHvHUXAwmBTDxJ5JPA+gKOEpS4mIiIiIiIgo2qkFeAxqS+wlhAASADjEzCTnKnujXofTirKRlmjEuG7NkJnsajGvNhd0/fTl+HndIZyoqvf4eL7GQ0QUbh3yUt226VQ+9EwN0XqrjZlJwWIM9wCo6TAziaKNsr4pERERERERUaxQzUzy8dvX7iMzKTvFjA1PjXd/LJVjzd92FPO3HcWC+0d5fDyTgevOiSiyPXp2Vxj0Ouj1Onz+VzEA9c9XIThuZWZS0PAbIoYxM4mijfQkmi9bIiIiIiIiiiVqv3N9ZQLZHO7BpNLyWgDAqVqr58fyclhvE6tGlrkjogiXmWzGixf1wqCibHGbWmBe+DxjZlLwMJgUw5iZRNFGmr7Ply0RERERERHFErUKHAZfmUmSuE9DdTs8N2MLAGBzSYXH+1XV2zwf00swyczMJCKKEtI5b7W4vFDmjj2TgoffEDFMzEwK7zCIvJKeN28tPSXZzlcuERERERERxQ7VMnc+MpMm9igQLzsc2idEf99y2ONtFpvn46QnmTQ/BhFROEkzO9V60rkykxhMChYGk2KYA8xMIiIiIiIiIiKKBIHMzjw8sSv+b1gRAECYDk0xGwCor8QXZCWbPd627kCZ6vb/XDeAPZOIKGpIPwO99UyyMTMpaPgNEcPYM4mIiIiIiIiIKDIoK3B0KUjzeZ9EkwEX9msJwFUafnD7HADAc5N6BjSOh7/boLp9SPvcgI5HRBQOst7rXoJJdj+yOsk7BpNiGHsmUbTp1zoz3EMgIiIiIiIiahLK2ZnW2cma7ifM6wiLhuduOQIAMHpJTbIGsBLfZOD8ERFFD3nPJPfPL+EjkplJwcNgUgxjZhJFm+wUz2n4RERERERERNFMOdm5/kC5X/dT9kya67Uvkt3P0QFGlrgjoigi65mk1pNO/OwM1YhiH78lYpjwRmFmEkUyvjqJiIiIiIgoHiinZ0orajXdT5gvVS6un9S3pcf7WKz+BZMykkx+7U9EFG56va/MJJa5CzYGk2KY8EZhLImihbJ+NBEREREREVG80ykmRFtmJgEACrOSPN7H4mdZJwaTiCjaeKn06by9YQcbg0lBw2BSDHMwmERRhi9VIiIiIiIiilWBzs+ImUkNAaKDZTUAAJOXsnT1GjKTpD2XquqsgQ2OiChMDD56Jglt4BhLCh4Gk2IYy9xRtOFrlYiIiIiIiGJVoNU4pH0/SstdpfG8BZNqLDafxz23dwvx8vGq+oDGRkQULnqNPZNsfmZqkmcMJsWwH9aVAGBdSIoejCURERERERFRrPJVksnz/Vylmg6crBa3mwyeD/jkud0AALePao+BbbNU98lNNQc2ICKiCCBdlG5QmVTsXJCKXtl2dMxPDeWwYpox3AOgprdqX1m4h0DkkXRlFoNJREREREREFKt0ARZ3F34r2x0OJBgN4najl8ykqwa1wejO+WiekYgr//2X6j45qQni5e4t0gMaGxFRuEg/AtUyPy/s2xKJJeswsW8Lt9soMAwmxYGaet+pzUSRINATayIiIiIiIqJIp5zrvGN0B033MzSkNNkdQEqCK5jUIiPR6/1aZCYBAIweMphyUlyZSa9d2lvTWIiIIsWJKot42Wr33SeOGo/BpBhll9SCFE4eiCIeY0lEREREREQUo5Q/eTs201Z6SSjlZLc7IEz3ZCSZNPdgMnvIYBrZOQ8vXNAThytq0aWAmUlEFF2q6qziZYuNwaRQYDApRlkk0djsFFMYR0KknZ517oiIiIiIiChWKX7yGvXaWpnrJWXuHA19sf3pv2Q2qj9OgsGAKwe11n4gIqII4oArmcLh8LIjBY22by2KOhab6x2UaDJ42ZMocgTajJSIiIiIiIgo0ikXUHoqP+d2P0mZO5sYTNL+A9rkITPJoPHxiYgikdngmvNmMCk0GEyKURarKzMp2cxgEkUHnsYSERERERFRrFL+5jVqXFEpDRzZGurcaS1xB3gOJml9fCKiSDS2WzPxsjRLiZoOg0kxSlonsmtBWhhHQuQdT12JiIiIiIgoHigDQAaNwRyD5H6zNx0G4G+ZO/WdGUwiomgmLeHJzKTQYDApRiWaDRjaPgdpJgeGtM8J93CINGHPJCIiIiIiIopV7plJ2qbldJLd3vx9B4AglbljMImIYgRjSaHBYFKMSk804aPr++O5AbZwD4VIs8ENgc8uzKYjIiIiIiKiGKOM/wSSmeTvfQHPwSR/SuUREUWy5XtOhHsIccEY7gEQEQnSk0zY8sxZsjRVIiIiIiIiolgQaJm7xlbx4G9sIiIKBgaTiCiiJJkN4R4CERERERERUdC5ZyZpu59aNbyDZTWaH9es8kBpCZwSJCIi/3BpAhGFFbPqiYiIiIiIKB4of/4aNPZMamxmUnmNxW3b0+d3b9QxiYgo/jCYREQRg3ElIiIiIiIiilVuZe40Bom07ufJWT0K3LY1NkBFRETxh8EkIooYbP5JREREREREsUr5i1djYlKjK3p0bpYW9GMSEVH8YTCJiIiIiIiIiIioiSmzgYwao0lqCy9vHFqk+XHTk0w+x0JEROSLX8GkKVOmYODAgUhLS0N+fj4mTZqEbdu2yfa59dZb0b59eyQlJSEvLw/nn38+tm7dKtunuLgYZ599NpKTk5Gfn4/7778fVqu18X8NEUU1nsoSERERERFRrFLGbwyNWOJ9fp8Wmvc16N1/bTOYRERE/vLra2vBggWYPHkyli1bhjlz5sBisWDcuHGoqqoS9+nfvz+mT5+OLVu24LfffoPD4cC4ceNgs9kAADabDWeffTbq6+uxZMkSfPzxx/joo4/wxBNPBPcvI6KowNJ2REREREREFI8MWuvcqd7Xv9/Sj07sKrvu592JiIhg9GfnWbNmya5/9NFHyM/Px6pVqzBixAgAwC233CLe3rZtWzz33HPo3bs39u7di/bt22P27NnYvHkz5s6di2bNmqFPnz549tln8eCDD+Kpp56C2Wx2e9y6ujrU1dWJ1ysqKgAAFosFFovFnz8hrgjPDZ8jihZWmzUiX698LxEFD99PRMHB9xJRcPC9RBQ8fD/5ZrXaZdcLUo0BP182P38/Xz+4FfYeq8Rny/cDAOx2G/+tIhTfS0T+0+nc3zN8L2njz/PjVzBJqby8HACQnZ2tentVVRWmT5+OoqIitGrVCgCwdOlS9OzZE82aNRP3Gz9+PG677TZs2rQJffv2dTvOlClT8PTTT7ttnz17NpKTkxvzJ8SFOXPmhHsIRD44P4pWrVqFut2OMI/FM76XiIKH7yei4OB7iSg4+F4iCh6+nzxzxpJcU3Gzf5vlcV938im8xYsWYW+Kf4+/v1gPoUjRmtWrYd0bub+/ie8lIi2SDQZU23Rok+LAzJkzVffhe8m76upqzfsGHEyy2+245557MHToUPTo0UN22zvvvIMHHngAVVVV6Ny5M+bMmSNmHJWWlsoCSQDE66WlpaqP9fDDD+Pvf/+7eL2iogKtWrXCuHHjkJ6eHuifEPMsFgvmzJmDsWPHwmRyb7ZIFCnuXjobADBgwACc0TkvzKNxx/cSUfDw/UQUHHwvEQUH30tEwcP3k28Wmx3/+GuueH3ixIma7yv8bhaMHDEcnZql+fX4q2duxcLSYgDAgAH9MaZLvl/3p9Dge4lIuy4Dq/Dp8v24ZXhbFKQnym7je0kboQqcFgEHkyZPnoyNGzdi0aJFbrddddVVGDt2LEpKSvDqq6/i0ksvxeLFi5GYmKhyJN8SEhKQkJDgtt1kMvGFoAGfJ4oWJqMhol+rfC8RBQ/fT0TBwfcSUXDwvUQUPHw/eaY3yDOBGvM8JZj9f55NBoPrstHIf6cIx/cSkW+dW2Ti2UmZXvfhe8k7f56bgDr93XHHHfjll1/wxx9/oLCw0O32jIwMdOzYESNGjMA333yDrVu34vvvvwcAFBQU4PDhw7L9hesFBQWBDIeIiIiIiIiIiCii6YJ5LJ3/R9PrdaqXiYiItPArmORwOHDHHXfg+++/x7x581BUVKTpPg6HA3V1dQCAwYMHY8OGDThy5Ii4z5w5c5Ceno5u3br5OXwiIiIiIiIiIqLIF8wAjj6QYJLkPoHcn4iI4ptfwaTJkyfj008/xeeff460tDSUlpaitLQUNTU1AIDdu3djypQpWLVqFYqLi7FkyRJccsklSEpKEuvAjhs3Dt26dcM111yDdevW4bfffsNjjz2GyZMnq5ayIyIiIiIiIiIiimfn9Gouu24IKJikfpmIiEgLv4JJ06ZNQ3l5OUaNGoXmzZuL/3311VcAgMTERPz555+YOHEiOnTogMsuuwxpaWlYsmQJ8vOdTf0MBgN++eUXGAwGDB48GFdffTWuvfZaPPPMM8H/64iIiIiIiIiIiKKcQ3E9kMQiZiYREVFjGP3Z2eFQfnXJtWjRAjNnzvR5nDZt2mjaj4jiiy6oFaSJiIiIiIiIYoRiSi6QknnS+zCWRERE/vIrM4mIqCk53NZaEREREREREcWO7BRzQPfbUlIhux5ImToDM5OIiKgRGEwiIiIiIiIiIiIKgUBDOLuPVcmuB9IzySCZBWQwiYiI/MVgEhFFDJa5IyIiIiIiIvLN5qMVhRqdJIBk4IwgERH5iV8dREREREREREREUaTOYvf7PgZZzyQu5iQiIv8wmERERERERERERBQCwegU3Do7GYVZSX7fjz2TiIioMRhMIiIiIiIiIiIiihLz/jESxgDq1EnjR3rGkoiIyE8MJhEREREREREREUWJQAJJgLzMHTOTiIjIXwwmERERERERERERxbjjlfXhHgIREUUxBpOIiIiIiIiIiIhCwOEIrGvSXWd0aPRjl9dYxMtMTCIiIn8xmERERERERERERBTBWmUnN/oYDrgCWTowmkRERP5hMImIiIiIiIiIiCgEAstLAnRBTiViZhIREfmLwSQiIiIiIiIiIqIIFozYT4AV9oiIiAAwmERERERERERERBTROjZLbfQx7JJgEjOTiIjIX8ZwD4CISMSTWSIiIiIiIiI3vQoz8e7V/VGYlRSU47FnEhER+YvBJCIiIiIiIiIiohBoTKm5s3oUNPbRG3l/IiKKZyxzR0REREREREREFOMcLHNHRESNwGASERERERERERFRjGtMVhQRERGDSURERERERERERDHOISlzx8QkIiLyF4NJREREREREREREIeAIY3pQ84wk8TLL3BERkb8YTCIiIiIiIiIiIopxN49oJ7nGaBIREfmHwSQiIiIiIiIiIqIYl2QyiJdNBgaTiIjIP8ZwD4CIiIiIiIiIiIialkGvw/VD2qKixoLW2cnhHg4REUUZBpOIiIiIiIiIiIhCIHwdk5yeOq97mEdARETRimXuiIiIiIiIiIiIiIiIyCMGk4goYrBiMxEREREREREREVHkYTCJiIiIiIiIiIiIiIiIPGIwiYgiRrhrRxMRERERERERERGROwaTiIiIiIiIiIiIiIiIyCMGk4goYrBnEhEREREREREREVHkYTCJiIiIiIiIiIgoFFjfnYiIohSDSUREREREREREREREROQRg0lERERERERERERERETkEYNJRERERERERERERERE5BGDSURERERERERERCHAlklERBStGEwiIiIiIiIiIiIiIiIijxhMIqKIodPpwj0EIiIiIiIiIiIiIlJgMImIiIiIiIiIiIiIiIg8YjCJiCKGw8Hq0URERERERBS7+LuXiIiiFYNJRERERERERERERERE5BGDSUQUMdgziYiIiIiIiIiIiCjyMJhEREREREREREREREREHjGYREREREREREREFALsmERERNGKwSQiIiIiIiIiIiIiIiLyyBjuATQlm80Gi8US7mGEjcVigdFoRG1tLWw2W7iHE1HMZjP0esZSiYiIiIiIiIiIiIh8iclgksPhQGlpKcrKysI9lLByOBwoKCjA/v37odPpwj2ciKLX61FUVASz2RzuoRARERERERERERERRbSYDCYJgaT8/HwkJyfHbSDFbrejsrISqampzMKRsNvtOHToEEpKStC6deu4fX0QERERERERUWg52DSJiIiiVMwFk2w2mxhIysnJCfdwwsput6O+vh6JiYkMJink5eXh0KFDsFqtMJlM4R4ONWBYj4iIiIiIiIiIiCjyxFyEQeiRlJycHOaRUCQTytuxl1Rk4QItIiIiIiIiimUO/vIlIqIoFXPBJAFLl5E3fH0QEREREREREREREWkTs8EkIiIiIiIiIiIiIiIiajwGk+JU27Zt8cYbb4R7GB7Nnz8fOp0OZWVl4R4KhRDzxYiIiIiIiIiIiIgiD4NJFJBRo0bhnnvuabLjDxkyBCUlJcjIyGiyxyAiIiIiIiIiCiUHWyYREVGUYjApztTX14d7CD5ZLBaYzWYUFBSwtxERERERERERERERUZgxmBQhRo0ahTvvvBP33HMPsrKy0KxZM/z73/9GVVUVbrjhBqSlpaFDhw749ddfxfvYbDb83//9H4qKipCUlITOnTvjzTfflB339ttvxwUXXIDnn38eLVq0QOfOnVUf/4MPPkBmZiZ+//13AMDGjRsxYcIEpKamolmzZrjmmmtw7NgxAMD111+PBQsW4M0334ROp4NOp8PevXtVj9u2bVs8++yzuOKKK5CSkoKWLVti6tSpsn10Oh2mTZuG8847DykpKXj++edVy9wtXrwYo0aNQnJyMrKysjB+/HicPHkSAGC32zFlyhTxuejduze++eYbv/4NiIiIiIiIiIiaEtfMEhFRtIr5YJLD4UB1vTUs/zn8zF3++OOPkZubi+XLl+POO+/EbbfdhksuuQRDhgzB6tWrMW7cOFxzzTWorq4G4AygFBYW4n//+x82b96MJ554Ao888gi+/vpr2XHnzZuHbdu2Yc6cOfjll1/cHvfll1/GQw89hNmzZ2PMmDEoKyvDGWecgb59+2LlypWYNWsWDh8+jEsvvRQA8Oabb2Lw4MG4+eabUVJSgpKSErRq1crj3/XKK6+gd+/eWLNmDR566CHcfffdmDNnjmyfp556ChdccAE2bNiAG2+80e0Ya9euxZgxY9CtWzcsXboUixYtwrnnngubzQYAmDJlCv773//i3XffxaZNm3Dvvffi6quvxoIFC/z6NyAiIiIiIiIiaiosc0dERNHKGO4BNLUaiw3dnvgtLI+9+ZnxSDZrf4p79+6Nxx57DADw8MMP48UXX0Rubi5uvvlmAMATTzyBadOmYf369Tj99NNhMpnw9NNPi/cvKirC0qVL8fXXX4uBHwBISUnBBx98ALPZ7PaYDz74ID755BMsWLAA3bt3BwD861//Qt++ffHCCy+I+3344Ydo1aoVtm/fjk6dOsFsNiM5ORkFBQU+/66hQ4fioYceAgB06tQJixcvxj//+U+MHTtW3OfKK6/EDTfcIF7fvXu37Bgvv/wyBgwYgHfeeUfcJoy3rq4OL7zwAubOnYvBgwcDANq1a4dFixbhvffew8iRI32OkYiIiIiIiIiIiIiI1MV8MCma9OrVS7xsMBiQk5ODnj17ituaNWsGADhy5Ii4berUqfjwww9RXFyMmpoa1NfXo0+fPrLj9ujRQzWQ9Nprr6GqqgorV65Eu3btxO3r1q3DH3/8gdTUVLf77Nq1C506dfLr7xICPNLrb7zxhmzbgAEDvB5j7dq1uOSSS1Rv27lzJ6qrq2XBKcDZH6pv375+jZWIiIiIiIiIiIiIiORiPpiUZDJg8zPjw/bY/jCZTLLrOp1Otk3XUFjXbrcDAL788kvcd999eO211zB48GCkpaXhlVdewV9//SU7TkpKiurjDR8+HDNmzMDXX38tZg4BQGVlJc4991y89NJLbvdp3ry5X3+TVp7GKEhKSvJ4W2VlJQBgxowZaNmypey2hISExg+OQoa1o4mIiIiIiIiIiIgiT8wHk3Q6nV+l5qLJ4sWLMWTIENx+++3itl27dmm+/2mnnYY77rgDZ511FoxGI+677z4AQL9+/fDtt9+ibdu2MBrVnzuz2Sz2K/Jl2bJlbte7du2qeZyAM2vr999/l5X1E3Tr1g0JCQkoLi5mSbsox9rRREREREREFMv4s5eIiKKVPtwDoMB17NgRK1euxG+//Ybt27fj8ccfx4oVK/w6xpAhQzBz5kw8/fTTYum5yZMn48SJE7jiiiuwYsUK7Nq1C7/99htuuOEGMYDUtm1b/PXXX9i7dy+OHTsmZkupWbx4MV5++WVs374dU6dOxf/+9z/cfffdfo3z4YcfxooVK3D77bdj/fr12Lp1K6ZNm4Zjx44hLS0N9913H+699158/PHH2LVrF1avXo23334bH3/8sV+PQ0REREREREREREREcgwmRbFbb70VF154IS677DIMGjQIx48fl2UpaTVs2DDMmDEDjz32GN5++220aNECixcvhs1mw7hx49CzZ0/cc889yMzMhF7vfMncd999MBgM6NatG/Ly8lBcXOzx+P/4xz+wcuVK9O3bF8899xxef/11jB/vX+nBTp06Yfbs2Vi3bh1OO+00DB48GD/++KOYOfXss8/i8ccfx5QpU9C1a1ecddZZmDFjBoqKivx+PoiIiIiIiIiIiIiIyCU2679Fofnz57tt27t3r9s2h6QOWEJCAqZPn47p06fL9pkyZYp4+Z133kF6errPY48YMULsPQQ4s56+++47j+Pt1KkTli5d6vF2qfT0dHz99dceb3eo1DYbNWqU2/aRI0di8eLFqsfQ6XS4++67/c54osjCnklEREREREREREREkYeZSURERERERERERKHApklERBSlGEwioohh1PMjiYiIiIiIiIiIiCjSsMwdNSm1Un1ESreOaIddRysxqCg73EMhIiIiIiIiIiIiIgUGk4go7B6e2DXcQyAiIiIiIiIiIiIiD1hTioiIiIiIiIiIKAQcbJpERERRisEkIiIiIiIiIiIiIiIi8ojBJCIiIiIiIiIiIiIiIvKIwSQiIiIiIiIiIiIiIiLyiMEkIiIiIiIiIiKiEHCwZRIREUUpBpOIiIiIiIiIiIiIiIjIIwaTKGieeuop9OnTJ9zDICIiIiIiIiKKSC2zksI9BCIiooAwmERERERERERERBQC/7luAEZ2ysO3tw0J91CIiIj8Ygz3AIiIiIiIiIiIiOJBh/w0fHzjaeEeBhERkd/iJzOpqsrzf7W12vetqdG2bwDq6upw1113IT8/H4mJiRg2bBhWrFgBAJg/fz50Oh1mzJiBXr16ITExEaeffjo2btwoO8aiRYswfPhwJCUloU2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" ] @@ -1112,11 +1195,15 @@ } ], "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", "axs.plot(data['close'], color='tab:blue', label='market price')\n", "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", - "axs.axhline(y=243, color='red', linestyle='--', label='open_close')\n", - "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", "axs.grid()\n", "axs.legend(loc='lower left')\n", @@ -1139,13 +1226,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 141, "metadata": { "tags": [] }, "outputs": [], "source": [ - "def run_sim(period, open_close, slippage, K_1, K_2, hat_L, L):\n", + "def run_sim(period, open_close, slippage, max_txs, L):\n", + " global ocs\n", " # Initialize everything\n", " with open(\"Files/StgyApp_config.json\") as json_file:\n", " config = json.load(json_file)\n", @@ -1155,22 +1243,33 @@ " # Period of Simulations\n", " # period = [\"2019-09-01\",\"2019-12-31\"]\n", " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", - " floor = open_close / (1+slippage)\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", " # Load target_prices + intervals in stgy.historical_data\n", " # First we calculate weighted vol\n", - " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", - " for i in range(len(periods_for_vol)):\n", - " N = periods_for_vol[i]\n", - " log_returns = np.log(stgy.historical_data[-N:]['close']) \\\n", - " - np.log(stgy.historical_data[-N:]['close'].shift(1))\n", - " global()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - " vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", - " K = K_1\n", + " last_date = period[1]+' 00:00:00'\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", " # Now we define prices and intervals given K and vol\n", - " stgy.parameter_manager.define_target_prices(stgy, slippage, K_1, vol, floor)\n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor)\n", + " # We create five equidistant OCs\n", + " oc1 = stgy.trigger_prices['open_close']\n", + " # oc2 = oc1 * (1+6/2/100)\n", + " ocs = [oc1]\n", + " for i in range(1,5):\n", + " globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", + " ocs.append(globals()[\"oc\"+str(i+1)])\n", + " # But we start with the first oc1\n", + " stgy.trigger_prices['open_close'] = oc1\n", " stgy.parameter_manager.define_intervals(stgy)\n", - " stgy.parameter_manager.load_intervals(stgy)\n", + " \n", + " # print(\"Volatility:\", vol)\n", + " # print(\"Floor:\", stgy.trigger_prices['floor'])\n", + " # print(\"Open_close1:\", oc1)\n", + " # print(\"Open_close2:\", oc2)\n", + " # print(\"1-OC2/OC1 - 1:\", 1-oc2/oc1)\n", " #########################\n", " # Save historical data with trigger prices and thresholds loaded\n", " # checking if the directory demo_folder \n", @@ -1179,7 +1278,7 @@ " # if the demo_folder directory is not present \n", " # then create it.\n", " os.makedirs(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", - " stgy.historical_data.to_csv(\"~/Cruize Simulations/Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data_.csv\" \n", + " stgy.historical_data.to_csv(\"Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", " % (period[0], period[1], open_close))\n", " #########################\n", " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", @@ -1187,14 +1286,14 @@ " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", " # As we calculate vol using first week of data, we initialize simulations from that week on\n", " initial_index = 1\n", - " stgy.launch(config)\n", "\n", " # Stk eth\n", " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", "\n", " # AAVE\n", " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", - " stgy.aave.interval_current = stgy.historical_data['interval'][initial_index]\n", + " # stgy.aave.interval_current = stgy.historical_data['interval'][initial_index]\n", + " stgy.aave.interval_current = stgy.parameter_manager.find_interval(stgy, stgy.aave.market_price)['interval']\n", "\n", " # What is the price at which we place the collateral in AAVE given our initial_index?\n", " stgy.aave.entry_price = stgy.aave.market_price\n", @@ -1216,31 +1315,21 @@ "\n", " # DyDx\n", " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", - " stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index]\n", + " # stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index]\n", + " stgy.dydx.interval_current = stgy.parameter_manager.find_interval(stgy, stgy.dydx.market_price)['interval']\n", " stgy.dydx.collateral = stgy.aave.debt\n", " stgy.dydx.equity = stgy.dydx.equity_calc()\n", " stgy.dydx.collateral_status = True\n", " #########################\n", - " # Change or define prices that aren't defined yet if the period of simulations involves those prices\n", - " # For ex if we are executing periods of time in which ltv_limit or repay_aave are already defined\n", - "\n", - " # price_floor = stgy.intervals['open_close_1'].left_border\n", - " stgy.trigger_prices['ltv_limit'] = stgy.aave.price_to_ltv_limit\n", - " previous_position_order = stgy.intervals['open_close'].position_order\n", - " stgy.intervals['floor'] = Interval(stgy.aave.price_to_ltv_limit, floor,\n", - " 'floor', previous_position_order + 1)\n", - " stgy.intervals['minus_infty'] = Interval(-math.inf, stgy.aave.price_to_ltv_limit,\n", - " 'minus_infty', previous_position_order + 2)\n", - "\n", - " #########################\n", " # Load interval_old\n", - " interval_old = stgy.historical_data['interval'][initial_index]\n", + " # interval_old = stgy.historical_data['interval'][initial_index]\n", + " interval_old = stgy.aave.interval_current\n", " #########################\n", " # Clear previous csv data for aave and dydx\n", - " stgy.data_dumper.delete_results(stgy, period)\n", + " stgy.data_dumper.delete_results(stgy, period, open_close)\n", " #########################\n", " # add header to csv of aave and dydx\n", - " stgy.data_dumper.add_header(stgy, period)\n", + " stgy.data_dumper.add_header(stgy, period, open_close)\n", " ##################################\n", " # Run through dataset\n", " #########################\n", @@ -1251,16 +1340,18 @@ " # for i in range(initial_index, len(stgy.historical_data)):\n", " i = initial_index\n", "\n", - "\n", + " maker_fees_counter = []\n", " while(i < len(stgy.historical_data)):\n", " # for i in range(initial_index, len(stgy.historical_data)):\n", " # pass\n", " \n", " # We reset costs in every instance\n", " stgy.parameter_manager.reset_costs(stgy)\n", - " new_interval_previous = stgy.historical_data[\"interval\"][i-1]\n", - " new_interval_current = stgy.historical_data[\"interval\"][i]\n", - " new_market_price = stgy.historical_data[\"close\"][i]\n", + " # new_interval_previous = stgy.historical_data[\"interval\"][i-1]\n", + " interval_previous = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i-1])['interval']\n", + " # new_interval_current = stgy.historical_data[\"interval\"][i]\n", + " interval_current = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i])['interval']\n", + " market_price = stgy.historical_data[\"close\"][i]\n", " #########################\n", " # This case is when P crossed open_close_2 while increasing (therefore we had to close short), I_old = I_open_close_2, \n", " # but then it goes below open_close_2 again. \n", @@ -1274,8 +1365,8 @@ "# time_dydx = stgy_instance.dydx.open_short(new_market_price, new_interval_current, stgy)\n", " # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late\n", " # therefore not taking the actions needed as soon as they are needed\n", - " if new_interval_previous != new_interval_current:\n", - " interval_old = new_interval_previous\n", + " if interval_previous != interval_current:\n", + " interval_old = interval_previous\n", " # print(interval_old.name)\n", " #########################\n", " # Update parameters\n", @@ -1284,14 +1375,57 @@ " # AAVE: market_price, interval_current, lending and borrowing fees (and the diference),\n", " # debt value, collateral value and ltv value\n", " # DyDx: market_price, interval_current, notional, equity, leverage and pnl\n", - " stgy.parameter_manager.update_parameters(stgy, new_market_price, new_interval_current)\n", + " stgy.parameter_manager.update_parameters(stgy, market_price, interval_current)\n", " # Here we identify price movent direction by comparing current interval and old interval\n", " # and we also execute all the actions involved since last price was read\n", - " time_used = stgy.parameter_manager.find_scenario(stgy, new_market_price, new_interval_current, interval_old, i)\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, interval_current, interval_old, i)\n", + " ##############################\n", + " # We update vol and ocs if short_status = False\n", + " # if not stgy.dydx.short_status:\n", + " # current_date = list(stgy.historical_data.index)[i]\n", + " # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol)\n", + " # mu, sigma = vol\n", + " # oc1 = floor * (1+slippage) * (1+mu+2*sigma)\n", + " # ocs = [oc1]\n", + " # for i in range(1,5):\n", + " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", + " # ocs.append(globals()[\"oc\"+str(i+1)])\n", " #########################\n", " # If we executed more txs than hat_L*20 then we change to K_2\n", - " if (stgy.dydx.maker_fees_counter > hat_L * 20) and (stgy.dydx.short_status):\n", - " K = K_2\n", + " if (stgy.dydx.maker_fees_counter >= max_txs):\n", + " # stgy.historical_data = stgy.historical_data_OC2\n", + " # print(stgy.dydx.maker_fees_counter)\n", + " current_date = list(stgy.historical_data.index)[i]\n", + " current_oc = stgy.trigger_prices['open_close']\n", + " vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", + " ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", + " # if short = open and if there are up_choices available, we take the last option (the furthest)\n", + " # if there isn't options we take max_distance\n", + " # random.seed(4)\n", + " if stgy.dydx.short_status:\n", + " if len(ocs_choices['up_choices']) != 0:\n", + " stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", + " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", + " else:\n", + " pass\n", + " # if short = close and if there are down_choices available, we take the first option (the furthest)\n", + " # if there isn't options we take max_distance\n", + " else:\n", + " if len(ocs_choices['down_choices']) != 0:\n", + " stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][-1]\n", + " # oc_choice_down = random.choice(range(len(ocs_choices['down_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][oc_choice_down]\n", + " else:\n", + " pass\n", + " # If we didnt change oc we dont clean maker_fees_counter\n", + " if current_oc != stgy.trigger_prices['open_close']:\n", + " maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " 'txs': stgy.dydx.maker_fees_counter, \n", + " # 'index': i,\n", + " 'date': str(stgy.historical_data.index[i])})\n", + " stgy.dydx.maker_fees_counter = 0\n", + " stgy.parameter_manager.define_intervals(stgy)\n", " ########################\n", " # Funding rates\n", " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", @@ -1299,7 +1433,7 @@ " # Calling it before find_scenarios will overwrite the funding by 0\n", " # We have to check all the indexes between old index i and next index i+time_used\n", " # for index in range(i, i+time_used):\n", - " if i % (8 * 60) == 0:\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", " stgy.dydx.add_funding_rates()\n", " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", " #########################\n", @@ -1311,92 +1445,468 @@ " # We write the data into the google sheet or csv file acording to sheet value\n", " # (sheet = True --> sheet, sheet = False --> csv)\n", " stgy.data_dumper.write_data(stgy,\n", - " new_interval_previous, interval_old, i, period,\n", + " interval_previous, interval_old, i, period, open_close,\n", " sheet=False)\n", " #########################\n", - " # Update trigger prices and thresholds\n", - " # We update trigger prices and thresholds every day\n", - " # if (i+time_used - initial_index) % (1*24*60) == 0:\n", - " # # We call the paramater_manager instance with updated data\n", - " # stgy.parameter_manager.define_target_prices(stgy, N_week, data_for_thresholds, floor)\n", - " # stgy.parameter_manager.define_intervals(stgy)\n", - " # stgy.parameter_manager.load_intervals(stgy)\n", - " # save = True\n", - " # stgy.data_dumper.plot_data(stgy)#, save, factors, vol, period)\n", - "\n", " # we increment index by the time consumed in executing actions\n", " # i += time_used\n", - " i += 1" + " i += 1\n", + " return maker_fees_counter" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-01 00:00:00'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(historical_data.index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, + "outputs": [], "source": [ - "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "# periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", - "# [[\"2020-01-01\",\"2020-05-01\"],135], [[\"2020-05-01\",\"2020-09-01\"],240]]\n", - "periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "open_close_2 < floor!\n", - "(floor-open_close_2)/floor= 1.0000000001384752e-06\n" + "data": { + "text/plain": [ + "(0.01716814159292035, -0.034270575164515926)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu_returns_max, mu_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'mu_abs_log_returns' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m K \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[0;32m----> 2\u001b[0m condition \u001b[38;5;241m=\u001b[39m (mu_abs_log_returns\u001b[38;5;241m-\u001b[39mK\u001b[38;5;241m*\u001b[39mstd_ema_log_returns\u001b[38;5;241m<\u001b[39mlog_returns)\u001b[38;5;241m&\u001b[39m(log_returns\u001b[38;5;241m<\u001b[39mmu_abs_log_returns\u001b[38;5;241m+\u001b[39mK\u001b[38;5;241m*\u001b[39mstd_ema_log_returns)\n", + "\u001b[0;31mNameError\u001b[0m: name 'mu_abs_log_returns' is not defined" ] - }, + } + ], + "source": [ + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(log_returns[condition]),\u001b[38;5;28mlen\u001b[39m(log_returns),\u001b[38;5;28mlen\u001b[39m(log_returns[condition])\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(log_returns)\n", + "\u001b[0;31mNameError\u001b[0m: name 'log_returns' is not defined" ] } ], "source": [ + "len(log_returns[condition]),len(log_returns),len(log_returns[condition])/len(log_returns)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([3.800e+01, 4.800e+01, 3.500e+01, 4.000e+01, 3.700e+01, 4.000e+01,\n", + " 4.100e+01, 3.600e+01, 4.500e+01, 5.100e+01, 4.100e+01, 5.300e+01,\n", + " 5.400e+01, 2.400e+01, 5.800e+01, 3.900e+01, 5.900e+01, 7.100e+01,\n", + " 3.100e+01, 8.600e+01, 7.800e+01, 1.700e+01, 7.400e+01, 7.500e+01,\n", + " 1.300e+01, 9.000e+01, 8.600e+01, 1.800e+01, 8.500e+01, 1.500e+01,\n", + " 8.400e+01, 5.975e+03, 0.000e+00, 1.220e+02, 8.700e+01, 2.000e+00,\n", + " 8.600e+01, 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9.01197153e-04, 9.28149446e-04, 9.55101739e-04,\n", + " 9.82054032e-04, 1.00900632e-03, 1.03595862e-03, 1.06291091e-03,\n", + " 1.08986320e-03, 1.11681550e-03, 1.14376779e-03, 1.17072008e-03,\n", + " 1.19767237e-03, 1.22462467e-03, 1.25157696e-03, 1.27852925e-03,\n", + " 1.30548155e-03, 1.33243384e-03, 1.35938613e-03, 1.38633842e-03,\n", + " 1.41329072e-03, 1.44024301e-03, 1.46719530e-03, 1.49414760e-03,\n", + " 1.52109989e-03, 1.54805218e-03, 1.57500447e-03, 1.60195677e-03,\n", + " 1.62890906e-03, 1.65586135e-03, 1.68281364e-03, 1.70976594e-03,\n", + " 1.73671823e-03, 1.76367052e-03, 1.79062282e-03, 1.81757511e-03,\n", + " 1.84452740e-03]),\n", + " )" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(log_returns[condition], bins=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11521" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(log_returns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "# periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", + "L = 5 * 0.07\n", "for period_n_open_close in periods_n_open_close:\n", " period = period_n_open_close[0]\n", " open_close = period_n_open_close[1]\n", - " slippage = 0.001\n", - " run_sim(period, open_close, slippage)" + " slippage = 0.0005\n", + " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'oc': 186.11, 'txs': 9, 'date': '2019-09-14 16:44:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-10-11 06:36:00'},\n", + " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-10-14 18:11:00'},\n", + " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-10-26 03:02:00'},\n", + " {'oc': 188.35001996000003, 'txs': 9, 'date': '2019-10-28 01:20:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-10-29 10:52:00'},\n", + " {'oc': 186.11, 'txs': 8, 'date': '2019-10-29 12:57:00'},\n", + " {'oc': 185.0, 'txs': 8, 'date': '2019-10-29 14:51:00'},\n", + " {'oc': 186.11, 'txs': 9, 'date': '2019-11-02 17:36:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-04 16:08:00'},\n", + " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-11-04 21:02:00'},\n", + " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-11-06 01:38:00'},\n", + " {'oc': 188.35001996000003, 'txs': 9, 'date': '2019-11-06 16:31:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-07 08:56:00'},\n", + " {'oc': 186.11, 'txs': 8, 'date': '2019-11-08 01:04:00'},\n", + " {'oc': 185.0, 'txs': 8, 'date': '2019-11-10 13:46:00'},\n", + " {'oc': 186.11, 'txs': 9, 'date': '2019-11-11 23:06:00'},\n", + " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-12 07:31:00'},\n", + " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-11-13 10:47:00'},\n", + " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-11-13 17:49:00'}]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "maker_fees_counter" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(maker_fees_counter)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"Files/From_2020-05-31_to_2020-06-07_open_close_at_240/dydx_results.csv\")\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(239.4380835398584, 240.0, 247.20000000000002)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slippage = 0.0005\n", + "K_1 = 2\n", + "K_2 = 6\n", + "mu = 0.0004973569978282845\n", + "sigma = 0.0006742666391824819\n", + "floor = 240 / ((1+slippage)*(1+mu+K_1*sigma))\n", + "p_open_close_1 = floor * (1+slippage) * (1+mu+K_1*sigma)\n", + "p_open_close_2 = p_open_close_1 * (1+K_2/K_1/100)\n", + "floor, p_open_close_1, p_open_close_2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0050452283113396" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1+slippage)*(1+mu+6*sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "max_loss = 0.05\n", + "p_open_close_1 = floor * (1+slippage) * (1+mu+K_1*sigma)\n", + "oc1 = p_open_close_1\n", + "for i in range(1,5):\n", + " globals()['oc'+str(i+1)] = oc1 * 1.01**i # jumps of 1%" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.029126213592233108" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_open_close_1/p_open_close_2-1" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = periods_n_open_close[0][0]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=floor, color='green', linestyle='--', label='floor')\n", + "for i in range(len(ocs)):\n", + " axs.axhline(y=ocs[i], color='red', linestyle='--', label='oc'+str(i))\n", + "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" ] }, { @@ -1518,7 +2028,7 @@ "metadata": {}, "outputs": [], "source": [ - "dydx_results = pd.read_csv(\"~/Cruize Simulations/Files/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + "dydx_results = pd.read_csv(\"Files/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" ] }, { @@ -1841,7 +2351,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1855,7 +2365,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.6" } }, "nbformat": 4, From a70ac7d267c711395e6d5640e792ef71f4b99d10 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Tue, 11 Oct 2022 10:02:55 -0300 Subject: [PATCH 04/16] updates --- jupyter-lab/Simulations_lab.ipynb | 251 ++++++++++++++++++++---------- 1 file changed, 170 insertions(+), 81 deletions(-) diff --git a/jupyter-lab/Simulations_lab.ipynb b/jupyter-lab/Simulations_lab.ipynb index fe2a0ef..10e09a3 100644 --- a/jupyter-lab/Simulations_lab.ipynb +++ b/jupyter-lab/Simulations_lab.ipynb @@ -14,36 +14,36 @@ "Requirement already satisfied: pygsheets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (2.0.5)\n", "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", "Requirement already satisfied: numpy>=1.21.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (1.23.3)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2022.2.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2.8.2)\n", "Requirement already satisfied: google-api-python-client>=1.5.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (2.63.0)\n", "Requirement already satisfied: google-auth-oauthlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages 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requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2022.9.24)\n" ] } ], @@ -451,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -650,24 +650,27 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "class ParameterManager(object):\n", " # auxiliary functions\n", " @staticmethod\n", - " def define_target_prices(stgy_instance, slippage, vol, floor):\n", + " def define_target_prices(stgy_instance, slippage, vol, floor, trailing):\n", " mu = vol[0]\n", " sigma = vol[1]\n", " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", + " p_trailing = floor * (1-trailing)\n", " ##########################################################\n", " # We define the intervals\n", " list_of_intervals = [\"open_close\",\n", " \"floor\",\n", + " \"trailing_stop\",\n", " \"ltv_limit\"]\n", " list_of_trigger_prices = [p_open_close,\n", " floor,\n", + " p_trailing, \n", " stgy_instance.aave.price_to_ltv_limit]\n", " # We define/update trigger prices\n", " for i in range(len(list_of_intervals)):\n", @@ -683,12 +686,15 @@ " \"open_close\": Interval(stgy_instance.trigger_prices['floor'],\n", " stgy_instance.trigger_prices['open_close'],\n", " \"open_close\", 1),\n", - " \"floor\": Interval(stgy_instance.trigger_prices['ltv_limit'],\n", + " \"floor\": Interval(stgy_instance.trigger_prices['trailing_stop'],\n", " stgy_instance.trigger_prices['floor'],\n", " \"floor\", 2),\n", + " \"trailing_stop\": Interval(stgy_instance.trigger_prices['ltv_limit'],\n", + " stgy_instance.trigger_prices['trailing_stop'],\n", + " \"trailing_stop\", 3),\n", " \"minus_infty\": Interval(-math.inf,\n", " stgy_instance.trigger_prices['ltv_limit'],\n", - " \"minus_infty\", 3)}\n", + " \"minus_infty\", 4)}\n", "\n", " # function to assign interval_current to each market_price in historical data\n", " @staticmethod\n", @@ -824,6 +830,10 @@ " if list(stgy_instance.intervals.keys())[i+1] == 'open_close':\n", " actions.append('close_short')\n", " \n", + " # CASE: open_close_1 APPROACH\n", + " elif list(stgy_instance.intervals.keys())[i+1] == 'trailing_stop':\n", + " actions.append('close_short')\n", + " \n", " # CASE: TOO MANY FEES FOR open_close_1 APPROACH\n", "# if list(stgy_instance.intervals.keys())[i+1] == 'open_close_2':\n", "# actions.append('close_short')\n", @@ -840,6 +850,10 @@ " # from inside the for loop of the run_sims\n", " if list(stgy_instance.intervals.keys())[i] == 'open_close':\n", " actions.append('open_short')\n", + " \n", + " elif list(stgy_instance.intervals.keys())[i] == 'trailing_stop':\n", + " actions.append('open_short')\n", + " \n", " else:\n", " actions.append(list(stgy_instance.intervals.keys())[i])\n", " # print(actions)\n", @@ -887,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1050,7 +1064,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -1101,17 +1115,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "weighted mu: 1.4462763455814235e-05%\n", - "weighted sigmas: 0.17999807112824498%\n", + "weighted mu: -3.1125433306818375e-05%\n", + "weighted sigmas: 0.1798150316808595%\n", "[min_6m_change, max_6m_change]: ['-6.786594905713236%', '9.135956592119358%']\n", - "avg movement: (1+slip)(1+mu+2vol): 1.1305428909776651%\n" + "avg movement: (1+slip)(1+mu+2vol): 0.40977873739736026%\n" ] } ], @@ -1120,7 +1134,7 @@ "last_date = \"2021-06-01\"\n", "slippage = 0.0005\n", "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", - "data = historical_data.loc[:last_date][-periods_for_vol[0]:]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]-3*60:-3*60]\n", "for i in range(len(periods_for_vol)):\n", " N = periods_for_vol[i]\n", " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", @@ -1133,10 +1147,31 @@ "print(\"weighted mu: \", str(mu*100)+'%')\n", "print(\"weighted sigmas: \", str(vol*100)+'%')\n", "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", - "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+6*vol)*100-100)+'%')\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+2*vol)*100-100)+'%')\n", "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "weighted sigmas: 0.20636032309050903%\n", + "avg movement: (1+mu+2vol): 0.4123904345313889%\n" + ] + } + ], + "source": [ + "vol = sigma_2\n", + "mu = mu_2\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"avg movement: (1+mu+2vol): \", str((1+mu+2*vol)*100-100)+'%')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1226,13 +1261,13 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 55, "metadata": { "tags": [] }, "outputs": [], "source": [ - "def run_sim(period, open_close, slippage, max_txs, L):\n", + "def run_sim(period, open_close, slippage, max_txs, L, trailing):\n", " global ocs\n", " # Initialize everything\n", " with open(\"Files/StgyApp_config.json\") as json_file:\n", @@ -1253,16 +1288,16 @@ " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", " # Now we define prices and intervals given K and vol\n", - " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor)\n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", " # We create five equidistant OCs\n", - " oc1 = stgy.trigger_prices['open_close']\n", + " oc1 = floor\n", " # oc2 = oc1 * (1+6/2/100)\n", " ocs = [oc1]\n", - " for i in range(1,5):\n", - " globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", + " for i in range(1,4):\n", + " globals()[\"oc\"+str(i+1)] = oc1 * (1+0.01)**i # We define 5 OCs based on a top width of 3%\n", " ocs.append(globals()[\"oc\"+str(i+1)])\n", " # But we start with the first oc1\n", - " stgy.trigger_prices['open_close'] = oc1\n", + " stgy.trigger_prices['open_close'] = oc4\n", " stgy.parameter_manager.define_intervals(stgy)\n", " \n", " # print(\"Volatility:\", vol)\n", @@ -1320,6 +1355,10 @@ " stgy.dydx.collateral = stgy.aave.debt\n", " stgy.dydx.equity = stgy.dydx.equity_calc()\n", " stgy.dydx.collateral_status = True\n", + " \n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= stgy.trigger_prices['open_close']):\n", + " stgy.dydx.open_short(stgy)\n", " #########################\n", " # Load interval_old\n", " # interval_old = stgy.historical_data['interval'][initial_index]\n", @@ -1341,6 +1380,8 @@ " i = initial_index\n", "\n", " maker_fees_counter = []\n", + " \n", + " stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['floor'] * (1-trailing)\n", " while(i < len(stgy.historical_data)):\n", " # for i in range(initial_index, len(stgy.historical_data)):\n", " # pass\n", @@ -1352,6 +1393,7 @@ " # new_interval_current = stgy.historical_data[\"interval\"][i]\n", " interval_current = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i])['interval']\n", " market_price = stgy.historical_data[\"close\"][i]\n", + " previous_price = stgy.historical_data[\"close\"][i-1]\n", " #########################\n", " # This case is when P crossed open_close_2 while increasing (therefore we had to close short), I_old = I_open_close_2, \n", " # but then it goes below open_close_2 again. \n", @@ -1379,6 +1421,23 @@ " # Here we identify price movent direction by comparing current interval and old interval\n", " # and we also execute all the actions involved since last price was read\n", " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, interval_current, interval_old, i)\n", + " ############################## \n", + " # We update trailing\n", + " # Everytime price moves down more than trailing we update trailing_stop\n", + " if market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']:\n", + " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", + " stgy.parameter_manager.define_intervals(stgy)\n", + " # If price moves above trailing we move trailing up in order to save that profit\n", + " # Is important to change trailing after finding scenarios (because we need to close the short first)\n", + " elif market_price*(1+trailing) > stgy.trigger_prices['trailing_stop']:\n", + " stgy.trigger_prices['trailing_stop'] = market_price\n", + " stgy.parameter_manager.define_intervals(stgy)\n", + " \n", + " # If price goes above floor again, we start at oc1 = floor, trailing_stop = floor * (1-trailing) and repeat the process\n", + " # We need to write the case market > floor but in terms of trailing in order to not change ocs at the beginning of the sims\n", + " # if stgy.trigger_prices['trailing_stop'] >= stgy.trigger_prices['floor']:\n", + " # stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['floor'] * (1-trailing)\n", + " # stgy.trigger_prices['open_close'] = stgy.trigger_prices['floor'] # = oc1\n", " ##############################\n", " # We update vol and ocs if short_status = False\n", " # if not stgy.dydx.short_status:\n", @@ -1402,28 +1461,26 @@ " # if short = open and if there are up_choices available, we take the last option (the furthest)\n", " # if there isn't options we take max_distance\n", " # random.seed(4)\n", - " if stgy.dydx.short_status:\n", + " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # 'index': i,\n", + " # 'date': str(stgy.historical_data.index[i])})\n", + " if not stgy.dydx.short_status:\n", + " if stgy.trigger_prices['open_close'] == oc4:\n", + " stgy.trigger_prices['open_close'] = oc1\n", + " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", + " elif stgy.dydx.short_status:\n", " if len(ocs_choices['up_choices']) != 0:\n", " stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", - " else:\n", - " pass\n", - " # if short = close and if there are down_choices available, we take the first option (the furthest)\n", - " # if there isn't options we take max_distance\n", - " else:\n", - " if len(ocs_choices['down_choices']) != 0:\n", - " stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][-1]\n", - " # oc_choice_down = random.choice(range(len(ocs_choices['down_choices'])))\n", - " # stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][oc_choice_down]\n", - " else:\n", - " pass\n", " # If we didnt change oc we dont clean maker_fees_counter\n", " if current_oc != stgy.trigger_prices['open_close']:\n", " maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " 'txs': stgy.dydx.maker_fees_counter, \n", - " # 'index': i,\n", - " 'date': str(stgy.historical_data.index[i])})\n", + " 'txs': stgy.dydx.maker_fees_counter, \n", + " # 'index': i,\n", + " 'date': str(stgy.historical_data.index[i])})\n", " stgy.dydx.maker_fees_counter = 0\n", " stgy.parameter_manager.define_intervals(stgy)\n", " ########################\n", @@ -1700,19 +1757,19 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "# periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + "periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 56, "metadata": { "tags": [] }, @@ -1720,44 +1777,37 @@ "source": [ "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", "L = 5 * 0.07\n", + "trailing = 0.01\n", "for period_n_open_close in periods_n_open_close:\n", " period = period_n_open_close[0]\n", " open_close = period_n_open_close[1]\n", " slippage = 0.0005\n", - " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L)" + " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing)" ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'oc': 186.11, 'txs': 9, 'date': '2019-09-14 16:44:00'},\n", - " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-10-11 06:36:00'},\n", - " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-10-14 18:11:00'},\n", - " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-10-26 03:02:00'},\n", - " {'oc': 188.35001996000003, 'txs': 9, 'date': '2019-10-28 01:20:00'},\n", - " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-10-29 10:52:00'},\n", - " {'oc': 186.11, 'txs': 8, 'date': '2019-10-29 12:57:00'},\n", - " {'oc': 185.0, 'txs': 8, 'date': '2019-10-29 14:51:00'},\n", - " {'oc': 186.11, 'txs': 9, 'date': '2019-11-02 17:36:00'},\n", - " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-04 16:08:00'},\n", - " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-11-04 21:02:00'},\n", - " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-11-06 01:38:00'},\n", - " {'oc': 188.35001996000003, 'txs': 9, 'date': '2019-11-06 16:31:00'},\n", - " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-07 08:56:00'},\n", - " {'oc': 186.11, 'txs': 8, 'date': '2019-11-08 01:04:00'},\n", - " {'oc': 185.0, 'txs': 8, 'date': '2019-11-10 13:46:00'},\n", - " {'oc': 186.11, 'txs': 9, 'date': '2019-11-11 23:06:00'},\n", - " {'oc': 187.22665999999998, 'txs': 8, 'date': '2019-11-12 07:31:00'},\n", - " {'oc': 188.35001996000003, 'txs': 8, 'date': '2019-11-13 10:47:00'},\n", - " {'oc': 189.48012007975998, 'txs': 8, 'date': '2019-11-13 17:49:00'}]" + "[{'oc': 239.49397412360375, 'txs': 8, 'date': '2020-05-31 04:02:00'},\n", + " {'oc': 241.8889138648398, 'txs': 9, 'date': '2020-05-31 07:23:00'},\n", + " {'oc': 244.30780300348817, 'txs': 8, 'date': '2020-05-31 10:22:00'},\n", + " {'oc': 246.7508810335231, 'txs': 8, 'date': '2020-05-31 14:34:00'},\n", + " {'oc': 239.49397412360375, 'txs': 9, 'date': '2020-06-01 12:22:00'},\n", + " {'oc': 241.8889138648398, 'txs': 9, 'date': '2020-06-01 16:11:00'},\n", + " {'oc': 244.30780300348817, 'txs': 8, 'date': '2020-06-02 14:51:00'},\n", + " {'oc': 246.7508810335231, 'txs': 8, 'date': '2020-06-02 23:05:00'},\n", + " {'oc': 239.49397412360375, 'txs': 9, 'date': '2020-06-03 06:16:00'},\n", + " {'oc': 241.8889138648398, 'txs': 9, 'date': '2020-06-03 08:45:00'},\n", + " {'oc': 244.30780300348817, 'txs': 8, 'date': '2020-06-03 19:32:00'},\n", + " {'oc': 246.7508810335231, 'txs': 8, 'date': '2020-06-04 01:06:00'}]" ] }, - "execution_count": 143, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1768,16 +1818,16 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "20" + "4" ] }, - "execution_count": 144, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1788,9 +1838,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "-100215.65907717952" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dydx_results = pd.read_csv(\"Files/From_2020-05-31_to_2020-06-07_open_close_at_240/dydx_results.csv\")\n", "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" @@ -1798,7 +1859,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1807,7 +1868,7 @@ "(239.4380835398584, 240.0, 247.20000000000002)" ] }, - "execution_count": 17, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1879,12 +1940,12 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - 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"text/plain": [ "
" ] @@ -1909,6 +1970,34 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "timestamp\n", + "2020-06-01 00:17:00 233.48\n", + "2020-06-01 00:18:00 233.48\n", + "2020-06-01 01:29:00 233.48\n", + "2020-06-01 01:30:00 233.48\n", + "2020-06-01 01:31:00 233.48\n", + "2020-06-01 01:32:00 233.48\n", + "2020-06-02 16:00:00 233.48\n", + "Name: close, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['close'].loc[data['close']==233.48]" + ] + }, { "cell_type": "markdown", "metadata": {}, From ae64601a3507c5d1a451ac14c3a1abee64571c28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Wed, 12 Oct 2022 10:10:27 -0300 Subject: [PATCH 05/16] trailing + ocs below floor approach updated --- hedge_scripts/Long_short/stgyapp.py | 1 - hedge_scripts/Short_only/aave.py | 20 +- hedge_scripts/Short_only/command_center.py | 107 +- hedge_scripts/Short_only/interval.py | 7 +- hedge_scripts/Short_only/parameter_manager.py | 38 +- hedge_scripts/Short_only/stgyapp.py | 1 - jupyter-lab/Simulations_lab.ipynb | 2974 +++++++++++++++-- 7 files changed, 2739 insertions(+), 409 deletions(-) diff --git a/hedge_scripts/Long_short/stgyapp.py b/hedge_scripts/Long_short/stgyapp.py index 03711ec..a923ad0 100644 --- a/hedge_scripts/Long_short/stgyapp.py +++ b/hedge_scripts/Long_short/stgyapp.py @@ -36,7 +36,6 @@ def __init__(self, config): self.historical_data = None - self.data_dumper = DataDamperNPlotter() def launch(self, config): diff --git a/hedge_scripts/Short_only/aave.py b/hedge_scripts/Short_only/aave.py index 0edb6e0..fdf3a5b 100644 --- a/hedge_scripts/Short_only/aave.py +++ b/hedge_scripts/Short_only/aave.py @@ -118,7 +118,7 @@ def ltv_calc(self): return self.debt / self.collateral_usd() def price_to_liquidation(self, dydx_class_instance): - return self.entry_price - (dydx_class_instance.short_pnl() + return self.entry_price - (dydx_class_instance.pnl() + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth def price_to_ltv_limit_calc(self): @@ -162,12 +162,12 @@ def repay_aave(self, stgy_instance): time = 0 if self.usdc_status: # update parameters - short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.short_entry_price) + short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price) new_short_size = dydx_class_instance.short_size - short_size_for_debt # pnl_for_debt = dydx_class_instance.pnl() # We have to repeat the calculations for pnl and notional methods, but using different size_eth - pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.short_entry_price) + pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price) self.debt = self.debt - pnl_for_debt self.ltv = self.ltv_calc() @@ -175,10 +175,10 @@ def repay_aave(self, stgy_instance): self.costs = self.costs + gas_fees dydx_class_instance.short_size = new_short_size - dydx_class_instance.short_notional = dydx_class_instance.short_notional_calc() - dydx_class_instance.short_equity = dydx_class_instance.short_equity_calc() - dydx_class_instance.short_leverage = dydx_class_instance.short_leverage_calc() - dydx_class_instance.short_pnl = dydx_class_instance.short_pnl_calc() + dydx_class_instance.notional = dydx_class_instance.notional_calc() + dydx_class_instance.equity = dydx_class_instance.equity_calc() + dydx_class_instance.leverage = dydx_class_instance.leverage_calc() + dydx_class_instance.pnl = dydx_class_instance.pnl_calc() # dydx_class_instance.price_to_liquidation = \ # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance) @@ -186,9 +186,9 @@ def repay_aave(self, stgy_instance): # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees dydx_class_instance.simulate_maker_taker_fees() notional_for_fees = abs(short_size_for_debt) * self.market_price - dydx_class_instance.short_costs = dydx_class_instance.short_costs \ - + dydx_class_instance.maker_taker_fees * notional_for_fees \ - + pnl_for_debt * dydx_class_instance.withdrawal_fees + dydx_class_instance.costs = dydx_class_instance.costs \ + + dydx_class_instance.maker_taker_fees * notional_for_fees \ + + pnl_for_debt * dydx_class_instance.withdrawal_fees # Note that a negative self.debt is actually a profit # We update the parameters diff --git a/hedge_scripts/Short_only/command_center.py b/hedge_scripts/Short_only/command_center.py index f1bc180..57cad75 100644 --- a/hedge_scripts/Short_only/command_center.py +++ b/hedge_scripts/Short_only/command_center.py @@ -12,7 +12,7 @@ from hedge_scripts.Short_only.stgyapp import StgyApp -def run_sim(period, open_close, slippage, max_txs, L): +def run_sim(period, open_close, slippage, max_txs, L, trailing): global ocs # Initialize everything with open("Files/StgyApp_config.json") as json_file: @@ -33,16 +33,16 @@ def run_sim(period, open_close, slippage, max_txs, L): # floor just in order to get triger_price['open_close_1'] = open_close_1 floor = open_close / ((1 + slippage) * (1 + mu + 2 * sigma)) # Now we define prices and intervals given K and vol - stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor) + stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing) # We create five equidistant OCs - oc1 = stgy.trigger_prices['open_close'] + oc1 = floor # oc2 = oc1 * (1+6/2/100) ocs = [oc1] - for i in range(1, 5): - globals()["oc" + str(i + 1)] = oc1 * (1 + 0.03 / 5) ** i # We define 5 OCs based on a top width of 3% + for i in range(1, 4): + globals()["oc" + str(i + 1)] = oc1 * (1 + 0.01) ** i # We define 5 OCs based on a top width of 3% ocs.append(globals()["oc" + str(i + 1)]) # But we start with the first oc1 - stgy.trigger_prices['open_close'] = oc1 + stgy.trigger_prices['open_close'] = oc4 stgy.parameter_manager.define_intervals(stgy) # print("Volatility:", vol) @@ -76,7 +76,7 @@ def run_sim(period, open_close, slippage, max_txs, L): stgy.aave.interval_current = stgy.parameter_manager.find_interval(stgy, stgy.aave.market_price)['interval'] # What is the price at which we place the collateral in AAVE given our initial_index? - stgy.aave.short_entry_price = stgy.aave.market_price + stgy.aave.entry_price = stgy.aave.market_price # We place 90% of staked as collateral and save 10% as a reserve margin stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3) stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3) @@ -87,10 +87,10 @@ def run_sim(period, open_close, slippage, max_txs, L): # What is the usdc_status for our initial_index? stgy.aave.usdc_status = True - stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.short_entry_price) * stgy.aave.borrowed_percentage - stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.short_entry_price) * stgy.aave.borrowed_percentage + stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage + stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage # debt_initial - stgy.aave.price_to_ltv_limit = round(stgy.aave.short_entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), + stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3) # stgy.total_costs = 104 @@ -98,9 +98,13 @@ def run_sim(period, open_close, slippage, max_txs, L): stgy.dydx.market_price = stgy.historical_data['close'][initial_index] # stgy.dydx.interval_current = stgy.historical_data['interval'][initial_index] stgy.dydx.interval_current = stgy.parameter_manager.find_interval(stgy, stgy.dydx.market_price)['interval'] - stgy.dydx.short_collateral = stgy.aave.debt - stgy.dydx.short_equity = stgy.dydx.short_equity_calc() - stgy.dydx.short_collateral_status = True + stgy.dydx.collateral = stgy.aave.debt + stgy.dydx.equity = stgy.dydx.equity_calc() + stgy.dydx.collateral_status = True + + # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor'])) + if (stgy.dydx.market_price <= stgy.trigger_prices['open_close']): + stgy.dydx.open_short(stgy) ######################### # Load interval_old # interval_old = stgy.historical_data['interval'][initial_index] @@ -122,6 +126,8 @@ def run_sim(period, open_close, slippage, max_txs, L): i = initial_index maker_fees_counter = [] + + stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['floor'] * (1 - trailing) while (i < len(stgy.historical_data)): # for i in range(initial_index, len(stgy.historical_data)): # pass @@ -133,6 +139,7 @@ def run_sim(period, open_close, slippage, max_txs, L): # new_interval_current = stgy.historical_data["interval"][i] interval_current = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i])['interval'] market_price = stgy.historical_data["close"][i] + previous_price = stgy.historical_data["close"][i - 1] ######################### # This case is when P crossed open_close_2 while increasing (therefore we had to close short), I_old = I_open_close_2, # but then it goes below open_close_2 again. @@ -161,6 +168,23 @@ def run_sim(period, open_close, slippage, max_txs, L): # and we also execute all the actions involved since last price was read time_used = stgy.parameter_manager.find_scenario(stgy, market_price, interval_current, interval_old, i) ############################## + # We update trailing + # Everytime price moves down more than trailing we update trailing_stop + if market_price * (1 + trailing) < stgy.trigger_prices['trailing_stop']: + stgy.trigger_prices['trailing_stop'] = market_price * (1 + trailing) + stgy.parameter_manager.define_intervals(stgy) + # If price moves above trailing we move trailing up in order to save that profit + # Is important to change trailing after finding scenarios (because we need to close the short first) + elif market_price * (1 + trailing) > stgy.trigger_prices['trailing_stop']: + stgy.trigger_prices['trailing_stop'] = market_price + stgy.parameter_manager.define_intervals(stgy) + + # If price goes above floor again, we start at oc1 = floor, trailing_stop = floor * (1-trailing) and repeat the process + # We need to write the case market > floor but in terms of trailing in order to not change ocs at the beginning of the sims + # if stgy.trigger_prices['trailing_stop'] >= stgy.trigger_prices['floor']: + # stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['floor'] * (1-trailing) + # stgy.trigger_prices['open_close'] = stgy.trigger_prices['floor'] # = oc1 + ############################## # We update vol and ocs if short_status = False # if not stgy.dydx.short_status: # current_date = list(stgy.historical_data.index)[i] @@ -183,22 +207,20 @@ def run_sim(period, open_close, slippage, max_txs, L): # if short = open and if there are up_choices available, we take the last option (the furthest) # if there isn't options we take max_distance # random.seed(4) - if stgy.dydx.short_status: + # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], + # 'txs': stgy.dydx.maker_fees_counter, + # # 'index': i, + # 'date': str(stgy.historical_data.index[i])}) + if not stgy.dydx.short_status: + if stgy.trigger_prices['open_close'] == oc4: + stgy.trigger_prices['open_close'] = oc1 + # oc_choice_up = random.choice(range(len(ocs_choices['up_choices']))) + # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] + elif stgy.dydx.short_status: if len(ocs_choices['up_choices']) != 0: stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0] # oc_choice_up = random.choice(range(len(ocs_choices['up_choices']))) # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] - else: - pass - # if short = close and if there are down_choices available, we take the first option (the furthest) - # if there isn't options we take max_distance - else: - if len(ocs_choices['down_choices']) != 0: - stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][-1] - # oc_choice_down = random.choice(range(len(ocs_choices['down_choices']))) - # stgy.trigger_prices['open_close'] = ocs_choices['down_choices'][oc_choice_down] - else: - pass # If we didnt change oc we dont clean maker_fees_counter if current_oc != stgy.trigger_prices['open_close']: maker_fees_counter.append({'oc': stgy.trigger_prices['open_close'], @@ -232,4 +254,37 @@ def run_sim(period, open_close, slippage, max_txs, L): # we increment index by the time consumed in executing actions # i += time_used i += 1 - return maker_fees_counter \ No newline at end of file + return maker_fees_counter + +if __name__ == '__main__': + # Track historical data + # symbol = 'ETHUSDC' + # freq = '1m' + # initial_date = "1 Jan 2019" + # stgy.get_historical_data(symbol=symbol, freq=freq, + # initial_date=initial_date, save=True) + + # Load historical data if previously tracked and saved + + historical_data = pd.read_csv("Files/ETHUSDC-1m-data_since_1 Sep 2019.csv")[] + # # assign data to stgy instance + define index as dates + timestamp = pd.to_datetime(historical_data['timestamp']) + historical_data = pd.DataFrame(historical_data["close"], columns=['close']) + historical_data.index = timestamp + # + # ####################################################### + periods_n_open_close = [[["2019-09-01", "2019-12-31"], 148], [["2019-09-01", "2019-12-31"], 185], + [["2020-01-01", "2020-05-01"], 135]] # , [["2020-05-01","2020-09-01"],240]] + periods_n_open_close = [[["2019-09-01", "2019-12-31"], 185]] + periods_n_open_close = [[["2020-05-31", "2020-06-07"], 240]] + ########################################################## + max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late) + L = 5 * 0.07 + trailing = 0.01 + for period_n_open_close in periods_n_open_close: + period = period_n_open_close[0] + open_close = period_n_open_close[1] + slippage = 0.0005 + maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing) + ########################################################## + print(maker_fees_counter) \ No newline at end of file diff --git a/hedge_scripts/Short_only/interval.py b/hedge_scripts/Short_only/interval.py index f892bc6..485e018 100644 --- a/hedge_scripts/Short_only/interval.py +++ b/hedge_scripts/Short_only/interval.py @@ -1,5 +1,10 @@ class Interval(object): - def __init__(self, left_border, right_border, name, position_order): + + def __init__(self, + left_border, + right_border, + name, + position_order): self.left_border = left_border self.right_border = right_border self.name = name diff --git a/hedge_scripts/Short_only/parameter_manager.py b/hedge_scripts/Short_only/parameter_manager.py index fb79023..856d511 100644 --- a/hedge_scripts/Short_only/parameter_manager.py +++ b/hedge_scripts/Short_only/parameter_manager.py @@ -8,17 +8,20 @@ class ParameterManager(object): # auxiliary functions @staticmethod - def define_target_prices(stgy_instance, slippage, vol, floor): + def define_target_prices(stgy_instance, slippage, vol, floor, trailing): mu = vol[0] sigma = vol[1] p_open_close = floor * (1 + slippage) * (1 + mu + 2 * sigma) + p_trailing = floor * (1 - trailing) ########################################################## # We define the intervals list_of_intervals = ["open_close", "floor", + "trailing_stop", "ltv_limit"] list_of_trigger_prices = [p_open_close, floor, + p_trailing, stgy_instance.aave.price_to_ltv_limit] # We define/update trigger prices for i in range(len(list_of_intervals)): @@ -34,12 +37,15 @@ def define_intervals(stgy_instance): "open_close": Interval(stgy_instance.trigger_prices['floor'], stgy_instance.trigger_prices['open_close'], "open_close", 1), - "floor": Interval(stgy_instance.trigger_prices['ltv_limit'], + "floor": Interval(stgy_instance.trigger_prices['trailing_stop'], stgy_instance.trigger_prices['floor'], "floor", 2), + "trailing_stop": Interval(stgy_instance.trigger_prices['ltv_limit'], + stgy_instance.trigger_prices['trailing_stop'], + "trailing_stop", 3), "minus_infty": Interval(-math.inf, stgy_instance.trigger_prices['ltv_limit'], - "minus_infty", 3)} + "minus_infty", 4)} # function to assign interval_current to each market_price in historical data @staticmethod @@ -124,17 +130,17 @@ def update_parameters(stgy_instance, new_market_price, new_interval_current): # DYDX stgy_instance.dydx.market_price = new_market_price stgy_instance.dydx.interval_current = new_interval_current - stgy_instance.dydx.short_notional = stgy_instance.dydx.short_notional_calc() - stgy_instance.dydx.short_equity = stgy_instance.dydx.short_equity_calc() - stgy_instance.dydx.short_leverage = stgy_instance.dydx.short_leverage_calc() - stgy_instance.dydx.short_pnl = stgy_instance.dydx.short_pnl_calc() + stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc() + stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc() + stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc() + stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc() # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client) @staticmethod def reset_costs(stgy_instance): # We reset the costs in order to always start in 0 - stgy_instance.aave.short_costs = 0 - stgy_instance.dydx.short_costs = 0 + stgy_instance.aave.costs = 0 + stgy_instance.dydx.costs = 0 def find_scenario(self, stgy_instance, new_market_price, new_interval_current, interval_old, index): actions = self.actions_to_take(stgy_instance, new_interval_current, interval_old) @@ -174,6 +180,10 @@ def actions_to_take(stgy_instance, new_interval_current, interval_old): if list(stgy_instance.intervals.keys())[i + 1] == 'open_close': actions.append('close_short') + # CASE: open_close_1 APPROACH + elif list(stgy_instance.intervals.keys())[i + 1] == 'trailing_stop': + actions.append('close_short') + # CASE: TOO MANY FEES FOR open_close_1 APPROACH # if list(stgy_instance.intervals.keys())[i+1] == 'open_close_2': # actions.append('close_short') @@ -187,10 +197,14 @@ def actions_to_take(stgy_instance, new_interval_current, interval_old): else: for i in range(interval_old.position_order + 1, new_interval_current.position_order + 1): - # In both cases we open at open_close_1 bc for open_close_2 case we manage the opening + # In both cases we open at open_close_1 bc for open_close_2 case we manage the opening # from inside the for loop of the run_sims if list(stgy_instance.intervals.keys())[i] == 'open_close': actions.append('open_short') + + elif list(stgy_instance.intervals.keys())[i] == 'trailing_stop': + actions.append('open_short') + else: actions.append(list(stgy_instance.intervals.keys())[i]) # print(actions) @@ -212,9 +226,9 @@ def simulate_fees(stgy_instance): @staticmethod def update_pnl(stgy_instance): - stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.short_costs - stgy_instance.dydx.short_costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees + stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees @staticmethod def add_costs(stgy_instance): stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \ - + stgy_instance.aave.short_costs + stgy_instance.dydx.short_costs \ No newline at end of file + + stgy_instance.aave.costs + stgy_instance.dydx.costsnce.dydx.short_costs \ No newline at end of file diff --git a/hedge_scripts/Short_only/stgyapp.py b/hedge_scripts/Short_only/stgyapp.py index 03711ec..a923ad0 100644 --- a/hedge_scripts/Short_only/stgyapp.py +++ b/hedge_scripts/Short_only/stgyapp.py @@ -36,7 +36,6 @@ def __init__(self, config): self.historical_data = None - self.data_dumper = DataDamperNPlotter() def launch(self, config): diff --git a/jupyter-lab/Simulations_lab.ipynb b/jupyter-lab/Simulations_lab.ipynb index 10e09a3..c0a9b06 100644 --- a/jupyter-lab/Simulations_lab.ipynb +++ b/jupyter-lab/Simulations_lab.ipynb @@ -2,48 +2,49 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.5.0)\n", - "Requirement already satisfied: scipy in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.9.1)\n", - "Requirement already satisfied: pygsheets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (2.0.5)\n", - "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", 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"/usr/lib/python3/dist-packages/secretstorage/util.py:19: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", + " from cryptography.utils import int_from_bytes\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.0.5)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (1.4.1)\n", + "Requirement already satisfied: pygsheets in /home/agustin/.local/lib/python3.8/site-packages (2.0.5)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.2.2)\n", + "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.19.4)\n", + "Requirement already satisfied: pytz>=2017.2 in /usr/lib/python3/dist-packages (from pandas) (2019.3)\n", + "Requirement already satisfied: python-dateutil>=2.6.1 in 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/usr/local/lib/python3.8/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.0.6)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/agustin/.local/lib/python3.8/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.26.8)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.8)\n" ] } ], @@ -89,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 41, "metadata": { "tags": [] }, @@ -185,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -233,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -574,8 +575,8 @@ " self.costs = self.costs + self.maker_taker_fees * self.notional\n", "\n", "\n", - " price_floor = stgy_instance.trigger_prices['floor']\n", - " floor_position = intervals['floor'].position_order\n", + " trailing_stop = stgy_instance.trigger_prices['trailing_stop']\n", + " trailing_interval_position = intervals['trailing_stop'].position_order\n", "\n", " price_to_repay_debt = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", " aave_class_instance)\n", @@ -583,22 +584,22 @@ " stgy_instance.trigger_prices['repay_aave'] = price_to_repay_debt\n", " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", " if price_to_ltv_limit < price_to_repay_debt:\n", - " intervals['floor'] = Interval(price_to_repay_debt, price_floor,\n", - " 'floor', floor_position)\n", + " intervals['trailing_stop'] = Interval(price_to_repay_debt, trailing_stop,\n", + " 'trailing_stop', trailing_interval_position)\n", " intervals['repay_aave'] = Interval(price_to_ltv_limit, price_to_repay_debt,\n", - " 'repay_aave', floor_position + 1)\n", + " 'repay_aave', trailing_interval_position + 1)\n", " intervals['minus_infty'] = Interval(-math.inf, price_to_ltv_limit,\n", - " 'minus_infty', floor_position + 2)\n", + " 'minus_infty', trailing_interval_position + 2)\n", " else:\n", " print(\"CAUTION: P_ltv > P_repay\")\n", " print(\"Difference of: \", price_to_ltv_limit - price_to_repay_debt)\n", " price_to_repay_debt = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", - " intervals['floor'] = Interval(price_to_ltv_limit, price_floor,\n", - " 'floor', floor_position)\n", + " intervals['trailing_stop'] = Interval(price_to_ltv_limit, trailing_stop,\n", + " 'trailing_stop', trailing_interval_position)\n", " intervals['ltv_limit'] = Interval(price_to_repay_debt, price_to_ltv_limit,\n", - " 'repay_aave', floor_position + 1)\n", + " 'repay_aave', trailing_interval_position + 1)\n", " intervals['minus_infty'] = Interval(-math.inf, price_to_repay_debt,\n", - " 'minus_infty', floor_position + 2)\n", + " 'minus_infty', trailing_interval_position + 2)\n", " self.order_status = False\n", " return 0\n", "\n", @@ -650,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -661,7 +662,7 @@ " mu = vol[0]\n", " sigma = vol[1]\n", " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", - " p_trailing = floor * (1-trailing)\n", + " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", " ##########################################################\n", " # We define the intervals\n", " list_of_intervals = [\"open_close\",\n", @@ -683,12 +684,12 @@ " stgy_instance.intervals = {\"infty\": Interval(stgy_instance.trigger_prices['open_close'],\n", " math.inf,\n", " \"infty\", 0),\n", - " \"open_close\": Interval(stgy_instance.trigger_prices['floor'],\n", + " \"open_close\": Interval(stgy_instance.trigger_prices['trailing_stop'],\n", " stgy_instance.trigger_prices['open_close'],\n", " \"open_close\", 1),\n", - " \"floor\": Interval(stgy_instance.trigger_prices['trailing_stop'],\n", - " stgy_instance.trigger_prices['floor'],\n", - " \"floor\", 2),\n", + "# \"floor\": Interval(stgy_instance.trigger_prices['trailing_stop'],\n", + "# stgy_instance.trigger_prices['floor'],\n", + "# \"floor\", 2),\n", " \"trailing_stop\": Interval(stgy_instance.trigger_prices['ltv_limit'],\n", " stgy_instance.trigger_prices['trailing_stop'],\n", " \"trailing_stop\", 3),\n", @@ -901,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -960,7 +961,7 @@ " data_dydx.append(stgy_instance.total_pnl)\n", " data_dydx.append(mkt_price_index)\n", " # print(interval_old.name)\n", - " # print(data_dydx, list(dydx_instance.__dict__.keys()))\n", + "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", " if sheet == True:\n", " gc = pygsheets.authorize(service_file=\n", " 'stgy-1-simulations-e0ee0453ddf8.json')\n", @@ -968,8 +969,8 @@ " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", " else:\n", - " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " with open(path_to_aave, 'a') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", " writer.writerow(data_aave)\n", @@ -980,8 +981,8 @@ "\n", " @staticmethod\n", " def delete_results(stgy_instance, period, oc1):\n", - " file_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " file_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", " os.remove(file_aave)\n", " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", @@ -1035,8 +1036,8 @@ " \"total_stgy_pnl\",\n", " \"index_of_mkt_price\"]\n", " \n", - " path_to_aave = 'Files/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = 'Files/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " with open(path_to_aave, 'a') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", " writer.writerow(aave_headers)\n", @@ -1064,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1077,7 +1078,7 @@ "\n", "# Load historical data if previously tracked and saved\n", "\n", - "historical_data = pd.read_csv(\"Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "historical_data = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", "# # assign data to stgy instance + define index as dates\n", "timestamp = pd.to_datetime(historical_data['timestamp'])\n", "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", @@ -1095,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1115,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1153,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1181,23 +1182,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0005989101310066664,\n", - " 0.0011978202620133327,\n", - " 0.0023956405240266655,\n", - " 0.0035934607860399984)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# normal_std = std\n", "# medium_std = 2*std\n", @@ -1215,17 +1202,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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0traitdUziXddXR0AwGq1wmq1BvNPSCnCd8PviKhteCwRBcZud4iPZ32yHjNHdJG9fqCinsdTDAhl7hx2e1x8706Haz+x2x1xsT2JiscSUXjwWCIKHx5PROHBY4koPHgsBSaY70fnbMPsx3v37sWAAQOwZcsWDBs2DICrzN22bdvgdDpRWFiImTNn4qGHHkJmZiYA4OGHH8YPP/yAjRs3ius5cOAA+vbti99//x2jR4/2+pxHH30Ujz32mNfyTz75RFwvERERxdbCEh2+P+jJPrq0rx2f7fc8H5jnwKwhDrW3UgTdv8aAJrsOD4yyoXNGrLcGmHtYh5+LDTipswOX9OX+QERERERERBQrTU1NuPzyy1FbW4vc3FyfbUOevMDhcOD222/HxIkTxUASAFx++eXo1asXunbtis2bN+Pee+/Frl278M033wAASktL0bmzvOyN8Ly0tFT1s+6//37ceeed4vO6ujr06NED06dP9/sPTGVWqxXz58/HtGnTYDKZYr05RAmLxxJRYI4uL8L3B3eLz4cOGw7s3y4+79W1ENOmDeXxFGUPblgI2G2YOuUU9O6QFevNwf5F+/Bz8T706NETZ589JNabk7D420QUHjyWiMKHxxNRePBYIgoPHkuBEarABSLkYNKsWbOwdetWLFu2TLb8hhtuEB8PHz4cXbp0wWmnnYZ9+/ahX79+IX1WWloa0tLSvJabTCbuCAHg90QUHjyWiHwz6BVzIikm6XE4IR5DPJ6ix+Euc2eOk+/caHDtJ3q9Li62J9HxWCIKDx5LROHD44koPHgsEYUHjyXfgvluQpqK+eabb8bs2bOxaNEidO/e3WfbE044AYCrJB4AFBYWoqysTNZGeK41zxIRERElHiGIIbA5Qq6sS20gfO16nS62G+ImbEbohZaJiIiIiIiIKNqCCiY5nU7cfPPN+Pbbb7Fw4UL06dPH73uEuZG6dHFNwj1hwgRs2bIF5eXlYpv58+cjNzcXQ4aw1AkREVGiUsYqLDb5fDh2BpNiwuGO2uj18RJMcm0Hg0lEREREREREiSOoMnezZs3CJ598gu+//x45OTniHEd5eXnIyMjAvn378Mknn+Dss89Ghw4dsHnzZtxxxx2YPHkyRowYAQCYPn06hgwZgiuvvBLPPfccSktL8eCDD2LWrFmqpeyIiIgoMX265pDsuc3h0GhJkSQGk+IjliRygtEkIiIiIiIiokQRVGbSa6+9htraWkyZMgVdunQR//v8888BAGazGQsWLMD06dMxePBg3HXXXbjwwgvx448/iuswGAyYPXs2DAYDJkyYgCuuuAJXXXUVHn/88fD+y4iIiCimyupaZM+ZmRQbwtduYJk7IiIiIiIiIgpRUJlJTj93/T169MDixYv9rqdXr16YM2dOMB9NRERECabRYpc9t9oZPYgFIYini5dgEuJjO4iIiIiIiIgocEFlJhERERGFiplJvpXXteCdZQdQ22QN2zqlA4EMcVbnjnsDERERERERUeIIKjOJiIiIKFQ2BpN8+vP7a7H1SB3WHKjEG1eOC8s6pQG8eIklxUmCFBEREREREREFgZlJREREFBX+yuWmuq1H6gAAv2wrC/g9TqcTn605hM3FNaqvW+wO8bHZyMs+IiIiIiIiIgoNM5OIiIgoKhhLCr/fdh/Dfd9sAQAUPTPD6/VPVh8SH5sN8RVM4v5ARERERERElDjiq1eBiIiIkpaTs+SE3YFjjeLjQ5VNstdarHY8+dMO8bkxToJJrHJHRERERERElHjio1eBiIiIkl4yTZn0v2UHMOHpX3GgotF/4xDY7A6sK6pCQ6vN67WiikZc/tYqvL10PxyS9J4jNc2ydmrvJSIiIiIiIiIKBcvcERERUVQ4kqiu2eOztwMAzv/vcmx6ZHqb13esvlV83CknDc/O3Ym3lh7A2F7t8PVfTxJfe/rnHXhj8X4AwIp9lbJ1pJs8Y4TeXX4Aj/24vc3bFUnMVCMiIiIiIiJKHAwmERERUVhoxYrSTXq0WB1IxtiBze4Iy3pKJFlFx+pb8dbSAwCA9QerxeV3fL4R3244orkO4estq2vxCiRdPK57WLYzHHSsc0dERERERESUcFjmjoiIiMJCK/Mo3WTw+XoiS3P/29qqxWr328ZXIAkALn1zFVqsdry9dL9seXaaEc9dNLJN2xcRybc7EBERERERESUtBpOIiIgoLLRiAxnugEsyxg7SjOG5lGqxaWc4BRJoAgCLzYGv1hfj3eVF4rIhXXLxn8tGtXHrwksHpiYRERERERERJRoGk4iIiCgstDKPpg4uEF//eM1hvLDFgKpGSzQ3LWycTifWHKgSn4ctmOQjYFTTZA14PfUtNkzo10F8Pue2k3Hq4M5t2jYiIiIiIiIiIgaTiIiIKCy0qtj9caxrvh6HA3j0xx0oatDhvRUHo7hl4TNnSykufmOl+Dw9TGXu9h9r1Hztpy1HVZd3zDZ7LSutbUZdsyv49MIlcVjaTiIZM9WIiIiIiIiIkhWDSURERBQWDod6eMCg9y5rlqiBhLnbSmXPpZlJK/dV4q4vNuFwVVPQ63127k7N11qsdtXvVi1j6f2VB7GpuBaAp7xgvNGxyh0RERERERFRwjHGegOIiIgoOWgFiPTu6IFdEhDJSU/MS5BMRYAmzeh6/sXaw7jn680AgMNVTfjixgkBr1MrCCewO5yyEoLPXjgcO47Wo6rRgh82lWi+zxymEnxEREREREREROxlICIiorDQmjNJ0NBqEx/nppsivTkR0bdTluy50eAKlAmBJADYWVoX8PqcTif+NW+XzzY2uwN2yXd71vAuePTcoejeLsPn+9KN8ZmZJHD62V+IiIiIiIiIKH4wmERERERhoZVgI2QmSYNJQhAm0WSa5QEaIYA2vFueuKyuxQYtdocTP2wqwa7SegDAtpI6vPrbPvH1S8b18HrPfxbuhcPheW5wf59d8rWDSb06ZGJMr3Y+/iVERERERERERIFjMImIiIjCQyPTRK9ytWH3U9otXlnt8u0W/hlZaYFlAS3fW4FbP92Ac15eCrvDibK6FvG1vp2ycOHY7qrvk2Z9CXNQXTKuB3p1yAQA/POiEbL2714zHulxOmcSERERERERESWexJywgIiIiOKOVnxILcZkszu8FyYAm0O+3cJ8R00Wu7jsuC65mu8vdQePrHYnvt94BBsP14ivPX/xKFmQrTA3XWz/6ZpD4nJ3YhLMRj0W3z1VXH73V55SeyZD/I8XSsxwIhEREREREVFqYjCJiIiIwkJrziS1LCRbgmYmHatvlT0X5jLaU9YgLrP6CJRJ5wm684tN4uNxvdphVI98OBxOnDq4AD3bZ2LBjjLx9Sd/2iE+Fsrc+RLPwSRdANtPRERERERERPGFwSQiIiIKC63wkGpmUoIGk77bWCJ7vuFQDd5fUYRmqyczaW95A2x2B4wqAR1lmTzBtCGdAQB6vQ7/u2Y8AGDfsQYUVzd7tRXK3PmSborfYJJAI/ZIRERERERERHEo/nsaiIiIKCFoZiapLK9rtkV6cyIi0+w9D9EjP2zzWvbUnJ2q79fKWlLLJLr3zMGqbbUyexb9bQr6F2TjjtMHIj/TrNomHjAviYiIiIiIiCjxMJhEREREYaGVaaJW5q6qyRLhrQm/JosNByubAAA/3DzR6/U/jO4mPv7f8gOq69AKJp3Yt4PXsmHd8vDGlWMD3r4+HbOw4M5TcNvpAwJ+DxERERERERFRIBhMIiIiorBwaJSuU8tYsmmUe4tnpbUtAIA0ox5DuuR6vX7Z8T19vv/3Q9X4cl0xAOCcEV1kr3XOTVN9zxlDC/HMBcND2dy4l3h7ABEREREREVHqYjCJiIiIwkIrOKAWOJKWvltXVIXn5+/WzNqJF5WNrmyqwrx0GA16dM1Ll70+tKsnwGRWlK3bXFyDC15dgT3lDQBc8x5dOKa7+HqHbPVgEgB0a5fR5m2PJxpV+oiIiIiIiIgojjGYRERERGGhNWfSoMIcr2V2SYDpotdX4j+/7sFnaw9HbNvCobKhFQDQIcs1H9E9kjmNzAY9MkwGXD2hFwDghL7tZe/dcKhG9nzmiK6wOQILnmWajaFuMhERERERERFRWDCYRERERGGhNWdSe3fwRUptHqWV+yrCvUlhVdNkBQDkZ7r+PQa9PMVGr9dhbG9XEEn57+uUI888ys80ITfdFNDnjumZLz7OMBmC2uZ45tTaYYiIiIiIiIgo7nCoKxEREYVFMMEBISunvsUqLpuzpTTs2xRODa02AEBWmuvyySgJJv11Sj/ZMps7mDR7cwlW7KtE7w6ZsnWlmwy4blIfrC2qwgVjuvn8XJ1Ohx2Pn4mPVx/EtCGdw/OPiSFWuSMiIiIiIiJKPAwmERERUVioJBv5bOt0OvHu8iLZ8tLaFhQq5iKKF00WOwAgO82VHaSXBJPS3RlDeveEQGsOVOHiN1ZizYEqAN5ZTJlmA3p3zMLc2ycH9NkZZgOuP7lv2/4BcYZ5SURERERERESJg2XuiIiIKCzU5kwqUJR3E9gcDszdWorn5++WLX/1t70R2bZwaLS4MpOEOYykmUmtNlegSRo0EgJJgLzs3f+d3Ad9OmZFdFuJiIiIiIiIiMKJwSQiIiIKCyFcMq5XO3GZkKmjZHc4caSm2Wt5ehzPCdSoKHMnjZ2d0KcDAKDFave5jpkju+LvM4ZAp/G9pIJU/rcTERERERERJSoGk4iIiCgshDmTpgzqJC7TihvYHE5ZZo8gnoNJc7e65nQSytw1SwJH43q7AmjZ6b4rCKcZeeklYp07IiIiIiIiooTBHg0iIiIKC4fD9X9p5omQmfTshcMVbZ2qcyw5VUrlxYuO2fKSfcd1yQEAdMlLh8nguqQ6uX9Hn+tgMEk7wEhERERERERE8cv38FkiIiKiAAlzJqkFC4Z1y5M9tzmcqnMs2dQiTHHCandFy0Z2zwcA9C/IwdzbT0amyXM5ZTTocen4Hvhs7WHVdaQZ4zfzioiIiIiIiIhIC4fHEhERUVgIYSDpPEl695WGMm5kdzhhVwkc2dwBG8HcraW4/v21qGmyiMv2HWvAT5uPwhHlwJPV7vo8kyS7aHBhLnp2yJS1u2BMd+h0wOnHdcY7V4+TvTbYnc1EgJN17oiIiIiIiIgSBjOTiIiIKCyETCO9DnhwxnF48qcdeO7Ckapt7U71MnfKzKQbP1oPAHh6zk48e9EI2OwOnPbvxQCAj647AZMG+C4rF6zFu4+h2WLDmcO6eL0mZCaZ9L7H4hzfpz1W338a2meZYTTosePxM1HXYkWr1eEVeEpFrHJHRERERERElHgYTCIiIqKwELKPdNDh+pP74qoJvWHWmCPIrlHmTi1bCQAOVDYCAOpabOKyktrmNm3v9pI6fP17Mf7v5L4ozEuH3eHE1f9bI2vTOTcNr10xFmN6tpNkJvkPhxTkpouPM8wGZJhZ3o6IiIiIiIiIEhfL3BEREVFYOBVzJmkFkgBgW0k9yupavJbvPFqv2r7BHUSqbGj1+rxQ3fnFRryz7ACe/nmH7DOkyupacckbK1HfYkWF+7ONfjKTKDBt/PMRERERERERURSxN4SIiIjCQkgqks6Z5MsHKw96LVtTVKXa1u5wwmJzYNoLS8RlFptDtW2gdpa6AleLdx8DAGw5Uqvazmp3Yvij88TnaT6CZBSAAPcPIiIiIiIiIoof7A0hIiKisHAoMpOk2pqFcqyhFR+tkgefWtsYTBI4HE602uy44p3VAbXvmp8Rls9NdcxMIiIiIiIiIkocDCYRERFRWAixAbXMpP4F2QGvZ8Z/lqKxVV5yrqrRIpajE4QrmFTXYsP3G0rE593baQeLMkwGGPTMrGkLfntEREREREREiYfBJCIiIgoLYQ4jtVhLhtmALY9Ox3MXDPO7nm0ldZj1ye9ey2eO6Cp7Lg0mVTS04txXluFfv+wC4CqBt3Bnmeq8TGqKq5vEx3NuOxlXTeil2u7FS0cFtD4iIiIiIiIiomRijPUGtEljI2AweC83GID0dHk7LXo9kJERWtumJu0aLTodkJkZWtvmZsDhY7R1VlZgba1W+fOWFsBuD2y9/tpmZnrqGLW2AjbvSctDapuR4fqeAcBi8f43hNo2Pd2zrwTT1mp1tdeSlgYYjcG3tdlc34UWsxkwmYJva7e7/nZaTCZX+2DbOhyufS0cbY1G13cBuI6JpqbwtA3muA/2HGE0Bt42kc4RyrY8RwTflucI12P3cW9sbkaGpQXGlmb5seJum5NuQu926ciweK93QEE29pQ3wK43wGI04bddxwCnU9Y2294qe25v9jz+fsMR7Nlfhj37y/C3Sd3x8bIDeO6XXRjYORvf3zxJdtzbHU488OEq2bps9fXIsLRg6uBOyIUdj583DPecORhGvQ73vL8S87eXAQBybK3yf1syX0co24bpHGFodv1bnEIuG88Rwbe12YDGRhhaWlz7nHCMC+L0HBGWtol4HcFzRPBto3kdYbWqH0uJfo5IwOuIsLTlOcIlVucI5fGUDOeIcLTlOcKF54jA2wrHUnOz/Lcp0c8RvtryHBF8W54jXI99HfdWK3TS7zNZzhGCcF1H+HqfkjMB1dbWOgE4a13/ZO//zj5b/obMTPV2gNN5yinyth07arcdN07etlcv7bZDhsjbDhmi3bZXL3nbceO023bsKG97yimabR2Zmc7vvvvOabFYXG3PPlt7vcpd4aKLfLdtaPC0vfpq323Lyz1tb7rJd9sDBzxt//Y33223bvW0feQR323XrPG0fe45320XLfK0feUV321nz/a0ffdd322/+MLT9osvfLd9911P29mzfbd95RVP20WLfLd97jlP2zVrfLd95BFP261bfbf92988bQ8c8N32pps8bcvLfbe9+mpP24YG320vusgp46ttkOcIi8XiOZaS6BzhzMyUt+U5woXnCJcInSMsu3f7bPv+6BnOXvfOdva6d7bfc8SGU88T1/vmnM2+t0FyjtheUuu7reIcYU/R64hIniMG3/GV84YP1rra8hzhwnOESxJeR8jwHOHC6wgXniNceI7w4DnChecIF54jXFL4HGGfPFnelucIF54jXHiOcPFzjth9/vmevvEkO0eE6zqiFnACcNbW1jr9SezMJCIiIkpZe8sb8O33W9FideCHFXvwfwG+77K3VmFjEJ/DmsBERERERERElOp0rmBYYqmrq0NeXh5qS0qQm5vr3SCV08olrFYr5ixejLPPPhsmk4kpo0wZZVp5KG31eliNRsyZM8d1LPn6GyfYOcKrLc8RwbflOcL12H3c3/TxeizaeQxPnD8UF43todrW2tqKm176ActKPSGaX24/GT07ZOG4h+aKZe4A4MnzhuIfX64X2w0oyMKe8kYM756HLcW1srZwOpFhdX0P95wxCAt3lmPdwWoAwI4nzhSP+6V7juHKd9aoltoDgNOHFODlK8bzOkKtbZjOEZ+tPYT7ft6H6UML8eZV43iOCPEcYW1owC+//IIzzjjDdZ0nFafniLC0TcDrCJ4jQmgbxesIq9Wqfiwl+DkiEa8jwtKW5wiXGJ0jvI6nJDhHhKUtzxEuPEcE3FY8ls46CyZpv2eCnyN8tuU5Ivi2PEe4Hvs47q1WK35esABnnXee63cpSc4RojBdR9TV1SGva1fU1taqx1okEjszKStLfjLx1S6YdQZKejINZ1vpH7YtbZUnH+mO6E8wbdPSPAdPONuazZ4TQ6zamkze8xCEo63RKJ+HJ1xtDYbA9+Fg2ur1kWmr00WmLRDettJjKZnOEUo8RwTflucIF/dx32LOQLM5HY4MH7/Pej0c6eloNnuCSea8XCArHc1m+X714PfbAMmyBmM6ms121BvSvNpCpxOXPbbooHvFrue29AwYDXpY7Q48NWcnAHi/3+28kwZ679+peh2hFKZzhCMjy3PDBvAcEUpboxHIyoI9Pd21z/l6XxydI8LeNlGuI0Jtm6LnCC+RPkdYrf6PpUQ8RyTgdUTY2/IcEVrbtpwjfB1PiXqOiGVbniNcUvEcIRxLymMs0c8RvvAcEXxbniNcfB33Viuc0n93spwjwtFWetz7Cs4qsHILERERhYWQ7CyNE6gxSq4+uualo3Ou64L9pH4dfL7PYneNart4XPegtqvZaseJT/2KsU/MhzQh22z0vgzKMBuCWjcRERERUaiqGy1IwIJBRESUohhMIiIiorBwuO+D9X6iSUad54b5nWvGQ+du/8n/nYjTBhdovu9IjSsdfXzv9rhgTLeAt2vZngqU1rWgrsWGnaX1AIAXLhmJrY+e4dXWZOClUbSw24SIiIhS2cp9lRj9xHzc8fnGWG8KERFRQNhjQkRERGHhCCEzyaiXN37nmvHolKNeWkAYtGk06DC6Z7uAt6uo0rt2cZbZqJqZZDL42XhqM3/7BxEREVEqePW3vQCA7zaWxHhLiIiIAsNgEhEREYWFEEzyl5kkjR8Z9N5tM/2UmrPZnZg5ogvOH9UVGSZ528uO7+nVft72Uq9lEzRK6qkFmCgyWNGFiIiIUplOcs1c32L10ZKIiCg+sMeEiIiI2szpdGL53krXYz8FzAx+gknKAJH3ZwH5mWa8eOlozLtjsuy1qyb08mq/4VCN7Hl+pgk56eqTj/bqEMSElRQSJiYRERERAXXNngDSQZVMeiIionjDYBIRERG1WUOrTXxc2WDx2dZfZpK/eYuGdcsVH3dvlyF7rX9Bts/3Aq7MJkFehjyolJ1m9Pt+IiIiIqK2sjkcsd4EIiKioDCYRERERG0mzUVSCxBJSS8+jPrg5y2SlgTR6XSyAJDJoEfRMzNQ9MwMHNclV+3taLR4Al+zb5kkPj6xb3ufn0vhxjp3RERElLqkA5zeWLIf/120N4ZbQ0RE5B+H3xIREVGb2SU3w/4yi/yVuTtUFVyZD6fG5DtZfuZeAoAe7TOx9J6p+HVHGc4YVhjU51Jo/EypRURERJQSpJewP24qwY+bgAvHdEdhXnrsNoqIiMgHZiYRERFRm1klZTr0fqIFJU2e19WSmCokZfIeP2+o3892aCS49Gifqbo8R1HKrkf7TFwzsQ+65GWoticiIgrG6v2V+H7jEc3BDkREAGBXOUc0W+0x2BIiIqLAMDOJiIiI2kxapsNvW0l5+FzFnEVKmWb/lypOjXJpyk68C0Z3w+wtR3HHtIH+N5Iijn2sRJSsbvxoPaqbrGifZcbJAzrFenOIKE45VC6G1JYRERHFCwaTiIiIqM2CCSZBko2kVhLPZNDB6l6fslTda38ao7I69Uyohlab7Pl9Zw3G85eMCnw7KSK0/l5ERMmiuskKACiqaGQwiYg0OVTS6612h0pLIiKi+MAyd0RERNRm0jJ3WplCgtO7OtA1Lx1/m66eIfTcRSPExxmKYNJZw7t4tb9uUh8AwPF92suW33fWYNlzf3M5UXRx3C0RJSNpVqxerZYrEZGbWpm7ViuDSUREFL+YmURERERtFkxmUn4asPhvk2EyqZe4O65Lrvg4zegJJt08tb9q+7umD8SEfh0wtGuubHn/ghxMHtgJS3YfAwCYjAwmxQX2rRJRErNJMg0M7jkE524txY+bSnDX9IHo2yk7VptGRHHGrnL93MI5k4iIKI4xmERERERtZnOEbxRlP0lHm7RufJ+OWartdTodJvbv6He9JgOjGEREFFmtkokBhcykGz9aD8D1O/TipaNjsl1EFH9UM5NszEwiIqL4xWASERERtZk0M0maWRQKaTm6/EwTXr5sNNYWVeH80d3atl49M5PiiZMTTBNREpr58jLxsV6nw75jDeLz7zaWoLLRgnevGQ8jS68SpbzsNCPK0CpbVlrXEqOtISIi8o/BJCIiImozaWbSmJ7t2ry+1/40BkdqmjG0ax6Gds3DzJFd27xOzl0RH/hXIKJkdqCiUXz8ty83eb2+dE8FdpXVY2jXvGhuFhHFoZkju+LFBXtky+75ajNOHtARXfIyYrRVRERE2hhMIiIiojazujOT+nVSL0UXrLOGdwnLejJNBv+NiIiIwiDQjMvaZmuEt4SIEoFepz7E5sCxRgaTiIgoLjG3noiIiNpMKHNnirOyPeN6tz1LiiKDRe6IKNlY7YGd2eoUwSSn04ktxbUor2d5K6JU4tAIQFsdvEoiIqL4FF89PkRERJSQrO4yd0ZDfBUxu2R8D3TOTcOpgwtivSnkptMYhUtElOikJV+lJg/sJHuuzEz6YVMJZr6yDKf9ezEsNvV1EFHy0YoZ2ew8DxARUXxiMImIiIjaTMhMMurj69IiJ92EZfeeineuHhfrTSEioiSnlpk0oW8HjOwunx9JGUw6WNkEAKhvsaGuhSXwwsFqdwRcdpAoVrSCRte9vw5WBpSIiCgOxVePDxERESUku5CZpI+/rBOTQc9smDjEPj4iSjZqHcOf3nCi10ALIXgkvk+SntBssUdm41JIbZMVJzz1K27+dEOsN4XIp8ZWm+Zrt3++EXaWuyMiojjDYBIRERG1mTAaO97K3FH84R5CRMmquskiPj79uAIxKzbTbJC1W7GvUvZcmoHQamMwqa1mbylBVaMFP20+GutNIfKp3kcw6afNR/HV+sPi89omK9YfrGKAiYiIYorBJCIiImozYZ4Ik4GXFhQYdoUQUbL5bdcx8fHbV4/Hacd1BgBcfkJPWbsDFY1okHQiSzOami0sbdVWWWZjrDeBKCCtkjnSPrzueK/XNxfXio8ven0FLnxtJV5dtDcq20ZERKSGPT5ERETUZmJmUhyWuaP4woqDRJSsappc8x2N7dVOtjwrzTu4sXBnOUprW7DvWAOqmzzzJLUwM6nN8jJM4uN1RVUx3BIi34R5vR4/byhOHtAJ/QuyZa8b3NfVTqcTe8obAAD/nr8bDmYnERFRjDCYRERERG1mE8vc8dKCiIhSk5BtdGLf9l6vDeuWK3t+66cbcOLTv+K0fy/GV+uLxeV7yhoiu5EpIDvdE7y76PWVWLSrPIZbQ6TNndgvzu05+5ZJsteFYFKTYi61NQySEhFRjLDHh4iIiNrMU+aOaScUGGE0LhFRshB+C41679vsL/4yAb/edQqundjb5zoe+HZLJDYtpSh/Xt5bXhST7SDyx+HeWYXE/nSTASO654mvpxld863VtVhl76tssICIiCgWGEwiIiKiNvOUueOlBfnGMndElKyELF21gRWZZiP6dcpGptkQ7c1KOcrBCtL5qYjiiVCtTi+5OEozeq6l9x9zZSrWNcv3YSFwTUREFG3s8SEiIqI2s4ujsRkpICKi1GQNoORr347Zmq8JOB9K2yi/vW0ltbj4jZX4Yt3hmGwPkTbX3iq9ep46uEB8nOEOPtcrMpPsPEcQEVGMMJhEREREbWYVR2Pz0oKIiFKTLYCBFV3y02XPTxnYyatNfQszadpCWeauxerAmgNVuOerzbHZICINaplJ0sdFFY045+WluOj1lbL32RhMIiKiGDH6b0JERETkW6vN1YFmNjKYRL7pwOw1IkpOtgAGVghzoAhe/dMYHK5uwpHqZlz3/joAwEerD2LW1P6R29Ak5/TKTSKKT8KcSdISwFmSUpibimtV3yeca4iIiKKNPT6EFXsr8OGqg5wIm4iIQmZhMImIiFKc1e7OTFKZM0kgnQ/luC65yEozYnBhLk47rjPyM00AgMoGS2Q3NNnxtpYShFpm0kVje/h9n51zJhERUYywx4dw+dur8dB3W7F8b2WsN4WIiBIUg0kULI5hIaJkUtNkwbztZQAAk177tzBTknVQXtcie+3PE/sAAKoaWyOwhamDPy+UKIQBvdJTRobZgNf+NEa1fZc8V5lMlrkjIqJYYY8PiYqrm2K9CURElKAsdjsAwMw5k8gPHavcEVESevzH7eLjPHeGkZpeHbLExx2z02SvbS+pAwB8t7EEP2wqCfMWpg5fgxXWHKhCbZM1ehtD5INTJTMJAHQaF0sFOa5zxq7S+ohuFxERkRb2+JCIg1uIiChUzEyiYHFOCyJKJt9Lgj/9OmVptjPodfjyxgkY3TMfD50zRPZauyyz+Pjv324J/0amCF+/Lxe/sRLn/ndZFLeGAuV0OsVSkanCoRH5NOjlwaTC3HQ8fM4QdM3PAAD8fqg64ttGRESkJqgen6effhrjx49HTk4OCgoKcP7552PXrl2qbZ1OJ8466yzodDp89913std0Op3Xf5999lnI/wgKD60LGSIiIn+EYFIag0lERJSC7JKRef0Lcny2Hd+7Pb69aSImDegoW37xuO7i4/oWW3g3MIX4u609WMmKHPHozi824fh/LEB1Y+rMGSb0wSgzkxSxJDx1wTD8eVIfnNSvAwAg02yMyvYREREpBdXjs3jxYsyaNQurVq3C/PnzYbVaMX36dDQ2Nnq1ffHFFzVTcwHg3XffxdGjR8X/zj///KA3ntrOIbnp2VNWL9bsJSIiCobFzswkIiJKXddO7N3mdSg7iCsaOHdSKCJ9R/vByiLM+uT3lMuiibRvNxxBdZMVX/9eHOtNiRqHRpk7vSKa1L1dJgCgR3vX/7nvERFRrAQ1nGHu3Lmy5++99x4KCgqwfv16TJ48WVy+ceNG/Pvf/8a6devQpUsX1XXl5+ejsLAwoM9tbW1Fa6vnQrquzlVL2mq1wmplvWMtwnej9R2tPlCFN5ceEJ+/v/Ig+nfKwqXju6u2J0pV/o4lolRmdzjxj593Yc6WUgCAAU6fxwqPJ7K759dyOHzvK+QbjyWi0O0pb8A7y4tw0yl90SXHNb9RKMfSwaomvPTrXlw2vgfaZbhurS8e2y3k49Jhl2cjjXtyAZbdPRmdc9NDWl+qstn8Z3U1NreGNADG6XTi4e+3AQDOHVGIUwd1CnodgpomK56btxsXjO6Kcb3ahbyeeBKO3ya73Z4yv20Oh8P9f/m/2em+VhIUZptgtVqhh6u9xZY631Gq4nUeUXjwWApMMN+PztmGVJS9e/diwIAB2LJlC4YNGwYAaGpqwrhx4/D000/jvPPOg06nw7fffivLPNLpdOjatStaW1vRt29f3Hjjjbj22ms1M5keffRRPPbYY17LP/nkE2RmZoa6+SnvX5sNONwo/87zzU48Ntau8Q4iIiK5lWU6fLbfID6/or8d4zsxy5W0/V6hw/t7DBiQ68DNQzmyloii7741BjTbdSjMcOL+UaHf+7y9U48t1XrkmpyYVOjAnMMGnFTgwCX9Qju32R3Anau9x3vePcKG7trTMJHCtmod3txp8NnmqXE2ZJmCX7fNAdzl/hv93yA7hrUP/Zrn4716rDnmCmi9NIFlDW9b6fpez+tlx6ldU+Na8sWtBhyo1+HPA+0Y2cHzb95Vo8OrOzz78L9PsMGoB/bVAf/ZZkSndCceHM1+GyIiCo+mpiZcfvnlqK2tRW5urs+2IRdadTgcuP322zFx4kQxkAQAd9xxB0466SScd955mu99/PHHceqppyIzMxPz5s3DTTfdhIaGBtx6662q7e+//37ceeed4vO6ujr06NED06dP9/sPTGVWqxXz58/HtGnTYDJ5Xyk/t2MJgBbZMqM5DWefPSU6G0iUIPwdS0SpbNPPu4D9B8Xn48eMxtnDtTOPeTyRY/NRvL9nCzp27Iizzx4X681JWDyWiEJ328p5AIDSZh2mTZsW8rH06KZFAKyos+rQv/9A4PA+9OrVE2efPSTkbcvsX44bP94oW9ap/yicPbpryOtMNRm7juHNnRt8tpk05VR0yQs+46vFagdW/woAGDN2LE4/riCkbQSA999aA6AGAHD22WeHvJ540pbfJuG47NqrP86eNiASmxd33iteDdTXYvw4+b7Ubn8lXt2xXnw+c4ZrPvJNxbX4z7bVONaiS5p9htTxOo8oPHgsBUaoAheIkINJs2bNwtatW7Fs2TJx2Q8//ICFCxdiwwbfF24PPfSQ+Hj06NFobGzEP//5T81gUlpaGtLS0ryWm0wm7ggB0Pqe1HLS7A4nv1MiDTznEHmzKgZfZ6QFdpzweEpdBqNw+anjPhAGPJaI2kY4fkI5luyS+Wehd2WYmIyGNh2T6Wbv97Y6wOM8CAaD76wkALA5Q/sNsjo9lT2cOn2b/i7SyizJ9vcN9niSzuV8qKYl6b4PLU649gGj0Sj7N5sV/36z2QwAyMnw9Isda7Sha35GFLaSYonXeUThwWPJt2C+m5Bmyb755psxe/ZsLFq0CN27e+bXWbhwIfbt24f8/HwYjUYY3Z0FF154IaZMmaK5vhNOOAHFxcWyeZEo8hwqwSSbPTXSyYmIKDycimmuQ5l/gFKLelFjIqLEY5XcOwnV4/UapdsDZdR7/442W1gCLRiBFPJvtoZWIky6bpvDiblbj2LJ7mMhrYu/hx4Wu2d0Um56yGOeE8rWI7XYeLgGAKBX7Axa55GBnbPFxxUN7D8jIqLoC+pX2ul04pZbbsG3336L3377DX369JG9ft999+H666+XLRs+fDheeOEFzJw5U3O9GzduRLt27VSzjyhyHCpX2V3yObkrERGFLs3ofzQwEeAdiCQiioV9xxphC3H6NmlAQshSamMsCQZlrzKAJovnc47WNuPCV1fglEGd8PQFI9r2YUlK+HUZ2SMfz188El3zMnDcw3NlbVpCDCZJ76FLa5vx1JydAIB0kx4fX38CxvZqH9J6U12rJNX90zWHMX1oIaYOCr2EYCI452VPlR9l8MigMTZLp9Ohe7sMFFc3yzMjiYiIoiSo4cOzZs3CRx99hE8++QQ5OTkoLS1FaWkpmpubAQCFhYUYNmyY7D8A6Nmzpxh4+vHHH/H2229j69at2Lt3L1577TU89dRTuOWWW8L8TyN/1C49OucymERERIE7Ut0se87MJPKnrR2tREThdOZ/luOtnW3/7Xr1t30AgGP1bcsWMBq8T5LNkmDStxuOoKS2BZ+uOdymz0lmQpaYDkC/TtnIMBuw68kz8eaVY2F299I3W0KLIEr77ysbLeLjFqsDj8/eEdS6+Hvo0WqTB/eufXdtjLYkNpRBZJ2PnUNoy1gSERHFQlBXza+99hpqa2sxZcoUdOnSRfzv888/D3gdJpMJ//3vfzFhwgSMGjUKb7zxBp5//nk88sgjQW88tY1TJTPJag9xWB4REaWkRbvkpV3SGEwiIqIEs7NWj/nby8Oyrnnby9r0frXMpDeW7MfWI7U4WtuMYskgDpvdgTlbjqK8rqVNn5lshLtc6VeZZjRg+tBCDO2WCwB49be92FZSixcX7Mbfv92CQ5VNwa0cgFHxtzpcFeA6ksSX6w7jD68ux6JdbT92WkNND0xQy/ZUyJ6P691O9twgCSYp40pCFpNafw4REVGkBV3mLljK95x55pk488wzg14PhZ/aSBaObiEiIn92ldbjb19uwp3TBnq91iWPGa4UGPaBEFE8ue2LTTh7ZLc2ryfD1LZyr8oAhUAoidWrQ6a4rP/ffwYAnDuyK/5z2eiA1r+3vAHl9S2Y0LeDz+yHRCb8vqj9+4SO+BX7KjHjP54yY2lGAx6eOcTvuqVl7pSlyZLz29R291ebAQBPzt7e5pJ0yswkwFXSsUteRpvWG6+en79LfPzTrZOQaZZ3zUmDyl//9STZa8Jux74bIiKKBQ4fTmFqWUgOXpEQEZEff/14PbYcqcW178lLkJx+XAE6ZHP+Q/JNl3LdbUSUCKz28NwHtfV+Si0zSeqgSgbND5tKAlq3xebAmS8uweVvrcbqA1UhbV9i8JS5U3ruIvV5phpbbQGtWRpMUs5ZE2xwLll+D6Xl/kLV0OodTNp5tL7N641XI7rni4/7dcr2ej1dEpTu3SFL9poQxFSbA5uIiCjSGExKYRaVVHI7L0iIiMiP2iar6vL+BTlR3hIiIqLYEebfkbI62lauy6iXr/OT608I6H0vLdjjt019ixU2dwCkLIlL43kyk7xf69cpG386oafX8kD/btK7ZZtXMCnQLUwuyn02FPO3l3otS+bSdz3auzIMTx1cIAscCaRlo5XzqOnFzCT23RARUfQxmJSinE6neHHWUTKKnJlJRETkj1ZnSbqJlxUUuD3lDbHeBCIiUfssk8/XP1x10CsDKDvdu2q8rY0ZTtKO45un9ofeT6aS4NM1h/y2abZ6sj+cTlc2zvcbj6CuRX2QSKIS/gJamT9qpQQDzUyTduB/vOqg7LVj9a2BbaBbsgzkNBtCi6KV17XgyndW4+4vN2H1fu9MudLaZpV3JQdhOohclXMIIO+jSTfKg01iZlLyxtqIEsYbi/fhb19u4hxmlFLY65OiqiSp6B2zzeLjZLmgJSKi6FMbWUmktKu0DoD8WoSIKJpGds/zWpbp4zfsYGUjHvpuK279dIOsw0jtPHbeqLbNuyQNdJw1vNBrXh4tanPOKB2oaBQf2xxOPPjdVtz22Ubc9cWm4Dc0jjk90SRVJ/Tt4LXMGmgWjOR2udHi/Z03WQIrl9dqs2P9wWrxeSIP6jSqZOgFYuHOcizdU4Ev1xfjUJV3+cZHf9zuVUowWQj7qNbxnWE2YP4dk7HgzskwG+XfL8vcEcWPp3/eia/WF2OVSkCcKFkxmJSiyuo8o6ZyJKNhVKZRIiIiUlC/8T2pn3fnDJHS9iSeA4GIEkNeptlrma+SWpuLa8XHLVZXuw9WFonL7j1zMObefjJevGQUnjh/aJu2TTpnklGvh1Y//ZxbT5Y9D6Qk2JwtR8XHdocD3244AgCYv70shC2NX04fcyYBwFnDCnHbaQNky2wBpnn4i200qsz9o6a4Wp51s6m4JqECJ9JAalaaenaNP02SYFyzSmAOAJ6Y7Tug1GqzxzwQ53Q6Veej9kUIBPmaZ2tA5xzVEtJCVUEGk4jiR0sAAzqIkgWDSSlKuNjplp+Bqyb0FpfH+kKMiIjin9p9b8dss2wyYSItdtZlIaIYa1HpuG7xEYx5du5O8fGf3l6Fj1YdxMPfbxOX/XlSbwwuzMX5o7sh0xxax7pAOv+M0aBTzVzIzzRhSNdcTB/SWVymNh+uUl2zJ2tGOd/P8/N3h7K5ccnXnEmu5Tr8dUo/2TJLCGXupIRSvy3WwDoU1xXJR7H/4dUV+O+ivQG9Nx40tHj2pXaZvktEapF2vmp1xL63oggz/rMUVY0Wr6BSRUMrTnzqV/zxjZUhfX643PDhepz87CI0tgaWlQZ4gpIBVrGUEc4JjCURxQ9Dqk6aRymJwaQUJYy8Mhl0mDmyK544fxgAlrkjIiL/1G58HzpnSPQ3hBKSsgOTiCjaGlQ6fVttDizfW4G7vtiEO7/YiO83HhFfk2aR/H6oBg9+t1V8vuzeqUgzhq/Ma26GJxjVPtOsGkzq1T4TAPDK5WPw2Q0nAnCdW6v9lA+VZi8pBxGu2lcZ8jbHG39zJgFAmqJ0mM1HZsmyPRVY6f5+tH7BhFK/n6897HNdgl93lHstk2aOxTtpv0EoXQgOhxPPzd0lPvc1Z9XO0nqMeWI++j0wB7XNnvm9NhfXoLrJivUHq/HH11f4nPurttmK2qbIzA02f3sZSutasHRPRcDvEYKSgZaxlNKxzB1RXJCWvb3xo/Wy8xNRMmMwKcXUNFnw+uJ92F3mmvRaqG88sCAbAC9IiIjIP7XOGelIaiJf2jo5PRFRWzWqzGtjsTnw2I/b8PXvxfjm9yO456vNfifULsxNR/d2mWHdtkyzEXNvPxmzb5mEdllmWdk7QUFuOgDAbNRjbK924vIVfgJC0lJcrTaHbHBIc4AZNYnAKZYQ026j0+nQvV2G+FyrTFllQyuueGc1LntrFeparJqVPNLdAcVXFu3F7M3+g0Jq33dZXYvf98ULaZZQKH0Ii/cc8/l6305ZqssfkgRypYnOa4uqxYCf0qu/7cXIx+Zh5OPzwp79Jc1EUztWpRpbbWJns7CPhnL5LHwMx+YQxZY0CN5kseOlBXtiuDVE0cOenxTz+uL9eObnnbj/my0AAJM7mCRc+LDMHRERhSLEuZcpBSXSnBBElJykJbqkhAF3gCvY4u98FakAzODCXAzrlgfAOyDy4Izj8Oi5nnmZTAY9Bhe65lXxV2JNmrnx5E87ZJ3RyRRMEvhL+vjx5km4blIfANqZMdK5gWqbrJpZONJAwoZD1Zqf2WyxY1tJrWrwytf8ObHW2GrDV+uLUVrbArvDKQsghfKrfriqSXX5iX3b47Lje2JQZ++5ggDgh00l4mPlPqtV6nH1fk9JwbWK8oJt9dGqg+LjmibtzECLzYFT//0bRj42D0UVjeKxF8rfXMhm4vUUUWwpz+MVDa0aLYmSC7t+Usyq/fLROmaD60JE7774LapsCigtn4iIUle1ys1yPHeAUHyxcs4kIoqxSj/l4ARCWc4uea5MoD+d0FP2erPK3Evhpsx2uP7kvuiWnyFb1tNd9s7XBODbSmqx4VCN5uvNFjsqGlrx9tL9fsvlxbsadzkzfyXE2mWZcfKAjgC0M5OkpQFbbQ44VUIn95w5CEdqPKUQa3yUOpr+4mLM+M8yrNof3qBGpL28cC/+9uUmnPj0rzjrpSW464tN4mv+MvjUtFrVv++7zxiEpy8Yrhm0G9Q5By8u2I11RVVewSStv6F0ua8AzNYjtSiudgW5Gltt+HnLUfywqcRnkOiYpPP47q82a/alVDdZUFbnartkzzFJmTvNVWsyiHMmMZhEFEvKags/bCrB5uKa2GwMURQxmJRihnTNlT1vct8A5aZ7Js30Vx6BiIhS24DO2V7LQqn5TqlphHu0PRFRLByoaAy4rdDxLGStXHFiL+SkeeY0skRhEJ50Uu/Hzxuq2kaYr2e1jwDFir3q93iXjOsBwJXl8X8frMOTP+3A6Cfm456vNiVkZ/XBykY88sO2gNsLlTq0SrDKg0l2r9JiD844DjdN6S9bVqURjGux2nG4qlm2zCxJ7Y7XK6lfd5Th9cX7xOe7yxqw5Uit+DyUBJlDGplJwvWkWtAOAHaV1ePFBXtwxxcbvTLxtIJJ0r+t1i59oKIR57y8DJOeXYSftxzFIz9sw18//h23froB9329RfPfocyGqmiwqAaZpdvWYvXsR6HNmeT6PxOTiGJrf0WD17I7JYF2omTFYFKKMSvqEI3umQ8A6F/g6Rj0NXElERHRyO75XstY5o4C9acTe8V6E4gohR2sDDyY9O2GIwAAuzuj0qjXoVXSKdxPY16XcJJm/o7r1V61jdCpvu+Yd8eWQMi4yDIbZMtvmtoPgCsAIs1c+mJdsWaHf7xxOp3ifENfrisWlwfSUS8Ek7QCEbXNnsDQw99vwy2f/i57XQjk9eno2RcyTPLvWKCWjSOdtyneLNtTgbNeWorr3l/ns12wQcfaZis+lJSHkwp0Ds7DVc1ewSSLIiDocDhR3WiRZQ+pZSbtKavHvV9tFp//9ePf8dV6z35UUtvs9R7xMxXBpBOf/hWjn5iHrZJgGyAPaFntTs+8Xppr1ibs15zvmii21Epr7i3X/h0mShbs+kkhR2ub0dAqrw/eq4PnondSf1eKP2vvEhGRL2o3ryxzR4Eyumu6SEf3ExFFS4u7Qz833XMOyjGp3/88+N1WAJ5ydwa9LuCSWeEiLXOXZlK/fT/9uM4AALNR+/b+x81HAQAPzDhOtjzTrH0u1pqDJt68uWQ/TnjqV7y1ZL+sI09ZIlCN0V32XasE646j9eLj9QersfVInez1/ExXhY8XLxklDtzU2i/sKtdPOZL9MN4upa54ZzV2HK3z2y7Yw6DcHfhTk+3+PgKJkzw1Z6fs+aerD8me3/fNZox+Yr4sG1H5N2ix2nHWS0uxxsdcSr6OA7XSzy1WBzYpSl3ZHPLzhrAZoVw/17rLKO6R7OuHq5qwubgmIbMJiRIVu04pVTGYlCIqGlox4emFshE2gPxCV7jY1krxJyIiAtQ7SQzx1gNCcYuBRyKKJXGuEmmQxs9dsfC7Z9TrZZ3c547sGvbtU5LGQ9I0gkUdss0AXJkYWhpaXIMKu+SlY80Dp+HW0wbgk+tPQF6GCe2zzKrvsSbIfeHTP7uCCv+Ys0OWTRXIfDRCAMhqC+3fKsynNbJHPp66YDgAT/BRSW0wzqDCHPFxRUNizlXlhCvA0WSx+W27rqhKVobw5AEdcaUkY7kw1/V9DpZ8L4HafrROlq30xbpirzbKYEtdi1Xz79Wrg2suMq1g0vcbj2DOllLV16yK96wrqhYf7y6rxwcriwCEVuZuuzvA959f9wAAjtW34uTnFuHcV5bju41Hgl4fEYWG2YGUqhhMShHrD1arLk+XjG4TRgozM4mIiHxRqwQjLe9C5IvQbcKrDSKKBaHzZ2DnHFw6vgdumdoX/uIIYmaSQd7x+5dT+kVkG6UKctKRl2FCu0wTOmanqbYRAmNqmS+AqwNdmN9peLd8FOSm485pA3FS/44wG/WYe9vJOHlAR6/3aZV+iyc2xTZul2TShFrmbu7Wo/jfsgMAgP3HfJdFTDN6Str5u59WBvumDemMx88bhv87uQ8AeZZSIqlutGDiMwsx+blFXn8PpT+9vVo2R/NJ/Tri0XOH4sVLRuGLv0xAhrsM401T+2PW1MCOr3euHic+9jePmfJvo1Z6EADevXY8Xrp0NABgf0Ujnp+3y6vN3K3qgSTAtR9Kg1D3feOZd2n25qOocwd3Awl4+lNa68n0Wn+wmtlJRApHa5tR2xT+6TzYd0qpisGkFFHfoj5K6PzR3cTHQmaSVoo/ERER4D0K64LR3dCjfWaMtoYSFTs7iCgWhL4fg06HZy4cgVtP7Y8ai6dH97tZE2XtqxstYqewsrM/KwrlOjPMBiy9dyqW3DNVnJ9HScgO1upHb5V0ameavddRkJuOi8f1EJ/3dmdk2BLgvnC7jzJsQZW5k3x5N370Ox6fvR3bSmqxs9R3mTdptphY6UPje1MG+966ahzSTQZcMr4ngNDmz4mUXaX1/hu5HapqQkOrDRUNFrzw617Ndk6nU7YvAsCNp/SFQa/D+aO74fg+njnB0k0G3H3GYL+ffc+Zg3DKwE7ic2nA7rguueJjoQSkMtlOLfi0+dHpmDqoQDbf9H8Wev+7hO/opUtHoas7Q004vr5YV4yHv9/qd/urVMrkBUu6v3206hCufW+t+HxzcQ2+33iE11yUsqobLZjw9EKMfHxe2NfNzCRKVQwmpQi7xgVtQU66+HhzsWuSyL9/u5UXG0REpEk5Cmtwl+BLkVDqEgaK80qDiGLBKZa5U399VI98XDreE1ipaGgF4JobJzfdFPHtU5ObbkKOj88WghhaZe6aLZ7SX1oBqTOHFeKmKf3w+hVjYHR3olv8pGzZ7A5ZWbFIKatrwXXvrcXi3cdQVNGIx3/cjv3HXPPFtGhklgDyUoZaxDJ3KiX9Smpa/AYMg8lM0louBKTiqazgGS8uCel9by4t0nztWH2r17K2lr7VQScLGkpL1glZUm9eORavXzEGgPdAFmH+IcHrV4wVj/O+nbJkJSClAcf3lh/AfvdcTNlpRrz35+Px4iWjcIIkIPbZ2sM44akF6HP/T5rbr5UZFQzlfrXmgGf+p3NfWY7bPtuI695f1+bPIUpEu8oCD4wHSyuY5G8QAlGiYzApRQSSfVkqmQizvtV/vWMA2Ftej798uA63f7ZBvNEiIqLkprxw9jV5N5GSLq7GXhNRqhE6Xn2VQLv51P4AXCXBP197GACQ5f6t+4O7ssMNk/tGcjODIvxbtDJimt0BH7NBr5mtYzLocc+Zg3HmsC5i6bfL3lql2Sm2s7QO/f/+MwY/NBfL9lS09Z/g08Pfb8WvO8tx9f/W4KHvt+J/yw/ggW9dZcO05rMBAitzJ2QmCd+dtEzbhkPVaHV/dy9eMgqzb5nk9X6zJDNJCMJpzpkk2dR3rx3vtY5mq11WsiwR9WiXofnakZrmsH/eH8d1h06nEweqSAOqwt8hP9Ms7gt2RbDpgldXyNZ3Uv8O4uN0kwHr/n66GOwrkWz/Esk+37dTNgZ2zsH5o7uh0SIPrpbVtcLXON22BmM/WX3Ia39T+7yFO8vb9DlEiUr6mxfuQfPCz8WoHvnY9PB0cfkPG0vC+jlE8YbBpBQR7DnT1+StALBoZznmbDmKmS8vxy/byvDdxhIs2F7Whi0kIqJEoQwmHcfMJAqCmJkUPwOwiSiFCLc5vgINRnfaUovVgbfdc+cIc80+fcFwfP3XCbj7jEGR3dAgiJlJGudVIZgknS/XF2kGyZkvLsXaIk+mQ22TFR+tOogzX1wqLrvindX4YGVRkFsduLI6z/YsdXfir9rv2iZf8zoZAhi7YJJkJtU0WWRlz8rqWsXvrmN2Gjpkm73e3ynHM4+VkJm04VAN+tz/E657b62s81Ioc5eTZsTUQQVe2wAAf//WM7dOvPvwuuO9lh2ubvYqJSdoS+ZVYa6nokqvDpmYOqgTVj9wmjiPmFjqUfJ9C/uGyaATj3fpMbJcMndTXoYJz104wiv7UK/Xobs7QLbhUI24XMhoumlKP9m8occVBndNnBbgManlgW+3YIu7wozAydxvIpH0tz7ccxwJ98QGvQ55mSacO7IrAN8Zs0TJgMGkFKGWfqkclSZN4fZ1kj1Q0Yhr31uLmz7+Xby4BvxPdklERMlB+RshLfFCFCh2dhBRLAj3RWoJOheP6w5Afa6dC8a4Xks3GTC2V3tZACDWhE3RuocTytxlqMyXpOaisd1lz/89b5f4+IUFu/Hgd95zwew4GrlSQr6q1Snn4JG9L4AydxmSsn//+XWvrOyYXgfsLnOV00sz6WVz6Aik+4p0PiqnE/h1Zzl+2VYqZuQI+54yjinNbvo1jjNIvrpxguy5VkB2RZlO9e8iZH3pdMCgzjl47Nyhfj/zuQtHoGf7TDx67lC8+qcxuPXU/vjyLxPw7rXHo7MkwKRXKTFYXO363k2SjLxmi6cCS5OkGsvTFwzHxZLyllLtMl39JN9uOCIua3S/98S+HWRtbzltAPIyAi+Hef3Jbc9w/MecHbLnWl05nMqAUpH0HB3uUqIOh/x6QggsW+yRL/9KFEvxcwVMYVXVaMG5/12JT/bq4XSqd9e8fdU42fMvJReHyslBpSo1ytn5y2YiIqLkIB07MKZnvmyCY6JAsU+DiGLBKRlJLLhjmA1/ndwHj583DIArk0HpEo2O5nigLOF15xcbcc7LS8XMjFabO5ikMV+SkrIzvLrRM6/MwcpG1fdozdEbDlql+QB/mUn+g0lZaUYM7JwNwDU/ljQI0iQZONmzfaZsvqm+nbLwxV/kwZWxvdp5rf/Gj37HZW+uAuC5X1b+e9T2t1hS+04fPmcIxvVujwV3ngIAOL53e82itV8dMOAfc3Z6LRcGnw7pkotf7piMq0/q7XdbLh7fA0vumYozhxXi7OFdcOf0QSiQBJEEwt9a2A0bJIGigtw08bOLKpvEc4BV0n/ROdeTYaY0ske+a92SCxchMylXcax0zE7DnNtO1lxXpiKgO6an9z7TZhrXV9tKOI8LpRan04kDFQ3ic2uYf6fs4uAU1/lHGBjgq/wqUTJgMClJ2RwO7Citx+pjehyqbvYahfLgjOMwdXCBbFm/Ttliar6vc6xmDWh2ChERpYQFO1xlTZ+7cAS+uWmiz04eIqU2zrVNRNQmQj+5TnIy6p0D3DltgBgsUGbw3H76ALGcVjzylLlz3ZB98/sRbD1Sh9XuUnDNFtc/Oj3AYFJ2mrzdrrJ6MVCVrSgDVuAu82YL84hvKa0MmF93lLV5ziQAuOakPgBc5QBX7vfMhdPQ4glIdMgyIyvNiLvPGIQLxnTDjzdPwvF92svWY9TIVjtU5QpgPOXOIKlusspeV2Y83frpBuw71oBYEQIlUkI5tv4F2dj08HR8eP3xsmNI6dO1xV7LhH1E63tqC+EYEDp3rZL9okNWGvaWeb5PIdAkzVLyFdQR/s5CNpLT6USZe77pQpXAVrd87XmjojHPqDToJa0+U91kifhnE8WTj1cfwh2fbxKfhzvII/SBCucfYX61L9YVx/QcThRpDCYlKeloMovV4TX6V+vCTy+pNfzthmLc8ME6NEkusgDvE3C7TNdnqZXSIyKi5LL+YLX4OJDyMURKwjUIrxqIKBr2ltfLOsd9lbkTKMu33n76wIhsW7gYJCW+1EpZCaXJAy1zl5PuXaZrc3ENAO9qFDee0g+A9oDDcNAatPLhqoM+S60Hep0iZIvM314m63gUgg5GvU4MgMya2h/PXzwKWWnBBQVabQ4s2nVM9TXlvfkPm0rwkEopwWhR3tf37ZiFM4YWis/zMk1IMxqCHhwiZDyZI5CJZVCUuZPuj3odcM7ILuLzhe5Sgo2truNi5siuPgNjWe4A0O+HavCXD9fB5nCKncha85A9dM4QAMDdZwxCR8lcW9LMpLOHF3q9Lxykfz1p2T/O40Kp5rXf9sme7y0PT4DHZnfg+fm7sWq/a941oR9VWv72hfm7w/JZRPGIwaQklWY0iKnaFrvD6+Je6/rNPdcsHA4n7vh8E+ZtL8ObS/bL2ijT3oWbLcaSiIiSX3F1k/g4jqaLoAQiXoLwuoGIImxzcQ1Of34JLnljJQCgutGCNQdc2Tr+slZuObU/AOCMoZ0ju5FhYHTfxNkdTvx30V5xuRBL2V3mms8o0DJ3UwcXeGVX1LuzdJT3gkb3jaUtgmXutP5Wh6qa8PPWUs33BXqdkmZUbygMoAk0o8uXuhbvbB9fVuyrbPNnhkr5p/zixgmqmXmBZn4JhLJywv4aTsrsPCGIYjLooNPp0CXPsz/f9tlGABAHzWb5CbJK57T6ZVuZ7BjQClheN6kPfn9oGm6a0k8MWgHyYNK//zjK3z8rJNJgoPSx9BqeKBUoz+2BZtDWNFlQ7+Oc/eGqg/jPr3vwyepDADzngYn9O4ptijRKwhIlA3YDJTEhKm6xO7wu+vsX5Ki+x6Cotw1ATOEGgK/XF+O699fJ3iOkvDMziYgo+Uk7VILtRCCSUp/RkYgoPN5euh/nvrIcALCztB6frz2Es15aih82lQDwn7Vy1/RB2PrYGXj9irER39a2EjqoKxst+Nc8z2joJosdH606iH/+sgtA4EGRvAwTnrpguGzZDR+67gGVgxSFwEBEy9xp/K32H2vEkt3q2T4A0DnHuwSZmn4F2T5f95X9pHT1hF4AgMtP6In7zxqMnHRXVstX6z1l3567aETA64sF5fzJWvvNsG65yM/0zmLTIpSeM2kE79pCOW+Y0P+hldW28XANmiyuII+/0nNGxUjcVkmGj695udpnmaHT6TCkq2du0aO1nr6VcMbUDHodTj/OFfiW/vmkj//lPg8QpQqzMpgUwKCH0toWHP+PXzHxmYViaUulLcW18s9xnyN6d8gUlzETkJIZg0lJTKi9bLE5YFNcAE8a0FHtLeKFuvQCUnqxdNeXm7ze08k9SolzJhERJT/pqOZWXiRTCBiDJKJoePKnHbLnD3y7FaWSQXLK+yM12WlGn+Wv4kW2Rsm16z9Yh02Ha8Tn54/uFvA6lQMFW6wOOBxO2SDF/EyTOOduJMvcpQcQfOial46+nbJwysBOeP2KsXj9ijH4v8l9A1q/v2p4z1443HcDiUfPHYp1D56Op/4wHH85pZ+Y0fPcXE9HvrRkXDxSljLU2r8yzUYsvWcqLhzTHQDQJU87eGexOVDjLjdpikCZZCELTQgm2VWyoF66dJT4+Nmfd4odxZn+MpMUKW4tNk+mUSDzhj46c6j4WFpyU4fwfA9je7XDvqfOlu2nTqcT6w9WiaUaAaDRYsdGyfkgXJostqAz74iiwWQIPjPpQEUjLHYH6lpsOFrbrNqmS778XNe9nSuIJJ0PboCfQQpEiYzBpCQmRMc/WXMY1gBHigkXQ9Ja260+Jql788qx6NfJdZJkZhIRUfLLkkzKPaxbXgy3hBKV0HnCywYiiia7ooN8+9G6GG1J+AnZL2qEDvx//GEYzh3ZNeB1nty/I2aO7IoZwz1zzYx9cj6W7qkQnz98zhAxa2PhznKv7zhc+ks65YwanffThnTGwrum4P0/H48zhxXizGFdAs7EUgYMleUAAy0PKKxLWhLukZlDvNqoldV7VKWdcu7iaAnm75iTbsL9Zw/GdZP64NU/jRH3xXG98mXtLnp9BZ6YvR2Ad6ZPOAgZQn98fSUcDqcY3JR+1nmjPMHUlfsr8f7KgwCAzDTff1/l9v5TEhgMJEt/ePc8PDpzCF6+bLRsebji1EI5Rul+3GSx47K3Vnu13eKe+6ytvttwBP/4aTtqm6047d+LMfHphbJAGVE8GFQor8gUyKCHQPpCldmM7TI986I9cZ4reOwrazbSaputqG3i8UiRw2BSErO4A0g1zdaAa1gLF0N7yz31PVslI2+kpg3pjOlDC8X0bLXJXomIKLlIT/XSsh1EgRL6OnjVQESxZPExYC7RGA16LLhzstfyLLNB7FDKzzB7ve5vnS9fNhr//dMYjOzuGjxSLemc+ui6E3DBmO7o2d5T1md7SWQCdNJgjlpnYN+OWbjshJ4hr19Zquzaib1lc2XVtYQe1JkyqADTh3jWZTbovTJdAOCaiX28gkyr91eF/LltIa1SMq5XO7/tO2an4aFzhmB0z3b4+1mDAABZks5Wp9OJzZKyUMpsgXBosrr6LJqtdhytaxH7P7SCj1JZfsrcKbf3mw1HxMeBZCYBrr/vTEUwN9whNemmDH3kF9k5bkzPfADagcLaJitW768MqE/H7nDi9s834q2lB/DQd1txtLYF9a027DvW0KbtJwq3UMrcSc9/WtcJygwnaXaj8EqnHO955qLhzSX7MPKxeRj5+Dy8uGC3/zcQhYDBpCR281RXWr/D4Qy4hnVVowUAcONH68VlC3aUY4970lYp4cQsjIBhmTsiouQnnOv7dsqK7YZQwuMgFCKKlGaL+mA4qatP6h35DYkitTlxGy12rClyBSSCmdtG6dubJnotm9CvAwBgXO/24n3hByuLQv4MX/xlcPzvmvEYXBj6ABdldkluhkmcfwZoe6f/bacPwMXjuuMPo7vh2YuGa84Bpez4fHz2dq+5j6NBWubu7avHBfVe4d8g3W7l3CEHKsI/MX2NJNA58ZmFOPPFpQDkZe4A4Ozh3iUG1TLFpEw+JjcKtmLfWElwLtwlNH2VzRPKb2l1C017YTEueXMVXlywx+/nSDPmhDnoAKCqwRLglhJFx6p9lbLn5XWtft8jDbhqBZPsiqBU+yzPYI3jurh+i2JVIneVZBDCmgOxGZBAyY/BpCQmjLCyO4HFbUyxnPbCEq9OH2FElXABxTJ3RETJTzjXB1LWg0gN9xwiirQZ/1nq8/W3rhqHv57SL0pbEx+EDq5Q6PU6PPUHz3wspw4ukGVkDOzsKkNnj9D9oL9Bix3bOAJceUmjLBvY1k7BoV3z8NxFI/HCJaPwh9HdNdspgxoHKhrxD8XcX9Eg/B07ZpuRnxlcRpuQxWORBJOUJSW3RSiDTU1BrnzfeOicIbh+Uh/ZsuP7tPe5jg7ZZtVsMr0u+H1D+lmh7lWXHd9D9vz20we41uejd8/kLtWnnA9LUF7v6mR/6Vf/waRmq3qw/voP1uE/v+5BcXUTdpbW4Z+/7MTvh6r9ro8oEnYcrcN+ReD68dnbxX5Np9OJw1VNXv2c0n7NS95chUW7yr3WbZUcRxeN7Y6zJeVgY9k/ure8AQt3erY3GTKwtx6pxW+7yrGrtB67SutxqLIp1ptEYDApqQkjnhwOp6xusy+TB3bSfE2ZEi1ckOiZmURElDI8waQYbwglLpa5I6IIU3YgKU0e2DFmo4ZjoWf7TNnI6VC0k2Q2KUuHXeAOkAQ6T2+wfHXKXXNSb2Sn+S5T5o8yU6it6wuVWvm32ZuPRn07hPv+UAYOCX0E0mDS1iO1sjZDo1gmOS9DnpHXJS8DD54zBLec2h8A0DUvHX07Zau9VZSVZsRvd0/BnyfKg1CBlriTkr4j1FPQk+cPx5xbTxafn9Cng9e6lYQMLYfTibeX7sdJT/8acqdscXWz5mvPz9+NK99ZgzNfXIr/LtqH695bG9JnELWVVtnFWZ/8DgB4Zu5OnPzcIvx7nrwUnDIZ9MFvt8KmWCg8v2FyX/zrjyORISlzJ1xbxGKs/edrD8meW2KQ2RpOFQ2tOO+/y3HNu2txxotLcMaLSzD5n4vw3vIDsd60lMdgUhLzZCY5A06Pf+fqcbhr2kDV15T1qXt1cJU40osnS3YLERElO+FUz8wkCpVQhoWXDUTJqdVmx/+WHUBRBEpZhcPUQZ2QZjT4b5hEpPM5hEoa6FAGPUxCabMIjYL29XvRQzJnU6iUMYHS2hbZ82mSOY8iSVnmDgBqm6NfOkyo4BRKsEQsc2fTLhX1rz+ODH3jgqTVDzJran+8ceVYfHezdwlHNV3zM3DfWYPRLT9DXBZKQFr6llAD2ga9DoMLc2TP/a1PCPLZnU48+dMOlNS24Imftouvp5sC7xo8Vu+7VJi0jKF0njWiaFLLJgSAOVtKsfFwDd5YvB8A8MqivbLXlYMXjtQ045/zdsmW7S13BarU5mTzDLaP/o2OsqRoqzXxg0nC4IaO2WbxWmaXyjQsFF0MJiUxobTvxsO1WC2plfn+n4/XfI/JoEfnvHTV1+pa5BcCN7pLQ+himMZJRETRJZzrU2lEN4UXdx2i5Pb8/N14fPZ2TPnXb/ho1UHUNMV2Hg1l6bC2lHtLZSbJ92g0yE/kaQbveXLCydegRbOh7T8qygEyE/t3lD1XZrdEilrnp93h1CxNFikVDa5gwVFFUC0QZpUyd602V1m0C8Z0Q9EzM6J6DErnD5FKNxlwxtBCFOSo932oMRv1eP/P48Xnhhhe0Eg/WujP9hX7EzOTJPuS8HcGgJz0wPfxWMzjFaiaJgsmP7cIN7uzTxpbbWjRKMtHyS3NpD2I4ot1hzVfUzvfzt9WhhV7KzD8kV/Q5/6fsGiXaxoRZfAG8ByHsegeVZaa3VVWj1kf/+5VZSpR2NzZzoW56Vj34DTccqqrpGeksqApcAwmJTG1kUTvXjMep/goZQdoXxSd9aKn9viye6eK62eZOyKi1OEQM5Niux2UHJjVTJR8fpKU5Xrwu614du4uH60jb/UDp8mep1pWUriYJD/8yvtMk9G7tFk4+brPNGqMPg+GMpg0rFteTEqx7in3lGV671pX0MLh9B7UGfntCH3Ut0klsPjZWlfHbazKB4aTdF8JrcxdeC6gpYO6hMe+1m0QMpMkh2h9i018rMywcDqd+GLtYaw/6D3nkfC3lZYr7JqXjvl3TA7iXxAZi3cfw6GqJszefBTL91Zg9OPzMeaJ+djDTIaUoxxIIvXJ6kOar6nN/be/ohELdpSjvtUmCxIJ8wVKCcdhLAbb291BlhkjPHM4/bTFdSwkIqE6lnCuFc5TiRocSyYMJiUxtaCQWh1mr/eppmoClY2eUYXS9O5YTjBHRETR5ZkzidEkCg33HKLkprwl2FZSq94wCk4bXID8TDPOHl4oLksLopxTIrvyxF7i43Dcpkkzk0x6RZk79z3m0j0VaLaELwugtsmKHzaVoNFi02wzqkd+mz9Hevv7+hVj27y+cBjQOQcZ7pH1dc3a//5IEDKx+nTMCvq9ZjGY5NrpDlc1iXPsGPWJf+xJ+0oaWqP7d9GSl+EK0mldmh/fuz3y3X/TFxZ45ofZW94gHq/Sf5fd4cSWI7W45+vNuPC1FV4dt0IJw865nqwunU6HAZ1zZHOrAdEPIEq39U9vr4bF7kCTxY5NxbH7HaLYCKTvU3D1/9bg7aX7sau0XtyHlGVH/6cyT4/0GBAIx6G/391wZvi1WO34Yu1hfO7OuBrWNQ+//W2K+Hq5n9KUsTRvm6vsoBq7u+aqkA0tnKeUU7BQ9CX+rzlpUk4kCniXJPD3Pk8JO3kb6UgYfQwnmCMiouhyisGkGG8IJSzpNQSvHYiSj7K/OBYjSNtnmQEAN011leV+ZOZQ8bWc9MTPjgjEE+cPEx+Ho0xb/07ZaJdpgk4HjO3dTvaatGzZkj3H2vxZgus/WItbP92Ad5cXeb1m1OswsX+HsJRMa2u2SSR0y89ArjtIEO3MJKGPs3+B96h7f4S5eYR5kqTZLyf169D2jQvSiX3bh3V90n2le7sMHy3VhXMs1uPnDcUtp/ZH/4Ic1XW/dOkoLLjzFHxx4wTVuV0A4K8frwcg3++tdocsgLnjaJ3sPUIGoknStySc95WZnzZHdEviaX2/BysbmQ2f4kb1yMemh6fjl9sn4w+ju8leW7z7GJ78aQdu/Gi9OHAyw0eZPEFVo3cZ30DmTPrm92IMffgXLNxZFsw/QdPXvxfjnq83i89bbXb07piFM4a65vv7en0xvttwBGuL1Mt+xsrO0jrc8OF6nP/f5SiubvJ6XRiUIJy/hHOOLY5LbaaK1LiSTlFqmUmBnBAbJSNs1M5/pw0ukD0XOoUSNXWSiIgCJ9wTcs4kCpV0z+FtPVHyUd6DxCKYJJS3EUYmF+Sk4fITeqKivhUzhnfx9dakMKJ7HgDgkZlD8N3GEtwxbWCb19kuy4yV95+GVqsDeYrsg36dsmHU62BzOMM6P8naIu8SW4IND09Dljk83Rl62UDJsKyyTcb2cgXrctNNKKtrRV1zlINJTnkHXjCUZe6kpQ9PH9I5DFsXmB9vnoR9xxowZZDvEv/BkgZdenXIDPr94dy9rprQW/ZcWTXgvFGeDnO1gcYA8Jt77hfpv+ufv+zC1EGePp9jiqwGqxhM8owcED5bmfkZrvO/0+nEw99vQ7ssM+70cT7T6r9/eeFeHKxswsMzh6BjdlpYtoninWdnyM804ePrT0BWmhF5mSb8648jcfkJPbFgexneWLJfbFdS0yze6w7vlof8TBMaWm3icQIA/718DGa55+Qa1i3P61O1BuRL3fnFJgDAn99bh6JnZoT6DxQpj9Fm9++wENxdub8SK/dXAgB+vu3kuJk7sqrBE4z7an0xbj/ddWxXNrTipy1HsWhnOQBPVqvB/X9mJsUeg0lJTDkq8IoTe6qe7JRqmrQvVs1GPd65ZrxsmTDCo6SmOfiNJCKimLE7nGhosXl1CvniYGYShZHrGoI7E1EyUXZaxqIUdnWTq4NC6ODU6XR46g/Do74dsSIEAa6d2AfXTuwTtvWmmwxI1xiceGLfDli2tyJqGadZZqNmB3mwdJL7ZqEjsEued/miSHvhkpH476J9+PuM4wB4suiinpnkDhaE8v0KpaHq3BlJre5Ozb6dgi+ZF4qZI7vir6f0w5CuuRje3X/fR7CkAbZgymhFg6+/ls3HhPUOh1MsTwgA7yw7IMvgvPa9tbIObyGYJH2PMA3C4MIcHKz0ZBiEq9P3UFUTPlx1EABwy6n9Vb/78voWsZNezQ+bSrD6QCVW3Hda3GQgUuRId733rz0eWZKSiwa9DuN7t8f43u0xvHse7A4nbvtsI1ptDrGcncmgwyuXjwHgylxatucYhnTNxYwRXTCm16k4WtuCQYU5Xp/rqdwUvWsfZck84aPVxn6W17fiuDgZU2OV/JFeXLAHu8vq8eqfxuL5+bvxsWReqyM1QqlUzpkUL+Lr14/CSjoqsF+nLDx5/vCAfjSlN3xPXyC/6WqfafZqf86IrgCA6iYr6qN8oUtERKG75I2VGPn4PByu8k4r1yJcu3HOJAoVdx2i5Kb8fahssGDV/krU+hiwFk5HaprRYpXX2U8VwmjjC8d2j/pni/NERCHnVK8LLdChvT5JmXd3l/yk/h1xz5mD8L9rxoXtc/z5w+juWHDnKRjT05WZZFLMPxQtwseFkpkknSNn8e5jqHVnVaUb/VdIaYvvZk3ErKn98M+LRmBI18iNupfud/E2B5S0aoAyI2vOlqOa7zuiMii4rE6e6SC9VxD2R5NBj4+uOwGnDOyEZy8cAcA159htpw0QM0CdTlewypdj9a145uedOFjZqNlG2i+vlan3zjLPnDY92quXICyra8WBigaf20PJQbrfjfQxt945I7piprtPEwC2lbjKOhZKBhScMrAT/j5jCP4w2vXb2iUvQzxPKwmniMpGC+ZtK/V6XRkI2Vse+v54tLYZD323Ff9dtE+2XAhkST+rZ3tXJmU8zXWvLFc3Z0spftlWKs6zJxDmpxOu6YoqG/H20v14Z9kBHKoMvB+Dwie+fv0orKQXOicPCDy9Wzpq5bLje+K6SZ7RbMpJ6AB5LeWXFuwJdjOJiChG1h10lY+ZvVn7BlPJM2dSanXQUfjoJGNn4+d2hojCRVlupbLRgkvfXIXzX10elc//cVOJ+Fit7Hcy+/wvJ+KT/zsBl43vGbNtiEY/Vbg78aX7ifBQp9Phpin9cerg6JVmU/IE6KJLmPQ8lOMnX5LtfvX/1uCGD11z8qSbItv1NKpHPu4+Y7Bm5ly4SL+TOEtMklUNGKoIqE0fWqj5vmar3auD+dM1h2TPj9a2iI/FMndGHSYN6Ij3/3w8erg7qnU6He6YNhBPX+gZlOwvO+nWTzfg9cX7cO17azXbSLdv0rOLcLTWOwAmlMzKTjNi/h2naM6Pd7iKFXVSgbDHBDL3m949B5+UdK7FYEiDusL5T1DdaPHaz09/fjFWuUvQBeu9FUVixp6UcLhIpzkR506Mo5sv4Vwypme+uOwvH67H4t3yuRfHuUu/ZrpL2+4/1ognf9qBJ2Zvx91faWcjUuTE2c8fhZP0QieYUUV2xSSJ+ZIJW9WCSRlmA/p0dKWtN0jmWyIiosTw7NydeH9FUUBtHT7S5okCItl34mhwHBGFyfje6hPeH6jQHnUeTtL7nnBmrySC3HQTTurXMSb/7mjOpRjuElXSDLZ4GrUtDL6IZrkkwNP5H+r3nGX03t60CGcmRYv02ArpzxLB40Qnm/tL/jl/n3EcXrhkpOr7ft5S6nN+FwCyudDU5kxSkp6H/ZWkEuZy2X9M+zdCmp3XbLXjJ5WBcC0213bdMW0g0k0GfD9rIv6sUuZTWRIMAA5WNuLyt1bh7aX70Wqzo6rRgsqGVr9ZVRS/gi3NfkiSfTeuV7uQA9O+DvGzXlqKJYpACQA8+sO2gNbdYrVjxb4KVDS4Bu1oZeWo7bbC9xAvv3HldS34dsMRAOr9zABw17SBeHTmELxwySgArgyxv0zuiz+M7oZJ/TsCAKoaLXA6nbjyndUY88T8qF1rpjoGk5KY9OLPGMSwGeXIkaHdPKNazBrr+dMJrtFv4ZxwlYiIoueRAC9ibcJo1RTroKPwkd5kRaMcEhFFV6ec2E5unpvuGQgnzONB0ROdzKTwXoNIO8XrW+JncGSsBu4IHeihlolUe1ukM5OiRXr9e9nxwWcARutPqgzgZKcZ8YfR3VU71pfvq/DbwSwdNCwEdrT6hgD592R1eAdvgqUMAO0pa/AK9OwqdZUnE/a1vp2y8fDMIVh1/2nIkZRfVMuU+nj1IazYV4knf9qB0Y/Px5gn5mPskwtw/qvLox7MpfAQ5w0K8KiTZqy9dsXYkD9Xq3rHwcpGlNa1qL62s7QeX68v9rvuO7/YiMvfWo1pzy+G3eHU/L1SDc6Iczn5/ZiomPHyMvyyrQyAdmC6Z4dMXDOxj5j5mGE24P6zj8MLl4zCLaf2BwDYnU60WB1YuqcCVY0WTP3Xb6qlOym8kuMXnVRJT2LmIC4EJw1wRXhN7veMk4wurGxsVX1PmjtqX6NRv5aIws/pdOK+rzfjv4v2xnpTKEnUuCcs96XVPQ9FpMuIEBFRYop1x1uru9PxtMEF/K2KIuFuMxp/fUME58Ia0iVy8+0ESyxzF+VDasnuCgBAaa16x6c/agGLZDkWpYFM6aDbeCNkLijlSarOCNKMer/72JoDVeLjqkbX/YLvzCTPa/YQ5vxaua8Swx/9BYMf+hnfbzyCzcW1stc/X3cYfR+Yg798uA4tVjscDid2l7nmnlHOz1WYl461D56O4939WmqZSRabZ1mTxTNAenNxLRotHDCdiMRgUgg/F20ZFKM8/wnzAt3wwXqV1h5rDlShyWLzeQ21q7QegGu++sd+3Ibyetc5+uzhhXhGMt99ptl1DAjlLTtkmeMuM0laEjlX5bwE+N5WIWDtcDi92h2pZjAp0tSLiFJSkP62B5OZdFK/jvjqxgno1cFVui7b7NlNlBMxCoRanL/tOoaiikb0dpe9I6LI2XC4Bp+tPQwAmDW1f4y3hpLB64v3476zBvts02Jz3VAlywhTij7pPVac3M8QURjFuqOiyT2CPi9TvXOCIsMT+Ij83z/cmUkAsPL+U1FW14oBnXPCvu5QiWXuohCie3PJPizcWY72WWasKXIFDhbt8i7HFAi1WF+GOTmCSekmA+47azDsDicKctKDfn+ks80uGN0N32w4onlv6Co36BoAnJ1mFDOO/J23syWZPV+5Myh8ldOUvuRvziQ1c7ceFbMubvtsI07q10G13S/byrB8bwUmukteAcAElbbpJgPS3PcuamX3Mn3sn02tNtm/nxKDI8h5fq85qTfeW1GEv00f2KbPVX5eZaMFnXPTsausXrY802yQBS4/X3cYn687jMGFOZh7+2RxucPhxEerD6KmyYqKBs/Azw9WeuZKumpCb5zYtwNe+nUPjta24Ax3EOn04wrw+Q0nYkDnHFz/vmuupni49VJeJ/xt+iDV0pXSIK+ScP6xO52wK9ZnC0M2JPnGnqAkJj2JBZuiPq53ezEaH0jN7fG924mPdxytC+qziCg08VSGgxKPWmdPIOdvYQLeZKl9T9EXzXk1iCj6Yj3FxK87ygEAWWZ2/kVTVDOTIhBM6pKXgVE98sO+3raIVmZSk8WGp+bsxKr9VZizpVRcPmVQp5DWp9b1cOMp/ULdvLhz4yn94nYg3/OXjELRMzPEgcFK0sDkU+5MBrvDqbmPCZl60lJ1+e5A/ZAu2oFXnU4nVrrxN2dS307e2/q+pKMc8GRkqM2B9NGqg7KAVfsss+rnCJlUNpVMKbUAtRBAYhWQxCQEkwK97XjonCGYe/vJuGlKeI/tE576FfO2lXot1zpGd5bW48t1h8Xn6w9V4+Hvt+H5+btRq1EJKifdta/Ov/MU/Pa3KRhU6Do2dTodTujbAe2zzGL/cKyyx212B2ZvLkFZXYtXub8+GskInXO1A/YGnZCZBDgVsSN/5xxqOwaTkpj0Itukj+yfuleHLBzfx5U2zMOWKDrskov6WJeUocSjdo1lCmDgwbYSV8AplFGGRAAzk4iSWVFFozhqXU00rleEcilNLE0UVeJAgSic1wcUxE/2UDRE+rDRGqB29rAuIa1P2Xn754l9MDCOMr5iKdD5W6JB6Iy1qZSJ6pSThs9vOBGTB7oCim8s3o+JzyzE9xuPoKbJ1aE9onu+7/XrhfVrZwkcrmqSzb20/mA1AE/lG0Glu7TeiX3bQ+lARaNYSgzQzlwUlqvN4aR2XyNUYfhRJWOC4p/wFw00M8mg12FwYW5Ag+l9UXv/DR96l7j765R+eP2Ksbh+kneA9Mt1nuuo6kb/Zehz0lzXPdlpRs0qUbEqmyp4d3kRbv5kA2b8ZymemL3d6/WRKoM5Jg/QHtAgnF/sDrXMJPnzD1Ydwhf79bLzBLUNh2slMelJLJAOQl/6dszC/opGn+m/8VaDkyjZSUdVWe1OmI3xc3NC8U+tQ89X7XNBO/doxHYsH0RERAq/7Sr3+brDqZ614Gt9XfMzguqIPljZCACY2F+9LBJFhiczKfL3gg+cfVzEPyMeiCPJI/w5+441qC4PtrqJoKzZ875V95+Gwrzgy8Elq1gnZ0sv/4XOWOl8SIKJ/TrghL4dsGxvhbjsSE0zbvtso/jcX/ana94kh2aWwBdrD+OerzfLlh2sbMSoHvlotroGAxTkpKFcMrdKTrr3/UerzQGr5L5YK3NR2J/VMpPUtvH1K8biotdXqq6L4p9TLHMX3c8N9PM6Zae5SzI68fayA7LX1hRV4aNVB3HFib1gCSAAImQm+SIM+IjVeND5O8oAABUNFlkG7E1TXFmrH113PPYfa8Tlb60S5ynzXUpTUuZO8Y964JstaJfpylBssdqxv6IRgB5rD1Zj8qDCsP2bUhmDSUnMICtz17bMpCfPH4aHf9jms36oMMqGg9WJokP6o2lzOGBmsikFQT0zyf8+JNyA9euUHe5NohQh7UiJRqcjEUXHgYpGLN9X6bONw+mEIYCR+bVNVhypacY177pq/O984kykm/yXV61rsWJPuXsS9gDaU/hEc9Rzj/YZkf+QOBCueajsDicaWm3I05jkvLJBfeR7OMoJMpAUX6R7kloGzyMzh2B7SZ0YsPWV4Zmb4bs70ZOZpL7/LtnjPSdXs9WOKkkmhvKd0jlbs8wGNFrsaLHaxftio16nWU5ZyN6oUykVptzGE/u2F8v5cbB0YhIT0KIcwfWVCdU+ywyH04naZiv6ucs7al2rPPjdVmSYDAikyFR2IMEk9/9jde+l9Tt29xmDALgCxSN75OPEvh3w607fA5MAz/nF4XB6rftobYtYml+qqZUZ6+HCYFISM4QxM+mk/h2x4M5TfLYRTnIst0UUHVaHPDOJKBhqF5LKkhJqhFIVkZivgFKDtMQLLxmIkoPD4cRZLy1Bi9X3CNpAjvkjNc2Y+MxC2bLBD81F0TMzfL5v/vYyvLvcM7qX55fYiOTXPqFvB1w4trtqdkIyCtc8VJe+uRJri6qx+O4pqvN0WDVGvgcyyIiCE+urZ7XMJKnxvdvjWsm8RMXVTarruXhcd79zYArBqq1HalUHoallCL27vAj9JW0Lc9NxTJKZZJT0rAvbX91kxabiGtfrPvq9OrsDm0v3VuCW0wbIXlNmNtw8dYAnk4OjpROSECCM9i2rr4/LMBnw7ayT0Gyxo8A9H1Cmjwy/u77cpDpPmFIg52p9jDOT1K7JZozo4nUeuWPaQNS1WHHBmO4+1yf8k+1O7zJ3gKsP/J2rxwMArvrfGgDAm8uKcOaIbiFsPSnx6iCJSU+axgjPmeT6PGFCt4h/FBEBspqvrP9KwVI7V/fskOn3fcLNVlsHKVDqkmcmEVEysNgdfgNJQGAjvH/aXKK6XJiAXc3e8nr83wfrsEKSGcWMiGiL/HXBXdMH4qKxvjuYkkm45qFaW+Sah+b7jd7HlsXmwLxtZarv05p7xp/xnVznglMGas93karaOh9LOKkFk3op7gWKq5tV3xtIn48wz9Ftn21EbQDZQACwt7zBXZLKNdXClRN6eW3ziO55AIA/jushLhfm6vM1V3gX92/CmgNV2FJcq9gW+e+X2ahn/1YCszucuPEj1zxF0T7ifA24NBv1KMhJlwX1/Q3m/J9kkIyaC8YEFhyJ9eB/teP98uN7ei0b1i0PX954Ei5TeU1KLHPncKoGyIx6PSYP7ITJAzvhihNc54qR7nMHtR2DSUlMehILtd5xKJgGTBQdrTbPRS8zkyhYoZ6qhQtBQxQGKRARUWJQm2/ixUtGeS0L5LcnzajesVLR0Kq6/PuNR3D680u8lo/pme//wyhsolHmzmxMrWuPcJcl2lZS67Xs3eUHMHeba/6K/gXy7JFQ+xD+2MeBf104DP+5bHRI709maTHfhz37kjJY+O61472y/v7mLkF17cTesuXBZjcccAeIZFuicbL4x087AAB5mSZcLAkYAa598qPrT8AXf5mA+88ajCtOdHU4z9/uCogafOyz04Z0Fh/vLK0TH7/86x58tOqQrN3IHnmcEzyBldR4gqCBDHQJJ+Xv1HWTPJlFgwu953/MMHvaTx0kD8Ar41LKczQADApwTkmhMkSsdueNh2u8lrUl+1Va5k4te1DaH37/mYNw8xA7LhufOoNRIo1l7pKYtFZnNMoRxTptkijVtFg9NV+1ylMQaVG7MfJXxsHpdOKIe4RiqKNViaRYGpcoOShLjNw1bSBmjuwKi92Bt5fux+4y1zxGgXTKaXW2Sq97Xlm4B99tLMG//zhSNiG8lL8STBRe0ZiPIeWCSWJndnjW94tKBtKvOzxzU/TukIVmix1H3B2xPdt7l8QLRJoBOHtUV5hMqVGOMBixLh0oPQVnpsm7A3NV5l2ZOqgA6x48HR2yzMjLMOHFBXsAAMd18d+Bfen4Hvhs7WH356rcdyiW9e2Yhf0VjWhotQFQ/y0w6HXITTfh+D7tAQDts9IUn6mdzdAxOw0XjOmGb34/ImZNAcCPkmzYD/58PCa7M+rYv5W4pP2f0e4nkf5OnTm0EPefNRi3nT4AJTXNshKOAumcSRlmz+OPrjsBkwZ0FJ8frW1Gu0wzBj80V/Z+6Xt80cUwOLquqEp1efssc8jrFI7PRosd3204ovK657HZqMeAPCf6dAztN428MZiUxHLTjcgzOVFn02FggNHqttCLo9H4a0sUDc2SThWtiU2JtKjtMb72opomC0Y9Pl98zjmTKFQsc0eUfJSDEc4e0QUGvQ4Xj+uB80Z1xaAHXZ0fgXRitNPoXNhd1oDTjnONLP/XvN0AgHeW+S7/QtETlcyklJvDJ/IjyRstNvFxdpoBv909BaW1LbDaHeir0vFJbRPrgKh0VxreTV7ySSsrtGO2K2Bz++kD0bdTNmqaLF4ZQ2ruPXOwJ5ik8rry9vWicd3x3Nxd4vN0lfJfysFs0gDYeaO64r6zBvvcJuHfUlrbIi4T5mT6+q8TMLZXe3F5LDvfqW2kf7Fo95NIf6euPqk3jAY9cg165BaqB9elZe6kx6CypHyXvAzV97fLDCwgo4th2ca3lu73WqbXqWdaBapTjieQ/Pm6w16vs68islLtaiylGA16/H20HcvvPiVKwSTWlCWKJmnKNudMomCp3Rj5On8rLwKZmUSh0sV8+mkiCjdlmbtcSakkabUEf306docTv+0qV33t/RVFXsu0RhyfLBnNS9EhltCJ4GfEOqsj2jxTJkXuW22yeAanGQ16mAx69GifyUBShKSb4mcfNuh1uHPaQPF5jkpmktK5I7viqgm9VQM9Su2yzOjZ3jUHk9o9hvJeRDl3jJCZJA16KTuI8zI8vzWBdKp3znXNm7SnvB4HKxux9Ugtqptc8zn16Sjf54XPYv9W4pH2jUizmqNBmhWtVgJYKT/TjEGdc6DTQcy4A4COOWk+3uVy31mDZeUbfYlG2UaHw4kPVxbhzSX7ZN97s0qpwXNGdG3TZ6WbDHj+4pEA1Od2Y0n+yGJmUpJLM8gjtpHEkRtE0dUq+YEWLoKJAqV2qvbVWfHdBvmkzYHcRBIRUWpQlrmTdkpKg0n+KhjM316KT9d4jzCVqmvxXPMoy7sU5qbj+YtHYhTnS4o68c8cwXtBray1ZBWJr9ThcEIv6ZCvb5FmJrF7KNLOGtYFr/22T5YBE03Kc/De8gbxsRD4CSfP6d9/mTvlvYXw/PbTB+CWTzdgQEE2Ct3BIIH0t+amKf38bk+Pdq7sjoZWO07552/icqNeh/wMeeaIp8wd+7cSjXQ+6aOSLLRo69vJf1k1g16Hn26dhMZWO/IyTchOM6KyoRX9NAL6vTtkoqiyCY/OHIJrJvZRbaPGU4o2cjYcrsFD328D4OqH/sNo1xxFQnCvY3aaOP+lMngciu7ttM9ZKTb2JOp4tUBhI6ZNxng7iFKFdLTHs3N34uu/nhTDraFEo9ahV17fits+24AJfTvg0uPlNcdbbZ797aKx3XFi3w4R30YiIkoMDsWgU2mnoHQgub9BukWVTV7LJvbvgOV7K1HZ2Cqbuw8A2itGoudmGHFSf2YlxYIniyYyfrh5YsoFOyLxnTZb7Vh9oBI922eiV4cssWMPAArz0n28k8Ih3WTAvDtOidnnK/cladn0SMwzJ6xR7dwv/d0Y2SPfK2tLyEw67bjO2P74marrzzR7zgl5mf7n6DK517npcI1seX6mWRZkBThYOpHZJDtXQZQG10stvWcqaput6JqvXppOyWjQIy/TtW/OHOk7Y+fTG07E9pI6cW6vQHkqSUVuf5YO9qlu9Dy2uYN7N03ph8dnbwcAzJrav82fN6ZnPk4e0BFL91R4vWbgvJkRlVpXYxRR0UibJCIP6cX/1iO1MdwSSkRqp+rP1hyCwwl8v7EEl4zvIbuprGt2jVxdft+p6BbghTGRP7xkIEoO0syk80fJO0J0sjJ3vg/6eklHxDUn9cYl43ugV4dMDHn4F1jtTvy05ahs9PzXvxfL3s+yJrEXqfP6iO75kVlxHBPLwvr4UsvqWnCsvhVDu+ZqBgOyzAY0usvZfbrmEJ78aQcA4NU/jRHbXHZ8T/xxbPcwbTnFK+WuFOlKA77maRF+D166dBTOGdEVc7Yclb2uNYeT1Il9O+CMoZ3Rt1N2QO215l1Tzk8DSDOT/K6W4ozV5vmjPTxzSNQ/v0f7TPifVSw0XfIyNOdP8iUa8xpK58+sa7Fiw6FqjOieD6s7uFeYl46cNCOcALrmt33wgtGgxztXj8ekZxeivL5V9poyOEzhxWAShY1wscsfW6LIa7Ha8cU6TweKMJkoUaDUOvSk5+/9FY3o555kNzfdBIs7PT31Jr+mcONAMaLkI3QgpJv0ePHS0V6v63Wu3xh/wSRh/pYZw7vgkZlDvDrHfz9YIytzpCzzy/n8YkecM4mjBMJG2P1XH6jClRN6e73eYrXj1H/9hkaLHW9cORZnDC1UXY90npkPVx0UH9/8ye8AXOWGnr5gePg2nOKW8hx831mDUVTRiGtO6h2Rz/N0YGvP1Woy6GHQ67zO3+N6t/O7frNRjzeuHBfw9mjNu3bxOO+uf2WJVp1OhyM1zXh6zg7Utdgwc0QX/FHxvvoWK2qbrT7Lb1HkCcGLHu0z2jw3T7LQRSE4Kp0j6sUFe/Digj24c9pAMTMp3aTHqgdOg07nCgSFg9mox8K/TcHhqia8vfSAOMhIOb8ahRd7hChsxIGAvIEgirh3lh2QPb/8hJ4aLYnUSc/U01Um7iyva8WGQ9UY9fh8/OWj9eJydtQREZGS0IGgVVZE72N0upTQ4dCvU5YskHTraQMAABa7XRzcoIadBzEUga++T0fXfBODC3PCv/IEcKCiEQAwe/NR2YhvQVWjRcw42n+sUXM90ncelJSSFFbZv0B9bg5Kft3yM/DjLZNwYYSy0nzN0yJktOpVpkvo2ykL543qFvbtUctAAoBMs3dWk1qJ1h83lWD25qNYsvsYnp27U9a+sdWGic8sxKRnF+EbRdYsRZdwLWFitrJI2J99zZHcVmoDhp6fvxsNra4KJ0a9HllpRll5ynDITjPiuC652FteLy5jmbvIYmYShY000l3daEF+pikidXeJyFXSQkrrwphIi3Cxp9NBtZ7zj5tLsL2kDgAwf3uZuNzAfY2IiBTETkGNYI6rs9DpNzNJmOdAOWJVmDvDYnNg8e5jmu/n9VDsRGJy7045aThQ0YhbTh0QxrUmDmlJ69pmK9plyecIk86fKm2rJA1EGfQ62ehxgMdNSonyuF9PH5FaRQQhmOR63mzx7MOfXH9iRLZHqxSeELiWUpZoNUAnO+ZarPKBDSU1zahrcXWa7yqtB8WOzS5cS/DcJohGJSmbxsqFgRGR/nvkS+bRDCSzkULHMC2FjTCi5JEftmH0E/Px2I/bY7xFRMlLWWpM64ebSJN7l9FBXsZB8MnqQ9iomJwWYGYShRlPXURJQeis1soM8kxk7ns9wmhi5XqEYNKS3RV4Y/F+r/e1yzRhSJdcXKVSCoyiw9fcKCFzrytVLz2k12etNu+MPGlndouvYJL7ezxjaGf8/tA0r9e1Sn9R8on2ZZe4B6vOmeT6v7CfTx7YCV3z0nHuyK4ozGv7fCpqBhXm4JwRXWTLfrx5EqapVGmQZya5NlYaiLUqsmSlx6jVzgvcWLK6/05GZiaJolFJyi4peXx87/Zer6sFbcPpyfOH4aoJvTD7lkl49sIREf2sVMcji8JGeY3/3oqiWGwGUUowGeWnb7XSF0S+SG/ggkkiZQkhaivuQUTJR+g40+q4EScy93O9IgyOUWZKmN3XPaWKzGzBz7dNxpzbTsbMkZwbIVYikZnkyaJOzV8Oacf1nvJ6/O3LTbIyPqsPVIqPpVkdXutxf48PzxyKvAyT1+tmI7uFUkW05zRTK2Gn3BbhZ6NTThpW3H8a/nOZ97x74WLQ6/DK5WPE593yMzC8e57qOUY+Z5Lr/9IBnMrBnNKArt2hXY6VIk/ITGLWpUc0MpOE3+zxvdtjQr8Ostf0OsjmvIyEHu0z8fh5wzCsm/oxTeHDqwYKG/YvEkWPMjNpye6KGG0JJSqhXrJepwuqc58jvIiISKnJ4irtk52mXkLIMwm77/UII72VvzXS0iVKF4zuFrFR7BQ4z984fD1V0pK8qUgaTLrynTX4an0xLntrtbispKZZfNzkDiY1tNrw3Nyd2HHUVarYYnPA4s6YEOaQaJfpHVCi1DAwyvOPCcfu9xuPoNE9b4rAoZgzKZqEedgm9e+o2UavKHMHyI9Ju8MpO9/JMpNC6LH/bVc5Zr68DFe+sxrl9eoDJ1JJRUMrHvtxGxbuLPPfWEEc4MKsS1EkfqOVhGQ9tWP6xL4dGOBJIjyyKGxicRFAlKraK2qmrymqQmVDa4y2hhKReI+jC66ThgMHKJwiOQksEUVPo7sjW2tSZeE+Yfk+34Nf7BqZSdMVJYj+PLGP+PjmU/sHt7EUEZG4PBB+IVL1PlNtnplj9Z7rfYvNu8zdv37ZhVd/24ezXloKAHjkh61iGyG7/Mnzh/v9HEpOr1w+BheO6Y6fbp0U1c/9Yl0x7vtmCwBXZ/aq/ZUornYFQ2NxfL//5+Px6p/G4KGZQzTbSKsxHK5ybatNUb5Omp0ky0xyt6trsXqVw7vzi4248/ONsmVHa5txzbtrseVILZbuqcBvu7TnBgSA2ZtLcPX/1uCmj9eL89Ekm7eW7se7y4vw5/fWBf1ecf5F3riKpHPcR4q05LEyU/yqCb0i98EUdQwmUfjwPE0UNZ1y0ryWVTdZY7AllKickklvpaOE+hdk+3wfRxQREZGSUGIr3aR+e9lqc71+/zdbsHSPdieZVZwzSb6edJM842l/RYP4uJ2PrCWKvnDGJYROr1S98rD76fV7f+VB8XGzuyP790PVsjZCR3P7LDM6ZruOFWXJ4vEqc1tQcuqWn4F/XzwSQ7vmReXzpPcNP24qAQCsPlCFS99chRr3vWssgkmdc9Nx9vAuyE5THwAByMs/1ja7tlVZvk4aXCqr8wR6bQ4nKhpaMeLRebjwtRXi8qpGC775/Qi+2XAE1Y0WcXllg+excr1qnp+3G4t3H8OcLaX4Yt1hn23j0VtL9uPpOTvErGY1B46FHiSzimXu2OUtEE77kRw8YJdkG/YvyMaCO08RX+veLjNin0vRxyOLwqaq0eK/ERGFhdo1gHLUE5EvTrGDRifrrMgyq5coIgoXBiSJko/QOaFVClU6GfmV76zB8r3qGUriaGIf8xxkpxlxfB9P53e7LAaT4oFOnBsljB1VijlVUo1Wp195fYvX/GPKEmICofTWMxcMF/9GaYo5ktKMvPajyFBLDPnBHVTy1SZeDOmSC8AVrC2paZYFcAHPb9Zzc3figW+3yJb/tPkoAGBzcS0WbC+DxeYQ5/IBgA8k61LOv2T30+EvzYJq0jj249Whyib8Y84OvLFkPxbuLPd6/XBVE3rf9xPmbQ++vJ1AuObgnEke4fommiw23P3lJlz1vzW48/ONqGly9QO3WO243519KMTw+hdk419/HIknzx+GoV1zw7QFFA9S9LKMIsFfKi4RhY/ajTqDSRQMMZikk498/b/JfWO0RURElKiE35FAO/3/9PZq1eVaZe4AINM92GFUj3xcN6kPXrp0FNY9eHoIW0uREIkuOzEzKUUHIWhlJtU0WXHFO/JjSMhM2lxcK1veanXdH6RJsvvG95FnIimDS0ThonbofrL6kOy5MlMungi/Oy//ugev/bbP63Uhg2jFvkr5codTDCYBwPUfrMPHqw/K5lJ6YcFu8bEy40kZLPb6XMnr0rmaEkGT1RP8UguCv7xwT5s/w8Y5k7wIGYD//GUXNh2uUW1T1WhBeZ1rvq7HftyGp+fs8GqzZPcxfLm+GEt2H8M3G45gwQ5XQFAaGCzM9cxjedHY7rjixF4p+zuerHhkUcRwFABR5AiBgBP6tBdLylgS7EKSYksYSWfQ68TSDQCQYdIenXrp+B4R3y5KLZymgSg5CBkUbe0UFAbGqGU4vXTpaJwxtDMeOmcI0owGnDeqGzpme5f9pRgRJ/cO3yqF/SpV7yq1gkl1zVavzusmix3F1U2yZU6nE9uP1gGQB4yy04z49qaTxOcdVcpnE4WDLoCjNztdu9RcrGW5y+CtO1iteq/98A/bAHgHdOx2J3IzTLJlpbUtsowiKauPuZjUSLMWLTYHmi12rNhXIct8ilfS85pFpZyfNBARKuE+l32SEu6votXmwHn/Xe71stPpxJgn5uP4p37F74eq8e7yIryxZD/+8dN2WbvZkiAp4BnI8KI7OJpu0uPuMwdH4B9A8YTBJAobX/VmiSi8hMsunQ7o4a4/uz9JJ9+MFyv3VeKlBXv81q9PFDZx9LdeNpmzr2BSBkvgERGRCjEzKYiRp2odczYfpWmmDemMN64ch0GFOSFuJUWS0Gkczqskob80FnOqxAOtS86nJKPFTz+uAIArwNRilR9T0pHiZkX20age+Xjl8tH450UjcObQwjBtMZGc8tBVO+/npJm8lsWLh84ZIj7+XGVuoh83laC60YJWRZDI5nB4ZfxZ7A5sPSLPHBTbK4IqwWQmzdtehls+3YDL31oty3aKV9IkLLXgV34Y5kEUgnNapXdTkb/fUYvkb3HBq555vt5aekDW7khNs+y51ebAocom7C5zzWV589T+7BtOATyyKGyUpyZ/oymIKHROcaSmDjXurJJ1RVWx3KSkd9lbq/DCgt34fuORWG9KWHhGfyvmTPJx8Vda2xLx7aLkl5pdgkTJTfgdCSYz6cSnf0VlQ6tsmU1cD29TE40ugplJqRpM0hrA9PuhGvHxfWe5RoCX17dib3m9uLwwNx0Pf79NfN5JkcWn0+lwzoiu+OO4Hl6BJqJwUZa2enz2Nq82hXltz0SJFLVO8VMGdpI9H/3EfK9BnTaHU7zXEjJobXYn5m2TzwMk3NNbFWXu3l9ZJL6mxi4JPjW02rBgh2u97y0v8vXPiQvS+aDUyvQLWUVTBnm+52AHcwpBKl/zL6Ya5eVZg6LEYLNFPWsOALZIyqcKfwvhd8Nqd6DaPW8SAFx5Yu82biklAl41UMQ4nUCrTfuEFE3F1U3483trsUJjsl+iRKXTAScP6AjAlWFCkVdU2eS/UQLwjP7Wy0oljOieh0vGqZez21veEJVtIyKixBJKBklVowUbJJ3igKcTiR1AiUf4i6nN6xkq6fyOqcjuJzJ3Ur8OyDR7Orv/u8gzp4vd6US/gmzxeZc47rCn5KU8dD9aJZ8vaeFdp8R1MFPttyiQ0mm/7TomdrAL8y7ZHA7sLquXtetz/xy8tGAPtikyloqrm7GpWD2LCfB/bohn0sDQ5uJaLNtTgWV7KrCuqAotVjt2lrq+I2m1jIe+34rn5+1CY6sNn689hPUHfQ+iFStwcGCKSFlyUnlf/8rCvZrvnfnKMjFAJ2QXFrjLo1rtDnFdvTtkIi8zfjMNKXyYe0YR9eqifbhj2sBYbwbu/2YLlu6pwMKd5Sh6ZkasN4eozaQ31307ZgEIfsQOpTZph500mKTT6fDsRSPQ0GrDT1vkNZFH9ciP5iZSCuBZiyg52APMIDm+d3uskWRSX//BOozt1Q7vXjseuekmz0AHdgAlHLXMpNLaFvx73i5cfVJvDOuWF/Q6hcBUqgaT/JW6WrGvEjmS+Wakndx2hxN57jlbbpjclxPRU0xozREEAHecPhB9O2Vrvh4P1H6LAs3AXVtUDcATTLLYnKpl6V9euAe3nz7Aa3l9i81rmUAIlvxy+2Q8P38XfnFnPEXquvqtJfsxb3sp3rv2eJ9VLAIhve+cvfmo1xw8gkyzEUa9DjaHE5+sdgUh31iyX5yfateTZyLNqF6C3crMJC/r/ATg3l9Z5PP1/n//GQU5aSh3l8cXsvb+Nc9TWpG/M6mDf2mKqJ+3qv8wRBtLM1GyEW+uoRNLwRyrb/WZDk/hkSyXpJ5a0jrVQKRyZPEVJ/bEgzOGeLUjIkpFh6ua8OzcnSir4zUmIC1z57ud2kTr6w9W452lB1Be1yKOSGYHUHK49+vN+HJ9Mc55eVlI7xcuT5QjqlNFINkHOemeUeDS8nd2h1Ocx6V3h6ywbxtRIGrd5diVpg7qhNtUAijxRu23SC2AYdTr8MaVY1XXIQSTDlQ0iAHfeXdMxnvXjgfgCgw1tHoH3Xwd/8Jvbn6mCZ1y0jTbhcs/5uzA2qJqvwGHQKjdd6r9G7LSDHjqguH4w+hu4rJWyZxbtU3q+xbgCSaxcovHvmPyQKaydL/V7v13+dt0eWJAuWSe5dwM7wwkX8FjSi48siglMEJOyUaamWR0j476dWc5xjwxH9tKtFPiU8mW4lqc8/LSsJS3rJDM6ZAso2PlZe68X5d23HTMTsOT5w9n2jqFRbIcQ5Q6vttwBGe8sAS/H6oWl131vzV47bd9+MuH62O4ZfFDGGnsb8R2jkowCQDqWqw4/qlfxefd8jPCt3EUFcJ1g3Rg0/ajdeLjHZLHgfLMmdTGjUtQBskP5kfXnYAcjYyAK07s6bXM4XCKHX9pcVxGjJJbk8Y8LIkyL55aMGmkSqWGdJMBZwwtxJsqASXhd/FARSNarK4gR/ssM07o00FsU9/iHRjRqjqyt7xBNk+hsM5oaPCRLRUotYzLG0/p57UsK82Ii8f1wAuXjMJzF43wel0rUAlI73NT9McjAO8q5tc6Y2hnrzadctJUlwPAdZP6YPoQ+WvR3BcpthLjDE4JQe2nLl6SJMz8EaEkIz229JI77OomK+ZvL1N5R+q5+t012HqkDpe/vbrN63ppwZ4wbFF8sdhdN3cmg161cz/N5LlEYMYbEaWy2z/fiF1l9bLfggPuUjUbD9fEaKvii9Cx5a/MXX6GCddN6oPrJvXBk+cPE5crOzV6tM8M+zZSZAl/+n/N242v1xcDcGXNC5TzYwVEmIsrRaNJ0o7sSQM64r9/GiN7/Z2rxwFwlQtTqm+1iecnZvpRrFhs6p3LidLJr1bm7uoJvbyWCQFbteDZ4MJcAK77dEGGySCbK+r7jSVe79O6/fr7t1vEx2ajHsXVnvl8h3UNvpxoMIKdq+lwVRMuf2sVHv1hm891KPeH9llm/OmEnpqvA0CjRqASkFTg4KByv77feAQ3fLAOfTp6l5w06PWyAaZ9OmbhD6O74e4zBmH6kM5486pxOG9UV/H1aUMKorLNFHucM4kiKl66H3UcBk1JRji2dDod9h2TT57Y7OPCKpVUNVrCtq6dpZ7RtMlSauVIdTMA1yhxm8oIsXaZZvHxacfxwpAig4FKSiSVja3+G6Uo6ShpXwx6PR46x1UydXtJ8JkqFL+kt1t3fbkJF47tjt4dMlFU6eroFMoOBUPITEqOK6/gmfR6tMDzvSnLRJ7Y15XZ0CE7DV3z0lGiUdrdplK+iCgaWm1amUmJcVQrA9nrHzwdRoMe3950EnYcrccD7sBOuslVyq5UpfRtj/bembYZJgP0ep04J1BDqyvjZ2T3PGwqdlUZ0cpM6pybDsAVwMpNN8mrSeS47t8W7izDj5uO4v6zBqPA3T4cilTmfPLll22lWLGvEiv2VeLW0wagfZZZ9d9lVATtnrlgOLq38wwqUctk89XnIcwNbEqQ/SxWnE4nbvtso/uZ94DkZqsdGw57svLvPXMwzhxWKGvz/MWj8Ncp/aCDDgMK4nsONAofhmkpbOL5NN2tHUtlUHJxSm6ulfWCtcoJUOh6tvfUmu+QbfbRMnF8sc41ajjTbFAtf3Llia5Rd2ajHg+cfVxUt42IKB4Zkmxwks3ugC2EDn41QlzYX2aSNENCmEdC6bbT4n8eDVLj/bfPlwxMCWUuBengqVSknEekRzt5xl6WpOydiaXsKA5pxEMSci6ba07qjQ7ZrmNydM92uFySOSPcS80Y3sXrffkZ8nvHO04fKAaplAP63rlmPEZ2d2UXOTQGXGW4A1e3un8rpXGWOVtKAQA3fLAe3244gld/2xfYPy5A64qq/TeSkM5xJAQW1f5dyuxJr+cqQaEv1h1Gk0W97B4zk7ypxdVu/3yjz/c0ttpkpQ3VSt4Z9DoMLszFoMKclM0iTkU8siiisjRuEqNNuh1vLdkfwy0hCg/PzbX3iC8GkyIrWTIp0t1l7PoX5KBrnnfAvXfHLBQ9MwO7nzxL1hlE1Fap2ilIiS85zv4uLVY7pv77N5z+/GLNkePBsDsDK3Mn7RAqyPWecHvbY2fgjmneJbso/qn96aUlrkKZSyHV50x69YoxGNIlVyxnpzZJvcBX53wynbsoOSRKZlKghJJ1Pdpn4re/TZG9lpkm7xMrzPMcx3dKfu/G9mqHjtlpyDS7gsRamUlixqb7K1T+7v5+qFoMUgklR8NF+W/xR/pv2FJcC4fDiUd/2C5rc/pxnb3K2CkzldT2l283HMGD321V/VxhoAxLfHqo3c+rlVeUamy1iYE5gPdw5MFgEkXUsG6RrdkaKGnq7z/m7IjhlhCFifs3XQfvSapDKSNCvtkdnu/UmiSlQordZe5O6tcB9541GKcOLsDrV4zx8y4iotS1ubgWve/7CfO2lQb8nharXbNDKJZKappxuKoZRZVNmL+9rM3b6Clz57vdkK654uNMsxEr7jtV9ro004ISi1oXk0VyTdoSQtBSuPxK1Q6swYW5mHPbyTjtOPUJ0KUaW9VH6APJMxCKEtv7fz5efKyWaRLvfP1OSqs8KKtYpBnlARhpybZ2mSbxca67jKUQONG65XT4yQRevOuY+Ljex3khFPYg74Ol2c83fLger/62F4eqXKVPJ/bvgOcuHIGnLhjmFTxSBoG0MsO/+f2I6vLFu13fgdqcV6kqlOu8/gXZOGekK9vupH4dwr1JlMCCOrKefvppjB8/Hjk5OSgoKMD555+PXbt2qbZ1Op0466yzoNPp8N1338leO3ToEGbMmIHMzEwUFBTg7rvvhs0W3pMcxdbxvdsD8P7hjJUUvf+gJOaEMCJJhzunDcLF47rjLHf9WpuDwaRws0ouvpLh+3U4nDjqrqufYTagfZYZ/7tmPM4c5l2agSiS2L1FieiGD9cH1K6oohHjnlyAGf9ZGncBpWZJybGbP9mA059f3KYOZ4efOZN+vHkSnjh/mFcJoK75GXj3mvEwG/R47sIRIX8+xZ7a/ZZ0gFOrIjNpS3EtPl1zKKD9LgH7nSPmdHdgaWSPfNnyoxrzJQGeuZWIYkla2jQRM5O0ys4BQJpJ+99mVpSglGbh5GZ4gknZ6a7HwrnUoXHdIPQD6DUyk5Tb+duucs3tDoT0HF1S24Kr/rcGj/+4XXP7pKyKNv+at1t8/PJlY3Dx+B4oyElHtmIgSZZZ/twQZIZR+yxXQC+U8qrJKtDr0Osm9cFD5wzB3WcMwswRXXHfWYNx9xmD8Cyv0UgiqKFfixcvxqxZszB+/HjYbDY88MADmD59OrZv346srCxZ2xdffFF1BJHdbseMGTNQWFiIFStW4OjRo7jqqqtgMpnw1FNPte1fQ3HjxL7tsaaoKm46XRlMomTjlGQm5WWa8NxFI/Hx6oP4eWtp0mTOxBPpqKpgv9+nf94Bm90pTjgeD6ySc3Pfjlk+WhIRkT8tVrs4+bbUliO1aGi1YWdpPaqbLOiYrV2iKtpu/XSD7PmBikbYHE6vUjOB8lfmbnj3PAzvrl6xYOrgAmx97AyvDjdKbN9tOIKDlU3ic2Vm0sxXlgEAOmWn4fQh6pk3jgDLJ6aSf140Aot2leNslblZ1DxzwXD0aJ/pvyFRhEnn/PIVmIlXvrZYmpmkPF/lpCsCI5Jg08kDOomP093rEF7fWFyDE1TGZnv6AVztlKfHxlb5ufaHTSWYMqjAx9b7pgxCLNl9DEt2H8NFY7vLso0DeS8ADC7MwS2nDhADPgBwUv8OuPGUfiira0HP9pkYrqxwFOTuInxHo3u2C+6NSSzQvlmjXofrJvURnxfkpGPW1P6R2ixKUEEFk+bOnSt7/t5776GgoADr16/H5MmTxeUbN27Ev//9b6xbtw5dusgvcubNm4ft27djwYIF6Ny5M0aNGoUnnngC9957Lx599FGYzUHMy9DYCBhUzq4GA5CeLm+nRa8HMjJCa9vU5DlLKel0QGZmaG2bmz05/WqkgTtfba1W+fOWFsDuIzIvXa+/tpmZnl+t1lbAZkOGpQU29wR46ZYWZFhaXN+n0+nVVlNGhmcGQYvF+98QYludpCPYZLf6/junp3v2K6vVtW4taWmA0Rh8W5vN9V1oMZsBkyn4tna762+nxWRytQ+2rcPh2tfC0dZodH0XgGvfaGoKT9tgjvtgzxFGY+Bto3SO0Dc3IcPSgjRLs7hN6a2u4668ruX/2bvr8DaurA3gr8gyU2KIA47DzNxQw1DuFlLacrdNKd3ibtstp0y7he12m8J+ZW6SJk2ahpmZOXGc2InZFn9/SDOaGY3IlizJfn/P06fS6EoaK9JodM895+C/yw/h0r4tkam1BX48CfMxIiRjg/ncO+xwaAI8nkg/94qxFpsdSzcdgfAvazVLHsfP577YAvx7ibNP270j2yJN5+NMuAGPEXazzXmMhvN4jSrXa95Axwhdrev7wWDwHBuLx4jGeh6hHBvKY0Qw5wZhOkYENdbHMcLn2HCeR1RVef8s8Twi+LE+PvfC8VJg12ph0rt/s2zadRxD2zf3eEhNdRWMFhNMBiNKhWBSlBwjTp4ogbJb3oothzGooBl2ldtx+Xsr0SojAYunD4be1zy+63NvtztgtJoRLzkv8TYWgMcxIg4ApG/ThjyPsFjUP0uxfoxo4N8acbU1MNgssOicYx/4YgMSrO7XwV5RKXtvCGP3nq7AuC5Zqp/7OFM1EsxmaKR/dxQeI+o1NsjziIykBFzWr5XzuuRzrzxOOTRArSEe3fPSPMZ6COV5hPLz1Ih/a/AYoTJWcYyQvi/T7GbZMSJmziNcjDqNx+ezT6YBe05VYEJ+svNviY8Xg0nC355mM8teB2NtrThWWESRYK6F0eT8/rSUVSDBXIsflu9HXDsbptTUyL6b9K55AENtNVBV5Z57A2DXaPDthmPi2HhLLcpLyjDtjYXo1Sodj03p6t75AM8jrBYb4i21qDXE49mLu+OdPw7gXEkZKkpKgTQvlYhcn2WLzQ6jxQSt5HF/vHGEcwGO8FomJcGo1+HRyV3cn/sa+b9JcdFZJJhrURPnPq4arWZo7XbVY6auxjkPKZbL4zECNrsDepsVBpv3v82sN0ArBDsb05ylxQKN9PVs4ucRXn9r+LqfkqMe9u3b5wDg2LZtm7itqqrK0bVrV8ePP/7ocDjzIR0//PCDePsTTzzh6N27t+xxDh486ADg2Lhxo+rz1NbWOsrKysT/jh075gDgKHP+yR7/2SZPdpjNZvE/e2Ki6jgH4LCNHCkf27y597H9+8vH5ud7HWvv2lU+tmtX72Pz82Vjbf37ex/bvLl87MiR3scmJjp+/PFHR1VVlXPs5MlexzoA+eNedpnvsefOucdef73vsSdOiGOtf/mL77F797rHPvCA77GbNrnHPv64z7EPPvSBI/+R2Y78R2Y7nh99k8+xlgUL3I/71lu+x/74ozjW8uGHvsd+/rl77Oef+x774YfusT/+6HOs9a233GMXLPA9duZM99iVK32Pffxx93ti0ybfYx94wD12717fY//yF/fYEyd8jrVdf7177LlzvsdedpnsPexzbJDHiKqqKvGzFO3HiOKEVPG9Pu3fK/0eI2SP28iOEeNufkd8LfwdIywrV7ofd+ZMn2P/78VZAR8jDs36QtyH0nfe970PTeQYUb1zp++xMXiMaMznEeE6RnSZ8a0j/5HZjsKzFTFxHhHMMYLnEa6xjfg84vd2A8Rje/4jsx2W+ASvY1e17uHIf2S245Vfd8bEMUJ6HpH/yGxH6aCh3h9Xcox4ce4Ox+/tBvh83WLtPILHCNdnLohjxPOjbxLfOxfe8LrPsW+cN82R/8hsx32fb3CUrl3vc+yZO+6OqWNENJxH7GnWxpH/yGzH0eLyRnkewWOEa2yMHiMe+npTzJxHPP3TNseAZ39z7Nl33PdY1zGiptbkyH9ktu+xrmNEWWWNI/+R2Y4qg9H7/o4YIftslKekex27Obej7Dv8WGq217HBnEccS8125D8y21FRXeu48O2ljs25Hb0/ruQYMfrlRY5VrXt4HxvkMUL6t83ufJ7PsV1mfOtYvf80jxGuY0TbR2c7/jrlfp9j77z4UceLc3dE9BgRrt8aey+5RJwb53mE+nlEGeAA4CgrK/MbD6pzh1O73Y77778f5513Hnr06CFunzFjBoYNG4aLL75Y9X6nTp1CTo48jV24fuqUejPbmTNn4umnnw54306fPo01c+eK16fabF5TsM6WlGCFZOwksxneClCUlZVhqWTs+OpqeEsYr6isxB+SsedXVsJbAmhNdTUWSMaOLCuDt2RMs9mMeZKx55WUwHMdpJPNtZJnwYIFAIDBp08j18tYAJgredwBp06hpY+x8+fPh80VJe17/Dja+Bi7cOFCmNPSAAC9jhxBgY+xf/zxB2pc74duBw+io4+xy5YtQ8WRIwCAzvv2oYuPseXlFUC6jwESq1evRokrIluwYwd8VQZdv349ilyXW2/ZAl+t6zdt2oSTrpUfeZs2YaCPsVu3bMEx179Hzvr1GOJj7I4dO3DINbbZtm0Y7mPs7t27sd81Nn3fPozyMXbfvn3Y4xqbcvQoxvgYe/DgQex0jU0oKsIEH2OPHjmCra6xcWVlmOxj7PHjx7HJNVZXW4sLfIwtPHUK6yXvYfUjkFPQxwjXZ2jBggVRf4yQWnnwLM76OUZIP/eN7Rghtc/PMWLFihUoPe2sJ91h92509zH2+PGT4mvh7xixfccOQOf8BG/bvsPn55PHCKeYPEY04vOIcB0jBAsW/o5RMXAeEcwxgucRTo35GKFkD6B0yK69+zHXtDcqjhGDzpYh0A555eXlSPNym/QYse+I1ud7Eoi98wgeI5yCOUbUxY9bClG+6jA+8jHm5Inj4ndtLBwjouE8Il7nwKAsOzYsWwSNpvGdR/AY4RSrx4hjx45hxZE9MXEe0dtxAL17Ans3LEcnH2OFY4TDAfgrAiUcI6x2/2PPnT0rO0aMDlEriWDOIwQL5s+D0eS7JK30GHGy1HcP9WCPEcFas3olTm3nMaIIQKDT/wf3H8Bc875G91sDcM+N8zwisPMIXzSu7KGg3Xnnnfj111+xfPlytGrlTLP++eef8de//hWbNm1CcnKy8wk0Gvzwww+45JJLAAC33347jhw5gvnz54uPVV1djaSkJMydOxeTJ3v+M5lMJpgkKXPl5eVo3bo1io8cQWqqyuGO5WkAABaLBQtWrsT48eNhMBjCnlY+7KUlqDQ5UyYfmdgRL83fh0ndcvDyDYMinjJ6wxfbsOpIOQBnSYXtj41yp28qNXBa+dGz1bjgX6sAAEseHIGMxLjYShkNdmwMpoxa9HosWLDA+Vny9W/cgMeIb9YdxbNz9mBMlyy8eaXz9OHdxQfw/tLDsvTvfX8bAdjtWLqvGP9bfRR/ndARnXNS3I/ViEtPdJq5TCxzt++J0XVKK6+12PDFumN4Y+EBcei0ER3wmND/yM/nfltxLS770Nmo/fe7B6NNsvMYUFFrRXmNBS0zJO+XEJWeqDJZUVJlRhuhPr7KMeJslRmjX1sGANj8+Bj38bABjhEWkwmL5szBmDFjnN9NSjF4jGis5xEeY0N4jOj04nI4oMGqR0ahuQFNvvRE0GOtVlgqK7Fo0SL1zxLPI4If6+Vz3+uZ3z2GKsvc3TMoB/eO8awn/+2GE3hq7h6YDEZcP6QNnpzaJeBjxP7TlXjh+02Y0CULVw9s7Tm2HseIHo/+Iis5oyQ9j/jvFV0wsn0z7/vs+ty/NH8vPlu8FzcNbom/jvcy3Ral5xEWi0X9sxTjx4iGLk8zc94efLahUCxhpbXbYJSUuRtSkIEPru+HnSfLcfWH62DV6cSxGocdex8b4fGYA174A2arHfP+Ogp52a6wZpQdI+o9NkTnEasOnsUd/9sEAFj20EikJcZF5DzC4/PUiH9r8BihMlZxjDjv5SWoqLWiU04yvr1jMLo9vxgWnQFX9m+J5y/s0mjPIzo+8ZtYem7FwyNx3stLxWFbnxwrjrXZHejyjwVIMNfi0r55ePrCriipNOHjVUfwyapj0GscWPO382GQzHs+9MlqzN95Go9N6oRpg5znB2arHasOnsX0r7bCZDDi7at6oU1mIq5+ezE0ko/9QxM64vohbXC6vBYL9hRDl5SEC3rlItmolx0jZnyzFb/vOgMAaJFmxMlyEyzGBOx+ejxMFhs27yvEbZ9sBADMv3cYWqQrCue6Psvdn14ITU0Nvr99IDpJ5yBUxgLw+bnv9czvqImLh06rQeecZBw4VoKxHTPx1lW9xTF7iiow/YvNOF1uRo3BiJ+mD0W3Fqk8Ruj16PjEb2KZu/HdsrFgpzNwdct5+WidmYinftkFs96A6WM74Z4x7RtVmTuLxYKFS5Zg3JQpzu+lKDhGAIi684jy8nI0z89HWVmZeqxFok6ZSXfffTdmz56NpUuXioEkAFi0aBEOHDiA9PR02fjLL78cI0aMwOLFi5Gbm4u1a9fKbi8qcq6lyM1Vj0EbjUYYhX8cCUN6uuyg6pVif0I2Ns3bOr16jlWbXKvLWNfBx2AwOD8woXpcL2Nr4+JR43AedHVpaaiJi0eNMQEGaR+sMO+DNzat+61u0RngSEmFQaVRsurjSk+sgxhbZbJCr9PAqFd5HoNB/ABrrQbxR3utMRmG9ESvYwPaB+kBJ1RjAfcBMtRjg+mTFszYUH7upZ+lQN8PQFiPEfakFNTExcOakAiDa/+vHdcDb6yWZ3gKx8j7Zq9BpcmKmiXH8eXtQ70+bjD7EO1jxX5JAAzSk9QAH/f6/67Bsn3Fzm2SiTWbVueebPJzjNCUn3PvT1w8DOnOhRYj/zEflSYrlj18vnpT5GA/95KxV7+5FLtPVeDJC7rh5uEFnmPj46HVmcTjjjHTy9rTMB4jbPHxzu/wQP69Y+EYUdex0X4eEc6xGg3gAPR6AwzJQXxnxMLfphxbx/MIv2P1+sA+SzyPqNtY12dZk5yEarOPCU0AFfoE8btYyppYCpPB+bd8tvoonr2kp8cxotZiww0frUWtxYaPbxqETNe/5axVR7HsRA2WnTiK68f39L+/QRwjhH0KhD0hWfVv86SBSR8HR3JKYOOj5fMJABaL/89SLB4j6nge4Xesl8+9PTHZ3QsFgF2rQ02c+3fQhhILDOnpsJbZZQFLwHnOVqpLQFaK+73pcDhQrouDQwskpqbI/22i6BgR0bHSz32yRXxd4zMzYDDqvY/1pz7vd1+fp2j63MfS2Bg+Rnx67/n4YOlBzBjXCYb0RHcAWaOFIT6+UZ9HiL+1MjJkxzzpd6TeNYlbExcPW0ISDOnpyE0Hbpucjvc3OIM52mT58c+ckIiauHhoUtzftwYAw9PScNGRKrRIi8dFfVvDYrOj1iB/fY9b9DCkp2Pm3I2YvbUQAHCuxop7x3aUHSNqDAniPh+sAWCIR2qcTpxbHN67ANrkPagy25xza+mev7XLqi0wW+2AwYgWrXJgSArgtfPx2fjTyM74bPUR3DumI3JSjXi0sALm+ETZ6/mPL3biSK1W/O2eYIxrkLnQiI8N4HN/Ue88/LzlJIZ2yUVKZiJq4pyL7R+8fAB2nCxDzfxDAIDstAT3a9bA5xH1Ggt4/9xbLHC43rviZykKjhHRdh5h0PrOOpQKfCScJ3R33303fvjhByxatAgFBfJJqkcffRRbt27F5s2bxf8A4I033sCsWbMAAEOHDsW2bdtw2pW+BzhTzVJTU9GtW7dgdoeimMG1yt0aohTc+rLZ5auwzLbw7leN2Ybu/5iPQc97rmZVskj2ZcPRs+HcLYpCZTUWnP/qYrw0b3dQ9xPe0Rq4M+yaJxuRGq++RkDIGtxxorxO+9kUiYEkBYvdy6pOFXbJClCz1f1ZF/49Vh0sqePeebf7VAUA4JnZO72OEY6JOm8ZmkQNxIHAP09EkdCrlf8JWG/nlVWKINS5Ks+VoztOlmHtobPYerwMf/lsg3tstY8VqSHUtYXvhXm2AM/lba7vO52G3ytNlb9/+vJaK85UmGCyqr+nzntpET5cdhA/bDoOq80Oq90hJtLE6YKatmiSpN+mep7fUZTokpuK16/so754rgkY1zUHWh8HR42X2+L07mOeRXGOIX4tK+4bb9DhlSt644EJnQGofx8L83Ony91ZJK8v2IuCx+ag7aNz8MOm47DZHeLv3ewUIy7t2xKX9m2JmZfJi6npxDk/z3P5lQeK0fuZ38TrGYlBBEe8ePaSHtjy5ATcN64jDK7vBLPN+dyHiqsw7YPV2HDknOw+PBa6zbysJ968qg/+dU0/JMbJF7xnp7gDNu2zkht61ygGBXVWNn36dPzvf//D559/jpSUFJw6dQqnTp1CjSs9LDc3Fz169JD9BwBt2rQRA08TJkxAt27dcP3112PLli2YP38+Hn/8cUyfPl01+4hih0Hyhad3HdwttuiYJFJ+wb0ybw9Ol/tIl6ynA2cqATgDBf4qSUonmGd8tSVs+0TR6bNVh3GouArvLT7gf7CU632lPEcsr3WnZKv98DaFOZDaFFiDeA2tkmPgjpNl4didOhEn/XiCTUTkkzDxfVm/lrikTx7SVSZELF4mx1/8Vb5QRC3oJD1XXnvYvahIenj+cdMJnCj1UY6jHh6f2tXn7YF85TkcDqw64Fwcwe+Vpku6wEnQo2UqNj0xXrw+ZObv2FVYoXp/s9WO5+bswoyvtmDR7tOyoJN0YpXUSX9z8nNI0a6pLCbKSjHKfq/7+84V6CUZAsq5LOG18/cxV2vrIJxz1Frli12Ew8eMr7Zg6d4z4nnN4xd0wxtX9cEbV/XB1F7ybotCQEe5cBsAvlh7TLx8Ue88r0GzYKW5zsGEuUdhP+duK/RYpDmyUxbymwVRnaSRSzLqcUnflkhLMOCqga3RITsZt7qqmKRIFiS3z+JrRv4FdVb23nvvoaysDKNHj0aLFi3E/7766quAH0On02H27NnQ6XQYOnQorrvuOtxwww145plngt55ii7/uWEAslOM+Nc1fWHQ1S8z6ZctJ/HGgr2qX0x1oVxV+dnqI3hj4d6QPLYa6Xelv4Ca2koOajrMXiag/BEzkxTnZW2buVd9qU141fX5yM0aRJDcJvlh/9C3W8WMpEiz27mCnCKL7zyKdvtPV+KlebtRVOZcfHRJn5Z48+q+yE7xXPz2zYbjmPzWMtnqYbVzWLVzPm/fKdLJ4Pu/2ozr/7smoP222x3YfqIs4O97vVaDgW29lDtFYOfyS/cVi1mxoZowosYhxWhAhqS0kc3uwLNeMqcHF2QiL825OvpMpQnVZuc5k0YDxBsYTPJHun6R53cU7erWuT32JBh0ssyk+EBaLUB+DlBrkQd+hFMJtQC+r8cBnIsiay02bD3uXOT44Q0DsPbvY/HCpe5yumcqTOL5TJzO+3MIj11S6Zl1XeM6fl/YOw8vXd7L4/b6EvZr1cESnK6oRZXrN/bE7jn47s5hOPjCFHx68yAG1r3okJ2ChQ+MwuOuPtDxBh1eurwnXrysJ7JTgygrR01WUD2T/GVYBHqf/Px8zJ07N+jHoujWPz8Da/8+DgAwx1V/tS6ZSQ6HA/d8sUl8zJGdsuq9b2o/1I+UVGPlgWL0apXubDgYQtIvdovN7nM1nTJtmZqWusYSHV5OImde1gvT/rPaOaY+O9bIFFea0DyYviw+BFXmTvHxrjJZkRQX2I+IujhdIc+4tNsdqqvSTrpWuNdYfPcBISJqqq54f6Ws1JwwAaTzUk98V2E55mwtxCV9W8Jqs2PSW8s8xthUzkctKsGac1VmzN0m74EoBLW8WbizCOeqzaioteKZ2Tsxpks2Pri+v1gtwBu9TiNbAe2xzwF85/2x212+nJPYTZfaP73JGth5xgPjO+HesR1x9+cbcXJrIcxWO6pNzvsmxekZpAyANNND7dyPiCJDGtAINLghLc/24fLDeOLCHuJ1YY41kIfSaTSwSY4NVrsDP246IV7PTYtHdko8rhncBkv3nsG8HadgstnF37sGH+cQwunBtP+sRmKcDn+d0Bl/HpoPvU4rZpaO6ZKFhDD89pXOrw16/neMcs0Zts9KRv987wtkyLurBraJ9C5QDOESHwoLvZCZVIdAifSHe3ltaOrFCz+EpV86Kw+U4Jr/rMFtn6wPyXNISX+T/7r9lPeB8F4ahRq3WosNP285ieX71fvy+ONwR5NkpO/x0mrPVUJNlfSkub5+2XIy4LFmm+ckirTM0cPfbsXKOr4H1EjrXwPeMx9v/nhdyJ6TqF4Y9aYopexZJGRGGCSrdEd3li94EkrRna0yY//pSo/HVAvgKwNMZqsd//h5h3j96oGtneN8LOqrtdhw66fr8dC3W8V+eYt2n0aHv/+KoyXVsrFlir+rc26qeN6upsZiw5S3lmHAcwuxt6hCdaGgdKKIrW2aLrV30T1jO6qP1QB/m9JFvC58voTzWJPVjmpXz7FAV/ITUexoKqd/CXFaWdDHVwBI+l0qDQgrS4MKX8O+ejG5H0d+3WKzo0jye7F7nrtvonD8NVvt4hyVrwUpxZXux6k22/Ds7J34y/824sNlB3HEde5h1Ifn+N09T97PcsneMwCAlPj692YiIv94uk9h4S5zF/xpwjxJ8MVqc2DPqQqMf30Jhr+0CPN3+A7MSJXVWPDgN1swe+tJ8Qf4+K45HuOUtVVDQbrI9MFvfPdB8ta0mRq3t37fh3u/2ITNx0rrdH+xzJ1iu1HWrLOpnKark9b+DfXnLJBM3b1FFbj5Y3mw2mZ3oNYi35fP1x4N2T499v02j+dTo2wKT0REcsrmxO7MJPc3b4u0BNkYkytY5O38d+KbS1Gk6NmpLCNX41psIsh0lQjzlSFU7eOYPvKVP7CrsFy8ftUHq8TLn948CMlGvWwF9H9uGIAnLuiGjtnOBsx7T1VgZ2E5iitNmPDGUpz/6mKP70DpyuXUBE7kNFVq85pJcc5zsVYZ8s+KwwGkJ7jL36W6JgCFicfC0hrYXe8zNlAPTFMpG0aNQ2N/v/7jwm7o3SoNtw5vJ8usVCtNd83gNojTaTGxe67qYynPKexeFpWqUWYLW20OsV/SzecVyPZNCCaVVZvF6jkGH4tN1CzcVYTn5uzC0bPOYFJqmII7OanObCpB71ZpmNqzBS7ukxeW5yMiOQaTKCyEchl1mcwW6mMDzhrxr/62B/tOV+L4uZqgMgI+X3MU3244jrs/34SDZ6oAAEPaNwt6f+oimF5PytdoSx2DCxRb3lt8oF73F88hFSeIRjYoFklPXl+etwfX/3eNRxk4b85WeWZ1XdavpXi577ML/AYCX1I0XgecJ//SYxwAWYPp+jhwphLbTpTJtqmtZFfW3SaKBJYsominzDqKd01ySye2W2fKJ8jfXrQfDodDVl75g+v7y8ZsP1GGbcfLxGOxcpJIeYwWglq+Fmj5648kzZI/fs6ZPdU8OQ7ndWju/JskwaC0BANuGV4g9vj7ZNUR2WMdLqnGGwv3qe4jAPRrw/IyTZWv4/q71/bz2DapZy5uGV6Am88rwOSezsbuwsfrx80nJavvQ76rjRLb8BJFj5vOK8BPdw+X9YwD1IPuz1/SA1v+MQFDvcxVKeeWhGuBZSbJx1SZreI8REKcfN5AGPn2ov3Y58qujqtDunGPlqm4tG9L3DW6PQa3ywz6/oGSBt9e+lMvvHNtP+SlJ/i4BxGFCmcdKSzqU+ZOmUGwYGeReDmYFSxCTxCpLrkpQe9PXUjr3/tbTaesJf6iygQ0kZKwkln57lL256rLZ7CxUJbtWbavGIt3n1Ed+9nqIxjz6mIs3+csOadWiihNstq6tNqCmXN3+Xx+tZrYdjtQZZI/dqBN0v2pNHnu8xqVzMsaZiUREfmlnLwRynBJj+2tMxI97ldjsYnnganxekxQrDR+fu4uXPiv5Xj0u60APPt6KjOXElyZHQ4HsHTvGdhVZoz9fY8Ie7z9RJkYJFowY5T4t0hXLguLUgp99GiS9khyOBziuevozlno0TLN292okVP7xSOsoJdmIQlS4w144oJuePLCbuI5VtcWzpJLzZLjxPty8UFgslJC0xuUqCE4mkyhO7nEOM9e3RqNxmdfIWExyYPfbMHA5xeKZc0D6pmk9fw9LMhOiZfdZjR4Tg/7KnP30MTOqtu75qbijav64OFJXXz2XKov6UKWUPdAJyLfGEyisBC+NOpS5s7XD2J7PfKhs1OM6N9AqyWlExBWu0P1h7/g7s83ya6vOliC3afKvYymps5ud+CHTcfxwlznxM3cbYWy25V1iYWsl6b4A1Pt/FqYRFN64sftOFhchZfnO19XtSCcsma/vwxEtZNnm0pmkkXyXBabHRf8cxl6/mM+lu1TD3x5I6xmb9c8Sdx2yyfrsfKAvCeTNBOqJVdvUYQ1zakEigXKQ7zR9R1gUGTxKNns7swkte8BIVv+x83ObPvdp+S9ED5afkh2XTpZcsNHa2Ul8AS7/Jw32uwOVNRacME/l4vbpOXo9hS59yEnVT65pCbB9VrsOFkme8yRHbO83YWaApUTL+E3kEEfWEBICEaaLHZ3SWfGkgLSp3U6/j6lK/6tyIYkosibMa4TxnXNwdiu2UHf12q341RZLb7dcBxnKkzY6SpdG8ixUVnmTurKAa1l17vkpnqM8VXm7i+j2uPRyV08tjdLbph5B2nWFINJRA2LwSQKCyEbx1KXzCQfwaRgyscppSYYoNVq8OzF3ev8GIFS/t1qWQ6+bDxSGsK9oVgRyPv72w3HMeMrdx8uZcBWmZkkBBjiJSuNfAU3Aefk0LGz1T7HxIJslQmxlQeKUVFrURntVFLpLG+nduzKVgTk/JUWyE3zfH67w4EPlh6UbZMe846drcb2E+WoMFmxZE/dgknKoJfQkFTt+QYVhK/0ABFRLFP2BUpQ6Zmk02rQQnGstzvc/T8DWQT1/hJ52VshyCRIUkyQ/LpdvogEAL5ad0x2/YvbhuC2EQXidYvdjgOuIJZ03wXFFe4m2sJ31x0j23ndZ5PVhlUHSjD17eXYcdIdyLp5eIHX+1Djp9YLRDjlzFWck10r6XUhJZyvmqw28fMTSCkncrptZDuvfVeIosmw9s0jvQsN6r5xHfHhnwd4/E4LhFGvQ3GlyWN7XcrcCR6f2tUjG0qtoo6vzCKdVoMrB7RGeqIBBp0G947pgCsHtMKdo9v73a9QkJ5jKc+ViCi8GEyisBAzk+rQM2mRpHSGUjCxJOV3qzAJoPaF+sOm44E/cACUf7evxshqPlhav346FJs2HDnnd8xqRdmyzjny0o0ewSSr0DzTvd1Xj56i8lpMfXs5Rrz8h999iUULd51G76d/85qhJBw3zFb3Z/ixyV3wwqU9cdVA+eotf+UZzlV79l2647MNmL1VPhEoDVxJA4rVQQahhX/XeEWJghaKCRyzzf24D4zvFNRzEIUKpwaBRbuLMPiFhWJ5TYouynNOYXWuXhFMev3KPrhxWFv3/ewOvLFwLwDgXLVz8UKf1umqz+Gvh13zZCOGKXooqPUjbZ/lzkg9/OJUDG3fDB0l5wdWmwN7iyo87id4YILzu6B3K3eJuocmdsYvdw/HgRemYEK3HNl4k9WO/62R91JqihnQ5F9P13tKWaru4Ymeq9kBd4Z9caUZ51z9KxlLImo8lj9yPv51TV9c1rel/8EEABjqpe9QICVAq7385h3SzrM/k1qJdn9l6jKT4rD6sbHY9OQEPDChM17+U2/VrO1waJPpLjUcznJ6ROSJnzgKC7Fnkj34zCRfP0aVq0R92XJc3ohe2Ce1FRzKEiP1pcww8VWuKk8le+FwSexnhVDwAvm8NFd8Pt5XlLIwKoJJJtdElUHr3u5r8uqI5L2nzM4xWW1YsLMIR0qqlHeLSt4ysOwOYMeJMtXbhMODUBquZXoC7hjVHtcMbuNR49pfcLus2jMDar+rmamUNLhnkxzjPl9z1PcTAJi3vRDT/28jDhVXif+uylKHZTXyHxHC82WnGNE607PfBxE1jJs/Xo+ichOu+++aSO8KqVCeywmTNnrJ96lOq8HQ9s3wjwu7idvUspG+/ctQbHh8nMf2MxWeK42lvrx9MFLj5ZMyalnMwlPeMcqdTXRR7zzxstVml333K8vW3DisLT6/dTA+uXmQuE2v06JnqzTotBqPc+fdpyo8vmODOUenxkn6Nln4wEhsfWqC10lFZV9LQV66+3fRVtdvOWYmETUerTIScUGvPK8ZM+T256HODE6b3aHaPiKQV7DKy6Lmziq9xNUCMok+ejkJ4g26iJSZa5ZsxNx7R2DJQ6Mb/LmJmjoGkygshB/aaqsn/fFVGi/QnkkWmx1bjpXKtj00wdkgUK1urK0O++nv+aUe+HqLl5GAxXViMLqzvM78gTOek87UuP3hIytPoFwxlJkob2isTE+vtTjfi9K3vVpvBavNjnu+2IQ3XaupAaBUEQy5/dMNuO3T9bjg7eXKu0clX83Dpanwh4rdwbEM1+v53JxdAIATpTWy+6XGu+/nb+LM34+kRyY5V+WavWQmAcApH38DAPzlfxsxZ1shXv1tD0wWd2bS2C7uetw/bTkhu49Q5k6ZxUYUCZx/pmjl7ZxTJ5kEFya5NRqN+D1rU7mfXqdFs2SjrL4/IM9cb9ss0WOldlpCnMf3utqxW1gkYJQ8frxBhxnjnBlHZptDdk6u7Jen0WgwrENzpCvOKQTXDckHIM9cUn4/3nQeS9w1ddIV8Ea9ziMQKuUtmGTU69A9z9m3QygTzlgSETVFwjmDxWbHtuOlHre3k2QleyNd7CLolJOsGjhSy0xSlsKLNt3yUpHfzP/rQEShxZkkCgthxaNaE3t/fJXGC7TM3eXvrfTYluL6QaM2wau20qM+guntJLxGwzvI6wYXlfueRKbYphZn+M+yQ54bAZyrMosrgJWTW6kJ8lVAGo0Gn0pWFtdabVh3+Kys5NovWzz7LWw5XoZftpzEygPuMnrSoOixs9Vi750Kk9Vv36VIq7XYPCa6pITPvMPhwCvzd4vb4w06n5/fdY+PwzOuvmv+XgFhFfiVA1rJegWM7JSFwy9OxWBXyQJvZe6kj+HPgdOVMFndPZMev8D9wyEnRZ79KE46MphEROSV8HUbp9PilT/1Ercry9wJhMDS8XPev3uUgabCMvdYrUaD16/qI55Dd8xORlaKEVqtBtMGufvL5KR6ZvDP3eb8XjcqejEIE/aFZTVYd+is6n4HYnjH5ljzt7H4/q7zxG3KrP7rBucH9ZjU+Hy+1p1R7e89Js3wUxJWuNe4gq2MJRFRUyQEfMxWu0dJO71Wg+55aWp38xin5C3bU21sQh16PBFR48cuZRQWemEVRR0mnIWJ1Zf/1Au9W6WjtNqM1QfP4o2Fe31mJn297hgOl1ThvnEdxbIIANCtRSoKspLQtYUzlVetnGpdyvH5/BuC+LuFzJGxXXNgttnx8rw9ru3qk8g1ZhveXbwf47vloFer9HrvKzU8u90RcGB0z6kKTHxzKQBnzwVpSbS/ju+kWit5ZKcsdMhOxv7TlbjsXc/A6m87TmHmZT1l29Teb9LArrLXgtXuQFwUlycor/UsMSclZOcs2FmEudtOidtNVjsOFXvPCjTqdWiR5lzR7S+jQnhNR3fOxsJd7qyzRNdJubDa7NjZGtjtDmi1Go9gUqDHpt2nKvDETztc+6hFQfMkvH9df/zlfxs8MiXdmUn8cUCR09RXmh9lOduoJ5xzvnplb1nJuHjJsVMalNdpNLDBIfveVU7MDO/QXFyYAQALdxV5PO8Pd52HX7acxD1jO4rbZl7WE7mp8a5zYfn4iloLSly9ZZQlxYTA1OI98nLLynKogchR9t+TnI+sfmws0hIbpkcCRS/p4jy1YFJuajxOuRbL+Qo2CQ3qhcwklrkjoqZIyEResOs02mfLy9Ipv5O90akE7r0df5XbB7XNZC8iIlLFIwOFhUFb98wkoQxHTmo8OuemYHC7Zmjb3NnXw9vkbbXZioe/24p3Fx+QNbLWaoC5943AO9f0EwNcanOzwWQS+eJwOLD6YAl2F3qWEVNjstrEH0p6rQZ3je6AQQXObAVvJbreXbwf/1y0Hxf9a0VI9pkanloJHIHVZsdhSdm1L9e5V3luPlaKXa731hX9W+HuMR28Po60HJuSMOkkVa1ST9ki+bAob7/hozVR3TTeX6BHCI7tV5STNFlsuPN/G33eVzjN9p+Z5M4AOit5zRftcQaWpJMjfZ9dgD2nKjwC5mZr8Memga5jiDCJqAwmVZudZWhY5o4ocj5eeTjSu0B+CMdj5ZzL9UPzMa5rNv48NB+dc9yTO2rz3dmKPoedcpJl1ytrJT3tXPfv0TINj03p6tF/QJjPUWYGl9W4F09c3CdPdpu37I+uLVJVt9fF41O7Ilel/yc1PdL3u1rVh/vGdfTYpkYI0jKYRERNmc31W/xUuQmbFC0c6sNbMKlZsvsYXtA8CV/cPiRkz0lEjQszkygshFJydYnRCBOf0ubAQvaFt8wk6erIarMNzZONKK404e1pfT3Gqv2A9lVaLxhbjpfh6g9WBzy+sNQdMGrh+iFucv1w2leknh2xq7BCdTvFBpvdgZ83n/R6e4e//wrA2Qz7qYu649hZ9dXr2alG1awkwRMXdMOlKllJ0v2QnkgKAQYp6edC+VSrD56FVrMfGg2w/3QlpvRsgawUz9I7keKvv5pQOiVesTrbbLWLq2YB4L9/HuBxX/G18PMcwkSKckVXh6xk+ePAORn43uL9slJGzsfwHZDPSjHiTIUJl/ZtiQcndkZynF5cHS6WRlAc3+75YhMAeW8Nokhx+A3LNk6lNe4A86hOWT5GUqQIh1/lRHaPlmn48M8DPcarTc48fXEP2fXWmYmy69JAkD/C6mLlAihhsUdmUpxY0llg8LJo4P4AJ/UDwYl+EqQlGFBUbgIAZCV7nhMGunhPp1iUyLcYETVFXXLdC1Z+Uswf+CrnLnVUZS7B2/d2vzbp+Oe0vjhVVovx3XKCLolLRE0Hg0kUFtIvKIfD4XPSW0ltAlb4HvM2QSz9ceJwPScAdMhO9hgbb/D8YR2qzKRCH1/quwrLPQJZta4eJ82S4sTMKWFFiLdmh/xBFdv+b80RPOkqR+bLxysP45FJXWTl0aR0ft4I/k7+rHY7dFr3e6zK5M48SorTocpskwUy1J7vZGkNrv1wDQBg24kyvHpFb5/P2ZCEz7RRr8XkHrn4UXECbnZNUDRLljcbN1ntmNQ9F99vOoFpg9pgbNccj8cWXgp/Rw1hNZmyzJEQ5Jb+QACAs9UWj6y1v3y2AR0kK9+bJcXhiQu6ITPJud/Cv8stwws8GqoLx1Blhqjz+OzAEFfPJiJqWMv3FeP7jSfE6/xej07eMpO8UZucGd9N/h1y9cA2yE2NxyPfbcW5agv+UJSf80U4LVaes1aZnItBElXOGw0qO3/PmA4eQa364GQTqVHLfnb4Sxt3ERYlCu/1YH5HEhE1FqGYo7KpLEz09r2t0WhwYe881duIiKS4LJnCQvr9FOx3oJAxIG32pxUzk5zXT5bW4Iu1R3HOVTpK+hwOhwMVrrIhqfGe9dulQaqpPVsAqFtvJzW+HucfKgEEoQxWvORvbZ+VBADYcbJc/Puk+Js9tv3uJTikpqhcvdQh4P+Htb+VwsqT0+0nnX3GLumTJ/ZckGcmeT6esPoUgOp7NZKE+QqdVqM6oSH0MzIqbqsyW/H9Juckr7IckUDjqkXkb05ECIzrtBqxPxLgDnJrNBqM6ZItbrfZ7R5lOE+W1WLp3jPifz9sOoGFO909NmziZKfnv49amTuT1Sb23bpleDvffwBRGGmaWEv13afKMXTm7/hy7VFM/1xeSjNUC1ootOw+jq9qKk2eGb5KcXotJnTPRZ/W6R63PTyxi8/7Cvthczjw/pIDWLTb+V0gZCYlxXmuEdSrZKC2zqhfIEmZhazliSm5+DsvCjgzSSOcvwQX0CUiakxCE0zy3OZvUSoRkT8MJlFYSCee/ZWbUhLKbUkzc4QfEcKKtvu+3ITHvt+G5+fukm133t8mZh2kqPSNkU4sC6s4f9nivexYMCxW+bf1pO654mW1UibihLYkW0r4Ub507xlMfXuZx32a2gRcYxPM5+FctfcAjb+VwGq3S7PyLIrSZ6Wu56o02cTJp+JKd7BIbTWpUMse8N0HKhKEk2+tRoPL+7XyuH3+jlMA3K9DrquJaWm1+3PqtUG5n0xJ5T7odRrcP95ZUujGYW1lY568oBu65zkzFlfsL8Grv+3xeJxRnbLw+pW90atVGgDAJPlVIPy7qP17C4Fz6b91haQ/R7KPvlpEFFoPfrMFhWW1ePT7bbIyvgCDSdFK+GcJRxk36WM2T47DhsfHYVKPXB/3cGe5rthfghd/3Y2bP14PQJKZZFTJTFK81x6a2Lneq47n3z9Sdp2TUiTwdyQL9FAnlrlzrbBhKUUiaopCUUJe7feql3aKREQB42GEwkKemRT4JMmxs9Uod012SjOTNIrMpHWHzwEA5m8/JdsOAD9ISseordKUZiblScpCvblwLx74arNHY+NgKBvdSyfvhR9GP20+gds/XY9KkxVnKpyT9ekJ7gwq6Y/yk2W1Hvsj/fJ/Y8HeOu8rRUYwMRdfmUn+VmmqBReelfRusNkdeO23Pbj+v2twpsIk/lDv0TJV7Bkk1GI+WlKNmb/u9vl80TYZKi1PNLhdM7x3bT/Z7XuLKvHSvN1i/yC18kBJKhNzgBhL8p+ZZBMCPVrcMbI9frl7OP4+tatsTNvmSbh3rLt3xWaV5qpdW6Tisn6txNXk0mOCO2jm+fzuYJL7uDTPdcx07hcnZyjyoiwOHTbbT5SLl5V91FYeKEG7x+bgmV92NvRukQ/i8TUMv5bMkuNypckqa3rtjXDMli70APxkJkl2Pk6vxfTzO3gtoxyozKQ43DK8QLwunMsS+StjF+hvQuGnkHAexVgSETVFg9pmIDehfifKar/R+RuQiOqLwSQKC3nPpMDvJ2QLABB7gkgfT/kjRHgaaVbE2sNn3fdTXa3v3nZJ35bi5TcX7sP3m05g8d7Ay5ApKYNJep0WrTKcASvhS/u+Lzfjt51F+PeSAyhxTQi0SHMHtZT9VSyKulfZKfHi5bd+31fnfaXIUL5H3rmmn2q5GwB4b/EBr4/jr6yM8ubL+rbEFQNai9tNVhv+uWg/lu0rxpM/bYfZlVWXlWJEz5bODBiNRgOHw4GRr/yh2rxTKtgMxHATg0muP7izoj8RIH991bLAhrZvpvrYQnDbf88kV2aSVgOdVoOerdI8JpGF26XuH9cRz1/qDvwJ2ZTSHgIHzlTi122FYq8rtTKEcXrPMnf/XMRjBlGk6XWen1e7A/hk1eGG3xnyyioew0P/c2nZvmLxslDy2B+17/2rP1glltdTWxSRJlmsZLYG9jyBeOKCbuJlq0o/Bmqa/J0Xje6cBQBI9ZMZLSyss7JnEhE1YRqNBhNa1e87Vm3KIMHA6hREVD8MJlFYaOtY5k5YqTmiY3NZHyHhS1C5sEL4Ya2WTXT9kHzV50iJN2DmZT3x8p96oaB5EjIS5X2VKk021fsFwqwoHVZZaxXLWm07UYYdrr40AFBcaRbLT0lL7yknC5TlyJpJgmwUe6RlxhLjdJjaqwW++ctQ1bFbjpepbgf8l5VRlgQRgpnCpFiV5H3+6/ZT2HHSuWreoNOiRZozYGmz2bFA0p8HcH42/3fLYI/ni77MJOf/hdcpVTKh1kUlsJRklJ9UZ6cYZYFbKWXZTW+ECTa1iWMp5eqw+8d1Ev8NACDOdX/hYcw2Oya/tQx3/t9G8ZiptsJM+LeWHkOau1a/CyXziCKmic4N6rUatG2WFLbHdzgc2FdUAatakXwKivAa+juGC/q2SRcv3ze2Iz6/1fO7sj7UvvdXHzyLY67FHsrvMQDISXVnPPX2snClrm4c1hZZKUZcplJKlkhNh+wULH3ofKx8bKzPccI5zYYjzkoUXERPRE1VfddrTj+/A1qmJ+CB8Z3wzMXdcUX/VrhPUhWDiKguGEyisJD+3g1mjlkoZ9BK0RxYmBjfcqwUFbUWj+1qX7LSEnZK0wa1wZUDWgNw9iMJFWXWyc7Cclk5EeFHEQAs2l0kliaRZkspJwvMVju+33gcE95Ygk1Hz8GieEH9TWhTdJGWthH+rdWyVfzZWVju83ZlcOHG89rKttda5EFToaRdnE4rZspY7Q7M2VYoG6fXapCRJA/AAsF9zhtCuaJHWfNkI966ug/uG9sR/7t1MNpnySdzH5kkb3zuq59QoH3LrJLMJF+kq97HdskGIO/XJLw/hEBzlcnqscJctcydK0gtfc+lxjv/7W4d0S6gv4Eo3KLs0BF2Wq0GnXM8A9pAaHrP/Hf5IYx/Yyke+HpLvR+rqROO4YF+R/99iruM6YzxnTCsQ/OA7vfmVX0CGuetb8zPrr6fauXrslPdCxNapqsvkKirpy7qjnV/H4eC5uELjlKMCeCA3qZZIpJVAp9SyoV1jCURUVNlq+eJcnZqPFY8Ogb3ju2IG4a2xStX9EZPLiokonpiMInCQvqDN5iMBWEVqLJhsPRXxOI9Z8TLQlaRTSWg4vEYXujrMJHvjUUxwVtrscl6P/1v9RHxclG5CV+uOwrAd2ZSjcWGB77egr1FlXjypx0eq43NXH0cU6T/uhUmd5bSTa5gT6BW7C/2ebty0kkodSNMNpVWWzzuAzjfi3qdO6NF2cOntMaiOrFWn15j4XD9f9cCAEqq3OXrLu7TEjPGdxKzcwTr/j4OF/bOEwM5gDOo5o3w0vqL49okPZN8kQb+hM+/NDMp13VZmGhWK1WkNskoHAOF8Q6HA6sOlgCQ96QjooajgfucpXtequw2s82Obk/OQ8Fjc/DAV5uDfuzyWguem7MLgDvAQHUnLBDytyBAMKBtJh6b3AUfXN8/qOfxVupWyVussbDM2efQpFIuLy3BgNtGFKB3qzRcN1g9Y58oVEJ1JqgMrHsLpBIRNXY8+hFRNGKxTAoL6eRoWbVFVrPdF4uX+vQdspIBOCe6y2o8J8HVSulJAzS+BBp0CoQyM6l9VrKshv3eokrZ7cIEgHRyXplRct6Li8TLu0+Ve/RxKauxIDuFE8OxwtsP4mCzk569uIfP25XvI+HzkJFowNkqM26ctVa8zajXwuQKOCQZ9eJnwmqzo0/rdBwpcfdLqjHbVCfW1AK6at5cuBeHiqvw96ldvZaRC4Uai+9yldJ/h3RXUFr6b6DWe0Ig3NPhZ9ok4MwkyTFo9QFnsKddVjK+/ctQVNRaMaKjc3W78G+qlpWm1ktDGhArq7bAZHW/Jm2bJXqMJ6LwOCbpOafRuBfZqB33hYzlX7efwutXBf4cX68/hh83najfjpKMrQ49k+4Y1T7o5wn0fNVf3xhvff7+PrWb6naiUAtVtQTlKQ2DSUTUVKWwwwERRSFmJlFYSH8EvL/0gPeBCr/vcvZnUQZ4hB/aZqsdz7tW3QLAgTNVOFNhUv3xEujkvHLSvT4/hISeSR2ykzGkXSb+PrVrQGVOpBMJvsrcWGwOfLD0oGxbYWltHfeWIkH6z/v0Rd3Fy76Cmpf1a+kRkB3e0ff7yiOY5Po8jOuaA8Ad6Egx6vH6lX3EcUPbNYMQLnltwV6P1VA2u0P1s2W22v1+dk6W1uDNhfvw0+aTmLf9lM+x4Sb9d5CW9RPc46uWtEpm0tkqM37afEJWPlCYiFTrZyQl7eEkzVYb0DYT53fJFjPFhIlEaeN2gdpxQ/qe2VNUIQYMAaCjlzJbRA2lqUwNmqw2THhjqXg9Nd4gHhu8Tf4D/oPVSg9/uxUrXcFoKbPVHnU97WKF0G8u0J5JdRXo+aq/vYi2DGFqekL1DvRYoNRUvjCIiBQ6p/G7nYiiD4NJFBbS1ZOVtVYfI932nKoQM3eUk6/S68qMg4NnKqFW6S3QH+fBrDj1R8hMGt8tB1/ePhQ9WqYhNd6A7BSjz/tN6JYrXlbLMPDFXwYGRa+pvVqIl329D2eM64Qt/5gg2ybtqaNGuYpTCFg+rOgNZNBrMblHLj6/dTA2PzkecXotdp4sE2//cbO8VNLhkirVz9aOk+X46ze+e3QUltWIl9/+fR+e/mVHxCa/pH2PhOPVdUPaoFVGAu4Y2Q7nd872dlfxvtKMyJtmrcV9X27Ga7/tEbdZ7YGVSEqJdwd9fC2+9XVIM6qsbNdoNBjSLhMAsOnoObEkZqqPflBEDa2x9/2bv6NI9j19tsosBneS4nReg831fVkGF2SivNaC815ahIv+tZyBhjoQjuGhzGBXI5Rs9kd5mpCiOJYbDfxZR5EVqsO5VdEkJBT95IiIYpHyNPHGYW3RKScZL13eMzI7REQEBpOoAQRS/spqs2PDkXPideXkisHHRPui3adVy9wF+uM/HGXulJPtvgJbVw1ojf75GeJ1tUlhX67+YDX2n670P5CigjT7RZqR5q3MTdtmiWid6SxJ9vCkzgCcwQB/71vpJFPv1ulijxydViN7LoNOA61Wg2EdmiM90ZlHn5vmvfycxebwukr7+40nUFGr3otJuK+guNKMWSsOY8dJz5JtDWFPUYXHttGds7H8kTF4TNJEXY3YM0mybctxZwDuk5XOvmh2uwPCP3Uwfdl8HS7VJlMu7dsSz1zcHRlJ6jUQhKDj7lMV4vEp0JJKRFR/ZdVm2XWr3YEv1x0D4Oyn5i1rqL5zshoNcOB0Jc5UmLDjZDmKK031fMSmR5jQDuWiIzWBfkdoFOkZKUZ5MGl8t5yQ7RNRXQSbUemN8jyF5y1ERE4PTOiE32aMwlUD20R6V4ioCeOZGYWdv9WwZqsdE95Yir/9sE3c1k8SXAEAncrktRB0+ffSg6g0eWY/xQWamRRkrxopm90h6+EkTtb6meh3lhJzMujlYyd2z8WFvfOC2o+v1x8LajxFjjRLJZD36GFJv6Jbhhfg39f3x093D/fbOyHeoMP8+0fil7uH46fp58nGS/sBqf1Af2hiZ6+P2zI9wWdwV1mGUUot6Guxq6QVRjnxlVQ5tJltdny+5qgsiO6vzJ2Ur/eEWtbiG1f1wQ1D23q9T0HzJOd9NRpYrN77tBBReCT7yAT0mbVYzzlZZZBq7eGz9XvAJkjITArmGB6sYB7b19d+TqoRiXHMOqXGQXleE+7sQCKiWNCvTTpS4wPLZiYiCifOKFHY+avVv+90BQ4WV4nXu+elepSYUptw+d+tg8XL56rMHrcHOmFqqMckwZX/XoXeT/8mNtc2e5msVc69SyeXlCtes1KM+Oe0vkHtB8vXxA7hBHBMl2zEG9xBncV7TouXByiCqQKjXoeJ3XPFAIE/nXNT0LNVmsf20mp3AFTtc9Ih27Ofzp+H5mNEx+b4zw0DfPaPkD62klrWTbh6eXTITgYAfHjDANXbhVJv3fNSg35sITDnbc/XHiqR/V3+ytwBwF/HdwIAPDq5i9cxdSnz0iojAQDw3cbjYqktBpMoGrBqke+ytvVd4V9SZYb08Orr2EzqhMykcB4zgzkFVS4ikfbY81f6lqghhKrMXYfsZDwmOR/ieQsRked5ABFRpPDMjMLOX0+fA2eqZNe7tfCc3FVOxi6YMRID22aK29WyIQwBlkTQKYI5n6w87DGm2mzFf5YexK5CeUkuoTTfL1udfWW8lbm7akBr2XXpj61AV6Uqa+p/d+dQ5LnKkf2y9SSOlFSp3Y2ijJCxcqXiPSENKjZLVi9ZFg6BZvD1bZOBz24ZjG55qUgy6nFJnzy0a56E32aMlJXa8RVoUptksKg1PAuhJKP6Su0vbx+Ky/q1xL+v7x/0Y4pl7lx/kMkqP8bVWuyycoaBfMbvHtMBqx4bg5uHF3gdI80oC5Q0G0zIYLSG+TUnCkYjb5nk0ftDylegua6vy/3jOgIADp6pkn3+F+wsqtsDNlEOh0M8jvv6XquraYOc5wD3j+sU8H2UeyH9brlqYGsQRVooj+fS3p8MJhERBbcAhYgonHhmRmEn7YWkRtljRa2BsHIyVijNJfzQUOvLFHiZO/ljbzxa6jFm1orDeH7uLtzx2QbVxxDq2IvBJEUg687RHfD9XcMC2h81T1zQDSmSlOa0BAP652fiT66ARFG5CRe/s6LOj08NR5icUr6npQEkacZSuAVah175OXnz6r5Y9OBodMpJwfJHxojbfa2OVitzF67MJCHQ420BV7e8VLx+ZR+0ykgM+rGFhxT2vKJWXmbTZLWhRNKfJJDMJI1GgxZpCT7HXD2oDa4b4q6P3T7Lf4aaNKj17YbjfscTUWj5Osb5zkzy7mRpDb5Ye1TMNpJmJwtldI16rWx7io9ye+RJ2uPPV2nXunrukp6Yf/9I3DW6fcD30Sq+0KSlbq4ZxN4J1LhI3+4MJhERefZOJCKKFP6ypLDrqpJpJGWyyFfJqzU6Vqb0Cj+ghc1mq7uuvTBxE6cP7Ms2kDrcaw45ew0cPevuX1MqaaotlKPx1jNJp9WgXxt36TLpn5PsJXNC0K1FKm4ZXoAv1x6VPR7g7hvl3B8L7HaHz8kpijxhck8ZYJAGWqwNWLawxuw7c1CQ5KMXQ5oka65NpvfgjFowydeq/foQHjUcnwaxzJ3rSZSTxbUWO/70/irxeqj6beSlJ+C5S3riztEdsPZQCYZIeq95o7ZKeFBBZkj2h4j8kx7Ps1KMOFMRWKDZ4WOJ/71fbML6I+fwe5csTE2XL6jJSHIuTLDZHbLts7cW4u2reY4QqI9WHBIvq/XtrC+dVoPOuZ4lZX1RLo64bUQBvtt4Ahf2zhP/3YkiyddxK1jyzCQet4iIWOWOiKIFl/lQ2HTOcf5I7toixWcpK5NVfpu3idcLe+eJl9MSnJPXwg8N4TESJBkdaQmB/bBWC15JfwzN2VqIpXvPeIyZvbVQvPzyvD04UVqD1QedQSd/K+hapifg1St644ah+bh2sO/VpMIEvPR1ES5Xm+UZERY7y1dFO2FiUTmh11qSIXPd4Pyw7sPfp3R1P9cQ9ecaqghU9Gmd7vMxp/ZsAQD4av0x1Hopbak2xRC2wJnrYcMxcSqWuXM9ifJvqLXaxD5uV/RvFfL61i3TE3Bp31Z+M5m84QpfigZNZXWlEGwe1zUbPRQ92nQaDX69bwSm9MwVtwkZRL6OjOtdGd8Ld5/BJ3vlGUjCIhOr3QHlKcGOk/JSveTdi7/uFi8Hkl3aEJR7kZYYhx+nn4dbfJRHJWpIoTyjk37seN5CRE1Zx2xnNYqL+7SM8J4QETnxzIzCZlIP5+TIkZJqdPz7r2j76Bwck2T2CP7Yczqgx5Nm4QgTxMIPjf2nKwHIS3alK3oMeaO22m1vUaV4efrnG1XvV1YjL89306y14v54W5j33rX90KtVGm4ZXoA/9W+FZy7ugezUeJ/7pxpMEoJoiqyucJUMo9Dxlpl0z9iOuHJAK3x2yyAMbd8M/7lhALrkpuDz2waHfB+SJeWOzuugnt3ywQ390TLdHawQArjeCGXwthwrxWerjqiOUVuxujBMfTyEz01YMpOE53B9/Gw2z8wkIcD0iKSBdCTYVY4JgZY2JKL6E44FiXF66BUTojqtBl1bpOLRSe4Av1CiN9AF/pvPamUBbWmp0ev+u0Y2tkqxAIXUHThTKbseLRPZyoUJOi5Rpijzl1HOso2Te+T6GemfdDFQtHwGiYgi4avbBuHz2wbjavZHJKIowTMzChvhN+6yfcXithtnrfUYt9ZVQk6gVgoL8MyUADyzDqQlEQKdMBV6DkhVmpwTLoeKq7zer6i8VnZ9b1Gl+JxdWqiXLpncswV+vns4WvsoBaYkBIhqJNkeQmBJOY9gCVPJMAodq91dklEq2ajHy3/qjREdswAA47vlYN79IzGsffOQ74P0udUy8wAgJd6A7pJV9P4yfKQ/9Jfu88zkA9zBl96t0tDO1e/nt52nAtrnYIll7sIw16ac0LMqlv9XmdwTtuHotREMtSMCJ2WIGs6qA85zIL1Wg1zF4hEhCC89pNQl2Cs9bzL6uL8yE5zkzlSYsHTvGYx9bYlse6hKldaX8vssSnaLSHTD0Hz8NmMk/jmtb70fS3quZQiwdDkRUWOUEm/AsPbNWaqYiKIGeyZR2KiVsDlwRh6cUVs172017tReLVBaY8HYLtlen1Oa7RHoJG6//AyPbVZXWT5pXySlapVeM8LqYF+TOYFq2ywRh0uqxQyvg5LXztvEhtVHOUGKDkJwMJKTU9JsPIOP92owJeikn+XsFPVsO2GEVqvBcxf3wDUfrsG5agtMVptsNX0ouI8jYShzJz6H80mUAXBpb7Vw9NoIhtr7rG2zwIPZRFQ/Ql/E8lornr64O6x2O75YewyAewGMt0bzDocjoDKZ0nMro8FHMMlLCVJynj9NfmsZiitNsu1dguxrFE5axXsh1CVUiepLo9GgU05oPjOyMncRXphDRERERG48M6Ow8fYb96CkfMhTv+zwuN1bZlK8QYdbhhegbfMkcZuyF5NeMnGrD3ASd1j7Zh79YIRJdF+ZPmp9Yaw2oYRZ/T9a3945DG9d3Qf3ju3ocZtezExSZkgwMyna2VTKFjY0neT9afCxH4MLMgN+zIOSLL7mKer9yqSl5wZLMg3VsgPrS+hnFI6X2d0zycnX5y7SvTYu6+dZW/uGoW0bfkeIFJrKPLhQEndCtxykxBtwUW/3Z1InluxVz6oOuNTd8TIAzn5LvjIPaxhM8qrSZPUIJAHAQxM7R2Bv1Ck/Mv56GRLFMulxMVqyA4mIiIiIwSQKI2+n/WNeWyL2OPpUpbdKXBAlmGolPYO656WivyTLKNBJXI1G4/GDXAhSKYNVUspyMc2S4ryWMKuL5slGXNynpWrGhjDZNLCtfLKfwaToZxMDjpH7YRxoU+ObzivAExd0w8IHRvp9zED6dQmZPFqNBjqtBolxzve2WmC2voTKc+FYuS1kXRaW1eJEaY0YRFYT6ZJyLdISsOmJ8bJtLJFA0STQgEmsEsrmprh61UkzQ4VjoPQwJWwDvC+uyU4xyq5vP1EOwPm94uu75b4vNwe+402MWgnAX+8bgbFdcyKwN+qk75ObzytAbprvnptEsUx6KFt1sCRyO0JEREREMgwmUdj4msM9JikDpZRorFv1xTn3jkBqvEHy/IFPmCp7FAgZSWaVYNKXa48CcE+Aj+jo7GnTpUWKOKkc7glkYX/Hdc3Ge9f2E7ezzF30s0kCKpEinZ/0VeYuTq/FLcML0CHbf8kS6aSnWvlK6fMKf3u8QQgmhe99G45XubTGXf7y5lnrUFHrnCxWm8SNhrhNRpJ6phgRhZ9wfBOOd9LFJmkJznMWaVlgaTDJW5xNud0sCYRoNBqkxns/j3KoBKh8LZxpKkwq30NdW6SqjIwcaXnloz7Oo4kaA+nvOJboJCIiIooeDCZR2PgK5phtdtmExtgu2ZjYPQfZKUZcM6hN0M8lTJw4vE69+KbMhnph7i4AwNythQDkEwoHi6twrsosTjxkJDonamstdjEzKNzlGIRglUajweSeLcQJKV9l+Sg6CBk8gZZhDLdQZUhJ40dqGXILdha5MxFdTxnvCmQFkplUXmvB6oMlXgNVSsLxJRwxO2k/gD1FFZj2n9UAgIQ4zyzCaOtp0TzZ6H8QEYWMyeo8vgm9jNplJaNZUhyyUozokJ0MQB50ToxzB4LWHz6nGvwRFo7Eux5TCDIMa+9c3PLLPcM9FskIqhT9HosrTRj4/EI88PXmYP+0mPH9xuO45j+rcdun61W/bz5cdhAjX/lDti0aj5XVZqt4OU4fXd8tRKEmXXTFjGoiIiKi6MFgEkWE2WrHAUnvpLen9cX71/XHikfH1KlsxzhXGZK69ipSTrocKq7CgTOV+GbDcQDArsJy3DGqHQDgg6UH0ffZBVh7+CwAID3RGcjZcOSceH9DmAMFyuCX8HyBlBqjyBIDjpHMTJJcDlUWXVdJk3Ll+/CPPadx26frxTIlwpyAOzPJfzBp2gercfUHq/H1+mMB7Y+wB+HIAMtJVT9Gje6cLbs+pWduyJ+7vib1iJ6STdS0CZ/Mui4CiRVC+TShZG1aggErHxuDFY+MQYqQTS05TEmD8dP+sxqbj5V6PKaQBR3veswqV5DB6DqXyW+WhInd3ccfIYMaANYekpeL+nbDcZRWW/D9xhN1+fOi3rGz1Xjg6y1YeaAEC3YWied1gv2nK/HcnF2ybYMLMvHmVX0acC8DI31vmK2N+3NDJI0fRfKcmYiIiIjkGEyisPF13m+x2XHsXA0AIClOhySjHhqNps4T2/GujIDbRzoDPqM6ZQV1f6PKCt5DZ6pk171NSguZSVL6MJS5u39cR/GyMvglZEJJS9UcO1uNilpLyPeD6sfWQNlrvkhXuodqPx6Y0Em8rAwmLdhZJLsufJaMQjBJpVeF0o6Tzp4g3yomAr3x1msknB6ZJG/U/u61/Rt8H/xp7P1piKKNkMUsPc8w6nWy73Hp+cWIDu7ADyBfqCIQSvAK2U6FZbWy6wBw03ltMaZLNkZ1ysIdI9uL22/+eL1sMU+iSkZlY1JWIz8P2na8VLzscDjw4q+7Zbd3yknGV3cMxfCO8n+HaCD9ar20b8vI7QhRA2BmEhEREVF0YjCJwkbjo1tJldmGwlLn5MeQds3q/VzC6ty89ATsf34yPrpxYFD3T00wyK4b9VqsUaze9fY7ZnC7TNn1W4YXILmOfZ98kU4iKINuQkbWxe+swNpDZ/H1+mMY8fIfGPnyH6gxs854NBHL3NUxiy4UwhFQaJWRiL+OdwaUlMEk5fNpxMykwMvcCSwBl7mTP1e4je2SjVYZiQ3zZPXAWBJRw6p1lbmLN3g/5ksPU8pzIrVydUKGSoIrIH/CdT4lZHsCQL82GfjoxoH45OZBGN6xOfq1SRdve3b2Tuw/7QwopUnOf4rKawP4i2KLsvelSbJ44fuNJ7Bwl3yxg3BuGo2kZV6jMfOVKJSk52+MJRERERFFDwaTKGx8TeI+8eN27DnlzDRI8dEoOlAJce63sl6nDTrb4tK+LTGoIBPXDHb2a7LZHbKATWKczmuJhdR490TMiI7N8cQF3YJ67kBd0b+VeFnZ50Za5u6lebux6ahzJfO5akujnByKZUKgJYKxpLCVldK53oeePZPk14XVpkIQeLarN1kgtqiUfFIjPKOvoHYoPTypS4M8T301T/LMpCSKhKYS2HRnJnnPALJJIu5C6VzB6XKT7LrD4RC/R6TBIwDo0zrd63O8f507U3LxnjMY9/oSAPLFNGerzF7vH2scDgem/99GXOPqaSeQ9qRae+isx/2E4F80ki7UiLZ+fEShJs1MimQ2PxERERHJMZhEYePvtP+TVUcAhKanSXpC/SZI4w06fH3HUDzgyqyw2h2yCfG+bdK9/nCXrjYO54/7DMkksPJppGX1jpRUy3ofeE7sUyRFQ2ZSuAhBTrvfzCTnOCGQfOJcdcj3RSjlF66X+fzO8lKa3prdR4vnLumBER2b4/qhbSO9K0QAgGpX1uwalQn9xsLhcMDkCk4YfWQmGXXuoJB0gQoA/OuP/bLrFpv7gKrMduqQnez1ObJT48UFM4Jv1h+THa+royiTucZsw+HiKv8DXZQZriVVZszZVohaizwzSfqanat2B8/aZyVhRMfm+G3GqDrucfhFonwrUaRIfx/2bZ0RwT0hIiIiIqnonv2imBZoXKU+AZiHJ3XGgPwMXDukjf/BAZBm/JgkExOvXdHHa9DL12rjUBKyj9RI99tqt6NleoLsOkUPW5iDHIHolJMSlscVPiPSAKbVZsei3acV45z/v3FYWwBApcka8n0Ry9yFKTPpoj55suvC51OZVRAtrhuSj89uGYysFGOkd4VI5t9LDkR6F8LGaneIJWp9nSukJRrw/nX98NGNA/wGpqVl2xIVmUnZKfE+76vsD/nQt1tl2S7RVBZ36j+XYfSri7E5gGzUp37egV5P/4YNR9yBSZOXXnyzVhzG0r1nAMgzsWbdOAif3TIYBc2T6rfjYaQsIUvUmEmTka4bkh+5HSEiIiIiGQaTKGwCncStT+WCu0Z3wLd3DpOVLakPaRmFrSfKAAD3ju2I3LR4r/vpa7VxKBm00gwo+W3SySWbzSG7brVx8iFa2O0OMcgRycyk7nlp+OjGAZh//8iQPq4Q1LTa7dh49BwKy2rw/aYTOF0hL9MkvH2FSVOzl0m/+hDL3IUpWVDZtywj0Zk5OLlHCwBAq4wEj/sQUdNSbXIHZ3z1TAKAST1aYEyXHL/HrINnKsXLl/drKV7OS4v3GyxW6+smzXapNoc+sF9XB884s5IueWcFft5y0iPjVerjlYdhttrx7yUHxW0WH98rN3y0Fna7A1WS4FlmcvSXALUxM4maEOmx0NeCOiIiIiJqWKGZgSdSsdHVt8efUJS5CxXpBP+mo6UAgDjXDxitl2hSUogCWf5In18ZqMtMjMNBOCdeLHY7LNJgEleyRg3pv0Wk67+P6ZIT8sfUuQIsc7edwtxtpxCn13o0kwcAnetzJgSTLEEGPO12h9fPo0Asc9cAwaSL++Qhyeg8Djw+tSvaNU/CpB5sjk4UiMb8DSWUUUuM04Usi1k4Xmo08p6TE7r7P+ZcN6QNnp29U7Zt6b5i8XKNJXoyk6Tu/WITmifFYViH5j7H1boCSLUWm0d5wKwUI85IFjZUma2yrO6kuIbJMq8PtWAgUVMQTb8ViYiIiJo6ZiZR2Gw/WRbQuGhqHaM2wS/0I/L2Q0aYRG5Qil2RNt2utdhlmSCbj54TJ9YpsqQrwCMdTAoHneIzYrbaxXJCgp4t03Ctq2+HEJDxVo4IAMpqLLhp1lrZtgW7ivzuiztuF57XOTfVXU6qe16qeDnJqMdtI9uhdSYn/YiaurWuflDpCYGXv/Q3ZyqUOitonoR8yXEmM8l/Zo1Rr8Pb0/rKtn2+5qh4OZp6JimVVJlx/Fw1lu8rxvYTZarnNUL20pythfh2w3Fx+8YnxuPTmwfJxh4pqRZLAg9t1yysPS9D5YKeLTBjXCf8362DI70rRA2KwSQiIiKi6MHMJAqbQMvcRdMPeL3KBH9haQ0A3xkOqfF6lNdaZZPK4eTvFZPOsTz1y05kp8ZjSs8WYd2nUDlTYcJPm0/gT/1bIT0x+svOBKqk0oRHv98mXld7r8U6X3/Tn4fm4+mLe8i2CZlJxZUmtbsAAFYdKMYfe+QBqRPnavzuizDRGK7DizQjgJMcRPXQCNc6nK0y48J/LscJ1/lDMH+iv+OJ0AdRr9Ugv5k7mNQ6M7DSmiM7es/uieZg0k+bT+CeLzaJ1/99fX9MVGRjCYG2/ZJSgIAz0CbN2AaAQ8VVmLfjFADgUkm5wGim1Wpw37iOkd4NogYh/S2jiaKFh0RERERNHU/NKGwCnSuPpjl1tdJZQmkyXxM8H/55IG4dXoA7RrYL275JKQNwnXNTfI4/XFIVzt0JqXu+2Ijn5uySTRo1Br/tLMKCne6MmsaYmeSr9Nw1gz2bJzdPcvf38BZQUstaqrX6n/AUeyb5HVk30jJ3DCYRkdQPm06IgSQAqKwNvBeRv6OJEDDRabXQaTWY1MqO8zs39wiseJOWYECKl4zqmijqmSRkDQn7uupAiez2Y2erPe4jZP+u3O8u3XfL8AIAQE5qPP5xYTdx+9xtheLlds2TQrTXRBQOysx3IiIiIoocBpMobPz1NBHHRfkPBKEPwVlX7wM1gwoy8fgF3Rosk0b5il3WrxWevKCb6lgAMFm8lxGLNqsPOssCLZP0cWgMTIpeFI3xh7GvzCS1gGdaorv0k9CjTEmtn1JtIO9n193CdXyRBgMbYVyQqOE0ws+PMnikD6J5vL9sbWGBi3C8ndzajg+u64fEAPs3ajQazJsxUvW2aMpMEo6xQgZrlWLf1PpBCpkMya7M0fvGdsQTknOjm84rwDWuMqu/bj8lbu+fnxG6HSeikIv234pERERETQmDSRQ2gZ74R/sPBGHSv8oUPSt2lS+ZTqvBzcMLMEmyMrl3qzQMaZcJwHdPmmgTSN+HWKSMiQQabI0l9nr05rrt0/U4W+UZsJ2/wz3hJ5SRnLe90GOct30J1+FFGjhrjFlmRA2mkZW5+3DZQSzac1q2rUVaYCXogAAyk2xCZlLdjzst0xPw0Y0DPLZHUzBJOIZLs0ClbCrBJJvrPhWuYJ5a6WEh40kwvEPzqCq3TESe+BElIiIiih4MJlHYqM1zKH/EA9H/A0H4O5T19iPJ2+RKW0mplo45KejXxrna1hRAWbBo0a1Fw/Sdamh2lYmvxuZwcf3KKfZ7dgH2FVXItgmlAbNTjOLq8VYZiR73VXKXuQt/ZhInIonqrjEdGTcfK8Vzc3Zhy7FS2fZHJncJ+DHUDidnq8z4+w/bcOf/Nog9k+obxB7TJcdjm/DY0UBYm2DQq/+dasGkDUfOYeDzC7H1eBkAqGZrXevKTBIYgsgaI6LIiPaFh0RERERNSWA1MYjqQHrif8vwAhj1Wtx1fgdc+5/V2OL6oa8cF5VcuxdN2T05qUbV7clGnXjZZLWL5WHMUbTv/jga1dSim02StXOrq4dDYzOwILPej7F4zxl0zHGWxHt38X5xe1mNBT1bpgFQn0RUEl7u8GUmuQO6jnpkZBFRbDhdUYufN59EUXkt/jysrWpQ+3R5rce2y/q1xKhOWQE/j1pwetQrf4jZNkJWaygyIvc8NwkLd57G7lPl+Oei/RDWzNjsDlzx/kq0SEvAO9f2q9NjV9RacNG/ViDBoMMP04fBqNf5v5OEkJkU52XxzGZFwE5wpsLZfy8lXi9+Z0ilJ8bhvA7NsGK/sweTcJ5ERNFFembFBHAiIiKi6MFfUBQ20iBRn9bpeHhSFyQb9bh/XCfFuIbes+AIf4e0rNXUni0AAFN6BtbwOlQen9oVfVqn4/aR7VVv75/vnsw/cLpSnLyJpkCYP8IED9C4snmEAMgV/VvhcR/9rWLZ8A7N6/0Y0lJ57/1xQLyclmAQJ0/VyumdKK3B6oMlYmBHCEqGLZgkWc3eeN6lROTNJf9agefm7MJ/lh3CbZ9uUB2jtjjmL6PUv6+DUSHpwTRnq7PMp7cgSzCMeh2m9moBoyugInzn7i2qwMajpZizrbDOWdm7CitwqLgKOwvLcaSkOuD71ZhtqDZbxe9Mb5nYi3afVt0OAHPvHYEtT06Q9eWT0kkWA8QFGeQiooYh7YumD8HxjoiIiIhCg2dmFDaS3+qKklCKcVGemSTs3dWDnKVRrhvSBq9d2Rtf3T4Er1/Zp0H35dYR7fDj9POQlqA+QTK0fTPxslbrXnEbS8EkKbUG27FKmKRrzP11QlHuTZrBJX3f3jqiQDxWqAWTrnx/Fa7+YDXWHT7nGhO6fVIj/XdsTEFPooYWK5l9J8vcWUe7CstVxyiP71cPbI1OrkzLuhjpymiSlghumZ6AHi1Tcd2Q/Do/rpIQXLGJfYrcf0ddy5dKM6IPBfgYDocDE99cin7PLkCNxVme1xhA5pCyhHLXFik++xJKFweFIihHRKFni6Kym0RERETkxl9QFDbSIJH0N70yeBTt/UaECYl+bTKw5ckJePbiHog36DC4XTPEG6J3RatWoxEnYUyW2OmZJFVaY470LoSMMEnna4KrMRkmCWwGw+FwTigu31cMs2tF/A93DcNtI9qJr51yfmHe9kKcKK0BAGw+ds71QM7/hevVlq6WT4lXD+4SUeyz2R34ZOXhgMYqT2faZyXX67l7tnT2EEySlLBd8MBIzL5nBCb1CF1mtHA4EwLj0oUcwWQVSZlt7vOOOz7bgCqT1cdop0qTFUfPVqPWYhezsbxlJkkpA5L+ziulQT+WuSOKThZbbCw0ICIiImpq+AuKwkYjCyapX3Zeb7BdqhPp7qUlGqI++CXQSIJJ5jqWqWloHy0/JLs+6PnfA5qAigViZlKMvH/qq65/pt3uwP9WH8F1/10jbmufnQyNRiMeK2yKicMHv9nqfl7XJzbcZe50Wg1ev7I3bh/ZDpMbuNwlUWOzcGdRwNkrDe39JQfwj593yLYlxakvJFGe3+Smxdfruds1dwajpFk+4chuFfZbOLaaLO7nm731JP7wUVLOG2Wvxo8DCMhVqnzfBxJMCjY5VJqZFEjmExE1vED6YxIRERFRw+MvKAob6XSHxkuWkvN6dE2uPzhB3tMpVoJHShpIytxZYiOY9F9FMAkAdnopJxRrhEm6xlzmTkqr0eC2EQUAgBmKPmlSyok8uwPYf7pStk0oQyQE4pSr0KUTkFtPlGHZvjPi5GI4jy+X9WuFv03pisQ4fdieg6ixO1xSjVs/XY/zX10c6V3xYLXZ8cr8PR7bc7wEiZTHm7pmvSx5aDR+vW8EslONAOQlP8OxIEH4XhImb6ULUH7cfBI3fbwOR4PMUDpdYZJdf2X+Hmw9XurzPpW1KsGkAF5DR5Cd65iZRBT9rDGyEI6Iqq1DrQAAZXxJREFUiIioqeEvKAobb3PmyjJf0Ta3fveYjriod554Pdr2L1BajXtFrzVG6o6r9c5oLP0MhN/E0RY8DadHJnXBL3cPx91jOngd89GNA3HbiAJc2rclAGc/JGWPL+E9oBF7Jrlv+3DZQdnYX7acxPX/XStOijadV5uIQu3oWfUASve8NNXtWsXXVV0DFfnNktC1RaoY9CiU9GsKx4IE4THtDgf2n65EsSQQlJHoLON5qCS4zLG9RRUe25TZx0rbT5Z5bIvTyf/eO0a18xhTn8wkg47fEkTRqF9+RqR3gYiIiIhUNI5ZWopK3ibNldtbZSQ2xO4ERbqLsZqZpNVoxAkTaxSXivhly0msPFAMANCpTOrUxmi/JyWHmJkU4R0JsxSjM0vn/M7Z0Ou06Nkqzefk53kdmuPvU7shNd55P4cimPTEBd3EALRy9fypslo8N2eX7PGyU4zyJ4jNjy8RRYFp/1mtut1bY3jleoh4ff36KuqV0SmE55xEOC/bfLQU415fgjv/byMAoFerNPRqlQ4A+NeifQCAE6U1+Gj5IZ8laOduK8T/Vh8FAIzpko2RnbIAqJexk5rx1RaPbcqA3PmdswEA8Qb3drWFKL4kGt3ZpMlG9rwjikbd89Lww13DsOZvYyO9K0REREQkwdo8FDbSDCTpD33pvPKlfVviigGtGnK3AqL1UZYvVmg1Guh18sn3aHOkpAr3fLEJAHD4xamqE2fVimBSjdmGtxftw+CCTIx2TSrFgj2uVdrKzLzGZsEDo7D28FlMCbI5vDBBumTvGWw57lyd/uzF3XH90LYeYzcfK0XBY3MwuCBTtn394+Mwe8tJPPXLTvfjMppERHVUVG5S3W710hhe+l07rmsO+rZJr9fzN9TiAyFQf1KSAQU4y5C2zkwAAKw7fA7fbjiOJ3/ajmqzDXuLKvDi5b1UH+8uVzAKAPrnZ6BlegKW7j2DWh8ld0sq1V9rZXZygsEZoKu12FFrsSHeoJMF8Ya1b+b1OQS3DC+Aw+GAQafF5f1b+h1PRJHRtw2zk4iIiIiiTSNfI0+RJJ0zl067SFfVDmybGZWZP7LMpBidjNZo3KuaLV4mviKtWDJ5ZLc7ZO+Zds2TADiDR1LfbTyO9xYfwI2z1jXIPobK3lPOYFLzJKOfkbEtNy0eF/XOgz7IWVAhgCsEkgCgebL8tZJONjocwOqDZ8Vxh1+ciubJRvx5WFvF4wa1G0REAIBnJEFpJW/ZvkIwqWfLNHz45wGIN9QvM0mnssAiHLz1YYrTazH9fHeZ0ge/2YJq13fy77tPB/TYGYlx4utQ4yXTuLCsBv2fWyhef3xqV7TJTMT5nbPQJlOevZ4Q535Nez39G/YWVcDuiia9e20/fHLzIL/71D4rGTMv64VnLu6B7BT1/ldERERERETkicEkChvvZe7cl6O1Vr1036Mw1hUQZzBJyEyKzp5JcTr3pNDpChOaSQItWa5yZdWKYFJhWU3D7FwI2e0OccX3RX3y/IxumpRBn5vOa4tx3XJk2yxeJnClPTU0Gg3SEw2y60QUG+xRlEX70Qp5f5+X/9QL413HJH/BpFBloHqU7QwTb/tr1OvQIi0BXXJTPG47U2FS/fcyWd3f2ed3zsLUni3EANCGI+fw8rzdHvdZub9EvDxtUBvcOqIdlj58PmbdNEjs/SjIb5aIIe2cWalmqx0T3liKc9UWAECnnBSP8URERERERBQ6/MVFYRNIz6S6NqcON+m8SqzORWs1GrF0TbT2TNJLggBDZv6Og8XOBt9/HpqPjMQ4AECNWd5jISHIld5Wmx2zVhzCbztO1XNv684iCeZJV1WTW0mVWXb96oFtPCYFLVb1oKhBcRwpdU0sAmyZRBRLLFGy8EFZGvb96/rjygGtcUGvFgCc3yuq9xN644XowJOXnoC/ju8kXr+od3gWI3iLvwgl5rz1Ltx3utJj29xtheLlV67ojbREg7iwBQDeXXwAP246IV7ffqIM/156AADQMj0Bz1/SQ/Z40v5G3905FEa9Dl/ePhQvXd7T47mZiUpERERERBRe7JlEYeMtCJMS737bpSZEZ+PjMxXuclregmLRbmDbTLHUmLf+DpFmVzTNFsredWmRiopaZwmzarMNL8/bjcV7ziC/WSL657vrpx8pqUJ+sySfz7HyQAmedpUr2vnMRCTGNfxhT1pm0NBAZYtizdpDZ2XX9SqzsVbJRLNG425272sleox+fImapGjp76cMnhS4yq4KpWO9LdAQMnXU+v/V1T1jO+K1BXsBAAMVfeJCxdt5jtHg/DuUvZQE0iwkQUmle2GAUKpU+e96/1eb0atVGtplJePWT9bjVLnz8bu2SPHIkrqkTx6Ona1GZlIc+kn6p1w1sA1apCXgho/W+v07iIiIiIiIKDQYTKKwGd05G8v2FQOArDlyu6xkvHhZT1SarBjeoXmE9s63P/acES/H2tzEor+OwuI9Z3DN4DbY4+rTc/RsdUT25WRpDb5cdwwmqw1GnRZXDWqDlukJ4u3eJg51Go2YwVNaY8F7i52rlncWliNJskq5rMaien+pilp3ZlNlrTUiwSTpKvZoLe0YaRbFSn9/QbfkOD0qTM5/W2Uw6aoBrfHV+mMAWOaOKJZESxatMpgU7wqq6MTSser7aRXL3IVnv7q1SA3L4+q8pPQImUlD2zXDkr1nPG43q2SLCq/Bn/q3ErepHYbv/2ozfr57uBhIAtSDQc2SjXjqou6q+5eXLu93xMM9ERERERFReHGJPIXNn4fme73taldN/FiobR9rK13bZSXj5uEFiDfokJPqnmipVpSLq6tTZbUoqTT5HwjgnT/24+3f9+HfSw7i7UX78a9F+2S3e5s41Go1SHQFk4RAkkAadKi1+C+JZJNEMgMZHw5myT57m7Rr6pSTs2qZSVcNbIO2zRJxx8h2SJZkOCoDdBf0biFejrGPL1GTZouSLFqLYj+E8qrCsWa/Snk3wJ1tG+rj/MIHRuLDGwbIMnNDSecnM+nFy3tixrhOHrebVcr92cTsLPdjDi5ohm4tUnFp35bia7j1eJnHfVcfLPHY5kteeoLYIy890SD2WiQiIiIiIqLwiP6ZfIpZelmgKDomiOoiluf+M5PixMuhWPF9psKEITN/x5jXlsDh8P94pa7MoTRXOUNplhDgIzNJCwxoq17OR/p3fLH2qN99sElKo4169Q9sOHLO731CTSgzGKfTMlPGC+X7U6/ywUtLMGDxQ+fjsSldkWyUBpPkX2UaSackvtpEsSNaMpOUJViNrmCSECQqq7GofpcI32mhXoTSITsF47rlhPQxpZSl5QRxOuff3SItATcPb+txuzLo5tzm/M6VBtTi9FrMvW8E3riqD966ui8AoF+bdI/7ltcGt+glMU6PpQ+fj7n3jsDSh8+PSOYxERERERFRU8JgEpFfsTsdLZ2Qlza8rqv5O04BcE6kVZr8T/oIJXBapDkzpJTxpxqzelNvrUaDid1zMbCt5yps6cr1HwL4m6QLpx0O4NnZO/3eJ9SEYJJatg05KRva6/1kLUozk5RZANJ5XAbviGKHtC9aJCmDSUKZO2ng+pctJz3up5aVEwuUmUmpruNrlxYp4ja1ANmfP1qLzo//im9cZUUB/6+B8FoeKQlN+d3UeAO65aUiNT46e3ASERERERE1JgwmEfkRY3NCMtLVxqEIokgziTYfK/U7XggmCau6lRN02096lrkBIJaqef+6/h63KVeuHyqu8rkPdsX4YxHoHyWUAoqFso6Rovx3FbLZvBnarpl4uVzRO0v6kY3lzy9RU/PN+uOR3gUAgDSmddWA1jDq5ZlJgHr/O+E7MtbKmSr/ruWPjsHSh87HlQNaq46RMlnteOjbrVjq6qkkHMu9LQgQTgNKqswBl8wlIiIiIiKi6MCZTSI/Gktmg1o5mmDVSJqS7zlVIbtt4c4i/GfpQcVzuoJJeuehRpmZ5K0UkBAoaJZsRLaiB4JNsXL9SInvYFI0lE0SVturTT6S07OX9BAvf37bYL+TsQ9P6iJetqj07RBoYjizkKipefv3ff4HNQBh4UOKUY+X/tRL3J4r6UOYISkjKxB69MVar0XpwhOLzYHUeAPaNEuUj1H8TS9d3hN/m+I+Dt/w0Vo4HA4xy9RbZpK03O2p8tp67zsRERERERE1HAaTqEEkxHAd+xhbYBxyp8pqMeOrzViy9wyqJWXpnpuzC8WSVcW3froez8/dJesjIWYmuYJJysykalepvKm9WiDD1UT7jpHtZAE8ZSxIGRy6cdY6n/tvC6C3U7hZrM59YGaSd1cOaI31j4/D4RenYlj75kHd1yNgKCtzF4KdI6IGMbVXi0jvAgD3d5Xy+NG2eRJyUp0LHD5decTzfrGamRTAgVL5N2WnxCM9UR5QM1ntWOLKUPL2GtRKFqV465tIRERERERE0YkzmxRWf5vSBZf0ycOIDsFNDkeTxtTQef/pCv+DFN76fR9+2HQCt326HjVmeZ+kdYfOeoyXBphMrmBSvKvMnXLaSAhOtcpIwKrHxmLjE+Px2JSusjEORTAo2Mmnc1Vm+eMFde/QsLgyk9gzybfmyUb/gyQ65zj7eUhL3hFR7MpvlhTpXQDgXsSgVQmIXOUq/Xa6ohal1e7vl582n8ATP+3wer9opg3g14DyTxpYkOmRfXS63CQex6u89FXs0yZdvBwNmcNEREREREQUuMYzS05R6faR7SO9C3UyoVsOfttZBMDdv6cxkJaXCdTuU+UAnFlG0jJ3gLsXkJS0FI4wmZTiauatDAytOlgCAEg06BFv0IlBJ6n8ZokokQSEgp18en3B3qDGh4PFyp5J4fDpLYOw4cg5jOyUJdsuLW0Xa+WmiJoyIZs10uw+ytXNGN8J/1tzFGerzFi0+zQu69cKby7cizcXukv0eSvxFq0COU5KM4bbZyUh2aj3yD66+oNVaJGeAAAY2l49yN8lN1W8bLU5oNF4lsAlIiIiIiKi6MSZTSIVr13ZG29e1QfbnpoQ6V0JqfouAt5xslx2XejDtK/InfEkxEvsdgcOFjv7GaXGG1SfX3i8VQeLvT7n4xd0kwX0YrEsjhAAMwSy/JsClpMajyk9WyDZ6H1dBGNJRLEjFoJJGo0G47pmAwAKy2pxurxWFkgCYq/MXbDfqw9O6AzA8+88WVaLU2XOPkhGlcUhgk45yQCcJe8YSCIiIiIiIoodnNkkUpESb8AlfVsixRUEiWXCpJdT8LM2dskk06ajpbLbLK7MpClvLxO3CauXi6vc5e4KmjtLFykzkwRHSqq9Pn+/NhlY9/dx6J+fAUA9M8nb46qJRDBq01FnHymWuWsY0vlfvuJEscOiku0aCa7KpF57Jupdqybe+WO/uGhCKjXGzh2kWcFtMhP9js9JiwegnoF1orQGgO9sJ51rYUW1onTuDUPz/e8sERERERERRQyDSUSN3H9uGICWrrIzdVkBbLZ5v5PVNfFnkYwRGnmbLK4+QVoNklyZI97iOMo+SWqEaSnhOaVBMl+l7/q0Tpdd99bHIZyKK51l+pIaUf+taCadwmSZO6LY4YhIVztPvjKTAHcQpdpsw1M/7/C4XSjtGiv6tk7HjcPaok1mIj788wCv4x4Y3wlXDWiNvq7vVZ2PbFtfyVkG18KKKpO7dO6v943APy7sHtyOExERERERUYOKrV+7RBQ0jUYDo9454VOXpJzOOcnYVSgvb6fXamC1O3Co2DOjSJh8M7nKFSUZ9eKkkvLpMxINOFdtQdfcFL/7ITyuUBpPWtrMZnfAW0Wd/GaJ2HysVLxutTvgcDhk/R/CTXgtRnRs3mDPSU6MJRHFjmgpeeYOJqnfLi3vtvuUs8xrr1ZpSIzToajchAndcsO+j6Gk1Wrw1EXd8dRFvoM5947tKLvua3HGoIJMr7cJr9+8HacAOINLXVukeh1PRERERERE0YHBJKKmQAjmBDFTd6bChEe+24rl+zz7GaUlGFBSZcZHKw55lKkRJok2ukq7GfVacUJf+fxCRpNBF3ySZJ4r2woA9hZVoFerdNVxagG0DUfOYUBb7xNdoWZz1UzSxlgfjVglDRQ2ZNCQiKLT/tOVaNssUSxP54/wveHtmK1W3q1VRgLevbZ/nfcxFvnKCo7z8VofO+sshVdWYwEgz24mIiIiIiKi6MUyd0RNgJDVE8x0zSvzd2PR7tMwq/Sw+NOAVuLlL9cdk90mBIwe+W4rACBOrxWf3+4RTHKVwgugl9Daw2dl1+86v4N4+afNJ73ez64y2XXsnPceTeEgTLipTUBS6DF+RBSbwhFSeH/JAYx7fQn+uWh/wPfxV+ZOrbzbxO6xlY0UChf0aiFe7i0pKRun1/oM5I/vlgMAMFmcZe6U5WiJiIiIiIgoOgUVTJo5cyYGDhyIlJQUZGdn45JLLsGePXtkY+644w60b98eCQkJyMrKwsUXX4zdu3fLxmg0Go//vvzyy/r/NUSkSpjSUQZzfFm0+4zX2yb3aIFNT4xXvc3mcMBktYnlih6d3EWcVJI+vd3uEMu/+VrBrObDGwbIyty1zkjwOlb4mye4Jq8AYMX+kqCer75srmCSjsGkBsFXmSg2haPM3Yu/Os9B3/p9n2x7caUJpytqveyHvzJ3ntsu6JVX952MUfGS+rJJce7LQmldb4TXr8YVTAr2HICIiIiIiIgiI6hfb0uWLMH06dOxevVqLFiwABaLBRMmTEBVVZU4pn///pg1axZ27dqF+fPnw+FwYMKECbDZbLLHmjVrFgoLC8X/LrnkkpD8QUTkSVwgHMREXXGlyettiXE6ZCTFIb9ZosdtNrtD7GsEAFN6tFANZh0sdh83UhMMAe9Xh+xkjHMFhi7qned6XO/jhecc2SlL7FkkDUSFU0WtBQ9+s0XMnGJmEhGRL+Etd3a0xJmVarHZMeC5hRj0/O8wWW0e44SEXG+ZSWsOyjNlZ98zvMkvFkiNd3+Pq2UES+ldmV3CgpJAspOJiIiIiIgo8oIKJs2bNw833ngjunfvjt69e+Pjjz/G0aNHsWHDBnHM7bffjpEjR6Jt27bo168fnnvuORw7dgyHDx+WPVZ6ejpyc3PF/+Lj40PyBxGRp2DL3Nn8TAQJwZj++Rket9kdDlTWuvsoabUaSZk797ha14pkg04jW93sj7S/knBZrRSfe39c+6HRiKV0isprse14WVA9pOpi6d5ifLvhuHhdx9XXRERefbH2mPjdECrSIP7IV/7AdxuOo6TSLG6rMnk+n7AIwVultj2nKsTLH980ED1apoVob2PXqM5Z4uWZl/fyOVYIvAn/1oH2siIiIiIiIqLIqtfy/LKyMgBAZqZ6I/uqqirMmjULBQUFaN26tey26dOn49Zbb0W7du3wl7/8BTfddJPX+uomkwkmkztLorzcmfVgsVhgsVjq8yc0asJrw9eIhNpBZos1oPfD77tP+7w9K0kPi8UCo8pq4l0ny9AhOxkA0KtlKiwWC+x254SR3W4Xn7+yxvmZzktLCOo9atC639N6rfPvqjV7/l2frDqCuduL3KvFHTbA4Qw6/br9FH7dfgpvXNFT1vPBm7p+lipr5dldGoedn8cGIM2E5esdffjdRL78tv0kJvcIXf+hLrkp2C7Jlt1+ohTFFTXi9UOny3Hdzztxy3ltcVFv5/dBrVn4jtGovk8fnNAR//hlFwCgxhS5c9Fo+Cz98Jch2HK8FBf1zMFj328DAFitvs81hGN0UbnzO1Kv4fGAIisaPktEjQU/T0Shwc8SUWjwsxSYYF4fjaOOS/PtdjsuuugilJaWYvny5bLb3n33XTz88MOoqqpC586dMWfOHLRv3168/dlnn8WYMWOQmJiI3377Df/4xz/w8ssv495771V9rqeeegpPP/20x/bPP/8ciYmeZbaISO7lLTqcqNbgzq42dEn3/5G/b5X3OHOm0YF/9HNOBK0s0uCrg/KsoqmtbchJAD7aq0NBigP397BhS4lGdv1/+7RYV+xeifzWUCv8EfZpYJYd13VwBoW+PaTFslNaTGxpx5Q2dtXxgmntbSgzA3OPuff3gjY2jG8Zvuwk5eszrb0NQ7LDmw1FgMkGPLxWjwSdAy8OCm2WAxGFhrfvmT93tKFf89AdJz/ao8WWs+7vm1Et7EjQAfOOO7dlGh04a3IuOhiXZ0e7VAe+PKBFuUWDFgkOPNrH8xjicAD3r3bu/7UdbBiUxeO69DW5voMNA3y8JsI5iaBXph23dPaeYUxEREREREThU11djWuuuQZlZWVITU31ObbOmUnTp0/H9u3bPQJJAHDttddi/PjxKCwsxKuvvoorr7wSK1asEEvZPfHEE+LYvn37oqqqCq+88orXYNJjjz2GBx54QLxeXl6O1q1bY8KECX7/wKbMYrFgwYIFGD9+PAyGwHvSUOPz78OrcKK6AgMHDhT7Bvly36rfvN5miIvHlCmjAABTANxWUg1ogP8uP4wv1h3HsjNGV1NtB3KaZ2LKlIEw7DyNj/ZuRkZGBrK7d8S6VetkjzllyhS/+5TV7Ry+23QCj07sjPRE5/t5y697sOzUEbQpaIcpEzv5/Bv69O6F0+UmzD22X9zWuVNnTBnVzu9z1/WzdHbNUeDgbvF63969MaVv02vSHgnjxlsRp9PAGEQJRWoY/G4iwPv3TO8+fTAlgIzRQM0u3QycdWfbtmmTj4Q4HXD8MACIgSQAWHhSC5x037ewRuP1++nX8s1Yd+QcZlx5PtKC6PsXStH2Wbp/tfPftFfv3pjSx/t33Ys7lwKoFa+3zsvDlCm+S+MRhVO0fZaIYhk/T0Shwc8SUWjwsxQYoQpcIOoUTLr77rsxe/ZsLF26FK1atfK4PS0tDWlpaejYsSOGDBmCjIwM/PDDD5g2bZrq4w0ePBjPPvssTCYTjEajx+1Go1F1u8Fg4BshAHydSOsq9abR6er8XujXJh0bj5ZifPcc2WN0yHX2ikgyOreVS/olxRn0rvef81Cz/UQ57v5ii+xxJ3XPDWifhnXMxrCO2bJtxjjn49ocGr+PYdDrYTDIV5hrg3w9gv0s2RVt6fIykvhZbCCZfJ2jHr+bSI2uHt9TapQtAO3QoLTGfzaswNu+vH/9AFhsDsTpI9/vJ9o+Sxqt739Di6LPodEQ2n9zorqKts8SUSzj54koNPhZIgoNfpZ8C+a1CSqY5HA4cM899+CHH37A4sWLUVBQENB9HA6HrOeR0ubNzowFtYAREdWfBkLfIOBslRmZSXFBP8ZrV/bB3G2FuHpga9Xb41UyQAxCEMt13Wyzo6TKLBszsEC951og4lxNu5UTU2p0Wg10ir5sduUsY4g9O3unePmzWwZhWPtmYX0+IqJYV7fiy95ZFcf5/1tzNCSPq9FoEKdX7/XZ1OWk+j6fN1vl39l6lf6LREREREREFH2CCiZNnz4dn3/+OX766SekpKTg1KlTAJyZSAkJCTh48CC++uorTJgwAVlZWTh+/DhefPFFJCQkiGVCfvnlFxQVFWHIkCGIj4/HggUL8MILL+DBBx8M/V9HRDLPzN6JQ8VVmHXTQJzfOdv/HQCM6ZKNKwe0QkHzJEw/v4PXcULpOSlhxbbWx8LtaYPUg1OBEB5fOTGlRqPRiBlagobscjGiY1YDPhsRUWyyhziaZHMFk7rkpmD3qYqg7ju4HosdmqKPbhyAXYUVGN7Bdzndpy/ujhlfubOUDbrIZ3cRERERERGRf0EFk9577z0AwOjRo2XbZ82ahRtvvBHx8fFYtmwZ3nzzTZw7dw45OTkYOXIkVq5ciexs58S1wWDAO++8gxkzZsDhcKBDhw54/fXXcdttt4XmLyIiD9tOlAEADhVXAQBmzt0VcDDpoxsHBjSuT+t0j206V/AmPdF7JlRiXJ1bt8HgWs1ca/VskK6k1QDKxc+hXgHvzbWD2zTMExERxbhQJoyarDYs318MADDWoRzdy39iH59gjOmSgzFdcvyOu7RvK6w9dA5frHVmiTGYREREREREFBuCLnPnS15eHubOnetzzKRJkzBp0qRgnpaIQkyrCX1JmZzUeI9teekJAIC+KoGmUBDK3P20+STeurqvz7Fajcaj3JEjzLlJaQkGlNVYcNN5bcP6PEREjYW/c81AHC6uwt6iCmSluMutZQRZ3rVlegLymyXVe19InbQUnl7LMndERERERESxoO4pAUQUs3RhmLhJiJP3TLp/XEdcPyQfgLPE3EMTO+OV+XtC+py5ac4AlnTCUJAYp0O12Z2xZNBpcfxcjWxMmFsmiT2Z9L7q/BERkag+h+UV+4vxzfpj+HHzSQDuUqgAPHrm+XOitMb/IKozaQBJz8wkIiIiIiKimMBfb0RNUJKP0nJ7iyogzLk9NrlLwI+Z6BFM6oRmye4gz19Gtfe4z+jO9esjNKigGQDgTIUJtRZ5qbvUeHkPJ4NOI/bOEIW5zp3N9fjhyAQjImqM6pOZdO2Ha8RAEiDvpze6S2ClXalhSANIoe6TRUREREREROHBYBJRE5TppdzPmoMlmPDGUjHGIpSpC0RinB4tfYxXZkP9eWg+3riyT8CPryYj0SCWuiuuNMluU05Oxem0GN9N3ssh3NNXwj4wlkREFJi6Zowu31fs9bbp57fHRb3zvN7OUqQNT/q1OLBtZsT2g4iIiIiIiALHYBJRE5SaoJ6Z9NX6Y7LrwTbFnnPvcHx351AcfGGK6u33jOkAABjZKQsPTeoSdA8LJY1GA7PNufL8cHG17DZlMMmg12JkpyzMu38ErujfSnVMqNldi+LDUVaQiKgxqsthecZXm3Hdf9d4vf3Svi1h1Hv/PvvHhd3xwfX9g39iqrORndyZyUmKzGYiIiIiIiKKTgwmETVBVi9Lvy02RQBGF1wQJD0xDv3zM6H1Ejz564TOOPziVHx68yAkG0Pbsu3mj9fJriv/RCEw1iU3FcnxzucOd2UdO8vcEREFxRFkzujpilr8sOmEzzEdslP8Lo6Y0D0Xfx3fSbz+5AXdgtoPCk6CwR1AMhr4c4SIiIiIiCgW8NcbURO0dO8Z1e3L9sm3B5uZFElmm122/x6ZSZLAmMZVYKehytxpY+dlJCKKKH9l7iw2O95cuBc7T5YDAJbt9V7eTspbhqiQMQsAk3rkipevHNg6oMelupEusuCCCyIiIiIiotjAKU6iJqi40ozn5+z0aHTeTFF2Ls5HWaBo0bt1unh5d2GFeNnmmpFMMOgwpF0mOmaniLcJ81bhzExyOBzipCgnyoiIAqP8XlL6afNJvLlwH6a8vQwAUFFrCfo5nr6oO2aM64RuLVJx7eB8cXvHnBT8+/r++OjGASHPniU56dcivyOJiIiIiIhiA38pEzVR/1l2CCM6Zsn6FlSbbbIxWSnGht6toP00/Tzc+8Um/LzlpNg/CXAHiubdPwL5zZJk99GIY8IXTZI+tI4TZUREAfF3WN5yrFS8PO71JZgsySZSkxLvPtX91zV9cbrchD8PawsAuG9cR4/xE7v7fjwKDQaTiIiIiIiIYk/0px0QUdicqzb7vJ6WYGjI3amz1ATnZKHJ6g4mCZlJapNUQk+ncJa5s0lmRDlRRkQUGHfmqAPnqpzfSSWVJpS6vp+kx9b9pyvxz0X7Zfd//tIeOPziVPF6Ypy7N88FvfJw8/CCcO06BUH6vcivSCIiIiIiotjAYBJRE6ZcAd46I1F2PRbK3AFAnM45WWhWCSbpdZ6zVA2RmSTt2cSeSUREgZm9tRA2uwNP/7ITfZ9dgNd/24P+zy1En2cWoKi8FlZJBqoag+uAm+3KrB3XNSfs+0zBY88kIiIiIiKi2MMpTqIm4KGJnQMa1zxZXtYuThcbhwgh6CUEkxwOh1jyzqD2N7jmrfw1eq8PaZyKE2VERIFZe+gsvlp3DB+vPAwAeFuSefTbziJY/Ry4da7M0+/uHIbHp3bF36d2Ddu+Ut1pJV+L/IokIiIiIiKKDbExU0xE9TJK0hfJF5NV3jMp5oJJNuf+Sycb1YJJGlc0adm+M1hzsCQs+2Szs8wdEVFdLNt3RnV7nE6D7zee8HlfIRu1dWYibh3RDolxbA8aldgziYiIiIiIKObExkwxEdVLvCGwj7q05xDg7i0U7YyuYJLF6gzgWCRlkNQCYsKftbeoEld9sBpVJmvI94ll7oiI6kbvZSHDz1tO+r2vLka+t5o69kwiIiIiIiKKPZziJGoCMhLjVLfbFT2DlMGkWCEEjITSdtLeSQa1nkmKTRW1YQgmSV5KrromIgqcwUtAaMV+/5mkRr0u1LtDYSALJkVwP4iIiIiIiChwrP1B1AQ0Szbi6oGtsfV4GXYWlovbLTY71h8+i+bJRrRtniSWubtxWFtM6BY7TcuVPZOEoJJGo75KXaOYujKHIYgmDdTpGEwiIgpYXbKLpvZsgXiDDud1aBaGPaJQY88kIiIiIiKi2MNgElET8eLlvVBUXovBL/wubtt6vAyPfLcNALD5yfEwWZxBlSsHtEa3vNSI7GddCMEkIbPKYnMGcuJ0WmhUZqmUm4ReS6Ek7dvEiTIiosDVJZtzxviO6JCdEoa9oXBQLuogIiIiIiKi6Mcyd0RNiHK197rDZ8XLW46X4XSFCQBgDLDHUrQwKMrcWVxBJbV+SQA8Akznqi0h3ydhX+L06gEtIiJSF0hvpPZZSbLrOjaniyka/nMRERERERHFHP6UI2pCMhW9k/YWVYqXP1h6QLxs1MfWocFd5s6ZYWRxBXIMXv4OZWjnivdX4XRFbUj3SQhoGb0EtIiISF2NxX+2aHK8QXZdOO5TbJBnn3HBBRERERERUSzgLCdRE6L10YdC2tg81hqYCxlIyp5JBp3631urMlH5mKvcX6iY/QS0iIiashuG5tfr/pmJ8mBSeoLBy0iKRnVoi0VEREREREQRxllOIvIQa2XuhEwqIYAjBJUMXrKCFu0+7bHtd5Vtag4XV+FsldnvuGqzM2DlrdQeEVFTVt9YwoiOWeLlaYPaIDs1vp6PSA2JPZOIiIiIiIhiD2c5ichD7Ja5cwaRDp6pcm73EsipqLXW6XmOnK3G6FcXY/JbS/2OfW/xfgCefaqIiKj+4g3uDNqRHZtHcE+oLthKkIiIiIiIKPbE1owxETWIWMumEYJJe4sqYbLaUFZjAQAcP1ejOt5cx94auworAABF5SZc9e9V2HDkrNexwlNkpxrr9FxEROSdXlLG1FcJV4pO0p5JDCwRERERERHFhtiaMSaiBqGJsZmdjtnJ4uXDxdUor3UGky7t21J1/IW9WtTpeRwOh3h5zaGzeOjbreL1b9Yfww0frcUzv+yEw+GAyeosc3f9kPr1BSEiaur2Pz/ZY5teEkDSxdh3Fsl7Jkm+WomIiIiIiCiKMZhERDEvPTEOGa5m7BabHYeKnWXuctLUe2g8OrlrSJ5XKKcHAC/N24Ole8/goxWHsKeoAiZXyT2jXuft7kRETVagixbGdMmGXiVbVlpClOVEY0+sLVohIiIiIiIiBpOIqJFIjNMDAKx2h9gTqWW6ejApIS70AR6TxSZerjbbsP6wswRerPWfIiKKJm9d3Ud1u8XmTmdxgKktsUarAbrkpqBlegLaNkuM9O4QERERERFRAPSR3gEiii4v/6lXpHehTgyu/hlWmx02u3NiURvilc/SyUslm6ROj8Vqh2sXYGAwiYiozpKN6qeqR0rcmaElleaG2h0KEY1Gg7n3joDd4VDNPCMiIiIiIqLow19vRCQzvmtOpHehToTJKIvNAbsrsBPq0kdmm93rbUIASxgX59qfDpJ+TkREFLgWafFey6G1SEsQL9davR+bKXpptRoGkoiIiIiIiGIIf8ERkYxOF9t9DKrNVjGwE0wwqXteqt8xlgCDSRabXcxU0rOXBxFRnbxwWU+vt/2pfyvxMo+yREREREREROHHYBIRyehitCn2/tOVAICHvt1ap2DSjpPlOFNh8jlm6/Fyj22ny2sByMvc7ThRHrZSe0RETYX0++i6IW3EywkGHeL0Wjw0sTMG5Gfgsn4tI7F7RERERERERE0Kg0lEJBPq0nAN7WyVGWsOnQUQfGDsxllrfd7+7cYTHttOltXC4XBAEkuCRZKlFOuvJxFRpEiPnxf0yhMv/zZjJABg+vkd8O2dw5AYxxagREREREREROHGYBIRycRqJs3fpnTx2KYNIJBzcR/3BOWOk56ZR/5YbXZZiTsAMEv6dzCYRERUN9Ljp/Ryq4wEteFEREREREREFEYMJhGRTKz2+OndKt1jWyCZSdkpxno9r8XmwP4zlbJtJqvNvQ8x+noSEYWT8vD8z2l98fjUrrJt0uOnUe8+ZdXE6KIHIiIiIiIioljGYBJRE6bTavD0Rd1l2wLJ5olGRoPOY5uvQE5inHP8hO659XreVQdLMOnNZbJtJmlmEic9iYj8urB3HlplJMq2SY/hPfLSMK5rNm4Ymt/Qu0ZEREREREREAFhknqiJunZwGzxxQTfEG3T4x887Ir079ZafmeixzVdgbM3fxuJclQWJRs8glD9vXtUH/11+CNtOlOHt3/d53G6yuINJWobsiYgCosyMlQbjtVoNPvzzwIbeJSIiIiIiIiJy4TQnURPzw13DcFnflrhnTEfEq2TzxKqMpDis/ftYPDbZ3TvJV1ZQSrwBbZolBtUjKsHgPGT2z8/wmfW0Yn9xQPtARETA3ed3AOBZ+o5lQomIiIiIiIiiB4NJRE1M3zYZeP2qPshNixe33TW6fQT3KHSyU+IxrluOeD2QrKBgpirNNgcAwKDTwqDzfs9T5bXiZU6GEhF50kiOvud1aA4AHsF9Hj+JiIiIiIiIogeDSUTkUVoolmUkxomXW6V7lr5TCjQzye4AbHZnMClOr4VeEam6bUQBBrXNlG3TaNgonogoYMxMIiIiIiIiIopa7JlERNA1osY+mUlx+N8tg2HQadCmmf9gkibAP93qboPkDCYpMpPaZCbisn6tMPmtZeI2o77xvK5EROEixNyVwf1gypASERERERERUXgxmERE0DWymMfwjs0DHhvoZKXV4b4cp9PCoHjREuP0yEtLkG3725SuAe8HEVFTpVH8383hsYWIiIiIiIiIIqORTSETUV00psykYAW67l2amWTQaTzKL+l1GhgN7tcxMykONwxtW/8dJCJqhKRxfKEcqDK4b7ExmEREREREREQULZruDDIRiRpbZlIwAslMOldtRlGNe5xGo4FBUebOoNPKytqxOBMRUWCEw7DycCz0qSMiIiIiIiKiyGOZOyJq2plJislLi80uK2H35sK9eHPhPvRppshEUrxmeq1GXF1PRETBUx5C89IT1AcSERERERERUYNrujPIRCQa3TkLAJCWYIjwnjQ85eSlSVrPDsCbC/cBADaXOA+XgwsyATjL2kkpeyhN6pEbyt0kImq0hAxRjSKnMzMpLhK7Q0REREREREQqmJlERGiflYylD52PjKSmF0xSlrn7Ys1R3DayndfxCXE6AIBBmZmkCC4FUj6PiKipkh4hhR50Wh42iYiIiIiIiKIWM5OICADQplkiUuIZTHp+7i6cKK3xOr68xuK8n2LWs3VGIgCgpassU7/89BDuJRFR46UVeyYxmkREREREREQUrRhMIqImTW0l/Gu/7fE6vnNuCgDgxmFtxW1z7h2Ots2TAACf3DwQb1zVGxf2ygvpfhIRNVZCUJ+ZSURERERERETRi2XuiKhJU1sJL+2b1KNlKrafKBevZ6fEA3AGlX6afh6MBi265KaKt3fITkGH7JQw7jERUeMi9kySHI5fvrxXhPaGiIiIiIiIiNQwM4mISMEiCSblpsbLbtNJls73bp0uCyQREVFgpIEjoQWdNLifZOR6JyIiIiIiIqJowmASEZGC2eYOJjkc8tt0rMNERBRSYmaSZBvbJxERERERERFFFwaTiIgUTBZnMOnY2Wr8vvu07DYGk4iIQks4rGolESQeaYmIiIiIiIiiC4NJREQKQmbSdxuPe9ymZzCJiCik1HomMTOJiIiIiIiIKLowmEREpGCy2gAANrvD4zYtZziJiOpN2h9JOK7Kj6881hIRERERERFFEwaTiIgUzFZnZlKczvMQyTJ3REShpRakZ9yeiIiIiIiIKLowmEREpGByBZMS4nQetzGYREQUWhr2TCIiIiIiIiKKegwmEVGT9/CkzrLrQmaSRmVpPINJREShJRxX7Q53adGSKnOkdoeIiIiIiIiIVDCYRERN3l2jO8iu11qcPZMqa60eY3WsvUREVG8Hz1SKl4WMJLPNLm4TgvpEREREREREFB0YTCIiUqgy2eBwOPDGwr0etzEziYio/tQyj6QBpDg9T1GJiIiIiIiIogl/qRMRKZhtdrFvkhKDSURE9ZcSbxAvm6w21//dx10eaomIiIiIiIiiC4NJREQqhr/0h+p2LWc4iYjqLU7nPgVtk5kIAMhIdAeYJO2TiIiIiIiIiCgKMJhERKSiuNKkul3PYBIRUQi4o0UaV8+kXq3SVW4lIiIiIiIiomjAYBIRURCERvFERFR3/jKPmJlEREREREREFF0YTCIiAvDwpM4BjWPPJCKi8HMwN4mIiIiIiIgoqjCYREQE4K7RHXD4xalISzD4HMcyd0RE4cfMJCIiIiIiIqLowmASEZFEWY3F5+1aBpOIiOrNX6yIsSQiIiIiIiKi6MJgEhFREHTsmUREVG8ONk0iIiIiIiIiiikMJhERBYE9k4iIwo+hJCIiIiIiIqLowmASEZEPKfF62XWDjsEkIqJwY2ISERERERERUXRhMImIyAdlJlLPVmkR2hMiosbDb88kRpOIiIiIiIiIogqDSUREPih7JBn1ugjtCREREREREREREVFkMJhEROSDlj2SiIhCzl/iEfOSiIiIiIiIiKILg0lERD4oM5OIiCj8WOWOiIiIiIiIKLowmERE5IOyZxIREdWf355JDbIXRERERERERBQoBpOIiHw4UVqDv03uDAC4fUTbyO4MEVET4WBqEhEREREREVFUYTCJiEjih7uGoUtuimzbTcPy8dZQKx6a0ClCe0VE1Lj4CxZ1yE5uoD0hIiIiIiIiokAwmEREJNG3TQbm3T8y0rtBRNQk/Tj9PMy8rCdGdcqK9K4QERERERERkYQ+0jtARERERAQAfVqno0/r9EjvBhEREREREREpMDOJiIiIiIiIiIiIiIiIvGIwiYiIiIiIiIiIiIiIiLxiMImIiIiIGpTDEek9ICIiIiIiIqJgMJhERERERA3KAUaTiIiIiIiIiGJJUMGkmTNnYuDAgUhJSUF2djYuueQS7NmzRzbmjjvuQPv27ZGQkICsrCxcfPHF2L17t2zM0aNHMXXqVCQmJiI7OxsPPfQQrFZr/f8aIiIiIiIiIiIiIiIiCqmggklLlizB9OnTsXr1aixYsAAWiwUTJkxAVVWVOKZ///6YNWsWdu3ahfnz58PhcGDChAmw2WwAAJvNhqlTp8JsNmPlypX45JNP8PHHH+PJJ58M7V9GRERERERERERERERE9aYPZvC8efNk1z/++GNkZ2djw4YNGDlyJADg9ttvF29v27YtnnvuOfTu3RuHDx9G+/bt8dtvv2Hnzp1YuHAhcnJy0KdPHzz77LN45JFH8NRTTyEuLi4EfxYRERERRSv2TCIiIiIiIiKKLUEFk5TKysoAAJmZmaq3V1VVYdasWSgoKEDr1q0BAKtWrULPnj2Rk5Mjjps4cSLuvPNO7NixA3379vV4HJPJBJPJJF4vLy8HAFgsFlgslvr8CY2a8NrwNSKqH36WiEKHnycCALvdLl7me6Fu+FkiCg1+lohCh58notDgZ4koNPhZCkwwr4/G4ajb2lC73Y6LLroIpaWlWL58uey2d999Fw8//DCqqqrQuXNnzJkzB+3btwfgzFw6cuQI5s+fL46vrq5GUlIS5s6di8mTJ3s811NPPYWnn37aY/vnn3+OxMTEuuw+EZFPj6/XocKigQYOvDnUFundISJqVP61Q4t95c5qy28NZd9MIiIiIiIiokiorq7GNddcg7KyMqSmpvocW+fMpOnTp2P79u0egSQAuPbaazF+/HgUFhbi1VdfxZVXXokVK1YgPj6+Ts/12GOP4YEHHhCvl5eXo3Xr1pgwYYLfP7Aps1gsWLBgAcaPHw+DwRDp3SGKKe37VeCl+Xtx/9gO6JqTyM8SUYjwu4kA4ItT67Cv/BwAYMqUKRHem9jEzxJRaPCzRBQ6/DwRhQY/S0Shwc9SYIQqcIGoUzDp7rvvxuzZs7F06VK0atXK4/a0tDSkpaWhY8eOGDJkCDIyMvDDDz9g2rRpyM3Nxdq1a2Xji4qKAAC5ubmqz2c0GmE0Gj22GwwGvhECwNeJKHg9Wmfis1uHAHCne/KzRBQ6/Dw1cRqNeJHvg/rhZ4koNPhZIgodfp6IQoOfJaLQ4GfJt2BeG20wD+xwOHD33Xfjhx9+wKJFi1BQUBDQfRwOh9jzaOjQodi2bRtOnz4tjlmwYAFSU1PRrVu3YHaHiIiIiGJQ3YosExEREREREVGkBJWZNH36dHz++ef46aefkJKSglOnTgFwZiIlJCTg4MGD+OqrrzBhwgRkZWXh+PHjePHFF5GQkCCWMJkwYQK6deuG66+/Hi+//DJOnTqFxx9/HNOnT1fNPiIiIiIiIiIiIiIiIqLICSoz6b333kNZWRlGjx6NFi1aiP999dVXAID4+HgsW7YMU6ZMQYcOHXDVVVchJSUFK1euRHZ2NgBAp9Nh9uzZ0Ol0GDp0KK677jrccMMNeOaZZ0L/1xEREREREREREREREVG9BJWZ5PBTkyQvLw9z5871+zj5+fkBjSMiIiKixodV7oiIiIiIiIhiS1CZSURERERE9cZoEhEREREREVFMYTCJiIiIiIiIiIiIiIiIvGIwiYiIiIiIiIiIiIiIiLxiMImIiIiIGpSDde6IiIiIiIiIYgqDSUREREREREREREREROQVg0lERERE1KAcTEwiIiIiIiIiiikMJhEREREREREREREREZFXDCYRERERUYNiYhIRERERERFRbGEwiYiIiIiIiIiIiIiIiLxiMImIiIiIGpSDTZOIiIiIiIiIYgqDSUREREREREREREREROQVg0lERERE1KCYl0REREREREQUWxhMIiIiIiIiIiIiIiIiIq8YTCIiIiKiBsWWSURERERERESxhcEkIiIiIiIiIiIiIiIi8kof6R0IJ5vNBovFEundiBiLxQK9Xo/a2lrYbLZI705UiYuLg1bLWCoREVEkMDGJiIiIiIiIKLY0ymCSw+HAqVOnUFpaGuldiSiHw4Hc3FwcO3YMGo0m0rsTVbRaLQoKChAXFxfpXSEiIiIiIiIiIiIiimqNMpgkBJKys7ORmJjYZAMpdrsdlZWVSE5OZhaOhN1ux8mTJ1FYWIg2bdo02fcHERFRxLBpEhEREREREVFMaXTBJJvNJgaSmjVrFundiSi73Q6z2Yz4+HgGkxSysrJw8uRJWK1WGAyGSO8OEREREREREREREVHUanQRBqFHUmJiYoT3hKKZUN6OvaSIiIgaHvOSiIiIiIiIiGJLowsmCVi6jHzh+4OIiIiIiIiIiIiIKDCNNphERERERNGJLZOIiIiIiIiIYguDSU1U27Zt8eabb0Z6N7xavHgxNBoNSktLI70rRERERERERERERERNGoNJVCejR4/G/fffH7bHHzZsGAoLC5GWlha25yAiIqLIcLBrEhEREREREVFMYTCpiTGbzZHeBb8sFgvi4uKQm5vL3kZERERERERERERERBHGYFKUGD16NO655x7cf//9yMjIQE5ODv7zn/+gqqoKN910E1JSUtChQwf8+uuv4n1sNhtuueUWFBQUICEhAZ07d8Zbb70le9y77roLl156KZ5//nnk5eWhc+fOqs//4YcfIj09Hb///jsAYPv27Zg8eTKSk5ORk5OD66+/HsXFxQCAG2+8EUuWLMFbb70FjUYDjUaDw4cPqz5u27Zt8eyzz2LatGlISkpCy5Yt8c4778jGaDQavPfee7jooouQlJSE559/XrXM3YoVKzB69GgkJiYiIyMDEydOxLlz5wAAdrsdM2fOFF+L3r1749tvvw3q34CIiIgaBnsmEREREREREcWWRh9McjgcqDZbI/KfI8iZkk8++QTNmzfH2rVrcc899+DOO+/EFVdcgWHDhmHjxo2YMGECrr/+elRXVwNwBlBatWqFb775Bjt37sSTTz6Jv/3tb/j6669lj7to0SLs2bMHCxYswOzZsz2e9+WXX8ajjz6K3377DWPHjkVpaSnGjBmDvn37Yv369Zg3bx6Kiopw5ZVXAgDeeustDB06FLfddhsKCwtRWFiI1q1be/27XnnlFfTu3RubNm3Co48+ivvuuw8LFiyQjXnqqadw6aWXYtu2bbj55ps9HmPz5s0YO3YsunXrhlWrVmH58uW48MILYbPZAAAzZ87Ep59+ivfffx87duzAjBkzcN1112HJkiVB/RsQEREREREREREREZGcPtI7EG41Fhu6PTk/Is+985mJSIwL/CXu3bs3Hn/8cQDAY489hhdffBHNmzfHbbfdBgB48skn8d5772Hr1q0YMmQIDAYDnn76afH+BQUFWLVqFb7++msx8AMASUlJ+PDDDxEXF+fxnI888gg+++wzLFmyBN27dwcA/Otf/0Lfvn3xwgsviOM++ugjtG7dGnv37kWnTp0QFxeHxMRE5Obm+v27zjvvPDz66KMAgE6dOmHFihV44403MH78eHHMNddcg5tuukm8fvDgQdljvPzyyxgwYADeffddcZuwvyaTCS+88AIWLlyIoUOHAgDatWuH5cuX49///jdGjRrldx+JiIio4TAziYiIiIiIiCi2NPpgUizp1auXeFmn06FZs2bo2bOnuC0nJwcAcPr0aXHbO++8g48++ghHjx5FTU0NzGYz+vTpI3vcHj16qAaSXnvtNVRVVWH9+vVo166duH3Lli34448/kJyc7HGfAwcOoFOnTkH9XUKAR3r9zTfflG0bMGCAz8fYvHkzrrjiCtXb9u/fj+rqallwCnD2h+rbt29Q+0pERERERERERERERHKNPpiUYNBh5zMTI/bcwTAYDLLrGo1Gtk2j0QBwlrcDgC+//BIPPvggXnvtNQwdOhQpKSl45ZVXsGbNGtnjJCUlqT7fiBEjMGfOHHz99ddi5hAAVFZW4sILL8RLL73kcZ8WLVoE9TcFyts+ChISErzeVllZCQCYM2cOWrZsKbvNaDTWf+eIiIgopJiYRERERERERBRbGn0wSaPRBFVqLpasWLECw4YNw1133SVuO3DgQMD3HzRoEO6++25MmjQJer0eDz74IACgX79++O6779C2bVvo9eqvXVxcnNivyJ/Vq1d7XO/atWvA+wk4s7Z+//13WVk/Qbdu3WA0GnH06FGWtCMiIiIiIiIiIiIiCjFtpHeA6q5jx45Yv3495s+fj7179+KJJ57AunXrgnqMYcOGYe7cuXj66afF0nPTp0/H2bNnMW3aNKxbtw4HDhzA/PnzcdNNN4kBpLZt22LNmjU4fPgwiouLxWwpNStWrMDLL7+MvXv34p133sE333yD++67L6j9fOyxx7Bu3Trcdddd2Lp1K3bv3o333nsPxcXFSElJwYMPPogZM2bgk08+wYEDB7Bx40b885//xCeffBLU8xARERERERERERERkRyDSTHsjjvuwGWXXYarrroKgwcPRklJiSxLKVDDhw/HnDlz8Pjjj+Of//wn8vLysGLFCthsNkyYMAE9e/bE/fffj/T0dGi1zrfMgw8+CJ1Oh27duiErKwtHjx71+vh//etfsX79evTt2xfPPfccXn/9dUycGFzpwU6dOuG3337Dli1bMGjQIAwdOhQ//fSTmDn17LPP4oknnsDMmTPRtWtXTJo0CXPmzEFBQUHQrwcRERGFl8PBQndEREREREREsaRx1n+LQYsXL/bYdvjwYY9t0skXo9GIWbNmYdasWbIxM2fOFC+/++67SE1N9fvYI0eOFHsPAc6sp++//97r/nbq1AmrVq3yertUamoqvv76a6+3q00ojR492mP7qFGjsGLFCtXH0Gg0uO+++4LOeCIiIiIiIiIiIiIiIt+YmUREREREREREREREREReMZhEREREREREREREREREXrHMHYWVWqk+IiIiatrYMomIiIiIiIgotjAziYiIiIgalAOMJhERERERERHFEgaTiIiIiIiIiIiIiIiIyCsGk4iIiIiIiIiIiIiIiMgrBpOIiIiIqEGxZxIRERERERFRbGEwiYiIiIgaFGNJRERERERERLGFwSQiIiIiIiIiIiIiIiLyisEkCpmnnnoKffr0ifRuEBERERERERERERFRCDGYREREREQNysGmSUREREREREQxhcEkIiIiImpQDCURERERERERxRYGk6KIyWTCvffei+zsbMTHx2P48OFYt24dAGDx4sXQaDSYM2cOevXqhfj4eAwZMgT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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1251,7 +1240,7 @@ "source": [ "Next we define a function that will\n", "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices and the intervals\n", - "- Create a new directory \"Files/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Create a new directory \"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit + defining the first interval_old to be the first interval in the dataset stgy.historical_data\n", "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", "- Run through the code executing everything as discussed in the dev doc.\n", @@ -1261,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 111, "metadata": { "tags": [] }, @@ -1270,7 +1259,7 @@ "def run_sim(period, open_close, slippage, max_txs, L, trailing):\n", " global ocs\n", " # Initialize everything\n", - " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " with open(\"/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json\") as json_file:\n", " config = json.load(json_file)\n", "\n", " # Initialize stgyApp\n", @@ -1290,14 +1279,15 @@ " # Now we define prices and intervals given K and vol\n", " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", " # We create five equidistant OCs\n", - " oc1 = floor\n", + " oc1 = open_close\n", " # oc2 = oc1 * (1+6/2/100)\n", " ocs = [oc1]\n", - " for i in range(1,4):\n", - " globals()[\"oc\"+str(i+1)] = oc1 * (1+0.01)**i # We define 5 OCs based on a top width of 3%\n", + " for i in range(1,7):\n", + " globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", " ocs.append(globals()[\"oc\"+str(i+1)])\n", + " print(ocs)\n", " # But we start with the first oc1\n", - " stgy.trigger_prices['open_close'] = oc4\n", + " stgy.trigger_prices['open_close'] = oc1\n", " stgy.parameter_manager.define_intervals(stgy)\n", " \n", " # print(\"Volatility:\", vol)\n", @@ -1309,11 +1299,11 @@ " # Save historical data with trigger prices and thresholds loaded\n", " # checking if the directory demo_folder \n", " # exist or not.\n", - " if not os.path.exists(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " if not os.path.exists(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", " # if the demo_folder directory is not present \n", " # then create it.\n", - " os.makedirs(\"Files/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", - " stgy.historical_data.to_csv(\"Files/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " os.makedirs(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " stgy.historical_data.to_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", " % (period[0], period[1], open_close))\n", " #########################\n", " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", @@ -1381,11 +1371,10 @@ "\n", " maker_fees_counter = []\n", " \n", - " stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['floor'] * (1-trailing)\n", + " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", " while(i < len(stgy.historical_data)):\n", " # for i in range(initial_index, len(stgy.historical_data)):\n", " # pass\n", - " \n", " # We reset costs in every instance\n", " stgy.parameter_manager.reset_costs(stgy)\n", " # new_interval_previous = stgy.historical_data[\"interval\"][i-1]\n", @@ -1394,21 +1383,23 @@ " interval_current = stgy.parameter_manager.find_interval(stgy, stgy.historical_data['close'][i])['interval']\n", " market_price = stgy.historical_data[\"close\"][i]\n", " previous_price = stgy.historical_data[\"close\"][i-1]\n", + " \n", + " # We make a copy in case open_close, trailing change at the end of the iteration\n", + " interval_current_copy = Interval(interval_current.left_border,\n", + " interval_current.right_border,\n", + " interval_current.name,\n", + " interval_current.position_order)\n", + " interval_previous_copy = Interval(interval_previous.left_border,\n", + " interval_previous.right_border,\n", + " interval_previous.name,\n", + " interval_previous.position_order)\n", " #########################\n", - " # This case is when P crossed open_close_2 while increasing (therefore we had to close short), I_old = I_open_close_2, \n", - " # but then it goes below open_close_2 again. \n", - " # So before updating I_old the bot will read I_current = I_open_close_2 and I_old = I_open_close_2.\n", - " # So in order to be protected we manage this case as it names indicates open_close_2:\n", - " # we open and close at this price.\n", - " # Note that this also includes a situation in which price crossed floor while decreasing and the it crosses it again going up\n", - " # I_old = I_open_close_2 and before updating new I_old we have I_current= I_open_close_2.\n", - " # But here we do nothing because short is still open.\n", - "# if (new_interval_current == stgy.intervals[\"open_close_2\"]) & (interval_old == stgy.intervals[\"open_close_2\"]):\n", - "# time_dydx = stgy_instance.dydx.open_short(new_market_price, new_interval_current, stgy)\n", " # We need to update interval_old BEFORE executing actions bc if not the algo could read the movement late\n", " # therefore not taking the actions needed as soon as they are needed\n", - " if interval_previous != interval_current:\n", - " interval_old = interval_previous\n", + " print(interval_previous.name, interval_current.name, interval_old.name)\n", + " if interval_previous_copy != interval_current_copy:\n", + " interval_old = interval_previous_copy\n", + "# print(interval_previous.name, interval_current.name, interval_old.name)\n", " # print(interval_old.name)\n", " #########################\n", " # Update parameters\n", @@ -1430,15 +1421,19 @@ " # If price moves above trailing we move trailing up in order to save that profit\n", " # Is important to change trailing after finding scenarios (because we need to close the short first)\n", " elif market_price*(1+trailing) > stgy.trigger_prices['trailing_stop']:\n", - " stgy.trigger_prices['trailing_stop'] = market_price\n", + " if market_price >= oc4:\n", + " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " else:\n", + " stgy.trigger_prices['trailing_stop'] = market_price\n", " stgy.parameter_manager.define_intervals(stgy)\n", - " \n", - " # If price goes above floor again, we start at oc1 = floor, trailing_stop = floor * (1-trailing) and repeat the process\n", - " # We need to write the case market > floor but in terms of trailing in order to not change ocs at the beginning of the sims\n", - " # if stgy.trigger_prices['trailing_stop'] >= stgy.trigger_prices['floor']:\n", - " # stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['floor'] * (1-trailing)\n", - " # stgy.trigger_prices['open_close'] = stgy.trigger_prices['floor'] # = oc1\n", - " ##############################\n", + " ################################\n", + " ################################\n", + " # OC LOGIC\n", + " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", + " if market_price > oc1:\n", + " stgy.trigger_prices['open_close'] = oc1\n", + "\n", + " \n", " # We update vol and ocs if short_status = False\n", " # if not stgy.dydx.short_status:\n", " # current_date = list(stgy.historical_data.index)[i]\n", @@ -1449,7 +1444,8 @@ " # for i in range(1,5):\n", " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", " # ocs.append(globals()[\"oc\"+str(i+1)])\n", - " #########################\n", + "\n", + " \n", " # If we executed more txs than hat_L*20 then we change to K_2\n", " if (stgy.dydx.maker_fees_counter >= max_txs):\n", " # stgy.historical_data = stgy.historical_data_OC2\n", @@ -1466,8 +1462,8 @@ " # # 'index': i,\n", " # 'date': str(stgy.historical_data.index[i])})\n", " if not stgy.dydx.short_status:\n", - " if stgy.trigger_prices['open_close'] == oc4:\n", - " stgy.trigger_prices['open_close'] = oc1\n", + " if stgy.trigger_prices['open_close'] == oc1:\n", + " stgy.trigger_prices['open_close'] = oc4\n", " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", " elif stgy.dydx.short_status:\n", @@ -1484,6 +1480,7 @@ " stgy.dydx.maker_fees_counter = 0\n", " stgy.parameter_manager.define_intervals(stgy)\n", " ########################\n", + " ########################\n", " # Funding rates\n", " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", @@ -1513,348 +1510,2609 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2019-09-01 00:00:00'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "str(historical_data.index[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "returns = data['close'].pct_change().dropna()\n", - "log_returns = np.log(data['close']) \\\n", - " - np.log(data['close'].shift(1))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ - "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - "mu_log_returns = log_returns.mean()\n", - "mu_abs_log_returns = abs(log_returns).mean()\n", - "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", - "mu_log_returns_max = log_returns.max()\n", - "mu_log_returns_min = log_returns.min()\n", - "mu_returns = returns.mean()\n", - "mu_abs_returns = abs(returns).mean()\n", - "mu_returns_max = returns.max()\n", - "mu_returns_min = returns.min()" + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "periods_n_open_close = [[[\"2020-05-15\",\"2020-06-15\"],240]]" ] }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, + "execution_count": 112, + "metadata": { + "tags": [] + }, "outputs": [ { - "data": { - "text/plain": [ - "(0.01716814159292035, -0.034270575164515926)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mu_returns_max, mu_returns_min" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + "[240, 238.8, 237.60600000000002, 236.41797, 235.23588014999999, 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"trailing_stop trailing_stop trailing_stop\n", + "trailing_stop trailing_stop trailing_stop\n", + "trailing_stop trailing_stop trailing_stop\n", + "trailing_stop trailing_stop trailing_stop\n", + "trailing_stop trailing_stop trailing_stop\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m K \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[0;32m----> 2\u001b[0m condition \u001b[38;5;241m=\u001b[39m (mu_abs_log_returns\u001b[38;5;241m-\u001b[39mK\u001b[38;5;241m*\u001b[39mstd_ema_log_returns\u001b[38;5;241m<\u001b[39mlog_returns)\u001b[38;5;241m&\u001b[39m(log_returns\u001b[38;5;241m<\u001b[39mmu_abs_log_returns\u001b[38;5;241m+\u001b[39mK\u001b[38;5;241m*\u001b[39mstd_ema_log_returns)\n", - "\u001b[0;31mNameError\u001b[0m: name 'mu_abs_log_returns' is not defined" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mopen_close\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mperiod_n_open_close\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mslippage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0005\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmaker_fees_counter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_sim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen_close\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslippage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_txs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrailing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_sim\u001b[0;34m(period, open_close, slippage, max_txs, L, trailing)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;31m# We write the data into the google sheet or csv file acording to sheet value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0;31m# (sheet = True --> sheet, sheet = False --> csv)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m stgy.data_dumper.write_data(stgy,\n\u001b[0m\u001b[1;32m 244\u001b[0m \u001b[0minterval_previous\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval_old\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen_close\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 245\u001b[0m sheet=False)\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mwrite_data\u001b[0;34m(stgy_instance, new_interval_previous, interval_old, mkt_price_index, period, oc1, sheet)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mpath_to_aave\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mperiod\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_to_aave\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0mwriter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlineterminator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_aave\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.8/_bootlocale.py\u001b[0m in \u001b[0;36mgetpreferredencoding\u001b[0;34m(do_setlocale)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlocale\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpreferredencoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo_setlocale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mgetpreferredencoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo_setlocale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdo_setlocale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutf8_mode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "K = 3\n", - "condition = (mu_abs_log_returns-K*std_ema_log_returns 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(log_returns[condition]),\u001b[38;5;28mlen\u001b[39m(log_returns),\u001b[38;5;28mlen\u001b[39m(log_returns[condition])\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(log_returns)\n", - "\u001b[0;31mNameError\u001b[0m: name 'log_returns' is not defined" - ] + "data": { + "text/plain": [ + "-93714.29797685935" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "len(log_returns[condition]),len(log_returns),len(log_returns[condition])/len(log_returns)" + "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", + "dydx_results = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/\" + directory)\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([3.800e+01, 4.800e+01, 3.500e+01, 4.000e+01, 3.700e+01, 4.000e+01,\n", - " 4.100e+01, 3.600e+01, 4.500e+01, 5.100e+01, 4.100e+01, 5.300e+01,\n", - " 5.400e+01, 2.400e+01, 5.800e+01, 3.900e+01, 5.900e+01, 7.100e+01,\n", - " 3.100e+01, 8.600e+01, 7.800e+01, 1.700e+01, 7.400e+01, 7.500e+01,\n", - " 1.300e+01, 9.000e+01, 8.600e+01, 1.800e+01, 8.500e+01, 1.500e+01,\n", - " 8.400e+01, 5.975e+03, 0.000e+00, 1.220e+02, 8.700e+01, 2.000e+00,\n", - " 8.600e+01, 9.000e+01, 6.000e+00, 8.200e+01, 6.700e+01, 2.100e+01,\n", - " 9.100e+01, 5.900e+01, 3.300e+01, 8.200e+01, 4.000e+01, 4.300e+01,\n", - " 6.600e+01, 3.700e+01, 5.700e+01, 5.100e+01, 4.100e+01, 5.000e+01,\n", - " 5.200e+01, 3.900e+01, 4.000e+01, 3.900e+01, 4.500e+01, 4.000e+01,\n", - " 3.100e+01, 4.200e+01, 3.700e+01, 3.800e+01, 3.700e+01, 3.400e+01,\n", - " 3.200e+01, 3.400e+01, 3.700e+01, 2.600e+01, 4.000e+01, 3.200e+01,\n", - " 3.100e+01, 2.300e+01, 2.100e+01, 2.300e+01, 2.500e+01, 2.000e+01,\n", - " 3.000e+01, 1.900e+01, 2.800e+01, 2.500e+01, 1.500e+01, 2.000e+01,\n", - " 2.300e+01, 2.200e+01, 2.000e+01, 1.300e+01, 1.500e+01, 2.500e+01,\n", - " 1.500e+01, 1.300e+01, 2.000e+01, 1.400e+01, 1.700e+01, 1.600e+01,\n", - " 1.500e+01, 1.800e+01, 1.200e+01, 1.000e+01]),\n", - " array([-8.50701880e-04, -8.23749587e-04, -7.96797295e-04, -7.69845002e-04,\n", - " -7.42892709e-04, -7.15940416e-04, -6.88988123e-04, -6.62035831e-04,\n", - " -6.35083538e-04, -6.08131245e-04, -5.81178952e-04, -5.54226659e-04,\n", - " -5.27274366e-04, -5.00322074e-04, -4.73369781e-04, -4.46417488e-04,\n", - " -4.19465195e-04, -3.92512902e-04, -3.65560610e-04, -3.38608317e-04,\n", - " -3.11656024e-04, -2.84703731e-04, -2.57751438e-04, -2.30799145e-04,\n", - " -2.03846853e-04, -1.76894560e-04, -1.49942267e-04, -1.22989974e-04,\n", - " -9.60376813e-05, -6.90853885e-05, -4.21330957e-05, -1.51808029e-05,\n", - " 1.17714900e-05, 3.87237828e-05, 6.56760756e-05, 9.26283684e-05,\n", - " 1.19580661e-04, 1.46532954e-04, 1.73485247e-04, 2.00437540e-04,\n", - " 2.27389833e-04, 2.54342125e-04, 2.81294418e-04, 3.08246711e-04,\n", - " 3.35199004e-04, 3.62151297e-04, 3.89103589e-04, 4.16055882e-04,\n", - " 4.43008175e-04, 4.69960468e-04, 4.96912761e-04, 5.23865054e-04,\n", - " 5.50817346e-04, 5.77769639e-04, 6.04721932e-04, 6.31674225e-04,\n", - " 6.58626518e-04, 6.85578811e-04, 7.12531103e-04, 7.39483396e-04,\n", - " 7.66435689e-04, 7.93387982e-04, 8.20340275e-04, 8.47292567e-04,\n", - " 8.74244860e-04, 9.01197153e-04, 9.28149446e-04, 9.55101739e-04,\n", - " 9.82054032e-04, 1.00900632e-03, 1.03595862e-03, 1.06291091e-03,\n", - " 1.08986320e-03, 1.11681550e-03, 1.14376779e-03, 1.17072008e-03,\n", - " 1.19767237e-03, 1.22462467e-03, 1.25157696e-03, 1.27852925e-03,\n", - " 1.30548155e-03, 1.33243384e-03, 1.35938613e-03, 1.38633842e-03,\n", - " 1.41329072e-03, 1.44024301e-03, 1.46719530e-03, 1.49414760e-03,\n", - " 1.52109989e-03, 1.54805218e-03, 1.57500447e-03, 1.60195677e-03,\n", - " 1.62890906e-03, 1.65586135e-03, 1.68281364e-03, 1.70976594e-03,\n", - " 1.73671823e-03, 1.76367052e-03, 1.79062282e-03, 1.81757511e-03,\n", - " 1.84452740e-03]),\n", - " )" + "'2020-05-01'" ] }, - "execution_count": 74, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "plt.hist(log_returns[condition], bins=100)" + "period" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "11521" + "'2019-09-01 00:00:00'" ] }, - "execution_count": 55, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "len(log_returns)" + "str(historical_data.index[0])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 29, "metadata": {}, + "outputs": [], "source": [ - "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", - " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]" + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" ] }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "tags": [] - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ - "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", - "L = 5 * 0.07\n", - "trailing = 0.01\n", - "for period_n_open_close in periods_n_open_close:\n", - " period = period_n_open_close[0]\n", - " open_close = period_n_open_close[1]\n", - " slippage = 0.0005\n", - " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing)" + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'oc': 239.49397412360375, 'txs': 8, 'date': '2020-05-31 04:02:00'},\n", - " {'oc': 241.8889138648398, 'txs': 9, 'date': '2020-05-31 07:23:00'},\n", - " {'oc': 244.30780300348817, 'txs': 8, 'date': '2020-05-31 10:22:00'},\n", - " {'oc': 246.7508810335231, 'txs': 8, 'date': '2020-05-31 14:34:00'},\n", - " {'oc': 239.49397412360375, 'txs': 9, 'date': '2020-06-01 12:22:00'},\n", - " {'oc': 241.8889138648398, 'txs': 9, 'date': '2020-06-01 16:11:00'},\n", - " {'oc': 244.30780300348817, 'txs': 8, 'date': '2020-06-02 14:51:00'},\n", - " {'oc': 246.7508810335231, 'txs': 8, 'date': '2020-06-02 23:05:00'},\n", - " {'oc': 239.49397412360375, 'txs': 9, 'date': '2020-06-03 06:16:00'},\n", - " {'oc': 241.8889138648398, 'txs': 9, 'date': '2020-06-03 08:45:00'},\n", - " {'oc': 244.30780300348817, 'txs': 8, 'date': '2020-06-03 19:32:00'},\n", - " {'oc': 246.7508810335231, 'txs': 8, 'date': '2020-06-04 01:06:00'}]" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "maker_fees_counter" + "mu_returns_max, mu_returns_min" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "len(maker_fees_counter)" + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns Date: Wed, 12 Oct 2022 10:11:38 -0300 Subject: [PATCH 06/16] new approach comparing prices and not using intervals --- .../Simulations_lab_prices_logic.ipynb | 2189 +++++++++++++++++ 1 file changed, 2189 insertions(+) create mode 100644 jupyter-lab/Simulations_lab_prices_logic.ipynb diff --git a/jupyter-lab/Simulations_lab_prices_logic.ipynb b/jupyter-lab/Simulations_lab_prices_logic.ipynb new file mode 100644 index 0000000..646f335 --- /dev/null +++ b/jupyter-lab/Simulations_lab_prices_logic.ipynb @@ -0,0 +1,2189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/usr/lib/python3/dist-packages/secretstorage/dhcrypto.py:15: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", + " from cryptography.utils import int_from_bytes\n", + "/usr/lib/python3/dist-packages/secretstorage/util.py:19: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", + " from cryptography.utils import int_from_bytes\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.0.5)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (1.4.1)\n", + "Requirement already satisfied: pygsheets in /home/agustin/.local/lib/python3.8/site-packages (2.0.5)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/lib/python3/dist-packages 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"\n", + "import os\n", + "import pygsheets\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import csv\n", + "import pandas as pd\n", + "import numpy as np\n", + "import json\n", + "import math\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## StgyApp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class StgyApp(object):\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.stk = config[\"stk\"]\n", + " self.total_costs_from_aave_n_dydx = 0\n", + " self.total_pnl = 0\n", + " self.gas_fees = 0\n", + "\n", + " # prices and intervals\n", + " self.trigger_prices = {}\n", + " self.intervals = {}\n", + "\n", + " # clients for data\n", + " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", + " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", + " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", + " # self.historical_data =\n", + "\n", + " # We create attributes to fill later\n", + " self.aave = None\n", + " self.aave_features = None\n", + " self.aave_rates = None\n", + "\n", + " self.dydx = None\n", + " self.dydx_features = None\n", + "\n", + " # self.volatility_calculator = None\n", + "\n", + " self.parameter_manager = ParameterManager()\n", + "\n", + " self.historical_data = None\n", + "\n", + " self.data_dumper = DataDamperNPlotter()\n", + "\n", + " def launch(self, config):\n", + " # self.call_binance_data_loader()\n", + " self.initialize_aave(config['initial_parameters']['aave'])\n", + " self.initialize_dydx(config['initial_parameters']['dydx'])\n", + "\n", + " # call clients functions\n", + " def get_historical_data(self, symbol, freq,\n", + " initial_date, save):\n", + " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=save)\n", + " # self.historical_data = eth_historical\n", + " self.historical_data = eth_historical[\"close\"]\n", + " for i in range(len(self.historical_data)):\n", + " self.historical_data[i] = float(self.historical_data[i])\n", + " # self.load_intervals()\n", + "\n", + " # initialize classes\n", + " def initialize_aave(self, config):\n", + " # We initialize aave and dydx classes instances\n", + " self.aave = Aave(config)\n", + " # We load methods and attributes for aave and dydx to use later\n", + " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", + " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", + " \"keys\": list(self.aave.__dict__.keys())}}\n", + " # We create an attribute for historical data\n", + " self.aave_historical_data = []\n", + "\n", + " def initialize_dydx(self, config):\n", + " self.dydx = Dydx(config)\n", + " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", + " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", + " \"keys\": list(self.dydx.__dict__.keys())}}\n", + " self.dydx_historical_data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Interval class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This class represents an actual mathematical interval [left_border, right_border] and is used to be aware in which interval every price is and therefore being able to identify price movement direction by comparing intervals between a new given price read by the bot and the last interval in which the price was." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "class Interval(object):\n", + "\n", + " def __init__(self,\n", + " left_border,\n", + " right_border,\n", + " name,\n", + " position_order):\n", + " self.left_border = left_border\n", + " self.right_border = right_border\n", + " self.name = name\n", + " self.position_order = position_order\n", + "\n", + " def is_lower(self, another_interval):\n", + " if self.right_border <= another_interval.left_border:\n", + " return True\n", + " else:\n", + " return False\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aave and DyDx modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Aave" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "class Aave(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert self.dydx_class_instance == isinstance(dydx)\n", + " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", + " self.market_price = config['market_price']\n", + "\n", + " self.entry_price = config['entry_price']\n", + "\n", + " self.collateral_eth_initial = config['collateral_eth']\n", + " self.collateral_eth = config['collateral_eth']\n", + " self.collateral_usdc = config['collateral_usdc']\n", + "\n", + " self.reserve_margin_eth = 0\n", + " self.reserve_margin_usdc = 0\n", + "\n", + " self.borrowed_percentage = config['borrowed_pcg']\n", + " self.usdc_status = config['usdc_status']\n", + "\n", + " self.debt = config['debt']\n", + " self.debt_initial = config['debt']\n", + "\n", + " self.ltv = config['ltv']\n", + " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", + "\n", + " self.lending_rate = 0\n", + " self.lending_rate_hourly = 0\n", + " self.interest_on_lending_eth = 0 # aggregated fees\n", + " self.interest_on_lending_usd = 0\n", + " self.lending_fees_eth = 0 # fees between last 2 prices\n", + " self.lending_fees_usd = 0\n", + "\n", + " self.borrowing_rate = 0\n", + " self.borrowing_rate_hourly = 0\n", + " self.interest_on_borrowing = 0 # aggregated fees\n", + " self.borrowing_fees = 0 # fees between last 2 prices\n", + "\n", + " self.lend_minus_borrow_interest = 0\n", + "\n", + " self.costs = 0\n", + " # self.historical = pd.DataFrame()\n", + " # self.dydx_class_instance = dydx_class_instance\n", + " # self.staked_in_protocol = stk\n", + "\n", + " # def update_costs(self):\n", + " # \"\"\"\n", + " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " # \"\"\"\n", + " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", + " # self.costs = self.costs - self.lend_minus_borrow_interest\n", + "\n", + " def collateral_usd(self):\n", + " return self.collateral_eth * self.market_price\n", + "\n", + " def update_debt(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.debt = self.debt + self.borrowing_fees\n", + "\n", + " def update_collateral(self):\n", + " \"\"\"\n", + " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", + " self.collateral_usdc = self.collateral_usd()\n", + "\n", + " def track_lend_borrow_interest(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", + " in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", + "\n", + " def lending_fees_calc(self, freq):\n", + " self.simulate_lending_rate()\n", + " self.lending_rate_freq = self.lending_rate / freq\n", + "\n", + " # fees from lending are added to collateral? YES\n", + " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", + " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", + " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", + " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", + " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", + "\n", + " def borrowing_fees_calc(self, freq):\n", + " self.simulate_borrowing_rate()\n", + " self.borrowing_rate_freq = self.borrowing_rate / freq\n", + "\n", + " # fees from borrow are added to debt? YES\n", + " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", + " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", + " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", + "\n", + " def simulate_lending_rate(self):\n", + " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", + "\n", + " # best case\n", + " # self.lending_rate = 1.5 / 100\n", + "\n", + " # worst case\n", + " self.lending_rate = 0.5 / 100\n", + "\n", + " def simulate_borrowing_rate(self):\n", + " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", + "\n", + " # best case\n", + " # self.borrowing_rate = 1.5/100\n", + "\n", + " # worst case\n", + " self.borrowing_rate = 2.5/100\n", + "\n", + " def ltv_calc(self):\n", + " if self.collateral_usd() == 0:\n", + " return 0\n", + " else:\n", + " return self.debt / self.collateral_usd()\n", + "\n", + " def price_to_liquidation(self, dydx_class_instance):\n", + " return self.entry_price - (dydx_class_instance.pnl()\n", + " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", + "\n", + " def price_to_ltv_limit_calc(self):\n", + " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", + "\n", + " def buffer_for_repay(self):\n", + " return 0.01\n", + "\n", + " def ltv_limit(self):\n", + " return 0.5\n", + "\n", + " # Actions to take\n", + " def return_usdc(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " time = 0\n", + " if self.usdc_status:\n", + " # simulate 2min delay for tx\n", + " # update parameters\n", + " # AAVE parameters\n", + " self.usdc_status = False\n", + " # self.collateral_eth = 0\n", + " # self.collateral_usdc = 0\n", + " self.debt = 0\n", + " self.ltv = 0\n", + " self.price_to_ltv_limit = 0\n", + " # self.lending_rate = 0\n", + " # self.borrowing_rate = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def repay_aave(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " dydx_class_instance = stgy_instance.dydx\n", + " # aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " #\n", + " time = 0\n", + " if self.usdc_status:\n", + " # update parameters\n", + " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price)\n", + " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", + "\n", + " # pnl_for_debt = dydx_class_instance.pnl()\n", + " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", + " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price)\n", + " self.debt = self.debt - pnl_for_debt\n", + " self.ltv = self.ltv_calc()\n", + "\n", + " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " dydx_class_instance.short_size = new_short_size\n", + " dydx_class_instance.notional = dydx_class_instance.notional_calc()\n", + " dydx_class_instance.equity = dydx_class_instance.equity_calc()\n", + " dydx_class_instance.leverage = dydx_class_instance.leverage_calc()\n", + " dydx_class_instance.pnl = dydx_class_instance.pnl_calc()\n", + " # dydx_class_instance.price_to_liquidation = \\\n", + " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", + "\n", + " # fees\n", + " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + " dydx_class_instance.simulate_maker_taker_fees()\n", + " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", + " dydx_class_instance.costs = dydx_class_instance.costs \\\n", + " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", + " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + "\n", + " # Note that a negative self.debt is actually a profit\n", + " # We update the parameters\n", + " if self.debt > 0:\n", + " self.usdc_status = True\n", + " else:\n", + " self.usdc_status = False\n", + " # simulate 2min delay for tx\n", + " time = 1\n", + " return time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### DyDx" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "class Dydx(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert aave_class == isinstance(aave)\n", + " self.market_price = config['market_price']\n", + " \n", + " self.entry_price = config['entry_price']\n", + " self.short_size = config['short_size']\n", + " self.collateral = config['collateral']\n", + " self.notional = config['notional']\n", + " self.equity = config['equity']\n", + " self.leverage = config['leverage']\n", + " self.pnl = config['pnl']\n", + " # self.price_to_liquidation = config['price_to_liquidation']\n", + " self.collateral_status = config['collateral_status']\n", + " self.short_status = config['short_status']\n", + " self.order_status = True\n", + " self.withdrawal_fees = 0.01/100\n", + " self.funding_rates = 0\n", + " self.maker_taker_fees = 0\n", + " self.maker_fees_counter = 0\n", + " self.costs = 0\n", + "\n", + " # auxiliary functions\n", + " def pnl_calc(self):\n", + " return self.short_size * (self.market_price-self.entry_price)\n", + "\n", + " def notional_calc(self):\n", + " return abs(self.short_size)*self.market_price\n", + "\n", + " def equity_calc(self):\n", + " return self.collateral + self.pnl_calc()\n", + "\n", + " def leverage_calc(self):\n", + " if self.equity_calc() == 0:\n", + " return 0\n", + " else:\n", + " return self.notional_calc() / self.equity_calc()\n", + "\n", + " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", + " return self.entry_price \\\n", + " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", + "\n", + " @staticmethod\n", + " def price_to_liquidation_calc(dydx_client_class_instance):\n", + " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", + "\n", + " def add_funding_rates(self):\n", + " self.simulate_funding_rates()\n", + " self.costs = self.costs - self.funding_rates * self.notional\n", + "\n", + " def simulate_funding_rates(self):\n", + " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", + "\n", + " # best case\n", + " # self.funding_rates = 0.0075 / 100\n", + "\n", + " # average -0.00443%\n", + "\n", + " # worst case\n", + " self.funding_rates = -0.0075 / 100\n", + "\n", + " def simulate_maker_taker_fees(self):\n", + " # We add a counter for how many times we call this function\n", + " # i.e. how many times we open and close the short\n", + " self.maker_fees_counter += 1\n", + " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", + " \n", + " # maker fees\n", + " self.maker_taker_fees = 0.05 / 100 # <1M\n", + " # self.maker_taker_fees = 0.04 / 100 # <5M\n", + " # self.maker_taker_fees = 0.035 / 100 # <10M\n", + " # self.maker_taker_fees = 0.03 / 100 # <50M\n", + " # self.maker_taker_fees = 0.025 / 100 # <200M\n", + " # self.maker_taker_fees = 0.02 / 100 # >200M\n", + "\n", + " # Actions to take\n", + " def remove_collateral(self, stgy_instance):\n", + " self.cancel_order()\n", + " time = 0\n", + " if self.collateral_status:\n", + " self.collateral_status = False\n", + " withdrawal_fees = self.collateral * self.withdrawal_fees\n", + " self.collateral = 0\n", + " # self.price_to_liquidation = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + withdrawal_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + "\n", + " def open_short(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " if (not self.short_status) and self.order_status:\n", + " self.short_status = True\n", + " # dydx parameters\n", + " # if self.market_price <= stgy_instance.trigger_prices['floor']:\n", + " # print(\"CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['floor'] - self.market_price)\n", + "\n", + " # if self.market_price <= stgy_instance.trigger_prices['open_close']:\n", + " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", + " self.entry_price = self.market_price\n", + " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + "\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", + " aave_class_instance)\n", + " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", + " i = 0\n", + " while stgy_instance.trigger_prices['ltv_limit'] > stgy_instance.trigger_prices['repay_aave']:\n", + " print(\"CAUTION: P_ltv > P_repay\")\n", + " print(\"Difference of: \", stgy_instance.trigger_prices['ltv_limit'] - stgy_instance.trigger_prices['repay_aave'])\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", + " i += 1\n", + " print(\"P_repay defined to repay 0.5 (half) of debt. This logic was repeated\" + str(i) + \" times.\")\n", + " self.order_status = False\n", + " return 0\n", + "\n", + " def close_short(self, stgy_instance):\n", + " if self.short_status:\n", + " # Next if is to move up the threshold if we didnt execute at exactly open_close\n", + " # if self.market_price >= stgy_instance.trigger_prices['open_close']:\n", + " # # new_open_close = self.market_price\n", + " # print(\"CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!\")\n", + " # print(\"Difference of: \", self.market_price - stgy_instance.trigger_prices['open_close'])\n", + " # stgy_instance.target_prices['open_close'] = self.market_price\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " self.pnl = self.pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.entry_price = 0\n", + " self.short_status = False\n", + " self.short_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + " self.place_order(stgy_instance.trigger_prices['open_close'])\n", + " return 0\n", + "\n", + " def place_order(self, price):\n", + " self.order_status = True\n", + " # self.\n", + "\n", + " def cancel_order(self):\n", + " self.order_status = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## ParameterManager Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterManager(object):\n", + " # auxiliary functions\n", + " @staticmethod\n", + " def define_target_prices(stgy_instance, slippage, vol, floor, trailing):\n", + " mu = vol[0]\n", + " sigma = vol[1]\n", + " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", + " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", + " ##########################################################\n", + " # We define the intervals\n", + " list_of_triggers = [\"open_close\",\n", + " \"floor\",\n", + " \"trailing_stop\",\n", + " \"ltv_limit\"]\n", + " list_of_trigger_prices = [p_open_close,\n", + " floor,\n", + " p_trailing, \n", + " stgy_instance.aave.price_to_ltv_limit]\n", + " # We define/update trigger prices\n", + " for i in range(len(list_of_triggers)):\n", + " trigger_name = list_of_triggers[i]\n", + " trigger_price = list_of_trigger_prices[i]\n", + " stgy_instance.trigger_prices[trigger_name] = trigger_price\n", + "\n", + " @staticmethod\n", + " def find_oc(current_oc, ocs, vol):\n", + " mu, sigma = vol\n", + " oc_up = current_oc * (1+slippage)*(1+mu+2*sigma)\n", + " oc_down = current_oc * (1+slippage)*(1+mu-2*sigma)\n", + " distances = []\n", + " next_oc_up = []\n", + " next_oc_down = []\n", + " for i in range(len(ocs)):\n", + " oci = ocs[i]\n", + " if oc_up < oci:\n", + " next_oc_up.append(oci)\n", + " # ocs['up'].append(oci)\n", + " elif oc_down > oci:\n", + " next_oc_down.append(oci)\n", + " # ocs['down'].append(oci)\n", + " distances.append(current_oc-oci)\n", + " # If we get here then we didnt return anything, so we return the farthest oc\n", + " # Furthest down (positive distance current_oc > oci)\n", + " max_value = max(distances)\n", + " max_index = distances.index(max_value)\n", + " # Furthest up (negative distance current_oc < oci)\n", + " min_value = min(distances)\n", + " min_index = distances.index(min_value)\n", + " # print(next_oc_up)\n", + " # print(next_oc_down)\n", + " return {'up_choices': next_oc_up,\n", + " 'down_choices': next_oc_down,\n", + " 'max_distance_up': ocs[min_index],\n", + " 'max_distance_down': ocs[max_index]}\n", + " \n", + " @staticmethod\n", + " def calc_vol(last_date, data):\n", + " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " vol = [mu, sigma]\n", + " return vol\n", + " \n", + " @staticmethod\n", + " # Checking and updating data\n", + " def update_parameters(stgy_instance, new_market_price):\n", + " # AAVE\n", + " stgy_instance.aave.market_price = new_market_price\n", + " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", + " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", + " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", + " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", + " # We have to execute track_ first because we need the fees for current collateral and debt values\n", + " stgy_instance.aave.track_lend_borrow_interest()\n", + " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", + " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", + " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", + " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", + "\n", + " # DYDX\n", + " stgy_instance.dydx.market_price = new_market_price\n", + " stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc()\n", + " stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc()\n", + " stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc()\n", + " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", + " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", + "\n", + " @staticmethod\n", + " def reset_costs(stgy_instance):\n", + " # We reset the costs in order to always start in 0\n", + " stgy_instance.aave.costs = 0\n", + " stgy_instance.dydx.costs = 0\n", + " \n", + " \n", + " def find_scenario(self, stgy_instance, market_price, previous_market_price, index):\n", + " actions = self.actions_to_take(stgy_instance, market_price, previous_market_price)\n", + " self.simulate_fees(stgy_instance)\n", + " time = 0\n", + " time_aave = 0\n", + " time_dydx = 0\n", + " for action in actions:\n", + " if action == \"borrow_usdc_n_add_coll\":\n", + " time_aave = stgy_instance.aave.borrow_usdc(stgy_instance)\n", + " market_price = stgy_instance.historical_data[\"close\"][index + time_aave]\n", + " time_dydx = stgy_instance.dydx.add_collateral(stgy_instance)\n", + " time_aave = 0\n", + " elif action in stgy_instance.aave_features[\"methods\"]:\n", + " time_aave = getattr(stgy_instance.aave, action)(stgy_instance)\n", + " elif action in stgy_instance.dydx_features[\"methods\"]:\n", + " time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance)\n", + " time += time_aave + time_dydx\n", + " # print(stgy_instance.aave_features[\"methods\"])\n", + " # print(stgy_instance.dydx_features[\"methods\"])\n", + " return time\n", + " # stgy_instance.append(action)\n", + "\n", + " @staticmethod\n", + " def actions_to_take(stgy_instance, market_price, previous_market_price):\n", + " actions = []\n", + " \n", + " # Case P decreasing: \n", + " # We need to ask both P_t-1 > trigger and trigger > P_t bc if we only ask the later we will execute\n", + " # the action for all prices below trigger. Same logic for Case P increasing.\n", + " if (previous_market_price > stgy_instance.trigger_prices['open_close']) and \\\n", + " (stgy_instance.trigger_prices['open_close'] > market_price):\n", + " actions.append('open_short')\n", + " \n", + " elif (previous_market_price > stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " (stgy_instance.trigger_prices['trailing_stop'] > market_price):\n", + " actions.append('open_short')\n", + " \n", + " elif (previous_market_price > stgy_instance.trigger_prices['repay_aave']) and \\\n", + " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", + " actions.append('repay_aave')\n", + " \n", + " \n", + " # Case P increasing\n", + " elif (previous_market_price < stgy_instance.trigger_prices['open_close']) and \\\n", + " (stgy_instance.trigger_prices['open_close'] < market_price):\n", + " actions.append('close_short')\n", + " elif (previous_market_price < stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " (stgy_instance.trigger_prices['trailing_stop'] < market_price):\n", + " actions.append('close_short')\n", + " \n", + " return actions\n", + "\n", + " @staticmethod\n", + " def simulate_fees(stgy_instance):\n", + " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", + "\n", + " # best case\n", + " # stgy_instance.gas_fees = 1\n", + "\n", + " # stgy_instance.gas_fees = 3\n", + "\n", + " # stgy_instance.gas_fees = 6\n", + "\n", + " # worst case\n", + " stgy_instance.gas_fees = 10\n", + "\n", + " @staticmethod\n", + " def update_pnl(stgy_instance):\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", + "\n", + " @staticmethod\n", + " def add_costs(stgy_instance):\n", + " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", + " + stgy_instance.aave.costs + stgy_instance.dydx.costs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## DataDamperNPlotter Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module will write the results and is also used for plotting (for analysis porpuses)." + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "class DataDamperNPlotter:\n", + " def __init__(self):\n", + " self.historical_data = None\n", + "\n", + " @staticmethod\n", + " def write_data(stgy_instance,\n", + " mkt_price_index, period,oc1,\n", + " sheet=False):\n", + " aave_instance = stgy_instance.aave\n", + " dydx_instance = stgy_instance.dydx\n", + " data_aave = []\n", + " data_dydx = []\n", + " aave_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\"]\n", + "\n", + " for i in range(len(aave_instance.__dict__.values())):\n", + " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", + " # print(list(aave_instance.__dict__.keys())[i])\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " for i in range(len(dydx_instance.__dict__.values())):\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", + " data_aave.append(stgy_instance.gas_fees)\n", + " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_aave.append(stgy_instance.total_pnl)\n", + " data_aave.append(mkt_price_index)\n", + "\n", + "\n", + " data_dydx.append(stgy_instance.gas_fees)\n", + " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_dydx.append(stgy_instance.total_pnl)\n", + " data_dydx.append(mkt_price_index)\n", + " # print(interval_old.name)\n", + "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", + " if sheet == True:\n", + " gc = pygsheets.authorize(service_file=\n", + " 'stgy-1-simulations-e0ee0453ddf8.json')\n", + " sh = gc.open('aave/dydx simulations')\n", + " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", + " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", + " else:\n", + " path_to_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_aave)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_dydx)\n", + "\n", + " @staticmethod\n", + " def delete_results(stgy_instance, period, oc1):\n", + " file_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", + " os.remove(file_aave)\n", + " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", + " os.remove(file_dydx)\n", + "\n", + " @staticmethod\n", + " def add_header(stgy_instance, period, oc1):\n", + " aave_headers = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\",\n", + " \"index_of_mkt_price\"]\n", + " dydx_headers = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"short_size\",\n", + " \"collateral\",\n", + " \"notional\",\n", + " \"equity\",\n", + " \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " \"collateral_status\",\n", + " \"short_status\",\n", + " \"order_status\",\n", + " \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\",\n", + " \"index_of_mkt_price\"]\n", + " \n", + " path_to_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(aave_headers)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(dydx_headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "# Track historical data\n", + "# symbol = 'ETHUSDC'\n", + "# freq = '1m'\n", + "# initial_date = \"1 Jan 2019\"\n", + "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", + "# initial_date=initial_date, save=True)\n", + "\n", + "# Load historical data if previously tracked and saved\n", + "\n", + "historical_data = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "# # assign data to stgy instance + define index as dates\n", + "timestamp = pd.to_datetime(historical_data['timestamp'])\n", + "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", + "historical_data.index = timestamp\n", + "#\n", + "# #######################################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-11-01\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", + "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", + "where $\\sigma=vol$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First we calculate weighted vol\n", + "last_date = \"2021-06-01\"\n", + "slippage = 0.0005\n", + "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]-3*60:-3*60]\n", + "for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", + " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", + "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + "print(\"weighted mu: \", str(mu*100)+'%')\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+2*vol)*100-100)+'%')\n", + "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = sigma_2\n", + "mu = mu_2\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"avg movement: (1+mu+2vol): \", str((1+mu+2*vol)*100-100)+'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# normal_std = std\n", + "# medium_std = 2*std\n", + "# high_std = 4*std\n", + "# extreme_std = 6*std\n", + "# normal_std, medium_std, high_std, extreme_std" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find such a relevant price manually by taking a look at the price plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define a function that will\n", + "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices\n", + "- Create a new directory \"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit \n", + "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", + "- Run through the code executing everything as discussed in the dev doc.\n", + "\n", + "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(period, open_close, slippage, max_txs, L, trailing):\n", + " global ocs\n", + " # Initialize everything\n", + " with open(\"/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", + " # First we calculate weighted vol\n", + " last_date = period[1]+' 00:00:00'\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + " # Now we define prices \n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", + " # We create five equidistant OCs\n", + " oc1 = open_close\n", + " # oc2 = oc1 * (1+6/2/100)\n", + " ocs = [oc1]\n", + " for i in range(1,7):\n", + " globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", + " ocs.append(globals()[\"oc\"+str(i+1)])\n", + " print(ocs)\n", + " # But we start with the first oc1\n", + " stgy.trigger_prices['open_close'] = oc1\n", + " \n", + " # print(\"Volatility:\", vol)\n", + " # print(\"Floor:\", stgy.trigger_prices['floor'])\n", + " # print(\"Open_close1:\", oc1)\n", + " # print(\"Open_close2:\", oc2)\n", + " # print(\"1-OC2/OC1 - 1:\", 1-oc2/oc1)\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder \n", + " # exist or not.\n", + " if not os.path.exists(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " # if the demo_folder directory is not present \n", + " # then create it.\n", + " os.makedirs(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " stgy.historical_data.to_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " % (period[0], period[1], open_close))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + "\n", + " # Stk eth\n", + " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.dydx.collateral = stgy.aave.debt\n", + " stgy.dydx.equity = stgy.dydx.equity_calc()\n", + " stgy.dydx.collateral_status = True\n", + " \n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= stgy.trigger_prices['open_close']):\n", + " stgy.dydx.open_short(stgy)\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(stgy, period, open_close)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(stgy, period, open_close)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + " maker_fees_counter = []\n", + " \n", + " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " while(i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " market_price = stgy.historical_data[\"close\"][i]\n", + " previous_price = stgy.historical_data[\"close\"][i-1]\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, market_price)\n", + " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", + " # and we execute all the actions involved between both (current and previous prices)\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", + " ############################## \n", + " # We update trailing\n", + " # Everytime price moves down more than trailing we update trailing_stop\n", + " if market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']:\n", + " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", + " # If price moves above trailing we move trailing up in order to save that profit\n", + " # Is important to change trailing after finding scenarios (because we need to close the short first)\n", + " elif market_price*(1+trailing) > stgy.trigger_prices['trailing_stop']:\n", + " if market_price >= oc4:\n", + " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " else:\n", + " stgy.trigger_prices['trailing_stop'] = market_price\n", + " ################################\n", + " ################################\n", + " # OC LOGIC\n", + " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", + " if market_price > oc1:\n", + " stgy.trigger_prices['open_close'] = oc1\n", + "\n", + " \n", + " # We update vol and ocs if short_status = False\n", + " # if not stgy.dydx.short_status:\n", + " # current_date = list(stgy.historical_data.index)[i]\n", + " # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol)\n", + " # mu, sigma = vol\n", + " # oc1 = floor * (1+slippage) * (1+mu+2*sigma)\n", + " # ocs = [oc1]\n", + " # for i in range(1,5):\n", + " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", + " # ocs.append(globals()[\"oc\"+str(i+1)])\n", + "\n", + " \n", + " # If we executed more txs than hat_L*20 then we change to K_2\n", + " if (stgy.dydx.maker_fees_counter >= max_txs):\n", + " # stgy.historical_data = stgy.historical_data_OC2\n", + " # print(stgy.dydx.maker_fees_counter)\n", + " current_date = list(stgy.historical_data.index)[i]\n", + " current_oc = stgy.trigger_prices['open_close']\n", + " vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", + " ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", + " # if short = open and if there are up_choices available, we take the last option (the furthest)\n", + " # if there isn't options we take max_distance\n", + " # random.seed(4)\n", + " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # 'index': i,\n", + " # 'date': str(stgy.historical_data.index[i])})\n", + " if not stgy.dydx.short_status:\n", + " if stgy.trigger_prices['open_close'] == oc1:\n", + " stgy.trigger_prices['open_close'] = oc4\n", + " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", + " elif stgy.dydx.short_status:\n", + " if len(ocs_choices['up_choices']) != 0:\n", + " stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", + " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", + " # If we didnt change oc we dont clean maker_fees_counter\n", + " if current_oc != stgy.trigger_prices['open_close']:\n", + " maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " 'txs': stgy.dydx.maker_fees_counter, \n", + " # 'index': i,\n", + " 'date': str(stgy.historical_data.index[i])})\n", + " stgy.dydx.maker_fees_counter = 0\n", + " ########################\n", + " ########################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " stgy.data_dumper.write_data(stgy,\n", + " i, period, open_close,\n", + " sheet=False)\n", + " #########################\n", + " # we increment index by the time consumed in executing actions\n", + " # i += time_used\n", + " i += 1\n", + " return maker_fees_counter" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "periods_n_open_close = [[[\"2020-05-15\",\"2020-06-15\"],240]]" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[240, 238.8, 237.60600000000002, 236.41797, 235.23588014999999, 234.05970074925, 232.88940224550376]\n" + ] + } + ], + "source": [ + "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", + "L = 5 * 0.07\n", + "trailing = 0.01\n", + "for period_n_open_close in periods_n_open_close:\n", + " period = period_n_open_close[0]\n", + " open_close = period_n_open_close[1]\n", + " slippage = 0.0005\n", + " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'oc': 236.41797, 'txs': 8, 'date': '2020-05-31 07:45:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-01 16:18:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-03 10:30:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-03 16:32:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-03 17:16:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-04 10:39:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-05 22:10:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-06 02:07:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-06 08:06:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-07 19:46:00'},\n", + " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-11 17:00:00'},\n", + " {'oc': 240, 'txs': 9, 'date': '2020-06-12 09:19:00'}]" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "maker_fees_counter" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-93714.29797685935" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", + "dydx_results = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/\" + directory)\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2020-05-01'" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "period" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-01 00:00:00'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(historical_data.index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mu_returns_max, mu_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = periods_n_open_close[0][0]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=floor, color='green', linestyle='--', label='floor')\n", + "for i in range(len(ocs)):\n", + " axs.axhline(y=ocs[i], color='red', linestyle='--', label='oc'+str(i))\n", + "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "timestamp\n", + "2020-06-01 00:17:00 233.48\n", + "2020-06-01 00:18:00 233.48\n", + "2020-06-01 01:29:00 233.48\n", + "2020-06-01 01:30:00 233.48\n", + "2020-06-01 01:31:00 233.48\n", + "2020-06-01 01:32:00 233.48\n", + "2020-06-02 16:00:00 233.48\n", + "Name: close, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['close'].loc[data['close']==233.48]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count how many times a given price is cross given a dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_counter(data_set, price):\n", + " crossed_down = 0\n", + " crossed_up = 0\n", + " index_up = []\n", + " index_down = []\n", + " for index in range(1,len(data_set)):\n", + " previous_price = data_set['close'][index-1]\n", + " current_price = data_set['close'][index]\n", + " if previous_price <= price < current_price:\n", + " crossed_up += 1\n", + " index_up.append(index-1)\n", + " elif previous_price >= price > current_price:\n", + " crossed_down += 1\n", + " index_down.append(index-1)\n", + " return {'down':\n", + " {'crossed_down': crossed_down,\n", + " 'index_down': index_down},\n", + " 'up':\n", + " {'crossed_up': crossed_up,\n", + " 'index_up': index_up}}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "price = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosses = cross_counter(data_set, 240)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "market_price 176910\n", + "I_current 176910\n", + "I_old 176910\n", + "entry_price 53220\n", + "short_size 53220\n", + "collateral 176910\n", + "notional 53375\n", + "equity 176910\n", + "leverage 53375\n", + "pnl 53066\n", + "collateral_status 176910\n", + "short_status 53220\n", + "order_status 123690\n", + "withdrawal_fees 176910\n", + "funding_rates 176910\n", + "maker_taker_fees 133516\n", + "maker_fees_counter 133516\n", + "costs 421\n", + "gas_fees 176910\n", + "total_costs_from_aave_n_dydx 133516\n", + "total_stgy_pnl 176910\n", + "index_of_mkt_price 176910\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dydx_results.astype(bool).sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def count_maker_fees_increment(data_set):\n", + " index_of_maker_fee = []\n", + " for index in range(1,len(data_set)):\n", + " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", + " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", + " if previous_maker_fee_counter < current_maker_fee_counter:\n", + " index_of_maker_fee.append(index)\n", + " return {'indexes': index_of_maker_fee}" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "matches_up = 0\n", + "matches_down = 0\n", + "for index_up in crosses['up']['index_up']:\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " matches_up += 1\n", + "for index_down in crosses['down']['index_down']:\n", + " if index_down in results_maker_fee_counter['indexes']:\n", + " matches_down += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(155, 136, 291)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches_up, matches_down, matches_up + matches_down" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 156)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", + "- entry_price and short_size can be found at index_up -1\n", + "- close_price is market_price in index = index_up" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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43393240.70inftyminus_infty0.0000.000.00000-2.879624
43394239.74minus_inftyinfty-4334.634239.740.00001-522.470891
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" + ], + "text/plain": [ + " market_price I_current I_old short_size entry_price \\\n", + "43393 240.70 infty minus_infty 0.000 0.00 \n", + "43394 239.74 minus_infty infty -4334.634 239.74 \n", + "43395 240.94 infty minus_infty 0.000 0.00 \n", + "43396 240.86 infty minus_infty 0.000 0.00 \n", + "\n", + " pnl maker_fees_counter total_stgy_pnl \n", + "43393 0.0000 0 -2.879624 \n", + "43394 0.0000 1 -522.470891 \n", + "43395 -5201.5608 2 -6246.223689 \n", + "43396 0.0000 2 -6246.222332 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 1\n", + "index = crosses['up']['index_up'][i]\n", + "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "diff = []\n", + "cost = []\n", + "# we dont start the loop at i = 0 because the data_set started below open_close\n", + "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", + "for i in range(1,len(crosses['up']['index_up'])):\n", + " index_up = crosses['up']['index_up'][i]\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " entry_price = dydx_results.iloc[index-1]['entry_price']\n", + " close_price = dydx_results.iloc[index]['market_price']\n", + " short_size = dydx_results.iloc[index-1]['short_size']\n", + " diff.append(close_price-entry_price)\n", + " cost.append(short_size * (close_price-entry_price))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1999999999999886, -5201.560799999951)" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(diff), np.mean(cost)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From c4458173ae2a8eb6af625d055773759b3abfdc7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Thu, 13 Oct 2022 10:32:19 -0300 Subject: [PATCH 07/16] playing with trailing percentages and time for updates --- files/aave_results.csv | 973 ------- files/dydx_results.csv | 973 ------- ...b => Simulations_intervals_approach.ipynb} | 0 .../Simulations_lab_prices_logic.ipynb | 2189 ---------------- jupyter-lab/Simulations_prices_approach.ipynb | 2250 +++++++++++++++++ 5 files changed, 2250 insertions(+), 4135 deletions(-) delete mode 100644 files/aave_results.csv delete mode 100644 files/dydx_results.csv rename jupyter-lab/{Simulations_lab.ipynb => Simulations_intervals_approach.ipynb} (100%) delete mode 100644 jupyter-lab/Simulations_lab_prices_logic.ipynb create mode 100644 jupyter-lab/Simulations_prices_approach.ipynb diff --git a/files/aave_results.csv b/files/aave_results.csv deleted file mode 100644 index f5ca08d..0000000 --- a/files/aave_results.csv +++ /dev/null @@ -1,973 +0,0 @@ -market_price,I_current,I_old,entry_price,collateral_eth,usdc_status,debt,ltv,lending_rate,interest_on_lending_usd,borrowing_rate,interest_on_borrowing,lend_minus_borrow_interest,costs,gas_fees,total_costs_from_aave_n_dydx,total_stgy_pnl,index_of_mkt_price 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"/usr/lib/python3/dist-packages/secretstorage/dhcrypto.py:15: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", - " from cryptography.utils import int_from_bytes\n", - "/usr/lib/python3/dist-packages/secretstorage/util.py:19: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", - " from cryptography.utils import int_from_bytes\n", - "Defaulting to user installation because normal site-packages is not writeable\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.0.5)\n", - "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (1.4.1)\n", - "Requirement already satisfied: pygsheets in /home/agustin/.local/lib/python3.8/site-packages (2.0.5)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.2.2)\n", - "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/lib/python3/dist-packages (from 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"\n", - "import os\n", - "import pygsheets\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import norm\n", - "import csv\n", - "import pandas as pd\n", - "import numpy as np\n", - "import json\n", - "import math\n", - "import random" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Classes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## StgyApp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class StgyApp(object):\n", - "\n", - " def __init__(self, config):\n", - "\n", - " self.stk = config[\"stk\"]\n", - " self.total_costs_from_aave_n_dydx = 0\n", - " self.total_pnl = 0\n", - " self.gas_fees = 0\n", - "\n", - " # prices and intervals\n", - " self.trigger_prices = {}\n", - " self.intervals = {}\n", - "\n", - " # clients for data\n", - " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", - " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", - " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", - " # self.historical_data =\n", - "\n", - " # We create attributes to fill later\n", - " self.aave = None\n", - " self.aave_features = None\n", - " self.aave_rates = None\n", - "\n", - " self.dydx = None\n", - " self.dydx_features = None\n", - "\n", - " # self.volatility_calculator = None\n", - "\n", - " self.parameter_manager = ParameterManager()\n", - "\n", - " self.historical_data = None\n", - "\n", - " self.data_dumper = DataDamperNPlotter()\n", - "\n", - " def launch(self, config):\n", - " # self.call_binance_data_loader()\n", - " self.initialize_aave(config['initial_parameters']['aave'])\n", - " self.initialize_dydx(config['initial_parameters']['dydx'])\n", - "\n", - " # call clients functions\n", - " def get_historical_data(self, symbol, freq,\n", - " initial_date, save):\n", - " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", - " initial_date=initial_date, save=save)\n", - " # self.historical_data = eth_historical\n", - " self.historical_data = eth_historical[\"close\"]\n", - " for i in range(len(self.historical_data)):\n", - " self.historical_data[i] = float(self.historical_data[i])\n", - " # self.load_intervals()\n", - "\n", - " # initialize classes\n", - " def initialize_aave(self, config):\n", - " # We initialize aave and dydx classes instances\n", - " self.aave = Aave(config)\n", - " # We load methods and attributes for aave and dydx to use later\n", - " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", - " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", - " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", - " \"keys\": list(self.aave.__dict__.keys())}}\n", - " # We create an attribute for historical data\n", - " self.aave_historical_data = []\n", - "\n", - " def initialize_dydx(self, config):\n", - " self.dydx = Dydx(config)\n", - " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", - " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", - " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", - " \"keys\": list(self.dydx.__dict__.keys())}}\n", - " self.dydx_historical_data = []" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Interval class" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This class represents an actual mathematical interval [left_border, right_border] and is used to be aware in which interval every price is and therefore being able to identify price movement direction by comparing intervals between a new given price read by the bot and the last interval in which the price was." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [], - "source": [ - "class Interval(object):\n", - "\n", - " def __init__(self,\n", - " left_border,\n", - " right_border,\n", - " name,\n", - " position_order):\n", - " self.left_border = left_border\n", - " self.right_border = right_border\n", - " self.name = name\n", - " self.position_order = position_order\n", - "\n", - " def is_lower(self, another_interval):\n", - " if self.right_border <= another_interval.left_border:\n", - " return True\n", - " else:\n", - " return False\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aave and DyDx modules" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Aave" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "class Aave(object):\n", - "\n", - " def __init__(self, config):\n", - " # assert self.dydx_class_instance == isinstance(dydx)\n", - " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", - " self.market_price = config['market_price']\n", - "\n", - " self.entry_price = config['entry_price']\n", - "\n", - " self.collateral_eth_initial = config['collateral_eth']\n", - " self.collateral_eth = config['collateral_eth']\n", - " self.collateral_usdc = config['collateral_usdc']\n", - "\n", - " self.reserve_margin_eth = 0\n", - " self.reserve_margin_usdc = 0\n", - "\n", - " self.borrowed_percentage = config['borrowed_pcg']\n", - " self.usdc_status = config['usdc_status']\n", - "\n", - " self.debt = config['debt']\n", - " self.debt_initial = config['debt']\n", - "\n", - " self.ltv = config['ltv']\n", - " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", - "\n", - " self.lending_rate = 0\n", - " self.lending_rate_hourly = 0\n", - " self.interest_on_lending_eth = 0 # aggregated fees\n", - " self.interest_on_lending_usd = 0\n", - " self.lending_fees_eth = 0 # fees between last 2 prices\n", - " self.lending_fees_usd = 0\n", - "\n", - " self.borrowing_rate = 0\n", - " self.borrowing_rate_hourly = 0\n", - " self.interest_on_borrowing = 0 # aggregated fees\n", - " self.borrowing_fees = 0 # fees between last 2 prices\n", - "\n", - " self.lend_minus_borrow_interest = 0\n", - "\n", - " self.costs = 0\n", - " # self.historical = pd.DataFrame()\n", - " # self.dydx_class_instance = dydx_class_instance\n", - " # self.staked_in_protocol = stk\n", - "\n", - " # def update_costs(self):\n", - " # \"\"\"\n", - " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", - " # \"\"\"\n", - " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", - " # self.costs = self.costs - self.lend_minus_borrow_interest\n", - "\n", - " def collateral_usd(self):\n", - " return self.collateral_eth * self.market_price\n", - "\n", - " def update_debt(self):\n", - " \"\"\"\n", - " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", - " \"\"\"\n", - " self.debt = self.debt + self.borrowing_fees\n", - "\n", - " def update_collateral(self):\n", - " \"\"\"\n", - " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", - " \"\"\"\n", - " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", - " self.collateral_usdc = self.collateral_usd()\n", - "\n", - " def track_lend_borrow_interest(self):\n", - " \"\"\"\n", - " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", - " in order to use updated values of last earned fees\n", - " \"\"\"\n", - " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", - "\n", - " def lending_fees_calc(self, freq):\n", - " self.simulate_lending_rate()\n", - " self.lending_rate_freq = self.lending_rate / freq\n", - "\n", - " # fees from lending are added to collateral? YES\n", - " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", - " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", - " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", - " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", - " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", - "\n", - " def borrowing_fees_calc(self, freq):\n", - " self.simulate_borrowing_rate()\n", - " self.borrowing_rate_freq = self.borrowing_rate / freq\n", - "\n", - " # fees from borrow are added to debt? YES\n", - " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", - " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", - " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", - "\n", - " def simulate_lending_rate(self):\n", - " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", - "\n", - " # best case\n", - " # self.lending_rate = 1.5 / 100\n", - "\n", - " # worst case\n", - " self.lending_rate = 0.5 / 100\n", - "\n", - " def simulate_borrowing_rate(self):\n", - " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", - "\n", - " # best case\n", - " # self.borrowing_rate = 1.5/100\n", - "\n", - " # worst case\n", - " self.borrowing_rate = 2.5/100\n", - "\n", - " def ltv_calc(self):\n", - " if self.collateral_usd() == 0:\n", - " return 0\n", - " else:\n", - " return self.debt / self.collateral_usd()\n", - "\n", - " def price_to_liquidation(self, dydx_class_instance):\n", - " return self.entry_price - (dydx_class_instance.pnl()\n", - " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", - "\n", - " def price_to_ltv_limit_calc(self):\n", - " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", - "\n", - " def buffer_for_repay(self):\n", - " return 0.01\n", - "\n", - " def ltv_limit(self):\n", - " return 0.5\n", - "\n", - " # Actions to take\n", - " def return_usdc(self, stgy_instance):\n", - " gas_fees = stgy_instance.gas_fees\n", - " time = 0\n", - " if self.usdc_status:\n", - " # simulate 2min delay for tx\n", - " # update parameters\n", - " # AAVE parameters\n", - " self.usdc_status = False\n", - " # self.collateral_eth = 0\n", - " # self.collateral_usdc = 0\n", - " self.debt = 0\n", - " self.ltv = 0\n", - " self.price_to_ltv_limit = 0\n", - " # self.lending_rate = 0\n", - " # self.borrowing_rate = 0\n", - "\n", - " # fees\n", - " self.costs = self.costs + gas_fees\n", - "\n", - " time = 1\n", - " return time\n", - "\n", - " def repay_aave(self, stgy_instance):\n", - " gas_fees = stgy_instance.gas_fees\n", - " dydx_class_instance = stgy_instance.dydx\n", - " # aave_class_instance = stgy_instance.aave\n", - " # dydx_client_class_instance = stgy_instance.dydx_client\n", - " #\n", - " time = 0\n", - " if self.usdc_status:\n", - " # update parameters\n", - " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price)\n", - " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", - "\n", - " # pnl_for_debt = dydx_class_instance.pnl()\n", - " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", - " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price)\n", - " self.debt = self.debt - pnl_for_debt\n", - " self.ltv = self.ltv_calc()\n", - "\n", - " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", - " self.costs = self.costs + gas_fees\n", - "\n", - " dydx_class_instance.short_size = new_short_size\n", - " dydx_class_instance.notional = dydx_class_instance.notional_calc()\n", - " dydx_class_instance.equity = dydx_class_instance.equity_calc()\n", - " dydx_class_instance.leverage = dydx_class_instance.leverage_calc()\n", - " dydx_class_instance.pnl = dydx_class_instance.pnl_calc()\n", - " # dydx_class_instance.price_to_liquidation = \\\n", - " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", - "\n", - " # fees\n", - " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", - " dydx_class_instance.simulate_maker_taker_fees()\n", - " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", - " dydx_class_instance.costs = dydx_class_instance.costs \\\n", - " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", - " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", - "\n", - " # Note that a negative self.debt is actually a profit\n", - " # We update the parameters\n", - " if self.debt > 0:\n", - " self.usdc_status = True\n", - " else:\n", - " self.usdc_status = False\n", - " # simulate 2min delay for tx\n", - " time = 1\n", - " return time" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### DyDx" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "class Dydx(object):\n", - "\n", - " def __init__(self, config):\n", - " # assert aave_class == isinstance(aave)\n", - " self.market_price = config['market_price']\n", - " \n", - " self.entry_price = config['entry_price']\n", - " self.short_size = config['short_size']\n", - " self.collateral = config['collateral']\n", - " self.notional = config['notional']\n", - " self.equity = config['equity']\n", - " self.leverage = config['leverage']\n", - " self.pnl = config['pnl']\n", - " # self.price_to_liquidation = config['price_to_liquidation']\n", - " self.collateral_status = config['collateral_status']\n", - " self.short_status = config['short_status']\n", - " self.order_status = True\n", - " self.withdrawal_fees = 0.01/100\n", - " self.funding_rates = 0\n", - " self.maker_taker_fees = 0\n", - " self.maker_fees_counter = 0\n", - " self.costs = 0\n", - "\n", - " # auxiliary functions\n", - " def pnl_calc(self):\n", - " return self.short_size * (self.market_price-self.entry_price)\n", - "\n", - " def notional_calc(self):\n", - " return abs(self.short_size)*self.market_price\n", - "\n", - " def equity_calc(self):\n", - " return self.collateral + self.pnl_calc()\n", - "\n", - " def leverage_calc(self):\n", - " if self.equity_calc() == 0:\n", - " return 0\n", - " else:\n", - " return self.notional_calc() / self.equity_calc()\n", - "\n", - " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", - " return self.entry_price \\\n", - " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", - "\n", - " @staticmethod\n", - " def price_to_liquidation_calc(dydx_client_class_instance):\n", - " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", - "\n", - " def add_funding_rates(self):\n", - " self.simulate_funding_rates()\n", - " self.costs = self.costs - self.funding_rates * self.notional\n", - "\n", - " def simulate_funding_rates(self):\n", - " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", - "\n", - " # best case\n", - " # self.funding_rates = 0.0075 / 100\n", - "\n", - " # average -0.00443%\n", - "\n", - " # worst case\n", - " self.funding_rates = -0.0075 / 100\n", - "\n", - " def simulate_maker_taker_fees(self):\n", - " # We add a counter for how many times we call this function\n", - " # i.e. how many times we open and close the short\n", - " self.maker_fees_counter += 1\n", - " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", - " \n", - " # maker fees\n", - " self.maker_taker_fees = 0.05 / 100 # <1M\n", - " # self.maker_taker_fees = 0.04 / 100 # <5M\n", - " # self.maker_taker_fees = 0.035 / 100 # <10M\n", - " # self.maker_taker_fees = 0.03 / 100 # <50M\n", - " # self.maker_taker_fees = 0.025 / 100 # <200M\n", - " # self.maker_taker_fees = 0.02 / 100 # >200M\n", - "\n", - " # Actions to take\n", - " def remove_collateral(self, stgy_instance):\n", - " self.cancel_order()\n", - " time = 0\n", - " if self.collateral_status:\n", - " self.collateral_status = False\n", - " withdrawal_fees = self.collateral * self.withdrawal_fees\n", - " self.collateral = 0\n", - " # self.price_to_liquidation = 0\n", - "\n", - " # fees\n", - " self.costs = self.costs + withdrawal_fees\n", - "\n", - " time = 1\n", - " return time\n", - "\n", - "\n", - " def open_short(self, stgy_instance):\n", - " aave_class_instance = stgy_instance.aave\n", - " # dydx_client_class_instance = stgy_instance.dydx_client\n", - " if (not self.short_status) and self.order_status:\n", - " self.short_status = True\n", - " # dydx parameters\n", - " # if self.market_price <= stgy_instance.trigger_prices['floor']:\n", - " # print(\"CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!\")\n", - " # print(\"Difference of: \", stgy_instance.trigger_prices['floor'] - self.market_price)\n", - "\n", - " # if self.market_price <= stgy_instance.trigger_prices['open_close']:\n", - " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", - " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", - " self.entry_price = self.market_price\n", - " self.short_size = -aave_class_instance.collateral_eth_initial\n", - " # self.collateral = aave_class_instance.debt_initial\n", - " self.notional = self.notional_calc()\n", - " self.equity = self.equity_calc()\n", - " self.leverage = self.leverage_calc()\n", - " # Simulate maker taker fees\n", - " self.simulate_maker_taker_fees()\n", - " # Add costs\n", - " self.costs = self.costs + self.maker_taker_fees * self.notional\n", - "\n", - " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", - " aave_class_instance)\n", - " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", - " i = 0\n", - " while stgy_instance.trigger_prices['ltv_limit'] > stgy_instance.trigger_prices['repay_aave']:\n", - " print(\"CAUTION: P_ltv > P_repay\")\n", - " print(\"Difference of: \", stgy_instance.trigger_prices['ltv_limit'] - stgy_instance.trigger_prices['repay_aave'])\n", - " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", - " i += 1\n", - " print(\"P_repay defined to repay 0.5 (half) of debt. This logic was repeated\" + str(i) + \" times.\")\n", - " self.order_status = False\n", - " return 0\n", - "\n", - " def close_short(self, stgy_instance):\n", - " if self.short_status:\n", - " # Next if is to move up the threshold if we didnt execute at exactly open_close\n", - " # if self.market_price >= stgy_instance.trigger_prices['open_close']:\n", - " # # new_open_close = self.market_price\n", - " # print(\"CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!\")\n", - " # print(\"Difference of: \", self.market_price - stgy_instance.trigger_prices['open_close'])\n", - " # stgy_instance.target_prices['open_close'] = self.market_price\n", - " self.notional = self.notional_calc()\n", - " self.equity = self.equity_calc()\n", - " self.leverage = self.leverage_calc()\n", - " self.pnl = self.pnl_calc()\n", - " stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl\n", - " # We update short parameters after the calculation of pnl\n", - " self.entry_price = 0\n", - " self.short_status = False\n", - " self.short_size = 0\n", - " self.simulate_maker_taker_fees()\n", - " self.costs = self.costs + self.maker_taker_fees * self.notional\n", - " self.place_order(stgy_instance.trigger_prices['open_close'])\n", - " return 0\n", - "\n", - " def place_order(self, price):\n", - " self.order_status = True\n", - " # self.\n", - "\n", - " def cancel_order(self):\n", - " self.order_status = False" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## ParameterManager Module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "class ParameterManager(object):\n", - " # auxiliary functions\n", - " @staticmethod\n", - " def define_target_prices(stgy_instance, slippage, vol, floor, trailing):\n", - " mu = vol[0]\n", - " sigma = vol[1]\n", - " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", - " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", - " ##########################################################\n", - " # We define the intervals\n", - " list_of_triggers = [\"open_close\",\n", - " \"floor\",\n", - " \"trailing_stop\",\n", - " \"ltv_limit\"]\n", - " list_of_trigger_prices = [p_open_close,\n", - " floor,\n", - " p_trailing, \n", - " stgy_instance.aave.price_to_ltv_limit]\n", - " # We define/update trigger prices\n", - " for i in range(len(list_of_triggers)):\n", - " trigger_name = list_of_triggers[i]\n", - " trigger_price = list_of_trigger_prices[i]\n", - " stgy_instance.trigger_prices[trigger_name] = trigger_price\n", - "\n", - " @staticmethod\n", - " def find_oc(current_oc, ocs, vol):\n", - " mu, sigma = vol\n", - " oc_up = current_oc * (1+slippage)*(1+mu+2*sigma)\n", - " oc_down = current_oc * (1+slippage)*(1+mu-2*sigma)\n", - " distances = []\n", - " next_oc_up = []\n", - " next_oc_down = []\n", - " for i in range(len(ocs)):\n", - " oci = ocs[i]\n", - " if oc_up < oci:\n", - " next_oc_up.append(oci)\n", - " # ocs['up'].append(oci)\n", - " elif oc_down > oci:\n", - " next_oc_down.append(oci)\n", - " # ocs['down'].append(oci)\n", - " distances.append(current_oc-oci)\n", - " # If we get here then we didnt return anything, so we return the farthest oc\n", - " # Furthest down (positive distance current_oc > oci)\n", - " max_value = max(distances)\n", - " max_index = distances.index(max_value)\n", - " # Furthest up (negative distance current_oc < oci)\n", - " min_value = min(distances)\n", - " min_index = distances.index(min_value)\n", - " # print(next_oc_up)\n", - " # print(next_oc_down)\n", - " return {'up_choices': next_oc_up,\n", - " 'down_choices': next_oc_down,\n", - " 'max_distance_up': ocs[min_index],\n", - " 'max_distance_down': ocs[max_index]}\n", - " \n", - " @staticmethod\n", - " def calc_vol(last_date, data):\n", - " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", - " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", - " for i in range(len(periods_for_vol)):\n", - " N = periods_for_vol[i]\n", - " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", - " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", - " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", - " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", - " vol = [mu, sigma]\n", - " return vol\n", - " \n", - " @staticmethod\n", - " # Checking and updating data\n", - " def update_parameters(stgy_instance, new_market_price):\n", - " # AAVE\n", - " stgy_instance.aave.market_price = new_market_price\n", - " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", - " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", - " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", - " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", - " # We have to execute track_ first because we need the fees for current collateral and debt values\n", - " stgy_instance.aave.track_lend_borrow_interest()\n", - " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", - " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", - " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", - " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", - "\n", - " # DYDX\n", - " stgy_instance.dydx.market_price = new_market_price\n", - " stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc()\n", - " stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc()\n", - " stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc()\n", - " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", - " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", - "\n", - " @staticmethod\n", - " def reset_costs(stgy_instance):\n", - " # We reset the costs in order to always start in 0\n", - " stgy_instance.aave.costs = 0\n", - " stgy_instance.dydx.costs = 0\n", - " \n", - " \n", - " def find_scenario(self, stgy_instance, market_price, previous_market_price, index):\n", - " actions = self.actions_to_take(stgy_instance, market_price, previous_market_price)\n", - " self.simulate_fees(stgy_instance)\n", - " time = 0\n", - " time_aave = 0\n", - " time_dydx = 0\n", - " for action in actions:\n", - " if action == \"borrow_usdc_n_add_coll\":\n", - " time_aave = stgy_instance.aave.borrow_usdc(stgy_instance)\n", - " market_price = stgy_instance.historical_data[\"close\"][index + time_aave]\n", - " time_dydx = stgy_instance.dydx.add_collateral(stgy_instance)\n", - " time_aave = 0\n", - " elif action in stgy_instance.aave_features[\"methods\"]:\n", - " time_aave = getattr(stgy_instance.aave, action)(stgy_instance)\n", - " elif action in stgy_instance.dydx_features[\"methods\"]:\n", - " time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance)\n", - " time += time_aave + time_dydx\n", - " # print(stgy_instance.aave_features[\"methods\"])\n", - " # print(stgy_instance.dydx_features[\"methods\"])\n", - " return time\n", - " # stgy_instance.append(action)\n", - "\n", - " @staticmethod\n", - " def actions_to_take(stgy_instance, market_price, previous_market_price):\n", - " actions = []\n", - " \n", - " # Case P decreasing: \n", - " # We need to ask both P_t-1 > trigger and trigger > P_t bc if we only ask the later we will execute\n", - " # the action for all prices below trigger. Same logic for Case P increasing.\n", - " if (previous_market_price > stgy_instance.trigger_prices['open_close']) and \\\n", - " (stgy_instance.trigger_prices['open_close'] > market_price):\n", - " actions.append('open_short')\n", - " \n", - " elif (previous_market_price > stgy_instance.trigger_prices['trailing_stop']) and \\\n", - " (stgy_instance.trigger_prices['trailing_stop'] > market_price):\n", - " actions.append('open_short')\n", - " \n", - " elif (previous_market_price > stgy_instance.trigger_prices['repay_aave']) and \\\n", - " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", - " actions.append('repay_aave')\n", - " \n", - " \n", - " # Case P increasing\n", - " elif (previous_market_price < stgy_instance.trigger_prices['open_close']) and \\\n", - " (stgy_instance.trigger_prices['open_close'] < market_price):\n", - " actions.append('close_short')\n", - " elif (previous_market_price < stgy_instance.trigger_prices['trailing_stop']) and \\\n", - " (stgy_instance.trigger_prices['trailing_stop'] < market_price):\n", - " actions.append('close_short')\n", - " \n", - " return actions\n", - "\n", - " @staticmethod\n", - " def simulate_fees(stgy_instance):\n", - " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", - "\n", - " # best case\n", - " # stgy_instance.gas_fees = 1\n", - "\n", - " # stgy_instance.gas_fees = 3\n", - "\n", - " # stgy_instance.gas_fees = 6\n", - "\n", - " # worst case\n", - " stgy_instance.gas_fees = 10\n", - "\n", - " @staticmethod\n", - " def update_pnl(stgy_instance):\n", - " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", - "\n", - " @staticmethod\n", - " def add_costs(stgy_instance):\n", - " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", - " + stgy_instance.aave.costs + stgy_instance.dydx.costs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## DataDamperNPlotter Module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This module will write the results and is also used for plotting (for analysis porpuses)." - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [], - "source": [ - "class DataDamperNPlotter:\n", - " def __init__(self):\n", - " self.historical_data = None\n", - "\n", - " @staticmethod\n", - " def write_data(stgy_instance,\n", - " mkt_price_index, period,oc1,\n", - " sheet=False):\n", - " aave_instance = stgy_instance.aave\n", - " dydx_instance = stgy_instance.dydx\n", - " data_aave = []\n", - " data_dydx = []\n", - " aave_wanted_keys = [\n", - " \"market_price\",\n", - " \"entry_price\",\n", - " \"collateral_eth\",\n", - " \"usdc_status\",\n", - " \"debt\",\n", - " \"ltv\",\n", - " \"lending_rate\",\n", - " \"interest_on_lending_usd\",\n", - " \"borrowing_rate\",\n", - " \"interest_on_borrowing\",\n", - " \"lend_minus_borrow_interest\",\n", - " \"costs\"]\n", - "\n", - " for i in range(len(aave_instance.__dict__.values())):\n", - " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", - " # print(list(aave_instance.__dict__.keys())[i])\n", - " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", - " for i in range(len(dydx_instance.__dict__.values())):\n", - " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", - " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", - " data_aave.append(stgy_instance.gas_fees)\n", - " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", - " data_aave.append(stgy_instance.total_pnl)\n", - " data_aave.append(mkt_price_index)\n", - "\n", - "\n", - " data_dydx.append(stgy_instance.gas_fees)\n", - " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", - " data_dydx.append(stgy_instance.total_pnl)\n", - " data_dydx.append(mkt_price_index)\n", - " # print(interval_old.name)\n", - "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", - " if sheet == True:\n", - " gc = pygsheets.authorize(service_file=\n", - " 'stgy-1-simulations-e0ee0453ddf8.json')\n", - " sh = gc.open('aave/dydx simulations')\n", - " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", - " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", - " else:\n", - " path_to_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " with open(path_to_aave, 'a') as file:\n", - " writer = csv.writer(file, lineterminator='\\n')\n", - " writer.writerow(data_aave)\n", - " with open(path_to_dydx, 'a',\n", - " newline='', encoding='utf-8') as file:\n", - " writer = csv.writer(file, lineterminator='\\n')\n", - " writer.writerow(data_dydx)\n", - "\n", - " @staticmethod\n", - " def delete_results(stgy_instance, period, oc1):\n", - " file_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " file_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", - " os.remove(file_aave)\n", - " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", - " os.remove(file_dydx)\n", - "\n", - " @staticmethod\n", - " def add_header(stgy_instance, period, oc1):\n", - " aave_headers = [\n", - " \"market_price\",\n", - " \"entry_price\",\n", - " \"collateral_eth\",\n", - " \"usdc_status\",\n", - " \"debt\",\n", - " \"ltv\",\n", - " \"lending_rate\",\n", - " \"interest_on_lending_usd\",\n", - " \"borrowing_rate\",\n", - " \"interest_on_borrowing\",\n", - " \"lend_minus_borrow_interest\",\n", - " \"costs\",\n", - " \"gas_fees\",\n", - " \"total_costs_from_aave_n_dydx\",\n", - " \"total_stgy_pnl\",\n", - " \"index_of_mkt_price\"]\n", - " dydx_headers = [\n", - " \"market_price\",\n", - " \"entry_price\",\n", - " \"short_size\",\n", - " \"collateral\",\n", - " \"notional\",\n", - " \"equity\",\n", - " \"leverage\",\n", - " \"pnl\",\n", - " # \"price_to_liquidation\",\n", - " \"collateral_status\",\n", - " \"short_status\",\n", - " \"order_status\",\n", - " \"withdrawal_fees\",\n", - " \"funding_rates\",\n", - " \"maker_taker_fees\",\n", - " \"maker_fees_counter\",\n", - " \"costs\",\n", - " \"gas_fees\",\n", - " \"total_costs_from_aave_n_dydx\",\n", - " \"total_stgy_pnl\",\n", - " \"index_of_mkt_price\"]\n", - " \n", - " path_to_aave = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = '/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " with open(path_to_aave, 'a') as file:\n", - " writer = csv.writer(file, lineterminator='\\n')\n", - " writer.writerow(aave_headers)\n", - " with open(path_to_dydx, 'a',\n", - " newline='', encoding='utf-8') as file:\n", - " writer = csv.writer(file, lineterminator='\\n')\n", - " writer.writerow(dydx_headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Simulations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "# Track historical data\n", - "# symbol = 'ETHUSDC'\n", - "# freq = '1m'\n", - "# initial_date = \"1 Jan 2019\"\n", - "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", - "# initial_date=initial_date, save=True)\n", - "\n", - "# Load historical data if previously tracked and saved\n", - "\n", - "historical_data = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", - "# # assign data to stgy instance + define index as dates\n", - "timestamp = pd.to_datetime(historical_data['timestamp'])\n", - "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", - "historical_data.index = timestamp\n", - "#\n", - "# #######################################################\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Period of Simulations\n", - "period = [\"2020-05-01\",\"2020-11-01\"]\n", - "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", - "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", - "where $\\sigma=vol$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First we calculate weighted vol\n", - "last_date = \"2021-06-01\"\n", - "slippage = 0.0005\n", - "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", - "data = historical_data.loc[:last_date][-periods_for_vol[0]-3*60:-3*60]\n", - "for i in range(len(periods_for_vol)):\n", - " N = periods_for_vol[i]\n", - " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", - " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", - " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", - " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", - "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", - "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", - "print(\"weighted mu: \", str(mu*100)+'%')\n", - "print(\"weighted sigmas: \", str(vol*100)+'%')\n", - "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", - "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+2*vol)*100-100)+'%')\n", - "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vol = sigma_2\n", - "mu = mu_2\n", - "print(\"weighted sigmas: \", str(vol*100)+'%')\n", - "print(\"avg movement: (1+mu+2vol): \", str((1+mu+2*vol)*100-100)+'%')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# normal_std = std\n", - "# medium_std = 2*std\n", - "# high_std = 4*std\n", - "# extreme_std = 6*std\n", - "# normal_std, medium_std, high_std, extreme_std" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's find such a relevant price manually by taking a look at the price plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Period of Simulations\n", - "period = [\"2020-05-31\",\"2020-06-07\"]\n", - "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - "\n", - "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", - "axs.plot(data['close'], color='tab:blue', label='market price')\n", - "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", - "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", - "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", - "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", - "axs.grid()\n", - "axs.legend(loc='lower left')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we define a function that will\n", - "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices\n", - "- Create a new directory \"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", - "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit \n", - "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", - "- Run through the code executing everything as discussed in the dev doc.\n", - "\n", - "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def run_sim(period, open_close, slippage, max_txs, L, trailing):\n", - " global ocs\n", - " # Initialize everything\n", - " with open(\"/home/agustin/Git-Repos/HedgingScripts/files/StgyApp_config.json\") as json_file:\n", - " config = json.load(json_file)\n", - "\n", - " # Initialize stgyApp\n", - " stgy = StgyApp(config)\n", - " # Period of Simulations\n", - " # period = [\"2019-09-01\",\"2019-12-31\"]\n", - " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - " # For vol updates we take all data up to the last date\n", - " stgy.launch(config)\n", - " # First we calculate weighted vol\n", - " last_date = period[1]+' 00:00:00'\n", - " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", - " mu, sigma = vol\n", - " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", - " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", - " # Now we define prices \n", - " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", - " # We create five equidistant OCs\n", - " oc1 = open_close\n", - " # oc2 = oc1 * (1+6/2/100)\n", - " ocs = [oc1]\n", - " for i in range(1,7):\n", - " globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", - " ocs.append(globals()[\"oc\"+str(i+1)])\n", - " print(ocs)\n", - " # But we start with the first oc1\n", - " stgy.trigger_prices['open_close'] = oc1\n", - " \n", - " # print(\"Volatility:\", vol)\n", - " # print(\"Floor:\", stgy.trigger_prices['floor'])\n", - " # print(\"Open_close1:\", oc1)\n", - " # print(\"Open_close2:\", oc2)\n", - " # print(\"1-OC2/OC1 - 1:\", 1-oc2/oc1)\n", - " #########################\n", - " # Save historical data with trigger prices and thresholds loaded\n", - " # checking if the directory demo_folder \n", - " # exist or not.\n", - " if not os.path.exists(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", - " # if the demo_folder directory is not present \n", - " # then create it.\n", - " os.makedirs(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", - " stgy.historical_data.to_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", - " % (period[0], period[1], open_close))\n", - " #########################\n", - " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", - "\n", - " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", - " # As we calculate vol using first week of data, we initialize simulations from that week on\n", - " initial_index = 1\n", - "\n", - " # Stk eth\n", - " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", - "\n", - " # AAVE\n", - " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", - "\n", - " # What is the price at which we place the collateral in AAVE given our initial_index?\n", - " stgy.aave.entry_price = stgy.aave.market_price\n", - " # We place 90% of staked as collateral and save 10% as a reserve margin\n", - " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", - " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", - " stgy.reserve_margin_eth = stgy.stk * 0.1\n", - " # We calculate collateral and reserve current value\n", - " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", - " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", - "\n", - " # What is the usdc_status for our initial_index?\n", - " stgy.aave.usdc_status = True\n", - " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", - " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", - " # debt_initial\n", - " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", - " # stgy.total_costs = 104\n", - "\n", - " # DyDx\n", - " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", - " stgy.dydx.collateral = stgy.aave.debt\n", - " stgy.dydx.equity = stgy.dydx.equity_calc()\n", - " stgy.dydx.collateral_status = True\n", - " \n", - " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", - " if (stgy.dydx.market_price <= stgy.trigger_prices['open_close']):\n", - " stgy.dydx.open_short(stgy)\n", - " #########################\n", - " # Clear previous csv data for aave and dydx\n", - " stgy.data_dumper.delete_results(stgy, period, open_close)\n", - " #########################\n", - " # add header to csv of aave and dydx\n", - " stgy.data_dumper.add_header(stgy, period, open_close)\n", - " ##################################\n", - " # Run through dataset\n", - " #########################\n", - " # import time\n", - " # # run simulations\n", - " # starttime = time.time()\n", - " # print('starttime:', starttime)\n", - " # for i in range(initial_index, len(stgy.historical_data)):\n", - " i = initial_index\n", - "\n", - " maker_fees_counter = []\n", - " \n", - " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", - " while(i < len(stgy.historical_data)):\n", - " # for i in range(initial_index, len(stgy.historical_data)):\n", - " # pass\n", - " # We reset costs in every instance\n", - " stgy.parameter_manager.reset_costs(stgy)\n", - " market_price = stgy.historical_data[\"close\"][i]\n", - " previous_price = stgy.historical_data[\"close\"][i-1]\n", - " #########################\n", - " # Update parameters\n", - " # First we update everything in order to execute scenarios with updated values\n", - " # We have to update\n", - " # AAVE: market_price, lending and borrowing fees (and the diference),\n", - " # debt value, collateral value and ltv value\n", - " # DyDx: market_price, notional, equity, leverage and pnl\n", - " stgy.parameter_manager.update_parameters(stgy, market_price)\n", - " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", - " # and we execute all the actions involved between both (current and previous prices)\n", - " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", - " ############################## \n", - " # We update trailing\n", - " # Everytime price moves down more than trailing we update trailing_stop\n", - " if market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']:\n", - " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", - " # If price moves above trailing we move trailing up in order to save that profit\n", - " # Is important to change trailing after finding scenarios (because we need to close the short first)\n", - " elif market_price*(1+trailing) > stgy.trigger_prices['trailing_stop']:\n", - " if market_price >= oc4:\n", - " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", - " else:\n", - " stgy.trigger_prices['trailing_stop'] = market_price\n", - " ################################\n", - " ################################\n", - " # OC LOGIC\n", - " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", - " if market_price > oc1:\n", - " stgy.trigger_prices['open_close'] = oc1\n", - "\n", - " \n", - " # We update vol and ocs if short_status = False\n", - " # if not stgy.dydx.short_status:\n", - " # current_date = list(stgy.historical_data.index)[i]\n", - " # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol)\n", - " # mu, sigma = vol\n", - " # oc1 = floor * (1+slippage) * (1+mu+2*sigma)\n", - " # ocs = [oc1]\n", - " # for i in range(1,5):\n", - " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", - " # ocs.append(globals()[\"oc\"+str(i+1)])\n", - "\n", - " \n", - " # If we executed more txs than hat_L*20 then we change to K_2\n", - " if (stgy.dydx.maker_fees_counter >= max_txs):\n", - " # stgy.historical_data = stgy.historical_data_OC2\n", - " # print(stgy.dydx.maker_fees_counter)\n", - " current_date = list(stgy.historical_data.index)[i]\n", - " current_oc = stgy.trigger_prices['open_close']\n", - " vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", - " ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", - " # if short = open and if there are up_choices available, we take the last option (the furthest)\n", - " # if there isn't options we take max_distance\n", - " # random.seed(4)\n", - " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " # 'txs': stgy.dydx.maker_fees_counter, \n", - " # # 'index': i,\n", - " # 'date': str(stgy.historical_data.index[i])})\n", - " if not stgy.dydx.short_status:\n", - " if stgy.trigger_prices['open_close'] == oc1:\n", - " stgy.trigger_prices['open_close'] = oc4\n", - " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", - " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", - " elif stgy.dydx.short_status:\n", - " if len(ocs_choices['up_choices']) != 0:\n", - " stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", - " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", - " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", - " # If we didnt change oc we dont clean maker_fees_counter\n", - " if current_oc != stgy.trigger_prices['open_close']:\n", - " maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " 'txs': stgy.dydx.maker_fees_counter, \n", - " # 'index': i,\n", - " 'date': str(stgy.historical_data.index[i])})\n", - " stgy.dydx.maker_fees_counter = 0\n", - " ########################\n", - " ########################\n", - " # Funding rates\n", - " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", - " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", - " # Calling it before find_scenarios will overwrite the funding by 0\n", - " # We have to check all the indexes between old index i and next index i+time_used\n", - " # for index in range(i, i+time_used):\n", - " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", - " stgy.dydx.add_funding_rates()\n", - " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", - " #########################\n", - " # Add costs\n", - " stgy.parameter_manager.add_costs(stgy)\n", - " stgy.parameter_manager.update_pnl(stgy)\n", - " #########################\n", - " # Write data\n", - " # We write the data into the google sheet or csv file acording to sheet value\n", - " # (sheet = True --> sheet, sheet = False --> csv)\n", - " stgy.data_dumper.write_data(stgy,\n", - " i, period, open_close,\n", - " sheet=False)\n", - " #########################\n", - " # we increment index by the time consumed in executing actions\n", - " # i += time_used\n", - " i += 1\n", - " return maker_fees_counter" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", - " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "periods_n_open_close = [[[\"2020-05-15\",\"2020-06-15\"],240]]" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[240, 238.8, 237.60600000000002, 236.41797, 235.23588014999999, 234.05970074925, 232.88940224550376]\n" - ] - } - ], - "source": [ - "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", - "L = 5 * 0.07\n", - "trailing = 0.01\n", - "for period_n_open_close in periods_n_open_close:\n", - " period = period_n_open_close[0]\n", - " open_close = period_n_open_close[1]\n", - " slippage = 0.0005\n", - " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing)" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'oc': 236.41797, 'txs': 8, 'date': '2020-05-31 07:45:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-01 16:18:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-03 10:30:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-03 16:32:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-03 17:16:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-04 10:39:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-05 22:10:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-06 02:07:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-06 08:06:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-07 19:46:00'},\n", - " {'oc': 236.41797, 'txs': 8, 'date': '2020-06-11 17:00:00'},\n", - " {'oc': 240, 'txs': 9, 'date': '2020-06-12 09:19:00'}]" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "maker_fees_counter" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-93714.29797685935" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", - "dydx_results = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/\" + directory)\n", - "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2020-05-01'" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "period" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2019-09-01 00:00:00'" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "str(historical_data.index[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "returns = data['close'].pct_change().dropna()\n", - "log_returns = np.log(data['close']) \\\n", - " - np.log(data['close'].shift(1))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", - "mu_log_returns = log_returns.mean()\n", - "mu_abs_log_returns = abs(log_returns).mean()\n", - "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", - "mu_log_returns_max = log_returns.max()\n", - "mu_log_returns_min = log_returns.min()\n", - "mu_returns = returns.mean()\n", - "mu_abs_returns = abs(returns).mean()\n", - "mu_returns_max = returns.max()\n", - "mu_returns_min = returns.min()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mu_returns_max, mu_returns_min" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "K = 3\n", - "condition = (mu_abs_log_returns-K*std_ema_log_returns" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Period of Simulations\n", - "period = periods_n_open_close[0][0]\n", - "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", - "axs.plot(data['close'], color='tab:blue', label='market price')\n", - "axs.axhline(y=floor, color='green', linestyle='--', label='floor')\n", - "for i in range(len(ocs)):\n", - " axs.axhline(y=ocs[i], color='red', linestyle='--', label='oc'+str(i))\n", - "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", - "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", - "axs.grid()\n", - "axs.legend(loc='lower left')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "timestamp\n", - "2020-06-01 00:17:00 233.48\n", - "2020-06-01 00:18:00 233.48\n", - "2020-06-01 01:29:00 233.48\n", - "2020-06-01 01:30:00 233.48\n", - "2020-06-01 01:31:00 233.48\n", - "2020-06-01 01:32:00 233.48\n", - "2020-06-02 16:00:00 233.48\n", - "Name: close, dtype: float64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['close'].loc[data['close']==233.48]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define a function to count how many times a given price is cross given a dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def cross_counter(data_set, price):\n", - " crossed_down = 0\n", - " crossed_up = 0\n", - " index_up = []\n", - " index_down = []\n", - " for index in range(1,len(data_set)):\n", - " previous_price = data_set['close'][index-1]\n", - " current_price = data_set['close'][index]\n", - " if previous_price <= price < current_price:\n", - " crossed_up += 1\n", - " index_up.append(index-1)\n", - " elif previous_price >= price > current_price:\n", - " crossed_down += 1\n", - " index_down.append(index-1)\n", - " return {'down':\n", - " {'crossed_down': crossed_down,\n", - " 'index_down': index_down},\n", - " 'up':\n", - " {'crossed_up': crossed_up,\n", - " 'index_up': index_up}}" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Period of Simulations\n", - "period = [\"2020-05-01\",\"2020-09-01\"]\n", - "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - "price = 240" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", - "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", - "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", - "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", - "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", - "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", - "axs.grid()\n", - "axs.legend(loc='lower left')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crosses = cross_counter(data_set, 240)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "312" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "dydx_results = pd.read_csv(\"/home/agustin/Git-Repos/HedgingScripts/files/Tests/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "market_price 176910\n", - "I_current 176910\n", - "I_old 176910\n", - "entry_price 53220\n", - "short_size 53220\n", - "collateral 176910\n", - "notional 53375\n", - "equity 176910\n", - "leverage 53375\n", - "pnl 53066\n", - "collateral_status 176910\n", - "short_status 53220\n", - "order_status 123690\n", - "withdrawal_fees 176910\n", - "funding_rates 176910\n", - "maker_taker_fees 133516\n", - "maker_fees_counter 133516\n", - "costs 421\n", - "gas_fees 176910\n", - "total_costs_from_aave_n_dydx 133516\n", - "total_stgy_pnl 176910\n", - "index_of_mkt_price 176910\n", - "dtype: int64" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dydx_results.astype(bool).sum(axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "def count_maker_fees_increment(data_set):\n", - " index_of_maker_fee = []\n", - " for index in range(1,len(data_set)):\n", - " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", - " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", - " if previous_maker_fee_counter < current_maker_fee_counter:\n", - " index_of_maker_fee.append(index)\n", - " return {'indexes': index_of_maker_fee}" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [], - "source": [ - "matches_up = 0\n", - "matches_down = 0\n", - "for index_up in crosses['up']['index_up']:\n", - " if index_up in results_maker_fee_counter['indexes']:\n", - " matches_up += 1\n", - "for index_down in crosses['down']['index_down']:\n", - " if index_down in results_maker_fee_counter['indexes']:\n", - " matches_down += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(155, 136, 291)" - ] - }, - "execution_count": 170, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "matches_up, matches_down, matches_up + matches_down" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(156, 156)" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", - "- entry_price and short_size can be found at index_up -1\n", - "- close_price is market_price in index = index_up" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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market_priceI_currentI_oldshort_sizeentry_pricepnlmaker_fees_countertotal_stgy_pnl
43393240.70inftyminus_infty0.0000.000.00000-2.879624
43394239.74minus_inftyinfty-4334.634239.740.00001-522.470891
43395240.94inftyminus_infty0.0000.00-5201.56082-6246.223689
43396240.86inftyminus_infty0.0000.000.00002-6246.222332
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" - ], - "text/plain": [ - " market_price I_current I_old short_size entry_price \\\n", - "43393 240.70 infty minus_infty 0.000 0.00 \n", - "43394 239.74 minus_infty infty -4334.634 239.74 \n", - "43395 240.94 infty minus_infty 0.000 0.00 \n", - "43396 240.86 infty minus_infty 0.000 0.00 \n", - "\n", - " pnl maker_fees_counter total_stgy_pnl \n", - "43393 0.0000 0 -2.879624 \n", - "43394 0.0000 1 -522.470891 \n", - "43395 -5201.5608 2 -6246.223689 \n", - "43396 0.0000 2 -6246.222332 " - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "i = 1\n", - "index = crosses['up']['index_up'][i]\n", - "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [ - "diff = []\n", - "cost = []\n", - "# we dont start the loop at i = 0 because the data_set started below open_close\n", - "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", - "for i in range(1,len(crosses['up']['index_up'])):\n", - " index_up = crosses['up']['index_up'][i]\n", - " if index_up in results_maker_fee_counter['indexes']:\n", - " entry_price = dydx_results.iloc[index-1]['entry_price']\n", - " close_price = dydx_results.iloc[index]['market_price']\n", - " short_size = dydx_results.iloc[index-1]['short_size']\n", - " diff.append(close_price-entry_price)\n", - " cost.append(short_size * (close_price-entry_price))" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.1999999999999886, -5201.560799999951)" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(diff), np.mean(cost)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/jupyter-lab/Simulations_prices_approach.ipynb b/jupyter-lab/Simulations_prices_approach.ipynb new file mode 100644 index 0000000..7067232 --- /dev/null +++ b/jupyter-lab/Simulations_prices_approach.ipynb @@ -0,0 +1,2250 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas 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already satisfied: idna<4,>=2.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (3.4)\n" + ] + } + ], + "source": [ + "!pip install pandas scipy pygsheets matplotlib\n", + "\n", + "import os\n", + "import pygsheets\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import csv\n", + "import pandas as pd\n", + "import numpy as np\n", + "import json\n", + "import math\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## StgyApp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class StgyApp(object):\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.stk = config[\"stk\"]\n", + " self.total_costs_from_aave_n_dydx = 0\n", + " self.total_pnl = 0\n", + " self.gas_fees = 0\n", + "\n", + " # prices and intervals\n", + " self.trigger_prices = {}\n", + " self.intervals = {}\n", + "\n", + " # clients for data\n", + " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", + " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", + " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", + " # self.historical_data =\n", + "\n", + " # We create attributes to fill later\n", + " self.aave = None\n", + " self.aave_features = None\n", + " self.aave_rates = None\n", + "\n", + " self.dydx = None\n", + " self.dydx_features = None\n", + "\n", + " # self.volatility_calculator = None\n", + "\n", + " self.parameter_manager = ParameterManager()\n", + "\n", + " self.historical_data = None\n", + "\n", + " self.data_dumper = DataDamperNPlotter()\n", + "\n", + " def launch(self, config):\n", + " # self.call_binance_data_loader()\n", + " self.initialize_aave(config['initial_parameters']['aave'])\n", + " self.initialize_dydx(config['initial_parameters']['dydx'])\n", + "\n", + " # call clients functions\n", + " def get_historical_data(self, symbol, freq,\n", + " initial_date, save):\n", + " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=save)\n", + " # self.historical_data = eth_historical\n", + " self.historical_data = eth_historical[\"close\"]\n", + " for i in range(len(self.historical_data)):\n", + " self.historical_data[i] = float(self.historical_data[i])\n", + " # self.load_intervals()\n", + "\n", + " # initialize classes\n", + " def initialize_aave(self, config):\n", + " # We initialize aave and dydx classes instances\n", + " self.aave = Aave(config)\n", + " # We load methods and attributes for aave and dydx to use later\n", + " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", + " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", + " \"keys\": list(self.aave.__dict__.keys())}}\n", + " # We create an attribute for historical data\n", + " self.aave_historical_data = []\n", + "\n", + " def initialize_dydx(self, config):\n", + " self.dydx = Dydx(config)\n", + " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", + " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", + " \"keys\": list(self.dydx.__dict__.keys())}}\n", + " self.dydx_historical_data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aave and DyDx modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Aave" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Aave(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert self.dydx_class_instance == isinstance(dydx)\n", + " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", + " self.market_price = config['market_price']\n", + "\n", + " self.entry_price = config['entry_price']\n", + "\n", + " self.collateral_eth_initial = config['collateral_eth']\n", + " self.collateral_eth = config['collateral_eth']\n", + " self.collateral_usdc = config['collateral_usdc']\n", + "\n", + " self.reserve_margin_eth = 0\n", + " self.reserve_margin_usdc = 0\n", + "\n", + " self.borrowed_percentage = config['borrowed_pcg']\n", + " self.usdc_status = config['usdc_status']\n", + "\n", + " self.debt = config['debt']\n", + " self.debt_initial = config['debt']\n", + "\n", + " self.ltv = config['ltv']\n", + " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", + "\n", + " self.lending_rate = 0\n", + " self.lending_rate_hourly = 0\n", + " self.interest_on_lending_eth = 0 # aggregated fees\n", + " self.interest_on_lending_usd = 0\n", + " self.lending_fees_eth = 0 # fees between last 2 prices\n", + " self.lending_fees_usd = 0\n", + "\n", + " self.borrowing_rate = 0\n", + " self.borrowing_rate_hourly = 0\n", + " self.interest_on_borrowing = 0 # aggregated fees\n", + " self.borrowing_fees = 0 # fees between last 2 prices\n", + "\n", + " self.lend_minus_borrow_interest = 0\n", + "\n", + " self.costs = 0\n", + " # self.historical = pd.DataFrame()\n", + " # self.dydx_class_instance = dydx_class_instance\n", + " # self.staked_in_protocol = stk\n", + "\n", + " # def update_costs(self):\n", + " # \"\"\"\n", + " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " # \"\"\"\n", + " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", + " # self.costs = self.costs - self.lend_minus_borrow_interest\n", + "\n", + " def collateral_usd(self):\n", + " return self.collateral_eth * self.market_price\n", + "\n", + " def update_debt(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.debt = self.debt + self.borrowing_fees\n", + "\n", + " def update_collateral(self):\n", + " \"\"\"\n", + " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", + " self.collateral_usdc = self.collateral_usd()\n", + "\n", + " def track_lend_borrow_interest(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", + " in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", + "\n", + " def lending_fees_calc(self, freq):\n", + " self.simulate_lending_rate()\n", + " self.lending_rate_freq = self.lending_rate / freq\n", + "\n", + " # fees from lending are added to collateral? YES\n", + " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", + " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", + " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", + " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", + " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", + "\n", + " def borrowing_fees_calc(self, freq):\n", + " self.simulate_borrowing_rate()\n", + " self.borrowing_rate_freq = self.borrowing_rate / freq\n", + "\n", + " # fees from borrow are added to debt? YES\n", + " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", + " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", + " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", + "\n", + " def simulate_lending_rate(self):\n", + " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", + "\n", + " # best case\n", + " # self.lending_rate = 1.5 / 100\n", + "\n", + " # worst case\n", + " self.lending_rate = 0.5 / 100\n", + "\n", + " def simulate_borrowing_rate(self):\n", + " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", + "\n", + " # best case\n", + " # self.borrowing_rate = 1.5/100\n", + "\n", + " # worst case\n", + " self.borrowing_rate = 2.5/100\n", + "\n", + " def ltv_calc(self):\n", + " if self.collateral_usd() == 0:\n", + " return 0\n", + " else:\n", + " return self.debt / self.collateral_usd()\n", + "\n", + " def price_to_liquidation(self, dydx_class_instance):\n", + " return self.entry_price - (dydx_class_instance.pnl()\n", + " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", + "\n", + " def price_to_ltv_limit_calc(self):\n", + " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", + "\n", + " def buffer_for_repay(self):\n", + " return 0.01\n", + "\n", + " def ltv_limit(self):\n", + " return 0.5\n", + "\n", + " # Actions to take\n", + " def return_usdc(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " time = 0\n", + " if self.usdc_status:\n", + " # simulate 2min delay for tx\n", + " # update parameters\n", + " # AAVE parameters\n", + " self.usdc_status = False\n", + " # self.collateral_eth = 0\n", + " # self.collateral_usdc = 0\n", + " self.debt = 0\n", + " self.ltv = 0\n", + " self.price_to_ltv_limit = 0\n", + " # self.lending_rate = 0\n", + " # self.borrowing_rate = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def repay_aave(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " dydx_class_instance = stgy_instance.dydx\n", + " # aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " #\n", + " time = 0\n", + " if self.usdc_status:\n", + " # update parameters\n", + " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price)\n", + " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", + "\n", + " # pnl_for_debt = dydx_class_instance.pnl()\n", + " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", + " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price)\n", + " self.debt = self.debt - pnl_for_debt\n", + " self.ltv = self.ltv_calc()\n", + "\n", + " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " dydx_class_instance.short_size = new_short_size\n", + " dydx_class_instance.notional = dydx_class_instance.notional_calc()\n", + " dydx_class_instance.equity = dydx_class_instance.equity_calc()\n", + " dydx_class_instance.leverage = dydx_class_instance.leverage_calc()\n", + " dydx_class_instance.pnl = dydx_class_instance.pnl_calc()\n", + " # dydx_class_instance.price_to_liquidation = \\\n", + " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", + "\n", + " # fees\n", + " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + " dydx_class_instance.simulate_maker_taker_fees()\n", + " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", + " dydx_class_instance.costs = dydx_class_instance.costs \\\n", + " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", + " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + "\n", + " # Note that a negative self.debt is actually a profit\n", + " # We update the parameters\n", + " if self.debt > 0:\n", + " self.usdc_status = True\n", + " else:\n", + " self.usdc_status = False\n", + " # simulate 2min delay for tx\n", + " time = 1\n", + " return time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### DyDx" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Dydx(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert aave_class == isinstance(aave)\n", + " self.market_price = config['market_price']\n", + " \n", + " self.entry_price = config['entry_price']\n", + " self.short_size = config['short_size']\n", + " self.collateral = config['collateral']\n", + " self.notional = config['notional']\n", + " self.equity = config['equity']\n", + " self.leverage = config['leverage']\n", + " self.pnl = config['pnl']\n", + " # self.price_to_liquidation = config['price_to_liquidation']\n", + " self.collateral_status = config['collateral_status']\n", + " self.short_status = config['short_status']\n", + " self.order_status = True\n", + " self.withdrawal_fees = 0.01/100\n", + " self.funding_rates = 0\n", + " self.maker_taker_fees = 0\n", + " self.maker_fees_counter = 0\n", + " self.costs = 0\n", + "\n", + " # auxiliary functions\n", + " def pnl_calc(self):\n", + " return self.short_size * (self.market_price-self.entry_price)\n", + "\n", + " def notional_calc(self):\n", + " return abs(self.short_size)*self.market_price\n", + "\n", + " def equity_calc(self):\n", + " return self.collateral + self.pnl_calc()\n", + "\n", + " def leverage_calc(self):\n", + " if self.equity_calc() == 0:\n", + " return 0\n", + " else:\n", + " return self.notional_calc() / self.equity_calc()\n", + "\n", + " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", + " return self.entry_price \\\n", + " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", + "\n", + " @staticmethod\n", + " def price_to_liquidation_calc(dydx_client_class_instance):\n", + " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", + "\n", + " def add_funding_rates(self):\n", + " self.simulate_funding_rates()\n", + " self.costs = self.costs - self.funding_rates * self.notional\n", + "\n", + " def simulate_funding_rates(self):\n", + " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", + "\n", + " # best case\n", + " # self.funding_rates = 0.0075 / 100\n", + "\n", + " # average -0.00443%\n", + "\n", + " # worst case\n", + " self.funding_rates = -0.0075 / 100\n", + "\n", + " def simulate_maker_taker_fees(self):\n", + " # We add a counter for how many times we call this function\n", + " # i.e. how many times we open and close the short\n", + " self.maker_fees_counter += 1\n", + " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", + " \n", + " # maker fees\n", + " self.maker_taker_fees = 0.05 / 100 # <1M\n", + " # self.maker_taker_fees = 0.04 / 100 # <5M\n", + " # self.maker_taker_fees = 0.035 / 100 # <10M\n", + " # self.maker_taker_fees = 0.03 / 100 # <50M\n", + " # self.maker_taker_fees = 0.025 / 100 # <200M\n", + " # self.maker_taker_fees = 0.02 / 100 # >200M\n", + "\n", + " # Actions to take\n", + " def remove_collateral(self, stgy_instance):\n", + " self.cancel_order()\n", + " time = 0\n", + " if self.collateral_status:\n", + " self.collateral_status = False\n", + " withdrawal_fees = self.collateral * self.withdrawal_fees\n", + " self.collateral = 0\n", + " # self.price_to_liquidation = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + withdrawal_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + "\n", + " def open_short(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " if (not self.short_status) and self.order_status:\n", + " self.short_status = True\n", + " # dydx parameters\n", + " # if self.market_price <= stgy_instance.trigger_prices['floor']:\n", + " # print(\"CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['floor'] - self.market_price)\n", + "\n", + " # if self.market_price <= stgy_instance.trigger_prices['open_close']:\n", + " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", + " self.entry_price = self.market_price\n", + " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + "\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", + " aave_class_instance)\n", + " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", + " i = 0\n", + " while stgy_instance.trigger_prices['ltv_limit'] > stgy_instance.trigger_prices['repay_aave']:\n", + " print(\"CAUTION: P_ltv > P_repay\")\n", + " print(\"Difference of: \", stgy_instance.trigger_prices['ltv_limit'] - stgy_instance.trigger_prices['repay_aave'])\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", + " i += 1\n", + " print(\"P_repay defined to repay 0.5 (half) of debt. This logic was repeated\" + str(i) + \" times.\")\n", + " self.order_status = False\n", + " return 0\n", + "\n", + " def close_short(self, stgy_instance):\n", + " if self.short_status:\n", + " # Next if is to move up the threshold if we didnt execute at exactly open_close\n", + " # if self.market_price >= stgy_instance.trigger_prices['open_close']:\n", + " # # new_open_close = self.market_price\n", + " # print(\"CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!\")\n", + " # print(\"Difference of: \", self.market_price - stgy_instance.trigger_prices['open_close'])\n", + " # stgy_instance.target_prices['open_close'] = self.market_price\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " self.pnl = self.pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.entry_price = 0\n", + " self.short_status = False\n", + " self.short_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + " self.place_order(stgy_instance.trigger_prices['open_close'])\n", + " return 0\n", + "\n", + " def place_order(self, price):\n", + " self.order_status = True\n", + " # self.\n", + "\n", + " def cancel_order(self):\n", + " self.order_status = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## ParameterManager Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterManager(object):\n", + " # auxiliary functions\n", + " @staticmethod\n", + " def define_target_prices(stgy_instance, slippage, vol, floor, trailing):\n", + " mu = vol[0]\n", + " sigma = vol[1]\n", + " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", + " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", + " ##########################################################\n", + " # We define the intervals\n", + " list_of_triggers = [\"open_close\",\n", + " \"floor\",\n", + " \"trailing_stop\",\n", + " \"ltv_limit\"]\n", + " list_of_trigger_prices = [p_open_close,\n", + " floor,\n", + " p_trailing, \n", + " stgy_instance.aave.price_to_ltv_limit]\n", + " # We define/update trigger prices\n", + " for i in range(len(list_of_triggers)):\n", + " trigger_name = list_of_triggers[i]\n", + " trigger_price = list_of_trigger_prices[i]\n", + " stgy_instance.trigger_prices[trigger_name] = trigger_price\n", + "\n", + " @staticmethod\n", + " def find_oc(current_oc, ocs, vol):\n", + " mu, sigma = vol\n", + " oc_up = current_oc * (1+slippage)*(1+mu+2*sigma)\n", + " oc_down = current_oc * (1+slippage)*(1+mu-2*sigma)\n", + " distances = []\n", + " next_oc_up = []\n", + " next_oc_down = []\n", + " for i in range(len(ocs)):\n", + " oci = ocs[i]\n", + " if oc_up < oci:\n", + " next_oc_up.append(oci)\n", + " # ocs['up'].append(oci)\n", + " elif oc_down > oci:\n", + " next_oc_down.append(oci)\n", + " # ocs['down'].append(oci)\n", + " distances.append(current_oc-oci)\n", + " # If we get here then we didnt return anything, so we return the farthest oc\n", + " # Furthest down (positive distance current_oc > oci)\n", + " max_value = max(distances)\n", + " max_index = distances.index(max_value)\n", + " # Furthest up (negative distance current_oc < oci)\n", + " min_value = min(distances)\n", + " min_index = distances.index(min_value)\n", + " # print(next_oc_up)\n", + " # print(next_oc_down)\n", + " return {'up_choices': next_oc_up,\n", + " 'down_choices': next_oc_down,\n", + " 'max_distance_up': ocs[min_index],\n", + " 'max_distance_down': ocs[max_index]}\n", + " \n", + " @staticmethod\n", + " def calc_vol(last_date, data):\n", + " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " vol = [mu, sigma]\n", + " return vol\n", + " \n", + " @staticmethod\n", + " # Checking and updating data\n", + " def update_parameters(stgy_instance, new_market_price):\n", + " # AAVE\n", + " stgy_instance.aave.market_price = new_market_price\n", + " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", + " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", + " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", + " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", + " # We have to execute track_ first because we need the fees for current collateral and debt values\n", + " stgy_instance.aave.track_lend_borrow_interest()\n", + " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", + " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", + " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", + " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", + "\n", + " # DYDX\n", + " stgy_instance.dydx.market_price = new_market_price\n", + " stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc()\n", + " stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc()\n", + " stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc()\n", + " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", + " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", + "\n", + " @staticmethod\n", + " def reset_costs(stgy_instance):\n", + " # We reset the costs in order to always start in 0\n", + " stgy_instance.aave.costs = 0\n", + " stgy_instance.dydx.costs = 0\n", + " \n", + " \n", + " def find_scenario(self, stgy_instance, market_price, previous_market_price, index):\n", + " actions = self.actions_to_take(stgy_instance, market_price, previous_market_price)\n", + " self.simulate_fees(stgy_instance)\n", + " time = 0\n", + " time_aave = 0\n", + " time_dydx = 0\n", + " for action in actions:\n", + " if action == \"borrow_usdc_n_add_coll\":\n", + " time_aave = stgy_instance.aave.borrow_usdc(stgy_instance)\n", + " market_price = stgy_instance.historical_data[\"close\"][index + time_aave]\n", + " time_dydx = stgy_instance.dydx.add_collateral(stgy_instance)\n", + " time_aave = 0\n", + " elif action in stgy_instance.aave_features[\"methods\"]:\n", + " time_aave = getattr(stgy_instance.aave, action)(stgy_instance)\n", + " elif action in stgy_instance.dydx_features[\"methods\"]:\n", + " time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance)\n", + " time += time_aave + time_dydx\n", + " # print(stgy_instance.aave_features[\"methods\"])\n", + " # print(stgy_instance.dydx_features[\"methods\"])\n", + " return time\n", + " # stgy_instance.append(action)\n", + "\n", + " @staticmethod\n", + " def actions_to_take(stgy_instance, market_price, previous_market_price):\n", + " actions = []\n", + " \n", + " # Case P decreasing: \n", + " # We need to ask both P_t-1 > trigger and trigger > P_t bc if we only ask the later we will execute\n", + " # the action for all prices below trigger. Same logic for Case P increasing.\n", + " if (previous_market_price > stgy_instance.trigger_prices['open_close']) and \\\n", + " (stgy_instance.trigger_prices['open_close'] > market_price):\n", + " actions.append('open_short')\n", + " \n", + " elif (previous_market_price >= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " (stgy_instance.trigger_prices['trailing_stop'] > market_price):\n", + " actions.append('open_short')\n", + " \n", + " elif (previous_market_price > stgy_instance.trigger_prices['repay_aave']) and \\\n", + " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", + " actions.append('repay_aave')\n", + " \n", + " \n", + " # Case P increasing\n", + " elif (previous_market_price < stgy_instance.trigger_prices['open_close']) and \\\n", + " (stgy_instance.trigger_prices['open_close'] < market_price):\n", + " actions.append('close_short')\n", + " elif (previous_market_price <= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " (stgy_instance.trigger_prices['trailing_stop'] < market_price):\n", + " actions.append('close_short')\n", + " \n", + " return actions\n", + "\n", + " @staticmethod\n", + " def simulate_fees(stgy_instance):\n", + " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", + "\n", + " # best case\n", + " # stgy_instance.gas_fees = 1\n", + "\n", + " # stgy_instance.gas_fees = 3\n", + "\n", + " # stgy_instance.gas_fees = 6\n", + "\n", + " # worst case\n", + " stgy_instance.gas_fees = 10\n", + "\n", + " @staticmethod\n", + " def update_pnl(stgy_instance):\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", + "\n", + " @staticmethod\n", + " def add_costs(stgy_instance):\n", + " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", + " + stgy_instance.aave.costs + stgy_instance.dydx.costs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## DataDamperNPlotter Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module will write the results and is also used for plotting (for analysis porpuses)." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "class DataDamperNPlotter:\n", + " def __init__(self):\n", + " self.historical_data = None\n", + "\n", + " @staticmethod\n", + " def write_data(stgy_instance, previous_price,\n", + " date, period,oc1,\n", + " sheet=False):\n", + " aave_instance = stgy_instance.aave\n", + " dydx_instance = stgy_instance.dydx\n", + " data_aave = []\n", + " data_dydx = []\n", + " aave_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\"]\n", + " dydx_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"short_size\",\n", + " # \"collateral\",\n", + " \"notional\",\n", + " # \"equity\",\n", + " # \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " # \"collateral_status\",\n", + " \"short_status\",\n", + " # \"order_status\",\n", + " # \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " # \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\"]\n", + " # \"gas_fees\"]\n", + "\n", + " \n", + " data_aave.append(date)\n", + " data_dydx.append(date)\n", + " for i in range(len(aave_instance.__dict__.values())):\n", + " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", + " # print(list(aave_instance.__dict__.keys())[i])\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " for i in range(len(dydx_instance.__dict__.values())):\n", + " if list(dydx_instance.__dict__.keys())[i] in dydx_wanted_keys:\n", + " if list(dydx_instance.__dict__.keys())[i] == \"market_price\":\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " data_dydx.append(previous_price)\n", + " data_dydx.append(stgy_instance.trigger_prices['open_close'])\n", + " data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " else:\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", + " data_aave.append(stgy_instance.gas_fees)\n", + " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_aave.append(stgy_instance.total_pnl)\n", + " # data_aave.append(mkt_price_index)\n", + "\n", + "\n", + " # data_dydx.append(stgy_instance.gas_fees)\n", + " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_dydx.append(stgy_instance.total_pnl)\n", + " # data_dydx.append(mkt_price_index)\n", + " # print(interval_old.name)\n", + "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", + " if sheet == True:\n", + " gc = pygsheets.authorize(service_file=\n", + " 'stgy-1-simulations-e0ee0453ddf8.json')\n", + " sh = gc.open('aave/dydx simulations')\n", + " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", + " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", + " else:\n", + " path_to_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_aave)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_dydx)\n", + "\n", + " @staticmethod\n", + " def delete_results(stgy_instance, period, oc1):\n", + " file_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", + " os.remove(file_aave)\n", + " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", + " os.remove(file_dydx)\n", + "\n", + " @staticmethod\n", + " def add_header(stgy_instance, period, oc1):\n", + " aave_headers = [\n", + " \"date\",\n", + " \"market_price\",\n", + " \"previous_price\",\n", + " \"open_close\",\n", + " \"trailing_stop\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + " # \"index_of_mkt_price\"]\n", + " dydx_headers = [\n", + " \"date\",\n", + " \"market_price\",\n", + " \"previous_price\",\n", + " \"open_close\",\n", + " \"trailing_stop\",\n", + " \"entry_price\",\n", + " \"short_size\",\n", + " # \"collateral\",\n", + " \"notional\",\n", + " # \"equity\",\n", + " # \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " # \"collateral_status\",\n", + " \"short_status\",\n", + " # \"order_status\",\n", + " # \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " # \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\",\n", + " # \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + " # \"index_of_mkt_price\"]\n", + " \n", + " path_to_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(aave_headers)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(dydx_headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Track historical data\n", + "# symbol = 'ETHUSDC'\n", + "# freq = '1m'\n", + "# initial_date = \"1 Jan 2019\"\n", + "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", + "# initial_date=initial_date, save=True)\n", + "\n", + "# Load historical data if previously tracked and saved\n", + "\n", + "historical_data = pd.read_csv(\"Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "# # assign data to stgy instance + define index as dates\n", + "timestamp = pd.to_datetime(historical_data['timestamp'])\n", + "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", + "historical_data.index = timestamp\n", + "#\n", + "# #######################################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-11-01\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", + "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", + "where $\\sigma=vol$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First we calculate weighted vol\n", + "last_date = \"2021-06-01\"\n", + "slippage = 0.0005\n", + "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]-3*60:-3*60]\n", + "for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", + " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", + "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + "print(\"weighted mu: \", str(mu*100)+'%')\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+2*vol)*100-100)+'%')\n", + "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = sigma_2\n", + "mu = mu_2\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"avg movement: (1+mu+2vol): \", str((1+mu+2*vol)*100-100)+'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# normal_std = std\n", + "# medium_std = 2*std\n", + "# high_std = 4*std\n", + "# extreme_std = 6*std\n", + "# normal_std, medium_std, high_std, extreme_std" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find such a relevant price manually by taking a look at the price plot." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define a function that will\n", + "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices\n", + "- Create a new directory \"Files/Tests/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit \n", + "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", + "- Run through the code executing everything as discussed in the dev doc.\n", + "\n", + "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(period, open_close, slippage, max_txs, L, trailing, trailing_update_hours):\n", + " global ocs\n", + " # Initialize everything\n", + " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", + " # First we calculate weighted vol\n", + " last_date = period[1]+' 00:00:00'\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + " # Now we define prices \n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", + " # We create five equidistant OCs\n", + " oc1 = open_close\n", + " # oc2 = oc1 * (1+6/2/100)\n", + " ocs = [oc1]\n", + " # print(\"oc1=\",round(oc1,3))\n", + " for i in range(1,7):\n", + " globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", + " ocs.append(globals()[\"oc\"+str(i+1)])\n", + " # print(\"oc\"+str(i+1)+\"=\",round(globals()[\"oc\"+str(i+1)],3))\n", + " # print(ocs)\n", + " # But we start with the first oc1\n", + " stgy.trigger_prices['open_close'] = oc1\n", + " \n", + " # print(\"Volatility:\", vol)\n", + " # print(\"Floor:\", stgy.trigger_prices['floor'])\n", + " # print(\"Open_close1:\", oc1)\n", + " # print(\"Open_close2:\", oc2)\n", + " # print(\"1-OC2/OC1 - 1:\", 1-oc2/oc1)\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder \n", + " # exist or not.\n", + " if not os.path.exists(\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " # if the demo_folder directory is not present \n", + " # then create it.\n", + " os.makedirs(\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " stgy.historical_data.to_csv(\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " % (period[0], period[1], open_close))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + "\n", + " # Stk eth\n", + " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.dydx.collateral = stgy.aave.debt\n", + " stgy.dydx.equity = stgy.dydx.equity_calc()\n", + " stgy.dydx.collateral_status = True\n", + " \n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= stgy.trigger_prices['open_close']):\n", + " stgy.dydx.open_short(stgy)\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(stgy, period, open_close)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(stgy, period, open_close)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + " maker_fees_counter = []\n", + " \n", + " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " while(i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " market_price = stgy.historical_data[\"close\"][i]\n", + " previous_price = stgy.historical_data[\"close\"][i-1]\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, market_price)\n", + " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", + " # and we execute all the actions involved between both (current and previous prices)\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", + " ############################## \n", + " # We update trailing\n", + " # Everytime price moves down more than trailing we update trailing_stop\n", + " if (market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']):\n", + " if stgy.dydx.short_status:\n", + " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", + " # # If price moves above trailing we move trailing up in order to save that profit\n", + " # # Is important to change trailing after finding scenarios (because we need to close the short first)\n", + " elif (market_price > stgy.trigger_prices['trailing_stop']):\n", + " if trailing_update_hours == 0:\n", + " pass\n", + " elif (i % (trailing_update_hours*60) == 0):\n", + " if not stgy.dydx.short_status:\n", + " stgy.trigger_prices['trailing_stop'] = min(stgy.trigger_prices['open_close']* (1-trailing), market_price)\n", + " ################################\n", + " ################################\n", + " # OC LOGIC\n", + " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", + " if market_price > oc1:\n", + " stgy.trigger_prices['open_close'] = oc1\n", + "\n", + " \n", + " # We update vol and ocs if short_status = False\n", + " # if not stgy.dydx.short_status:\n", + " # current_date = list(stgy.historical_data.index)[i]\n", + " # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol)\n", + " # mu, sigma = vol\n", + " # oc1 = floor * (1+slippage) * (1+mu+2*sigma)\n", + " # ocs = [oc1]\n", + " # for i in range(1,5):\n", + " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", + " # ocs.append(globals()[\"oc\"+str(i+1)])\n", + "\n", + " \n", + " # If we executed more txs than hat_L*20 then we change to K_2\n", + " if (stgy.dydx.maker_fees_counter >= max_txs):\n", + " # stgy.historical_data = stgy.historical_data_OC2\n", + " # print(stgy.dydx.maker_fees_counter)\n", + " current_date = str(stgy.historical_data.index[i])\n", + " current_oc = stgy.trigger_prices['open_close']\n", + " vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", + " ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", + " # if short = open and if there are up_choices available, we take the last option (the furthest)\n", + " # if there isn't options we take max_distance\n", + " # random.seed(4)\n", + " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # 'index': i,\n", + " # 'date': str(stgy.historical_data.index[i])})\n", + " if not stgy.dydx.short_status:\n", + " if stgy.trigger_prices['open_close'] == oc1:\n", + " stgy.trigger_prices['open_close'] = oc4\n", + " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", + " elif stgy.dydx.short_status:\n", + " if len(ocs_choices['up_choices']) != 0:\n", + " stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", + " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", + " # If we didnt change oc we dont clean maker_fees_counter\n", + " if current_oc != stgy.trigger_prices['open_close']:\n", + " maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " 'txs': stgy.dydx.maker_fees_counter, \n", + " # 'index': i,\n", + " 'date': str(stgy.historical_data.index[i])})\n", + " stgy.dydx.maker_fees_counter = 0\n", + " ########################\n", + " ########################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " current_date = str(stgy.historical_data.index[i])\n", + " stgy.data_dumper.write_data(stgy, previous_price,\n", + " current_date, period, open_close,\n", + " sheet=False)\n", + " #########################\n", + " # we increment index by the time consumed in executing actions\n", + " # i += time_used\n", + " i += 1\n", + " return maker_fees_counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fees counter for (pcg = 0.003, update_time = 0) = 33\n", + "PnL for (pcg = 0.003, update_time = 0) = -358952.261280538\n", + "Fees counter for (pcg = 0.005, update_time = 0) = 35\n", + "PnL for (pcg = 0.005, update_time = 0) = -381885.1626136181\n", + "Fees counter for (pcg = 0.01, update_time = 0) = 37\n", + "PnL for (pcg = 0.01, update_time = 0) = -361068.78503932577\n", + "Fees counter for (pcg = 0.02, update_time = 0) = 41\n", + "PnL for (pcg = 0.02, update_time = 0) = -429863.24218090175\n", + "Fees counter for (pcg = 0.03, update_time = 0) = 45\n", + "PnL for (pcg = 0.03, update_time = 0) = -492909.3027702088\n", + "Fees counter for (pcg = 0.05, update_time = 0) = 51\n", + "PnL for (pcg = 0.05, update_time = 0) = -554535.386025746\n", + "Fees counter for (pcg = 0.003, update_time = 1) = 575\n", + "PnL for (pcg = 0.003, update_time = 1) = -2930937.173964457\n", + "Fees counter for (pcg = 0.005, update_time = 1) = 450\n", + "PnL for (pcg = 0.005, update_time = 1) = -2242325.8231389998\n", + "Fees counter for (pcg = 0.01, update_time = 1) = 299\n", + "PnL for (pcg = 0.01, update_time = 1) = -1570120.5245088509\n", + "Fees counter for (pcg = 0.02, update_time = 1) = 167\n", + "PnL for (pcg = 0.02, update_time = 1) = -1023727.8108453712\n", + "Fees counter for (pcg = 0.03, update_time = 1) = 131\n", + "PnL for (pcg = 0.03, update_time = 1) = -965629.536657554\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [133], line 14\u001b[0m\n\u001b[1;32m 12\u001b[0m open_close \u001b[38;5;241m=\u001b[39m period_n_open_close[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 13\u001b[0m slippage \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0005\u001b[39m\n\u001b[0;32m---> 14\u001b[0m maker_fees_counter \u001b[38;5;241m=\u001b[39m run_sim(period, open_close, slippage, max_txs, L, trailing, trailing_time)\n\u001b[1;32m 15\u001b[0m maker_fees_counter_lengths[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpcg = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trailing) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, update_time = \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trailing_time)]\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(maker_fees_counter)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFees counter for (pcg = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trailing) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, update_time = \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trailing_time) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) = \u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m 17\u001b[0m maker_fees_counter_lengths[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpcg = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trailing) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, update_time = \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trailing_time)])\n", + "Cell \u001b[0;32mIn [128], line 213\u001b[0m, in \u001b[0;36mrun_sim\u001b[0;34m(period, open_close, slippage, max_txs, L, trailing, trailing_update_hours)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;66;03m#########################\u001b[39;00m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;66;03m# Write data\u001b[39;00m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;66;03m# We write the data into the google sheet or csv file acording to sheet value\u001b[39;00m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;66;03m# (sheet = True --> sheet, sheet = False --> csv)\u001b[39;00m\n\u001b[1;32m 212\u001b[0m current_date \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(stgy\u001b[38;5;241m.\u001b[39mhistorical_data\u001b[38;5;241m.\u001b[39mindex[i])\n\u001b[0;32m--> 213\u001b[0m \u001b[43mstgy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_dumper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstgy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprevious_price\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[43m \u001b[49m\u001b[43mcurrent_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopen_close\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 215\u001b[0m \u001b[43m \u001b[49m\u001b[43msheet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;66;03m#########################\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# we increment index by the time consumed in executing actions\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;66;03m# i += time_used\u001b[39;00m\n\u001b[1;32m 219\u001b[0m i \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "Cell \u001b[0;32mIn [37], line 52\u001b[0m, in \u001b[0;36mDataDamperNPlotter.write_data\u001b[0;34m(stgy_instance, previous_price, date, period, oc1, sheet)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(aave_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mvalues())):\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(aave_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())[i] \u001b[38;5;129;01min\u001b[39;00m aave_wanted_keys:\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m# print(list(aave_instance.__dict__.keys())[i])\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m data_aave\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43maave_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__dict__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(dydx_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mvalues())):\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(dydx_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())[i] \u001b[38;5;129;01min\u001b[39;00m dydx_wanted_keys:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", + "L = 5 * 0.07\n", + "trailings = [0.003,0.005,0.01,0.02, 0.03,0.05] #[0.02, 0.03]\n", + "# trailing_time = 0\n", + "trailing_update_hours = [0, 1, 3, 8, 12, 24]\n", + "maker_fees_counter_lengths = {}\n", + "pnl_results = {}\n", + "for period_n_open_close in periods_n_open_close:\n", + " for trailing_time in trailing_update_hours:\n", + " for trailing in trailings:\n", + " period = period_n_open_close[0]\n", + " open_close = period_n_open_close[1]\n", + " slippage = 0.0005\n", + " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing, trailing_time)\n", + " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)]=len(maker_fees_counter)\n", + " print(\"Fees counter for (pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time) + \") = \", \n", + " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)])\n", + " directory = \"From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\"\n", + " dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", + " pnl_results[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", + " print(\"PnL for (pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time) + \") = \", \n", + " pnl_results[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)])\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(maker_fees_counter)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = periods_n_open_close[0][0]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "parameter_manager = ParameterManager()\n", + "last_date = period[1]+' 00:00:00'\n", + "vol = parameter_manager.calc_vol(last_date, data)\n", + "mu, sigma = vol\n", + "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", + "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=floor, \n", + " color='green', \n", + " linestyle='--', \n", + " label='floor='+str(round(floor,3)))\n", + "for i in range(len(ocs)):\n", + " axs.axhline(y=ocs[i], \n", + " color='red', \n", + " linestyle='--', \n", + " label='oc'+str(i)+\"=\"+str(round(ocs[i],3)))\n", + "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-93714.29797685935" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", + "dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2020-05-01'" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "period" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-01 00:00:00'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(historical_data.index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mu_returns_max, mu_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns= price > current_price:\n", + " crossed_down += 1\n", + " index_down.append(index-1)\n", + " return {'down':\n", + " {'crossed_down': crossed_down,\n", + " 'index_down': index_down},\n", + " 'up':\n", + " {'crossed_up': crossed_up,\n", + " 'index_up': index_up}}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "price = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosses = cross_counter(data_set, 240)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"Files/Tests/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "market_price 176910\n", + "I_current 176910\n", + "I_old 176910\n", + "entry_price 53220\n", + "short_size 53220\n", + "collateral 176910\n", + "notional 53375\n", + "equity 176910\n", + "leverage 53375\n", + "pnl 53066\n", + "collateral_status 176910\n", + "short_status 53220\n", + "order_status 123690\n", + "withdrawal_fees 176910\n", + "funding_rates 176910\n", + "maker_taker_fees 133516\n", + "maker_fees_counter 133516\n", + "costs 421\n", + "gas_fees 176910\n", + "total_costs_from_aave_n_dydx 133516\n", + "total_stgy_pnl 176910\n", + "index_of_mkt_price 176910\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dydx_results.astype(bool).sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def count_maker_fees_increment(data_set):\n", + " index_of_maker_fee = []\n", + " for index in range(1,len(data_set)):\n", + " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", + " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", + " if previous_maker_fee_counter < current_maker_fee_counter:\n", + " index_of_maker_fee.append(index)\n", + " return {'indexes': index_of_maker_fee}" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "matches_up = 0\n", + "matches_down = 0\n", + "for index_up in crosses['up']['index_up']:\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " matches_up += 1\n", + "for index_down in crosses['down']['index_down']:\n", + " if index_down in results_maker_fee_counter['indexes']:\n", + " matches_down += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(155, 136, 291)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches_up, matches_down, matches_up + matches_down" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 156)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", + "- entry_price and short_size can be found at index_up -1\n", + "- close_price is market_price in index = index_up" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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43396240.86inftyminus_infty0.0000.000.00002-6246.222332
\n", + "
" + ], + "text/plain": [ + " market_price I_current I_old short_size entry_price \\\n", + "43393 240.70 infty minus_infty 0.000 0.00 \n", + "43394 239.74 minus_infty infty -4334.634 239.74 \n", + "43395 240.94 infty minus_infty 0.000 0.00 \n", + "43396 240.86 infty minus_infty 0.000 0.00 \n", + "\n", + " pnl maker_fees_counter total_stgy_pnl \n", + "43393 0.0000 0 -2.879624 \n", + "43394 0.0000 1 -522.470891 \n", + "43395 -5201.5608 2 -6246.223689 \n", + "43396 0.0000 2 -6246.222332 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 1\n", + "index = crosses['up']['index_up'][i]\n", + "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "diff = []\n", + "cost = []\n", + "# we dont start the loop at i = 0 because the data_set started below open_close\n", + "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", + "for i in range(1,len(crosses['up']['index_up'])):\n", + " index_up = crosses['up']['index_up'][i]\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " entry_price = dydx_results.iloc[index-1]['entry_price']\n", + " close_price = dydx_results.iloc[index]['market_price']\n", + " short_size = dydx_results.iloc[index-1]['short_size']\n", + " diff.append(close_price-entry_price)\n", + " cost.append(short_size * (close_price-entry_price))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1999999999999886, -5201.560799999951)" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(diff), np.mean(cost)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 70be20c3673ae9beb27bb5c406c006f20a9379fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Fri, 14 Oct 2022 09:53:51 -0300 Subject: [PATCH 08/16] updates trailing cases --- jupyter-lab/Simulations_prices_approach.ipynb | 329 ++++++++++-------- 1 file changed, 182 insertions(+), 147 deletions(-) diff --git a/jupyter-lab/Simulations_prices_approach.ipynb b/jupyter-lab/Simulations_prices_approach.ipynb index 7067232..dd6403a 100644 --- a/jupyter-lab/Simulations_prices_approach.ipynb +++ b/jupyter-lab/Simulations_prices_approach.ipynb @@ -13,36 +13,36 @@ "Requirement already satisfied: scipy in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.9.1)\n", "Requirement already satisfied: pygsheets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (2.0.5)\n", "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", - 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"execution_count": 106, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +602,7 @@ " mu = vol[0]\n", " sigma = vol[1]\n", " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", - " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", + " p_trailing = p_open_close * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", " ##########################################################\n", " # We define the intervals\n", " list_of_triggers = [\"open_close\",\n", @@ -724,7 +724,7 @@ " # Case P decreasing: \n", " # We need to ask both P_t-1 > trigger and trigger > P_t bc if we only ask the later we will execute\n", " # the action for all prices below trigger. Same logic for Case P increasing.\n", - " if (previous_market_price > stgy_instance.trigger_prices['open_close']) and \\\n", + " if (previous_market_price >= stgy_instance.trigger_prices['open_close']) and \\\n", " (stgy_instance.trigger_prices['open_close'] > market_price):\n", " actions.append('open_short')\n", " \n", @@ -732,16 +732,17 @@ " (stgy_instance.trigger_prices['trailing_stop'] > market_price):\n", " actions.append('open_short')\n", " \n", - " elif (previous_market_price > stgy_instance.trigger_prices['repay_aave']) and \\\n", - " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", - " actions.append('repay_aave')\n", + " if stgy_instance.dydx.short_status:\n", + " if (previous_market_price >= stgy_instance.trigger_prices['repay_aave']) and \\\n", + " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", + " actions.append('repay_aave')\n", " \n", " \n", " # Case P increasing\n", - " elif (previous_market_price < stgy_instance.trigger_prices['open_close']) and \\\n", + " if (previous_market_price <= stgy_instance.trigger_prices['open_close']) and \\\n", " (stgy_instance.trigger_prices['open_close'] < market_price):\n", " actions.append('close_short')\n", - " elif (previous_market_price <= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " if (previous_market_price <= stgy_instance.trigger_prices['trailing_stop']) and \\\n", " (stgy_instance.trigger_prices['trailing_stop'] < market_price):\n", " actions.append('close_short')\n", " \n", @@ -789,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -843,8 +844,14 @@ " data_dydx.append(date)\n", " for i in range(len(aave_instance.__dict__.values())):\n", " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", - " # print(list(aave_instance.__dict__.keys())[i])\n", - " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " if list(aave_instance.__dict__.keys())[i] == \"market_price\":\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " data_aave.append(previous_price)\n", + " data_aave.append(stgy_instance.trigger_prices['open_close'])\n", + " data_aave.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " else:\n", + " # print(list(aave_instance.__dict__.keys())[i])\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", " for i in range(len(dydx_instance.__dict__.values())):\n", " if list(dydx_instance.__dict__.keys())[i] in dydx_wanted_keys:\n", " if list(dydx_instance.__dict__.keys())[i] == \"market_price\":\n", @@ -972,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1089,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1135,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 110, "metadata": { "tags": [] }, @@ -1165,11 +1172,11 @@ " # We create five equidistant OCs\n", " oc1 = open_close\n", " # oc2 = oc1 * (1+6/2/100)\n", - " ocs = [oc1]\n", - " # print(\"oc1=\",round(oc1,3))\n", - " for i in range(1,7):\n", - " globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", - " ocs.append(globals()[\"oc\"+str(i+1)])\n", + " # ocs = [oc1]\n", + " # # print(\"oc1=\",round(oc1,3))\n", + " # for i in range(1,7):\n", + " # globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", + " # ocs.append(globals()[\"oc\"+str(i+1)])\n", " # print(\"oc\"+str(i+1)+\"=\",round(globals()[\"oc\"+str(i+1)],3))\n", " # print(ocs)\n", " # But we start with the first oc1\n", @@ -1248,7 +1255,8 @@ "\n", " maker_fees_counter = []\n", " \n", - " stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " # stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " stgy.trigger_prices['trailing_stop'] = stgy.trigger_prices['open_close'] * (1-trailing)\n", " while(i < len(stgy.historical_data)):\n", " # for i in range(initial_index, len(stgy.historical_data)):\n", " # pass\n", @@ -1271,22 +1279,21 @@ " # We update trailing\n", " # Everytime price moves down more than trailing we update trailing_stop\n", " if (market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']):\n", - " if stgy.dydx.short_status:\n", - " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", + " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", " # # If price moves above trailing we move trailing up in order to save that profit\n", " # # Is important to change trailing after finding scenarios (because we need to close the short first)\n", - " elif (market_price > stgy.trigger_prices['trailing_stop']):\n", - " if trailing_update_hours == 0:\n", - " pass\n", - " elif (i % (trailing_update_hours*60) == 0):\n", - " if not stgy.dydx.short_status:\n", - " stgy.trigger_prices['trailing_stop'] = min(stgy.trigger_prices['open_close']* (1-trailing), market_price)\n", + " # elif (market_price > stgy.trigger_prices['trailing_stop']):\n", + " # if trailing_update_hours == 0:\n", + " # pass\n", + " # elif (i % (trailing_update_hours*60) == 0):\n", + " # if not stgy.dydx.short_status:\n", + " # stgy.trigger_prices['trailing_stop'] = min(stgy.trigger_prices['open_close']* (1-trailing), market_price)\n", " ################################\n", " ################################\n", " # OC LOGIC\n", " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", - " if market_price > oc1:\n", - " stgy.trigger_prices['open_close'] = oc1\n", + " # if market_price > oc1:\n", + " # stgy.trigger_prices['open_close'] = oc1\n", "\n", " \n", " # We update vol and ocs if short_status = False\n", @@ -1302,37 +1309,37 @@ "\n", " \n", " # If we executed more txs than hat_L*20 then we change to K_2\n", - " if (stgy.dydx.maker_fees_counter >= max_txs):\n", - " # stgy.historical_data = stgy.historical_data_OC2\n", - " # print(stgy.dydx.maker_fees_counter)\n", - " current_date = str(stgy.historical_data.index[i])\n", - " current_oc = stgy.trigger_prices['open_close']\n", - " vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", - " ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", - " # if short = open and if there are up_choices available, we take the last option (the furthest)\n", - " # if there isn't options we take max_distance\n", - " # random.seed(4)\n", - " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " # 'txs': stgy.dydx.maker_fees_counter, \n", - " # # 'index': i,\n", - " # 'date': str(stgy.historical_data.index[i])})\n", - " if not stgy.dydx.short_status:\n", - " if stgy.trigger_prices['open_close'] == oc1:\n", - " stgy.trigger_prices['open_close'] = oc4\n", - " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", - " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", - " elif stgy.dydx.short_status:\n", - " if len(ocs_choices['up_choices']) != 0:\n", - " stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", - " # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", - " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", - " # If we didnt change oc we dont clean maker_fees_counter\n", - " if current_oc != stgy.trigger_prices['open_close']:\n", - " maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " 'txs': stgy.dydx.maker_fees_counter, \n", - " # 'index': i,\n", - " 'date': str(stgy.historical_data.index[i])})\n", - " stgy.dydx.maker_fees_counter = 0\n", + " # if (stgy.dydx.maker_fees_counter >= max_txs):\n", + " # # stgy.historical_data = stgy.historical_data_OC2\n", + " # # print(stgy.dydx.maker_fees_counter)\n", + " # current_date = str(stgy.historical_data.index[i])\n", + " # current_oc = stgy.trigger_prices['open_close']\n", + " # vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", + " # ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", + " # # if short = open and if there are up_choices available, we take the last option (the furthest)\n", + " # # if there isn't options we take max_distance\n", + " # # random.seed(4)\n", + " # # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # # 'index': i,\n", + " # # 'date': str(stgy.historical_data.index[i])})\n", + " # if not stgy.dydx.short_status:\n", + " # if stgy.trigger_prices['open_close'] == oc1:\n", + " # stgy.trigger_prices['open_close'] = oc4\n", + " # # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", + " # elif stgy.dydx.short_status:\n", + " # if len(ocs_choices['up_choices']) != 0:\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", + " # # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", + " # # If we didnt change oc we dont clean maker_fees_counter\n", + " # if current_oc != stgy.trigger_prices['open_close']:\n", + " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # 'index': i,\n", + " # 'date': str(stgy.historical_data.index[i])})\n", + " # stgy.dydx.maker_fees_counter = 0\n", " ########################\n", " ########################\n", " # Funding rates\n", @@ -1360,7 +1367,7 @@ " # we increment index by the time consumed in executing actions\n", " # i += time_used\n", " i += 1\n", - " return maker_fees_counter" + " return stgy.dydx.maker_fees_counter" ] }, { @@ -1372,19 +1379,21 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "periods_n_open_close = [[[\"2020-05-01\",\"2020-09-01\"],240]]" + "periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]\n", + "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", + "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],243]]" ] }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 117, "metadata": { "tags": [] }, @@ -1393,98 +1402,103 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fees counter for (pcg = 0.003, update_time = 0) = 33\n", - "PnL for (pcg = 0.003, update_time = 0) = -358952.261280538\n", - "Fees counter for (pcg = 0.005, update_time = 0) = 35\n", - "PnL for (pcg = 0.005, update_time = 0) = -381885.1626136181\n", - "Fees counter for (pcg = 0.01, update_time = 0) = 37\n", - "PnL for (pcg = 0.01, update_time = 0) = -361068.78503932577\n", - "Fees counter for (pcg = 0.02, update_time = 0) = 41\n", - "PnL for (pcg = 0.02, update_time = 0) = -429863.24218090175\n", - "Fees counter for (pcg = 0.03, update_time = 0) = 45\n", - "PnL for (pcg = 0.03, update_time = 0) = -492909.3027702088\n", - "Fees counter for (pcg = 0.05, update_time = 0) = 51\n", - "PnL for (pcg = 0.05, update_time = 0) = -554535.386025746\n", - "Fees counter for (pcg = 0.003, update_time = 1) = 575\n", - "PnL for (pcg = 0.003, update_time = 1) = -2930937.173964457\n", - "Fees counter for (pcg = 0.005, update_time = 1) = 450\n", - "PnL for (pcg = 0.005, update_time = 1) = -2242325.8231389998\n", - "Fees counter for (pcg = 0.01, update_time = 1) = 299\n", - "PnL for (pcg = 0.01, update_time = 1) = -1570120.5245088509\n", - "Fees counter for (pcg = 0.02, update_time = 1) = 167\n", - "PnL for (pcg = 0.02, update_time = 1) = -1023727.8108453712\n", - "Fees counter for (pcg = 0.03, update_time = 1) = 131\n", - "PnL for (pcg = 0.03, update_time = 1) = -965629.536657554\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [133], line 14\u001b[0m\n\u001b[1;32m 12\u001b[0m open_close \u001b[38;5;241m=\u001b[39m period_n_open_close[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 13\u001b[0m slippage \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0005\u001b[39m\n\u001b[0;32m---> 14\u001b[0m maker_fees_counter \u001b[38;5;241m=\u001b[39m run_sim(period, open_close, slippage, max_txs, L, trailing, trailing_time)\n\u001b[1;32m 15\u001b[0m maker_fees_counter_lengths[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpcg = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trailing) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, update_time = \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trailing_time)]\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(maker_fees_counter)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFees counter for (pcg = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trailing) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, update_time = \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trailing_time) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) = \u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m 17\u001b[0m maker_fees_counter_lengths[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpcg = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trailing) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, update_time = \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trailing_time)])\n", - "Cell \u001b[0;32mIn [128], line 213\u001b[0m, in \u001b[0;36mrun_sim\u001b[0;34m(period, open_close, slippage, max_txs, L, trailing, trailing_update_hours)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;66;03m#########################\u001b[39;00m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;66;03m# Write data\u001b[39;00m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;66;03m# We write the data into the google sheet or csv file acording to sheet value\u001b[39;00m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;66;03m# (sheet = True --> sheet, sheet = False --> csv)\u001b[39;00m\n\u001b[1;32m 212\u001b[0m current_date \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(stgy\u001b[38;5;241m.\u001b[39mhistorical_data\u001b[38;5;241m.\u001b[39mindex[i])\n\u001b[0;32m--> 213\u001b[0m \u001b[43mstgy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_dumper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstgy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprevious_price\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[43m \u001b[49m\u001b[43mcurrent_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopen_close\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 215\u001b[0m \u001b[43m \u001b[49m\u001b[43msheet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;66;03m#########################\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# we increment index by the time consumed in executing actions\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;66;03m# i += time_used\u001b[39;00m\n\u001b[1;32m 219\u001b[0m i \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "Cell \u001b[0;32mIn [37], line 52\u001b[0m, in \u001b[0;36mDataDamperNPlotter.write_data\u001b[0;34m(stgy_instance, previous_price, date, period, oc1, sheet)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(aave_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mvalues())):\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(aave_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())[i] \u001b[38;5;129;01min\u001b[39;00m aave_wanted_keys:\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m# print(list(aave_instance.__dict__.keys())[i])\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m data_aave\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43maave_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__dict__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(dydx_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mvalues())):\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(dydx_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())[i] \u001b[38;5;129;01min\u001b[39;00m dydx_wanted_keys:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "Fees counter for (pcg = 0.005, update_time = 0) = 344\n", + "PnL for (pcg = 0.005, update_time = 0) = -425400.1595972965\n" ] } ], "source": [ "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", "L = 5 * 0.07\n", - "trailings = [0.003,0.005,0.01,0.02, 0.03,0.05] #[0.02, 0.03]\n", - "# trailing_time = 0\n", - "trailing_update_hours = [0, 1, 3, 8, 12, 24]\n", + "trailings = [0.005]#[0.001, 0.003,0.005,0.01,0.02, 0.03,0.05] #[0.02, 0.03]\n", + "trailing_time = 0\n", + "# trailing_update_hours = [0, 1, 3, 8, 12, 24]\n", "maker_fees_counter_lengths = {}\n", "pnl_results = {}\n", "for period_n_open_close in periods_n_open_close:\n", - " for trailing_time in trailing_update_hours:\n", - " for trailing in trailings:\n", - " period = period_n_open_close[0]\n", - " open_close = period_n_open_close[1]\n", - " slippage = 0.0005\n", - " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing, trailing_time)\n", - " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)]=len(maker_fees_counter)\n", - " print(\"Fees counter for (pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time) + \") = \", \n", - " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)])\n", - " directory = \"From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\"\n", - " dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", - " pnl_results[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", - " print(\"PnL for (pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time) + \") = \", \n", - " pnl_results[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)])\n", - " \n" + " # for trailing_time in trailing_update_hours:\n", + " for trailing in trailings:\n", + " period = period_n_open_close[0]\n", + " open_close = period_n_open_close[1]\n", + " slippage = 0.0005\n", + " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing, trailing_time)\n", + " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)]=maker_fees_counter\n", + " print(\"Fees counter for (pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time) + \") = \", \n", + " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)])\n", + " directory = \"From_2020-05-01_to_2020-08-01_open_close_at_240/dydx_results.csv\"\n", + " dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", + " pnl_results[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", + " print(\"PnL for (pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time) + \") = \", \n", + " pnl_results[\"pcg = \"+str(trailing) + \", update_time = \" + str(trailing_time)])\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "price_jump_in_open = {}\n", + "price_jump_in_close = {}\n", + "\n", + "for i in range(len(dydx_results)-1):\n", + " if dydx_results['entry_price'][i]==0 and dydx_results['entry_price'][i+1]!=0:\n", + " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)\n", + " elif dydx_results['entry_price'][i]!=0 and dydx_results['entry_price'][i+1]==0:\n", + " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "15" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at open: 0.0042%\n", + "Mean price jump at open: 0.2203%\n", + "Max price jump at open: 4.383900000000001%\n" + ] } ], "source": [ - "len(maker_fees_counter)" + "print(\"Min price jump at open:\",str(round(min(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at open:\",str(round(np.mean(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at open:\",str(round(max(list(price_jump_in_open.values())),6)*100)+\"%\")" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at close: 0.0042%\n", + "Mean price jump at close: 0.1929%\n", + "Max price jump at close: 3.9785%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at close:\",str(round(min(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at close:\",str(round(np.mean(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at close:\",str(round(max(list(price_jump_in_close.values())),6)*100)+\"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -1495,25 +1509,26 @@ ], "source": [ "# Period of Simulations\n", + "periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", "period = periods_n_open_close[0][0]\n", "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", "parameter_manager = ParameterManager()\n", "last_date = period[1]+' 00:00:00'\n", "vol = parameter_manager.calc_vol(last_date, data)\n", "mu, sigma = vol\n", + "open_close = 243\n", "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", "axs.plot(data['close'], color='tab:blue', label='market price')\n", - "axs.axhline(y=floor, \n", + "axs.axhline(y=240, \n", " color='green', \n", " linestyle='--', \n", " label='floor='+str(round(floor,3)))\n", - "for i in range(len(ocs)):\n", - " axs.axhline(y=ocs[i], \n", - " color='red', \n", - " linestyle='--', \n", - " label='oc'+str(i)+\"=\"+str(round(ocs[i],3)))\n", + "axs.axhline(y=243, \n", + " color='red', \n", + " linestyle='--', \n", + " label='open_close='+str(round(open_close,3)))\n", "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", "axs.grid()\n", @@ -1521,6 +1536,26 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07894394589673559" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['close'].pct_change(1*24*60).dropna().max()" + ] + }, { "cell_type": "code", "execution_count": 92, From 442b37197b88b4c3f1df986e86c049d795e26545 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Fri, 14 Oct 2022 09:54:54 -0300 Subject: [PATCH 09/16] using trigger ranges instead of trigger prices --- jupyter-lab/Simulations_oc_range.ipynb | 2359 ++++++++++++++++++++++++ 1 file changed, 2359 insertions(+) create mode 100644 jupyter-lab/Simulations_oc_range.ipynb diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb new file mode 100644 index 0000000..8c79c44 --- /dev/null +++ b/jupyter-lab/Simulations_oc_range.ipynb @@ -0,0 +1,2359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.5.0)\n", + "Requirement already satisfied: scipy in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.9.1)\n", + "Requirement already satisfied: pygsheets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (2.0.5)\n", + "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", + "Requirement 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pandas as pd\n", + "import numpy as np\n", + "import json\n", + "import math\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## StgyApp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class StgyApp(object):\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.stk = config[\"stk\"]\n", + " self.total_costs_from_aave_n_dydx = 0\n", + " self.total_pnl = 0\n", + " self.gas_fees = 0\n", + "\n", + " # prices and intervals\n", + " self.trigger_prices = {}\n", + " self.intervals = {}\n", + "\n", + " # clients for data\n", + " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", + " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", + " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", + " # self.historical_data =\n", + "\n", + " # We create attributes to fill later\n", + " self.aave = None\n", + " self.aave_features = None\n", + " self.aave_rates = None\n", + "\n", + " self.dydx = None\n", + " self.dydx_features = None\n", + "\n", + " # self.volatility_calculator = None\n", + "\n", + " self.parameter_manager = ParameterManager()\n", + "\n", + " self.historical_data = None\n", + "\n", + " self.data_dumper = DataDamperNPlotter()\n", + "\n", + " def launch(self, config):\n", + " # self.call_binance_data_loader()\n", + " self.initialize_aave(config['initial_parameters']['aave'])\n", + " self.initialize_dydx(config['initial_parameters']['dydx'])\n", + "\n", + " # call clients functions\n", + " def get_historical_data(self, symbol, freq,\n", + " initial_date, save):\n", + " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=save)\n", + " # self.historical_data = eth_historical\n", + " self.historical_data = eth_historical[\"close\"]\n", + " for i in range(len(self.historical_data)):\n", + " self.historical_data[i] = float(self.historical_data[i])\n", + " # self.load_intervals()\n", + "\n", + " # initialize classes\n", + " def initialize_aave(self, config):\n", + " # We initialize aave and dydx classes instances\n", + " self.aave = Aave(config)\n", + " # We load methods and attributes for aave and dydx to use later\n", + " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", + " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", + " \"keys\": list(self.aave.__dict__.keys())}}\n", + " # We create an attribute for historical data\n", + " self.aave_historical_data = []\n", + "\n", + " def initialize_dydx(self, config):\n", + " self.dydx = Dydx(config)\n", + " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", + " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", + " \"keys\": list(self.dydx.__dict__.keys())}}\n", + " self.dydx_historical_data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aave and DyDx modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Aave" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Aave(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert self.dydx_class_instance == isinstance(dydx)\n", + " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", + " self.market_price = config['market_price']\n", + "\n", + " self.entry_price = config['entry_price']\n", + "\n", + " self.collateral_eth_initial = config['collateral_eth']\n", + " self.collateral_eth = config['collateral_eth']\n", + " self.collateral_usdc = config['collateral_usdc']\n", + "\n", + " self.reserve_margin_eth = 0\n", + " self.reserve_margin_usdc = 0\n", + "\n", + " self.borrowed_percentage = config['borrowed_pcg']\n", + " self.usdc_status = config['usdc_status']\n", + "\n", + " self.debt = config['debt']\n", + " self.debt_initial = config['debt']\n", + "\n", + " self.ltv = config['ltv']\n", + " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", + "\n", + " self.lending_rate = 0\n", + " self.lending_rate_hourly = 0\n", + " self.interest_on_lending_eth = 0 # aggregated fees\n", + " self.interest_on_lending_usd = 0\n", + " self.lending_fees_eth = 0 # fees between last 2 prices\n", + " self.lending_fees_usd = 0\n", + "\n", + " self.borrowing_rate = 0\n", + " self.borrowing_rate_hourly = 0\n", + " self.interest_on_borrowing = 0 # aggregated fees\n", + " self.borrowing_fees = 0 # fees between last 2 prices\n", + "\n", + " self.lend_minus_borrow_interest = 0\n", + "\n", + " self.costs = 0\n", + " # self.historical = pd.DataFrame()\n", + " # self.dydx_class_instance = dydx_class_instance\n", + " # self.staked_in_protocol = stk\n", + "\n", + " # def update_costs(self):\n", + " # \"\"\"\n", + " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " # \"\"\"\n", + " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", + " # self.costs = self.costs - self.lend_minus_borrow_interest\n", + "\n", + " def collateral_usd(self):\n", + " return self.collateral_eth * self.market_price\n", + "\n", + " def update_debt(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.debt = self.debt + self.borrowing_fees\n", + "\n", + " def update_collateral(self):\n", + " \"\"\"\n", + " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", + " self.collateral_usdc = self.collateral_usd()\n", + "\n", + " def track_lend_borrow_interest(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", + " in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", + "\n", + " def lending_fees_calc(self, freq):\n", + " self.simulate_lending_rate()\n", + " self.lending_rate_freq = self.lending_rate / freq\n", + "\n", + " # fees from lending are added to collateral? YES\n", + " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", + " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", + " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", + " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", + " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", + "\n", + " def borrowing_fees_calc(self, freq):\n", + " self.simulate_borrowing_rate()\n", + " self.borrowing_rate_freq = self.borrowing_rate / freq\n", + "\n", + " # fees from borrow are added to debt? YES\n", + " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", + " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", + " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", + "\n", + " def simulate_lending_rate(self):\n", + " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", + "\n", + " # best case\n", + " # self.lending_rate = 1.5 / 100\n", + "\n", + " # worst case\n", + " self.lending_rate = 0.5 / 100\n", + "\n", + " def simulate_borrowing_rate(self):\n", + " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", + "\n", + " # best case\n", + " # self.borrowing_rate = 1.5/100\n", + "\n", + " # worst case\n", + " self.borrowing_rate = 2.5/100\n", + "\n", + " def ltv_calc(self):\n", + " if self.collateral_usd() == 0:\n", + " return 0\n", + " else:\n", + " return self.debt / self.collateral_usd()\n", + "\n", + " def price_to_liquidation(self, dydx_class_instance):\n", + " return self.entry_price - (dydx_class_instance.pnl()\n", + " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", + "\n", + " def price_to_ltv_limit_calc(self):\n", + " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", + "\n", + " def buffer_for_repay(self):\n", + " return 0.01\n", + "\n", + " def ltv_limit(self):\n", + " return 0.5\n", + "\n", + " # Actions to take\n", + " def return_usdc(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " time = 0\n", + " if self.usdc_status:\n", + " # simulate 2min delay for tx\n", + " # update parameters\n", + " # AAVE parameters\n", + " self.usdc_status = False\n", + " # self.collateral_eth = 0\n", + " # self.collateral_usdc = 0\n", + " self.debt = 0\n", + " self.ltv = 0\n", + " self.price_to_ltv_limit = 0\n", + " # self.lending_rate = 0\n", + " # self.borrowing_rate = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def repay_aave(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " dydx_class_instance = stgy_instance.dydx\n", + " # aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " #\n", + " time = 0\n", + " if self.usdc_status:\n", + " # update parameters\n", + " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price)\n", + " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", + "\n", + " # pnl_for_debt = dydx_class_instance.pnl()\n", + " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", + " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price)\n", + " self.debt = self.debt - pnl_for_debt\n", + " self.ltv = self.ltv_calc()\n", + "\n", + " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " dydx_class_instance.short_size = new_short_size\n", + " dydx_class_instance.notional = dydx_class_instance.notional_calc()\n", + " dydx_class_instance.equity = dydx_class_instance.equity_calc()\n", + " dydx_class_instance.leverage = dydx_class_instance.leverage_calc()\n", + " dydx_class_instance.pnl = dydx_class_instance.pnl_calc()\n", + " # dydx_class_instance.price_to_liquidation = \\\n", + " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", + "\n", + " # fees\n", + " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + " dydx_class_instance.simulate_maker_taker_fees()\n", + " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", + " dydx_class_instance.costs = dydx_class_instance.costs \\\n", + " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", + " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + "\n", + " # Note that a negative self.debt is actually a profit\n", + " # We update the parameters\n", + " if self.debt > 0:\n", + " self.usdc_status = True\n", + " else:\n", + " self.usdc_status = False\n", + " # simulate 2min delay for tx\n", + " time = 1\n", + " return time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### DyDx" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Dydx(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert aave_class == isinstance(aave)\n", + " self.market_price = config['market_price']\n", + " \n", + " self.entry_price = config['entry_price']\n", + " self.short_size = config['short_size']\n", + " self.collateral = config['collateral']\n", + " self.notional = config['notional']\n", + " self.equity = config['equity']\n", + " self.leverage = config['leverage']\n", + " self.pnl = config['pnl']\n", + " # self.price_to_liquidation = config['price_to_liquidation']\n", + " self.collateral_status = config['collateral_status']\n", + " self.short_status = config['short_status']\n", + " self.order_status = True\n", + " self.withdrawal_fees = 0.01/100\n", + " self.funding_rates = 0\n", + " self.maker_taker_fees = 0\n", + " self.maker_fees_counter = 0\n", + " self.costs = 0\n", + "\n", + " # auxiliary functions\n", + " def pnl_calc(self):\n", + " return self.short_size * (self.market_price-self.entry_price)\n", + "\n", + " def notional_calc(self):\n", + " return abs(self.short_size)*self.market_price\n", + "\n", + " def equity_calc(self):\n", + " return self.collateral + self.pnl_calc()\n", + "\n", + " def leverage_calc(self):\n", + " if self.equity_calc() == 0:\n", + " return 0\n", + " else:\n", + " return self.notional_calc() / self.equity_calc()\n", + "\n", + " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", + " return self.entry_price \\\n", + " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", + "\n", + " @staticmethod\n", + " def price_to_liquidation_calc(dydx_client_class_instance):\n", + " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", + "\n", + " def add_funding_rates(self):\n", + " self.simulate_funding_rates()\n", + " self.costs = self.costs - self.funding_rates * self.notional\n", + "\n", + " def simulate_funding_rates(self):\n", + " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", + "\n", + " # best case\n", + " # self.funding_rates = 0.0075 / 100\n", + "\n", + " # average -0.00443%\n", + "\n", + " # worst case\n", + " self.funding_rates = -0.0075 / 100\n", + "\n", + " def simulate_maker_taker_fees(self):\n", + " # We add a counter for how many times we call this function\n", + " # i.e. how many times we open and close the short\n", + " self.maker_fees_counter += 1\n", + " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", + " \n", + " # maker fees\n", + " self.maker_taker_fees = 0.05 / 100 # <1M\n", + " # self.maker_taker_fees = 0.04 / 100 # <5M\n", + " # self.maker_taker_fees = 0.035 / 100 # <10M\n", + " # self.maker_taker_fees = 0.03 / 100 # <50M\n", + " # self.maker_taker_fees = 0.025 / 100 # <200M\n", + " # self.maker_taker_fees = 0.02 / 100 # >200M\n", + "\n", + " # Actions to take\n", + " def remove_collateral(self, stgy_instance):\n", + " self.cancel_order()\n", + " time = 0\n", + " if self.collateral_status:\n", + " self.collateral_status = False\n", + " withdrawal_fees = self.collateral * self.withdrawal_fees\n", + " self.collateral = 0\n", + " # self.price_to_liquidation = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + withdrawal_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + "\n", + " def open_short(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " if (not self.short_status) and self.order_status:\n", + " self.short_status = True\n", + " # dydx parameters\n", + " # if self.market_price <= stgy_instance.trigger_prices['floor']:\n", + " # print(\"CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['floor'] - self.market_price)\n", + "\n", + " # if self.market_price <= stgy_instance.trigger_prices['open_close']:\n", + " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", + " self.entry_price = self.market_price\n", + " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + "\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", + " aave_class_instance)\n", + " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", + " i = 0\n", + " while stgy_instance.trigger_prices['ltv_limit'] > stgy_instance.trigger_prices['repay_aave']:\n", + " print(\"CAUTION: P_ltv > P_repay\")\n", + " print(\"Difference of: \", stgy_instance.trigger_prices['ltv_limit'] - stgy_instance.trigger_prices['repay_aave'])\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", + " i += 1\n", + " print(\"P_repay defined to repay 0.5 (half) of debt. This logic was repeated\" + str(i) + \" times.\")\n", + " self.order_status = False\n", + " return 0\n", + "\n", + " def close_short(self, stgy_instance):\n", + " if self.short_status:\n", + " # Next if is to move up the threshold if we didnt execute at exactly open_close\n", + " # if self.market_price >= stgy_instance.trigger_prices['open_close']:\n", + " # # new_open_close = self.market_price\n", + " # print(\"CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!\")\n", + " # print(\"Difference of: \", self.market_price - stgy_instance.trigger_prices['open_close'])\n", + " # stgy_instance.target_prices['open_close'] = self.market_price\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " self.pnl = self.pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.entry_price = 0\n", + " self.short_status = False\n", + " self.short_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + " self.place_order(stgy_instance.trigger_prices['open_close'])\n", + " return 0\n", + "\n", + " def place_order(self, price):\n", + " self.order_status = True\n", + " # self.\n", + "\n", + " def cancel_order(self):\n", + " self.order_status = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## ParameterManager Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterManager(object):\n", + " # auxiliary functions\n", + " @staticmethod\n", + " def define_target_prices(stgy_instance, slippage, vol, floor, trailing):\n", + " mu = vol[0]\n", + " sigma = vol[1]\n", + " p_open_close = math.inf#floor * (1+slippage) * (1+mu+2*sigma)\n", + " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", + " ##########################################################\n", + " # We define the intervals\n", + " list_of_triggers = [\"open_close\",\n", + " \"floor\",\n", + " \"trailing_stop\",\n", + " \"ltv_limit\"]\n", + " list_of_trigger_prices = [p_open_close,\n", + " floor,\n", + " p_trailing, \n", + " stgy_instance.aave.price_to_ltv_limit]\n", + " # We define/update trigger prices\n", + " for i in range(len(list_of_triggers)):\n", + " trigger_name = list_of_triggers[i]\n", + " trigger_price = list_of_trigger_prices[i]\n", + " stgy_instance.trigger_prices[trigger_name] = trigger_price\n", + "\n", + " @staticmethod\n", + " def find_oc(current_oc, ocs, vol):\n", + " mu, sigma = vol\n", + " oc_up = current_oc * (1+slippage)*(1+mu+2*sigma)\n", + " oc_down = current_oc * (1+slippage)*(1+mu-2*sigma)\n", + " distances = []\n", + " next_oc_up = []\n", + " next_oc_down = []\n", + " for i in range(len(ocs)):\n", + " oci = ocs[i]\n", + " if oc_up < oci:\n", + " next_oc_up.append(oci)\n", + " # ocs['up'].append(oci)\n", + " elif oc_down > oci:\n", + " next_oc_down.append(oci)\n", + " # ocs['down'].append(oci)\n", + " distances.append(current_oc-oci)\n", + " # If we get here then we didnt return anything, so we return the farthest oc\n", + " # Furthest down (positive distance current_oc > oci)\n", + " max_value = max(distances)\n", + " max_index = distances.index(max_value)\n", + " # Furthest up (negative distance current_oc < oci)\n", + " min_value = min(distances)\n", + " min_index = distances.index(min_value)\n", + " # print(next_oc_up)\n", + " # print(next_oc_down)\n", + " return {'up_choices': next_oc_up,\n", + " 'down_choices': next_oc_down,\n", + " 'max_distance_up': ocs[min_index],\n", + " 'max_distance_down': ocs[max_index]}\n", + " \n", + " @staticmethod\n", + " def calc_vol(last_date, data):\n", + " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " vol = [mu, sigma]\n", + " return vol\n", + " \n", + " @staticmethod\n", + " # Checking and updating data\n", + " def update_parameters(stgy_instance, new_market_price):\n", + " # AAVE\n", + " stgy_instance.aave.market_price = new_market_price\n", + " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", + " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", + " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", + " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", + " # We have to execute track_ first because we need the fees for current collateral and debt values\n", + " stgy_instance.aave.track_lend_borrow_interest()\n", + " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", + " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", + " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", + " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", + "\n", + " # DYDX\n", + " stgy_instance.dydx.market_price = new_market_price\n", + " stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc()\n", + " stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc()\n", + " stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc()\n", + " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", + " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", + "\n", + " @staticmethod\n", + " def reset_costs(stgy_instance):\n", + " # We reset the costs in order to always start in 0\n", + " stgy_instance.aave.costs = 0\n", + " stgy_instance.dydx.costs = 0\n", + " \n", + " \n", + " def find_scenario(self, stgy_instance, market_price, previous_market_price, index):\n", + " actions = self.actions_to_take(stgy_instance, market_price, previous_market_price)\n", + " self.simulate_fees(stgy_instance)\n", + " time = 0\n", + " time_aave = 0\n", + " time_dydx = 0\n", + " for action in actions:\n", + " if action == \"borrow_usdc_n_add_coll\":\n", + " time_aave = stgy_instance.aave.borrow_usdc(stgy_instance)\n", + " market_price = stgy_instance.historical_data[\"close\"][index + time_aave]\n", + " time_dydx = stgy_instance.dydx.add_collateral(stgy_instance)\n", + " time_aave = 0\n", + " elif action in stgy_instance.aave_features[\"methods\"]:\n", + " time_aave = getattr(stgy_instance.aave, action)(stgy_instance)\n", + " elif action in stgy_instance.dydx_features[\"methods\"]:\n", + " time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance)\n", + " time += time_aave + time_dydx\n", + " # print(stgy_instance.aave_features[\"methods\"])\n", + " # print(stgy_instance.dydx_features[\"methods\"])\n", + " return time\n", + " # stgy_instance.append(action)\n", + "\n", + " @staticmethod\n", + " def actions_to_take(stgy_instance, market_price, previous_market_price):\n", + " actions = []\n", + " \n", + " # Case P decreasing: \n", + " # We need to ask both P_t-1 > trigger and trigger > P_t bc if we only ask the later we will execute\n", + " # the action for all prices below trigger. Same logic for Case P increasing.\n", + "# if (previous_market_price >= stgy_instance.trigger_prices['open_close']) and \\\n", + "# (stgy_instance.trigger_prices['open_close'] > market_price):\n", + "# actions.append('open_short')\n", + " \n", + "# elif (previous_market_price >= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + "# (stgy_instance.trigger_prices['trailing_stop'] > market_price):\n", + "# actions.append('open_short')\n", + " \n", + " if stgy_instance.dydx.short_status:\n", + " if (previous_market_price >= stgy_instance.trigger_prices['repay_aave']) and \\\n", + " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", + " actions.append('repay_aave')\n", + " \n", + " \n", + " # Case P increasing\n", + " # if (previous_market_price <= stgy_instance.trigger_prices['open_close']) and \\\n", + " # (stgy_instance.trigger_prices['open_close'] < market_price):\n", + " # actions.append('close_short')\n", + " # if (previous_market_price <= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " # (stgy_instance.trigger_prices['trailing_stop'] < market_price):\n", + " # actions.append('close_short')\n", + " \n", + " return actions\n", + "\n", + " @staticmethod\n", + " def simulate_fees(stgy_instance):\n", + " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", + "\n", + " # best case\n", + " # stgy_instance.gas_fees = 1\n", + "\n", + " # stgy_instance.gas_fees = 3\n", + "\n", + " # stgy_instance.gas_fees = 6\n", + "\n", + " # worst case\n", + " stgy_instance.gas_fees = 10\n", + "\n", + " @staticmethod\n", + " def update_pnl(stgy_instance):\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", + "\n", + " @staticmethod\n", + " def add_costs(stgy_instance):\n", + " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", + " + stgy_instance.aave.costs + stgy_instance.dydx.costs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## DataDamperNPlotter Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module will write the results and is also used for plotting (for analysis porpuses)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class DataDamperNPlotter:\n", + " def __init__(self):\n", + " self.historical_data = None\n", + "\n", + " @staticmethod\n", + " def write_data(stgy_instance, previous_price, last_outside, current_outside,\n", + " date, period,oc1,\n", + " sheet=False):\n", + " aave_instance = stgy_instance.aave\n", + " dydx_instance = stgy_instance.dydx\n", + " data_aave = []\n", + " data_dydx = []\n", + " aave_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\"]\n", + " dydx_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"short_size\",\n", + " # \"collateral\",\n", + " # \"notional\",\n", + " # \"equity\",\n", + " # \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " # \"collateral_status\",\n", + " \"short_status\",\n", + " # \"order_status\",\n", + " # \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " # \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\"]\n", + " # \"gas_fees\"]\n", + "\n", + " \n", + " data_aave.append(date)\n", + " data_dydx.append(date)\n", + " for i in range(len(aave_instance.__dict__.values())):\n", + " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", + " if list(aave_instance.__dict__.keys())[i] == \"market_price\":\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " data_aave.append(stgy_instance.open_close_range[0])\n", + " data_aave.append(stgy_instance.open_close_range[1])\n", + " data_aave.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " else:\n", + " # print(list(aave_instance.__dict__.keys())[i])\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " for i in range(len(dydx_instance.__dict__.values())):\n", + " if list(dydx_instance.__dict__.keys())[i] in dydx_wanted_keys:\n", + " if list(dydx_instance.__dict__.keys())[i] == \"market_price\":\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " data_dydx.append(stgy_instance.open_close_range[0])\n", + " data_dydx.append(stgy_instance.open_close_range[1])\n", + " data_dydx.append(current_outside)\n", + " data_dydx.append(last_outside)\n", + " data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " else:\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", + " data_aave.append(stgy_instance.gas_fees)\n", + " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_aave.append(stgy_instance.total_pnl)\n", + " # data_aave.append(mkt_price_index)\n", + "\n", + "\n", + " # data_dydx.append(stgy_instance.gas_fees)\n", + " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_dydx.append(stgy_instance.total_pnl)\n", + " # data_dydx.append(mkt_price_index)\n", + " # print(interval_old.name)\n", + "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", + " if sheet == True:\n", + " gc = pygsheets.authorize(service_file=\n", + " 'stgy-1-simulations-e0ee0453ddf8.json')\n", + " sh = gc.open('aave/dydx simulations')\n", + " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", + " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", + " else:\n", + " path_to_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_aave)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_dydx)\n", + "\n", + " @staticmethod\n", + " def delete_results(stgy_instance, period, oc1):\n", + " file_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", + " os.remove(file_aave)\n", + " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", + " os.remove(file_dydx)\n", + "\n", + " @staticmethod\n", + " def add_header(stgy_instance, period, oc1):\n", + " aave_headers = [\n", + " \"date\",\n", + " \"market_price\",\n", + " \"oc_range_0\",\n", + " \"oc_range_1\",\n", + " \"trailing_stop\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + " # \"index_of_mkt_price\"]\n", + " dydx_headers = [\n", + " \"date\",\n", + " \"P\",\n", + " \"oc_r_0\",\n", + " \"oc_r_1\",\n", + " \"out\",\n", + " \"l_out\",\n", + " \"trail_stp\",\n", + " \"entry\",\n", + " \"short_size\",\n", + " # \"collateral\",\n", + " # \"notional\",\n", + " # \"equity\",\n", + " # \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " # \"collateral_status\",\n", + " \"short_status\",\n", + " # \"order_status\",\n", + " # \"withdrawal_fees\",\n", + " \"funding_rates\",\n", + " # \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\",\n", + " # \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + " # \"index_of_mkt_price\"]\n", + " \n", + " path_to_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " with open(path_to_aave, 'a') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(aave_headers)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(dydx_headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Track historical data\n", + "# symbol = 'ETHUSDC'\n", + "# freq = '1m'\n", + "# initial_date = \"1 Jan 2019\"\n", + "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", + "# initial_date=initial_date, save=True)\n", + "\n", + "# Load historical data if previously tracked and saved\n", + "\n", + "historical_data = pd.read_csv(\"Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "# # assign data to stgy instance + define index as dates\n", + "timestamp = pd.to_datetime(historical_data['timestamp'])\n", + "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", + "historical_data.index = timestamp\n", + "#\n", + "# #######################################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-11-01\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", + "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", + "where $\\sigma=vol$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First we calculate weighted vol\n", + "last_date = \"2021-06-01\"\n", + "slippage = 0.0005\n", + "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]-3*60:-3*60]\n", + "for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", + " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", + "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + "print(\"weighted mu: \", str(mu*100)+'%')\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+2*vol)*100-100)+'%')\n", + "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = sigma_2\n", + "mu = mu_2\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"avg movement: (1+mu+2vol): \", str((1+mu+2*vol)*100-100)+'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# normal_std = std\n", + "# medium_std = 2*std\n", + "# high_std = 4*std\n", + "# extreme_std = 6*std\n", + "# normal_std, medium_std, high_std, extreme_std" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find such a relevant price manually by taking a look at the price plot." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define a function that will\n", + "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices\n", + "- Create a new directory \"Files/Tests/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit \n", + "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", + "- Run through the code executing everything as discussed in the dev doc.\n", + "\n", + "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(period, open_close, slippage, max_txs, L, trailing, increment):\n", + " global ocs\n", + " # Initialize everything\n", + " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", + " # First we calculate weighted vol\n", + " last_date = period[1]+' 00:00:00'\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + " # Now we define prices \n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", + " # We create five equidistant OCs\n", + " oc1 = open_close\n", + " # oc2 = oc1 * (1+6/2/100)\n", + " # ocs = [oc1]\n", + " # # print(\"oc1=\",round(oc1,3))\n", + " # for i in range(1,7):\n", + " # globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", + " # ocs.append(globals()[\"oc\"+str(i+1)])\n", + " # print(\"oc\"+str(i+1)+\"=\",round(globals()[\"oc\"+str(i+1)],3))\n", + " # print(ocs)\n", + " # But we start with the first oc1\n", + " # stgy.trigger_prices['open_close'] = oc1\n", + " \n", + " # print(\"Volatility:\", vol)\n", + " # print(\"Floor:\", stgy.trigger_prices['floor'])\n", + " # print(\"Open_close1:\", oc1)\n", + " # print(\"Open_close2:\", oc2)\n", + " # print(\"1-OC2/OC1 - 1:\", 1-oc2/oc1)\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder \n", + " # exist or not.\n", + " if not os.path.exists(\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " # if the demo_folder directory is not present \n", + " # then create it.\n", + " os.makedirs(\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " stgy.historical_data.to_csv(\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " % (period[0], period[1], open_close))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + "\n", + " # Stk eth\n", + " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.dydx.collateral = stgy.aave.debt\n", + " stgy.dydx.equity = stgy.dydx.equity_calc()\n", + " stgy.dydx.collateral_status = True\n", + " \n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= open_close_range[1]):\n", + " stgy.dydx.open_short(stgy)\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(stgy, period, open_close)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(stgy, period, open_close)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + " maker_fees_counter = []\n", + " \n", + " # stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " stgy.trigger_prices['trailing_stop'] = open_close_range[0] * (1-trailing)\n", + " \n", + " market_price = stgy.historical_data['close'][i-1]\n", + " stgy.open_close_range = [open_close * (1-increment), \n", + " open_close * (1+increment)]\n", + " stgy.trailing_stop_range = [stgy.trigger_prices['trailing_stop'] * (1-increment), \n", + " stgy.trigger_prices['trailing_stop'] * (1+increment)]\n", + " if (stgy.open_close_range[1] < market_price):\n", + " last_outside = 1\n", + " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", + " last_outside = False\n", + " elif (market_price < stgy.open_close_range[0]):\n", + " last_outside = -1\n", + " \n", + " if (stgy.trailing_stop_range[1] < market_price):\n", + " last_trailing_outside = 1\n", + " elif (stgy.trailing_stop_range[0] <= market_price) and (market_price <= stgy.trailing_stop_range[1]):\n", + " last_trailing_outside = False\n", + " elif (market_price < stgy.trailing_stop_range[0]):\n", + " last_trailing_outside = -1\n", + " \n", + " while(i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " market_price = stgy.historical_data[\"close\"][i]\n", + " previous_price = stgy.historical_data[\"close\"][i-1]\n", + " \n", + " if (stgy.open_close_range[1] < market_price):\n", + " outside = 1\n", + " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", + " outside = False\n", + " elif (market_price < stgy.open_close_range[0]):\n", + " outside = -1\n", + " \n", + " if (stgy.trailing_stop_range[1] < market_price):\n", + " trailing_outside = 1\n", + " elif (stgy.trailing_stop_range[0] <= market_price) and (market_price <= stgy.trailing_stop_range[1]):\n", + " trailing_outside = False\n", + " elif (market_price < stgy.trailing_stop_range[0]):\n", + " trailing_outside = -1\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, market_price)\n", + " \n", + " # open_close_range action\n", + " if (last_outside == 1) and (outside == -1):\n", + " stgy.dydx.open_short(stgy)\n", + " last_outside = outside\n", + " i += 1\n", + " elif (last_outside == -1) and (outside == 1):\n", + " stgy.dydx.close_short(stgy)\n", + " last_outside = outside\n", + " i += 1\n", + " else:\n", + " i += 1\n", + " \n", + " # open_close_range action\n", + " if (last_trailing_outside == 1) and (trailing_outside == -1):\n", + " stgy.dydx.open_short(stgy)\n", + " last_trailing_outside = trailing_outside\n", + " i += 1\n", + " elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", + " stgy.dydx.close_short(stgy)\n", + " last_trailing_outside = trailing_outside\n", + " i += 1\n", + " else:\n", + " i += 1\n", + " \n", + " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", + " # and we execute all the actions involved between both (current and previous prices)\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", + " ############################## \n", + " # We update trailing\n", + " # Everytime price moves down more than trailing we update trailing_stop\n", + " if (market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']):\n", + " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", + " stgy.trailing_stop_range = [stgy.trigger_prices['trailing_stop'] * (1-increment), \n", + " stgy.trigger_prices['trailing_stop'] * (1+increment)]\n", + " # # If price moves above trailing we move trailing up in order to save that profit\n", + " # # Is important to change trailing after finding scenarios (because we need to close the short first)\n", + " # elif (market_price > stgy.trigger_prices['trailing_stop']):\n", + " # if trailing_update_hours == 0:\n", + " # pass\n", + " # elif (i % (trailing_update_hours*60) == 0):\n", + " # if not stgy.dydx.short_status:\n", + " # stgy.trigger_prices['trailing_stop'] = min(stgy.trigger_prices['open_close']* (1-trailing), market_price)\n", + " ################################\n", + " ################################\n", + " # OC LOGIC\n", + " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", + " # if market_price > oc1:\n", + " # stgy.trigger_prices['open_close'] = oc1\n", + "\n", + " \n", + " # We update vol and ocs if short_status = False\n", + " # if not stgy.dydx.short_status:\n", + " # current_date = list(stgy.historical_data.index)[i]\n", + " # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol)\n", + " # mu, sigma = vol\n", + " # oc1 = floor * (1+slippage) * (1+mu+2*sigma)\n", + " # ocs = [oc1]\n", + " # for i in range(1,5):\n", + " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", + " # ocs.append(globals()[\"oc\"+str(i+1)])\n", + "\n", + " \n", + " # If we executed more txs than hat_L*20 then we change to K_2\n", + " # if (stgy.dydx.maker_fees_counter >= max_txs):\n", + " # # stgy.historical_data = stgy.historical_data_OC2\n", + " # # print(stgy.dydx.maker_fees_counter)\n", + " # current_date = str(stgy.historical_data.index[i])\n", + " # current_oc = stgy.trigger_prices['open_close']\n", + " # vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", + " # ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", + " # # if short = open and if there are up_choices available, we take the last option (the furthest)\n", + " # # if there isn't options we take max_distance\n", + " # # random.seed(4)\n", + " # # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # # 'index': i,\n", + " # # 'date': str(stgy.historical_data.index[i])})\n", + " # if not stgy.dydx.short_status:\n", + " # if stgy.trigger_prices['open_close'] == oc1:\n", + " # stgy.trigger_prices['open_close'] = oc4\n", + " # # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", + " # elif stgy.dydx.short_status:\n", + " # if len(ocs_choices['up_choices']) != 0:\n", + " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", + " # # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", + " # # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", + " # # If we didnt change oc we dont clean maker_fees_counter\n", + " # if current_oc != stgy.trigger_prices['open_close']:\n", + " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", + " # 'txs': stgy.dydx.maker_fees_counter, \n", + " # # 'index': i,\n", + " # 'date': str(stgy.historical_data.index[i])})\n", + " # stgy.dydx.maker_fees_counter = 0\n", + " ########################\n", + " ########################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " current_date = str(stgy.historical_data.index[i-1])\n", + " stgy.data_dumper.write_data(stgy, previous_price, last_outside, outside,\n", + " current_date, period, open_close,\n", + " sheet=False)\n", + " #########################\n", + " # we increment index by the time consumed in executing actions\n", + " # i += time_used\n", + " # i += 1\n", + " return stgy.dydx.maker_fees_counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "periods_n_open_close = [[[\"2020-05-31\",\"2020-09-01\"],240]]\n", + "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", + "periods_n_open_close = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", + "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],243]]\n", + "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fees counter for (pcg = 0.005, increment = 0.002) = 56\n", + "PnL for (pcg = 0.005, increment = 0.002) = -22581.88022449637\n", + "Fees counter for (pcg = 0.005, increment = 0.003) = 32\n", + "PnL for (pcg = 0.005, increment = 0.003) = 50200.7031820752\n", + "Fees counter for (pcg = 0.005, increment = 0.005) = 20\n", + "PnL for (pcg = 0.005, increment = 0.005) = 65653.33378332424\n", + "Fees counter for (pcg = 0.005, increment = 0.01) = 14\n", + "PnL for (pcg = 0.005, increment = 0.01) = -11485.19616509662\n" + ] + } + ], + "source": [ + "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", + "L = 5 * 0.07\n", + "trailings = [0.005]#[0.001, 0.003,0.005,0.01,0.02, 0.03,0.05] #[0.02, 0.03]\n", + "trailing_time = 0\n", + "# trailing_update_hours = [0, 1, 3, 8, 12, 24]\n", + "increments = [0.002, 0.003, 0.005, 0.01]\n", + "# increment = 0.003\n", + "maker_fees_counter_lengths = {}\n", + "pnl_results = {}\n", + "for period_n_open_close in periods_n_open_close:\n", + " for increment in increments:\n", + " for trailing in trailings:\n", + " period = period_n_open_close[0]\n", + " open_close = period_n_open_close[1]\n", + " slippage = 0.0005\n", + " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing, increment)\n", + " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=maker_fees_counter\n", + " print(\"Fees counter for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", + " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])\n", + " directory = \"From_2020-05-31_to_2020-06-30_open_close_at_240/dydx_results.csv\"\n", + " dydx_results = pd.read_csv(\"Files/Tests/\" + directory, low_memory=False)\n", + " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", + " print(\"PnL for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", + " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "price_jump_in_open = {}\n", + "price_jump_in_close = {}\n", + "\n", + "for i in range(len(dydx_results)-1):\n", + " if dydx_results['entry_price'][i]==0 and dydx_results['entry_price'][i+1]!=0:\n", + " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)\n", + " elif dydx_results['entry_price'][i]!=0 and dydx_results['entry_price'][i+1]==0:\n", + " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at open: 0.0041%\n", + "Mean price jump at open: 0.1688%\n", + "Max price jump at open: 2.5665%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at open:\",str(round(min(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at open:\",str(round(np.mean(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at open:\",str(round(max(list(price_jump_in_open.values())),6)*100)+\"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at close: 0.0082%\n", + "Mean price jump at close: 0.1646%\n", + "Max price jump at close: 1.717%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at close:\",str(round(min(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at close:\",str(round(np.mean(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at close:\",str(round(max(list(price_jump_in_close.values())),5)*100)+\"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", + "period = periods_n_open_close[0][0]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "parameter_manager = ParameterManager()\n", + "last_date = period[1]+' 00:00:00'\n", + "vol = parameter_manager.calc_vol(last_date, data)\n", + "mu, sigma = vol\n", + "open_close = 243\n", + "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", + "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=240, \n", + " color='green', \n", + " linestyle='--', \n", + " label='oc1='+str(round(240,3)))\n", + "axs.axhline(y=243, \n", + " color='red', \n", + " linestyle='--', \n", + " label='oc2='+str(round(243,3)))\n", + "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07894394589673559" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['close'].pct_change(1*24*60).dropna().max()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-93714.29797685935" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", + "dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2020-05-01'" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "period" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-01 00:00:00'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(historical_data.index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mu_returns_max, mu_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns= price > current_price:\n", + " crossed_down += 1\n", + " index_down.append(index-1)\n", + " return {'down':\n", + " {'crossed_down': crossed_down,\n", + " 'index_down': index_down},\n", + " 'up':\n", + " {'crossed_up': crossed_up,\n", + " 'index_up': index_up}}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "price = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosses = cross_counter(data_set, 240)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"Files/Tests/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "market_price 176910\n", + "I_current 176910\n", + "I_old 176910\n", + "entry_price 53220\n", + "short_size 53220\n", + "collateral 176910\n", + "notional 53375\n", + "equity 176910\n", + "leverage 53375\n", + "pnl 53066\n", + "collateral_status 176910\n", + "short_status 53220\n", + "order_status 123690\n", + "withdrawal_fees 176910\n", + "funding_rates 176910\n", + "maker_taker_fees 133516\n", + "maker_fees_counter 133516\n", + "costs 421\n", + "gas_fees 176910\n", + "total_costs_from_aave_n_dydx 133516\n", + "total_stgy_pnl 176910\n", + "index_of_mkt_price 176910\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dydx_results.astype(bool).sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def count_maker_fees_increment(data_set):\n", + " index_of_maker_fee = []\n", + " for index in range(1,len(data_set)):\n", + " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", + " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", + " if previous_maker_fee_counter < current_maker_fee_counter:\n", + " index_of_maker_fee.append(index)\n", + " return {'indexes': index_of_maker_fee}" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "matches_up = 0\n", + "matches_down = 0\n", + "for index_up in crosses['up']['index_up']:\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " matches_up += 1\n", + "for index_down in crosses['down']['index_down']:\n", + " if index_down in results_maker_fee_counter['indexes']:\n", + " matches_down += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(155, 136, 291)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches_up, matches_down, matches_up + matches_down" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 156)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", + "- entry_price and short_size can be found at index_up -1\n", + "- close_price is market_price in index = index_up" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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market_priceI_currentI_oldshort_sizeentry_pricepnlmaker_fees_countertotal_stgy_pnl
43393240.70inftyminus_infty0.0000.000.00000-2.879624
43394239.74minus_inftyinfty-4334.634239.740.00001-522.470891
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43396240.86inftyminus_infty0.0000.000.00002-6246.222332
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" + ], + "text/plain": [ + " market_price I_current I_old short_size entry_price \\\n", + "43393 240.70 infty minus_infty 0.000 0.00 \n", + "43394 239.74 minus_infty infty -4334.634 239.74 \n", + "43395 240.94 infty minus_infty 0.000 0.00 \n", + "43396 240.86 infty minus_infty 0.000 0.00 \n", + "\n", + " pnl maker_fees_counter total_stgy_pnl \n", + "43393 0.0000 0 -2.879624 \n", + "43394 0.0000 1 -522.470891 \n", + "43395 -5201.5608 2 -6246.223689 \n", + "43396 0.0000 2 -6246.222332 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 1\n", + "index = crosses['up']['index_up'][i]\n", + "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "diff = []\n", + "cost = []\n", + "# we dont start the loop at i = 0 because the data_set started below open_close\n", + "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", + "for i in range(1,len(crosses['up']['index_up'])):\n", + " index_up = crosses['up']['index_up'][i]\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " entry_price = dydx_results.iloc[index-1]['entry_price']\n", + " close_price = dydx_results.iloc[index]['market_price']\n", + " short_size = dydx_results.iloc[index-1]['short_size']\n", + " diff.append(close_price-entry_price)\n", + " cost.append(short_size * (close_price-entry_price))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1999999999999886, -5201.560799999951)" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(diff), np.mean(cost)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 9bfad1404b770c4dd9f378ea91d7177bf80cfa3c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Fri, 14 Oct 2022 10:23:01 -0300 Subject: [PATCH 10/16] tried 6month period --- jupyter-lab/Simulations_oc_range.ipynb | 30 +++++++++++++------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb index 8c79c44..d651b1d 100644 --- a/jupyter-lab/Simulations_oc_range.ipynb +++ b/jupyter-lab/Simulations_oc_range.ipynb @@ -1444,23 +1444,23 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "periods_n_open_close = [[[\"2020-05-31\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2020-05-31\",\"2020-12-01\"],240]]\n", "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", - "periods_n_open_close = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", + "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],243]]\n", "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 98, "metadata": { "tags": [] }, @@ -1469,14 +1469,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fees counter for (pcg = 0.005, increment = 0.002) = 56\n", - "PnL for (pcg = 0.005, increment = 0.002) = -22581.88022449637\n", - "Fees counter for (pcg = 0.005, increment = 0.003) = 32\n", - "PnL for (pcg = 0.005, increment = 0.003) = 50200.7031820752\n", - "Fees counter for (pcg = 0.005, increment = 0.005) = 20\n", - "PnL for (pcg = 0.005, increment = 0.005) = 65653.33378332424\n", - "Fees counter for (pcg = 0.005, increment = 0.01) = 14\n", - "PnL for (pcg = 0.005, increment = 0.01) = -11485.19616509662\n" + "Fees counter for (pcg = 0.005, increment = 0.002) = 82\n", + "PnL for (pcg = 0.005, increment = 0.002) = -108273.56530499744\n", + "Fees counter for (pcg = 0.005, increment = 0.003) = 52\n", + "PnL for (pcg = 0.005, increment = 0.003) = -27727.044891427304\n", + "Fees counter for (pcg = 0.005, increment = 0.005) = 36\n", + "PnL for (pcg = 0.005, increment = 0.005) = -29320.37499655358\n", + "Fees counter for (pcg = 0.005, increment = 0.01) = 22\n", + "PnL for (pcg = 0.005, increment = 0.01) = -98362.28852459832\n" ] } ], @@ -1500,7 +1500,7 @@ " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=maker_fees_counter\n", " print(\"Fees counter for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])\n", - " directory = \"From_2020-05-31_to_2020-06-30_open_close_at_240/dydx_results.csv\"\n", + " directory = \"From_2020-05-31_to_2020-12-01_open_close_at_240/dydx_results.csv\"\n", " dydx_results = pd.read_csv(\"Files/Tests/\" + directory, low_memory=False)\n", " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", " print(\"PnL for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", @@ -1567,12 +1567,12 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] From 1770f98f4b135f35771e18e6308bf70e186a5d28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Mon, 17 Oct 2022 10:44:37 -0300 Subject: [PATCH 11/16] better results by taking trailing_pcg based on range length --- jupyter-lab/Simulations_oc_range.ipynb | 277 +++++++++++++++++-------- 1 file changed, 196 insertions(+), 81 deletions(-) diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb index d651b1d..bf81469 100644 --- a/jupyter-lab/Simulations_oc_range.ipynb +++ b/jupyter-lab/Simulations_oc_range.ipynb @@ -13,37 +13,40 @@ "Requirement already satisfied: scipy in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.9.1)\n", "Requirement already satisfied: pygsheets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (2.0.5)\n", "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", + "Requirement already satisfied: numpy>=1.21.0 in 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urllib3<1.27,>=1.21.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.26.12)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], @@ -591,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -790,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -860,7 +863,9 @@ " data_dydx.append(stgy_instance.open_close_range[1])\n", " data_dydx.append(current_outside)\n", " data_dydx.append(last_outside)\n", + " data_dydx.append(stgy_instance.trailing_stop_range[0])\n", " data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " data_dydx.append(stgy_instance.trailing_stop_range[1])\n", " else:\n", " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", @@ -928,11 +933,13 @@ " dydx_headers = [\n", " \"date\",\n", " \"P\",\n", - " \"oc_r_0\",\n", - " \"oc_r_1\",\n", + " \"oc_rge_0\",\n", + " \"oc_rge_1\",\n", " \"out\",\n", " \"l_out\",\n", + " \"trail_stp_rge_0\",\n", " \"trail_stp\",\n", + " \"trail_stp_rge_1\",\n", " \"entry\",\n", " \"short_size\",\n", " # \"collateral\",\n", @@ -1146,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 145, "metadata": { "tags": [] }, @@ -1238,8 +1245,14 @@ " stgy.dydx.equity = stgy.dydx.equity_calc()\n", " stgy.dydx.collateral_status = True\n", " \n", + " stgy.open_close_range = [open_close * (1-increment), \n", + " open_close * (1+increment)]\n", + " # stgy.trigger_prices['trailing_stop'] = stgy.open_close_range[0] * (1-trailing)\n", + " stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-increment)**2, \n", + " stgy.open_close_range[0]]\n", + " \n", " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", - " if (stgy.dydx.market_price <= open_close_range[1]):\n", + " if (stgy.dydx.market_price <= stgy.open_close_range[1]):\n", " stgy.dydx.open_short(stgy)\n", " #########################\n", " # Clear previous csv data for aave and dydx\n", @@ -1260,13 +1273,9 @@ " maker_fees_counter = []\n", " \n", " # stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", - " stgy.trigger_prices['trailing_stop'] = open_close_range[0] * (1-trailing)\n", + " \n", " \n", " market_price = stgy.historical_data['close'][i-1]\n", - " stgy.open_close_range = [open_close * (1-increment), \n", - " open_close * (1+increment)]\n", - " stgy.trailing_stop_range = [stgy.trigger_prices['trailing_stop'] * (1-increment), \n", - " stgy.trigger_prices['trailing_stop'] * (1+increment)]\n", " if (stgy.open_close_range[1] < market_price):\n", " last_outside = 1\n", " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", @@ -1315,36 +1324,40 @@ " if (last_outside == 1) and (outside == -1):\n", " stgy.dydx.open_short(stgy)\n", " last_outside = outside\n", - " i += 1\n", + " # i += 1\n", " elif (last_outside == -1) and (outside == 1):\n", " stgy.dydx.close_short(stgy)\n", " last_outside = outside\n", - " i += 1\n", - " else:\n", - " i += 1\n", + " # i += 1\n", + " # else:\n", + " # i += 1\n", " \n", " # open_close_range action\n", " if (last_trailing_outside == 1) and (trailing_outside == -1):\n", " stgy.dydx.open_short(stgy)\n", " last_trailing_outside = trailing_outside\n", - " i += 1\n", + " # i += 1\n", " elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", " stgy.dydx.close_short(stgy)\n", " last_trailing_outside = trailing_outside\n", - " i += 1\n", - " else:\n", - " i += 1\n", - " \n", + " # i += 1\n", + " # else:\n", + " # i += 1\n", + " i += 1\n", " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", " # and we execute all the actions involved between both (current and previous prices)\n", " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", " ############################## \n", " # We update trailing\n", - " # Everytime price moves down more than trailing we update trailing_stop\n", - " if (market_price*(1+trailing) < stgy.trigger_prices['trailing_stop']):\n", - " stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", - " stgy.trailing_stop_range = [stgy.trigger_prices['trailing_stop'] * (1-increment), \n", - " stgy.trigger_prices['trailing_stop'] * (1+increment)]\n", + " # Everytime price moves down more than (1+trailing) / (1-increment) we update trailing_stop\n", + " # (1+trailing) is to contemplate a pcg between current_price and trailing stop\n", + " # 1/(1-increment) is to assure that we update when the price crossed the whole trailing_range\n", + " # if (market_price*(1+trailing) <= stgy.trigger_prices['trailing_stop']):\n", + " # if (market_price*(1+trailing)/(1-increment) <= stgy.trigger_prices['trailing_stop']):\n", + " if (market_price <= stgy.trailing_stop_range[0]):\n", + " # stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", + " stgy.trailing_stop_range = [market_price, \n", + " market_price * (1+increment)**2]\n", " # # If price moves above trailing we move trailing up in order to save that profit\n", " # # Is important to change trailing after finding scenarios (because we need to close the short first)\n", " # elif (market_price > stgy.trigger_prices['trailing_stop']):\n", @@ -1357,8 +1370,11 @@ " ################################\n", " # OC LOGIC\n", " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", - " # if market_price > oc1:\n", - " # stgy.trigger_prices['open_close'] = oc1\n", + " # if market_price >= stgy.open_close_range[1]:\n", + " # stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-increment)**2, \n", + " # stgy.open_close_range[0]]\n", + " # trailing_outside = 1\n", + " # last_trailing_outside = 1\n", "\n", " \n", " # We update vol and ocs if short_status = False\n", @@ -1444,7 +1460,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ @@ -1452,7 +1468,14 @@ " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", "periods_n_open_close = [[[\"2020-05-31\",\"2020-12-01\"],240]]\n", - "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", + "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-02\"],240]]\n", + "# Worst cases\n", + "worst_1_week = [[[\"2020-05-31\",\"2020-06-07\"],240]]\n", + "worst_1_month = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", + "worst_3_month = [[[\"2020-05-31\",\"2020-09-01\"],240]]\n", + "worst_6_month = [[[\"2020-02-20\",\"2020-09-01\"],240]]\n", + "worst_1_year = [[[\"2019-09-01\",\"2020-09-01\"],170]]\n", + "# p = 243\n", "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],243]]\n", "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]" @@ -1460,7 +1483,33 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "354" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Period of Simulations\n", + "periods_n_open_close = worst_6_month\n", + "period = periods_n_open_close[0][0]\n", + "p = periods_n_open_close[0][1]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "crosses = cross_counter(data_set, p)\n", + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 167, "metadata": { "tags": [] }, @@ -1469,14 +1518,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fees counter for (pcg = 0.005, increment = 0.002) = 82\n", - "PnL for (pcg = 0.005, increment = 0.002) = -108273.56530499744\n", - "Fees counter for (pcg = 0.005, increment = 0.003) = 52\n", - "PnL for (pcg = 0.005, increment = 0.003) = -27727.044891427304\n", - "Fees counter for (pcg = 0.005, increment = 0.005) = 36\n", - "PnL for (pcg = 0.005, increment = 0.005) = -29320.37499655358\n", - "Fees counter for (pcg = 0.005, increment = 0.01) = 22\n", - "PnL for (pcg = 0.005, increment = 0.01) = -98362.28852459832\n" + "Fees counter for (pcg = 0.005, increment = 0.0005) = 322\n", + "PnL for (pcg = 0.005, increment = 0.0005) = -159700.58932288198\n", + "Fees counter for (pcg = 0.005, increment = 0.001) = 260\n", + "PnL for (pcg = 0.005, increment = 0.001) = -157572.0970661439\n", + "Fees counter for (pcg = 0.005, increment = 0.002) = 194\n", + "PnL for (pcg = 0.005, increment = 0.002) = -104398.10832875544\n", + "Fees counter for (pcg = 0.005, increment = 0.003) = 136\n", + "PnL for (pcg = 0.005, increment = 0.003) = -40176.11423351267\n", + "Fees counter for (pcg = 0.005, increment = 0.005) = 96\n", + "PnL for (pcg = 0.005, increment = 0.005) = -52110.249370017846\n", + "Fees counter for (pcg = 0.005, increment = 0.007) = 74\n", + "PnL for (pcg = 0.005, increment = 0.007) = -9654.954697517012\n", + "Fees counter for (pcg = 0.005, increment = 0.01) = 56\n", + "PnL for (pcg = 0.005, increment = 0.01) = 25778.04723751844\n" ] } ], @@ -1486,7 +1541,7 @@ "trailings = [0.005]#[0.001, 0.003,0.005,0.01,0.02, 0.03,0.05] #[0.02, 0.03]\n", "trailing_time = 0\n", "# trailing_update_hours = [0, 1, 3, 8, 12, 24]\n", - "increments = [0.002, 0.003, 0.005, 0.01]\n", + "increments = [0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", "# increment = 0.003\n", "maker_fees_counter_lengths = {}\n", "pnl_results = {}\n", @@ -1500,13 +1555,33 @@ " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=maker_fees_counter\n", " print(\"Fees counter for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])\n", - " directory = \"From_2020-05-31_to_2020-12-01_open_close_at_240/dydx_results.csv\"\n", + " directory = \"From_%s_to_%s_open_close_at_%s/dydx_results.csv\" % (period[0], period[1], open_close)#\"From_2020-05-31_to_2020-12-01_open_close_at_240/dydx_results.csv\"\n", " dydx_results = pd.read_csv(\"Files/Tests/\" + directory, low_memory=False)\n", " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", " print(\"PnL for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])" ] }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'From_2019-09-01_to_2020-09-01_open_close_at_170'" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], p)" + ] + }, { "cell_type": "code", "execution_count": 89, @@ -1567,12 +1642,50 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-01\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", + " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-01\"],185]]" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "354" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Period of Simulations\n", + "# periods_n_open_close = worst_1_month\n", + "period = periods_n_open_close[0][0]\n", + "p = periods_n_open_close[0][1]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "crosses = cross_counter(data_set, p)\n", + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 163, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ESZdHv1bnR2brZGGpW78zxWWa9NoydXjoK016bZmjfehzC936hsvSHcf0/or9AZ+3M7fAZX+tl8+s8yOzHYkkZ+sPntZLC3boT/N36NAp/6clMkpJmc2xHRcd0CtIBFGJxVZ9pyCJijLp6n5tXNpKreG7vj/2HS/0ebxzi8o1oHwlkpzlnimpVUx1jcVqk93uPjWizVbZNrJ7S7fjl1ZpWzp1hCRp+pU9ghwhjBJQZVJSUpJ69uzp0paYmKjU1FRH+2233aYpU6aoWbNmSk5O1m9+8xtlZWVp6NChkqQxY8YoMzNTN954o55//nllZ2frscce0+TJkz1WHwEAAAAAAMA/d7+/xrFdWGr10bNSbn6xmjeOU1SUfy9OPdl/wvcLWk+2Z5/R2JeXOvbnbva8lnaFfSdcEygFJWVKjDXr+jdXyGq36z93ZgUcQ009+ukm3TAkI6BzFm3NddlfvueEOjz0laZf2UM3ZnXwep7FalOMOUpXzvze0bbpcL7aNq0b64ifKbZo6kcbdHmf1prYu5WjvdhS+fvn70t51H8lVZJHtU1mlVltKimz+ayyDERSvO9xmiXG6rsHRyixFtMy1kc2m137ThSqZXK8ev5+rjJbJWv2fRe79HGuQB3fq5UWbnN9pi2qst8+NcGlshL1X9C/FvDSSy/psssu06RJkzRs2DClp6frk08+cRw3m8368ssvZTablZWVpV/84he66aabNG3atGCHAgAAAAAAEFHyzrpON+fp2+XOluw4psHPLtQjn9ZuZfTHP9sc8DnOiSRJ+nLD0YDOf3n+DuXkl2j5nhNaufekjpw+G3AM/urcItGtzRJgxUW02XNC5fHPNut/aw9Lkl6cv8PteNdHv3Zre+A/6wK6dii9tni35mzO1uQP1ri0z9ucY1BEcFaLHHGNfFXlPnauUKuJLo9+rR6/n6vdx9yr9Woiyimx+bOB7Tz2ads0QU0TY4Nyvfqi0yOzdemflqjn7+dKkrYczVdplT+7Sa8td2xf2TfwqT5R/9U6mbR48WK9/PLLjv34+HjNnDlTJ0+eVGFhoT755BO3tZAyMjI0e/ZsFRUV6dixY3rhhRcUHR1Z2V4AAAAAAIBQW777hMf289LOTeX06qKdkqQPVx0MW0zBYjabXL4pPyWECZZJA9q6tRWVWvXivO16bfFuv8YY3s19WqgKv/33Otlsdv154U6PxzccOu2yX1hqdXvRGy5FpWUqc0qkHfMyxdd/fqx/v1MIPucKtdoY+aclQRnH6jRV272j6v7aY/6oek8Gy/bsM7pwxiIt233c7ViMOUr/uTNLLZOYaSySMGEpAAAAAABAA3X9WyscVS/OLNZzL1TNYShbSE+OD8m4byzZozEvVVY3/bDnZEiu482ibTn686Jd+sOcbS4vqL2JruazHv2S95flV7z6vVvbr99fXX2QQVZYUqbeT87TaKfPverUVhWK/JxmEQ3b019tqfG51VVW1oStfMzk+Gi1adIo4POvH9LeY7tRyd38Yosyn5jrck8Gy+WvfqfDp8/q+jdXeDw+uGMzffLrCxz7D47rHvQYULeQTAIAAAAAAGjAfvvvdZKkIR2bOdoqXnyGOgEz6vw0x8vbcBj+x2+06XBe0Mf19CPc/+/1ju01B05VO0Z1n8PuY4GtO7Vga66OF3iuCgqVDYfyVGaza+/xQpWUnUsWnSgs9djXnwQbQuPNmwY6to1er+pUkaX6Tl7k5Nf89zvvrEU2D7+DFfdhTaexG9cj3WN7cZkxydMf9517hu89Hvi6dYFY6+UZ18zpc7y8TyuPfdBwkEwCAAAAAABoIHxVATknM04WlobkW/9VPXVlDwWaU4g1R+m6wZ7XMqnOvhNFuuwv39XoXF+qS4xc+/pyn8f9GaMmBj69IOhjevPhygNavqdy2sTJ769x6/PN9soqpcMhXMMKvo3OTDM6hKD4ZO2hGp330Y8H1eepeer0yGy3Y9O+OFcptf9EUY3G9lbNWWIxpjIpGPyZIu8nf13msb1RjFmjzk/TsPNa1KjSC/ULCxUBAAAAAAAYJNj5nOz8Yo/tczdnq8wpmXHWYlXHh91ftAbbuZeLldd9flJv/e6/G3yes+OZ8ZKkIR1T9bele7TlaH4oQ/RLbRJBdrtdJpOpXlfq7Mot0EOfbHRpW3PgtCSpZVKccsvXTfrLwp0a4WNtKKDCk59v1o/7T+rjuy5QfIzZ7bjVZtfzc7Y79r1NL+fJ1I+9P2PWH6pd5aK3Qq9grQ0VqAVbPU8zGYjSWqy3ZDKZ9NbNA6vviAaByiQAAAAAAIAG7s53V3uc8snZeY997bVayWK16eBJ12/ynywsdbT1b9/E67jOQ/50kHvFkbcqpKv6tdHs+y52a//VRR29XitUajpV3w97TmjQMwv09cajjmRSpxaJQYsrtYZTdQVqn4cptC7vfW5Kqyv6tHa0bcs+ozKrTV9uOBKWuFD3zP3tMLe2qs8OSZq1bJ82Hc7XvC05HscpKC5z2f9gxQGd9DKloi8V0zHabHaPcQTKJC+VSQatmVSbZe/KrDb99I3l+tTDunq+fDb5wppfFPUaySQAAAAAAIAQqzqNUOsU79PRhUpRqe9vzpeW2XTLO6s8HvvlrFW6+Plv9M22ym/B958+Xxc//41W7j3pqFJx9rcbB0hyT8S0TIpz2X/8skx/wne4d1TXavucreZnrarYYtVts1bpvR/2ezzuT1XRrtwCt7br3vxBxwtKdff7a3S84NyLcLNTacOfr+sXUJxVeVuvKNg8vShPTzk3pVVCbGVVidlk0sXPf6N7PlgblrhQvUYeqn5CqVt6ktY/MUa/ubSLo+3i57/x2v/BjzfozaV7tHLvSdekpYckSf/p8wOP57E5Ki2z6fefb/YZh7+8JW9KDFozaUBG0xqfe+PbK7Vy70k9+ummgM7r065Jja+J+o1kEgAAAAAAQAi9smCnej45V9uzzyjWfO5VjMnbXEkhtNNDsqOqJTuOeWz/dudxSdI/lu9zO/bTNzyvFzSmfKH6qmmY34x0TQYlxFauwjD7XvdKpFd+3tdlPzk+xuP1nB0+HVgFwqxl+7RwW64e+98m5eYXa8Oh0/rj3G06XnBu+jarH5VJo15c4lb54Hza5A/OrTFkjjLp47uy9Or1/XRFn9Zq5SWxmBzvvjrFB78a4u+PFFTxMe6vEP8wZ5vyzloU75RMOlNSpqN5rlMtPj+pd8jjg7upY7vp8j6tdUHn1LBfOyUhRlE+nnG2KlNuPjN7q376xnINf2Gx/rXygCRpZ86ZgK+7bPdx7T/hXkW3PfuM3q2SKL53ZPVJaU9ioj2/Ti8OwppJNZkq74xTBZc/ax85c14DDfAHySQAAAAAAIAQemnBDhVbbHp29lbZy1MrUfXojcyJ8oSKVFlV4206vArOU59VnV5v9Plpbv23Pz1Oax4frczWyW7Hruzbxq3tuwdHaGT3yrV5Hpt4vsvxWHNg1RgvL9jh2B787EJd8er3mvnNbj3wn/WSKpNCbZs20oIpl3gdZ+SLS/y63sAOzXRZ73Of0YVdmnvsM7/KdS45r4Uu6NJcPxvoOi1gXpFFdru92j+T2vCWGOjz1DzFRfv+rH/S3/3PD6E3eUQX/eW6foqqzTxoteArX376rMXrsYfL1+aqukaXs3mbs7U92zXZtCPnjK5/c4Uu+eNit/6Xv/qdW1vbJo28B+hDUpxrkrdT83PTVta2MmnDodPq/vgcTftiS0DnpSZWVnqGY1W2d24dFIaroK6qR//pAgAAAAAAUH+VltlksZ573edt3Y264GypVX9ZuNPxsvYpp5ebFQu1e1uw/bmre2nvcxNcpm+rmuNIT4l3rLFUUWkTF21WswDW/2nbNEFv3zLI8SJ3bHkVVIVA1zjyVlWwZMcxlZRZ9a8V56olJvZqpS4tG+u6we099i/1Y92UbVVegnt615+eHK+05HhldaqsKmlcXqn0h2t6q6/TNFPLdh/XTX9fqUmvLat2Xawa8/Hrmptf7P2gpBgzrx/hasizC6rtM6C95+nblu06rjveXa2xLy/V6v0nHe1vLt0TUAyvfrMroP4VurRs7LIfW16p5M9UmL48N3ubJOnv3+8NKDHsnLQL5Ln34rztHtsv691KH92V5fW8Czt7Tn4jMvA0BwAAAAAACAPnKYWCXSxgs9n1i7dWBGWslxfs0J/m79DYl5dKkr7fddxxrGK6O29Jk77tmrhN4efp9eYnv75Q+2ZM1AVeqnL8Nee3w7T+iTFq1yxBf7ymcjo1f6al89f/fbRBZ0rOTSX12bojkqTfX56pv1zXT+ufGOPxnONO1VzVqZrI2vzUWC393QhJ0mV9Wjnar3Kq0HKeGu/u99fo253HtebAaa0+cMrv6wbCavX+eX6y9nBIron6zVvC3GazO5Lq3nR46Cv9+8eDHo9d7/Scm/TauSk2Z32/Vx+tPhRQfD8f3K76Th6YTCaXZHJF1d7n5c+GmtpyNN+xXd36dt7489g7cvqsFm3L0Z8XeU6mvXp9fw3q0MzjsRuHZjiSZ4hM/OkDAAAAAACEWbDXTNp8JF/fOSV9auONKt/wP1FY6rK/6XCe12RSoxj3Kc8CrRLyxTlhJJ2rCkhJOLeG0rUD26lp+XYwp3z7Yn3lS+Ls8iqc+BizLu/T2nFtZ3//bq8GPr1AFz+/yK/xP1/v+hI6MS7a8cL254Paq0NqgjJbJbtM6+ft1+e+f63165qBKvKxlsuxM/4nzhA5ThWVemzfeDgvoHEGZDRV77YpPvs8GeDUcJJ020UdAz6ngvPzxVz+zYCPVh9STjVVer7kOU39V1bDKid/nrUXzFikX876sdp+3dKSHNvv3DpIj0zormlX9qhRXGg4SCYBAAAAAACEWZmt9ou1OwtmJU51Vu8/5XWau3bNEtzaBpZ/y72Jh8SLv1Y9Okrv3DpIk/q39dmvokogVLO9+WPal+debB88ebbWY5mjTFo8dYRm33exy9o3v7m0q8f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H0u33iELlavPi7NIbweMKxz7bkXLOYZuWL8xUwpgZNssNBoO+uLOHkjrG6at7eql3szp6bEBLSdLtveM1Y9NxPTJ1nXLzyt/777ft1tfAT/4oKGv5S4lenIWOnLlod3luXr7WHzojkwdi8pbdqeesqsEU+s/dPXVZk5p2t9n40mC7ywEA1RPJJPi0zuPnkjApYd62VD313w26kJOrTUfSvR0OAAAAfEijWmFW81c5GG/ojU1GvTZzp96cU9RzqfgYRw1L7EeSKuN5/ZzcfJfHV+n/D9vxeve9nqQh7WPLdOydrw5VVKhtJfhfH6XEU0VrUCPU2yHATc6SSJK098R5qzHcSqodEawPRnSzjIkWdKkc5/+tOKhRU9dp+qbj6jx+rs12ZrNZCWNmKGHMDK0/dMbh/t+ev0sLdqZpT5p7Sec1B0/bLMsy5anFC7P05w+Wqd1Ls/22vOKvm6zHtRuZGK/9k5LUp1U9/fjwFVbrkjrGaf5TV6tGeJAAAChEMgk+zWwueNLs1enbvB2Kz7jvP2v047qj+mTxfj341VrL8tFDWissyKjpj16pD0dcZln+t9k7qOcOAABQTeSWuHm7K/WcDp7KtGmXlVdw43bpnpM6nn5Rb87ZYbU+NKjop6LBQZU7s9mshTvT7D789d6tXR3G+NI17RyuazVulsMeSvYs2JFmNR8QYNC/b++uVS8MsNv+y7t7WqaLJ46+uKuHQgKNWv3CQE28vr3+Nryjdr46VAcmJyvIyM/mihZpJ4lX0o6JQ/XFXT305o2dtOe1YVbr/Lm3iL8xm80ulYKzd92RpGeHtra7PNDOeZaZk2fpAWU2m5WWkaVle4t6UP75g2VKGDNDS3aftNpuwD8W6u35u3XXF6s1be0Ry/JzdiqfRAQb9ePDl1vmv7w0JppU8Ll65ZetVr3fTHlmdXh5jt3X4Ot2lUgATri+gwzFLvD/fSBRkvR8Uht9MKKbWsREVmp8AADfV/o3Nh+WmZMpY47RZrkxwKjQwFCrdo4EGAIUFhRWprYXTBccPjVnMBgUHhReprYXTReVb3b8ZTgiOKJMbbNys5SX77iXjzttw4PCLV86snOzlZvv+Mkcd9oWf3/NMsmsghg+XrJdTw6Ot2kbYCj4wpmTlyNTnuOSeKGBoTIGGJ22NZlMysoreN1BKnj6xpRnUk6e48EpQwJDFBgQ6Hbb3PxcZec6risfbAxWkDHIbtt8FZTTOJx+WvnKkkGBMihQo/q10L1XxivXnKPDZ7Ms7d5fuFVL9h7Rt/clKsgYpGBjQc33vPw8ZeU6Ls1RvG2+OV8XTfa7+7vbNjAgUCGBBaVCzGazLpgueKStO+c91wj7bf3lGlH8vM/Ky1JmTqaCzLZPzHn6GmGvrS9eI5y1dee85xrBNaJk29LO+2BDsMttK+sa4exc5hph25ZrRNW4RpzPOW/5HihJf+w5qqvfPKWxw9ro/j7NlJWbpeycbEubHalZ6jWpKHkToILXFmwMsFwjcvMvKl9Zysq9YPUahr29SodOF7yvZuXIrILryWd3dFdOXtH30Yhgoy7mFP1bfWvv+urbJlqPfbvebk97g0Is4zQV/01gz51TVlnaPjOkqSW+iJCi782/PnKFrn1vqQwKVl5+vr66p5feW7hdL1/bWueycrUj5Zx6No2wbHtDt3pOrxHF399TmRmqGRbBNULlu0Zk515QvrL0f3f31O3Fxrh69+Yu2p16UXHRkQoNMurqVnV10XRR2XkX9fbNbfXYt+slSc1f+EnbJwytEtcIe99r7bX11veIIW+ttJSCy1e2JLP+tWCz7r6imSTpjz0ntO7AGbWMjbLarvAacXPPGLvvyR+7T9i0laT7v1qql65ppwnFHjAteY249bOiXopLnuun3SdO2W3b4ZUZVteT23o10eghbRQcGHDpt3WwNh5J1+Pfrte4a1rpr//+Q3tP2P/7xY/5Vf1bx+rAqQt6qF8TJXWIsUrMFFfR3yMu5uTp65V79efL4hQaZHuPrLBt+qVxqc3K05ZX+tv8Hdo3DNHWCX0t56bkO9cIX/oeUXjvqPj5WpW+R5TWlvsR/Nbwxe8Rzs57FfsI8lvD8TXC2bbFGcyu1hDwIRkZGapRo4Y0RpKd3vBJLZM049aiurwRr0c4/INeHX+1Ft650DJf7816OnnhpN223Rt01+r7VlvmE95O0MH0g3bbtqvXTlsf3mqZb/9Be207Yb93TXyNeB144oBlvscnPbTm2Bq7beuG19WJ0UVfsvpO6atFB23LO0gFF8TM54s+CMlTkzVzt+On/MwvF30U/jLtL/p+2/cO254fe95yIb/z5zv15cYvHbZNeyZN9SLqSZJGzRilD9Z84LDt/sf3q+/kgvftTODnygj60WHbLQ9tUfuYggFwX1n4isYvGu+w7ap7V6lHwx6SpDeXvqln5z/rsO28EfM0sMVASdL7q97XI7Mecdh2+i3TldwqWZI0ZcMU3fW/uxy2/e+N/9Vf2v9FkjRt6zT99fu/Omz7xfVf6M4ud0qSZuyaoWu+ucZh29o5Dyoq7xodmJyshQcWqt+X/Ry2fWPgGxp9xWhJ0uqjq9Xz054O27589ct6pe8rkqStaVvV4cMODts+k/iM3hz8piTpwNkDavpOU4dtH+7+sN5Pfl+SdCLzhGL+HuOw7R2d79CUP02RVHBRi5zk+MmoG9vdqGl/mWaZN4x3PFA014gC/nqNSKiZIEl6evbT+ufKfzpsW1HXiAV3LFDfhL6S/OMa8d6w9zSq5yhJ4hrBNaJCrxE5z+do5syZSkpK0q0/3+r1a8TouaP19+V/d9iWa0QBrhEFqvI1wpgfo0bZn0uSJt3QUf/cONzhNSLAHK3GWVMlSQcmJ5d6jaiX8V/LfFrwK7potL9fSYq/ON0y3aPbFKfXiMYXv7cktU4GvaXMwN8ctm108WsZVTA4fK+uP+m/Oz5z2LZh1meacnuyBraL5RrhQ9eIhfvWqt//dXfYtjpcI0wmk2bOnKl7d96rkxd9+3tExKkplvmU4DHKNm6x29ZgDlGTrB8s8+5cI04ET9IF41KHbct6jTgV9KHOB9qO4VSoYdZnCjQXlMks7X5E/az3FWwueOD1bODXSg/6xmHbir5GJIyZofPG+ToV/LbDtv+98b96f2ZdHTh1QZkBS3QyZLLDtr52jeB7xELLfHX+reFr9yP4HnGnJP+4RjzV6yn1ye6jpKQkHc08yjXiEptrRJakyVJ6erqio6Mdxkd/fVQJeS50swcAAACqs7E/bvZ2CPrHXzq71b5L45outw0Ltv9EfnE9Emq7dfyKkJ9vVm5evvaccD7mTHVx6ycrvR0CKkGdyBCn61uX6MkEzzuW7vgpfwAAXOHXPZOOnThmN1NGl1H7bf2ly2izsbMk2Za0WDFmgNXgj4VdRi/m5Kn767PUvn6EvrjLftba1TJ3c+bM0fXJ1ys0pODz42tdRtcePK3bPltl1dagQO2YcI3Cgo2WbqBJ7y7W/pPWWevtE4ZWmW7l9vhDl9HiuEaUrW3xruKZWZmaPnO6hgwZoqAgyty50ra6lJ6wh2tEkYosc1fYMynPkOf1awSlJ7hGVKdrxLbjJzT0ncWO912snMP2if3V+kXHT/4Wtj0wOdlyjbh7yiot32c7KH3x/RaWpVrzwkBFhATqm5UHNWHG9qLjjr9ee0+cV8eGNZSdl225RpzOzNGyvSc1sG2suk6cV/D6FKIH+jTXjd0aKb5OsN3zvu1Lsy1tC0tYbXy5nwKN9t+z3Lx8BRhCFBVa8FkrzzXCbDarXbExU/a//ifLeZ+dm62W46arpAXPXK246DCFGEPU4oU5l96zXJlV9NrmPdlHjWoV/V2ryzUifsyvMqvgtf33gd7q2LCmw7bFrxFfLN2vN+bstLTz5zJ3hT2Trh54td3vtcXbFvLG94gLObnq8eoSy3xhmTuHr6/YNWLHqwOcfje4Z8omrbh0nTErR2/c2EGjv99kP+ZL5/2uV4ep5bhfHJbC3DZ+iPammXT9+8su7bfoHsP2CUOt2rZ9abYMCpbh0jPX9kpsbp8wVEfOXNCgtxaX2laS/nVLVw1sG1vh3yMSxsywuZ6UtPe169TyhbmX4s3Ttgn2x5WTfO8a4WvfIwrvHRX/Herv3yP84beGr92P4LeG/1wjlC/NnzNfSUlJMgYa+a1xScnzPiMjQw3qNSi1Z5Jfj5kUERxhdTFx1s6dfbqq+MXUk22L/2E92bb4B9GTbUMCQxQi508ZlaWtQUEyqOiLdGBAqCKCbeP634ajyswyaNX+CwoLDFdAgOOuglLBhax4DeBCJoNJocaii7JUcHEqvOiVxp22gQGBCgx27fQrbHvs7EWN/GyT1RfyQoVPYRoDjIoIjtDQdgn69+J9lvW9mta2+WwXtnVFgCGgQtoaDIYKaStV3HnPNcL9thV1jQg2BivUGKqI4AiHP7qLt7V33pe3rS9dI1zhznnPNaLi21a1a4TJVPSjx1euERVx3nONcL8t14iytXXnvL/hgzV2vyPas2LfOf25c1P9b+PxUtsWXiMCA8JK3b9Bwfpl1BWKiSooJ9WzaQMFaL9lfURIoDo1qinJ+hoRERyhm7rXklR04/m5oW30UN/mljb2znt78dQIc/09K+95X/z4hb8fDp26oD5vLrAb24C/r9ScJ/poyNtFSajCsU8LDXlrlfa9nmT394wnrhFHzlzQrtRz6tc6ptS29lTUNcKgABkuvWfdmtRXkNFxAZXi1whTbrDVe13yeP56jSjte20hb3yPaP+SdYm4ABf/TX6kX4tSvxs8NqClVuxbqdoRwfrHX3qoa5Oaek677LbtkVBLU+/rrcAAg819g0J7XhumQGOA2tQvuuFc2PaLu3rY/F1Knrcl97todF9FBEeodWyEfn9qsH7fkabXZ+6w27bQ9mM5ur6z9XHK8j3i4KlMNagZ5vTcKHk9KakwkVTQ1vXz0xeuEb72PaLw3pGz89UXridV7bdGWdvyW8P9tlXtt0bx36n81ihS8rzPC3acoC3Or5NJqF42H03XgGjbfzDenr/bMj1/e6oGt4+rzLAqzfK9p+wu/3DEZTbLLm9R1yqZFBXKqQ4AAFBVZZkcP/Vb0l1frC61zXu3drWaL+z5U5rOxUrSFSaO3LHuxUFKSc9SuwaOn4YsdOflCZqy7IDbx/C05vWKfrT3eXOB07ZD3nbce6xQs+cLeo0dmJxcvsDsuPJvBfF9dkd3DWgb6/H9l8XZC9ZPSTu7WV5SyVLn+fnmUh8sRNllZts+rf/A1c3070X77LQuUCs8SGvHDXLp73J587pa/+Ig1QwPksFgsDneQ32b65nBrWUssa+PbrtMpzJzNKJXvNYePK3vVh/WmGFtFXjpsxQYYPuZKp5QLTSiVxN9vfKQAgMMyi3x2frb8I6Kr1N0rreIiVKLmCgN61Bf87alasL0orFmGtcO0+HTBU+9F78+lNXsLcf14FfrJEkTrm+v23rFO3w//9Ktkf42vJPlOuLIjd0alTsuAED1xJhJ8DnhDmqdhzvIdKdkFHV7HP+r/QEDq4LnfrDfxX9Yx/o2y/q0rKt7rmyqy5vXkSSZ8vyumiUAAAC8YO24gbqmUwO3tnlyYCstHm07oPKfuhTsZ1A71xIXtSOCXUokSdKL17RzPcAKtPeE43IirmoZYztg8x2fr3JYcsiROVtTlDBmhhbsSNPRsxfV980F+sfcnTbtVu63LVlY2cxms96ev0ujpq6zLNv0ymC39tEqznqMnZ2p5/T7jlRtO5bhkRhhbfT3G63mD0xO1thhbZ1u42oiqVCtiGBLuaniCZ2BbWP03NA2NokkSRraob5G9IqXJHWLr603buys2hFFT/UX3+ab+3rr96evtnvs8de1108PX67tE4fq10eu1BUt6mjdi4N0YHKyburRxO42jWuH6+4rrQdy/+PZ/hp4KVlbMinlqvx8s37fkap521ItiSRJeul/W20SRd+uOmSZvveqZgoIMOi1P3dQ7YhgPZ/Uxu7+/+7m2HUAABSiuwJ8zpS7euq+/6xR+kXrGqEhQfZzn3/q0kA/bzgmSTp61kmNTD/nzhdRg8GgF69pp2lrDmvZ3lNatOtEBUYGAAAAf/TnLvX104aicnc7Xx2qkEDbB7s2Hj7rdD+PD2xpd/mkGzppaIc4XdWyXrnitKfkTeXCh6j8yWVNaqp7Qm09PqCl2hcbg0mSFu06oaZjZ+qHhy5Xt/hape5r67F0PfB/ayVJd00p6n32r9/36FRmjqauLLrh7AvDJl/5twU2v92iQ10rv1Pomo71deJctiZe6hUy7J0/LOsWPNNXm4+ma86WFN2eGK/ezerQc6mcZm5Osbv8/+7pqduLjeu7+ZXBMgYYZJChXO93dGigeibUVkaWSZ+M7F7m/Uiu9fQLNAaoa5OCc61joxr6+t7ebu0/LSNLdSMLymMFBtgmxNxRWs+ihDEF5QbHX9deL/+y1bK8VkTBOTSiV7xG9IrXwVOZllJ8hdaMG1immAAAkOiZBB/Us2ltbXhpkEb1a261fMnuk3bb92ha22r++Z82a/aW0mvAe0KWKU+/70jVmcwcj/0oM5vNen/BHoevt7hH+7dwun7amiOW6Zxc18ufAAAAwD+sP3TG4boPRlymqff10vjr2ttdHxkSqG/vL7phai+RJEnn7JS3KrRj4lCH68KCjRraob4iQirmGcZPi91g/tvwThVyDFesOXDabg+gQl2Klf8rdFXLuvrx4Sv0fFJbp+/P8A+XOT12Xr5Zqw+cVvK7Sxy2KZ5IKthGOp5+UQljZujeL1d7JbnkiYcAAwIMuuvyBLvr+v19oR77Zr1mbD6umz9eoYQxM9Ts+ZmWm/Bwz/xtqVbzgcWSRFe1rKcXkop6KEWFBik8ONAyrm9ZGQwGffdAb816/CpLbyVfFhMdakmeBRoL/p+XV/Ab/Kn/btDtn63Uwp1p6vjKHGVkmXT49AVd+bff9X8rDpb5mMUTSZIUE2U9LMBFU9H4F10a19Se14ZZEl4AAJQFPZPgkwwGg54Z3FrvL9hrWfbPebv02ADbpx5DS/zonbrykKauPFQhdcZLGvvjZv20/qgkaUCbGH12Z49y73PutlS9Oafgx+joIa11XecGalzbelC01S8MVHZunhrWdD6Y4eqDRSUs/j53p55Pcl6GAAAAAP7lzx/YTzY8M7iVki6VQ768eV2bm45SQe+e3s3qaPqjV6pmuOu9Qr66p5du+2ylZR/eMrBdrA5MTpbZbPbqzeYbP1puNf/FnT105OxFvfjzFo2/rr3uuDxBXSfMVY+E2hp/fXutO3hWSR1dH+f1syX7dU+xUlpms1mjv9+k1QdOq0/Lem7fjP586X59vnS/JGn+9jQ1HVvUC2L/pKQKfy/TzmXZLHtmcKsy7assPV+e/G6D/vnXzn6RoPAV9/5njdX8nteTrObvvrKpGtUKs/Ts8RR//RsVjmN36PRF/d+Kg/pxXcE9gz8uPTDa6ZW5lrYv/rxFL/68RZL03f29NWrqerv73Pt6kpo76bH0w0OJNssa1yq6j/DzqCvcfBUAANiiZxJ8lsFg0F9KGRgyL9+s/yw/YHfdlqPpdpdf994SJYyZoVfs/KB2V2EiSZJ+25Fm6aGUdi5Lo6dttBlQ1hWF5Skk6c05O3XVGwu0/bh13e96USFqVCu81C/XfYqVFPl48T59snif+r65QCnptj/gAAAA4F/2pJ23u/z9Wy/Tw32d92CXpCnLC3qsdGhYQ41qhZfSusDmVwarWbFB5Y0+cLPX1244R4QE6vbe8do6fojuuNRzZv1Lg/XxyO6qXyNMyZ3q28R8WZOakqS7rkiw2V9hGbdCS/ac1Pdrj+jgqQvl6tVgT9OxM5Wbly+z2SxTXsVUNphQYpzbOy9P0CP97ZdKrAg/rT+qpmNnKr+MJcgqS1pGlkZ8ukLH/KCUuzHAoGEd6yuuRmjpjauB+dsLenJ9vnS/JVHkips+XqGT57Mt8z2b1ta/bumqA5OTZQwwaNULA+xuN/3RK9UtvrbN8oiQQK0dN1Cb3RyPDAAAR+iZBJ82Nqmtpq0tKtX2+45U9W9TNIDv49+u18Yj9pNG6w6dUYeGNayWffrHPm261H7KsgN6xUHJj7LqOnGeBrSJ0W870iRJ09Ye8UgPqeJJq49u6+bydg/0aWY1XtJrM7dLKuhmP/U+12tAAwAAwPfsP5lpNb/gmb5qWjfCQWvPiAoNUlRokMYlt1VkSCBj0NjRI6Ggd4Y75f2mPXi5jp29qMa1wzWkfZwa1gzTVW8ssNv26JmKTS60eGGW+rSqp23HMvT7M1e7PZZRaYonRx7r30JPDW7t0f27qtnzM/WvW7rq2s4NvHL80vR8/TdJ0uWTf9ctPRurTkSIRvVrUe7yce66mJNnNT96iHf+XtXRJ7d3V41ivUZjokL1zs1ddDEnT2N+3KzOjWvq05HdVS/Kcem6OpS1AwB4ED2T4NNqRwRbzd89xbp7/fRNjsdGCgm0/nhvPZauV2ds91xwDhQmksoiy5Rnd/nHi/dZStrFRrv+ZbB3M/sDES/be8r94AAAAOBT/j7HepyehDqOexc9eHVzm2U3XtawzMe+96pmurlnkzJvX1UNv6xRmXpKGQMMltLWvZvVsSlnXbyX0JgfN5e6v6tb1dP/ylHWavGuEzp5PlszL/3eenPODiWMmaF7pqx2uUdPRpZJ09Ycthm79f4+zSzTnk4k/fFsP7faP/rNenV4eY4OnMzUm3N2KP2CSVJBryB75fgqS8lxrL5ZdVjvLdijti/N1pEzF/TKL1u1oBy/O91RMmn9cF/bawk8q05EsPZPSrJKJBW6vktD3dyziQ5MTtb/Rl3hNJEEAICn0TMJfmdX6jm1io3SE9/aryVc6LkfNuumHkU/cGdudpx48hXnnQxuXDhIbZDR9RwwT4oCAABUXTtTz1nNO0tijBnWRs8Nba1ur87X6cyCUszPDSnbODWwr3VslP7x184e2VfJ7/FfrTiou65o6qB1gcua1NQnI7vrzIUctYiJkiS9d2tXGWTQ0A5xev7HzfpuzWGrbdaOGyhJys7N1+WTf7fZ579+36NB7WItY9n+tiNNU1cd0m2947V41wmdOJet4Q5KkxeOCzP6+03q2qSmQgONeqR/CxXmxXo2tS3LVR61I4JV10EvjM2vDFbHYuPUFHc+O1d9/75QkvT+gr1aPLqf+rxZ0Ctsz2vDFOjG7y9Pybjo+HfhHZ+v0t4TmZqy7ECZq2BsPpKu79Yc0lODWts8wFnSrmLXmS3jh/hcWUl/cmuvJoqJCtHb83crqWOcTHlmPT6gpTo0rKGLOXkKDSr4rPEeAwB8Fckk+J0hby/W/knJ+nnDMbe2K/wBVNz/NhzV9V3K9kRmyafFyis/36w3Z+8stZ07ySSp4KnE4qXuAAAAUPW4clPZYDDoz10b6rMl+yVJESGVWy6rKqkdEWxJyhWa9lBihR3vu9WHS00mff/g5QoIMFiVtbqmU1EJt+eT2tokk4q3tfe74ejZi+r26nyrZeN+3qJxxcaBeXraRu2YOFShQQWfp3NZJptxntYfOitJWr6vqEJCkNEzN8w/Gdldk2Zt1zs3dZXRzsN0+15PUkCAQcvG9FffNxcqp5SxoAoTSVJByb/5T/WxJOfK4nRmjh77Zr1e+3MHxddxrQzl2kOnHa47Vexzl59vLtMDhNe+t0SSNGPTcd17VTPdeXmCw7KM7RtEW6Yj3SjdWJ19dFs3PfhV0VjIJa/Pj/RrYZOkrOzyhQAAlAVl7uB3PJnDefzbDWXe9q35u11qd+TMBZfazdh83ObHnT0HT2WW2qa4Z7xUgxwAAAAVq9alEkgRbtyEfODqohJj7j6k1DIm0q32VVmDmqE2yzw9tlCXxjUt0ztSzjluqIKeZ6UlFWqEB+nKFnUdrv/y7p66qqXj9c60eXG2/m/FQZnNZnV8Za7+u+ZIqducOp9TahtXDGoXq9+f7quOjWrYJKh+f/pqy/vSoGaYdkwcqsWj3SuFN/Cfi8sVX4/X5mvJnpO6+s2FLrU35eVblXd/bmgbq/VnL5XikwrGCS6PMxdMenPOTrV/eY7DNsfSC8r9xUXbfuZh39AOcWoTV5CAfKx/C5v13ujtBgCAJ/AvGKq8tIyscn/Jtufd31xLJt0zZY0yskxKGDNDCWNmOKwxPv7XrTbL+reJsVnWoWENt+Ls2Mi99gAAAPAPZy7dVM7MsT/upj0xUaFa/tzVerOn4zJajoTTK8HCw0UK7PrhocttljkaY/XwadceYPvq3l5O1398e3eX9mPPiz9v0Uk3EkSlJcjKomR5sGb1rBOgBT23nJd1s+fU+ewyx5RX7Pdf+kWTk5bSnK2pavnCLKtlD/Vt7rDnYViwURlZJp3JzJHZbNbBU5nKzs1z+fNQXMKYGTbLer/+m+74fJUkKSXDe2NI+aPv7k/UpyO769EBLb0dCgAAHsOvAfi8Dg2jteVohtWylcXKIziTl29Wz9d/c9rGbDbbrUl8PjtXp8/nqImTgYwLdYuvpbUH7SesdqaeU/K7f1jmmz0/0+bHwN9m77D54VXYpviX+kf7t1CDEoPxuuLKFnW1ZM9Jy3yHhtFOWgMA4NgXS/croU6E+tl54AGAf6gbGaKyVFTyUFUyuMheybZfNtov9f3Ste08cszyltrq8Zp1SbxVzw/QnhPndesnK8u137Lo6OAhvPAyvMZur863m9D5ef1RbU/J0JMDW1nK/DnTefxczXzsKiW9+4fev/UyJXeqb7X+kW83uhVX8rtLHK5rExelWY9f5db4O+kXTaoRVtDDLumdP0gglUON8CANbBfr7TAAAPAoeibB5+1OPW+z7KaPV7i07cNfry21jaMnOfv/faH6vLlAu1NLf2Lun6UMtHv49EWr+YQxM6zGXPpwoe14TvY8XcaSdUkdrX+k5OZVwqOUAIAqZ/WB0xr/6zbdNWW1t0MB4AX2khvVVfH783Oe6KOlY/pXyHGuKZZsyM3L194T1r+NDkxO1oHJyQoJdD1BMvmGjpKkWY9f5VYs2ycMtVm28aXBDtsvea6fYqJDdXnzutr3epJNMua9W7u6dXx3vXJde7vLDQaDEuw8MLjq+QG6oWtDfXNfb+2flKT9k5JKPcYT323QvxftU5sXZysnN99mXN33F+yx2Sbp0oOGo6au07Pfb3Q6Fu/8p/pYpq/r3MBhO3t2pJxT07EzdcvHK/TQV2v17m+7lX7BpPx8s5rXKxi7qeQp3Xn8XJ3PLui1uO14RsldAgCAao6eSfB52bnOB0h1Zs7W1FLbvDl7h0b1a6GwEmdD2rmCUgZzt6WqZazzAVfj60TowORkfbXioNVgtM7sPZGpFjGROnr2os26LeOH2Czr17qeS/u15+YejXXgVKZy88z6fOn+CikpAQCo+v634ai3QwBgxy09m1TKcQLc6OFQnbSOc/5boTyu69xA0zcdlyQ9PW2jzmUVlSf86WHbMniuuLlnE93s5DOz6oUB+nHdUU2etcNqeViwUbtfG6aLpjwdO3tRDWqGORwnKio0UI1qFSVsCsct+tctXfXoN+slScklHnjzlKVj+islPUvd4ms5bLPw0rhJaeeytCftvC5vXjBW1D9v6uLycUqOZdv91XmKDAnUzMevUs3wglJ6b87Z6XQf/11zxDK+1M3NbM+v5sXK9L17S1e9e0tXu+XonFl+qarHrC0p+ue8XVbrPruzh9o3iFbP14qqeXR4eY4e7tvcqt1VLevq/+5xXiIRAABUffRMgs+rFxXi0f3VLVEj+8vlB9Xz9d/UccJvMuVLpzOty83Ze1Ks5BOBhYZf1sjlOAb+c5He+323MrOt69X/8NDliixWj37pmP6afENHfTyy7PXLAwIMej6prerXKBo0NSWdkgUAAPd8teKQt0MAYMcdl8dXynHcHbsT5Xeq2G+T9Ism/b4jzTLftYnjZEl5xESF6sGrm6vZpd4rE69vr72vF/TSCTIGKDo0SG3ioi2JpBsua2izj5XPD7C772s61ddX9/TSqhcGuFV+zR0Na4Y5TSQVFxMVakkklabkeFVXv7nQaj4jK1fH0rPUZcI87Uix7dUTGuT89su3+6x7lzkqUedo/KSy2JlyTjFRoTbLPyhWOWNUv+YkkgAAgCSSSfADzwxu5dH9/fTwFfr7X+yXpXtmZaB6TV6oZ78vqlWdZ6dj1IB/LLJMz3miqPRAWLBRW8cP0fKxrpW5+PvcXValBT4ccZnND5+GNcN0c88mCjKW/3Q9XyxxNebHTeXeHwCg+irPYOQASrdgZ5qe/G6DzmWZHLYpvDkdEVyxBSdmPHalHu3fQk8N8uz3cn82oldBAq9X09oVepzuxX4bLNx5okKPVdLvT/fV/klJuj0xwWmJw8k3dNI/iv2+OjA5WeEOPpMGg0FXtqxrN4Hh6wa9VfQbMNfej8Rihr79h8aW+L217sVBLh9rwvXt1ba+++Pc3tS9sSRpmYtlF++8PEGS8wTVFS1cS7YBAICqjzJ38HnxdSJKbVM4iKkr6kQG68ZujfTMNMeDmxaWGpCkz5fu1+MDWzpsGxxoneSJCAlURIjrp9bnSw9YpodVUKmHQu0bFP0g2XwkvUKPBQCo2o6evag6kZ7tPQygyF1fFIxNVis8WC9d285uG9OlcTBLfh/1tPYNaqh9A3olFXdzj8bq2LCGWsRElt64HJrYGdunMrnSeyg4MEDDuzXS8G6uV2nwR8XHwX3xf1tLbf/NqsOW6TXjBio8ONCStCmtVN3tvd3rbTjlrh7q2zpGkvS3GztJKkgQFT/OzMeuUnCgQV+tOKTv1x7R6zd0VGhQ6WNtORnSCQAAVDP0TILPC3PhC267Bq4/teXoKTlH0i86fhpUkoKM5SvPMHVl5ZUMuqpl0bhLhT82AAAoi29XHy69EYBy25maoUemrtPsLSlWy/PzzcrLL7jL64ke7HCPwWBQh4Y1XLoZXx4hgRW7fzj31k1FPa6euPSAYXZunr5Z5d5vuKhQx79Bm9Yteniyf+t6OjA5udQkXrtivZYOTE52+NtuwTN9JUmfjuyudg2i1SImSq9c115bxg/RdZ0bWLXtkVDUC65ZsZgi3XhQEgAAVG18K4DPCwt2/gNqRK/KGXC40PhfrZ9Cy8l1XuKgpKn39tKtn670ZEguK16nu239ihsoGABQ9bWs4KfxARRYuueUJGn6puPaPylJu9POK6FOhPKLdRcILOfDTfBtPZvW1qr9p62WfXNfby9FU738uWsjzdh0XPO3p1l66Jy9YP2w4VUt66plTJQ+X7rf4X4cJQU7NaqhXx65Ujk5OZo1a5aSkrq6FFdi8zradtx2XKaSmtaNcHmMpU9Gdte8baka1rG+IoKNuuOL1Uq/aLKqbgEAAKo3HmGDzwst5Wm81/7cUZL0QJ9mkqS/dGukj267zKMxFD71OWrqOn1RrCydJMVE26/33bd1PbvLHemZULH11qWCJygLB9Kt6KcoAQBVW6tYHkoAKtsNHy7T4LcWq9W4WUrLKBq3LNDJeDbwfyUTSZK0+oDtMlSMRrUKSg2+89tu7Uw5Z1O5Irljfb10bTsdmJysv3Z3rdTf5Bs6qkntcL11UxdJrpUTLO6JgS11S88mmnpfL7e2c6ZmeLD+0r2xIkMCZTAY9OVdPfTzw5crkJ6PAADgEnomwee5+qTl6CGtNbh9rDo2rOl0oOJCX9zVw1KLvjQ3/Xu5bu7ZRDM2Hbda/saNnRx2+7+lZxOrQXJ7JtTWl3f3VGZOrt323z1QOU8XRoUGSZIOnMyslOMBAKoGc4lBE06ez3bQEkBFWX/orGW6z5sLLNPulnGG/2tbn94ilaV4snbI24ut1jWsGaabejS2zE+6oZPV+LuStGX8EJt93tyziW7uWfYKG1GhQZp0Q8cyb+8KdxNcAACg6uMRE/g8V2vABxoD1C2+toIDAxwOCF78oc1+rWOU2KyOpIIa0s6sOXhGz0zbaLP8r90b22ldIO1c0U22d2/pqv8+mKiwYKPqRoZo0ei+Nu0r68v6xsNnJUmfLnFchgEAgJJy862TSY9/u8E7gQDVQMnkrTOujC+Kqqd2RLC3Q6g2nPXMWTqmv9XvOGOJXoIjE+MZcwgAAFQZfKuBzwt28uW9TZx7JXZWvTDQav6b+3vLbDZXSCKnebFBS0sObhpfJ0KzHr9KMzcfV+9mddSwZpjHjw8AgCddNOVV2L7z8s3KMuUpghtugCRpZ+o5l9tW5LkJ39OoVpiOp2cxjk0lys517xxbPLqfDp7O1FUt3St7DgAA4Ov4xQ6f56zM3aHTF1zax1Ut6+rFa9qprp0eSxXVIyixeR09NqCl2jkoQdG2fjTlKQAAPu10Zo7+MXenburRWAEV2IM2+d0/tCPlnFY9P8DhWIRAdZKb53rPJFR9zw1to7/N3iFJWjS6n3Lz8xVSyriy8JySY+aWpkmdcDWpE14xwQAAAHgRyST4vFAnpTtudbHO9G294z06UPhVLevq8zt7OG1jMBj01KBWHjtmRcjMzuUpcACAQ+N+3qyZm1P09cpDFXqcHSkFvTB6vv6btk8YqrBgbpIChR7t30L/+n2Pt8OAF8VGFz0QZwwwyBjANdIXbLUzFhIAAEBVxphJ8Hkl604Xd2sv15JJ7g4S/sNDl2vr+CEal9zW7vr/3N3T5bGcfNkT323wdggAAB82c3NKmbY7eyFHi3edcGncl5Jt2r40u0zHhO84evaisii9Vi75xc4Lew8n1QoPkiQFGQ3aMXFopcUF70juVF99W9fT2GFtvB1KtRQSWPS777H+LSRJNcKCeCgPAABUO3z7gV9rWmxcopICAwyWwcIX7EjTiF7xLu+3W3wtSdJUB09iV1RpvMo2b1uqt0MAAFRBXSbMkySNHtJao/q1cNq28N9qX/S/DUf168Zj+udNXRQdGuTtcPzChsNn9af3l0qSDkxO9nI0/mvu1qLvaAaDQR/d1k0PfrVWA9rE6O2buyiKz2O1EhJo1JS7eno7jGqrUa0w7T2RKUl6anBrPTqgZZV4sBAAAMBdfAOCX/hgxGV68OrmNsudJXWmP3alZbpn09qlHmNo+1ibZSfs9GhqXs9xAgsAABR5c87OUtuY8vJtlr3yy9aKCMdtj3+7QfO3p+mDBXu9HYrfKEwkSdK5LJPDdukXTfpl4zFdzLHtwZRr5zNRnZw8n63v1x6xWja0Q5z2T0rSZ3f2IJEEVLKS4+6SSAIAANUV34LgF5I61tcYN8s6xEQVDeDtygC1j/RtJkn6S7eGlmX2xmv6ZGR3t+IAAKAqGX5ZI4frzmRLU1cddmk/36w6pIQxM/T2/N0266YsO6Cdl8ZR8ob8fLNVIuTEuaKHS37fkaqEMTPUZcJcPfndBpdK+VUXObnWSaAXf95imU6/aFKP1+Zrye6TMpvN6jx+rh77Zr3G/7rVavvZW1LU/uU5+nXjsUqL25dsOZqu7q/OV0pGliSp+HNTVaVnPOBv3ryxs3o2ra3P7uB3IAAAqN5IJsGv3N+nmcttQ4OKPt6ulNBpHRelN3vm6rXr21mWFb95JEktYyLVrF6kyzEAAFDVRIQUPWix7ViG1bpX1gXq5V+3u7SfsT9uliR9vHif3fVD3l5cxgjL784pq9XxlbmW+R/WFfUSuXvKGknS2Qsm/bT+qJqOnalNR84qP98ss9msKUv3a8Phs5Udsk8o2Zvm5w1FCaHO4+fqxLls3fbZSg175w/L8m9XH9YXS/frlo9XqNW4WXrwq7XKzs3Xo9+sV+fxc/Xq9G2VFr8vuOZfS6zmyVUC3tekTrj++0CiBrS1rWQBAABQnZBMgl9pGeN6Iie0WG+kzo1quLRNsNH6qc/3bu1qtX532nmXj+8vSibMAAAo1LVJTZtlbetHW6aT3v3DZr09CWNmKGHMDP31o+VKzchSwpgZngqxQizedcKt9te9t1Rv/7Zbv2w8pld+3aY/vb9UWSbb8m1V3fM/bbZZZq/n1o4Svc7G/7pNy/edsmmXftGkT5fs165U7/VSAwAAAAAUIJkEv7Vj4lCn6wMCDErqGKdWsZHq3LhmmY5xTacGVvPd42uVaT++bMam6llGBgBQupL/7jWtG2EzVsTeE+e1yEnyZWWxJMGqA6fV6/XfXD7+4dMXXG5b0Q6eytTx9IsO17/72249/u0Gy3ybF2fbHQ+qumk6dma5k4ebjqR7KBr/89iAlt4OAQAAAAAkkUyCn1lz4Ixl2t54RiV9MKKb5jzRx2ODpH73QKJH9uNLXKgACACopor/GxFsDNBHt3VTSKD1v6kD/rFId3y+Sn+bs8vuPm76eIVLxxprZ2zEO79Y5XqwFezqNxcqcdLvbm3zt1k7Kiga33R1q3oVst+6kcEVsl9f9/ZNXfTkQJJJAAAAAHwDyST4lb0n3C8zV97Biv99ezd1bVJTC5/pK2OA/w983KfEjZ4J1WwsAgCAa46dvajPluy3zM95so9ax0UpOND+18dPlxwo87Hi64Tr/j7N9PHt3RQdGmhZvvdEpl75Zat+KDEWT0kp6Vka/uEy/W/D0VKPZa/sWkX5dMn+Sj2et13Zoq4kqVfT2h7db15+wVhUCWNm6OyFnDLvx5/+Fskd6+tPXRuW+3ssAAAAAHgKyST4lf9v777Do6j2P45/Nr0nJBBCT2jSu0DoSAcLylWxo/xEBFTEcgHBioKKFbErYr/q9VoA6U3pvRcpoQeQFgik7vz+CFmyZDfZJJvsbvJ+PQ+PM2fOnPnOZE827nfPOV3rRZf4NXs1jNH/hrVXbPngEr92cSiNU/UBAJyv3STrUThxl98Hu17j3PfihpXDNPfxTjKZTOrZMEYPda5ldfyL5Ql64sdNea6b8+yvW7XuwBmraeZseXv+bsWNmaX3Fv7tjNAdcviM/anxSpuMy0PZooowkmj9+B7678PttP3FXmp2eZrijYfO6vnfs7780uzFeQVu0zAMxY6eqbgxs3TgVLLdeiv2nlLfd/7U8j3/FCr2orqUdmWdrfvbx7okBgAAAACwh2QSPIqfk6arK8ucNeUfAKDs6FinvGXb3sgkR/RtHGPZ3vRcTyVM6qeZj3aUv8+VqWsfaB9n89yeby212+7c7cct21MX7bFb7+35WUmkyXNtT8mXLcgv/6l0HXUxR4KgNDp3KV2r959WRqZZW49krW0U7OeTz1m2NaoSpshgP7WsUU5Bfj6WUUhTFlr/TCfP2VWgds+nZli2O7++2GadPSfO645PVmr7sSTd+ekqnbuUXrDgneDhb9ZZtn34ew0AAACAm+H/UuBRyoeWzTnzncnXO/d0KRksEA4AyGHPCetpZf/8u2AjNWytndOsWoTeGdhcUtYop5zT2eUU6OetW1pUceg6h05f1P1Xrav0+pxdOnrWejTQsXOX9Pumo1ZlyTkSDNlS0jM1e2uiJQH017+72rzu493rqmWOkb53tK5udXxC/0aW7d825T/1nid78Mu1uu2jFar9zB+aueWYJPsJx1uaX/m59mtcSW/e1lSTb22qcf3qKzTAR5NvbWpVP+HURZvtvLdoj/5v+pp8YzObDa0/eEbDv1mfZ73Nh8+q+5vWycpbP1yeb/vOtnjXSct2On+bAQAAAHAzJJPgUW5sWkV3t62ud+9o7upQPNbVC6dL0vwdx/Xx0r0a+f0Gmc2es54AAKB4jP15S5HOn/5A61xl6Zlm+Xp7KWFSPy16skuea8H8vN6xBEzH1xZpUY4P4LM99v0Gy/bxpBTFT1yoR77bYFWn4XNzlHnVe964X7Zq6NdXRof4eXvlmm4sLMBHj3Wvo3cGNlO/JpX0v2HtNPGWxpbjPRpU1N1ta1j21yaccehePNXq/adzla07cEZfPtBaPRtUtJRFBfvplRzP6YEOsbqlRVX9q2VV/V/Hmtr8XE/Viwlz+Lrzd5zIt84TP27SLe8vz5UMHffLFp1JTtMPaw9pxd5TuvG9ZbnOrRMd6nAshXU6OU2xo2fq5Zm5169kWmIAAAAA7qZwc1AALuLtZdKE/o3zrwi7bE2bsjPxvGXqn+ubVFb3HB/+AADKntUJ1gmC9eN7FLiNIZ1q6uOl+yz7T/SsW+S4HBUW4GvZ3nDQfjKn2Qtz9fyNDbV87ylNGtBYP607bHXcx9tLA6+trmnLEixlm5/vJUmqWi5IU+9skavN65tUstq/s031XHVKu52J59WpbgV1qltBGw6e0etzdumZfvUV4OutpU911YHTyWpZI9LqnLySi/acT0lXaI6f9dX+t8F2UvLrlQf19cqDebYdHuQrwzB092er1L52eQ3rUrvA8V3NbDb0vw1H1KJGORmGoeveWCJJ+uTP/XqmXwOruoV5HgAAAABQnEgmAWWMt40PJ7ITSZI0ZdEekkkAUIbZWismMrjg08w+3r2uyof4KTk1U34+XrqunvPeW16euV2r8xjx4+975YsTSSm5p7PLdj41Q0/8uEmSVD0yKNfxQF9v1aoQbNnPuXbU1WY+2kEbD53VjU0rS5JaVI/Q+oNnFeDrvPWX3M3OxCSb5U2rRVi2m1cvp28fbGvZrx4VpOpRuZ91fnZN6K1zF9PV+pUFlrLGz89VwqR+BW7LEd+uOihfL5OW7TmlZXtOOSWZ9NHSfXp19k6bx2JHz7RsD+lUs8jXAgAAAABnI5kElDHeXnl/03XvVetk5PT7pqNKPJeiB/mQAwBKreun/Gm1v/Ol3gU6/++XekrKWvtoSKdaTosre5rWPSfO65M/9+dZ98DltXYyzYbNtQJteWv+7lxlgX7WiaBP7m1l9/yGlcPVsHK4Zd9dRpb8sPaQKoYF2FzHqqhGfr/RZnn3etFOv5a/j7eiw3In5pbsPmnz3o6du5SrLD+t4yKtpu2bvuKAZXvRzhPqWsT7spdIulrVcoFFug4AAAAAFAfWTALKGJ98PlS7Ntb+HP2PfLdBL8/aofV5TBkEAPBsh05bfwjvipE1EUFXpi67tWVVSVJ4YFbZhdTMfM/fdjRJg6atVq2xs/T4fzZZHbutVdY6PQWRMKmfEib187hRRjuOJenpnzbrvs9XF0v7vRrG2CwfUMDna88j12WNBrq9VTVL2cf3tLSqY+/ejp7NnUx6+eZGeV7v3YH21+S8/4s1VvuGYej1OTv126ajebZZGP2bV3F6mwAAAABQVCSTgDImyC/vAYmLdp20fCv3nwupmrXlmNIzzTp38cq0R7e8v7xYYwQAuIeYsACXXPeW5lnJiKbVInRfu1hJ0onzqdp46KzN6VptWbzrZK6yX4e312v/aqrHutVxWqzu7I8tx6z2v1t9UKP+s1Ep6fkn5ByxYu8py/bWF3ppSKea+uy+Vqoc4ZyRNY93r6uZj3awSgIZDp77zaorayI93KWWOtetoAEt8k5yVQzz15u3NbV7/Exymp77dau2HD6n+s/O1tRFe/XodxvU8NnZajVhvv65kOpgdHkLy2MdKAAAAABwFZJJQBlTKTz/DwZv+2iFJKnVhPka9s16fbx0n5q+ONeqzlcrD+jE+ZRiiREA4B5+f6SDw3WD/b01qK5zkhRP975GU+9soS/vb62EU8mW8v5Tl6kos8dlr+UT7F9yMz0bjmY/isG6HCOJ9568oDE/b9HPG46o3vjZVl8SKazVCVemhAvx99HYvvXVrb7z1sby8jKpYeVw+Xhf+V+WTnUqqHKOv2XubFM913nnLqbr5/VHLPv/7l1P0x9orQBfb215vqelvFfDito/sa9l5JnJZFL/ZrZHBbWOjVTzl+Zp+ooDuuG9v5SSbrYcS07L1D8XUtVqwnzNuiqBJ2WNYvpm1QGrspzTL/734XaW7fmjOtu8PgAAAAC4GskkoIy5JiZUNaKC1CzH4ti2nE+58iHT63N25To+/petav3yAmWaXfgpGQCg2DSoFKYKof4O118/9jo1j3LOe0KAr7f6Namk8CBfmWSdPVq860Su+j88FK8P726Zq9yeyGC/fOvMe7yTw+3Z4g4rJi3bc2XkULc3llgdu/pLIvZcSsvUhdQMm8e6X04cBfuV3PR/gX7eWj6mmx69PAXe1SPV0jLMed5baICvZj3aUSO719E7A5vnWtvKy87akrUrhjgU37Bv1ispxTpR978NR/TM/7Za9l8d0Fi7J/TRz8PaafPzPdWyRjn1b1ZZ3etHq1aFYIeuAwAAAAAljWQSUMb4entp4RNd9L9h7fKsdybZsW8s1xo7S2YSSgBQamR/2eDxHnXt1vlleHt9dNXaNfY+hC+q5tUjrPYnz92dq07ruEj1bmR7/Z5sL9zY0Gr/nYHN7Nbd9GxP1akY6nCMpdHp5DQlp2ao/rOz1ei5OZYp7b5Ytl8fLdmrk+dTVTEsK9k4uENcice3/ViSpKyR0jltcGBdxwaVwzSye127a2ANujy1Yk7f5pg2Lz9Nnp9rieXI2UuatizB6ni/JpVlMpnUono5y5R2bw9srk/vuzZXcgsAAAAA3EWBkkkTJ07Utddeq9DQUEVHR6t///7atct6xEJKSoqGDx+uqKgohYSEaMCAATp+/LhVnYMHD6pfv34KCgpSdHS0nnrqKWVk2P7GIwDn8/YyOfXDippjZzmtLQCAa2WPOPXO46/EZtUi1KthjGpeHkUREVR8a7zkNz3r2L717B7z97lyE3ddNR3aTVdNZ9aqRjl9dl8r7Xixt8KL8X7cyQu/b9OCHcdzfSlk7rZEtXhpntpNWmgpu+OTlfpqRYKe/327Jv6xU9e+PN+yLlFACY5MyjZ/R+4RapJ0+8cri9z2czc0KHIbsaNn6ub3l6v9pIXacuSc1bGQEpxmEQAAAACcpUDJpCVLlmj48OFauXKl5s2bp/T0dPXs2VPJyVfmsn/88cf1+++/68cff9SSJUt09OhR3XLLLZbjmZmZ6tevn9LS0rR8+XJNnz5dX3zxhZ599lnn3RWAIjMXcJGHS2nOWScDAOBa2ckkLwe+dDD9/ta6rVVV/fhQfLHFYzKZVC0y0OaxhU901pBOtXKV14sJ1f6JfbX1hV6KKx+s5tUjrNbdseWnh9upW/2KCnR6YqRkR+/O3HxMvd9eqq1XJTBsmbYsQYOnr1XNsbOspq0d8tU6SdK5S9ajlMf/us1mO3uOXyhCxIUTaGNU0bFzl3KVfflA6wK37egXbiqHByhhUj/tfKm3w23vntCnwPEAAAAAgDsoUDJp9uzZGjRokBo2bKimTZvqiy++0MGDB7Vu3eX/4Tx3Tp999pnefPNNXXfddWrZsqWmTZum5cuXa+XKrG8Jzp07V9u3b9fXX3+tZs2aqU+fPnrppZc0depUpaWlOf8OARTKpsNnC1S//rOzdTqZPgwAni77ywTeDkxbVy0ySK/9q2mxTwn31m3NbJbXrGC9js2LNzVU7egQTbs/a7owX28vzXu8k/47NO+pXYuDq2YrG/7teu1MPK/rp/xVoPPiJy4o9DVPXyz59/8hnWpatmdsPqph36zLlfySpOgwx9f9yunzQa3Uo0FFu8cHtKiq5WO6Scpa4+u7B9vmu8ZY9/oV5efDLOMAAAAAPFOR5lg4dy7rG4+RkZGSpHXr1ik9PV3du3e31KlXr56qV6+uFStWqG3btlqxYoUaN26sihWv/M9Zr1699PDDD2vbtm1q3rx5ruukpqYqNTXVsp+UlDVHenp6utLTHVvXBchP9muJ11SWx77fWOBzPv9zrx7rVtv5wQA20GcBKT3TrPu+WKcmVcI0uvc1RW5v+ooD2pl4XpJkmDML1L+KtU8a5jyvme2OVlV0R6squY5lSsrMYwBtqxoRTo/buJyUy8go2HMsbrUrBGvPyeRc5SfOp+rshUsKLsQUbK/f0qjE73HvifOW7RHfbpAkzdqSaFVnZLfaqhUVWKjYOtaKVMdakaozfq6l7OsHWsnPx0ubD5/TvW2rW7XbqnqYlj/d2ap+Tj8Oaa1m1Zz/Oiss3kMB90KfBDwH/RXwLPRZxzj6fAqdTDKbzRo5cqTat2+vRo0aSZISExPl5+eniIgIq7oVK1ZUYmKipU7ORFL28exjtkycOFEvvPBCrvK5c+cqKCiosLcA2DRv3jxXh1CCnDtn/+adezQrNffC6EBxKlt9FrC26ZRJaxK8tSbhjJqY92rXWZP+d8BLQ+plKrKAAzKS0qQJ6668L/y8cLXO7Cz4FG3F0Se3nDZJsp7WzCRDs2YVdc2+rPu9cPa0E9qydvq0tyST1q9fr8wDxT/V3fp/TNp4yqT8Jh5IuXhBku1hU+/+ME/VQww58vfBfXUy1aicIT9vafnikv89vC0h6/na8058hnRxp2bN2lmk64T5eispPes6p3ZkzbRQQdIff9ie8q9nFS/NPWL9M3ikYYaOblmuo1uKFEqx4D0UcC/0ScBz0F8Bz0KfzdvFixcdqlfoT5KHDx+urVu36q+/CjaFRmGMGTNGo0aNsuwnJSWpWrVq6tmzp8LCwor9+igb0tPTNW/ePPXo0UO+vmVj4e3HVlh/ezY2KkgJpxz75SFJK/7dWfGvLrHsL030UoO6tfREjzpOixGwpyz2WZRdqRlm+Xmbcq3l8liOURAdr+upx15eKEl6Yb2Pdr/Yw+G1XyRp78lkad0yy/6IWzqrRqTjX9opzj4ZuucffbprvVXZ4ic6qXKE7bWUHJX9PlihQgX17duySG1d7aujq7Xv/Fk1b9FCvRvany7NWR6zMyLmaoeT7b8mPt+d/3pRD3aI1d1tqhX52RfVVu/d+uSvBLvH+/bt65TrbPPZrY//TFCTqmHq27dtvvX7SrlGJz060DmxOBPvoYB7oU8CnoP+CngW+qxjsmeCy0+hkkkjRozQjBkztHTpUlWtWtVSHhMTo7S0NJ09e9ZqdNLx48cVExNjqbN69Wqr9o4fP245Zou/v7/8/XN/vdbX15cXAZyuLL+uBrSoqjfmOT6yqFK5EAX6eutS+pW5gz5cul+j+zYojvAAm8pyn0XZcOpCqlq9PF8d61TQlw+0tpSnZ1pP/fbWgr1W+3WfnaedL/VWgK+31Tm+3lmjJg6dvqiOry2SJP3wULxu+2iF1fm1K4YXKt7i6JPB/n65ympUcOIXikwmp8fsZcp6zj7e3sX+O2rVvlN5Hp/3eCf1eGupJOV6325cJVxbjpyze+7Nzato1b5TOnouRZI0tl+DAiUpi4uPT96JL2c98yd71VfL2Ci1jYsqVJsLnujs1u9RvIcC7oU+CXgO+ivgWeizeXP02RRoBVjDMDRixAj973//08KFCxUXF2d1vGXLlvL19dWCBVcW8N21a5cOHjyo+Ph4SVJ8fLy2bNmiEydOWOrMmzdPYWFhatCAD6ABV2pWPcLhut8+2EaS9PsjHXIdS07NcFZIAFBmjfphox7+ep1mbjkmw5CW7j5pdXzrVQmAr1YeyNVGvfGzZTZnTbH27582q84zf2j8L1slyZJIkpQrkVQ+pIBz5BWzljXKWe3/8VhHp7Q7qF2sJOmJnkVfb8qVbv94pd1jAb5eqlUhxLJ/bVykdr7UW7+NaK/Vz3Sz+T6ebe247nrztqZa+nRXjetXXwuf6OwWiSRJltd1cfPz8VKvhjEKDyr4/3g+1Lmm1bMHAAAAAE9WoJFJw4cP17fffqtff/1VoaGhljWOwsPDFRgYqPDwcA0ePFijRo1SZGSkwsLC9Mgjjyg+Pl5t22ZNC9GzZ081aNBA99xzj1577TUlJiZq3LhxGj58uM3RRwBKTsc6FWyWb3q2p+ZuT1SLGuXU7Y2sae2yP2isHZ37Q5IpC/dodJ96xRcoAJRy5y6l6+f1RyRJf2y1XlNyy+FzKh/qp7+PX3CorZpjZylhUj/9Z+0hSVlJp+jQvP/mWjuueyGiLj4+3tbff6pfyTmjkp6/saH+3bueAv3yn97NU9WqECIvL5PKh/jpnwtpur5JJQX4eqtJ1Yh8z81+r/fxNun/OtYs5kgLpmJYgKtDsOv7IW01a8sxPdaNaX8BAAAAlB4FSiZ98MEHkqQuXbpYlU+bNk2DBg2SJL311lvy8vLSgAEDlJqaql69eun999+31PX29taMGTP08MMPKz4+XsHBwbrvvvv04osvFu1OABTaPW1r6KX+jWweqxQeoPAgX93aqpok6ene1+hEUqrqVgy11LmleRX9vOGIZT++VpRiR8+UpFxTLAEA8nfBzgjP2VuPaejX620ey8vfx89b7ec1pWn2yFN3FR7o3KkJijuRVDLjZ6z1a1JJMzcfk3Rl9NWsRztq0+Fz6l4/Olf9lWO6qe3EBbnK3dndbWto+ooEHbCx1uPGZ3u4IKIr2taMUtuaUS6NAQAAAACcrUDJJMPI/3+HAwICNHXqVE2dOtVunRo1amjWrFkFuTSAYvDh3S3107rDeqJnXbt1ute3XjR8WJfauerc0KyyVTLpg8V7LNu3fbRCv42wP4UOACC3f86n2iwvTCJJkmW9nPy8fXsztatVvlDXKCnT7r/W1SE4xkWzwdWJDtE7tzfTawOaaGdiklpUz5oiMDosQD0a2B7NExOeu/znYe2KNc6i8vPx0oMda2rc5Wkb372jua5vXEkmk9xmKj4AAAAAKE0KlEwCULr0bhSj3o1i8qzjyHR1Xa+J1rh+9TVh5g5J0sp9py3HNh+2v6g3AMC21+bsLPFr/vl0V1WLDCrx6zpq9dhuOnMxXdfEhOZf2Q1kr+ljduDLWM40b1RnSVlTA7asEVnodtx5Grls7WtfSXyG+HvLy4skEgAAAAAUF6/8qwAoy4L9Hcs5u9taCgDgyZbtOVXoc6cNulY1ohxLCo3oWluv3NxYW1/o5daJJClrZI2nJJIkae2BM5KkN+fuVlqGOc+6SSnp6vvOn5qy4O+SCM0hnpCXiSsf7OoQAAAAAKDMYGQSALse6kyCCADc3fdD2mr21kQ91esayxcAWsaW076Tyeo/dZnd825pUUVP9rqmpMIss/b9k6yWL81T70Yxev3WpjbrfLPyoLYfS9L2Y0l6pFsdm3WW7D6pLYfPqk/jSqpVIcRmnbdvb1boOB/qXFMfLdln2Y8M9it0W65QwgPAAAAAAKDMYWQSALtG985/ijsAQP6W7D6pacv2F+rcqGA/VQzztyprXztKkvTOwGZqWzNKz9/Y0GokaViAr5pVi8jVVs5kw/h+DQoVDwrufGqGflx3WP9csL0WVqY575FL5y6m677PV2vy3N3q9sYSXUrL1Kwtx/TD2kOSJO/Lw4iKMnKrXo5zNz3XU/4+3oVuyxXMJJMAAAAAoFgxMgmAXSxgDQDOcd/nqyVJ51MylJiUogc71nR4iq5143tIkvadvKDr3lii++Jr6IWbGjl0btOq4dqUY+26/s2rqH/zKgWMHs7SasJ8zR7ZUfViwhQ7eqakrMSNt5f973f99fc/uvuzVVZl9Z+dbdn+aMleZV7OpHgXYW66nGskhQf6FrodV6laLtDVIQAAAABAqUYyCYBNTaqGO6WdxlWc0w4AlAZvztstSfp21UElTOqnTLOhsxfTFBViPfLoxqaV9dumo7qxaWVLWc0KIfr75T7y9XZ8YHnnuhUsyaRlo69zwh2gqHq//ac61ilv2W/6wlw9lcd0g1cnkq6292SyZdvRdQ5tia8ZpYe71FJtO1Pouasfh8bryJlLql8pzNWhAAAAAECpxjR3AKxkT3Nzd9saBT73k3tb5Srz8+HXDICSl5SSrtjRM/Xsr1tdHYpdn/65T7XGzlLLCfO16dBZHTp9Uf/+abO2H02y/O6sV8l62rKCJJIkaVjX2rq9VTV9PqiVqkQwcsNd/Pn3P1b7/11/OFedS2mZemnG9gK1W5Sfsclk0r9719OAllUL3YYrXBsbyWg7AAAAACgBjEwCYOW/D7fTruPn1dzGWhv56V4/OldZRmbe60AAQHG47cMVkqQvVxzQ0M61VNkNEykTZu6wbD/wxRqdSk6TJP1n7SH1bFBRkuRXwOTR1QJ8vfXqv5oUqQ0Uv305RhfFjp6pnS/1tprKzhFta0Y6OywAAAAAACwYMgDASrC/j1pUL1eo9ZJsnZPBitgAXGBn4nnLdrtJC10YiWOyE0nZ5m4/LkmWtXBQttQb71giaWzfepbt74fEF1c4AAAAAACQTAJQvLYdTZJh8GEoANeKn7igxK+Z/buvKL8Dv1t90FnhwMM9e30DrRzTzars/vZxeuXmxlr8ZBfXBAUAAAAAKDNIJgEodvv/Sc6/EgAUo2PnUmyWf/rnPr0+Z6fTrzfm5y3qMnmxzqekF2l00Ru3NXViVPBkD3SIU0x4gGVdpNtaVZWvt5fubFNdseWDXRwdAAAAAKC0I5kEoNilpLNuEoCS1bdxTL51jielaMLMHZq6aK+2HD7n1Ot/t/qgDpy6qF83HrWacu/t25s53EZsVJBa1mAdnNLi+yFtC33upFsaW7b/81BbjetXX8/d0NAZYQEAAAAA4BCSSQCK3eEzF10dAoAyIvFcij5YvFfVIoNyHTt27pLVfs7fTXO3JxZLPCv3ndKahNOW/Z4NK1odH961ltX+T0OvrHvz87D2xRITXKNtzahCnZcwqZ8Gtq5u2a9aLkj/17Gmgv19nBUaAAAAAAD5IpkEoNgN+Wqdq0MAUAYkpaSr7cQFenX2Tn20ZF+u4/ETF2rrkSsjkNYknLFsT1m4p8jrux0+c1F/bDlmVTZj8zG98Pt2y36Aj7dl+6vBrTWye12r+q1iI7Xzpd7a/HxPRQb7FSkeuL+FT3TO83jrWEamAQAAAADcA8kkAABQKny36mC+da6f8pck6a15uzXpD+u1kv6z5lCRrt/h1UV6+Jv1ev63bXbreHmZLNsHT1+Ur7eXdk3orad7X6NZj3aUJAX4eisswLdIscD99W4Yo5oVQjR/VCe7dVrFlivBiAAAAAAAsI9kEgAAKBWOJ6XmKqsUHpCr7KGv1uqdBX/nKh/985ZCX/vsxTTL9hfLExw6p2eDrHWd/H28NaxLbTWoHFbo68PzPNnrGklSrQoh6l6/ovo0sl7nKyYsQMO71nZFaAAAAAAA5EIyCQAAlApLdp/IVfbJva1ylc3Zdtzp1+7+5hKH664a202LnuyiCqH+To8D7mnz8z0lSeVDrvzMa0eHSJJMJpM+va+VPri7pdU5n97XinWRAAAAAABug2QSgGLRoJL1N+wzMs0uigRAWbH3ZHKuMi+TyUZN5/vnQlq+dX4cGi9JqhgWoLjywcUdEtxE12sqFGrawvBApjoEAAAAALgPkkkAnGr+qM4a0qmmvhzc2qr8t01HXRQRgLJq/8S+MmQU6JzY0TO1aFfuEU7OsOnQ2WJpF+6tsAnNapFBTo4EAAAAAIDCI5kEwKlqR4dobN/6VlP5SFKMjXVLAKC47J/YVyYHP8SfNuhaq/37p60p8vXvi6+Rq+zm5lWK3C48T86XYfPqEZIkX++SGTEHAAAAAICzMBE7gGLj7+Ol1AymtwNQ8rITSX7e+X9vJjrMuWsXJUzqJ0mavuKApey6etGKCmGNpLLk373r6dM/9+mZfg0sZZNuaazYqCDd1qqaCyMDAAAAAKDgGJkEoNhseb6XZTsjs2BTTQGAM9SODtHNzavowY5xduvUiQ7NVWYYBfudFRqQ9f2cafdfa/P42L71CtQePN/DXWpp7bjuVutjRYX465l+DVSnYu7XnCTVi7FdDgAAAACAq5FMAlBs/Hy81LRquCQpw8wIJQDFq1GVMEmy+vDeZDLprdubWY0OkaRRPeqqbc1ITb2zhfx8cv85dPRcSoGuXSE0a9RRkK+3peyry2vHhfj7qLaNhBVKP0enWgQAAAAAwN0xzR2AYuVzeYqptAxGJgEoXm3iorT1SJJ6NYzJt+4j19XWo93q2D0+e2uiBnewP5rpaumZWQlz3xyJqY51KlimvAMcUcABcQAAAAAAlBhGJgEoVtmLjGd/0AqUZedT0jV5zi7tTExydSilkvnyJ/EOLJOU74iRqYv2OHTNE+dT1OedP3Xo9CVJkq8Xf1qh8IZ2qSlJ6u1AQhQAAAAAgJLEyCQAxcr38qe6THMHSK/N3qWvVh7Qe4v2KGFSP321IkERQX66oWllV4dWKpjNWckkLzuJok3P9dQ78//WsK618m3rdHKaQ9ds/fIC6xgYWoIiuLl5VbWoXk5VywW5OhQAAAAAAKzw9VkAxSo7mZTONHeAfl5/2LK99cg5jf91mx75boPWHThjVS8tw6xLaZklHZ7bmbMtUbGjZ2rRrhM2j3/21371fedPJV5e3+ifC3kngMIDffXsDQ1UPsS/QHEYBUgQVQoPKFDbwNVqRAXL24u1lgAAAAAA7oVkEoBi5XP5A7E0prkDlJwjQXT9lL8s2wM+WG5Vr/2rC9X0hblKSb9S3zAM7TlxQRmltC+lZ5o18OMVemXWDkvZQ1+tkyTdP22NzXNemrFd248lqe3EBYodPVMztxyTJP2+6ahTYsrINOv537YpbswsHU/KSljlTCyduFyWU3QYySQAAAAAAFD6MM0dgGI1d/txSdK4X7bq7rY1XBwN4L5e+H2bnruhoY6evaST51MlSQmnklUvJkxS1iidoV+vz6p7Y0M999s2zXu8k+pUDHVZzM60aOcJrdx3Wiv3ndbYvvVzHb+QmqEQf8f+bEk4dbHA1+9yTQUt3nXSqqz2M39Yttu8skDXN6mkGZuzElaRwX5qUjXcqv6+V/oW+LoAAAAAAACegJFJAAC4gWnLEiRJ7SYttJT5XZ4mMvFciiWRJEnP/bZNktTjraUlF2Axyzl6MXb0TMWOnml1/LrJi3Xz+8v0yHcb8m3rg7taFPj6797RXJNvbZpnnexEkpS1plLO5NPWF3rJi6nJAAAAAABAKUUyCQAAN7Ht6Dmr/UNnLkmS2k5cYPec2NEzdTEto1jjKgnmfJYlOnE+VRsOntXvm45q5b5TOncp3W7dkICCD7wOC/DVv1pWLfB5lms6OGoKAAAAAADAE5FMAlBiCrKIPVAWPfO/rVb7932+Wgt2HM/3vFYT5uuuT1fqoa/WFldoxS41x/pQ+Rn48Uo1fWGuVdkbOUYVta0ZVeg4utePLvS5AAAAAAAApRVfowVQYtIyzfL38XZ1GIDb2njobK6ywdPzTxBdTMvUsj2nJEmf/7VfD3SIc3Zoxa6oqeZbWlRRsL+36sWEyde78N+VaVYtQvN3nChiNAAAAAAAAKULI5MAlJi8pqUCyoI7Wlcr9mu8OGN7sV+jWBQhm3Rnm+oymUzq3aiSYssHFymMqBD/Ap+TMKlfka4JAAAAAADg7kgmASgx7y/a6+oQAJfKzG9hIEk+XqYiXyelAFPGuYuoEL/cZcF+Wjuue77nvty/kdPiKH9VMmnD+B656tzVprriL0+lt/HZ3McBAAAAAABKG5JJAErMF8sTXB0C4FJpGWarfZONvFFGHgmnGY90cOg6N723rEBxuQNbU9OtG98jV3Inp8m3NtWuCb1lsvUgC6l8jqTW/FGdVS7YOsk1qF2sJvRvpO+GtFXCpH6KCMqdBAMAAAAAAChtWDMJQLFqWzNSK/eddnUYgMttO3pOm4+csyrb83Jf1Ro7y6Hzfxwar0ZVwjWgRVVtO3pOp5LTdPJ8qs26u46fL3K8JS3TsE6ibXm+p2V750u9NWjaal3fpLLqxYTKz8dLTapGFEsc18SEWrb9Lie45j7eST3fWqp6MaF6/saGxXJdAAAAAAAAd0YyCUCx+vKBNqo77g9XhwG41KW0TPV79y+rshmPdJB3Aaa0uzY2UpL0xm1NJUnxExdYjv00NF7Vo4LU+uWssrxG87irlDTrqflCA3wt2wG+3vp+SHyJxOGXY4RUueCsGOpWDGVdJAAAAAAAUKYxzR2AYuXn46WWNcpJkgZ3iHNxNIBr3Py+9bRzrw5orEZVwiVJf7/cR9892DbP87vXr5irzJxjJE+r2EhFhwaobc2shNOAFlWKGnKJe+z7jZbtu9pUd1kcPt5emvFIB/06vL1VQgsAAAAAAKAsI5kEoNg1qZr1obm/D79yUDbtTLS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" ] @@ -1583,26 +1696,29 @@ ], "source": [ "# Period of Simulations\n", - "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", + "\n", + "worst_6_month = [[[\"2020-02-20\",\"2020-09-01\"],240]]\n", + "worst_1_year = [[[\"2019-09-01\",\"2020-03-01\"],170]]\n", + "periods_n_open_close = worst_3_month\n", "period = periods_n_open_close[0][0]\n", "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", "parameter_manager = ParameterManager()\n", "last_date = period[1]+' 00:00:00'\n", "vol = parameter_manager.calc_vol(last_date, data)\n", "mu, sigma = vol\n", - "open_close = 243\n", + "open_close = periods_n_open_close[0][1]\n", "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", "axs.plot(data['close'], color='tab:blue', label='market price')\n", - "axs.axhline(y=240, \n", + "axs.axhline(y=open_close, \n", " color='green', \n", " linestyle='--', \n", - " label='oc1='+str(round(240,3)))\n", - "axs.axhline(y=243, \n", - " color='red', \n", - " linestyle='--', \n", - " label='oc2='+str(round(243,3)))\n", + " label='oc1='+str(round(open_close,3)))\n", + "# axs.axhline(y=243, \n", + "# color='red', \n", + "# linestyle='--', \n", + "# label='oc2='+str(round(243,3)))\n", "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", "axs.grid()\n", @@ -1939,24 +2055,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# Period of Simulations\n", - "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2020-09-01\"],240]]\n", + "period = periods_n_open_close[0][0]\n", "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - "price = 240" + "price = periods_n_open_close[0][1]" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -1969,9 +2086,10 @@ "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", - "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=243, color='red', linestyle='--', label='open_close')\n", "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", - "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=170, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=130, color='red', linestyle='--', label='open_close')\n", "axs.grid()\n", "axs.legend(loc='lower left')\n", "plt.show()" @@ -1979,32 +2097,29 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 75, "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "crosses = cross_counter(data_set, 240)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "312" + "558" ] }, - "execution_count": 18, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Period of Simulations\n", + "p = 170\n", + "periods_n_open_close = [[[\"2019-09-01\",\"2020-09-01\"],p]]\n", + "period = periods_n_open_close[0][0]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "crosses = cross_counter(data_set, p)\n", "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" ] }, From 9aa5edc875e6fd643b76d8a820eb1984c6fef873 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Tue, 18 Oct 2022 10:31:28 -0300 Subject: [PATCH 12/16] playing with oc_increment and trailing_increment --- jupyter-lab/Simulations_oc_range.ipynb | 499 ++++++++++++------------- 1 file changed, 242 insertions(+), 257 deletions(-) diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb index bf81469..86bf598 100644 --- a/jupyter-lab/Simulations_oc_range.ipynb +++ b/jupyter-lab/Simulations_oc_range.ipynb @@ -13,37 +13,37 @@ "Requirement already satisfied: scipy in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.9.1)\n", "Requirement already satisfied: pygsheets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (2.0.5)\n", "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", - "Requirement already satisfied: numpy>=1.21.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (1.23.3)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2022.2.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: numpy>=1.21.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (1.23.3)\n", "Requirement already satisfied: google-api-python-client>=1.5.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (2.63.0)\n", "Requirement already satisfied: google-auth-oauthlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (0.5.3)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (4.37.3)\n", - "Requirement already satisfied: packaging>=20.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (21.3)\n", "Requirement already satisfied: cycler>=0.10 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (4.37.3)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n", "Requirement already satisfied: contourpy>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.0.5)\n", - "Requirement already satisfied: pyparsing>=2.2.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n", + "Requirement already satisfied: packaging>=20.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (21.3)\n", "Requirement already satisfied: pillow>=6.2.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (9.2.0)\n", - "Requirement already satisfied: httplib2<1dev,>=0.15.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (0.20.4)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n", "Requirement already satisfied: google-auth-httplib2>=0.1.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (0.1.0)\n", - "Requirement already satisfied: uritemplate<5,>=3.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (4.1.1)\n", "Requirement already satisfied: google-auth<3.0.0dev,>=1.19.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (2.12.0)\n", + "Requirement already satisfied: uritemplate<5,>=3.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (4.1.1)\n", "Requirement already satisfied: 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/home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2022.9.24)\n", "Requirement already satisfied: idna<4,>=2.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (3.4)\n", - "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.1.1)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.26.12)\n", + "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.1.1)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2022.9.24)\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" @@ -601,11 +601,11 @@ "class ParameterManager(object):\n", " # auxiliary functions\n", " @staticmethod\n", - " def define_target_prices(stgy_instance, slippage, vol, floor, trailing):\n", + " def define_target_prices(stgy_instance, slippage, vol, floor, trailing_increment):\n", " mu = vol[0]\n", " sigma = vol[1]\n", " p_open_close = math.inf#floor * (1+slippage) * (1+mu+2*sigma)\n", - " p_trailing = floor * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", + " p_trailing = floor * (1-trailing_increment) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", " ##########################################################\n", " # We define the intervals\n", " list_of_triggers = [\"open_close\",\n", @@ -793,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -802,8 +802,8 @@ " self.historical_data = None\n", "\n", " @staticmethod\n", - " def write_data(stgy_instance, previous_price, last_outside, current_outside,\n", - " date, period,oc1,\n", + " def write_data(stgy_instance, #previous_price, last_outside, current_outside,\n", + " date, file_location,\n", " sheet=False):\n", " aave_instance = stgy_instance.aave\n", " dydx_instance = stgy_instance.dydx\n", @@ -825,7 +825,7 @@ " dydx_wanted_keys = [\n", " \"market_price\",\n", " \"entry_price\",\n", - " \"short_size\",\n", + " # \"short_size\",\n", " # \"collateral\",\n", " # \"notional\",\n", " # \"equity\",\n", @@ -836,7 +836,7 @@ " \"short_status\",\n", " # \"order_status\",\n", " # \"withdrawal_fees\",\n", - " \"funding_rates\",\n", + " # \"funding_rates\",\n", " # \"maker_taker_fees\",\n", " \"maker_fees_counter\",\n", " \"costs\"]\n", @@ -861,10 +861,10 @@ " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", " data_dydx.append(stgy_instance.open_close_range[0])\n", " data_dydx.append(stgy_instance.open_close_range[1])\n", - " data_dydx.append(current_outside)\n", - " data_dydx.append(last_outside)\n", + " # data_dydx.append(current_outside)\n", + " # data_dydx.append(last_outside)\n", " data_dydx.append(stgy_instance.trailing_stop_range[0])\n", - " data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " # data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", " data_dydx.append(stgy_instance.trailing_stop_range[1])\n", " else:\n", " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", @@ -876,7 +876,7 @@ "\n", "\n", " # data_dydx.append(stgy_instance.gas_fees)\n", - " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " # data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", " data_dydx.append(stgy_instance.total_pnl)\n", " # data_dydx.append(mkt_price_index)\n", " # print(interval_old.name)\n", @@ -888,8 +888,8 @@ " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", " else:\n", - " path_to_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = file_location + 'dydx_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " with open(path_to_aave, 'a') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", " writer.writerow(data_aave)\n", @@ -899,16 +899,18 @@ " writer.writerow(data_dydx)\n", "\n", " @staticmethod\n", - " def delete_results(stgy_instance, period, oc1):\n", - " file_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " file_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " def delete_results(stgy_instance, file_location):\n", + " #period, oc1):\n", + " file_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = file_location + 'dydx_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", " os.remove(file_aave)\n", " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", " os.remove(file_dydx)\n", "\n", " @staticmethod\n", - " def add_header(stgy_instance, period, oc1):\n", + " def add_header(stgy_instance, file_location):\n", + " #period, oc1):\n", " aave_headers = [\n", " \"date\",\n", " \"market_price\",\n", @@ -935,13 +937,13 @@ " \"P\",\n", " \"oc_rge_0\",\n", " \"oc_rge_1\",\n", - " \"out\",\n", - " \"l_out\",\n", + " # \"out\",\n", + " # \"l_out\",\n", " \"trail_stp_rge_0\",\n", - " \"trail_stp\",\n", + " # \"trail_stp\",\n", " \"trail_stp_rge_1\",\n", " \"entry\",\n", - " \"short_size\",\n", + " # \"short_size\",\n", " # \"collateral\",\n", " # \"notional\",\n", " # \"equity\",\n", @@ -952,17 +954,17 @@ " \"short_status\",\n", " # \"order_status\",\n", " # \"withdrawal_fees\",\n", - " \"funding_rates\",\n", + " # \"funding_rates\",\n", " # \"maker_taker_fees\",\n", " \"maker_fees_counter\",\n", " \"costs\",\n", " # \"gas_fees\",\n", - " \"total_costs_from_aave_n_dydx\",\n", + " # \"total_costs_from_aave_n_dydx\",\n", " \"total_stgy_pnl\"]\n", " # \"index_of_mkt_price\"]\n", " \n", - " path_to_aave = 'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " path_to_dydx = 'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = file_location + 'dydx_results.csv' #'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " with open(path_to_aave, 'a') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", " writer.writerow(aave_headers)\n", @@ -992,7 +994,26 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'Files/ETHUSDC-1m-data_since_1 Sep 2019.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [7], line 10\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Track historical data\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# symbol = 'ETHUSDC'\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# freq = '1m'\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 7\u001b[0m \n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Load historical data if previously tracked and saved\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m historical_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFiles/ETHUSDC-1m-data_since_1 Sep 2019.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# # assign data to stgy instance + define index as dates\u001b[39;00m\n\u001b[1;32m 12\u001b[0m timestamp \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(historical_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/util/_decorators.py:317\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 312\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 313\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 314\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 315\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(inspect\u001b[38;5;241m.\u001b[39mcurrentframe()),\n\u001b[1;32m 316\u001b[0m )\n\u001b[0;32m--> 317\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m 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\u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/common.py:857\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 853\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m 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max_6m_change]: ['-6.786594905713236%', '9.135956592119358%']\n", + "avg movement: (1+slip)(1+mu+2vol): 0.40977873739736026%\n" + ] + } + ], "source": [ "# First we calculate weighted vol\n", "last_date = \"2021-06-01\"\n", @@ -1068,9 +1100,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "weighted sigmas: 0.20636032309050903%\n", + "avg movement: (1+mu+2vol): 0.4123904345313889%\n" + ] + } + ], "source": [ "vol = sigma_2\n", "mu = mu_2\n", @@ -1087,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1107,7 +1148,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1153,13 +1194,13 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 8, "metadata": { "tags": [] }, "outputs": [], "source": [ - "def run_sim(period, open_close, slippage, max_txs, L, trailing, increment):\n", + "def run_sim(stk, period, open_close, slippage, oc_increment, trailing_increment, file_location):\n", " global ocs\n", " # Initialize everything\n", " with open(\"Files/StgyApp_config.json\") as json_file:\n", @@ -1169,45 +1210,29 @@ " stgy = StgyApp(config)\n", " # Period of Simulations\n", " # period = [\"2019-09-01\",\"2019-12-31\"]\n", - " stgy.historical_data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + " stgy.historical_data = historical_data.loc[period[0]:period[1]]\n", " # For vol updates we take all data up to the last date\n", " stgy.launch(config)\n", " # First we calculate weighted vol\n", - " last_date = period[1]+' 00:00:00'\n", + " last_date = period[1]\n", " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", " mu, sigma = vol\n", " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", " # Now we define prices \n", - " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing)\n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing_increment)\n", " # We create five equidistant OCs\n", " oc1 = open_close\n", - " # oc2 = oc1 * (1+6/2/100)\n", - " # ocs = [oc1]\n", - " # # print(\"oc1=\",round(oc1,3))\n", - " # for i in range(1,7):\n", - " # globals()[\"oc\"+str(i+1)] = oc1 * (1-0.005)**i # We define 5 OCs based on a top width of 3%\n", - " # ocs.append(globals()[\"oc\"+str(i+1)])\n", - " # print(\"oc\"+str(i+1)+\"=\",round(globals()[\"oc\"+str(i+1)],3))\n", - " # print(ocs)\n", - " # But we start with the first oc1\n", - " # stgy.trigger_prices['open_close'] = oc1\n", - " \n", - " # print(\"Volatility:\", vol)\n", - " # print(\"Floor:\", stgy.trigger_prices['floor'])\n", - " # print(\"Open_close1:\", oc1)\n", - " # print(\"Open_close2:\", oc2)\n", - " # print(\"1-OC2/OC1 - 1:\", 1-oc2/oc1)\n", " #########################\n", " # Save historical data with trigger prices and thresholds loaded\n", " # checking if the directory demo_folder \n", " # exist or not.\n", - " if not os.path.exists(\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " if not os.path.exists(file_location):#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", " # if the demo_folder directory is not present \n", " # then create it.\n", - " os.makedirs(\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", - " stgy.historical_data.to_csv(\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", - " % (period[0], period[1], open_close))\n", + " os.makedirs(file_location)#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " stgy.historical_data.to_csv(file_location+'stgy.historical_data.csv')#\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " # % (period[0], period[1], open_close))\n", " #########################\n", " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", "\n", @@ -1216,7 +1241,7 @@ " initial_index = 1\n", "\n", " # Stk eth\n", - " stgy.stk = 1000000/stgy.historical_data['close'][initial_index]\n", + " stgy.stk = stk/stgy.historical_data['close'][initial_index]\n", "\n", " # AAVE\n", " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", @@ -1245,21 +1270,21 @@ " stgy.dydx.equity = stgy.dydx.equity_calc()\n", " stgy.dydx.collateral_status = True\n", " \n", - " stgy.open_close_range = [open_close * (1-increment), \n", - " open_close * (1+increment)]\n", + " stgy.open_close_range = [floor * ((1+slippage)*(1+mu+2*sigma)), \n", + " floor * ((1+slippage)*(1+mu+2*sigma)) * (1+oc_increment)]\n", " # stgy.trigger_prices['trailing_stop'] = stgy.open_close_range[0] * (1-trailing)\n", - " stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-increment)**2, \n", - " stgy.open_close_range[0]]\n", + " stgy.trailing_stop_range = [floor * (1-trailing_increment), \n", + " floor]\n", " \n", " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", - " if (stgy.dydx.market_price <= stgy.open_close_range[1]):\n", + " if (stgy.dydx.market_price <= stgy.open_close_range[0]):\n", " stgy.dydx.open_short(stgy)\n", " #########################\n", " # Clear previous csv data for aave and dydx\n", - " stgy.data_dumper.delete_results(stgy, period, open_close)\n", + " stgy.data_dumper.delete_results(stgy, file_location)#period, open_close)\n", " #########################\n", " # add header to csv of aave and dydx\n", - " stgy.data_dumper.add_header(stgy, period, open_close)\n", + " stgy.data_dumper.add_header(stgy, file_location)#period, open_close)\n", " ##################################\n", " # Run through dataset\n", " #########################\n", @@ -1324,49 +1349,28 @@ " if (last_outside == 1) and (outside == -1):\n", " stgy.dydx.open_short(stgy)\n", " last_outside = outside\n", - " # i += 1\n", " elif (last_outside == -1) and (outside == 1):\n", " stgy.dydx.close_short(stgy)\n", " last_outside = outside\n", - " # i += 1\n", - " # else:\n", - " # i += 1\n", " \n", " # open_close_range action\n", " if (last_trailing_outside == 1) and (trailing_outside == -1):\n", " stgy.dydx.open_short(stgy)\n", " last_trailing_outside = trailing_outside\n", - " # i += 1\n", " elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", " stgy.dydx.close_short(stgy)\n", " last_trailing_outside = trailing_outside\n", - " # i += 1\n", - " # else:\n", - " # i += 1\n", + "\n", " i += 1\n", " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", " # and we execute all the actions involved between both (current and previous prices)\n", " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", " ############################## \n", " # We update trailing\n", - " # Everytime price moves down more than (1+trailing) / (1-increment) we update trailing_stop\n", - " # (1+trailing) is to contemplate a pcg between current_price and trailing stop\n", - " # 1/(1-increment) is to assure that we update when the price crossed the whole trailing_range\n", - " # if (market_price*(1+trailing) <= stgy.trigger_prices['trailing_stop']):\n", - " # if (market_price*(1+trailing)/(1-increment) <= stgy.trigger_prices['trailing_stop']):\n", + " # Everytime price crosses the lower bound, we move the trailing range\n", " if (market_price <= stgy.trailing_stop_range[0]):\n", - " # stgy.trigger_prices['trailing_stop'] = market_price * (1+trailing)\n", " stgy.trailing_stop_range = [market_price, \n", - " market_price * (1+increment)**2]\n", - " # # If price moves above trailing we move trailing up in order to save that profit\n", - " # # Is important to change trailing after finding scenarios (because we need to close the short first)\n", - " # elif (market_price > stgy.trigger_prices['trailing_stop']):\n", - " # if trailing_update_hours == 0:\n", - " # pass\n", - " # elif (i % (trailing_update_hours*60) == 0):\n", - " # if not stgy.dydx.short_status:\n", - " # stgy.trigger_prices['trailing_stop'] = min(stgy.trigger_prices['open_close']* (1-trailing), market_price)\n", - " ################################\n", + " market_price * (1+trailing_increment)]\n", " ################################\n", " # OC LOGIC\n", " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", @@ -1387,41 +1391,6 @@ " # for i in range(1,5):\n", " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", " # ocs.append(globals()[\"oc\"+str(i+1)])\n", - "\n", - " \n", - " # If we executed more txs than hat_L*20 then we change to K_2\n", - " # if (stgy.dydx.maker_fees_counter >= max_txs):\n", - " # # stgy.historical_data = stgy.historical_data_OC2\n", - " # # print(stgy.dydx.maker_fees_counter)\n", - " # current_date = str(stgy.historical_data.index[i])\n", - " # current_oc = stgy.trigger_prices['open_close']\n", - " # vol = stgy.parameter_manager.calc_vol(current_date, stgy.historical_data)\n", - " # ocs_choices = stgy.parameter_manager.find_oc(current_oc, ocs, vol)\n", - " # # if short = open and if there are up_choices available, we take the last option (the furthest)\n", - " # # if there isn't options we take max_distance\n", - " # # random.seed(4)\n", - " # # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " # # 'txs': stgy.dydx.maker_fees_counter, \n", - " # # # 'index': i,\n", - " # # 'date': str(stgy.historical_data.index[i])})\n", - " # if not stgy.dydx.short_status:\n", - " # if stgy.trigger_prices['open_close'] == oc1:\n", - " # stgy.trigger_prices['open_close'] = oc4\n", - " # # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", - " # # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up] \n", - " # elif stgy.dydx.short_status:\n", - " # if len(ocs_choices['up_choices']) != 0:\n", - " # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][0]\n", - " # # oc_choice_up = random.choice(range(len(ocs_choices['up_choices'])))\n", - " # # stgy.trigger_prices['open_close'] = ocs_choices['up_choices'][oc_choice_up]\n", - " # # If we didnt change oc we dont clean maker_fees_counter\n", - " # if current_oc != stgy.trigger_prices['open_close']:\n", - " # maker_fees_counter.append({'oc':stgy.trigger_prices['open_close'], \n", - " # 'txs': stgy.dydx.maker_fees_counter, \n", - " # # 'index': i,\n", - " # 'date': str(stgy.historical_data.index[i])})\n", - " # stgy.dydx.maker_fees_counter = 0\n", - " ########################\n", " ########################\n", " # Funding rates\n", " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", @@ -1441,13 +1410,9 @@ " # We write the data into the google sheet or csv file acording to sheet value\n", " # (sheet = True --> sheet, sheet = False --> csv)\n", " current_date = str(stgy.historical_data.index[i-1])\n", - " stgy.data_dumper.write_data(stgy, previous_price, last_outside, outside,\n", - " current_date, period, open_close,\n", + " stgy.data_dumper.write_data(stgy, #previous_price, last_outside, outside,\n", + " current_date, file_location,#period, open_close,\n", " sheet=False)\n", - " #########################\n", - " # we increment index by the time consumed in executing actions\n", - " # i += time_used\n", - " # i += 1\n", " return stgy.dydx.maker_fees_counter" ] }, @@ -1460,21 +1425,21 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", - " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "periods_n_open_close = [[[\"2020-05-31\",\"2020-12-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01 00:00:00\",\"2019-12-31 00:00:00\"],148], [[\"2019-09-01 00:00:00\",\"2019-12-31 00:00:00\"],185], \n", + " [[\"2020-01-01 00:00:00\",\"2020-05-01 00:00:00 00:00:00\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", + "periods_n_open_close = [[[\"2019-09-01 00:00:00\",\"2019-12-31 00:00:00\"],185]]\n", + "periods_n_open_close = [[[\"2020-05-31 00:00:00\",\"2020-12-01 00:00:00\"],240]]\n", "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-02\"],240]]\n", "# Worst cases\n", - "worst_1_week = [[[\"2020-05-31\",\"2020-06-07\"],240]]\n", - "worst_1_month = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", - "worst_3_month = [[[\"2020-05-31\",\"2020-09-01\"],240]]\n", - "worst_6_month = [[[\"2020-02-20\",\"2020-09-01\"],240]]\n", - "worst_1_year = [[[\"2019-09-01\",\"2020-09-01\"],170]]\n", + "worst_1_week = [[[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]]\n", + "worst_1_month = [[[\"2020-05-31 00:00:00\",\"2020-06-30 00:00:00\"],240], [[[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]]]\n", + "worst_3_month = [[[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]]\n", + "worst_6_month = [[[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240]]\n", + "worst_1_year = [[[\"2019-09-01 00:00:00\",\"2020-09-01 00:00:00\"],170]]\n", "# p = 243\n", "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],243]]\n", @@ -1483,33 +1448,34 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 10, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "354" - ] - }, - "execution_count": 166, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'historical_data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [10], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m period \u001b[38;5;241m=\u001b[39m periods_n_open_close[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 4\u001b[0m p \u001b[38;5;241m=\u001b[39m periods_n_open_close[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m----> 5\u001b[0m data_set \u001b[38;5;241m=\u001b[39m historical_data\u001b[38;5;241m.\u001b[39mloc[period[\u001b[38;5;241m0\u001b[39m]:period[\u001b[38;5;241m1\u001b[39m]]\n\u001b[1;32m 6\u001b[0m crosses \u001b[38;5;241m=\u001b[39m cross_counter(data_set, p)\n\u001b[1;32m 7\u001b[0m crosses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdown\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrossed_down\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m crosses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mup\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrossed_up\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'historical_data' is not defined" + ] } ], "source": [ "# Period of Simulations\n", - "periods_n_open_close = worst_6_month\n", + "periods_n_open_close = worst_3_month[1]\n", "period = periods_n_open_close[0][0]\n", "p = periods_n_open_close[0][1]\n", - "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "data_set = historical_data.loc[period[0]:period[1]]\n", "crosses = cross_counter(data_set, p)\n", "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" ] }, { "cell_type": "code", - "execution_count": 167, + "execution_count": null, "metadata": { "tags": [] }, @@ -1518,48 +1484,48 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fees counter for (pcg = 0.005, increment = 0.0005) = 322\n", - "PnL for (pcg = 0.005, increment = 0.0005) = -159700.58932288198\n", - "Fees counter for (pcg = 0.005, increment = 0.001) = 260\n", - "PnL for (pcg = 0.005, increment = 0.001) = -157572.0970661439\n", - "Fees counter for (pcg = 0.005, increment = 0.002) = 194\n", - "PnL for (pcg = 0.005, increment = 0.002) = -104398.10832875544\n", - "Fees counter for (pcg = 0.005, increment = 0.003) = 136\n", - "PnL for (pcg = 0.005, increment = 0.003) = -40176.11423351267\n", - "Fees counter for (pcg = 0.005, increment = 0.005) = 96\n", - "PnL for (pcg = 0.005, increment = 0.005) = -52110.249370017846\n", - "Fees counter for (pcg = 0.005, increment = 0.007) = 74\n", - "PnL for (pcg = 0.005, increment = 0.007) = -9654.954697517012\n", - "Fees counter for (pcg = 0.005, increment = 0.01) = 56\n", - "PnL for (pcg = 0.005, increment = 0.01) = 25778.04723751844\n" + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.003]) : [96, '-5.754%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.005]) : [90, '-4.883%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.01]) : [80, '-4.614%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.015]) : [78, '-6.341%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.02]) : [74, '-5.004%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.003]) : [82, '-9.713%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.005]) : [76, '-8.842%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.01]) : [66, '-8.573%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.015]) : [64, '-10.3%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.02]) : [60, '-8.963%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.003]) : [52, '-1.937%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.005]) : [46, '-1.067%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.01]) : [36, '-0.797%']\n" ] } ], "source": [ - "max_txs = 8 # we wont execute more than 4 late closes (each one has a loss of ~-5k which means -5k/1M = -0.5% loss each time we close late)\n", - "L = 5 * 0.07\n", - "trailings = [0.005]#[0.001, 0.003,0.005,0.01,0.02, 0.03,0.05] #[0.02, 0.03]\n", - "trailing_time = 0\n", - "# trailing_update_hours = [0, 1, 3, 8, 12, 24]\n", - "increments = [0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", - "# increment = 0.003\n", + "# range's lenght = 2*increment\n", + "stk = 1000000\n", + "oc_increments = [0.003, 0.005, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", + "trailing_increments = [0.003, 0.005, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", "maker_fees_counter_lengths = {}\n", "pnl_results = {}\n", + "total_results = []\n", "for period_n_open_close in periods_n_open_close:\n", - " for increment in increments:\n", - " for trailing in trailings:\n", + " for oc_increment in oc_increments:\n", + " for trailing_increment in trailing_increments:\n", " period = period_n_open_close[0]\n", " open_close = period_n_open_close[1]\n", " slippage = 0.0005\n", - " maker_fees_counter = run_sim(period, open_close, slippage, max_txs, L, trailing, increment)\n", - " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=maker_fees_counter\n", - " print(\"Fees counter for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", - " maker_fees_counter_lengths[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])\n", - " directory = \"From_%s_to_%s_open_close_at_%s/dydx_results.csv\" % (period[0], period[1], open_close)#\"From_2020-05-31_to_2020-12-01_open_close_at_240/dydx_results.csv\"\n", - " dydx_results = pd.read_csv(\"Files/Tests/\" + directory, low_memory=False)\n", - " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", - " print(\"PnL for (pcg = \"+str(trailing) + \", increment = \" + str(increment) + \") = \", \n", - " pnl_results[\"pcg = \"+str(trailing) + \", increment = \" + str(increment)])" + " directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, oc_increment, trailing_increment)\n", + " maker_fees_counter = run_sim(stk, period, open_close, slippage, 2*oc_increment, 2*trailing_increment, directory)\n", + " maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", + " # print(\"Max txs for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", + " # maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])])\n", + " # directory = \"From_%s_to_%s_open_close_at_%s/dydx_results.csv\" % (period[0], period[1], open_close)\n", + " dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)\n", + " pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", + " print(\"Max txs and PnL for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", + " [maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])], \n", + " str(round(pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%'])\n", + " total_results.append([maker_fees_counter_lengths, pnl_results])" ] }, { @@ -1584,7 +1550,18 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, 0.005, 0.02)\n", + "maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", + "dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -1592,24 +1569,24 @@ "price_jump_in_close = {}\n", "\n", "for i in range(len(dydx_results)-1):\n", - " if dydx_results['entry_price'][i]==0 and dydx_results['entry_price'][i+1]!=0:\n", - " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)\n", - " elif dydx_results['entry_price'][i]!=0 and dydx_results['entry_price'][i+1]==0:\n", - " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)" + " if dydx_results['entry'][i]==0 and dydx_results['entry'][i+1]!=0:\n", + " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['P'][i+1] / dydx_results['P'][i]-1)\n", + " elif dydx_results['entry'][i]!=0 and dydx_results['entry'][i+1]==0:\n", + " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['P'][i+1] / dydx_results['P'][i]-1)" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Min price jump at open: 0.0041%\n", - "Mean price jump at open: 0.1688%\n", - "Max price jump at open: 2.5665%\n" + "Min price jump at open: 0.0714%\n", + "Mean price jump at open: 0.4439%\n", + "Max price jump at open: 1.4048%\n" ] } ], @@ -1621,16 +1598,16 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Min price jump at close: 0.0082%\n", - "Mean price jump at close: 0.1646%\n", - "Max price jump at close: 1.717%\n" + "Min price jump at close: 0.049%\n", + "Mean price jump at close: 0.5558%\n", + "Max price jump at close: 3.5869999999999997%\n" ] } ], @@ -1642,50 +1619,12 @@ }, { "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [], - "source": [ - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-01\"],148], [[\"2019-09-01\",\"2019-12-31\"],185], \n", - " [[\"2020-01-01\",\"2020-05-01\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-31\"],185]]\n", - "periods_n_open_close = [[[\"2019-09-01\",\"2019-12-01\"],185]]" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "354" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Period of Simulations\n", - "# periods_n_open_close = worst_1_month\n", - "period = periods_n_open_close[0][0]\n", - "p = periods_n_open_close[0][1]\n", - "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", - "crosses = cross_counter(data_set, p)\n", - "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" - ] - }, - { - "cell_type": "code", - "execution_count": 163, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { - "image/png": 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H0u33iELlavPi7NIbweMKxz7bkXLOYZuWL8xUwpgZNssNBoO+uLOHkjrG6at7eql3szp6bEBLSdLtveM1Y9NxPTJ1nXLzyt/777ft1tfAT/4oKGv5S4lenIWOnLlod3luXr7WHzojkwdi8pbdqeesqsEU+s/dPXVZk5p2t9n40mC7ywEA1RPJJPi0zuPnkjApYd62VD313w26kJOrTUfSvR0OAAAAfEijWmFW81c5GG/ojU1GvTZzp96cU9RzqfgYRw1L7EeSKuN5/ZzcfJfHV+n/D9vxeve9nqQh7WPLdOydrw5VVKhtJfhfH6XEU0VrUCPU2yHATc6SSJK098R5qzHcSqodEawPRnSzjIkWdKkc5/+tOKhRU9dp+qbj6jx+rs12ZrNZCWNmKGHMDK0/dMbh/t+ev0sLdqZpT5p7Sec1B0/bLMsy5anFC7P05w+Wqd1Ls/22vOKvm6zHtRuZGK/9k5LUp1U9/fjwFVbrkjrGaf5TV6tGeJAAAChEMgk+zWwueNLs1enbvB2Kz7jvP2v047qj+mTxfj341VrL8tFDWissyKjpj16pD0dcZln+t9k7qOcOAABQTeSWuHm7K/WcDp7KtGmXlVdw43bpnpM6nn5Rb87ZYbU+NKjop6LBQZU7s9mshTvT7D789d6tXR3G+NI17RyuazVulsMeSvYs2JFmNR8QYNC/b++uVS8MsNv+y7t7WqaLJ46+uKuHQgKNWv3CQE28vr3+Nryjdr46VAcmJyvIyM/mihZpJ4lX0o6JQ/XFXT305o2dtOe1YVbr/Lm3iL8xm80ulYKzd92RpGeHtra7PNDOeZaZk2fpAWU2m5WWkaVle4t6UP75g2VKGDNDS3aftNpuwD8W6u35u3XXF6s1be0Ry/JzdiqfRAQb9ePDl1vmv7w0JppU8Ll65ZetVr3fTHlmdXh5jt3X4Ot2lUgATri+gwzFLvD/fSBRkvR8Uht9MKKbWsREVmp8AADfV/o3Nh+WmZMpY47RZrkxwKjQwFCrdo4EGAIUFhRWprYXTBccPjVnMBgUHhReprYXTReVb3b8ZTgiOKJMbbNys5SX77iXjzttw4PCLV86snOzlZvv+Mkcd9oWf3/NMsmsghg+XrJdTw6Ot2kbYCj4wpmTlyNTnuOSeKGBoTIGGJ22NZlMysoreN1BKnj6xpRnUk6e48EpQwJDFBgQ6Hbb3PxcZec6risfbAxWkDHIbtt8FZTTOJx+WvnKkkGBMihQo/q10L1XxivXnKPDZ7Ms7d5fuFVL9h7Rt/clKsgYpGBjQc33vPw8ZeU6Ls1RvG2+OV8XTfa7+7vbNjAgUCGBBaVCzGazLpgueKStO+c91wj7bf3lGlH8vM/Ky1JmTqaCzLZPzHn6GmGvrS9eI5y1dee85xrBNaJk29LO+2BDsMttK+sa4exc5hph25ZrRNW4RpzPOW/5HihJf+w5qqvfPKWxw9ro/j7NlJWbpeycbEubHalZ6jWpKHkToILXFmwMsFwjcvMvKl9Zysq9YPUahr29SodOF7yvZuXIrILryWd3dFdOXtH30Yhgoy7mFP1bfWvv+urbJlqPfbvebk97g0Is4zQV/01gz51TVlnaPjOkqSW+iJCi782/PnKFrn1vqQwKVl5+vr66p5feW7hdL1/bWueycrUj5Zx6No2wbHtDt3pOrxHF399TmRmqGRbBNULlu0Zk515QvrL0f3f31O3Fxrh69+Yu2p16UXHRkQoNMurqVnV10XRR2XkX9fbNbfXYt+slSc1f+EnbJwytEtcIe99r7bX11veIIW+ttJSCy1e2JLP+tWCz7r6imSTpjz0ntO7AGbWMjbLarvAacXPPGLvvyR+7T9i0laT7v1qql65ppwnFHjAteY249bOiXopLnuun3SdO2W3b4ZUZVteT23o10eghbRQcGHDpt3WwNh5J1+Pfrte4a1rpr//+Q3tP2P/7xY/5Vf1bx+rAqQt6qF8TJXWIsUrMFFfR3yMu5uTp65V79efL4hQaZHuPrLBt+qVxqc3K05ZX+tv8Hdo3DNHWCX0t56bkO9cIX/oeUXjvqPj5WpW+R5TWlvsR/Nbwxe8Rzs57FfsI8lvD8TXC2bbFGcyu1hDwIRkZGapRo4Y0RpKd3vBJLZM049aiurwRr0c4/INeHX+1Ft650DJf7816OnnhpN223Rt01+r7VlvmE95O0MH0g3bbtqvXTlsf3mqZb/9Be207Yb93TXyNeB144oBlvscnPbTm2Bq7beuG19WJ0UVfsvpO6atFB23LO0gFF8TM54s+CMlTkzVzt+On/MwvF30U/jLtL/p+2/cO254fe95yIb/z5zv15cYvHbZNeyZN9SLqSZJGzRilD9Z84LDt/sf3q+/kgvftTODnygj60WHbLQ9tUfuYggFwX1n4isYvGu+w7ap7V6lHwx6SpDeXvqln5z/rsO28EfM0sMVASdL7q97XI7Mecdh2+i3TldwqWZI0ZcMU3fW/uxy2/e+N/9Vf2v9FkjRt6zT99fu/Omz7xfVf6M4ud0qSZuyaoWu+ucZh29o5Dyoq7xodmJyshQcWqt+X/Ry2fWPgGxp9xWhJ0uqjq9Xz054O27589ct6pe8rkqStaVvV4cMODts+k/iM3hz8piTpwNkDavpOU4dtH+7+sN5Pfl+SdCLzhGL+HuOw7R2d79CUP02RVHBRi5zk+MmoG9vdqGl/mWaZN4x3PFA014gC/nqNSKiZIEl6evbT+ufKfzpsW1HXiAV3LFDfhL6S/OMa8d6w9zSq5yhJ4hrBNaJCrxE5z+do5syZSkpK0q0/3+r1a8TouaP19+V/d9iWa0QBrhEFqvI1wpgfo0bZn0uSJt3QUf/cONzhNSLAHK3GWVMlSQcmJ5d6jaiX8V/LfFrwK7potL9fSYq/ON0y3aPbFKfXiMYXv7cktU4GvaXMwN8ctm108WsZVTA4fK+uP+m/Oz5z2LZh1meacnuyBraL5RrhQ9eIhfvWqt//dXfYtjpcI0wmk2bOnKl7d96rkxd9+3tExKkplvmU4DHKNm6x29ZgDlGTrB8s8+5cI04ET9IF41KHbct6jTgV9KHOB9qO4VSoYdZnCjQXlMks7X5E/az3FWwueOD1bODXSg/6xmHbir5GJIyZofPG+ToV/LbDtv+98b96f2ZdHTh1QZkBS3QyZLLDtr52jeB7xELLfHX+reFr9yP4HnGnJP+4RjzV6yn1ye6jpKQkHc08yjXiEptrRJakyVJ6erqio6Mdxkd/fVQJeS50swcAAACqs7E/bvZ2CPrHXzq71b5L45outw0Ltv9EfnE9Emq7dfyKkJ9vVm5evvaccD7mTHVx6ycrvR0CKkGdyBCn61uX6MkEzzuW7vgpfwAAXOHXPZOOnThmN1NGl1H7bf2ly2izsbMk2Za0WDFmgNXgj4VdRi/m5Kn767PUvn6EvrjLftba1TJ3c+bM0fXJ1ys0pODz42tdRtcePK3bPltl1dagQO2YcI3Cgo2WbqBJ7y7W/pPWWevtE4ZWmW7l9vhDl9HiuEaUrW3xruKZWZmaPnO6hgwZoqAgyty50ra6lJ6wh2tEkYosc1fYMynPkOf1awSlJ7hGVKdrxLbjJzT0ncWO912snMP2if3V+kXHT/4Wtj0wOdlyjbh7yiot32c7KH3x/RaWpVrzwkBFhATqm5UHNWHG9qLjjr9ee0+cV8eGNZSdl225RpzOzNGyvSc1sG2suk6cV/D6FKIH+jTXjd0aKb5OsN3zvu1Lsy1tC0tYbXy5nwKN9t+z3Lx8BRhCFBVa8FkrzzXCbDarXbExU/a//ifLeZ+dm62W46arpAXPXK246DCFGEPU4oU5l96zXJlV9NrmPdlHjWoV/V2ryzUifsyvMqvgtf33gd7q2LCmw7bFrxFfLN2vN+bstLTz5zJ3hT2Trh54td3vtcXbFvLG94gLObnq8eoSy3xhmTuHr6/YNWLHqwOcfje4Z8omrbh0nTErR2/c2EGjv99kP+ZL5/2uV4ep5bhfHJbC3DZ+iPammXT9+8su7bfoHsP2CUOt2rZ9abYMCpbh0jPX9kpsbp8wVEfOXNCgtxaX2laS/nVLVw1sG1vh3yMSxsywuZ6UtPe169TyhbmX4s3Ttgn2x5WTfO8a4WvfIwrvHRX/Herv3yP84beGr92P4LeG/1wjlC/NnzNfSUlJMgYa+a1xScnzPiMjQw3qNSi1Z5Jfj5kUERxhdTFx1s6dfbqq+MXUk22L/2E92bb4B9GTbUMCQxQi508ZlaWtQUEyqOiLdGBAqCKCbeP634ajyswyaNX+CwoLDFdAgOOuglLBhax4DeBCJoNJocaii7JUcHEqvOiVxp22gQGBCgx27fQrbHvs7EWN/GyT1RfyQoVPYRoDjIoIjtDQdgn69+J9lvW9mta2+WwXtnVFgCGgQtoaDIYKaStV3HnPNcL9thV1jQg2BivUGKqI4AiHP7qLt7V33pe3rS9dI1zhznnPNaLi21a1a4TJVPSjx1euERVx3nONcL8t14iytXXnvL/hgzV2vyPas2LfOf25c1P9b+PxUtsWXiMCA8JK3b9Bwfpl1BWKiSooJ9WzaQMFaL9lfURIoDo1qinJ+hoRERyhm7rXklR04/m5oW30UN/mljb2znt78dQIc/09K+95X/z4hb8fDp26oD5vLrAb24C/r9ScJ/poyNtFSajCsU8LDXlrlfa9nmT394wnrhFHzlzQrtRz6tc6ptS29lTUNcKgABkuvWfdmtRXkNFxAZXi1whTbrDVe13yeP56jSjte20hb3yPaP+SdYm4ABf/TX6kX4tSvxs8NqClVuxbqdoRwfrHX3qoa5Oaek677LbtkVBLU+/rrcAAg819g0J7XhumQGOA2tQvuuFc2PaLu3rY/F1Knrcl97todF9FBEeodWyEfn9qsH7fkabXZ+6w27bQ9mM5ur6z9XHK8j3i4KlMNagZ5vTcKHk9KakwkVTQ1vXz0xeuEb72PaLw3pGz89UXridV7bdGWdvyW8P9tlXtt0bx36n81ihS8rzPC3acoC3Or5NJqF42H03XgGjbfzDenr/bMj1/e6oGt4+rzLAqzfK9p+wu/3DEZTbLLm9R1yqZFBXKqQ4AAFBVZZkcP/Vb0l1frC61zXu3drWaL+z5U5rOxUrSFSaO3LHuxUFKSc9SuwaOn4YsdOflCZqy7IDbx/C05vWKfrT3eXOB07ZD3nbce6xQs+cLeo0dmJxcvsDsuPJvBfF9dkd3DWgb6/H9l8XZC9ZPSTu7WV5SyVLn+fnmUh8sRNllZts+rf/A1c3070X77LQuUCs8SGvHDXLp73J587pa/+Ig1QwPksFgsDneQ32b65nBrWUssa+PbrtMpzJzNKJXvNYePK3vVh/WmGFtFXjpsxQYYPuZKp5QLTSiVxN9vfKQAgMMyi3x2frb8I6Kr1N0rreIiVKLmCgN61Bf87alasL0orFmGtcO0+HTBU+9F78+lNXsLcf14FfrJEkTrm+v23rFO3w//9Ktkf42vJPlOuLIjd0alTsuAED1xJhJ8DnhDmqdhzvIdKdkFHV7HP+r/QEDq4LnfrDfxX9Yx/o2y/q0rKt7rmyqy5vXkSSZ8vyumiUAAAC8YO24gbqmUwO3tnlyYCstHm07oPKfuhTsZ1A71xIXtSOCXUokSdKL17RzPcAKtPeE43IirmoZYztg8x2fr3JYcsiROVtTlDBmhhbsSNPRsxfV980F+sfcnTbtVu63LVlY2cxms96ev0ujpq6zLNv0ymC39tEqznqMnZ2p5/T7jlRtO5bhkRhhbfT3G63mD0xO1thhbZ1u42oiqVCtiGBLuaniCZ2BbWP03NA2NokkSRraob5G9IqXJHWLr603buys2hFFT/UX3+ab+3rr96evtnvs8de1108PX67tE4fq10eu1BUt6mjdi4N0YHKyburRxO42jWuH6+4rrQdy/+PZ/hp4KVlbMinlqvx8s37fkap521ItiSRJeul/W20SRd+uOmSZvveqZgoIMOi1P3dQ7YhgPZ/Uxu7+/+7m2HUAABSiuwJ8zpS7euq+/6xR+kXrGqEhQfZzn3/q0kA/bzgmSTp61kmNTD/nzhdRg8GgF69pp2lrDmvZ3lNatOtEBUYGAAAAf/TnLvX104aicnc7Xx2qkEDbB7s2Hj7rdD+PD2xpd/mkGzppaIc4XdWyXrnitKfkTeXCh6j8yWVNaqp7Qm09PqCl2hcbg0mSFu06oaZjZ+qHhy5Xt/hape5r67F0PfB/ayVJd00p6n32r9/36FRmjqauLLrh7AvDJl/5twU2v92iQ10rv1Pomo71deJctiZe6hUy7J0/LOsWPNNXm4+ma86WFN2eGK/ezerQc6mcZm5Osbv8/+7pqduLjeu7+ZXBMgYYZJChXO93dGigeibUVkaWSZ+M7F7m/Uiu9fQLNAaoa5OCc61joxr6+t7ebu0/LSNLdSMLymMFBtgmxNxRWs+ihDEF5QbHX9deL/+y1bK8VkTBOTSiV7xG9IrXwVOZllJ8hdaMG1immAAAkOiZBB/Us2ltbXhpkEb1a261fMnuk3bb92ha22r++Z82a/aW0mvAe0KWKU+/70jVmcwcj/0oM5vNen/BHoevt7hH+7dwun7amiOW6Zxc18ufAAAAwD+sP3TG4boPRlymqff10vjr2ttdHxkSqG/vL7phai+RJEnn7JS3KrRj4lCH68KCjRraob4iQirmGcZPi91g/tvwThVyDFesOXDabg+gQl2Klf8rdFXLuvrx4Sv0fFJbp+/P8A+XOT12Xr5Zqw+cVvK7Sxy2KZ5IKthGOp5+UQljZujeL1d7JbnkiYcAAwIMuuvyBLvr+v19oR77Zr1mbD6umz9eoYQxM9Ts+ZmWm/Bwz/xtqVbzgcWSRFe1rKcXkop6KEWFBik8ONAyrm9ZGQwGffdAb816/CpLbyVfFhMdakmeBRoL/p+XV/Ab/Kn/btDtn63Uwp1p6vjKHGVkmXT49AVd+bff9X8rDpb5mMUTSZIUE2U9LMBFU9H4F10a19Se14ZZEl4AAJQFPZPgkwwGg54Z3FrvL9hrWfbPebv02ADbpx5DS/zonbrykKauPFQhdcZLGvvjZv20/qgkaUCbGH12Z49y73PutlS9Oafgx+joIa11XecGalzbelC01S8MVHZunhrWdD6Y4eqDRSUs/j53p55Pcl6GAAAAAP7lzx/YTzY8M7iVki6VQ768eV2bm45SQe+e3s3qaPqjV6pmuOu9Qr66p5du+2ylZR/eMrBdrA5MTpbZbPbqzeYbP1puNf/FnT105OxFvfjzFo2/rr3uuDxBXSfMVY+E2hp/fXutO3hWSR1dH+f1syX7dU+xUlpms1mjv9+k1QdOq0/Lem7fjP586X59vnS/JGn+9jQ1HVvUC2L/pKQKfy/TzmXZLHtmcKsy7assPV+e/G6D/vnXzn6RoPAV9/5njdX8nteTrObvvrKpGtUKs/Ts8RR//RsVjmN36PRF/d+Kg/pxXcE9gz8uPTDa6ZW5lrYv/rxFL/68RZL03f29NWrqerv73Pt6kpo76bH0w0OJNssa1yq6j/DzqCvcfBUAANiiZxJ8lsFg0F9KGRgyL9+s/yw/YHfdlqPpdpdf994SJYyZoVfs/KB2V2EiSZJ+25Fm6aGUdi5Lo6dttBlQ1hWF5Skk6c05O3XVGwu0/bh13e96USFqVCu81C/XfYqVFPl48T59snif+r65QCnptj/gAAAA4F/2pJ23u/z9Wy/Tw32d92CXpCnLC3qsdGhYQ41qhZfSusDmVwarWbFB5Y0+cLPX1244R4QE6vbe8do6fojuuNRzZv1Lg/XxyO6qXyNMyZ3q28R8WZOakqS7rkiw2V9hGbdCS/ac1Pdrj+jgqQvl6tVgT9OxM5Wbly+z2SxTXsVUNphQYpzbOy9P0CP97ZdKrAg/rT+qpmNnKr+MJcgqS1pGlkZ8ukLH/KCUuzHAoGEd6yuuRmjpjauB+dsLenJ9vnS/JVHkips+XqGT57Mt8z2b1ta/bumqA5OTZQwwaNULA+xuN/3RK9UtvrbN8oiQQK0dN1Cb3RyPDAAAR+iZBJ82Nqmtpq0tKtX2+45U9W9TNIDv49+u18Yj9pNG6w6dUYeGNayWffrHPm261H7KsgN6xUHJj7LqOnGeBrSJ0W870iRJ09Ye8UgPqeJJq49u6+bydg/0aWY1XtJrM7dLKuhmP/U+12tAAwAAwPfsP5lpNb/gmb5qWjfCQWvPiAoNUlRokMYlt1VkSCBj0NjRI6Ggd4Y75f2mPXi5jp29qMa1wzWkfZwa1gzTVW8ssNv26JmKTS60eGGW+rSqp23HMvT7M1e7PZZRaYonRx7r30JPDW7t0f27qtnzM/WvW7rq2s4NvHL80vR8/TdJ0uWTf9ctPRurTkSIRvVrUe7yce66mJNnNT96iHf+XtXRJ7d3V41ivUZjokL1zs1ddDEnT2N+3KzOjWvq05HdVS/Kcem6OpS1AwB4ED2T4NNqRwRbzd89xbp7/fRNjsdGCgm0/nhvPZauV2ds91xwDhQmksoiy5Rnd/nHi/dZStrFRrv+ZbB3M/sDES/be8r94AAAAOBT/j7HepyehDqOexc9eHVzm2U3XtawzMe+96pmurlnkzJvX1UNv6xRmXpKGQMMltLWvZvVsSlnXbyX0JgfN5e6v6tb1dP/ylHWavGuEzp5PlszL/3eenPODiWMmaF7pqx2uUdPRpZJ09Ycthm79f4+zSzTnk4k/fFsP7faP/rNenV4eY4OnMzUm3N2KP2CSVJBryB75fgqS8lxrL5ZdVjvLdijti/N1pEzF/TKL1u1oBy/O91RMmn9cF/bawk8q05EsPZPSrJKJBW6vktD3dyziQ5MTtb/Rl3hNJEEAICn0TMJfmdX6jm1io3SE9/aryVc6LkfNuumHkU/cGdudpx48hXnnQxuXDhIbZDR9RwwT4oCAABUXTtTz1nNO0tijBnWRs8Nba1ur87X6cyCUszPDSnbODWwr3VslP7x184e2VfJ7/FfrTiou65o6qB1gcua1NQnI7vrzIUctYiJkiS9d2tXGWTQ0A5xev7HzfpuzWGrbdaOGyhJys7N1+WTf7fZ579+36NB7WItY9n+tiNNU1cd0m2947V41wmdOJet4Q5KkxeOCzP6+03q2qSmQgONeqR/CxXmxXo2tS3LVR61I4JV10EvjM2vDFbHYuPUFHc+O1d9/75QkvT+gr1aPLqf+rxZ0Ctsz2vDFOjG7y9Pybjo+HfhHZ+v0t4TmZqy7ECZq2BsPpKu79Yc0lODWts8wFnSrmLXmS3jh/hcWUl/cmuvJoqJCtHb83crqWOcTHlmPT6gpTo0rKGLOXkKDSr4rPEeAwB8Fckk+J0hby/W/knJ+nnDMbe2K/wBVNz/NhzV9V3K9kRmyafFyis/36w3Z+8stZ07ySSp4KnE4qXuAAAAUPW4clPZYDDoz10b6rMl+yVJESGVWy6rKqkdEWxJyhWa9lBihR3vu9WHS00mff/g5QoIMFiVtbqmU1EJt+eT2tokk4q3tfe74ejZi+r26nyrZeN+3qJxxcaBeXraRu2YOFShQQWfp3NZJptxntYfOitJWr6vqEJCkNEzN8w/Gdldk2Zt1zs3dZXRzsN0+15PUkCAQcvG9FffNxcqp5SxoAoTSVJByb/5T/WxJOfK4nRmjh77Zr1e+3MHxddxrQzl2kOnHa47Vexzl59vLtMDhNe+t0SSNGPTcd17VTPdeXmCw7KM7RtEW6Yj3SjdWJ19dFs3PfhV0VjIJa/Pj/RrYZOkrOzyhQAAlAVl7uB3PJnDefzbDWXe9q35u11qd+TMBZfazdh83ObHnT0HT2WW2qa4Z7xUgxwAAAAVq9alEkgRbtyEfODqohJj7j6k1DIm0q32VVmDmqE2yzw9tlCXxjUt0ztSzjluqIKeZ6UlFWqEB+nKFnUdrv/y7p66qqXj9c60eXG2/m/FQZnNZnV8Za7+u+ZIqducOp9TahtXDGoXq9+f7quOjWrYJKh+f/pqy/vSoGaYdkwcqsWj3SuFN/Cfi8sVX4/X5mvJnpO6+s2FLrU35eVblXd/bmgbq/VnL5XikwrGCS6PMxdMenPOTrV/eY7DNsfSC8r9xUXbfuZh39AOcWoTV5CAfKx/C5v13ujtBgCAJ/AvGKq8tIyscn/Jtufd31xLJt0zZY0yskxKGDNDCWNmOKwxPv7XrTbL+reJsVnWoWENt+Ls2Mi99gAAAPAPZy7dVM7MsT/upj0xUaFa/tzVerOn4zJajoTTK8HCw0UK7PrhocttljkaY/XwadceYPvq3l5O1398e3eX9mPPiz9v0Uk3EkSlJcjKomR5sGb1rBOgBT23nJd1s+fU+ewyx5RX7Pdf+kWTk5bSnK2pavnCLKtlD/Vt7rDnYViwURlZJp3JzJHZbNbBU5nKzs1z+fNQXMKYGTbLer/+m+74fJUkKSXDe2NI+aPv7k/UpyO769EBLb0dCgAAHsOvAfi8Dg2jteVohtWylcXKIziTl29Wz9d/c9rGbDbbrUl8PjtXp8/nqImTgYwLdYuvpbUH7SesdqaeU/K7f1jmmz0/0+bHwN9m77D54VXYpviX+kf7t1CDEoPxuuLKFnW1ZM9Jy3yHhtFOWgMA4NgXS/croU6E+tl54AGAf6gbGaKyVFTyUFUyuMheybZfNtov9f3Ste08cszyltrq8Zp1SbxVzw/QnhPndesnK8u137Lo6OAhvPAyvMZur863m9D5ef1RbU/J0JMDW1nK/DnTefxczXzsKiW9+4fev/UyJXeqb7X+kW83uhVX8rtLHK5rExelWY9f5db4O+kXTaoRVtDDLumdP0gglUON8CANbBfr7TAAAPAoeibB5+1OPW+z7KaPV7i07cNfry21jaMnOfv/faH6vLlAu1NLf2Lun6UMtHv49EWr+YQxM6zGXPpwoe14TvY8XcaSdUkdrX+k5OZVwqOUAIAqZ/WB0xr/6zbdNWW1t0MB4AX2khvVVfH783Oe6KOlY/pXyHGuKZZsyM3L194T1r+NDkxO1oHJyQoJdD1BMvmGjpKkWY9f5VYs2ycMtVm28aXBDtsvea6fYqJDdXnzutr3epJNMua9W7u6dXx3vXJde7vLDQaDEuw8MLjq+QG6oWtDfXNfb+2flKT9k5JKPcYT323QvxftU5sXZysnN99mXN33F+yx2Sbp0oOGo6au07Pfb3Q6Fu/8p/pYpq/r3MBhO3t2pJxT07EzdcvHK/TQV2v17m+7lX7BpPx8s5rXKxi7qeQp3Xn8XJ3PLui1uO14RsldAgCAao6eSfB52bnOB0h1Zs7W1FLbvDl7h0b1a6GwEmdD2rmCUgZzt6WqZazzAVfj60TowORkfbXioNVgtM7sPZGpFjGROnr2os26LeOH2Czr17qeS/u15+YejXXgVKZy88z6fOn+CikpAQCo+v634ai3QwBgxy09m1TKcQLc6OFQnbSOc/5boTyu69xA0zcdlyQ9PW2jzmUVlSf86WHbMniuuLlnE93s5DOz6oUB+nHdUU2etcNqeViwUbtfG6aLpjwdO3tRDWqGORwnKio0UI1qFSVsCsct+tctXfXoN+slScklHnjzlKVj+islPUvd4ms5bLPw0rhJaeeytCftvC5vXjBW1D9v6uLycUqOZdv91XmKDAnUzMevUs3wglJ6b87Z6XQf/11zxDK+1M3NbM+v5sXK9L17S1e9e0tXu+XonFl+qarHrC0p+ue8XVbrPruzh9o3iFbP14qqeXR4eY4e7tvcqt1VLevq/+5xXiIRAABUffRMgs+rFxXi0f3VLVEj+8vlB9Xz9d/UccJvMuVLpzOty83Ze1Ks5BOBhYZf1sjlOAb+c5He+323MrOt69X/8NDliixWj37pmP6afENHfTyy7PXLAwIMej6prerXKBo0NSWdkgUAAPd8teKQt0MAYMcdl8dXynHcHbsT5Xeq2G+T9Ism/b4jzTLftYnjZEl5xESF6sGrm6vZpd4rE69vr72vF/TSCTIGKDo0SG3ioi2JpBsua2izj5XPD7C772s61ddX9/TSqhcGuFV+zR0Na4Y5TSQVFxMVakkklabkeFVXv7nQaj4jK1fH0rPUZcI87Uix7dUTGuT89su3+6x7lzkqUedo/KSy2JlyTjFRoTbLPyhWOWNUv+YkkgAAgCSSSfADzwxu5dH9/fTwFfr7X+yXpXtmZaB6TV6oZ78vqlWdZ6dj1IB/LLJMz3miqPRAWLBRW8cP0fKxrpW5+PvcXValBT4ccZnND5+GNcN0c88mCjKW/3Q9XyxxNebHTeXeHwCg+irPYOQASrdgZ5qe/G6DzmWZHLYpvDkdEVyxBSdmPHalHu3fQk8N8uz3cn82oldBAq9X09oVepzuxX4bLNx5okKPVdLvT/fV/klJuj0xwWmJw8k3dNI/iv2+OjA5WeEOPpMGg0FXtqxrN4Hh6wa9VfQbMNfej8Rihr79h8aW+L217sVBLh9rwvXt1ba+++Pc3tS9sSRpmYtlF++8PEGS8wTVFS1cS7YBAICqjzJ38HnxdSJKbVM4iKkr6kQG68ZujfTMNMeDmxaWGpCkz5fu1+MDWzpsGxxoneSJCAlURIjrp9bnSw9YpodVUKmHQu0bFP0g2XwkvUKPBQCo2o6evag6kZ7tPQygyF1fFIxNVis8WC9d285uG9OlcTBLfh/1tPYNaqh9A3olFXdzj8bq2LCGWsRElt64HJrYGdunMrnSeyg4MEDDuzXS8G6uV2nwR8XHwX3xf1tLbf/NqsOW6TXjBio8ONCStCmtVN3tvd3rbTjlrh7q2zpGkvS3GztJKkgQFT/OzMeuUnCgQV+tOKTv1x7R6zd0VGhQ6WNtORnSCQAAVDP0TILPC3PhC267Bq4/teXoKTlH0i86fhpUkoKM5SvPMHVl5ZUMuqpl0bhLhT82AAAoi29XHy69EYBy25maoUemrtPsLSlWy/PzzcrLL7jL64ke7HCPwWBQh4Y1XLoZXx4hgRW7fzj31k1FPa6euPSAYXZunr5Z5d5vuKhQx79Bm9Yteniyf+t6OjA5udQkXrtivZYOTE52+NtuwTN9JUmfjuyudg2i1SImSq9c115bxg/RdZ0bWLXtkVDUC65ZsZgi3XhQEgAAVG18K4DPCwt2/gNqRK/KGXC40PhfrZ9Cy8l1XuKgpKn39tKtn670ZEguK16nu239ihsoGABQ9bWs4KfxARRYuueUJGn6puPaPylJu9POK6FOhPKLdRcILOfDTfBtPZvW1qr9p62WfXNfby9FU738uWsjzdh0XPO3p1l66Jy9YP2w4VUt66plTJQ+X7rf4X4cJQU7NaqhXx65Ujk5OZo1a5aSkrq6FFdi8zradtx2XKaSmtaNcHmMpU9Gdte8baka1rG+IoKNuuOL1Uq/aLKqbgEAAKo3HmGDzwst5Wm81/7cUZL0QJ9mkqS/dGukj267zKMxFD71OWrqOn1RrCydJMVE26/33bd1PbvLHemZULH11qWCJygLB9Kt6KcoAQBVW6tYHkoAKtsNHy7T4LcWq9W4WUrLKBq3LNDJeDbwfyUTSZK0+oDtMlSMRrUKSg2+89tu7Uw5Z1O5Irljfb10bTsdmJysv3Z3rdTf5Bs6qkntcL11UxdJrpUTLO6JgS11S88mmnpfL7e2c6ZmeLD+0r2xIkMCZTAY9OVdPfTzw5crkJ6PAADgEnomwee5+qTl6CGtNbh9rDo2rOl0oOJCX9zVw1KLvjQ3/Xu5bu7ZRDM2Hbda/saNnRx2+7+lZxOrQXJ7JtTWl3f3VGZOrt323z1QOU8XRoUGSZIOnMyslOMBAKoGc4lBE06ez3bQEkBFWX/orGW6z5sLLNPulnGG/2tbn94ilaV4snbI24ut1jWsGaabejS2zE+6oZPV+LuStGX8EJt93tyziW7uWfYKG1GhQZp0Q8cyb+8KdxNcAACg6uMRE/g8V2vABxoD1C2+toIDAxwOCF78oc1+rWOU2KyOpIIa0s6sOXhGz0zbaLP8r90b22ldIO1c0U22d2/pqv8+mKiwYKPqRoZo0ei+Nu0r68v6xsNnJUmfLnFchgEAgJJy862TSY9/u8E7gQDVQMnkrTOujC+Kqqd2RLC3Q6g2nPXMWTqmv9XvOGOJXoIjE+MZcwgAAFQZfKuBzwt28uW9TZx7JXZWvTDQav6b+3vLbDZXSCKnebFBS0sObhpfJ0KzHr9KMzcfV+9mddSwZpjHjw8AgCddNOVV2L7z8s3KMuUpghtugCRpZ+o5l9tW5LkJ39OoVpiOp2cxjk0lys517xxbPLqfDp7O1FUt3St7DgAA4Ov4xQ6f56zM3aHTF1zax1Ut6+rFa9qprp0eSxXVIyixeR09NqCl2jkoQdG2fjTlKQAAPu10Zo7+MXenburRWAEV2IM2+d0/tCPlnFY9P8DhWIRAdZKb53rPJFR9zw1to7/N3iFJWjS6n3Lz8xVSyriy8JySY+aWpkmdcDWpE14xwQAAAHgRyST4vFAnpTtudbHO9G294z06UPhVLevq8zt7OG1jMBj01KBWHjtmRcjMzuUpcACAQ+N+3qyZm1P09cpDFXqcHSkFvTB6vv6btk8YqrBgbpIChR7t30L/+n2Pt8OAF8VGFz0QZwwwyBjANdIXbLUzFhIAAEBVxphJ8Hkl604Xd2sv15JJ7g4S/sNDl2vr+CEal9zW7vr/3N3T5bGcfNkT323wdggAAB82c3NKmbY7eyFHi3edcGncl5Jt2r40u0zHhO84evaisii9Vi75xc4Lew8n1QoPkiQFGQ3aMXFopcUF70juVF99W9fT2GFtvB1KtRQSWPS777H+LSRJNcKCeCgPAABUO3z7gV9rWmxcopICAwyWwcIX7EjTiF7xLu+3W3wtSdJUB09iV1RpvMo2b1uqt0MAAFRBXSbMkySNHtJao/q1cNq28N9qX/S/DUf168Zj+udNXRQdGuTtcPzChsNn9af3l0qSDkxO9nI0/mvu1qLvaAaDQR/d1k0PfrVWA9rE6O2buyiKz2O1EhJo1JS7eno7jGqrUa0w7T2RKUl6anBrPTqgZZV4sBAAAMBdfAOCX/hgxGV68OrmNsudJXWmP3alZbpn09qlHmNo+1ibZSfs9GhqXs9xAgsAABR5c87OUtuY8vJtlr3yy9aKCMdtj3+7QfO3p+mDBXu9HYrfKEwkSdK5LJPDdukXTfpl4zFdzLHtwZRr5zNRnZw8n63v1x6xWja0Q5z2T0rSZ3f2IJEEVLKS4+6SSAIAANUV34LgF5I61tcYN8s6xEQVDeDtygC1j/RtJkn6S7eGlmX2xmv6ZGR3t+IAAKAqGX5ZI4frzmRLU1cddmk/36w6pIQxM/T2/N0266YsO6Cdl8ZR8ob8fLNVIuTEuaKHS37fkaqEMTPUZcJcPfndBpdK+VUXObnWSaAXf95imU6/aFKP1+Zrye6TMpvN6jx+rh77Zr3G/7rVavvZW1LU/uU5+nXjsUqL25dsOZqu7q/OV0pGliSp+HNTVaVnPOBv3ryxs3o2ra3P7uB3IAAAqN5IJsGv3N+nmcttQ4OKPt6ulNBpHRelN3vm6rXr21mWFb95JEktYyLVrF6kyzEAAFDVRIQUPWix7ViG1bpX1gXq5V+3u7SfsT9uliR9vHif3fVD3l5cxgjL784pq9XxlbmW+R/WFfUSuXvKGknS2Qsm/bT+qJqOnalNR84qP98ss9msKUv3a8Phs5Udsk8o2Zvm5w1FCaHO4+fqxLls3fbZSg175w/L8m9XH9YXS/frlo9XqNW4WXrwq7XKzs3Xo9+sV+fxc/Xq9G2VFr8vuOZfS6zmyVUC3tekTrj++0CiBrS1rWQBAABQnZBMgl9pGeN6Iie0WG+kzo1quLRNsNH6qc/3bu1qtX532nmXj+8vSibMAAAo1LVJTZtlbetHW6aT3v3DZr09CWNmKGHMDP31o+VKzchSwpgZngqxQizedcKt9te9t1Rv/7Zbv2w8pld+3aY/vb9UWSbb8m1V3fM/bbZZZq/n1o4Svc7G/7pNy/edsmmXftGkT5fs165U7/VSAwAAAAAUIJkEv7Vj4lCn6wMCDErqGKdWsZHq3LhmmY5xTacGVvPd42uVaT++bMam6llGBgBQupL/7jWtG2EzVsTeE+e1yEnyZWWxJMGqA6fV6/XfXD7+4dMXXG5b0Q6eytTx9IsO17/72249/u0Gy3ybF2fbHQ+qumk6dma5k4ebjqR7KBr/89iAlt4OAQAAAAAkkUyCn1lz4Ixl2t54RiV9MKKb5jzRx2ODpH73QKJH9uNLXKgACACopor/GxFsDNBHt3VTSKD1v6kD/rFId3y+Sn+bs8vuPm76eIVLxxprZ2zEO79Y5XqwFezqNxcqcdLvbm3zt1k7Kiga33R1q3oVst+6kcEVsl9f9/ZNXfTkQJJJAAAAAHwDyST4lb0n3C8zV97Biv99ezd1bVJTC5/pK2OA/w983KfEjZ4J1WwsAgCAa46dvajPluy3zM95so9ax0UpOND+18dPlxwo87Hi64Tr/j7N9PHt3RQdGmhZvvdEpl75Zat+KDEWT0kp6Vka/uEy/W/D0VKPZa/sWkX5dMn+Sj2et13Zoq4kqVfT2h7db15+wVhUCWNm6OyFnDLvx5/+Fskd6+tPXRuW+3ssAAAAAHgKyST4lf9v777Do6j2P45/Nr0nJBBCT2jSu0DoSAcLylWxo/xEBFTEcgHBioKKFbErYr/q9VoA6U3pvRcpoQeQFgik7vz+CFmyZDfZJJvsbvJ+PQ+PM2fOnPnOZE827nfPOV3rRZf4NXs1jNH/hrVXbPngEr92cSiNU/UBAJyv3STrUThxl98Hu17j3PfihpXDNPfxTjKZTOrZMEYPda5ldfyL5Ql64sdNea6b8+yvW7XuwBmraeZseXv+bsWNmaX3Fv7tjNAdcviM/anxSpuMy0PZooowkmj9+B7678PttP3FXmp2eZrijYfO6vnfs7780uzFeQVu0zAMxY6eqbgxs3TgVLLdeiv2nlLfd/7U8j3/FCr2orqUdmWdrfvbx7okBgAAAACwh2QSPIqfk6arK8ucNeUfAKDs6FinvGXb3sgkR/RtHGPZ3vRcTyVM6qeZj3aUv8+VqWsfaB9n89yeby212+7c7cct21MX7bFb7+35WUmkyXNtT8mXLcgv/6l0HXUxR4KgNDp3KV2r959WRqZZW49krW0U7OeTz1m2NaoSpshgP7WsUU5Bfj6WUUhTFlr/TCfP2VWgds+nZli2O7++2GadPSfO645PVmr7sSTd+ekqnbuUXrDgneDhb9ZZtn34ew0AAACAm+H/UuBRyoeWzTnzncnXO/d0KRksEA4AyGHPCetpZf/8u2AjNWytndOsWoTeGdhcUtYop5zT2eUU6OetW1pUceg6h05f1P1Xrav0+pxdOnrWejTQsXOX9Pumo1ZlyTkSDNlS0jM1e2uiJQH017+72rzu493rqmWOkb53tK5udXxC/0aW7d825T/1nid78Mu1uu2jFar9zB+aueWYJPsJx1uaX/m59mtcSW/e1lSTb22qcf3qKzTAR5NvbWpVP+HURZvtvLdoj/5v+pp8YzObDa0/eEbDv1mfZ73Nh8+q+5vWycpbP1yeb/vOtnjXSct2On+bAQAAAHAzJJPgUW5sWkV3t62ud+9o7upQPNbVC6dL0vwdx/Xx0r0a+f0Gmc2es54AAKB4jP15S5HOn/5A61xl6Zlm+Xp7KWFSPy16skuea8H8vN6xBEzH1xZpUY4P4LM99v0Gy/bxpBTFT1yoR77bYFWn4XNzlHnVe964X7Zq6NdXRof4eXvlmm4sLMBHj3Wvo3cGNlO/JpX0v2HtNPGWxpbjPRpU1N1ta1j21yaccehePNXq/adzla07cEZfPtBaPRtUtJRFBfvplRzP6YEOsbqlRVX9q2VV/V/Hmtr8XE/Viwlz+Lrzd5zIt84TP27SLe8vz5UMHffLFp1JTtMPaw9pxd5TuvG9ZbnOrRMd6nAshXU6OU2xo2fq5Zm5169kWmIAAAAA7qZwc1AALuLtZdKE/o3zrwi7bE2bsjPxvGXqn+ubVFb3HB/+AADKntUJ1gmC9eN7FLiNIZ1q6uOl+yz7T/SsW+S4HBUW4GvZ3nDQfjKn2Qtz9fyNDbV87ylNGtBYP607bHXcx9tLA6+trmnLEixlm5/vJUmqWi5IU+9skavN65tUstq/s031XHVKu52J59WpbgV1qltBGw6e0etzdumZfvUV4OutpU911YHTyWpZI9LqnLySi/acT0lXaI6f9dX+t8F2UvLrlQf19cqDebYdHuQrwzB092er1L52eQ3rUrvA8V3NbDb0vw1H1KJGORmGoeveWCJJ+uTP/XqmXwOruoV5HgAAAABQnEgmAWWMt40PJ7ITSZI0ZdEekkkAUIbZWismMrjg08w+3r2uyof4KTk1U34+XrqunvPeW16euV2r8xjx4+975YsTSSm5p7PLdj41Q0/8uEmSVD0yKNfxQF9v1aoQbNnPuXbU1WY+2kEbD53VjU0rS5JaVI/Q+oNnFeDrvPWX3M3OxCSb5U2rRVi2m1cvp28fbGvZrx4VpOpRuZ91fnZN6K1zF9PV+pUFlrLGz89VwqR+BW7LEd+uOihfL5OW7TmlZXtOOSWZ9NHSfXp19k6bx2JHz7RsD+lUs8jXAgAAAABnI5kElDHeXnl/03XvVetk5PT7pqNKPJeiB/mQAwBKreun/Gm1v/Ol3gU6/++XekrKWvtoSKdaTosre5rWPSfO65M/9+dZ98DltXYyzYbNtQJteWv+7lxlgX7WiaBP7m1l9/yGlcPVsHK4Zd9dRpb8sPaQKoYF2FzHqqhGfr/RZnn3etFOv5a/j7eiw3In5pbsPmnz3o6du5SrLD+t4yKtpu2bvuKAZXvRzhPqWsT7spdIulrVcoFFug4AAAAAFAfWTALKGJ98PlS7Ntb+HP2PfLdBL8/aofV5TBkEAPBsh05bfwjvipE1EUFXpi67tWVVSVJ4YFbZhdTMfM/fdjRJg6atVq2xs/T4fzZZHbutVdY6PQWRMKmfEib187hRRjuOJenpnzbrvs9XF0v7vRrG2CwfUMDna88j12WNBrq9VTVL2cf3tLSqY+/ejp7NnUx6+eZGeV7v3YH21+S8/4s1VvuGYej1OTv126ajebZZGP2bV3F6mwAAAABQVCSTgDImyC/vAYmLdp20fCv3nwupmrXlmNIzzTp38cq0R7e8v7xYYwQAuIeYsACXXPeW5lnJiKbVInRfu1hJ0onzqdp46KzN6VptWbzrZK6yX4e312v/aqrHutVxWqzu7I8tx6z2v1t9UKP+s1Ep6fkn5ByxYu8py/bWF3ppSKea+uy+Vqoc4ZyRNY93r6uZj3awSgIZDp77zaorayI93KWWOtetoAEt8k5yVQzz15u3NbV7/Exymp77dau2HD6n+s/O1tRFe/XodxvU8NnZajVhvv65kOpgdHkLy2MdKAAAAABwFZJJQBlTKTz/DwZv+2iFJKnVhPka9s16fbx0n5q+ONeqzlcrD+jE+ZRiiREA4B5+f6SDw3WD/b01qK5zkhRP975GU+9soS/vb62EU8mW8v5Tl6kos8dlr+UT7F9yMz0bjmY/isG6HCOJ9568oDE/b9HPG46o3vjZVl8SKazVCVemhAvx99HYvvXVrb7z1sby8jKpYeVw+Xhf+V+WTnUqqHKOv2XubFM913nnLqbr5/VHLPv/7l1P0x9orQBfb215vqelvFfDito/sa9l5JnJZFL/ZrZHBbWOjVTzl+Zp+ooDuuG9v5SSbrYcS07L1D8XUtVqwnzNuiqBJ2WNYvpm1QGrspzTL/734XaW7fmjOtu8PgAAAAC4GskkoIy5JiZUNaKC1CzH4ti2nE+58iHT63N25To+/petav3yAmWaXfgpGQCg2DSoFKYKof4O118/9jo1j3LOe0KAr7f6Namk8CBfmWSdPVq860Su+j88FK8P726Zq9yeyGC/fOvMe7yTw+3Z4g4rJi3bc2XkULc3llgdu/pLIvZcSsvUhdQMm8e6X04cBfuV3PR/gX7eWj6mmx69PAXe1SPV0jLMed5baICvZj3aUSO719E7A5vnWtvKy87akrUrhjgU37Bv1ispxTpR978NR/TM/7Za9l8d0Fi7J/TRz8PaafPzPdWyRjn1b1ZZ3etHq1aFYIeuAwAAAAAljWQSUMb4entp4RNd9L9h7fKsdybZsW8s1xo7S2YSSgBQamR/2eDxHnXt1vlleHt9dNXaNfY+hC+q5tUjrPYnz92dq07ruEj1bmR7/Z5sL9zY0Gr/nYHN7Nbd9GxP1akY6nCMpdHp5DQlp2ao/rOz1ei5OZYp7b5Ytl8fLdmrk+dTVTEsK9k4uENcice3/ViSpKyR0jltcGBdxwaVwzSye127a2ANujy1Yk7f5pg2Lz9Nnp9rieXI2UuatizB6ni/JpVlMpnUono5y5R2bw9srk/vuzZXcgsAAAAA3EWBkkkTJ07Utddeq9DQUEVHR6t///7atct6xEJKSoqGDx+uqKgohYSEaMCAATp+/LhVnYMHD6pfv34KCgpSdHS0nnrqKWVk2P7GIwDn8/YyOfXDippjZzmtLQCAa2WPOPXO46/EZtUi1KthjGpeHkUREVR8a7zkNz3r2L717B7z97lyE3ddNR3aTVdNZ9aqRjl9dl8r7Xixt8KL8X7cyQu/b9OCHcdzfSlk7rZEtXhpntpNWmgpu+OTlfpqRYKe/327Jv6xU9e+PN+yLlFACY5MyjZ/R+4RapJ0+8cri9z2czc0KHIbsaNn6ub3l6v9pIXacuSc1bGQEpxmEQAAAACcpUDJpCVLlmj48OFauXKl5s2bp/T0dPXs2VPJyVfmsn/88cf1+++/68cff9SSJUt09OhR3XLLLZbjmZmZ6tevn9LS0rR8+XJNnz5dX3zxhZ599lnn3RWAIjMXcJGHS2nOWScDAOBa2ckkLwe+dDD9/ta6rVVV/fhQfLHFYzKZVC0y0OaxhU901pBOtXKV14sJ1f6JfbX1hV6KKx+s5tUjrNbdseWnh9upW/2KCnR6YqRkR+/O3HxMvd9eqq1XJTBsmbYsQYOnr1XNsbOspq0d8tU6SdK5S9ajlMf/us1mO3uOXyhCxIUTaGNU0bFzl3KVfflA6wK37egXbiqHByhhUj/tfKm3w23vntCnwPEAAAAAgDsoUDJp9uzZGjRokBo2bKimTZvqiy++0MGDB7Vu3eX/4Tx3Tp999pnefPNNXXfddWrZsqWmTZum5cuXa+XKrG8Jzp07V9u3b9fXX3+tZs2aqU+fPnrppZc0depUpaWlOf8OARTKpsNnC1S//rOzdTqZPgwAni77ywTeDkxbVy0ySK/9q2mxTwn31m3NbJbXrGC9js2LNzVU7egQTbs/a7owX28vzXu8k/47NO+pXYuDq2YrG/7teu1MPK/rp/xVoPPiJy4o9DVPXyz59/8hnWpatmdsPqph36zLlfySpOgwx9f9yunzQa3Uo0FFu8cHtKiq5WO6Scpa4+u7B9vmu8ZY9/oV5efDLOMAAAAAPFOR5lg4dy7rG4+RkZGSpHXr1ik9PV3du3e31KlXr56qV6+uFStWqG3btlqxYoUaN26sihWv/M9Zr1699PDDD2vbtm1q3rx5ruukpqYqNTXVsp+UlDVHenp6utLTHVvXBchP9muJ11SWx77fWOBzPv9zrx7rVtv5wQA20GcBKT3TrPu+WKcmVcI0uvc1RW5v+ooD2pl4XpJkmDML1L+KtU8a5jyvme2OVlV0R6squY5lSsrMYwBtqxoRTo/buJyUy8go2HMsbrUrBGvPyeRc5SfOp+rshUsKLsQUbK/f0qjE73HvifOW7RHfbpAkzdqSaFVnZLfaqhUVWKjYOtaKVMdakaozfq6l7OsHWsnPx0ubD5/TvW2rW7XbqnqYlj/d2ap+Tj8Oaa1m1Zz/Oiss3kMB90KfBDwH/RXwLPRZxzj6fAqdTDKbzRo5cqTat2+vRo0aSZISExPl5+eniIgIq7oVK1ZUYmKipU7ORFL28exjtkycOFEvvPBCrvK5c+cqKCiosLcA2DRv3jxXh1CCnDtn/+adezQrNffC6EBxKlt9FrC26ZRJaxK8tSbhjJqY92rXWZP+d8BLQ+plKrKAAzKS0qQJ6668L/y8cLXO7Cz4FG3F0Se3nDZJsp7WzCRDs2YVdc2+rPu9cPa0E9qydvq0tyST1q9fr8wDxT/V3fp/TNp4yqT8Jh5IuXhBku1hU+/+ME/VQww58vfBfXUy1aicIT9vafnikv89vC0h6/na8058hnRxp2bN2lmk64T5eispPes6p3ZkzbRQQdIff9ie8q9nFS/NPWL9M3ikYYaOblmuo1uKFEqx4D0UcC/0ScBz0F8Bz0KfzdvFixcdqlfoT5KHDx+urVu36q+/CjaFRmGMGTNGo0aNsuwnJSWpWrVq6tmzp8LCwor9+igb0tPTNW/ePPXo0UO+vmVj4e3HVlh/ezY2KkgJpxz75SFJK/7dWfGvLrHsL030UoO6tfREjzpOixGwpyz2WZRdqRlm+Xmbcq3l8liOURAdr+upx15eKEl6Yb2Pdr/Yw+G1XyRp78lkad0yy/6IWzqrRqTjX9opzj4ZuucffbprvVXZ4ic6qXKE7bWUHJX9PlihQgX17duySG1d7aujq7Xv/Fk1b9FCvRvany7NWR6zMyLmaoeT7b8mPt+d/3pRD3aI1d1tqhX52RfVVu/d+uSvBLvH+/bt65TrbPPZrY//TFCTqmHq27dtvvX7SrlGJz060DmxOBPvoYB7oU8CnoP+CngW+qxjsmeCy0+hkkkjRozQjBkztHTpUlWtWtVSHhMTo7S0NJ09e9ZqdNLx48cVExNjqbN69Wqr9o4fP245Zou/v7/8/XN/vdbX15cXAZyuLL+uBrSoqjfmOT6yqFK5EAX6eutS+pW5gz5cul+j+zYojvAAm8pyn0XZcOpCqlq9PF8d61TQlw+0tpSnZ1pP/fbWgr1W+3WfnaedL/VWgK+31Tm+3lmjJg6dvqiOry2SJP3wULxu+2iF1fm1K4YXKt7i6JPB/n65ympUcOIXikwmp8fsZcp6zj7e3sX+O2rVvlN5Hp/3eCf1eGupJOV6325cJVxbjpyze+7Nzato1b5TOnouRZI0tl+DAiUpi4uPT96JL2c98yd71VfL2Ci1jYsqVJsLnujs1u9RvIcC7oU+CXgO+ivgWeizeXP02RRoBVjDMDRixAj973//08KFCxUXF2d1vGXLlvL19dWCBVcW8N21a5cOHjyo+Ph4SVJ8fLy2bNmiEydOWOrMmzdPYWFhatCAD6ABV2pWPcLhut8+2EaS9PsjHXIdS07NcFZIAFBmjfphox7+ep1mbjkmw5CW7j5pdXzrVQmAr1YeyNVGvfGzZTZnTbH27582q84zf2j8L1slyZJIkpQrkVQ+pIBz5BWzljXKWe3/8VhHp7Q7qF2sJOmJnkVfb8qVbv94pd1jAb5eqlUhxLJ/bVykdr7UW7+NaK/Vz3Sz+T6ebe247nrztqZa+nRXjetXXwuf6OwWiSRJltd1cfPz8VKvhjEKDyr4/3g+1Lmm1bMHAAAAAE9WoJFJw4cP17fffqtff/1VoaGhljWOwsPDFRgYqPDwcA0ePFijRo1SZGSkwsLC9Mgjjyg+Pl5t22ZNC9GzZ081aNBA99xzj1577TUlJiZq3LhxGj58uM3RRwBKTsc6FWyWb3q2p+ZuT1SLGuXU7Y2sae2yP2isHZ37Q5IpC/dodJ96xRcoAJRy5y6l6+f1RyRJf2y1XlNyy+FzKh/qp7+PX3CorZpjZylhUj/9Z+0hSVlJp+jQvP/mWjuueyGiLj4+3tbff6pfyTmjkp6/saH+3bueAv3yn97NU9WqECIvL5PKh/jpnwtpur5JJQX4eqtJ1Yh8z81+r/fxNun/OtYs5kgLpmJYgKtDsOv7IW01a8sxPdaNaX8BAAAAlB4FSiZ98MEHkqQuXbpYlU+bNk2DBg2SJL311lvy8vLSgAEDlJqaql69eun999+31PX29taMGTP08MMPKz4+XsHBwbrvvvv04osvFu1OABTaPW1r6KX+jWweqxQeoPAgX93aqpok6ene1+hEUqrqVgy11LmleRX9vOGIZT++VpRiR8+UpFxTLAEA8nfBzgjP2VuPaejX620ey8vfx89b7ec1pWn2yFN3FR7o3KkJijuRVDLjZ6z1a1JJMzcfk3Rl9NWsRztq0+Fz6l4/Olf9lWO6qe3EBbnK3dndbWto+ooEHbCx1uPGZ3u4IKIr2taMUtuaUS6NAQAAAACcrUDJJMPI/3+HAwICNHXqVE2dOtVunRo1amjWrFkFuTSAYvDh3S3107rDeqJnXbt1ute3XjR8WJfauerc0KyyVTLpg8V7LNu3fbRCv42wP4UOACC3f86n2iwvTCJJkmW9nPy8fXsztatVvlDXKCnT7r/W1SE4xkWzwdWJDtE7tzfTawOaaGdiklpUz5oiMDosQD0a2B7NExOeu/znYe2KNc6i8vPx0oMda2rc5Wkb372jua5vXEkmk9xmKj4AAAAAKE0KlEwCULr0bhSj3o1i8qzjyHR1Xa+J1rh+9TVh5g5J0sp9py3HNh+2v6g3AMC21+bsLPFr/vl0V1WLDCrx6zpq9dhuOnMxXdfEhOZf2Q1kr+ljduDLWM40b1RnSVlTA7asEVnodtx5Grls7WtfSXyG+HvLy4skEgAAAAAUF6/8qwAoy4L9Hcs5u9taCgDgyZbtOVXoc6cNulY1ohxLCo3oWluv3NxYW1/o5daJJClrZI2nJJIkae2BM5KkN+fuVlqGOc+6SSnp6vvOn5qy4O+SCM0hnpCXiSsf7OoQAAAAAKDMYGQSALse6kyCCADc3fdD2mr21kQ91esayxcAWsaW076Tyeo/dZnd825pUUVP9rqmpMIss/b9k6yWL81T70Yxev3WpjbrfLPyoLYfS9L2Y0l6pFsdm3WW7D6pLYfPqk/jSqpVIcRmnbdvb1boOB/qXFMfLdln2Y8M9it0W65QwgPAAAAAAKDMYWQSALtG985/ijsAQP6W7D6pacv2F+rcqGA/VQzztyprXztKkvTOwGZqWzNKz9/Y0GokaViAr5pVi8jVVs5kw/h+DQoVDwrufGqGflx3WP9csL0WVqY575FL5y6m677PV2vy3N3q9sYSXUrL1Kwtx/TD2kOSJO/Lw4iKMnKrXo5zNz3XU/4+3oVuyxXMJJMAAAAAoFgxMgmAXSxgDQDOcd/nqyVJ51MylJiUogc71nR4iq5143tIkvadvKDr3lii++Jr6IWbGjl0btOq4dqUY+26/s2rqH/zKgWMHs7SasJ8zR7ZUfViwhQ7eqakrMSNt5f973f99fc/uvuzVVZl9Z+dbdn+aMleZV7OpHgXYW66nGskhQf6FrodV6laLtDVIQAAAABAqUYyCYBNTaqGO6WdxlWc0w4AlAZvztstSfp21UElTOqnTLOhsxfTFBViPfLoxqaV9dumo7qxaWVLWc0KIfr75T7y9XZ8YHnnuhUsyaRlo69zwh2gqHq//ac61ilv2W/6wlw9lcd0g1cnkq6292SyZdvRdQ5tia8ZpYe71FJtO1Pouasfh8bryJlLql8pzNWhAAAAAECpxjR3AKxkT3Nzd9saBT73k3tb5Srz8+HXDICSl5SSrtjRM/Xsr1tdHYpdn/65T7XGzlLLCfO16dBZHTp9Uf/+abO2H02y/O6sV8l62rKCJJIkaVjX2rq9VTV9PqiVqkQwcsNd/Pn3P1b7/11/OFedS2mZemnG9gK1W5Sfsclk0r9719OAllUL3YYrXBsbyWg7AAAAACgBjEwCYOW/D7fTruPn1dzGWhv56V4/OldZRmbe60AAQHG47cMVkqQvVxzQ0M61VNkNEykTZu6wbD/wxRqdSk6TJP1n7SH1bFBRkuRXwOTR1QJ8vfXqv5oUqQ0Uv305RhfFjp6pnS/1tprKzhFta0Y6OywAAAAAACwYMgDASrC/j1pUL1eo9ZJsnZPBitgAXGBn4nnLdrtJC10YiWOyE0nZ5m4/LkmWtXBQttQb71giaWzfepbt74fEF1c4AAAAAACQTAJQvLYdTZJh8GEoANeKn7igxK+Z/buvKL8Dv1t90FnhwMM9e30DrRzTzars/vZxeuXmxlr8ZBfXBAUAAAAAKDNIJgEodvv/Sc6/EgAUo2PnUmyWf/rnPr0+Z6fTrzfm5y3qMnmxzqekF2l00Ru3NXViVPBkD3SIU0x4gGVdpNtaVZWvt5fubFNdseWDXRwdAAAAAKC0I5kEoNilpLNuEoCS1bdxTL51jielaMLMHZq6aK+2HD7n1Ot/t/qgDpy6qF83HrWacu/t25s53EZsVJBa1mAdnNLi+yFtC33upFsaW7b/81BbjetXX8/d0NAZYQEAAAAA4BCSSQCK3eEzF10dAoAyIvFcij5YvFfVIoNyHTt27pLVfs7fTXO3JxZLPCv3ndKahNOW/Z4NK1odH961ltX+T0OvrHvz87D2xRITXKNtzahCnZcwqZ8Gtq5u2a9aLkj/17Gmgv19nBUaAAAAAAD5IpkEoNgN+Wqdq0MAUAYkpaSr7cQFenX2Tn20ZF+u4/ETF2rrkSsjkNYknLFsT1m4p8jrux0+c1F/bDlmVTZj8zG98Pt2y36Aj7dl+6vBrTWye12r+q1iI7Xzpd7a/HxPRQb7FSkeuL+FT3TO83jrWEamAQAAAADcA8kkAABQKny36mC+da6f8pck6a15uzXpD+u1kv6z5lCRrt/h1UV6+Jv1ev63bXbreHmZLNsHT1+Ur7eXdk3orad7X6NZj3aUJAX4eisswLdIscD99W4Yo5oVQjR/VCe7dVrFlivBiAAAAAAAsI9kEgAAKBWOJ6XmKqsUHpCr7KGv1uqdBX/nKh/985ZCX/vsxTTL9hfLExw6p2eDrHWd/H28NaxLbTWoHFbo68PzPNnrGklSrQoh6l6/ovo0sl7nKyYsQMO71nZFaAAAAAAA5EIyCQAAlApLdp/IVfbJva1ylc3Zdtzp1+7+5hKH664a202LnuyiCqH+To8D7mnz8z0lSeVDrvzMa0eHSJJMJpM+va+VPri7pdU5n97XinWRAAAAAABug2QSgGLRoJL1N+wzMs0uigRAWbH3ZHKuMi+TyUZN5/vnQlq+dX4cGi9JqhgWoLjywcUdEtxE12sqFGrawvBApjoEAAAAALgPkkkAnGr+qM4a0qmmvhzc2qr8t01HXRQRgLJq/8S+MmQU6JzY0TO1aFfuEU7OsOnQ2WJpF+6tsAnNapFBTo4EAAAAAIDCI5kEwKlqR4dobN/6VlP5SFKMjXVLAKC47J/YVyYHP8SfNuhaq/37p60p8vXvi6+Rq+zm5lWK3C48T86XYfPqEZIkX++SGTEHAAAAAICzMBE7gGLj7+Ol1AymtwNQ8rITSX7e+X9vJjrMuWsXJUzqJ0mavuKApey6etGKCmGNpLLk373r6dM/9+mZfg0sZZNuaazYqCDd1qqaCyMDAAAAAKDgGJkEoNhseb6XZTsjs2BTTQGAM9SODtHNzavowY5xduvUiQ7NVWYYBfudFRqQ9f2cafdfa/P42L71CtQePN/DXWpp7bjuVutjRYX465l+DVSnYu7XnCTVi7FdDgAAAACAq5FMAlBs/Hy81LRquCQpw8wIJQDFq1GVMEmy+vDeZDLprdubWY0OkaRRPeqqbc1ITb2zhfx8cv85dPRcSoGuXSE0a9RRkK+3peyry2vHhfj7qLaNhBVKP0enWgQAAAAAwN0xzR2AYuVzeYqptAxGJgEoXm3iorT1SJJ6NYzJt+4j19XWo93q2D0+e2uiBnewP5rpaumZWQlz3xyJqY51KlimvAMcUcABcQAAAAAAlBhGJgEoVtmLjGd/0AqUZedT0jV5zi7tTExydSilkvnyJ/EOLJOU74iRqYv2OHTNE+dT1OedP3Xo9CVJkq8Xf1qh8IZ2qSlJ6u1AQhQAAAAAgJLEyCQAxcr38qe6THMHSK/N3qWvVh7Qe4v2KGFSP321IkERQX66oWllV4dWKpjNWckkLzuJok3P9dQ78//WsK618m3rdHKaQ9ds/fIC6xgYWoIiuLl5VbWoXk5VywW5OhQAAAAAAKzw9VkAxSo7mZTONHeAfl5/2LK99cg5jf91mx75boPWHThjVS8tw6xLaZklHZ7bmbMtUbGjZ2rRrhM2j3/21371fedPJV5e3+ifC3kngMIDffXsDQ1UPsS/QHEYBUgQVQoPKFDbwNVqRAXL24u1lgAAAAAA7oVkEoBi5XP5A7E0prkDlJwjQXT9lL8s2wM+WG5Vr/2rC9X0hblKSb9S3zAM7TlxQRmltC+lZ5o18OMVemXWDkvZQ1+tkyTdP22NzXNemrFd248lqe3EBYodPVMztxyTJP2+6ahTYsrINOv537YpbswsHU/KSljlTCyduFyWU3QYySQAAAAAAFD6MM0dgGI1d/txSdK4X7bq7rY1XBwN4L5e+H2bnruhoY6evaST51MlSQmnklUvJkxS1iidoV+vz6p7Y0M999s2zXu8k+pUDHVZzM60aOcJrdx3Wiv3ndbYvvVzHb+QmqEQf8f+bEk4dbHA1+9yTQUt3nXSqqz2M39Yttu8skDXN6mkGZuzElaRwX5qUjXcqv6+V/oW+LoAAAAAAACegJFJAAC4gWnLEiRJ7SYttJT5XZ4mMvFciiWRJEnP/bZNktTjraUlF2Axyzl6MXb0TMWOnml1/LrJi3Xz+8v0yHcb8m3rg7taFPj6797RXJNvbZpnnexEkpS1plLO5NPWF3rJi6nJAAAAAABAKUUyCQAAN7Ht6Dmr/UNnLkmS2k5cYPec2NEzdTEto1jjKgnmfJYlOnE+VRsOntXvm45q5b5TOncp3W7dkICCD7wOC/DVv1pWLfB5lms6OGoKAAAAAADAE5FMAlBiCrKIPVAWPfO/rVb7932+Wgt2HM/3vFYT5uuuT1fqoa/WFldoxS41x/pQ+Rn48Uo1fWGuVdkbOUYVta0ZVeg4utePLvS5AAAAAAAApRVfowVQYtIyzfL38XZ1GIDb2njobK6ywdPzTxBdTMvUsj2nJEmf/7VfD3SIc3Zoxa6oqeZbWlRRsL+36sWEyde78N+VaVYtQvN3nChiNAAAAAAAAKULI5MAlJi8pqUCyoI7Wlcr9mu8OGN7sV+jWBQhm3Rnm+oymUzq3aiSYssHFymMqBD/Ap+TMKlfka4JAAAAAADg7kgmASgx7y/a6+oQAJfKzG9hIEk+XqYiXyelAFPGuYuoEL/cZcF+Wjuue77nvty/kdPiKH9VMmnD+B656tzVprriL0+lt/HZ3McBAAAAAABKG5JJAErMF8sTXB0C4FJpGWarfZONvFFGHgmnGY90cOg6N723rEBxuQNbU9OtG98jV3Inp8m3NtWuCb1lsvUgC6l8jqTW/FGdVS7YOsk1qF2sJvRvpO+GtFXCpH6KCMqdBAMAAAAAAChtWDMJQLFqWzNSK/eddnUYgMttO3pOm4+csyrb83Jf1Ro7y6Hzfxwar0ZVwjWgRVVtO3pOp5LTdPJ8qs26u46fL3K8JS3TsE6ibXm+p2V750u9NWjaal3fpLLqxYTKz8dLTapGFEsc18SEWrb9Lie45j7eST3fWqp6MaF6/saGxXJdAAAAAAAAd0YyCUCx+vKBNqo77g9XhwG41KW0TPV79y+rshmPdJB3Aaa0uzY2UpL0xm1NJUnxExdYjv00NF7Vo4LU+uWssrxG87irlDTrqflCA3wt2wG+3vp+SHyJxOGXY4RUueCsGOpWDGVdJAAAAAAAUKYxzR2AYuXn46WWNcpJkgZ3iHNxNIBr3Py+9bRzrw5orEZVwiVJf7/cR9892DbP87vXr5irzJxjJE+r2EhFhwaobc2shNOAFlWKGnKJe+z7jZbtu9pUd1kcPt5emvFIB/06vL1VQgsAAAAAAKAsI5kEoNg1qZr1obm/D79yUDbtTLS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"text/plain": [ "
" ] @@ -1697,13 +1636,13 @@ "source": [ "# Period of Simulations\n", "\n", - "worst_6_month = [[[\"2020-02-20\",\"2020-09-01\"],240]]\n", - "worst_1_year = [[[\"2019-09-01\",\"2020-03-01\"],170]]\n", - "periods_n_open_close = worst_3_month\n", + "# worst_6_month = [[[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240]]\n", + "# worst_1_year = [[[\"2019-09-01 00:00:00\",\"2020-03-01 00:00:00\"],170]]\n", + "periods_n_open_close = [[[\"2019-09-15 00:00:00\",\"2019-12-01 00:00:00\"],182]]\n", "period = periods_n_open_close[0][0]\n", - "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "data = historical_data.loc[period[0]:period[1]]\n", "parameter_manager = ParameterManager()\n", - "last_date = period[1]+' 00:00:00'\n", + "last_date = period[1]\n", "vol = parameter_manager.calc_vol(last_date, data)\n", "mu, sigma = vol\n", "open_close = periods_n_open_close[0][1]\n", @@ -1715,7 +1654,7 @@ " color='green', \n", " linestyle='--', \n", " label='oc1='+str(round(open_close,3)))\n", - "# axs.axhline(y=243, \n", + "# axs.axhline(y=185, \n", "# color='red', \n", "# linestyle='--', \n", "# label='oc2='+str(round(243,3)))\n", @@ -1726,6 +1665,52 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "178.97" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['close'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "396" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Period of Simulations\n", + "# periods_n_open_close = worst_1_month\n", + "period = periods_n_open_close[0][0]\n", + "p = periods_n_open_close[0][1]\n", + "data_set = historical_data.loc[period[0]:period[1]]\n", + "crosses = cross_counter(data_set, p)\n", + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, { "cell_type": "code", "execution_count": 38, @@ -2027,7 +2012,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ From 77736dabcb1f3a0e0b5a32f9cbc0d6864a913ad3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Wed, 19 Oct 2022 10:59:43 -0300 Subject: [PATCH 13/16] updates with oc_inc and trail_inc --- jupyter-lab/Simulations_oc_range.ipynb | 2556 ++++++++++++++++++++---- 1 file changed, 2114 insertions(+), 442 deletions(-) diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb index 86bf598..68d284c 100644 --- a/jupyter-lab/Simulations_oc_range.ipynb +++ b/jupyter-lab/Simulations_oc_range.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Libraries" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -18,32 +28,32 @@ "Requirement already satisfied: numpy>=1.21.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (1.23.3)\n", "Requirement already satisfied: google-api-python-client>=1.5.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (2.63.0)\n", "Requirement already satisfied: google-auth-oauthlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (0.5.3)\n", - "Requirement already satisfied: cycler>=0.10 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: pillow>=6.2.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (9.2.0)\n", "Requirement already satisfied: fonttools>=4.22.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (4.37.3)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n", "Requirement already satisfied: contourpy>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.0.5)\n", + "Requirement already satisfied: cycler>=0.10 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n", "Requirement already satisfied: packaging>=20.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (21.3)\n", - "Requirement already satisfied: pillow>=6.2.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (9.2.0)\n", "Requirement already satisfied: pyparsing>=2.2.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n", - "Requirement already satisfied: google-auth-httplib2>=0.1.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (0.1.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n", "Requirement already satisfied: google-auth<3.0.0dev,>=1.19.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (2.12.0)\n", - "Requirement already satisfied: uritemplate<5,>=3.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (4.1.1)\n", - "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (2.10.1)\n", + "Requirement already satisfied: google-auth-httplib2>=0.1.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (0.1.0)\n", "Requirement already satisfied: httplib2<1dev,>=0.15.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (0.20.4)\n", + "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (2.10.1)\n", + "Requirement already satisfied: uritemplate<5,>=3.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (4.1.1)\n", "Requirement already satisfied: six>=1.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n", "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-auth-oauthlib->pygsheets) (1.3.1)\n", - "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.56.2 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.56.4)\n", "Requirement already satisfied: protobuf<5.0.0dev,>=3.20.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (4.21.7)\n", "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.28.1)\n", - "Requirement already satisfied: rsa<5,>=3.1.4 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (4.9)\n", + "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.56.2 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.56.4)\n", "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (0.2.8)\n", + "Requirement already satisfied: rsa<5,>=3.1.4 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (4.9)\n", "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (5.2.0)\n", "Requirement already satisfied: oauthlib>=3.0.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib->pygsheets) (3.2.1)\n", "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (0.4.8)\n", - "Requirement already satisfied: idna<4,>=2.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (3.4)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.26.12)\n", "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.1.1)\n", "Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2022.9.24)\n", + "Requirement already satisfied: idna<4,>=2.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (3.4)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (1.26.12)\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" @@ -51,7 +61,7 @@ } ], "source": [ - "!pip install pandas scipy pygsheets matplotlib\n", + "!pip install pandas scipy pygsheets matplotlib python-binance\n", "\n", "import os\n", "import pygsheets\n", @@ -77,6 +87,7 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -172,7 +183,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "## Aave and DyDx modules" ] @@ -579,6 +593,7 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -793,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 265, "metadata": {}, "outputs": [], "source": [ @@ -890,9 +905,9 @@ " else:\n", " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " path_to_dydx = file_location + 'dydx_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " with open(path_to_aave, 'a') as file:\n", - " writer = csv.writer(file, lineterminator='\\n')\n", - " writer.writerow(data_aave)\n", + " # with open(path_to_aave, 'a') as file:\n", + " # writer = csv.writer(file, lineterminator='\\n')\n", + " # writer.writerow(data_aave)\n", " with open(path_to_dydx, 'a',\n", " newline='', encoding='utf-8') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", @@ -965,9 +980,9 @@ " \n", " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", " path_to_dydx = file_location + 'dydx_results.csv' #'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", - " with open(path_to_aave, 'a') as file:\n", - " writer = csv.writer(file, lineterminator='\\n')\n", - " writer.writerow(aave_headers)\n", + " # with open(path_to_aave, 'a') as file:\n", + " # writer = csv.writer(file, lineterminator='\\n')\n", + " # writer.writerow(aave_headers)\n", " with open(path_to_dydx, 'a',\n", " newline='', encoding='utf-8') as file:\n", " writer = csv.writer(file, lineterminator='\\n')\n", @@ -977,43 +992,132 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ - "## Simulations" + "## BinanceClient" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 257, "metadata": {}, + "outputs": [], "source": [ - "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + "import math\n", + "import pandas as pd\n", + "import os.path\n", + "from datetime import timedelta, datetime\n", + "from dateutil import parser\n", + "from binance.client import Client as Client_binance\n", + "\n", + "\n", + "class BinanceClient(object):\n", + "\n", + " def __init__(self,\n", + " config):\n", + " self.binance_api_key = config['binance_api_key']\n", + " self.binance_api_secret = config['binance_api_secret']\n", + "\n", + " self.client = Client_binance(api_key=self.binance_api_key, api_secret=self.binance_api_secret)\n", + " # self.initial_date = config['initial_date']\n", + " # self.symbol = config['symbol']\n", + " # self.freq = config['freq']\n", + " ### FUNCTIONS\n", + " def minutes_of_new_data(self, symbol, kline_size,\n", + " initial_date, data, source):\n", + " if len(data) > 0:\n", + " old = parser.parse(data[\"timestamp\"].iloc[-1])\n", + " elif source == \"binance\":\n", + " old = datetime.strptime(initial_date, '%d %b %Y')\n", + " if source == \"binance\":\n", + " new = pd.to_datetime(self.client.get_klines(symbol=symbol, interval=kline_size)[-1][0], unit='ms')\n", + " return old, new\n", + " \n", + " def get_all_binance(self, symbol, freq,\n", + " initial_date, save=False):\n", + " binsizes = {\"1m\": 1, \"5m\": 5, \"10m\": 10, \"15m\": 15, \"1h\": 60, \"6h\": 360, \"12h\": 720, \"1d\": 1440}\n", + " filename = 'Files/%s-%s-data_since_%s.csv' % (symbol, freq, initial_date)\n", + " data_df = pd.DataFrame()\n", + " oldest_point, newest_point = self.minutes_of_new_data(symbol, freq,\n", + " initial_date, data_df, source=\"binance\")\n", + " delta_min = (newest_point - oldest_point).total_seconds() / 60\n", + " available_data = math.ceil(delta_min / binsizes[freq])\n", + " if oldest_point == datetime.strptime(initial_date, '%d %b %Y'):\n", + " print('Downloading all available %s data for %s. Be patient..!' % (freq, symbol))\n", + " else:\n", + " print('Downloading %d minutes of new data available for %s, i.e. %d instances of %s data.'\n", + " % (delta_min, symbol, available_data, freq))\n", + " klines = self.client.get_historical_klines(symbol, freq,\n", + " oldest_point.strftime(\"%d %b %Y %H:%M:%S\"),\n", + " newest_point.strftime(\"%d %b %Y %H:%M:%S\"))\n", + " data = pd.DataFrame(klines,\n", + " columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av',\n", + " 'trades', 'tb_base_av', 'tb_quote_av', 'ignore'])\n", + " data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms')\n", + " # data.index = pd.to_datetime(data['timestamp'], unit='ms')\n", + " if len(data_df) > 0:\n", + " temp_df = pd.DataFrame(data)\n", + " data_df = data_df.append(temp_df)\n", + " else:\n", + " data_df = data\n", + " data_df.set_index('timestamp', inplace=True)\n", + " if save:\n", + " data_df.to_csv(filename)\n", + " print('All caught up..!')\n", + " print(initial_date)\n", + " return data_df" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 258, "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'Files/ETHUSDC-1m-data_since_1 Sep 2019.csv'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [7], line 10\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Track historical data\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# symbol = 'ETHUSDC'\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# freq = '1m'\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 7\u001b[0m \n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Load historical data if previously tracked and saved\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m historical_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFiles/ETHUSDC-1m-data_since_1 Sep 2019.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# # assign data to stgy instance + define index as dates\u001b[39;00m\n\u001b[1;32m 12\u001b[0m timestamp \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(historical_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/util/_decorators.py:317\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 312\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 313\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 314\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 315\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(inspect\u001b[38;5;241m.\u001b[39mcurrentframe()),\n\u001b[1;32m 316\u001b[0m )\n\u001b[0;32m--> 317\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1729\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1727\u001b[0m is_text \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1728\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1729\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1730\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1731\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1732\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1733\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1734\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1735\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1736\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1737\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1738\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1739\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1740\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", - "File \u001b[0;32m~/cruize/env/lib/python3.10/site-packages/pandas/io/common.py:857\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 853\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 865\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 866\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Files/ETHUSDC-1m-data_since_1 Sep 2019.csv'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading all available 1m data for BTCUSDC. Be patient..!\n", + "All caught up..!\n", + "1 Sep 2019\n" ] } ], + "source": [ + "# Track historical data\n", + "with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "symbol = 'BTCUSDC'\n", + "freq = '1m'\n", + "initial_date = \"1 Sep 2019\"\n", + "_binance_client_ = BinanceClient(config['binance_client'])\n", + "eth_historical = _binance_client_.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], "source": [ "# Track historical data\n", "# symbol = 'ETHUSDC'\n", @@ -1148,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1194,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 306, "metadata": { "tags": [] }, @@ -1231,7 +1335,7 @@ " # if the demo_folder directory is not present \n", " # then create it.\n", " os.makedirs(file_location)#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", - " stgy.historical_data.to_csv(file_location+'stgy.historical_data.csv')#\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " # stgy.historical_data.to_csv(file_location+'stgy.historical_data.csv')#\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", " # % (period[0], period[1], open_close))\n", " #########################\n", " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", @@ -1278,7 +1382,8 @@ " \n", " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", " if (stgy.dydx.market_price <= stgy.open_close_range[0]):\n", - " stgy.dydx.open_short(stgy)\n", + " print(\"Short position should be open for this first price!...breaking\")\n", + " break\n", " #########################\n", " # Clear previous csv data for aave and dydx\n", " stgy.data_dumper.delete_results(stgy, file_location)#period, open_close)\n", @@ -1357,9 +1462,14 @@ " if (last_trailing_outside == 1) and (trailing_outside == -1):\n", " stgy.dydx.open_short(stgy)\n", " last_trailing_outside = trailing_outside\n", + " # We will use the oc_range once trailing_stop is executed (ie trailing_range crossed going up)\n", + " # So we redefine oc_range to end at that market_price + update trailing_range to end at oc_range[0]\n", " elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", " stgy.dydx.close_short(stgy)\n", " last_trailing_outside = trailing_outside\n", + " stgy.open_close_range = [market_price * (1-oc_increment), market_price]\n", + " stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-trailing_increment), \n", + " stgy.open_close_range[0]]\n", "\n", " i += 1\n", " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", @@ -1373,24 +1483,12 @@ " market_price * (1+trailing_increment)]\n", " ################################\n", " # OC LOGIC\n", - " # If prices goes above the topmost oc (floor + slip + vol) then we repeat the oc logic\n", - " # if market_price >= stgy.open_close_range[1]:\n", - " # stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-increment)**2, \n", - " # stgy.open_close_range[0]]\n", + " # If prices goes above floor we restart oc_range\n", + " if market_price >= floor:\n", + " stgy.open_close_range = [floor * ((1+slippage)*(1+mu+2*sigma)), \n", + " floor * ((1+slippage)*(1+mu+2*sigma)) * (1+oc_increment)]\n", " # trailing_outside = 1\n", " # last_trailing_outside = 1\n", - "\n", - " \n", - " # We update vol and ocs if short_status = False\n", - " # if not stgy.dydx.short_status:\n", - " # current_date = list(stgy.historical_data.index)[i]\n", - " # vol = stgy.parameter_manager.calc_vol(current_date, data_for_vol)\n", - " # mu, sigma = vol\n", - " # oc1 = floor * (1+slippage) * (1+mu+2*sigma)\n", - " # ocs = [oc1]\n", - " # for i in range(1,5):\n", - " # globals()[\"oc\"+str(i+1)] = oc1 * (1+0.03/5)**i # We define 5 OCs based on a top width of 3%\n", - " # ocs.append(globals()[\"oc\"+str(i+1)])\n", " ########################\n", " # Funding rates\n", " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", @@ -1416,278 +1514,496 @@ " return stgy.dydx.maker_fees_counter" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "periods_n_open_close = [[[\"2019-09-01 00:00:00\",\"2019-12-31 00:00:00\"],148], [[\"2019-09-01 00:00:00\",\"2019-12-31 00:00:00\"],185], \n", - " [[\"2020-01-01 00:00:00\",\"2020-05-01 00:00:00 00:00:00\"],135]]#, [[\"2020-05-01\",\"2020-09-01\"],240]]\n", - "periods_n_open_close = [[[\"2019-09-01 00:00:00\",\"2019-12-31 00:00:00\"],185]]\n", - "periods_n_open_close = [[[\"2020-05-31 00:00:00\",\"2020-12-01 00:00:00\"],240]]\n", - "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-02\"],240]]\n", - "# Worst cases\n", - "worst_1_week = [[[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]]\n", - "worst_1_month = [[[\"2020-05-31 00:00:00\",\"2020-06-30 00:00:00\"],240], [[[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]]]\n", - "worst_3_month = [[[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]]\n", - "worst_6_month = [[[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240]]\n", - "worst_1_year = [[[\"2019-09-01 00:00:00\",\"2020-09-01 00:00:00\"],170]]\n", - "# p = 243\n", - "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-30\"],240]]\n", - "# periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],243]]\n", - "# periods_n_open_close = [[[\"2020-05-31\",\"2020-06-07\"],240]]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'historical_data' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [10], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m period \u001b[38;5;241m=\u001b[39m periods_n_open_close[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 4\u001b[0m p \u001b[38;5;241m=\u001b[39m periods_n_open_close[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m----> 5\u001b[0m data_set \u001b[38;5;241m=\u001b[39m historical_data\u001b[38;5;241m.\u001b[39mloc[period[\u001b[38;5;241m0\u001b[39m]:period[\u001b[38;5;241m1\u001b[39m]]\n\u001b[1;32m 6\u001b[0m crosses \u001b[38;5;241m=\u001b[39m cross_counter(data_set, p)\n\u001b[1;32m 7\u001b[0m crosses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdown\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrossed_down\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m crosses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mup\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrossed_up\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "\u001b[0;31mNameError\u001b[0m: name 'historical_data' is not defined" - ] - } - ], - "source": [ - "# Period of Simulations\n", - "periods_n_open_close = worst_3_month[1]\n", - "period = periods_n_open_close[0][0]\n", - "p = periods_n_open_close[0][1]\n", - "data_set = historical_data.loc[period[0]:period[1]]\n", - "crosses = cross_counter(data_set, p)\n", - "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.003]) : [96, '-5.754%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.005]) : [90, '-4.883%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.01]) : [80, '-4.614%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.015]) : [78, '-6.341%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.003, 0.02]) : [74, '-5.004%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.003]) : [82, '-9.713%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.005]) : [76, '-8.842%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.01]) : [66, '-8.573%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.015]) : [64, '-10.3%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.005, 0.02]) : [60, '-8.963%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.003]) : [52, '-1.937%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.005]) : [46, '-1.067%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.01]) : [36, '-0.797%']\n" - ] - } - ], - "source": [ - "# range's lenght = 2*increment\n", - "stk = 1000000\n", - "oc_increments = [0.003, 0.005, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", - "trailing_increments = [0.003, 0.005, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", - "maker_fees_counter_lengths = {}\n", - "pnl_results = {}\n", - "total_results = []\n", - "for period_n_open_close in periods_n_open_close:\n", - " for oc_increment in oc_increments:\n", - " for trailing_increment in trailing_increments:\n", - " period = period_n_open_close[0]\n", - " open_close = period_n_open_close[1]\n", - " slippage = 0.0005\n", - " directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, oc_increment, trailing_increment)\n", - " maker_fees_counter = run_sim(stk, period, open_close, slippage, 2*oc_increment, 2*trailing_increment, directory)\n", - " maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", - " # print(\"Max txs for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", - " # maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])])\n", - " # directory = \"From_%s_to_%s_open_close_at_%s/dydx_results.csv\" % (period[0], period[1], open_close)\n", - " dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)\n", - " pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", - " print(\"Max txs and PnL for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", - " [maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])], \n", - " str(round(pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%'])\n", - " total_results.append([maker_fees_counter_lengths, pnl_results])" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'From_2019-09-01_to_2020-09-01_open_close_at_170'" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], p)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, 0.005, 0.02)\n", - "maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", - "dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)" - ] - }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 426, "metadata": {}, "outputs": [], "source": [ - "price_jump_in_open = {}\n", - "price_jump_in_close = {}\n", - "\n", - "for i in range(len(dydx_results)-1):\n", - " if dydx_results['entry'][i]==0 and dydx_results['entry'][i+1]!=0:\n", - " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['P'][i+1] / dydx_results['P'][i]-1)\n", - " elif dydx_results['entry'][i]!=0 and dydx_results['entry'][i+1]==0:\n", - " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['P'][i+1] / dydx_results['P'][i]-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Min price jump at open: 0.0714%\n", - "Mean price jump at open: 0.4439%\n", - "Max price jump at open: 1.4048%\n" - ] - } - ], - "source": [ - "print(\"Min price jump at open:\",str(round(min(list(price_jump_in_open.values())),6)*100)+\"%\")\n", - "print(\"Mean price jump at open:\",str(round(np.mean(list(price_jump_in_open.values())),6)*100)+\"%\")\n", - "print(\"Max price jump at open:\",str(round(max(list(price_jump_in_open.values())),6)*100)+\"%\")" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Min price jump at close: 0.049%\n", - "Mean price jump at close: 0.5558%\n", - "Max price jump at close: 3.5869999999999997%\n" - ] - } - ], - "source": [ - "print(\"Min price jump at close:\",str(round(min(list(price_jump_in_close.values())),6)*100)+\"%\")\n", - "print(\"Mean price jump at close:\",str(round(np.mean(list(price_jump_in_close.values())),6)*100)+\"%\")\n", - "print(\"Max price jump at close:\",str(round(max(list(price_jump_in_close.values())),5)*100)+\"%\")" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Period of Simulations\n", + "def run_sim(stk, period, open_close, slippage, oc_increment, trailing_increment, file_location):\n", + " global ocs\n", + " # Initialize everything\n", + " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", "\n", - "# worst_6_month = [[[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240]]\n", - "# worst_1_year = [[[\"2019-09-01 00:00:00\",\"2020-03-01 00:00:00\"],170]]\n", - "periods_n_open_close = [[[\"2019-09-15 00:00:00\",\"2019-12-01 00:00:00\"],182]]\n", - "period = periods_n_open_close[0][0]\n", - "data = historical_data.loc[period[0]:period[1]]\n", - "parameter_manager = ParameterManager()\n", - "last_date = period[1]\n", - "vol = parameter_manager.calc_vol(last_date, data)\n", - "mu, sigma = vol\n", - "open_close = periods_n_open_close[0][1]\n", - "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", - "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", - "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", - "axs.plot(data['close'], color='tab:blue', label='market price')\n", - "axs.axhline(y=open_close, \n", - " color='green', \n", - " linestyle='--', \n", - " label='oc1='+str(round(open_close,3)))\n", - "# axs.axhline(y=185, \n", - "# color='red', \n", - "# linestyle='--', \n", - "# label='oc2='+str(round(243,3)))\n", - "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", - "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", - "axs.grid()\n", - "axs.legend(loc='lower left')\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0]:period[1]]\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", + " # First we calculate weighted vol\n", + " last_date = period[1]\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + " # Now we define prices \n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing_increment)\n", + " # We create five equidistant OCs\n", + " oc1 = open_close\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder \n", + " # exist or not.\n", + " if not os.path.exists(file_location):#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " # if the demo_folder directory is not present \n", + " # then create it.\n", + " os.makedirs(file_location)#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", + " # stgy.historical_data.to_csv(file_location+'stgy.historical_data.csv')#\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " # % (period[0], period[1], open_close))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + "\n", + " # Stk eth\n", + " stgy.stk = stk/stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.dydx.collateral = stgy.aave.debt\n", + " stgy.dydx.equity = stgy.dydx.equity_calc()\n", + " stgy.dydx.collateral_status = True\n", + " \n", + " stgy.open_close_range = [floor * ((1+slippage)*(1+mu+2*sigma)), \n", + " floor * ((1+slippage)*(1+mu+2*sigma)) * (1+oc_increment)]\n", + " # stgy.trigger_prices['trailing_stop'] = stgy.open_close_range[0] * (1-trailing)\n", + " stgy.open_close_range_2 = [floor * (1-oc_increment), \n", + " floor]\n", + " \n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= stgy.open_close_range[0]):\n", + " print(\"Short position should be open for this first price!...breaking\")\n", + " return\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(stgy, file_location)#period, open_close)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(stgy, file_location)#period, open_close)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + " maker_fees_counter = []\n", + " \n", + " # stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", + " \n", + " \n", + " market_price = stgy.historical_data['close'][i-1]\n", + " if (stgy.open_close_range[1] < market_price):\n", + " last_outside = 1\n", + " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", + " last_outside = False\n", + " elif (market_price < stgy.open_close_range[0]):\n", + " last_outside = -1\n", + " \n", + " if (stgy.open_close_range_2[1] < market_price):\n", + " last_outside_2 = 1\n", + " elif (stgy.open_close_range_2[0] <= market_price) and (market_price <= stgy.open_close_range_2[1]):\n", + " last_outside_2 = False\n", + " elif (market_price < stgy.open_close_range_2[0]):\n", + " last_outside_2 = -1\n", + " \n", + " stgy.trailing_stop_range = [floor * (1-trailing_increment), \n", + " floor] \n", + " if (stgy.trailing_stop_range[1] < market_price):\n", + " last_trailing_outside = 1\n", + " elif (stgy.trailing_stop_range[0] <= market_price) and (market_price <= stgy.trailing_stop_range[1]):\n", + " last_trailing_outside = False\n", + " elif (market_price < stgy.trailing_stop_range[0]):\n", + " last_trailing_outside = -1\n", + " \n", + " while(i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " market_price = stgy.historical_data[\"close\"][i]\n", + " previous_price = stgy.historical_data[\"close\"][i-1]\n", + " \n", + " if (stgy.open_close_range[1] < market_price):\n", + " outside = 1\n", + " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", + " outside = False\n", + " elif (market_price < stgy.open_close_range[0]):\n", + " outside = -1\n", + " \n", + " if (stgy.open_close_range_2[1] < market_price):\n", + " outside_2 = 1\n", + " elif (stgy.open_close_range_2[0] <= market_price) and (market_price <= stgy.open_close_range_2[1]):\n", + " outside_2 = False\n", + " elif (market_price < stgy.open_close_range_2[0]):\n", + " outside_2 = -1\n", + " \n", + " # if (stgy.trailing_stop_range[1] < market_price):\n", + " # trailing_outside = 1\n", + " # elif (stgy.trailing_stop_range[0] <= market_price) and (market_price <= stgy.trailing_stop_range[1]):\n", + " # trailing_outside = False\n", + " # elif (market_price < stgy.trailing_stop_range[0]):\n", + " # trailing_outside = -1\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, market_price)\n", + " \n", + " # open_close_range action\n", + " if (last_outside == 1) and (outside == -1):\n", + " stgy.dydx.open_short(stgy)\n", + " last_outside = outside\n", + " elif (last_outside == -1) and (outside == 1):\n", + " stgy.dydx.close_short(stgy)\n", + " last_outside = outside\n", + " \n", + " if (last_outside_2 == 1) and (outside_2 == -1):\n", + " stgy.dydx.open_short(stgy)\n", + " last_outside_2 = outside_2\n", + " elif (last_outside_2 == -1) and (outside_2 == 1):\n", + " stgy.dydx.close_short(stgy)\n", + " last_outside_2 = outside_2 \n", + " \n", + " # open_close_range action\n", + " # if (last_trailing_outside == 1) and (trailing_outside == -1):\n", + " # stgy.dydx.open_short(stgy)\n", + " # last_trailing_outside = trailing_outside\n", + " # # We will use the oc_range once trailing_stop is executed (ie trailing_range crossed going up)\n", + " # # So we redefine oc_range to end at that market_price + update trailing_range to end at oc_range[0]\n", + " # elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", + " # stgy.dydx.close_short(stgy)\n", + " # last_trailing_outside = trailing_outside\n", + " # stgy.open_close_range = [market_price * (1-oc_increment), market_price]\n", + " # # stgy.trailing_stop_range = stgy.open_close_range\n", + " i += 1\n", + " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", + " # and we execute all the actions involved between both (current and previous prices)\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", + " ############################## \n", + " # We update trailing\n", + " # Everytime price crosses the lower bound, we move the trailing range\n", + " # if (market_price <= stgy.trailing_stop_range[0]):\n", + " # stgy.trailing_stop_range = [market_price, \n", + " # market_price * (1+trailing_increment)]\n", + " ################################\n", + " # trailing_outside = 1\n", + " # last_trailing_outside = 1\n", + " ########################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " current_date = str(stgy.historical_data.index[i-1])\n", + " stgy.data_dumper.write_data(stgy, #previous_price, last_outside, outside,\n", + " current_date, file_location,#period, open_close,\n", + " sheet=False)\n", + " return stgy.dydx.maker_fees_counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [], + "source": [ + "# Best cases 0 to 50 crosses\n", + "best_1_week = [[\"2022-04-02 00:00:00\",\"2022-04-09 00:00:00\"],3400]\n", + "best_1_month = [[\"2022-04-02 00:00:00\",\"2022-05-01 00:00:00\"],3400]\n", + "\n", + "# Normal cases 50 to 150 crosses\n", + "normal_1_week = [[[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]]\n", + "normal_1_month = [[[\"2020-05-31 00:00:00\",\"2020-06-30 00:00:00\"],240],\n", + " [[\"2021-12-01 00:00:00\",\"2022-01-01 00:00:00\"],historical_data['close'].max()*0.8]]\n", + "# Worst cases 150+ crosses\n", + "worst_1_week = [ [[\"2019-10-26 05:00:00\",\"2019-11-02 00:00:00\"],183]]\n", + "worst_1_month = [[[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]]\n", + "\n", + "# worst_3_month = [ [[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]\n", + "# worst_6_month = [ [[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2020-03-15 00:00:00\"],182]]\n", + "# worst_1_year = [ [\"2019-09-01 00:00:00\",\"2020-09-01 00:00:00\"],170] " + ] + }, + { + "cell_type": "code", + "execution_count": 407, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 407, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Period of Simulations\n", + "periods_n_open_close = best_1_month\n", + "period = periods_n_open_close[0]\n", + "p = periods_n_open_close[1]\n", + "data_set = historical_data.loc[period[0]:period[1]]\n", + "crosses = cross_counter(data_set, p)\n", + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 408, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "\n", + "# worst_6_month = [[[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240]]\n", + "# worst_1_year = [[[\"2019-09-01 00:00:00\",\"2020-03-01 00:00:00\"],170]]\n", + "# periods_n_open_close = best_1_week\n", + "period = periods_n_open_close[0]\n", + "data = historical_data.loc[period[0]:period[1]]\n", + "parameter_manager = ParameterManager()\n", + "last_date = period[1]\n", + "vol = parameter_manager.calc_vol(last_date, data)\n", + "mu, sigma = vol\n", + "open_close = periods_n_open_close[1]\n", + "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", + "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=open_close, \n", + " color='green', \n", + " linestyle='--', \n", + " label='oc1='+str(round(open_close,3)))\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3891.8" + ] + }, + "execution_count": 332, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historical_data['close'].max()*0.8" + ] + }, + { + "cell_type": "code", + "execution_count": 245, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "178.97" + "0.002845840696203998" ] }, - "execution_count": 107, + "execution_count": 245, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data['close'][0]" + "mu_ema_log_returns+ 3 *std_ema_log_returns" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.002]) : [1, '-0.501%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.015, 0.002]) : [1, '-0.501%']\n", + "Max txs and PnL for ( [oc_inc, trail_inc] = [0.02, 0.002]) : [1, '-0.494%']\n" + ] + } + ], + "source": [ + "# range's lenght = 2*increment\n", + "stk = 1000000\n", + "# oc_increments = [round(mu_ema_log_returns+ 3 *std_ema_log_returns,4), \n", + "# round(mu_ema_log_returns+ 4 *std_ema_log_returns,4),\n", + "# round(mu_ema_log_returns+ 5 *std_ema_log_returns,4)]\n", + "oc_increments = [0.01, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", + "trailing_increments = [0.002]#, 0.003, 0.005]#, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", + "maker_fees_counter_lengths = {}\n", + "pnl_results = {}\n", + "total_results = []\n", + "# for period_n_open_close in periods_n_open_close:\n", + "for oc_increment in oc_increments:\n", + " for trailing_increment in trailing_increments:\n", + " period = periods_n_open_close[0]\n", + " open_close = periods_n_open_close[1]\n", + " slippage = 0.0005\n", + " directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, oc_increment, trailing_increment)\n", + " maker_fees_counter = run_sim(stk, period, open_close, slippage, 2*oc_increment, 2*trailing_increment, directory)\n", + " maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", + " # print(\"Max txs for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", + " # maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])])\n", + " # directory = \"From_%s_to_%s_open_close_at_%s/dydx_results.csv\" % (period[0], period[1], open_close)\n", + " dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)\n", + " pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", + " print(\"Max txs and PnL for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", + " [maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])], \n", + " str(round(pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%'])\n", + " total_results.append([maker_fees_counter_lengths, pnl_results])" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, 0.005, 0.02)\n", + "maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", + "dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "price_jump_in_open = {}\n", + "price_jump_in_close = {}\n", + "\n", + "for i in range(len(dydx_results)-1):\n", + " if dydx_results['entry'][i]==0 and dydx_results['entry'][i+1]!=0:\n", + " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['P'][i+1] / dydx_results['P'][i]-1)\n", + " elif dydx_results['entry'][i]!=0 and dydx_results['entry'][i+1]==0:\n", + " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['P'][i+1] / dydx_results['P'][i]-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at open: 0.0714%\n", + "Mean price jump at open: 0.4439%\n", + "Max price jump at open: 1.4048%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at open:\",str(round(min(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at open:\",str(round(np.mean(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at open:\",str(round(max(list(price_jump_in_open.values())),6)*100)+\"%\")" ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at close: 0.049%\n", + "Mean price jump at close: 0.5558%\n", + "Max price jump at close: 3.5869999999999997%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at close:\",str(round(min(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at close:\",str(round(np.mean(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at close:\",str(round(max(list(price_jump_in_close.values())),5)*100)+\"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 242, "metadata": {}, "outputs": [ { @@ -1696,309 +2012,1665 @@ "396" ] }, - "execution_count": 112, + "execution_count": 242, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Period of Simulations\n", - "# periods_n_open_close = worst_1_month\n", - "period = periods_n_open_close[0][0]\n", - "p = periods_n_open_close[0][1]\n", - "data_set = historical_data.loc[period[0]:period[1]]\n", - "crosses = cross_counter(data_set, p)\n", - "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + "# Period of Simulations\n", + "# periods_n_open_close = worst_1_month\n", + "period = periods_n_open_close[0][0]\n", + "p = periods_n_open_close[0][1]\n", + "data_set = historical_data.loc[period[0]:period[1]]\n", + "crosses = cross_counter(data_set, p)\n", + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Jumps + vol analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the whole period 2019.09.01 to 2022.09.01" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "data = historical_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate pct_change (returns/jumps) and log_returns." + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [], + "source": [ + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))\n", + "log_returns = log_returns.dropna()\n", + "abs_returns = abs(returns)\n", + "abs_log_returns=abs(log_returns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's calculate sma, ema, std of sma and std of ema." + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "# Of returns\n", + "emw_returns = returns.ewm(alpha=0.8, adjust=False)\n", + "mu_sma_returns = returns.mean()\n", + "mu_ema_returns = emw_returns.mean().mean()\n", + "\n", + "std_sma_returns = returns.std()\n", + "std_ema_returns = emw_returns.std().mean()\n", + "# Others\n", + "mu_sma_abs_returns = abs(returns).mean()\n", + "returns_max = returns.max()\n", + "returns_min = returns.min()\n", + "\n", + "# Of log-returns\n", + "emw_log_returns = log_returns.ewm(alpha=0.8, adjust=False)\n", + "mu_sma_log_returns = log_returns.mean()\n", + "mu_ema_log_returns = emw_log_returns.mean().mean()\n", + "\n", + "std_sma_log_returns = log_returns.std()\n", + "std_ema_log_returns = emw_log_returns.std().mean()\n", + "\n", + "\n", + "# Others\n", + "mu_sma_abs_log_returns = abs(log_returns).mean()\n", + "log_returns_max = log_returns.max()\n", + "log_returns_min = log_returns.min()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 279, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.07894394589673559" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data['close'].pct_change(1*24*60).dropna().max()" + "# Of returns\n", + "emw_abs_returns = abs_returns.ewm(alpha=0.8, adjust=False)\n", + "mu_sma_abs_returns = abs_returns.mean()\n", + "mu_ema_abs_returns = emw_abs_returns.mean().mean()\n", + "\n", + "std_sma_abs_returns = abs_returns.std()\n", + "std_ema_abs_returns = emw_abs_returns.std().mean()\n", + "# Others\n", + "mu_sma_abs_returns = abs(abs_returns).mean()\n", + "abs_returns_max = abs_returns.max()\n", + "abs_returns_min = abs_returns.min()\n", + "\n", + "# Of log-returns\n", + "emw_abs_log_returns = abs_log_returns.ewm(alpha=0.8, adjust=False)\n", + "mu_sma_abs_log_returns = abs_log_returns.mean()\n", + "mu_ema_abs_log_returns = emw_abs_log_returns.mean().mean()\n", + "\n", + "std_sma_abs_log_returns = abs_log_returns.std()\n", + "std_ema_abs_log_returns = emw_abs_log_returns.std().mean()\n", + "\n", + "\n", + "# Others\n", + "mu_sma_abs_log_returns = abs(abs_log_returns).mean()\n", + "abs_log_returns_max = abs_log_returns.max()\n", + "abs_log_returns_min = abs_log_returns.min()\n", + "std_ema_abs_log_returns = abs(abs_log_returns).ewm(alpha=0.8, adjust=False).std().mean()" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 280, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "-93714.29797685935" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Jumps of prices (Returns):\n", + "Mean price jump: 0.048924%\n", + "Std of mean: 0.10266%\n", + "Mean of EMA price jump: 0.048924%\n", + "Std of Mean EMA: 0.052945%\n" + ] } ], "source": [ - "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", - "dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", - "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + "print(\"Jumps of prices (Returns):\")\n", + "print(\"Mean price jump:\",str(round(mu_sma_abs_returns*100,6))+\"%\")\n", + "print(\"Std of mean:\",str(round(std_sma_abs_returns*100,6))+\"%\")\n", + "print(\"Mean of EMA price jump:\",str(round(mu_ema_abs_returns*100,6))+\"%\")\n", + "print(\"Std of Mean EMA:\",str(round(std_ema_abs_returns*100,6))+\"%\")" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 281, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'2020-05-01'" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Jumps of log(prices) (log_returns):\n", + "Mean price jump: 0.048926%\n", + "Std of mean: 0.102703%\n", + "Mean of EMA price jump: 0.048925%\n", + "Std of Mean EMA: 0.052947%\n" + ] } ], "source": [ - "period" + "print(\"Jumps of log(prices) (log_returns):\")\n", + "print(\"Mean price jump:\",str(round(mu_sma_abs_log_returns*100,6))+\"%\")\n", + "print(\"Std of mean:\",str(round(std_sma_abs_log_returns*100,6))+\"%\")\n", + "print(\"Mean of EMA price jump:\",str(round(mu_ema_abs_log_returns*100,6))+\"%\")\n", + "print(\"Std of Mean EMA:\",str(round(std_ema_abs_log_returns*100,6))+\"%\")" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 283, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of EMA +-1*Std of Mean EMA: ['-0.004%', '0.102%']\n", + "Percentage of jumps within Mean of EMA +-1*Std of Mean EMA: 82.618%\n", + "Mean of EMA +-2*Std of Mean EMA: ['-0.057%', '0.155%']\n", + "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 89.667%\n", + "Mean of EMA +-3*Std of Mean EMA: ['-0.11%', '0.208%']\n", + "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 93.911%\n", + "Mean of EMA +-4*Std of Mean EMA: ['-0.163%', '0.261%']\n", + "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 96.261%\n", + "Mean of EMA +-5*Std of Mean EMA: ['-0.216%', '0.314%']\n", + "Percentage of jumps within Mean of EMA +-5*Std of Mean EMA: 97.631%\n" + ] + }, { "data": { + "image/png": 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"text/plain": [ - "'2019-09-01 00:00:00'" + "
" ] }, - "execution_count": 28, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "str(historical_data.index[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" + "for K in range(1,6):\n", + " globals()[\"condition_\"+str(K)] = (mu_ema_abs_log_returns-K*std_ema_abs_log_returns\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Counting
0
0.1214521
0.1183791
0.0847291
0.0839671
0.0691261
......
-0.0662591
-0.0671821
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\n", + "

18486 rows × 1 columns

\n", + "" + ], + "text/plain": [ + " Counting\n", + "0 \n", + " 0.121452 1\n", + " 0.118379 1\n", + " 0.084729 1\n", + " 0.083967 1\n", + " 0.069126 1\n", + "... ...\n", + "-0.066259 1\n", + "-0.067182 1\n", + "-0.078588 1\n", + "-0.085284 1\n", + "-0.086744 1\n", + "\n", + "[18486 rows x 1 columns]" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "len(log_returns[condition]),len(log_returns),len(log_returns[condition])/len(log_returns)" + "jumps_2.sort_index(ascending=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ - "plt.hist(log_returns[condition], bins=100)" + "jumps_2.sort_index(ascending=False).to_csv('jumps.csv')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 228, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of jumps greater than 0.05 or lower than -0.05: 0.001522%\n", + "Percentage of jumps greater than 0.1 or lower than -0.1: 0.000127%\n", + "Percentage of jumps greater than 0.15 or lower than -0.15: 0.0%\n", + "Percentage of jumps greater than 0.25 or lower than -0.25: 0.0%\n", + "Percentage of jumps greater than 0.5 or lower than -0.5: 0.0%\n" + ] + } + ], "source": [ - "len(log_returns)" + "for pcg in [0.05,0.1,0.15,0.25,0.5]:\n", + " condition = (-pcg>=log_returns)|(log_returns>=pcg)\n", + " len(log_returns[condition])/len(log_returns)\n", + " print(\"Percentage of jumps greater than \"+str(pcg)+\" or lower than \" + str(-pcg)+ \":\",str(round(len(log_returns[condition])/len(log_returns)*100,6))+\"%\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 162, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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close
timestamp
2020-03-12 10:45:00140.51
2020-03-12 10:46:00134.73
2020-03-12 10:47:00131.06
2020-03-12 10:48:00125.76
2020-03-12 10:49:00142.00
2020-03-12 10:50:00140.21
\n", + "
" + ], "text/plain": [ - "(239.4380835398584, 240.0, 247.20000000000002)" + " close\n", + "timestamp \n", + "2020-03-12 10:45:00 140.51\n", + "2020-03-12 10:46:00 134.73\n", + "2020-03-12 10:47:00 131.06\n", + "2020-03-12 10:48:00 125.76\n", + "2020-03-12 10:49:00 142.00\n", + "2020-03-12 10:50:00 140.21" ] }, - "execution_count": 22, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "slippage = 0.0005\n", - "K_1 = 2\n", - "K_2 = 6\n", - "mu = 0.0004973569978282845\n", - "sigma = 0.0006742666391824819\n", - "floor = 240 / ((1+slippage)*(1+mu+K_1*sigma))\n", - "p_open_close_1 = floor * (1+slippage) * (1+mu+K_1*sigma)\n", - "p_open_close_2 = p_open_close_1 * (1+K_2/K_1/100)\n", - "floor, p_open_close_1, p_open_close_2" + "data.iloc[277763-3:277763+3]" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 89, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "1.0050452283113396" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Jumps of prices (Returns):\n", + "Mean price jump: 0.000246%\n", + "Std of mean: 0.145833%\n", + "Mean of EMA price jump: 0.000246%\n", + "Std of Mean EMA: 0.094817%\n" + ] } ], "source": [ - "(1+slippage)*(1+mu+6*sigma)" + "print(\"Jumps of prices (Returns):\")\n", + "print(\"Mean price jump:\",str(round(mu_sma_returns*100,6))+\"%\")\n", + "print(\"Std of mean:\",str(round(std_sma_returns*100,6))+\"%\")\n", + "print(\"Mean of EMA price jump:\",str(round(mu_ema_returns*100,6))+\"%\")\n", + "print(\"Std of Mean EMA:\",str(round(std_ema_returns*100,6))+\"%\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jumps of log(prices) (log_returns):\n", + "Mean price jump: 0.00014%\n", + "Std of mean: 0.145778%\n", + "Mean of EMA price jump: 0.00014%\n", + "Std of Mean EMA: 0.094815%\n" + ] + } + ], "source": [ - "max_loss = 0.05\n", - "p_open_close_1 = floor * (1+slippage) * (1+mu+K_1*sigma)\n", - "oc1 = p_open_close_1\n", - "for i in range(1,5):\n", - " globals()['oc'+str(i+1)] = oc1 * 1.01**i # jumps of 1%" + "print(\"Jumps of log(prices) (log_returns):\")\n", + "print(\"Mean price jump:\",str(round(mu_sma_log_returns*100,6))+\"%\")\n", + "print(\"Std of mean:\",str(round(std_sma_log_returns*100,6))+\"%\")\n", + "print(\"Mean of EMA price jump:\",str(round(mu_ema_log_returns*100,6))+\"%\")\n", + "print(\"Std of Mean EMA:\",str(round(std_ema_log_returns*100,6))+\"%\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 123, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of EMA +-2*Std of Mean EMA: ['-0.189%', '0.19%']\n", + "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 89.016%\n", + "Mean of EMA +-3*Std of Mean EMA: ['-0.284%', '0.285%']\n", + "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 95.305%\n", + "Mean of EMA +-4*Std of Mean EMA: ['-0.379%', '0.379%']\n", + "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 97.72%\n" + ] + }, { "data": { + "image/png": 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" ] }, - "execution_count": 18, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + } + ], + "source": [ + "for K in range(2,5):\n", + " globals()[\"condition_\"+str(K)] = (mu_ema_log_returns-K*std_ema_log_returns Date: Thu, 20 Oct 2022 10:19:09 -0300 Subject: [PATCH 14/16] new approach using several ocs --- .../Simulations_several_ocs_approach.ipynb | 2304 +++++++++++++++++ 1 file changed, 2304 insertions(+) create mode 100644 jupyter-lab/Simulations_several_ocs_approach.ipynb diff --git a/jupyter-lab/Simulations_several_ocs_approach.ipynb b/jupyter-lab/Simulations_several_ocs_approach.ipynb new file mode 100644 index 0000000..bba9e4a --- /dev/null +++ b/jupyter-lab/Simulations_several_ocs_approach.ipynb @@ -0,0 +1,2304 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in 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satisfied: charset-normalizer<3,>=2 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.5.5->pygsheets) (2.1.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install pandas scipy pygsheets matplotlib\n", + "\n", + "import os\n", + "import pygsheets\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import csv\n", + "import pandas as pd\n", + "import numpy as np\n", + "import json\n", + "import math\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## StgyApp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main class for initializing everything and running simulations through reading prices in the dataset, updating all the parameters involved and executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class StgyApp(object):\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.stk = config[\"stk\"]\n", + " self.total_costs_from_aave_n_dydx = 0\n", + " self.total_pnl = 0\n", + " self.gas_fees = 0\n", + "\n", + " # prices and intervals\n", + " self.trigger_prices = {}\n", + " self.intervals = {}\n", + "\n", + " # clients for data\n", + " # self.binance_client = binance_client_.BinanceClient(config[\"binance_client\"])\n", + " # self.dydx_client = dydx_client.DydxClient(config[\"dydx_client\"])\n", + " # self.sm_interactor = sm_interactor.SmInteractor(config[\"sm_interactor\"])\n", + " # self.historical_data =\n", + "\n", + " # We create attributes to fill later\n", + " self.aave = None\n", + " self.aave_features = None\n", + " self.aave_rates = None\n", + "\n", + " self.dydx = None\n", + " self.dydx_features = None\n", + "\n", + " # self.volatility_calculator = None\n", + "\n", + " self.parameter_manager = ParameterManager()\n", + "\n", + " self.historical_data = None\n", + "\n", + " self.data_dumper = DataDamperNPlotter()\n", + "\n", + " def launch(self, config):\n", + " # self.call_binance_data_loader()\n", + " self.initialize_aave(config['initial_parameters']['aave'])\n", + " self.initialize_dydx(config['initial_parameters']['dydx'])\n", + "\n", + " # call clients functions\n", + " def get_historical_data(self, symbol, freq,\n", + " initial_date, save):\n", + " eth_historical = self.binance_client.get_all_binance(symbol=symbol, freq=freq,\n", + " initial_date=initial_date, save=save)\n", + " # self.historical_data = eth_historical\n", + " self.historical_data = eth_historical[\"close\"]\n", + " for i in range(len(self.historical_data)):\n", + " self.historical_data[i] = float(self.historical_data[i])\n", + " # self.load_intervals()\n", + "\n", + " # initialize classes\n", + " def initialize_aave(self, config):\n", + " # We initialize aave and dydx classes instances\n", + " self.aave = Aave(config)\n", + " # We load methods and attributes for aave and dydx to use later\n", + " self.aave_features = {\"methods\": [func for func in dir(self.aave)\n", + " if (callable(getattr(self.aave, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.aave.__dict__.values()),\n", + " \"keys\": list(self.aave.__dict__.keys())}}\n", + " # We create an attribute for historical data\n", + " self.aave_historical_data = []\n", + "\n", + " def initialize_dydx(self, config):\n", + " self.dydx = Dydx(config)\n", + " self.dydx_features = {\"methods\": [func for func in dir(self.dydx)\n", + " if (callable(getattr(self.dydx, func))) & (not func.startswith('__'))],\n", + " \"attributes\": {\"values\": list(self.dydx.__dict__.values()),\n", + " \"keys\": list(self.dydx.__dict__.keys())}}\n", + " self.dydx_historical_data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aave and DyDx modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modules with parameters for the protocols involved in the strategy (Aave and DyDx), methods for updating all the parameters given a new price read by the bot and methods for executing the actions needed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Aave" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Aave(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert self.dydx_class_instance == isinstance(dydx)\n", + " # assert config['debt'] == config['collateral_eth'] * config['borrowed_pcg']\n", + " self.market_price = config['market_price']\n", + "\n", + " self.entry_price = config['entry_price']\n", + "\n", + " self.collateral_eth_initial = config['collateral_eth']\n", + " self.collateral_eth = config['collateral_eth']\n", + " self.collateral_usdc = config['collateral_usdc']\n", + "\n", + " self.reserve_margin_eth = 0\n", + " self.reserve_margin_usdc = 0\n", + "\n", + " self.borrowed_percentage = config['borrowed_pcg']\n", + " self.usdc_status = config['usdc_status']\n", + "\n", + " self.debt = config['debt']\n", + " self.debt_initial = config['debt']\n", + "\n", + " self.ltv = config['ltv']\n", + " self.price_to_ltv_limit = config['price_to_ltv_limit']\n", + "\n", + " self.lending_rate = 0\n", + " self.lending_rate_hourly = 0\n", + " self.interest_on_lending_eth = 0 # aggregated fees\n", + " self.interest_on_lending_usd = 0\n", + " self.lending_fees_eth = 0 # fees between last 2 prices\n", + " self.lending_fees_usd = 0\n", + "\n", + " self.borrowing_rate = 0\n", + " self.borrowing_rate_hourly = 0\n", + " self.interest_on_borrowing = 0 # aggregated fees\n", + " self.borrowing_fees = 0 # fees between last 2 prices\n", + "\n", + " self.lend_minus_borrow_interest = 0\n", + "\n", + " self.costs = 0\n", + " # self.historical = pd.DataFrame()\n", + " # self.dydx_class_instance = dydx_class_instance\n", + " # self.staked_in_protocol = stk\n", + "\n", + " # def update_costs(self):\n", + " # \"\"\"\n", + " # it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " # \"\"\"\n", + " # # We have to substract lend_minus_borrow in order to increase the cost (negative cost means profit)\n", + " # self.costs = self.costs - self.lend_minus_borrow_interest\n", + "\n", + " def collateral_usd(self):\n", + " return self.collateral_eth * self.market_price\n", + "\n", + " def update_debt(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.debt = self.debt + self.borrowing_fees\n", + "\n", + " def update_collateral(self):\n", + " \"\"\"\n", + " it requires having called lending_fees_calc() in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.collateral_eth = self.collateral_eth + self.lending_fees_eth\n", + " self.collateral_usdc = self.collateral_usd()\n", + "\n", + " def track_lend_borrow_interest(self):\n", + " \"\"\"\n", + " it requires having called borrowing_fees_calc() and lending_fees_calc()\n", + " in order to use updated values of last earned fees\n", + " \"\"\"\n", + " self.lend_minus_borrow_interest = self.interest_on_lending_usd - self.interest_on_borrowing\n", + "\n", + " def lending_fees_calc(self, freq):\n", + " self.simulate_lending_rate()\n", + " self.lending_rate_freq = self.lending_rate / freq\n", + "\n", + " # fees from lending are added to collateral? YES\n", + " # lending rate is applied to coll+lend fees every time or just to initial coll? COLL+LEND ie LAST VALUE\n", + " self.lending_fees_eth = self.collateral_eth * self.lending_rate_freq\n", + " self.lending_fees_usd = self.lending_fees_eth * self.market_price\n", + " self.interest_on_lending_eth = self.interest_on_lending_eth + self.lending_fees_eth\n", + " self.interest_on_lending_usd = self.interest_on_lending_usd + self.lending_fees_usd\n", + "\n", + " def borrowing_fees_calc(self, freq):\n", + " self.simulate_borrowing_rate()\n", + " self.borrowing_rate_freq = self.borrowing_rate / freq\n", + "\n", + " # fees from borrow are added to debt? YES\n", + " # borrowing rate is applied to debt+borrow fees every time or just to initial debt? DEBT+BORROW ie LAST VALUE\n", + " self.borrowing_fees = self.debt * self.borrowing_rate_freq\n", + " self.interest_on_borrowing = self.interest_on_borrowing + self.borrowing_fees\n", + "\n", + " def simulate_lending_rate(self):\n", + " # self.lending_rate = round(random.choice(list(np.arange(0.5/100, 1.5/100, 0.25/100))), 6) # config['lending_rate']\n", + "\n", + " # best case\n", + " # self.lending_rate = 1.5 / 100\n", + "\n", + " # worst case\n", + " self.lending_rate = 0.5 / 100\n", + "\n", + " def simulate_borrowing_rate(self):\n", + " # self.borrowing_rate = round(random.choice(list(np.arange(1.5/100, 2.5/100, 0.25/100))), 6) # config['borrowing_rate']\n", + "\n", + " # best case\n", + " # self.borrowing_rate = 1.5/100\n", + "\n", + " # worst case\n", + " self.borrowing_rate = 2.5/100\n", + "\n", + " def ltv_calc(self):\n", + " if self.collateral_usd() == 0:\n", + " return 0\n", + " else:\n", + " return self.debt / self.collateral_usd()\n", + "\n", + " def price_to_liquidation(self, dydx_class_instance):\n", + " return self.entry_price - (dydx_class_instance.pnl()\n", + " + self.debt - self.lend_minus_borrow_interest) / self.collateral_eth\n", + "\n", + " def price_to_ltv_limit_calc(self):\n", + " return round(self.entry_price * self.borrowed_percentage / self.ltv_limit(), 3)\n", + "\n", + " def buffer_for_repay(self):\n", + " return 0.01\n", + "\n", + " def ltv_limit(self):\n", + " return 0.5\n", + "\n", + " # Actions to take\n", + " def return_usdc(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " time = 0\n", + " if self.usdc_status:\n", + " # simulate 2min delay for tx\n", + " # update parameters\n", + " # AAVE parameters\n", + " self.usdc_status = False\n", + " # self.collateral_eth = 0\n", + " # self.collateral_usdc = 0\n", + " self.debt = 0\n", + " self.ltv = 0\n", + " self.price_to_ltv_limit = 0\n", + " # self.lending_rate = 0\n", + " # self.borrowing_rate = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + " def repay_aave(self, stgy_instance):\n", + " gas_fees = stgy_instance.gas_fees\n", + " dydx_class_instance = stgy_instance.dydx\n", + " # aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " #\n", + " time = 0\n", + " if self.usdc_status:\n", + " # update parameters\n", + " short_size_for_debt = self.debt / (self.market_price - dydx_class_instance.entry_price)\n", + " new_short_size = dydx_class_instance.short_size - short_size_for_debt\n", + "\n", + " # pnl_for_debt = dydx_class_instance.pnl()\n", + " # We have to repeat the calculations for pnl and notional methods, but using different size_eth\n", + " pnl_for_debt = short_size_for_debt * (self.market_price - dydx_class_instance.entry_price)\n", + " self.debt = self.debt - pnl_for_debt\n", + " self.ltv = self.ltv_calc()\n", + "\n", + " self.price_to_ltv_limit = round(self.entry_price * (self.debt / self.collateral_usdc) / self.ltv_limit(), 3)\n", + " self.costs = self.costs + gas_fees\n", + "\n", + " dydx_class_instance.short_size = new_short_size\n", + " dydx_class_instance.notional = dydx_class_instance.notional_calc()\n", + " dydx_class_instance.equity = dydx_class_instance.equity_calc()\n", + " dydx_class_instance.leverage = dydx_class_instance.leverage_calc()\n", + " dydx_class_instance.pnl = dydx_class_instance.pnl_calc()\n", + " # dydx_class_instance.price_to_liquidation = \\\n", + " # dydx_class_instance.price_to_liquidation_calc(dydx_client_class_instance)\n", + "\n", + " # fees\n", + " # withdrawal_fees = pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + " dydx_class_instance.simulate_maker_taker_fees()\n", + " notional_for_fees = abs(short_size_for_debt) * self.market_price\n", + " dydx_class_instance.costs = dydx_class_instance.costs \\\n", + " + dydx_class_instance.maker_taker_fees * notional_for_fees \\\n", + " + pnl_for_debt * dydx_class_instance.withdrawal_fees\n", + "\n", + " # Note that a negative self.debt is actually a profit\n", + " # We update the parameters\n", + " if self.debt > 0:\n", + " self.usdc_status = True\n", + " else:\n", + " self.usdc_status = False\n", + " # simulate 2min delay for tx\n", + " time = 1\n", + " return time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### DyDx" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Dydx(object):\n", + "\n", + " def __init__(self, config):\n", + " # assert aave_class == isinstance(aave)\n", + " self.market_price = config['market_price']\n", + " \n", + " self.entry_price = config['entry_price']\n", + " self.short_size = config['short_size']\n", + " self.collateral = config['collateral']\n", + " self.notional = config['notional']\n", + " self.equity = config['equity']\n", + " self.leverage = config['leverage']\n", + " self.pnl = config['pnl']\n", + " # self.price_to_liquidation = config['price_to_liquidation']\n", + " self.collateral_status = config['collateral_status']\n", + " self.short_status = config['short_status']\n", + " self.order_status = True\n", + " self.withdrawal_fees = 0.01/100\n", + " self.funding_rates = 0\n", + " self.maker_taker_fees = 0\n", + " self.maker_fees_counter = 0\n", + " self.costs = 0\n", + "\n", + " # auxiliary functions\n", + " def pnl_calc(self):\n", + " return self.short_size * (self.market_price-self.entry_price)\n", + "\n", + " def notional_calc(self):\n", + " return abs(self.short_size)*self.market_price\n", + "\n", + " def equity_calc(self):\n", + " return self.collateral + self.pnl_calc()\n", + "\n", + " def leverage_calc(self):\n", + " if self.equity_calc() == 0:\n", + " return 0\n", + " else:\n", + " return self.notional_calc() / self.equity_calc()\n", + "\n", + " def price_to_repay_aave_debt_calc(self, pcg_of_debt_to_cover, aave_class_instance):\n", + " return self.entry_price \\\n", + " + aave_class_instance.debt * pcg_of_debt_to_cover / self.short_size\n", + "\n", + " @staticmethod\n", + " def price_to_liquidation_calc(dydx_client_class_instance):\n", + " return dydx_client_class_instance.dydx_margin_parameters[\"liquidation_price\"]\n", + "\n", + " def add_funding_rates(self):\n", + " self.simulate_funding_rates()\n", + " self.costs = self.costs - self.funding_rates * self.notional\n", + "\n", + " def simulate_funding_rates(self):\n", + " # self.funding_rates = round(random.choice(list(np.arange(-0.0075/100, 0.0075/100, 0.0005/100))), 6)\n", + "\n", + " # best case\n", + " # self.funding_rates = 0.0075 / 100\n", + "\n", + " # average -0.00443%\n", + "\n", + " # worst case\n", + " self.funding_rates = -0.0075 / 100\n", + "\n", + " def simulate_maker_taker_fees(self):\n", + " # We add a counter for how many times we call this function\n", + " # i.e. how many times we open and close the short\n", + " self.maker_fees_counter += 1\n", + " # self.maker_taker_fees = round(random.choice(list(np.arange(0.01/100, 0.035/100, 0.0025/100))), 6)\n", + " \n", + " # maker fees\n", + " self.maker_taker_fees = 0.05 / 100 # <1M\n", + " # self.maker_taker_fees = 0.04 / 100 # <5M\n", + " # self.maker_taker_fees = 0.035 / 100 # <10M\n", + " # self.maker_taker_fees = 0.03 / 100 # <50M\n", + " # self.maker_taker_fees = 0.025 / 100 # <200M\n", + " # self.maker_taker_fees = 0.02 / 100 # >200M\n", + "\n", + " # Actions to take\n", + " def remove_collateral(self, stgy_instance):\n", + " self.cancel_order()\n", + " time = 0\n", + " if self.collateral_status:\n", + " self.collateral_status = False\n", + " withdrawal_fees = self.collateral * self.withdrawal_fees\n", + " self.collateral = 0\n", + " # self.price_to_liquidation = 0\n", + "\n", + " # fees\n", + " self.costs = self.costs + withdrawal_fees\n", + "\n", + " time = 1\n", + " return time\n", + "\n", + "\n", + " def open_short(self, stgy_instance):\n", + " aave_class_instance = stgy_instance.aave\n", + " # dydx_client_class_instance = stgy_instance.dydx_client\n", + " if (not self.short_status) and self.order_status:\n", + " self.short_status = True\n", + " # dydx parameters\n", + " # if self.market_price <= stgy_instance.trigger_prices['floor']:\n", + " # print(\"CAUTION: OPEN PRICE LESS OR EQUAL TO FLOOR!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['floor'] - self.market_price)\n", + "\n", + " # if self.market_price <= stgy_instance.trigger_prices['open_close']:\n", + " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", + " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", + " self.entry_price = self.market_price\n", + " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " # self.collateral = aave_class_instance.debt_initial\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " # Simulate maker taker fees\n", + " self.simulate_maker_taker_fees()\n", + " # Add costs\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + "\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(1 + aave_class_instance.buffer_for_repay(),\n", + " aave_class_instance)\n", + " # stgy_instance.trigger_prices['ltv_limit'] = price_to_ltv_limit\n", + " i = 0\n", + " while stgy_instance.trigger_prices['ltv_limit'] > stgy_instance.trigger_prices['repay_aave']:\n", + " print(\"CAUTION: P_ltv > P_repay\")\n", + " print(\"Difference of: \", stgy_instance.trigger_prices['ltv_limit'] - stgy_instance.trigger_prices['repay_aave'])\n", + " stgy_instance.trigger_prices['repay_aave'] = self.price_to_repay_aave_debt_calc(0.5, aave_class_instance)\n", + " i += 1\n", + " print(\"P_repay defined to repay 0.5 (half) of debt. This logic was repeated\" + str(i) + \" times.\")\n", + " self.order_status = False\n", + " return 0\n", + "\n", + " def close_short(self, stgy_instance):\n", + " if self.short_status:\n", + " # Next if is to move up the threshold if we didnt execute at exactly open_close\n", + " # if self.market_price >= stgy_instance.trigger_prices['open_close']:\n", + " # # new_open_close = self.market_price\n", + " # print(\"CAUTION: SHORT CLOSED AT A PRICE GREATER OR EQUAL TO CLOSE_SHORT!\")\n", + " # print(\"Difference of: \", self.market_price - stgy_instance.trigger_prices['open_close'])\n", + " # stgy_instance.target_prices['open_close'] = self.market_price\n", + " self.notional = self.notional_calc()\n", + " self.equity = self.equity_calc()\n", + " self.leverage = self.leverage_calc()\n", + " self.pnl = self.pnl_calc()\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl + self.pnl\n", + " # We update short parameters after the calculation of pnl\n", + " self.entry_price = 0\n", + " self.short_status = False\n", + " self.short_size = 0\n", + " self.simulate_maker_taker_fees()\n", + " self.costs = self.costs + self.maker_taker_fees * self.notional\n", + " self.place_order(stgy_instance.trigger_prices['open_close'])\n", + " return 0\n", + "\n", + " def place_order(self, price):\n", + " self.order_status = True\n", + " # self.\n", + "\n", + " def cancel_order(self):\n", + " self.order_status = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## ParameterManager Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module is in charge of defining trigger points and intervals, updating parameters given a new price, and fining/executing the needed actions." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterManager(object):\n", + " # auxiliary functions\n", + " @staticmethod\n", + " def define_target_prices(stgy_instance, slippage, vol, floor):\n", + " mu = vol[0]\n", + " sigma = vol[1]\n", + " p_open_close = floor * (1+slippage) * (1+mu+2*sigma)\n", + " # p_trailing = p_open_close * (1-trailing) # We dont use this trailing initially but we need to define it anyway in order to have the interval defined\n", + " ##########################################################\n", + " # We define the intervals\n", + " list_of_triggers = [\"open_close\",\n", + " \"floor\",\n", + " \"ltv_limit\"]\n", + " list_of_trigger_prices = [p_open_close,\n", + " floor,\n", + " # p_trailing, \n", + " stgy_instance.aave.price_to_ltv_limit]\n", + " # We define/update trigger prices\n", + " for i in range(len(list_of_triggers)):\n", + " trigger_name = list_of_triggers[i]\n", + " trigger_price = list_of_trigger_prices[i]\n", + " stgy_instance.trigger_prices[trigger_name] = trigger_price\n", + "\n", + " @staticmethod\n", + " def find_oc(current_oc, ocs, vol):\n", + " mu, sigma = vol\n", + " oc_up = current_oc * (1+slippage)*(1+mu+2*sigma)\n", + " oc_down = current_oc * (1+slippage)*(1+mu-2*sigma)\n", + " distances = []\n", + " next_oc_up = []\n", + " next_oc_down = []\n", + " for i in range(len(ocs)):\n", + " oci = ocs[i]\n", + " if oc_up < oci:\n", + " next_oc_up.append(oci)\n", + " # ocs['up'].append(oci)\n", + " elif oc_down > oci:\n", + " next_oc_down.append(oci)\n", + " # ocs['down'].append(oci)\n", + " distances.append(current_oc-oci)\n", + " # If we get here then we didnt return anything, so we return the farthest oc\n", + " # Furthest down (positive distance current_oc > oci)\n", + " max_value = max(distances)\n", + " max_index = distances.index(max_value)\n", + " # Furthest up (negative distance current_oc < oci)\n", + " min_value = min(distances)\n", + " min_index = distances.index(min_value)\n", + " # print(next_oc_up)\n", + " # print(next_oc_down)\n", + " return {'up_choices': next_oc_up,\n", + " 'down_choices': next_oc_down,\n", + " 'max_distance_up': ocs[min_index],\n", + " 'max_distance_down': ocs[max_index]}\n", + " \n", + " @staticmethod\n", + " def calc_vol(last_date, data):\n", + " periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + " last_six_months = data.loc[:last_date][-periods_for_vol[0]:]\n", + " for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(last_six_months[-N:]['close']) - np.log(last_six_months[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + " sigma = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + " vol = [mu, sigma]\n", + " return vol\n", + " \n", + " @staticmethod\n", + " # Checking and updating data\n", + " def update_parameters(stgy_instance, new_market_price):\n", + " # AAVE\n", + " stgy_instance.aave.market_price = new_market_price\n", + " # Before updating collateral and debt we have to calculate last earned fees + update interests earned until now\n", + " # As we are using hourly data we have to convert anual rate interest into hourly interest, therefore freq=365*24\n", + " stgy_instance.aave.lending_fees_calc(freq=365 * 24 * 60)\n", + " stgy_instance.aave.borrowing_fees_calc(freq=365 * 24 * 60)\n", + " # We have to execute track_ first because we need the fees for current collateral and debt values\n", + " stgy_instance.aave.track_lend_borrow_interest()\n", + " # stgy_instance.aave.update_costs() # we add lend_borrow_interest to costs\n", + " stgy_instance.aave.update_debt() # we add the last borrowing fees to the debt\n", + " stgy_instance.aave.update_collateral() # we add the last lending fees to the collateral and update both eth and usd values\n", + " stgy_instance.aave.ltv = stgy_instance.aave.ltv_calc()\n", + "\n", + " # DYDX\n", + " stgy_instance.dydx.market_price = new_market_price\n", + " stgy_instance.dydx.notional = stgy_instance.dydx.notional_calc()\n", + " stgy_instance.dydx.equity = stgy_instance.dydx.equity_calc()\n", + " stgy_instance.dydx.leverage = stgy_instance.dydx.leverage_calc()\n", + " stgy_instance.dydx.pnl = stgy_instance.dydx.pnl_calc()\n", + " # stgy_instance.dydx.price_to_liquidation = stgy_instance.dydx.price_to_liquidation_calc(stgy_instance.dydx_client)\n", + "\n", + " @staticmethod\n", + " def reset_costs(stgy_instance):\n", + " # We reset the costs in order to always start in 0\n", + " stgy_instance.aave.costs = 0\n", + " stgy_instance.dydx.costs = 0\n", + " \n", + " \n", + " def find_scenario(self, stgy_instance, market_price, previous_market_price, index):\n", + " actions = self.actions_to_take(stgy_instance, market_price, previous_market_price)\n", + " self.simulate_fees(stgy_instance)\n", + " time = 0\n", + " time_aave = 0\n", + " time_dydx = 0\n", + " for action in actions:\n", + " if action == \"borrow_usdc_n_add_coll\":\n", + " time_aave = stgy_instance.aave.borrow_usdc(stgy_instance)\n", + " market_price = stgy_instance.historical_data[\"close\"][index + time_aave]\n", + " time_dydx = stgy_instance.dydx.add_collateral(stgy_instance)\n", + " time_aave = 0\n", + " elif action in stgy_instance.aave_features[\"methods\"]:\n", + " time_aave = getattr(stgy_instance.aave, action)(stgy_instance)\n", + " elif action in stgy_instance.dydx_features[\"methods\"]:\n", + " time_dydx = getattr(stgy_instance.dydx, action)(stgy_instance)\n", + " time += time_aave + time_dydx\n", + " # print(stgy_instance.aave_features[\"methods\"])\n", + " # print(stgy_instance.dydx_features[\"methods\"])\n", + " return time\n", + " # stgy_instance.append(action)\n", + "\n", + " @staticmethod\n", + " def actions_to_take(stgy_instance, market_price, previous_market_price):\n", + " actions = []\n", + " \n", + " # Case P decreasing: \n", + " # We need to ask both P_t-1 > trigger and trigger > P_t bc if we only ask the later we will execute\n", + " # the action for all prices below trigger. Same logic for Case P increasing.\n", + "# if (previous_market_price >= stgy_instance.trigger_prices['open_close']) and \\\n", + "# (stgy_instance.trigger_prices['open_close'] > market_price):\n", + "# actions.append('open_short')\n", + " \n", + "# elif (previous_market_price >= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + "# (stgy_instance.trigger_prices['trailing_stop'] > market_price):\n", + "# actions.append('open_short')\n", + " \n", + " if stgy_instance.dydx.short_status:\n", + " if (previous_market_price >= stgy_instance.trigger_prices['repay_aave']) and \\\n", + " (stgy_instance.trigger_prices['repay_aave'] > market_price):\n", + " actions.append('repay_aave')\n", + " \n", + " \n", + " # Case P increasing\n", + " # if (previous_market_price <= stgy_instance.trigger_prices['open_close']) and \\\n", + " # (stgy_instance.trigger_prices['open_close'] < market_price):\n", + " # actions.append('close_short')\n", + " # if (previous_market_price <= stgy_instance.trigger_prices['trailing_stop']) and \\\n", + " # (stgy_instance.trigger_prices['trailing_stop'] < market_price):\n", + " # actions.append('close_short')\n", + " \n", + " return actions\n", + "\n", + " @staticmethod\n", + " def simulate_fees(stgy_instance):\n", + " # stgy_instance.gas_fees = round(random.choice(list(np.arange(1, 10, 0.5))), 6)\n", + "\n", + " # best case\n", + " # stgy_instance.gas_fees = 1\n", + "\n", + " # stgy_instance.gas_fees = 3\n", + "\n", + " # stgy_instance.gas_fees = 6\n", + "\n", + " # worst case\n", + " stgy_instance.gas_fees = 10\n", + "\n", + " @staticmethod\n", + " def update_pnl(stgy_instance):\n", + " stgy_instance.total_pnl = stgy_instance.total_pnl - stgy_instance.aave.costs - stgy_instance.dydx.costs + stgy_instance.aave.lending_fees_usd - stgy_instance.aave.borrowing_fees\n", + "\n", + " @staticmethod\n", + " def add_costs(stgy_instance):\n", + " stgy_instance.total_costs_from_aave_n_dydx = stgy_instance.total_costs_from_aave_n_dydx \\\n", + " + stgy_instance.aave.costs + stgy_instance.dydx.costs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## DataDamperNPlotter Module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This module will write the results and is also used for plotting (for analysis porpuses)." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "class DataDamperNPlotter:\n", + " def __init__(self):\n", + " self.historical_data = None\n", + "\n", + " @staticmethod\n", + " def write_data(stgy_instance, previous_price,\n", + " date, file_location,\n", + " sheet=False):\n", + " aave_instance = stgy_instance.aave\n", + " dydx_instance = stgy_instance.dydx\n", + " data_aave = []\n", + " data_dydx = []\n", + " aave_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\"]\n", + " dydx_wanted_keys = [\n", + " \"market_price\",\n", + " \"entry_price\",\n", + " # \"short_size\",\n", + " # \"collateral\",\n", + " \"notional\",\n", + " # \"equity\",\n", + " # \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " # \"collateral_status\",\n", + " \"short_status\",\n", + " # \"order_status\",\n", + " # \"withdrawal_fees\",\n", + " # \"funding_rates\",\n", + " # \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\"]\n", + " # \"gas_fees\"]\n", + "\n", + " \n", + " data_aave.append(date)\n", + " data_dydx.append(date)\n", + " for i in range(len(aave_instance.__dict__.values())):\n", + " if list(aave_instance.__dict__.keys())[i] in aave_wanted_keys:\n", + " if list(aave_instance.__dict__.keys())[i] == \"market_price\":\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " data_aave.append(previous_price)\n", + " # data_aave.append(stgy_instance.trigger_prices['open_close'])\n", + " # data_aave.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " else:\n", + " # print(list(aave_instance.__dict__.keys())[i])\n", + " data_aave.append(str(list(aave_instance.__dict__.values())[i]))\n", + " for i in range(len(dydx_instance.__dict__.values())):\n", + " if list(dydx_instance.__dict__.keys())[i] in dydx_wanted_keys:\n", + " if list(dydx_instance.__dict__.keys())[i] == \"market_price\":\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " data_dydx.append(previous_price)\n", + " # data_dydx.append(stgy_instance.trigger_prices['open_close'])\n", + " # data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", + " else:\n", + " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", + " # We add the index number of the appareance of market price in historical_data.csv order to find useful test values quicker\n", + " data_aave.append(stgy_instance.gas_fees)\n", + " data_aave.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_aave.append(stgy_instance.total_pnl)\n", + " # data_aave.append(mkt_price_index)\n", + "\n", + "\n", + " # data_dydx.append(stgy_instance.gas_fees)\n", + " data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", + " data_dydx.append(stgy_instance.total_pnl)\n", + " data_dydx.append(stgy_instance.total_pnl + stgy_instance.dydx.pnl)\n", + " # data_dydx.append(mkt_price_index)\n", + " # print(interval_old.name)\n", + "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", + " if sheet == True:\n", + " gc = pygsheets.authorize(service_file=\n", + " 'stgy-1-simulations-e0ee0453ddf8.json')\n", + " sh = gc.open('aave/dydx simulations')\n", + " sh[0].append_table(data_aave, end=None, dimension='ROWS', overwrite=False)\n", + " sh[1].append_table(data_dydx, end=None, dimension='ROWS', overwrite=False)\n", + " else:\n", + " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = file_location + 'dydx_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " # with open(path_to_aave, 'a') as file:\n", + " # writer = csv.writer(file, lineterminator='\\n')\n", + " # writer.writerow(data_aave)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(data_dydx)\n", + "\n", + " @staticmethod\n", + " def delete_results(stgy_instance, file_location):\n", + " file_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " file_dydx = file_location + 'dydx_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " if (os.path.exists(file_aave) and os.path.isfile(file_aave)):\n", + " os.remove(file_aave)\n", + " if (os.path.exists(file_dydx) and os.path.isfile(file_dydx)):\n", + " os.remove(file_dydx)\n", + "\n", + " @staticmethod\n", + " def add_header(stgy_instance, file_location):\n", + " aave_headers = [\n", + " \"date\",\n", + " \"market_price\",\n", + " \"previous_price\",\n", + " # \"open_close\",\n", + " # \"trailing_stop\",\n", + " \"entry_price\",\n", + " \"collateral_eth\",\n", + " \"usdc_status\",\n", + " \"debt\",\n", + " \"ltv\",\n", + " \"lending_rate\",\n", + " \"interest_on_lending_usd\",\n", + " \"borrowing_rate\",\n", + " \"interest_on_borrowing\",\n", + " \"lend_minus_borrow_interest\",\n", + " \"costs\",\n", + " \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_stgy_pnl\"]\n", + " # \"index_of_mkt_price\"]\n", + " dydx_headers = [\n", + " \"date\",\n", + " \"market_price\",\n", + " \"previous_price\",\n", + " # \"open_close\",\n", + " # \"trailing_stop\",\n", + " \"entry_price\",\n", + " # \"short_size\",\n", + " # \"collateral\",\n", + " \"notional\",\n", + " # \"equity\",\n", + " # \"leverage\",\n", + " \"pnl\",\n", + " # \"price_to_liquidation\",\n", + " # \"collateral_status\",\n", + " \"short_status\",\n", + " # \"order_status\",\n", + " # \"withdrawal_fees\",\n", + " # \"funding_rates\",\n", + " # \"maker_taker_fees\",\n", + " \"maker_fees_counter\",\n", + " \"costs\",\n", + " # \"gas_fees\",\n", + " \"total_costs_from_aave_n_dydx\",\n", + " \"total_realised_pnl\",\n", + " \"total_unrealised_pnl\"]\n", + " # \"index_of_mkt_price\"]\n", + " \n", + " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " path_to_dydx = file_location + 'dydx_results.csv' #'Files/Tests/From_%s_to_%s_open_close_at_%s/dydx_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", + " # with open(path_to_aave, 'a') as file:\n", + " # writer = csv.writer(file, lineterminator='\\n')\n", + " # writer.writerow(aave_headers)\n", + " with open(path_to_dydx, 'a',\n", + " newline='', encoding='utf-8') as file:\n", + " writer = csv.writer(file, lineterminator='\\n')\n", + " writer.writerow(dydx_headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all lets read the dataset containing prices for ETH in minutes basis from 2019-09-01 to 2022-09-01." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Track historical data\n", + "# symbol = 'ETHUSDC'\n", + "# freq = '1m'\n", + "# initial_date = \"1 Jan 2019\"\n", + "# stgy.get_historical_data(symbol=symbol, freq=freq,\n", + "# initial_date=initial_date, save=True)\n", + "\n", + "# Load historical data if previously tracked and saved\n", + "\n", + "historical_data = pd.read_csv(\"Files/ETHUSDC-1m-data_since_1 Sep 2019.csv\")\n", + "# # assign data to stgy instance + define index as dates\n", + "timestamp = pd.to_datetime(historical_data['timestamp'])\n", + "historical_data = pd.DataFrame(historical_data[\"close\"], columns=['close'])\n", + "historical_data.index = timestamp\n", + "#\n", + "# #######################################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to test pnl/costs of the whole strategy let's find a period of time and a relevant price (i.e. a price that is crossed many times)." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-11-01\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's analyze historical 6month weighted volatility to check if 5% is enough space to move between OCs. We will compare \n", + "$$5\\% \\text{ vs } (1+slippgae)(1+\\mu+2\\sigma),$$\n", + "where $\\sigma=vol$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First we calculate weighted vol\n", + "last_date = \"2021-06-01\"\n", + "slippage = 0.0005\n", + "periods_for_vol = [6*30*24*60, 3*30*24*60, 1*30*24*60]\n", + "data = historical_data.loc[:last_date][-periods_for_vol[0]-3*60:-3*60]\n", + "for i in range(len(periods_for_vol)):\n", + " N = periods_for_vol[i]\n", + " log_returns = np.log(data[-N:]['close']) - np.log(data[-N:]['close'].shift(1))\n", + " globals()['sigma_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + " globals()['mu_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().mean()\n", + " globals()['mu_max_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().max()\n", + " globals()['mu_min_'+str(i)] = log_returns.ewm(alpha=0.8, adjust=False).mean().min()\n", + "vol = sigma_0 * 0.1 + sigma_1 * 0.3 + sigma_2 * 0.6\n", + "mu = mu_0 * 0.1 + mu_1 * 0.3 + mu_2 * 0.6\n", + "print(\"weighted mu: \", str(mu*100)+'%')\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"[min_6m_change, max_6m_change]: \", [str(mu_min_0*100)+'%', str(mu_max_0*100)+'%'])\n", + "print(\"avg movement: (1+slip)(1+mu+2vol): \", str((1+slippage)*(1+mu+2*vol)*100-100)+'%')\n", + "# vol, mu, mu_max_0, mu_min_0, mu_0, (1+slippage)*(1+mu+2*vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = sigma_2\n", + "mu = mu_2\n", + "print(\"weighted sigmas: \", str(vol*100)+'%')\n", + "print(\"avg movement: (1+mu+2vol): \", str((1+mu+2*vol)*100-100)+'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We conclude that 5% is several times higher than the common movement of price within 1 minute, so we should have spaced enough OCs to choose if we executed too many txs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# normal_std = std\n", + "# medium_std = 2*std\n", + "# high_std = 4*std\n", + "# extreme_std = 6*std\n", + "# normal_std, medium_std, high_std, extreme_std" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's find such a relevant price manually by taking a look at the price plot." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-31\",\"2020-06-07\"]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "axs.axhline(y=247.2, color='red', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define a function that will\n", + "- Initiallize the main module + loading the data + definning the floor in a way that the open_close we get is the relevant price previously mentioned + define trigger_prices\n", + "- Create a new directory \"Files/Tests/From_\"from period\"_to_\"to period\"_open_close_at_\"relevant price\" + save the historical_data with the intervals of every price added\n", + "- Initiallize all the parameters for both protocols + add the trigger point price_to_ltv_limit \n", + "- Call data_dumper to create aave_results.csv and dydx_results.csv only with the headers\n", + "- Run through the code executing everything as discussed in the dev doc.\n", + "\n", + "This function is useful because we can run simulations for different periods of times and relevant prices (just by using a list of periods and relevant prices and looping thorugh it) and saving the results in descriptive directories." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(stk, period, open_close, slippage, oc_increment, file_location):\n", + " global ocs\n", + " # Initialize everything\n", + " with open(\"Files/StgyApp_config.json\") as json_file:\n", + " config = json.load(json_file)\n", + "\n", + " # Initialize stgyApp\n", + " stgy = StgyApp(config)\n", + " # Period of Simulations\n", + " # period = [\"2019-09-01\",\"2019-12-31\"]\n", + " stgy.historical_data = historical_data.loc[period[0]:period[1]]\n", + " # For vol updates we take all data up to the last date\n", + " stgy.launch(config)\n", + " # First we calculate weighted vol\n", + " last_date = period[1]\n", + " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", + " mu, sigma = vol\n", + " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", + " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + " # Now we define prices \n", + " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor)\n", + " #########################\n", + " # Save historical data with trigger prices and thresholds loaded\n", + " # checking if the directory demo_folder \n", + " # exist or not.\n", + " if not os.path.exists(file_location):#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", + " # if the demo_folder directory is not present \n", + " # then create it.\n", + " os.makedirs(file_location)\n", + " # stgy.historical_data.to_csv(\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", + " # % (period[0], period[1], open_close))\n", + " #########################\n", + " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", + "\n", + " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", + " # As we calculate vol using first week of data, we initialize simulations from that week on\n", + " initial_index = 1\n", + "\n", + " # Stk eth\n", + " stgy.stk = stk/stgy.historical_data['close'][initial_index]\n", + "\n", + " # AAVE\n", + " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", + "\n", + " # What is the price at which we place the collateral in AAVE given our initial_index?\n", + " stgy.aave.entry_price = stgy.aave.market_price\n", + " # We place 90% of staked as collateral and save 10% as a reserve margin\n", + " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", + " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", + " stgy.reserve_margin_eth = stgy.stk * 0.1\n", + " # We calculate collateral and reserve current value\n", + " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", + " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", + "\n", + " # What is the usdc_status for our initial_index?\n", + " stgy.aave.usdc_status = True\n", + " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", + " # debt_initial\n", + " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", + " # stgy.total_costs = 104\n", + "\n", + " # DyDx\n", + " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", + " stgy.dydx.collateral = stgy.aave.debt\n", + " stgy.dydx.equity = stgy.dydx.equity_calc()\n", + " stgy.dydx.collateral_status = True\n", + " \n", + " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", + " if (stgy.dydx.market_price <= stgy.trigger_prices['open_close']):\n", + " print(\"Short position should be open for this first price!...breaking\")\n", + " return None\n", + " #########################\n", + " # Clear previous csv data for aave and dydx\n", + " stgy.data_dumper.delete_results(stgy, file_location)\n", + " #########################\n", + " # add header to csv of aave and dydx\n", + " stgy.data_dumper.add_header(stgy, file_location)\n", + " ##################################\n", + " # Run through dataset\n", + " #########################\n", + " # import time\n", + " # # run simulations\n", + " # starttime = time.time()\n", + " # print('starttime:', starttime)\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " i = initial_index\n", + "\n", + " maker_fees_counter = []\n", + " open_closes = [open_close]\n", + " while(i < len(stgy.historical_data)):\n", + " # for i in range(initial_index, len(stgy.historical_data)):\n", + " # pass\n", + " # We reset costs in every instance\n", + " stgy.parameter_manager.reset_costs(stgy)\n", + " market_price = stgy.historical_data[\"close\"][i]\n", + " previous_price = stgy.historical_data[\"close\"][i-1]\n", + " #########################\n", + " # Update parameters\n", + " # First we update everything in order to execute scenarios with updated values\n", + " # We have to update\n", + " # AAVE: market_price, lending and borrowing fees (and the diference),\n", + " # debt value, collateral value and ltv value\n", + " # DyDx: market_price, notional, equity, leverage and pnl\n", + " stgy.parameter_manager.update_parameters(stgy, market_price)\n", + " \n", + " # We clean for duplicates\n", + " # open_closes = list(dict.fromkeys(open_closes))\n", + " # if (previous_price >= open_closes[oc_number]) and (open_closes[oc_number] > market_price):\n", + " # stgy.dydx.open_short()\n", + " # elif (previous_price <= open_closes[oc_number]) and (open_closes[oc_number] < market_price):\n", + " # stgy.dydx.close_short()\n", + " # open_close_2 = open_close * (1-0.004)\n", + " # open_closes.append(open_close_2)\n", + " \n", + " for open_close in open_closes:\n", + " if (previous_price >= open_close) and (open_close > market_price):\n", + " stgy.dydx.open_short(stgy)\n", + " elif (previous_price <= open_close) and (open_close < market_price):\n", + " stgy.dydx.close_short(stgy)\n", + " new_open_close = round(open_close * (1-oc_increment),3)\n", + " if new_open_close not in open_closes:\n", + " open_closes.append(new_open_close)\n", + " \n", + " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", + " # and we execute all the actions involved between both (current and previous prices)\n", + " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", + " ########################\n", + " # Funding rates\n", + " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", + " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", + " # Calling it before find_scenarios will overwrite the funding by 0\n", + " # We have to check all the indexes between old index i and next index i+time_used\n", + " # for index in range(i, i+time_used):\n", + " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", + " stgy.dydx.add_funding_rates()\n", + " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", + " #########################\n", + " # Add costs\n", + " stgy.parameter_manager.add_costs(stgy)\n", + " stgy.parameter_manager.update_pnl(stgy)\n", + " #########################\n", + " # Write data\n", + " # We write the data into the google sheet or csv file acording to sheet value\n", + " # (sheet = True --> sheet, sheet = False --> csv)\n", + " current_date = str(stgy.historical_data.index[i])\n", + " stgy.data_dumper.write_data(stgy, previous_price,\n", + " current_date, file_location,\n", + " sheet=False)\n", + " #########################\n", + " # we increment index by the time consumed in executing actions\n", + " # i += time_used\n", + " i += 1\n", + " print(open_closes)\n", + " return stgy.dydx.maker_fees_counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a list with some periods of time and relevant prices to use for calling the previous function and run several simulations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Best cases 0 to 50 crosses\n", + "best_1_week = [[\"2022-04-02 00:00:00\",\"2022-04-09 00:00:00\"],3400]\n", + "best_1_month = [[\"2022-04-02 00:00:00\",\"2022-05-01 00:00:00\"],3400]\n", + "\n", + "# Normal cases 50 to 150 crosses\n", + "normal_1_week = [[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]\n", + "normal_1_month = [[[\"2020-05-31 00:00:00\",\"2020-06-30 00:00:00\"],240],\n", + " [[\"2021-12-01 00:00:00\",\"2022-01-01 00:00:00\"],historical_data['close'].max()*0.8]]\n", + "# Worst cases 150+ crosses\n", + "worst_1_week = [ [\"2019-10-26 05:00:00\",\"2019-11-02 00:00:00\"],183]\n", + "worst_1_month = [[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]\n", + "\n", + "worst_3_month = [ [[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]\n", + "worst_6_month = [ [[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2020-03-15 00:00:00\"],182]]\n", + "worst_1_year = [ [\"2019-09-01 00:00:00\",\"2020-09-01 00:00:00\"],170] " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "396" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Period of Simulations\n", + "periods_n_open_close = worst_3_month[1]\n", + "period = periods_n_open_close[0]\n", + "p = periods_n_open_close[1]\n", + "data_set = historical_data.loc[period[0]:period[1]]\n", + "crosses = cross_counter(data_set, p)\n", + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "\n", + "# worst_6_month = [[[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240]]\n", + "# worst_1_year = [[[\"2019-09-01 00:00:00\",\"2020-03-01 00:00:00\"],170]]\n", + "# periods_n_open_close = best_1_week\n", + "period = periods_n_open_close[0]\n", + "data = historical_data.loc[period[0]:period[1]]\n", + "parameter_manager = ParameterManager()\n", + "last_date = period[1]\n", + "vol = parameter_manager.calc_vol(last_date, data)\n", + "mu, sigma = vol\n", + "open_close = periods_n_open_close[1]\n", + "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", + "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=open_close, \n", + " color='green', \n", + " linestyle='--', \n", + " label='oc1='+str(round(open_close,3)))\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[182, 181.454, 180.91, 180.367, 179.826, 179.287, 178.749, 178.213, 177.678, 177.145, 176.614, 176.084, 175.556, 175.029, 174.504, 173.98, 173.458, 172.938, 172.419, 171.902, 171.386, 170.872, 170.359, 169.848, 169.338, 168.83, 168.324, 167.819, 167.316]\n", + "Max txs, Realised and Unrealised PnL for ( [oc_inc] = [0.003]) : [3211, '-193.415%', '-180.799%']\n", + "[182, 181.272, 180.547, 179.825, 179.106, 178.39, 177.676, 176.965, 176.257, 175.552, 174.85, 174.151, 173.454, 172.76, 172.069, 171.381, 170.695, 170.012, 169.332, 168.655, 167.98, 167.308, 166.639, 165.972, 165.308, 164.647]\n", + "Max txs, Realised and Unrealised PnL for ( [oc_inc] = [0.004]) : [2733, '-160.114%', '-149.075%']\n", + "[182, 180.726, 179.461, 178.205, 176.958, 175.719, 174.489, 173.268, 172.055, 170.851, 169.655, 168.467, 167.288, 166.117, 164.954]\n", + "Max txs, Realised and Unrealised PnL for ( [oc_inc] = [0.007]) : [1737, '-100.144%', '-88.876%']\n" + ] + } + ], + "source": [ + "stk = 1000000\n", + "oc_increments = [0.003, 0.004, 0.007]\n", + "maker_fees_counter_lengths = {}\n", + "realised_pnl_results = {}\n", + "unrealised_pnl_results = {}\n", + "total_results = []\n", + "# for period_n_open_close in periods_n_open_close:\n", + "for oc_increment in oc_increments:\n", + " period = periods_n_open_close[0]\n", + " open_close = periods_n_open_close[1]\n", + " slippage = 0.0005\n", + " directory = \"Files/Several_OCs_Tests/From_%s_to_%s_open_close_at_%s_[oc_incr]_[%s]/\" % (period[0], period[1], open_close, oc_increment)\n", + " maker_fees_counter = run_sim(stk, period, open_close, slippage, oc_increment, directory)\n", + " maker_fees_counter_lengths[\"[oc_inc] = \"+str([oc_increment])]=maker_fees_counter\n", + " dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)\n", + " realised_pnl_results[\"[oc_inc] = \"+str([oc_increment])]=dydx_results['total_realised_pnl'][len(dydx_results)-1]\n", + " unrealised_pnl_results[\"[oc_inc] = \"+str([oc_increment])]=dydx_results['total_realised_pnl'][len(dydx_results)-1]+dydx_results['pnl'][len(dydx_results)-1]\n", + " print(\"Max txs, Realised and Unrealised PnL for ( [oc_inc] = \"+str([oc_increment]) + \") :\", \n", + " [maker_fees_counter_lengths[\"[oc_inc] = \"+str([oc_increment])], \n", + " str(round(realised_pnl_results[\"[oc_inc] = \"+str([oc_increment])]/stk*100,3))+'%',\n", + " str(round(unrealised_pnl_results[\"[oc_inc] = \"+str([oc_increment])]/stk*100,3))+'%'])\n", + " total_results.append([maker_fees_counter_lengths, realised_pnl_results, unrealised_pnl_results])" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "price_jump_in_open = {}\n", + "price_jump_in_close = {}\n", + "\n", + "for i in range(len(dydx_results)-1):\n", + " if dydx_results['entry_price'][i]==0 and dydx_results['entry_price'][i+1]!=0:\n", + " price_jump_in_open[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)\n", + " elif dydx_results['entry_price'][i]!=0 and dydx_results['entry_price'][i+1]==0:\n", + " price_jump_in_close[str(dydx_results['date'][i])] = abs(dydx_results['market_price'][i+1] / dydx_results['market_price'][i]-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at open: 0.0042%\n", + "Mean price jump at open: 0.2203%\n", + "Max price jump at open: 4.383900000000001%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at open:\",str(round(min(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at open:\",str(round(np.mean(list(price_jump_in_open.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at open:\",str(round(max(list(price_jump_in_open.values())),6)*100)+\"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min price jump at close: 0.0042%\n", + "Mean price jump at close: 0.1929%\n", + "Max price jump at close: 3.9785%\n" + ] + } + ], + "source": [ + "print(\"Min price jump at close:\",str(round(min(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Mean price jump at close:\",str(round(np.mean(list(price_jump_in_close.values())),6)*100)+\"%\")\n", + "print(\"Max price jump at close:\",str(round(max(list(price_jump_in_close.values())),6)*100)+\"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Period of Simulations\n", + "periods_n_open_close = [[[\"2020-06-02\",\"2020-07-22\"],240]]\n", + "period = periods_n_open_close[0][0]\n", + "data = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "parameter_manager = ParameterManager()\n", + "last_date = period[1]+' 00:00:00'\n", + "vol = parameter_manager.calc_vol(last_date, data)\n", + "mu, sigma = vol\n", + "open_close = 243\n", + "# floor just in order to get triger_price['open_close_1'] = open_close_1\n", + "floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data['close'], color='tab:blue', label='market price')\n", + "axs.axhline(y=240, \n", + " color='green', \n", + " linestyle='--', \n", + " label='floor='+str(round(floor,3)))\n", + "axs.axhline(y=243, \n", + " color='red', \n", + " linestyle='--', \n", + " label='open_close='+str(round(open_close,3)))\n", + "# axs.axhline(y=p_open_close_2, color='darkgoldenrod', linestyle='--', label='open_close2')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07894394589673559" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['close'].pct_change(1*24*60).dropna().max()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-93714.29797685935" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "directory = \"From_2020-05-15_to_2020-06-15_open_close_at_240/dydx_results.csv\"\n", + "dydx_results = pd.read_csv(\"Files/Tests/\" + directory)\n", + "dydx_results['total_stgy_pnl'][len(dydx_results)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2020-05-01'" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "period" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-01 00:00:00'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(historical_data.index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "data = historical_data.loc[periods_n_open_close[0][0][0]+' 00:00:00':periods_n_open_close[0][0][1]+' 00:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "returns = data['close'].pct_change().dropna()\n", + "log_returns = np.log(data['close']) \\\n", + " - np.log(data['close'].shift(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "std_ema_log_returns = log_returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "std_ema_returns = returns.ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns = log_returns.mean()\n", + "mu_abs_log_returns = abs(log_returns).mean()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()\n", + "mu_log_returns_max = log_returns.max()\n", + "mu_log_returns_min = log_returns.min()\n", + "mu_returns = returns.mean()\n", + "mu_abs_returns = abs(returns).mean()\n", + "mu_returns_max = returns.max()\n", + "mu_returns_min = returns.min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mu_returns_max, mu_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "K = 3\n", + "condition = (mu_abs_log_returns-K*std_ema_log_returns= price > current_price:\n", + " crossed_down += 1\n", + " index_down.append(index-1)\n", + " return {'down':\n", + " {'crossed_down': crossed_down,\n", + " 'index_down': index_down},\n", + " 'up':\n", + " {'crossed_up': crossed_up,\n", + " 'index_up': index_up}}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Period of Simulations\n", + "period = [\"2020-05-01\",\"2020-09-01\"]\n", + "data_set = historical_data.loc[period[0]+' 00:00:00':period[1]+' 00:00:00']\n", + "price = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 1, figsize=(21, 7))\n", + "axs.plot(data_set['close'], color='tab:blue', label='market price')\n", + "# axs.axhline(floor, color='darkgoldenrod', linestyle='--', label='floor')\n", + "axs.axhline(y=240, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=185, color='red', linestyle='--', label='open_close')\n", + "# axs.axhline(y=390, color='red', linestyle='--', label='open_close')\n", + "axs.grid()\n", + "axs.legend(loc='lower left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosses = cross_counter(data_set, 240)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crosses['down']['crossed_down'] + crosses['up']['crossed_up']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(\"Files/Tests/From_2020-05-01_to_2020-09-01_open_close_at_240/dydx_results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "market_price 176910\n", + "I_current 176910\n", + "I_old 176910\n", + "entry_price 53220\n", + "short_size 53220\n", + "collateral 176910\n", + "notional 53375\n", + "equity 176910\n", + "leverage 53375\n", + "pnl 53066\n", + "collateral_status 176910\n", + "short_status 53220\n", + "order_status 123690\n", + "withdrawal_fees 176910\n", + "funding_rates 176910\n", + "maker_taker_fees 133516\n", + "maker_fees_counter 133516\n", + "costs 421\n", + "gas_fees 176910\n", + "total_costs_from_aave_n_dydx 133516\n", + "total_stgy_pnl 176910\n", + "index_of_mkt_price 176910\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dydx_results.astype(bool).sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function to count down in which rows of the results a maker_fee is added. This will be helpful to analize the moments in which we close the short (therefore being able to calculate close_price - entry_price) and to compare if the amount of maker_fees is equal to the times the relevant price is crosses (both should coincide). " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def count_maker_fees_increment(data_set):\n", + " index_of_maker_fee = []\n", + " for index in range(1,len(data_set)):\n", + " previous_maker_fee_counter = data_set['maker_fees_counter'][index-1]\n", + " current_maker_fee_counter = data_set['maker_fees_counter'][index]\n", + " if previous_maker_fee_counter < current_maker_fee_counter:\n", + " index_of_maker_fee.append(index)\n", + " return {'indexes': index_of_maker_fee}" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "results_maker_fee_counter= count_maker_fees_increment(dydx_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's count down how many indexes in which price crossed relevant price downwards coincide with indexes in which a maker fee was added. Same for price crossing relevant price upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "matches_up = 0\n", + "matches_down = 0\n", + "for index_up in crosses['up']['index_up']:\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " matches_up += 1\n", + "for index_down in crosses['down']['index_down']:\n", + " if index_down in results_maker_fee_counter['indexes']:\n", + " matches_down += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(155, 136, 291)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches_up, matches_down, matches_up + matches_down" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 156)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(crosses['up']['index_up']), len(crosses['down']['index_down'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So almost all indexes for which price goes above relevant price coincide with indexes in which a maker fee was added. It means that in order to get the rows in which we close the short, we can use index_up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now calculate the average value of close_price - entry_price to have a notion of for how much usually we miss and a notion of an average amount of loss coming from closing late." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all note that if we look at rows of results for indexes between [index_up -2, index_up+2] we realise that \n", + "- entry_price and short_size can be found at index_up -1\n", + "- close_price is market_price in index = index_up" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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market_priceI_currentI_oldshort_sizeentry_pricepnlmaker_fees_countertotal_stgy_pnl
43393240.70inftyminus_infty0.0000.000.00000-2.879624
43394239.74minus_inftyinfty-4334.634239.740.00001-522.470891
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43396240.86inftyminus_infty0.0000.000.00002-6246.222332
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" + ], + "text/plain": [ + " market_price I_current I_old short_size entry_price \\\n", + "43393 240.70 infty minus_infty 0.000 0.00 \n", + "43394 239.74 minus_infty infty -4334.634 239.74 \n", + "43395 240.94 infty minus_infty 0.000 0.00 \n", + "43396 240.86 infty minus_infty 0.000 0.00 \n", + "\n", + " pnl maker_fees_counter total_stgy_pnl \n", + "43393 0.0000 0 -2.879624 \n", + "43394 0.0000 1 -522.470891 \n", + "43395 -5201.5608 2 -6246.223689 \n", + "43396 0.0000 2 -6246.222332 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 1\n", + "index = crosses['up']['index_up'][i]\n", + "dydx_results.iloc[index-2:index+2][['market_price', 'I_current','I_old','short_size','entry_price','pnl','maker_fees_counter','total_stgy_pnl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the difference close - open and the cost for each time we close the short (ie for every index_up)." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "diff = []\n", + "cost = []\n", + "# we dont start the loop at i = 0 because the data_set started below open_close\n", + "# so the first time price crossed open_close doesnt matter bc we didnt assume have the short position open\n", + "for i in range(1,len(crosses['up']['index_up'])):\n", + " index_up = crosses['up']['index_up'][i]\n", + " if index_up in results_maker_fee_counter['indexes']:\n", + " entry_price = dydx_results.iloc[index-1]['entry_price']\n", + " close_price = dydx_results.iloc[index]['market_price']\n", + " short_size = dydx_results.iloc[index-1]['short_size']\n", + " diff.append(close_price-entry_price)\n", + " cost.append(short_size * (close_price-entry_price))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1999999999999886, -5201.560799999951)" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(diff), np.mean(cost)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 7aca8966f8a25174597d3be73f8ccb178da5fa85 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Thu, 20 Oct 2022 10:21:14 -0300 Subject: [PATCH 15/16] updates --- jupyter-lab/Simulations_oc_range.ipynb | 461 +++++++------------------ 1 file changed, 131 insertions(+), 330 deletions(-) diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb index 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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" @@ -103,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": { "tags": [] }, @@ -201,6 +216,7 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -209,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -418,6 +434,7 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -426,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -808,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -893,6 +910,7 @@ " # data_dydx.append(stgy_instance.gas_fees)\n", " # data_dydx.append(stgy_instance.total_costs_from_aave_n_dydx)\n", " data_dydx.append(stgy_instance.total_pnl)\n", + " data_dydx.append(stgy_instance.total_pnl + stgy_instance.dydx.pnl)\n", " # data_dydx.append(mkt_price_index)\n", " # print(interval_old.name)\n", "# print(data_dydx, list(dydx_instance.__dict__.keys()))\n", @@ -975,7 +993,8 @@ " \"costs\",\n", " # \"gas_fees\",\n", " # \"total_costs_from_aave_n_dydx\",\n", - " \"total_stgy_pnl\"]\n", + " \"total_realised_pnl\",\n", + " \"total_unrealised_pnl\"]\n", " # \"index_of_mkt_price\"]\n", " \n", " path_to_aave = file_location + 'aave_results.csv'#'Files/Tests/From_%s_to_%s_open_close_at_%s/aave_results.csv' % (period[0], period[1], int(oc1))#int(stgy_instance.trigger_prices['open_close']))\n", @@ -1115,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1146,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1166,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1298,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": 306, + "execution_count": 45, "metadata": { "tags": [] }, @@ -1383,7 +1402,7 @@ " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", " if (stgy.dydx.market_price <= stgy.open_close_range[0]):\n", " print(\"Short position should be open for this first price!...breaking\")\n", - " break\n", + " return None\n", " #########################\n", " # Clear previous csv data for aave and dydx\n", " stgy.data_dumper.delete_results(stgy, file_location)#period, open_close)\n", @@ -1514,238 +1533,6 @@ " return stgy.dydx.maker_fees_counter" ] }, - { - "cell_type": "code", - "execution_count": 426, - "metadata": {}, - "outputs": [], - "source": [ - "def run_sim(stk, period, open_close, slippage, oc_increment, trailing_increment, file_location):\n", - " global ocs\n", - " # Initialize everything\n", - " with open(\"Files/StgyApp_config.json\") as json_file:\n", - " config = json.load(json_file)\n", - "\n", - " # Initialize stgyApp\n", - " stgy = StgyApp(config)\n", - " # Period of Simulations\n", - " # period = [\"2019-09-01\",\"2019-12-31\"]\n", - " stgy.historical_data = historical_data.loc[period[0]:period[1]]\n", - " # For vol updates we take all data up to the last date\n", - " stgy.launch(config)\n", - " # First we calculate weighted vol\n", - " last_date = period[1]\n", - " vol = stgy.parameter_manager.calc_vol(last_date, historical_data)\n", - " mu, sigma = vol\n", - " # floor just in order to get triger_price['open_close_1'] = open_close_1\n", - " floor = open_close / ((1+slippage)*(1+mu+2*sigma))\n", - " # Now we define prices \n", - " stgy.parameter_manager.define_target_prices(stgy, slippage, vol, floor, trailing_increment)\n", - " # We create five equidistant OCs\n", - " oc1 = open_close\n", - " #########################\n", - " # Save historical data with trigger prices and thresholds loaded\n", - " # checking if the directory demo_folder \n", - " # exist or not.\n", - " if not os.path.exists(file_location):#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close)):\n", - " # if the demo_folder directory is not present \n", - " # then create it.\n", - " os.makedirs(file_location)#\"Files/Tests/From_%s_to_%s_open_close_at_%s\" % (period[0], period[1], open_close))\n", - " # stgy.historical_data.to_csv(file_location+'stgy.historical_data.csv')#\"Files/Tests/From_%s_to_%s_open_close_at_%s/stgy.historical_data.csv\" \n", - " # % (period[0], period[1], open_close))\n", - " #########################\n", - " # Here we define initial parameters for AAVE and DyDx depending on the price at which we are starting simulations\n", - "\n", - " # Define initial and final index if needed in order to only run simulations in periods of several trigger prices\n", - " # As we calculate vol using first week of data, we initialize simulations from that week on\n", - " initial_index = 1\n", - "\n", - " # Stk eth\n", - " stgy.stk = stk/stgy.historical_data['close'][initial_index]\n", - "\n", - " # AAVE\n", - " stgy.aave.market_price = stgy.historical_data['close'][initial_index]\n", - "\n", - " # What is the price at which we place the collateral in AAVE given our initial_index?\n", - " stgy.aave.entry_price = stgy.aave.market_price\n", - " # We place 90% of staked as collateral and save 10% as a reserve margin\n", - " stgy.aave.collateral_eth = round(stgy.stk * 0.9, 3)\n", - " stgy.aave.collateral_eth_initial = round(stgy.stk * 0.9, 3)\n", - " stgy.reserve_margin_eth = stgy.stk * 0.1\n", - " # We calculate collateral and reserve current value\n", - " stgy.aave.collateral_usdc = stgy.aave.collateral_eth * stgy.aave.market_price\n", - " stgy.reserve_margin_usdc = stgy.aave.reserve_margin_eth * stgy.aave.market_price\n", - "\n", - " # What is the usdc_status for our initial_index?\n", - " stgy.aave.usdc_status = True\n", - " stgy.aave.debt = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", - " stgy.aave.debt_initial = (stgy.aave.collateral_eth_initial * stgy.aave.entry_price) * stgy.aave.borrowed_percentage\n", - " # debt_initial\n", - " stgy.aave.price_to_ltv_limit = round(stgy.aave.entry_price * stgy.aave.borrowed_percentage / stgy.aave.ltv_limit(), 3)\n", - " # stgy.total_costs = 104\n", - "\n", - " # DyDx\n", - " stgy.dydx.market_price = stgy.historical_data['close'][initial_index]\n", - " stgy.dydx.collateral = stgy.aave.debt\n", - " stgy.dydx.equity = stgy.dydx.equity_calc()\n", - " stgy.dydx.collateral_status = True\n", - " \n", - " stgy.open_close_range = [floor * ((1+slippage)*(1+mu+2*sigma)), \n", - " floor * ((1+slippage)*(1+mu+2*sigma)) * (1+oc_increment)]\n", - " # stgy.trigger_prices['trailing_stop'] = stgy.open_close_range[0] * (1-trailing)\n", - " stgy.open_close_range_2 = [floor * (1-oc_increment), \n", - " floor]\n", - " \n", - " # print((stgy.dydx.market_price <= stgy.trigger_prices['start']) and (stgy.dydx.market_price > stgy.trigger_prices['floor']))\n", - " if (stgy.dydx.market_price <= stgy.open_close_range[0]):\n", - " print(\"Short position should be open for this first price!...breaking\")\n", - " return\n", - " #########################\n", - " # Clear previous csv data for aave and dydx\n", - " stgy.data_dumper.delete_results(stgy, file_location)#period, open_close)\n", - " #########################\n", - " # add header to csv of aave and dydx\n", - " stgy.data_dumper.add_header(stgy, file_location)#period, open_close)\n", - " ##################################\n", - " # Run through dataset\n", - " #########################\n", - " # import time\n", - " # # run simulations\n", - " # starttime = time.time()\n", - " # print('starttime:', starttime)\n", - " # for i in range(initial_index, len(stgy.historical_data)):\n", - " i = initial_index\n", - "\n", - " maker_fees_counter = []\n", - " \n", - " # stgy.trigger_prices['trailing_stop'] = oc4 * (1-trailing)\n", - " \n", - " \n", - " market_price = stgy.historical_data['close'][i-1]\n", - " if (stgy.open_close_range[1] < market_price):\n", - " last_outside = 1\n", - " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", - " last_outside = False\n", - " elif (market_price < stgy.open_close_range[0]):\n", - " last_outside = -1\n", - " \n", - " if (stgy.open_close_range_2[1] < market_price):\n", - " last_outside_2 = 1\n", - " elif (stgy.open_close_range_2[0] <= market_price) and (market_price <= stgy.open_close_range_2[1]):\n", - " last_outside_2 = False\n", - " elif (market_price < stgy.open_close_range_2[0]):\n", - " last_outside_2 = -1\n", - " \n", - " stgy.trailing_stop_range = [floor * (1-trailing_increment), \n", - " floor] \n", - " if (stgy.trailing_stop_range[1] < market_price):\n", - " last_trailing_outside = 1\n", - " elif (stgy.trailing_stop_range[0] <= market_price) and (market_price <= stgy.trailing_stop_range[1]):\n", - " last_trailing_outside = False\n", - " elif (market_price < stgy.trailing_stop_range[0]):\n", - " last_trailing_outside = -1\n", - " \n", - " while(i < len(stgy.historical_data)):\n", - " # for i in range(initial_index, len(stgy.historical_data)):\n", - " # pass\n", - " # We reset costs in every instance\n", - " stgy.parameter_manager.reset_costs(stgy)\n", - " market_price = stgy.historical_data[\"close\"][i]\n", - " previous_price = stgy.historical_data[\"close\"][i-1]\n", - " \n", - " if (stgy.open_close_range[1] < market_price):\n", - " outside = 1\n", - " elif (stgy.open_close_range[0] <= market_price) and (market_price <= stgy.open_close_range[1]):\n", - " outside = False\n", - " elif (market_price < stgy.open_close_range[0]):\n", - " outside = -1\n", - " \n", - " if (stgy.open_close_range_2[1] < market_price):\n", - " outside_2 = 1\n", - " elif (stgy.open_close_range_2[0] <= market_price) and (market_price <= stgy.open_close_range_2[1]):\n", - " outside_2 = False\n", - " elif (market_price < stgy.open_close_range_2[0]):\n", - " outside_2 = -1\n", - " \n", - " # if (stgy.trailing_stop_range[1] < market_price):\n", - " # trailing_outside = 1\n", - " # elif (stgy.trailing_stop_range[0] <= market_price) and (market_price <= stgy.trailing_stop_range[1]):\n", - " # trailing_outside = False\n", - " # elif (market_price < stgy.trailing_stop_range[0]):\n", - " # trailing_outside = -1\n", - " #########################\n", - " # Update parameters\n", - " # First we update everything in order to execute scenarios with updated values\n", - " # We have to update\n", - " # AAVE: market_price, lending and borrowing fees (and the diference),\n", - " # debt value, collateral value and ltv value\n", - " # DyDx: market_price, notional, equity, leverage and pnl\n", - " stgy.parameter_manager.update_parameters(stgy, market_price)\n", - " \n", - " # open_close_range action\n", - " if (last_outside == 1) and (outside == -1):\n", - " stgy.dydx.open_short(stgy)\n", - " last_outside = outside\n", - " elif (last_outside == -1) and (outside == 1):\n", - " stgy.dydx.close_short(stgy)\n", - " last_outside = outside\n", - " \n", - " if (last_outside_2 == 1) and (outside_2 == -1):\n", - " stgy.dydx.open_short(stgy)\n", - " last_outside_2 = outside_2\n", - " elif (last_outside_2 == -1) and (outside_2 == 1):\n", - " stgy.dydx.close_short(stgy)\n", - " last_outside_2 = outside_2 \n", - " \n", - " # open_close_range action\n", - " # if (last_trailing_outside == 1) and (trailing_outside == -1):\n", - " # stgy.dydx.open_short(stgy)\n", - " # last_trailing_outside = trailing_outside\n", - " # # We will use the oc_range once trailing_stop is executed (ie trailing_range crossed going up)\n", - " # # So we redefine oc_range to end at that market_price + update trailing_range to end at oc_range[0]\n", - " # elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", - " # stgy.dydx.close_short(stgy)\n", - " # last_trailing_outside = trailing_outside\n", - " # stgy.open_close_range = [market_price * (1-oc_increment), market_price]\n", - " # # stgy.trailing_stop_range = stgy.open_close_range\n", - " i += 1\n", - " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", - " # and we execute all the actions involved between both (current and previous prices)\n", - " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", - " ############################## \n", - " # We update trailing\n", - " # Everytime price crosses the lower bound, we move the trailing range\n", - " # if (market_price <= stgy.trailing_stop_range[0]):\n", - " # stgy.trailing_stop_range = [market_price, \n", - " # market_price * (1+trailing_increment)]\n", - " ################################\n", - " # trailing_outside = 1\n", - " # last_trailing_outside = 1\n", - " ########################\n", - " # Funding rates\n", - " # We add funding rates every 8hs (we need to express those 8hs based on our historical data time frequency)\n", - " # Moreover, we nee.named to call this method after find_scenarios in order to have all costs updated.\n", - " # Calling it before find_scenarios will overwrite the funding by 0\n", - " # We have to check all the indexes between old index i and next index i+time_used\n", - " # for index in range(i, i+time_used):\n", - " if (i % (8 * 60) == 0) and (stgy.dydx.short_status):\n", - " stgy.dydx.add_funding_rates()\n", - " # stgy.total_costs = stgy.total_costs + stgy.dydx.funding_rates\n", - " #########################\n", - " # Add costs\n", - " stgy.parameter_manager.add_costs(stgy)\n", - " stgy.parameter_manager.update_pnl(stgy)\n", - " #########################\n", - " # Write data\n", - " # We write the data into the google sheet or csv file acording to sheet value\n", - " # (sheet = True --> sheet, sheet = False --> csv)\n", - " current_date = str(stgy.historical_data.index[i-1])\n", - " stgy.data_dumper.write_data(stgy, #previous_price, last_outside, outside,\n", - " current_date, file_location,#period, open_close,\n", - " sheet=False)\n", - " return stgy.dydx.maker_fees_counter" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1755,7 +1542,7 @@ }, { "cell_type": "code", - "execution_count": 398, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1764,37 +1551,37 @@ "best_1_month = [[\"2022-04-02 00:00:00\",\"2022-05-01 00:00:00\"],3400]\n", "\n", "# Normal cases 50 to 150 crosses\n", - "normal_1_week = [[[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]]\n", + "normal_1_week = [[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]\n", "normal_1_month = [[[\"2020-05-31 00:00:00\",\"2020-06-30 00:00:00\"],240],\n", " [[\"2021-12-01 00:00:00\",\"2022-01-01 00:00:00\"],historical_data['close'].max()*0.8]]\n", "# Worst cases 150+ crosses\n", - "worst_1_week = [ [[\"2019-10-26 05:00:00\",\"2019-11-02 00:00:00\"],183]]\n", - "worst_1_month = [[[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]]\n", + "worst_1_week = [[\"2019-10-26 05:00:00\",\"2019-11-02 00:00:00\"],183]\n", + "worst_1_month = [[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]\n", "\n", - "# worst_3_month = [ [[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]\n", - "# worst_6_month = [ [[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2020-03-15 00:00:00\"],182]]\n", - "# worst_1_year = [ [\"2019-09-01 00:00:00\",\"2020-09-01 00:00:00\"],170] " + "worst_3_month = [ [[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]\n", + "worst_6_month = [ [[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2020-03-15 00:00:00\"],182]]\n", + "worst_1_year = [ [\"2019-09-01 00:00:00\",\"2020-09-01 00:00:00\"],170] " ] }, { "cell_type": "code", - "execution_count": 407, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1" + "396" ] }, - "execution_count": 407, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Period of Simulations\n", - "periods_n_open_close = best_1_month\n", + "periods_n_open_close = worst_3_month[1]\n", "period = periods_n_open_close[0]\n", "p = periods_n_open_close[1]\n", "data_set = historical_data.loc[period[0]:period[1]]\n", @@ -1804,12 +1591,12 @@ }, { "cell_type": "code", - "execution_count": 408, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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nSa+//rr69eun9PR0paena9CgQfrll18kScePH9df//pXde/eXUlJSWrXrp3uuusu5eS4zzmxd+9ejRs3TsnJyWrWrJkeeOABWa3uD23mzJmj0047TQkJCerSpYvef//96v2UAFBP3H1+V10/qL1zPe5kibobBrf3t4u+9FOK7Uheic/2xy9yn9cnzljxqyTOFHxJvAPZRUH3hW9dmqXqt78PD2kfl1ihujZP9do+3KWMXijvZ1302jWn6ZvbB/vcRkk9AAAAAAAAdyFlOLVp00aTJk1S165d5XA49MEHH+iiiy7SqlWr5HA4dPDgQT377LPq1auX9uzZo9tuu00HDx7U119/LUmy2WwaN26cWrRooYULFyozM1PXX3+9zGaznnrqKUnSrl27NG7cON1222365JNPNGvWLN10001q2bKlRo8eHf5XAADqkFYNknT1me314aI9koKbE2l7Vr7P9i+W7fVqG9i+oXq1cs9eMhqkNg2TlFNoUccmKV77SNKFp7TST2sOurU9O21LpWODb+seHaV1+3PUr20DpcQHX/YuzmjQqkdGqu+j03V6h4Y6rV1Drz5dm6Xqgj4tlJYYp4S4+ltSr5zdT+lHMpwAAAAAAADchRRwuvDCC93Wn3zySb3++utavHixbrzxRn3zzTfObZ07d9aTTz6pa6+9VlarVXFxcZo+fbo2btyomTNnqnnz5jr11FP1+OOP6+9//7seffRRxcfH64033lDHjh313HPPSZJ69uyp3377TS+88ELAgFNJSYlKSiq+jZ+bmytJslgssljC+y3k8uOF+7hAXcT1UrPsNptkq3gQHid72Wtvt/vdp9hi9fn+ZPrIQPrT4HY++06/+2zZ7Q4ZHXZZLN7nenhsNyWYDLr8tFa68p1lkqSlu4979Qv2c1KfP09j+zRXokk6vX2GJIdXlrCrq05vo8+W7Xeut0hPUKJJ2vb4KEnyu+/L/9dPUv1+ncv5e42yC0t4faKM3y9A8LhegOBxvQDB43oBgsf1UjvxvgQnlNenynM42Ww2ffXVVyooKNCgQYN89snJyVF6erri4spOs2jRIvXt21fNmzd39hk9erRuv/12bdiwQf3799eiRYt0/vnnux1n9OjRuueeewKOZ+LEifr3v//t1T59+nQlJyeH+NMFZ8aMGRE5LlAXcb2Em+/b9+pVq+TY69CVnQyyOqTFc2dKkmx2//u001FNmTJFDod7ubVZ60yS3DOkCnes0JTdVRvxkATp8IY9znGUWr0DU1OmTAlwhLgg+8US/7+GJ55uVZJJ+vtSk0rsFe9DzpFMTZlyIOBxxrW1qV2qlFS8223b8CYFdei1qxk7ciVf79M783eoY9FWmev3NFe1Ar9fgOBxvQDB43oBgsf1AgSP66V24n0JrLCwMOi+IQec1q1bp0GDBqm4uFipqan67rvv1KtXL69+R48e1eOPP65bbrnF2Xbo0CG3YJMk5/qhQ4cC9snNzVVRUZGSkpJ8jmvChAn629/+5lzPzc1V27ZtNWrUKKWnVz55fSgsFotmzJihkSNHymw2h/XYQF3D9RIZdy+a7rP99IEDdH7PZhrrY9vflvje58wBp2rh7hP6Yvl+vXNdf53brakcDofuXuT+y7ZVRqIuvnBodYfud+ySNHasr5GXyWq4R09O2aKJl/TW2NNaV3sctUGg1+Ly35e9Fk+sn6OS/FJne6cO7TV2bE+3vo17Hte17y13rt9z2VB1aFxW3vD2K6TsQou2H8nXwPbeJfQQ2IHsIr28Yb5Xe4nNoHlFbfXcH/pGYVSQ+P0ChILrBQge1wsQPK4XIHhcL7UT70twyqvJBSPkgFP37t21evVq5eTk6Ouvv9YNN9yguXPnugWdcnNzNW7cOPXq1UuPPvpoqKeokoSEBCUkJHi1m83miH1YInlsoK7heqkZ8ea4kF9no9GoL5aXlV276aNV2j1pnBZsP+rW59Obz1Sf1hkRfw8DHf/moV106Wlt1TjV+15fF5W/FkkeczRZ7N6v0zndmuuHO87WRa8tkCS1apgqs7niV3zTDLOaZkQm27eu69DU/bW+9dxOenPuTknSj2sz9fLVp0VjWHDB7xcgeFwvQPC4XoDgcb0AweN6qZ14XwIL5bUJOeAUHx+vLl26SJIGDBigZcuW6aWXXtKbb74pScrLy9OYMWOUlpam7777zm0wLVq00NKlS92Od/jwYee28v+Xt7n2SU9P95vdBAAos3Z/js7r2bzyji6sNodX24+rD7qt92mdofTEyP3iPb1DQ119ZrtK+9WXYJOrxDj3gFOpzfd8XKe0baBp95RloKUkVLliLipht3tfLwAAAAAAAJCqPfOA3W5XSUmJpLLMplGjRik+Pl4//vijEhMT3foOGjRI69atU1ZWlrNtxowZSk9Pd2ZIDRo0SLNmzXLbb8aMGX7niQKA+ig+zvftO6fI/yR+p3fwXU5t0yH3tNiZGw/ri+X73NpS4yMbwPjqtsG6pH+biJ6jNhrTu0WlfVo2cP+yRcuMRD89pe4t0tS9RVq1xwX/jhWUVt7Jw5G8EuUWMwEpAAAAAACo20IKOE2YMEHz5s3T7t27tW7dOk2YMEFz5szRNddc4ww2FRQU6N1331Vubq4OHTqkQ4cOyWazSZJGjRqlXr166brrrtOaNWs0bdo0PfTQQ7rjjjuc5fBuu+027dy5Uw8++KA2b96s//73v/ryyy917733hv+nB4AYNef+YT7b/QWiJMls8r3tfwt2u63f9OFyt/V3bxgoo9EQ0vgQnPEX9PDZvunR853LEy91nyPo9mFdIjomBHZNEJl4rnKLLTr9yZnq96j/+boAAAAAAADqgpC+sp6VlaXrr79emZmZysjIUL9+/TRt2jSNHDlSc+bM0ZIlSyTJWXKv3K5du9ShQweZTCb9/PPPuv322zVo0CClpKTohhtu0GOPPebs27FjR02ePFn33nuvXnrpJbVp00bvvPOORo8eHYYfFwDqhlYNfJcYNQUIDHVplqqFO46FfK6OTVJC3gfB6eDntY1zCQ629nivUymXF1XtG1d+PSzbfVwtMxLVpmGydmTlO9sdDocMBoK3AAAAAACgbgrpqdW7777rd9uwYcPkcFQ+r0H79u01ZcqUgH2GDRumVatWhTI0AICkuAABpz+d3VEfLtoT8jFbBCjhhuq7f1Q3PTt9q3PdIOYIqs0qC/htPpSrP7yxSJL0+S1nuWUdHskrUbN0ricAAAAAAFA3VXsOJwBAdPzfwLaSpItPbeVsC5Q9kRCg3F4gyWGev+mNa08L6/Fi3alt3efWSjRFaSDwK94l4yzRHPgNWrc/x7l85VuLlesyr9oZT83ytQsAAAAAAECdQMAJAGLUU5f21cy/nav7RnV3tl19hv/5ZQJlP/nz3B9OqdLYAhnTp2XYjxnLzunaxG3dz1RbiKK7z+8aVL9ii00PfL3Wre2P/1sWiSEBAAAAAADUOjzWAoAYZTIa1KVZqltbRpLZb39jFQJOlw1oE/I+oRrQvmHlneqROB9v07d/GaxOTVL0vz+eXvMDQkAnCkqdyz0enlpp/1KrPZLDAQAAAAAAiBoCTgAQ45qmJTiX4wOUzUuOD61W24WntKq8UxjcN6pbjZwnVph9vIWntWuo2fcP0/AezWp+QFCHxil+t/V/fEZIx9qYmVvd4QAAAAAAANRK4Z2YAwBQ4xLNJq146HyZjAaZAmQxJcfH6c3rBujWj1YEddwdWfnhGmJAqQn8KnLlK8MJ0TW2bws9OKa7+rf1nY23PStfz0zbHNSxqlLaEgAA1E0Wm10Hs4v0zLQtyswp1he3nKU46isDAIAYxl8yAFAHNE5NUIPk+Er7je7dQg+M7l5pP0k67lIqLJLijPwq+uKWs5zLCaEloqEGGAwG/WVYFw3q3FiS1L9dA7ft5z8/V9M2HA7qWPO2HQn38AAAQIz68/vLdO4zc/Tz2kyt2HNCWw7nRXtIAAAA1cJTPgCoZ+x2R1D9DuUWR3gkZcwmMj7O7NTYuZwcF9z7g+h54uI+Vd736albwjgSAAAQy+ZvO+q2brHxdyAAAIhtBJwAoJ45XlgzmUvBomxImUcv7KWWGYm6tIM92kNBJQLN6SRJN53TsYZGAgAA6hJbkF8MAwAAqK14ygcA9UxRqc1n+8WntnJbT0usmbmVmNOmzB/P7qh59w9V06RojwSVSalk3rGHftdLn7uUSXR1af/WkRgSAACoA+wOAk4AACC2EXACgHomKd73JEETxvZ0LqcmxOndG06P2BjaNUp2LpvJcEIddFanxpr5t3O92gv9BHwBAAAsVjLdAQBAbKuZr68DAGoNq5/a8M3TE/XuDQPVKCVep7ZtIIMhcplH/73mNP3uld8kSXHM4YQYNLZvC01Zd8ir/cc7z3Yud2mWqvkPDtdPaw9qw4FcTV6XqakbDsnhcET0+gIAALGpxEbACQAAxDa+Vg4A9Yw1QG3483o2V/92DSP+MNz18GYjv4oQe/4yrIvP9obJ8W7rbRsl6y/DumhbVp6z7YKX5svKAyUAAOChxMLfBwAAILbxlA8A6pnUBN8l9aKFDCfEok5NU7zaRvVqrjYNfU/ClWSuuO42H8rTvhNFERsbAACo3aw2u16auc2rvZQvpAAAgBhHwAkA6pk/nt0x2kNQnEtWEwEnxCKDvD+3b10/0G924INjeritL9t1PCLjAgCgrrLbHZq39YgWbj+qDuMn6z9TN0d7SFX25fL9emHmVq/2DQdzojAaAACA8CHgBAD1TOsGSdo9aZxeuvLUqI2hY5MUNUlNUOemKYo38asIscczrnTdWe0D9m+Zkei2/tb8neEeEgAAddrUDYd0/XtLdfU7SyRJr8/ZEeURVd2incd8tr85l78PAABAbIuL9gAAANFhMlY8MR/StUmNnjs+zqiF40dIUsTniwIiwewRKP3373sH7N++sXsJvv5tG4R7SAAA1Gl/+WRltIcQFna7Qz+tORjtYQAAAEQEXysHgHrKtayda/CppsTHGRUfx68hxCbPa8ZYyTXk2f9ANnM4AQBQH63enx1w+4o9lN0FAACxiyd9AFBPuT7/To43RW8gQD20cIfvUjoAAKB+u+z1Rdp6OC/awwAAAKgSAk4AUE9Z7Q7nclqCOYojAWLTogkjdF6PZpp137nRHgoAAIgRSebKv+g16oV52ne8sAZGAwAAEF4EnACgnnJUxJtkMjGPEhCqlhlJevePp6tz09RoDwUAgHpp4pRN0R5CWEy9Z4hX25Cnf43CSAAAAKqHgBMA1FN2l4hT45T4KI4EqB9euvJU53LHJinRGwgAAHXEm/N2RnsIIfOVudQ0NSEKIwEAAAg/Ak4AUE+5BpxuPbdzFEcC1A8Xndpar17dX5LULI0HSwAA1Effrjzg1RZn8v1o5kB2UaSHAwAAEFYEnAAASk2Ii/YQgHohzlhWvtJ1DjUAAFB/DO/R1KstIc73o5lPFu+J9HAAAADCioATANRTzDsD1Lw4Y9mfXgScUJldRwtUYrVFexgAUGs0Sa0bJaAN8p47NdFs0i1DO3m1n9GxUU0MCQAAIGz4SjsA1FN9WmforesGqHXDpGgPBag3zCe/wWyx2qM8EtRmHcZPliSd3aWxPrnprCiPBgBqh1YNknQ0v9StzWT0Dt7UdrnFFrd1s6nsZ7j7vK56y2NOqkSzqcbGBQAAEA5kOAFAPTaqdwv1bpUR7WEA9Ub5QyWrnYATfLO7ZL8t2H4siiMBgNrF7DLP0b9/31uS+5ykseKJyZvc1u8a0VWSlJIQp7WPjnLb9uXyfTU2LgAAgHAg4AQAAFBDyh+WbT2cL0cMPiRD5JWQ/QYAPrn+3hzXr+XJNsXU79Plu4+7rZ/Xo5luHNLRuZ6eaNbuSeOc6/O2Hq2xsQEAAIQDAScAAIAa4vrt7JV7T0RxJKitPOdtWr0vOzoDAYBapjys9NZ1AxTnUkovlqZFvPyNRW7r7/7xdCXHM9MBAACoOwg4AQAA1BDXb2HnFVujOBLUVsUW9wynlXsITAKAJK3amy1JKii1ymCoCDjZYini5GLyXedU2uey01rXwEgAAADCh4ATAABADXHNcIqP488wePttu3v5pEU7mccJQP22cPtR/bo5y7n+4+qDMrllOMVGwKnY4p7B2qtleqX7vDlvZ6SGAwAAEBHkbgMAANSQ7i3SnMsJBJzgw7u/7XJbn7HxcJRGAgDRdzi3WFe/s8StLbfYKpNLhlNOkUWJZlNNDy1k0z3u565ZWp4ap8TrWEGpzuvRLNLDAgAACCuedAAAANQQs8mo1g2SJElWW2x8Ixs1K9HMn+cAakZesUWzNh1WqdVeeecoySmyeLW1SE+Ua6xmtkv2U23mCCETa1y/lpKk3q0zIjUcAACAiOBftAAAADWo5OSDvWMFpVEeCaLJbnfopg+W6cnJG93ay+cocXUop7iGRgWgPun/2Azd+MFyPTVlU7SH4pdr6bxy1w9q79Ze4lGqrrZyLatbGePJiFooQSoAAIDagIATAABADTqaXyJJuuuzVVEeCaLpj+8v08xNWXp7/i79ujlLdrtDCz3mbyp31sRZNTw6AHXZ6n3ZenbaFlntZcGM9xfuVn6J1WuOodrAV7zlzE6N3UrqxcfV/nJ6ktwyyTo2SQlqn1iZnwoAAKAcczgBAABEQfmDPtRP87YecS7/6f1llfYvtdr1xbK9Oqdr06AfVAKALxe/tsCr7bTHZ6hlRqLmPjA8CiPyz1+Gj9Elw6lto6SaGk613PPFaufyT389J2DfigynSI4IAAAg/MhwAgAAAGqZZy7v57Z+75er9fAPGzT82Tl+9ykosUZ4VADqqlKrXXuOFcpiq13zOQX6bkbfk/MbxeKciKkJgb/7Wx5P47spAAAg1hBwAgAAAGqQLYgniJcPaOO2PnltZsD+Xy7fp97/mqZvV+6v1tgA1G95xbUrcG21uwfAzujYyLkcZyqLytS2IFk4lGdwMYcTAACINQScAAAAgBoUzMNRg8v8JJXZfChXD369VpL0ty/XVHlcAJBbZHFb33W0QC/P2qa8YoufPSLrx9UH3db/c1lF9mfcyaBMMEH8WFP+Pqw/mBPlkQAAAISGgBMAAABQg4L9Nr7RT8zJ8xvvY16cX90hAYAkKSuvxG39dy/P1/Mztqrvo9OVXVha4+N5c95Ot/UOjZOdy3HGsscZljoYcPp82T5J0oLtx6I8EgAAgNAQcAIAAIiSwtLaVboIkZNdWKpft2TJZncEPd/Ir/cP89n+rx83OINOlFsCEE4vztzqtl5QanMuPzl5U00Px4tr9md5ST2bve6V1HPFfR4AAMQSAk4AAAA16P8GtnUubzyYG8WRoLpmbjyst+ftlD2Ib9f/4Y1F+tP/lul/C3bJUsnD0Scu7iNJat84xef2Dxft0cgX5mn6hkOavM57bidrHZzPBEDN6NY8ze+2HUfyI3LOfccL9emSvSq1ut+7DmYXua0/NK6n23p5ST1LkEH8WOI6V1WJlXs6AACIHQScAAAAatCyPcedy6HM04Pa56YPl+vJKZvU6R9T9PWK/W7bcoosenX2Nu05ViBJ2pZV9qD2icmb9M2KA36PueOpsbr2rPbO9R4tfD/83Z6Vr1s+WqFNmd5By6embA75ZwEASWqaluBc9sys8Qx8HM4t1rQNh4IKugcy/Nk5+sd36/Tm3B3Ott1HCzR40mzn+m3ndtZNQzq57RdnKnuc8eDXa7Vwx9FqjaEm/Xjn2ZX26dy04gsH1jpYMhAAANRdBJwAAABq0IETFd/YJt5Ud9z/1Rq39YlTNunZ6Vt14Su/efX9z1T/ASGTx8RNU+8Zqs2Pj9HuSeN89n/t1x1ebe8t2BXMkAHUQ7ZKgheuc8wVW9wDTBs8snLPf26ubv1ohb5Z6R5wD1V5QGWBS9Bo2LNz3PoMbN/Qa784l/vl1W8vqdYYwu3jxXt03btLVFBSUTo3Ia7s8UujlPhK92+enuhcJmsVAADEEgJOAAAANej2YZ2dy8SbYtf2rMClpeZvK3twmlsceJ6ut68fqJuHdJQkDerU2GefRLOpCiMEAG+WSoIXrtsrm2cw72Qw5duVB7zK4VXF4p3H/W6Lj/N+dFGe4VTuv3O2V3sM4fLQ9+s1f9tRfbBot7OtPNgXZ6z8McwtQyuyuepiyUAAAFB3EXACAACoQWd3aeJc5hFS7Pp86d6A2w+4zD3y5fJ9fvuN7NVc/xzXS5seG6NPbjozbOPzLPEHAJJ3wGnRhBEa1r2pc33ZrhPO5SKLLahjLtp5TN0e+kUdxk9WfkngIJWrY/klWrHHO8h0wGPuJsl3wMnskRH69NQtQZ+7ppRnODkcDmcmV5yp8q+bJMfHOTO4rJXM+wcAAFCbEHACAACoQa5lgVIT4qI4ElRHKFNqPPj12kr7JMWbZDQGfgj5wx2B5/24fEAb57JniT8AkCSrS7bMjqfGqmVGkt694XRddUY7SdLS3cd1oqBUkvxmLb0ya5vfbKI+/5oW9FhGvTBPl72+yKv9wa+971++Ak6HcouDPld1FJXa9K8f1mvV3hOVd/bw2q879PKsbW5ZSuYgMpykilKDS3f5z/wCAACobQg4AQAA1CCDwaAmqWXzN9gd5DjFKluAb5xXNkdKuUv7tw7pnKe0baCtT1zgc9vgzo317B9OcWu76NXf9FklmVgA6pfyDCeT0eCcM85kNGiRy/xJP689qGKLTbM2ZXntn1ds0XMztoYlm+jYycBWuaZpCZJ8B1jSfHxBY+GOY9UeQ2VKrXb1fGSqPli0R5f8d6Gz/eHv1+uCl+a7zdHkz/MztqrbQ784181xoRXUvfvz1SH1BwAAiCYCTgAAADXMYCh72ESVnNhlDRBUyi2yBHWMB8f0CPm88XFGzX9wuM92Sfrgz2c429bsz9GEb9eFfA4AdVfpyYBTnEdGZfP0ROfywz9s0H1frtGTUza59enbOkNFpcGV2asKm92hBduP+pyzKCPZHLHzBrJg+1Gf7R8t3qNNmbmauv5QyMdMjie7GQAA1F0EnAAAAGpY+XM+Mpxil9lU/T+jk+JNVdqvbaNkPX5Rb7e2Ub1aSJLO7dbU1y4AIEnOYE68xz3M5BGAmrwu02vfUqtdxZbIfVPieEGprnlnic9tGUnBBZyKg5x3KlgOj9kW958odFsPdX6l5Cre9zNzvOe1AgAAqI0IOAEAANQwU3mGEwGnmLNq7wnd+tFypST4f2j47582BHWsYB+g+nLdoA5a88goPX1ZP7105am68vS2fvtm5bnPc3Iwu0j5QZSBAlD3WE9mOJk95kQyBFHlLb/EqmJreAI6v272LtcXSEKc9z13VK/mXm3lJQPDZUdWgdv6Jf9dqK2H85zrof4ar+qXFQZNnF2l/QAAAGoaAScAAIAa5iypR7wp5lzy34WatuGwXvt1h9e2l2ZukyR9v/pgpcc5v6f3g9JQZSSbdcXpbXXRqa1lNPp/Wvz1iv3O5YPZRRo8abb6/GuaJv6yye8+AOomfyX1DKo84nQgu0ijXpgXlnH86f1l1T5GkY9spmDn0HNltdk1fcMhnfCYU0qSFu5wL6l3JK/E7TVYsz/Hax/PIL+rcGTHAgAA1Gb8tQMAAFDDjCf/AiPDqW55YebWoPvmFHk/2IyUzZkV38ZfsuuYc/nNuTtrbAwAaofyknqegY/BXRr73WfSpX0DHnNI1yZebQ6HQyXVzIb6+MYz9enNZ2rdo6N8bs8t9s7UDDS/nj9vz9+lWz5aoTOfmiWHx+/lMzq6vy49W6a7rX+2dK9u+XC5jrsEqwLN43c0vyTk8QEAAMQSAk4AAAA1zHgyw8nzwRbqD8+HlpH045qKjCtjMHWzANRZ5SX14j1K6t10Tief/U1Gg0b6KF1XbssTY/TRjWd6td/60Qp1f2iqFu+sCHJ/uGi3Plq8R5I0tm+LgOPc8sQYndO1iQZ3bqK0RN/lR//3x9O92qy20H+v/rD6gKSy7K8p6w65bftu1X639U2ZuV77T994WOc+86tzvag0cvNcAQAA1HYEnAAAAGpY+RxOYZ5qArWA1eVN7ds6w23bExf30RMX99EpbRvonvO71fTQJEklFvcPXXUzEADElvKSemaTe/A5Ps6omX8716t/k9R4JcfH+T1e/MlMqVvP7eR23OkbD0uSrnxrsXIKLXri54165IcNevj79crMKXIGdv5zWV9tfGy013F9zdnkqVFKvJ65vJ+uGNjGWSLQag/9F+vmQxVZoHd8utJt29bD+UEdI88l22rN/mzn8sRL++rZP5wS8pgk6c9nd3Rb3320wE9PAACA2oOAEwAAQA0rTzKhpF7d41rO6bZzO7ttG9y5sa49q71+uONsNUqJr9FxlVrLHsI++M1an+0A6ofyknpxRu9HAV2apSotwT241LNlupLifQd/TEaDc07Cq05vJ8l3oOjrlfv1zm+7nOu3fVwR1GnbKFlJ5sqDS/78YWBbPX35KUo4mbHlOofTkbzKy9dtPOidsVTup7WZVRrTQ9+vdy5fdUY7NUrxnaFVmfEX9HBbH/bsHP28tvI5AgEAAKKJgBMAAEANKy9rRsCp7nGdx6NXK/eyeYGyBCKt20O/+Hz4aqvCfCcAwmPr4TyNeXGepq4/VHnnMCm/DxT7yW7MK3GfF2nSpf0kSX8c3MGrr2sWp/lkwMdis8vikb77+M8b3dbX7Mt2LrdvnCKDwaDzezZztt1bhQxQkzPDqeye9tqv23X6kzP1wcLdfvc5mF2ksS/P92p3OBxafcygv321ztnWr02GV79gHcuv2px98XFGr0y0t+Yx9x4AAKjdCDgBAADUsPIHY8Sb6p4/v7/MudymYZLbtprOarr0tNZu66c/OdOrDwEnIHru+Xy1Nh/K020fr6ixc97/1RpJ0s4jlZdn++XuIWqRkShJum+UdxCoW/NU57L55O+1Eqs9YNaQp6apCZKkd244XV2bpSo53qTbh3WuZC9vcSb3DKdnpm2RJP3rxw0++2flFWvwpNk+t72/aK/+t9U96+rxi/oENY7CUqtXW1pixZcNykv/Beu3v49wW7dUYY4qAACAmkTACQAAoIYZnHM48eAo1p3RsZE+velM57rrXCCeDxbj42rmT+9Z952rr24bpCsGtq20L5/BumlTZq6+Wr5PDqLatZqv4ERt0rNlRZama9m7z24+S9ee1U7/HNvL2VYe8JGki15bEPQ5XO+LM/52rjY+NqZK98ry7FLPeeok6d8/eQedznhylt9jPfXLFq+2to2SddUZFffUNY+M8uqzPStPK/accK5f0r8s6D+se0X21twHh/s9ry/N0xPd1rcdzvPTEwAAoHaIXl0PAACAeqo8DrF6X7aGdmsa3cFESFGpTf+Zullj+rTQWZ0aR3s4YbFg+1Gvtqcu6aMGyb4zl8oDizWtc9NUdW4q7TteWGlfKwGnOumCl8rKhGUkmTWqd4sojwb+uAZpii02JVZjLqNgxccZqzR3W5zJqOUPnS9JapKaoEGd3e/rnqXfouWxnzfowz+f6db2vwW7dcOgDurQJMXZ1rpBkg5kFwV93LTEOD04uoeO5JXo8gFtlZLg/V7947v1srqUE5wwtmwOpkSzSbsmjpXN7nB7z6uCezYAAKjtyHACAACoYRtOlht6fsZWPT9ja8A5JmLVXZ+v0vsLd+vKtxZHeyhhc807S7zaUhPMauyjVN7A9g0lSR/++YyIj8sfz5J+vvDosm5zzbirab9uztLgibO0cEdZoHbFnhN67dftZNW5cM2CfPe3XTVyzktPZt3cN9L3PEmTLu0rSVryj/O8tjVJTVCTkyXwPJmrGUgJl2W7T+j2T7xLFP7lk5WSyuZn+mr5PhWEmF1mNhnVMCVe79xwusb0aeEzcLR013Gt3JvtXG+WVpGdZDAYqh1sAgAAiAVkOAEAAETRy7O2SZKuO6u9jCHO7VCbzdh4ONpDqBFJZpPPTKblJ8sqDe3WVNPuGapmab4f0kZSMBlWdh7+12nFFlvUzv2nk/OZXfPOEu2aOE6Xvb5QktQ0LSGoco/1gWvpuB1H8mvknOUBP5OfjKQrz2inK89oF/JxfQWckuNN2vjYGL3263bnnEquyjOmwm3OliNebRszy77oMXtzlh74em1Ix7vrvK5hGVc4jOjRrPJOAAAAUcRXbAAAAGqBwig+GEbVJZgr/3O6e4s0NfSRBVUbMMVP3fbR4j3RHoLXZ+zBr9dq3f6c6AymlklwCTgZa6gE56ZDZYGXqpTVC8Tk4wsTN57TUZI0undzZ5vBILXMSNTuSeP8ZktF0o0fLA95n7/5yQb759ieuvCUVtUdUkiYlw0AANR2BJwAAABqAUuYH/4h/MrL5LlKqMLk9jVpx1Nj1dFl3hJPdh5e1mlNo/BAPxgXvvobD84ltzmb9p+ofM61cFh/oCzg9Mrs7RE/150jukiSujRL03+vOU0///UcrXp4pH69f1j4zzW87Fzn96xaBtD3d5zts/2ukz+DLzcP7aRXrupfpfNVlY3LBgAA1HK1+1/IAAAA9YTFXncDTj+vPRjtIYSFr5JRwZStiyaT0aDnrzjFqz05vuxBNwGnum3n0YJoD0GSdM5/Znu15ZUEN4fOjiP56jB+spbvPh7uYUXd/G1HncuLd9bsz1cTc2klxFUE1Mb2bak+rTPUIDneLdAWLk1Sy7JIE8wmndaugc8+W3zMadYkNUG7J43TqW197zO8lpWwI1ALAABqOwJOAAAAtYCljn1t+YwOjZzLd366KoojCR9bCA/6avpb74H0b+eemTWmdwvn3DFM4VT3FZRYVRBkcCdS9p8o8mqbuv5QUPue99xcSdLlbywK65jquy7NUqM9hLAynfxCQFZusVbuzfbanhJv0vGCUq/2vwbIYJKkw7nFVRrPm9cNqNJ+vtznUtJv/rajuv69pZSlBAAAtRYBJwAAgFrAaqtbGU5H80uiPYSwO62dd0m9cvePcp/jo6bn9QjF69ee5pwvhm/L1329/zVNpz4xW4XRjTl5efDrtSHvY6kj90m73eF17fnLsAmX9QdyNHV9pjPQdNd5XSN6vppmPjmH1LLdJ5xtD43rqX+M7SFJKrHaddXbi732u/as9s7lR37Xy2t7t+ZplZ47zmP+qu1PXqDRvVsEN/AgjO3X0m193tYjuvDV32TnGwMAAKAWIuAEAABQC9S1DKfaFnA6lFOsDuMn673fdlX5GPEB5mu6c0RXNU+vnfPluOrdKl0Gg8H5Tf91B/iWfF1TYrX5bH9jU/jLmNW0Yovvny2WOBwOXfHmIl302gK39rX7syN63t+98ptu+3il9hwrK7OYWMvnnwtVnI+Spx2bpOiKgW0lSVYfwRmzySCTS7Doz+d01O5J47T1sZHq29Cui09pqU5NK88E+/r2wc7lJqnxPsdSHf6+F1BaRwKwAACgbqlbf2UCAADEKGsdmsPJZncotzh66RSLdx7TxCmb3B68j3yhrCzXYz9vrPJxbR7vkefk9IvGn6f/XNZXax4ZVeVzRMqaf43SRzeeocl3DXFrH//NuiiNCJGyYs8Jn+178mvffGO5xZZK+wzv3tS5vCnTew6eWFNssWv5nhNa61ESze6QPlq8J+LnL/9yg6856cLlj4M7aNXDIyN2fF88s4yksiCU6zxSnto3TvHZbjAYdFMPu565vG9Q53YtT/jL3UOD2icULTISfbaXWOrO3w0AAKDuIOAEAABQCxwvKNXmQ7l1osTZa79uj+r5r3xrsd6ct1MfLNztbMsLQwDM88vkb1030G3daDTo/05vp4xkc7XPFW4ZSWYN6drUq51vyNc96Yn+P3/5UZ7LSZIu6FNRaqy4tPKMJdfMlB/XHIjImGrSiULveYTKPfz9em3KzA37OXMKvQN7Jh8Bmuoaf0EPndGxkf4+pocapsSH/fiBxJm8fx6z0aCEAJlcB7O95xaritSEOM25f5h++/twNU0Lf6ZrakKc/ji4g1f7wz+s1/Ldx8N+PgAAgOog4AQAAFALXP32Eo15cb5enR3dYE04fLhod7SHIEnakVUgq82uKesyw3I8zwwnYwQe2ALVFaj042OTN9fgSLw1SY1Xm4ZJzvVgAmCl1orrLs4Y+/98nb7hUMDtF7w0P+znPJRb7NUWifmwbju3s768dZCS4mu+fKOvz4bJaAh4ny4MIuAZrA5NUtSmYXLYjufJV5bTj2sO6vI3FkXsnAAAAFUR+3+xAwAA1CHPzdiqdftje14dg8H7Ad+TkytK2dnsDi3YflR5QZTTqo7tR/L1zcr9+ssnK93aO4yfrLs/X6XFO4+FdDzXTIt7z+8WljEC4RYoSfK7VQd1MLtIDocjKtmUR/NLddOQTs71Ec/N1dytRwLu4xoYOc+jjGUsOprvP8OpXLjfG1/zei3dVbcyY/yV1KsrWjVIqrwTAABALVB3/gIDAACIEeXxmD+f3dHn9sd+3lCDowm/I3klXm1vz9/lXH7vt1265p0luvbdpREdx4o9JzR5ne9sgh9WH9SVby2Wze7Q+G/W6vr3lvp8KOtqx5ECSdLtwzrr7vO7hn28QDg4FDhYMXjSbHWcMEUdJ0yJ2BgO5Xhn1JRrnu6eqXHDe4HvA+VzDknS1sP51RtYLdC2UeWBg73HC8Nyrl+3ZOl3r8zX719d4LXNNdOsLjD5KKnnKwjlqmfL9EgNJ+x+17dltIcAAAAQFAJOAAAANWz2fcM04YIeun+07yyZZbtP1PCIao7D4dB/55SVDVyzLzvi5yutJIjU+R9T9PmyfZq39Yi6PzRV0wKUu5p3MhPj40V7wjrGaLv781XRHgJ8mLnxsOZvC5z940soyTE7jkQmgHP124urtF9RqU0vztyq3UcLnG2uGU6P/7zR124xJRJz/Pjzp/8t0/oDvueEqmsZMzab9wd/51Hfn++HxvVUw2SznvvDKZEeVthQwhUAAMQKAk4AAAA1rGOTFN16bmclx8fJ3zOkY/klEZljI9q6PfSLTviYwD5cbHb3h46Ld4ZWNurWj1YoMyfwRPJ5Qcw7E0t+WH0w2kOAh/wSq276cLmue3epCktD+7yVB5yapCZo8l3naN2jo3TX8M4++5733NzqDtWnnS4BI0macEEPSdK8B4b77H80vywrsucjU/XizG0a9uwcSdLU9Ye0+VCes98ZHRtFYLQ1y+ojMOLpkR+qn+Va2e+PET1ivzyhq+MF3qUKz+7SxGffG8/pqJUPj1SvVrGT4QQAABArCDgBAABEkd3Ps8cBT8zUpf9dWLODibDcYotbeaxI+Hlt9YMnf/207mf8XHpa62gPAQGUWiuCBblFIQacTpbUMxik3q0ylJZo1l9H+A44SWVZReFwNL9E17+3VNe8453ddOu5nbV70ji1a5zsc99f1mV6tV311mLd9vEKt7YzOsR+wMkzKC5Jfx3RRd/cPti5Xtm8VsG47t0lAbf7mmsvllns3gG2ZmmJPnqW/eyx+POvenikxlFaDwAA1HIEnAAAAGqpdQdyoj2EsImPM2rnkYLKO1bTuv3Vf82W76m7JQ3LNUqOd1t3hFKHDRHn+n6EmulYvqvn4/ThLX0f5+PF4SkR+Z9fNmve1iNasP2YW/vvT2nl1feGQe3d1n2VC1u085hXm6+gQqyx+gg43Tequwa0b+jWllfsPxO0xGrTe7/t0mo/ZUm/XrE/5OzOWDeoU2NJUmpCnFY/MlI7nxob5RGFX8OUeL12zWkyeVwv5RmCAAAAtQEBJwAAAIRVWmKcV1u/1hma4iOLIZwO5RTrnd92+d0+slfzsJznioFtwnKcaPL8cn+kM88QGtd3I9SAU/7Jko9Zee4PoS/uYNfc+4Zo+5MXaPuTFzjbU31cr1VxKLfYZ/tzV3jPkzNhbE89eUkf5/reY4VBneO3bUerNrha4IfVB/TD6gNeGU7XnNnOZ/++j073e6zXft2hx37eqItfW6DtWXle2+//ak31BhuDOjVN1a/3D9PCCSPUIDneLYh57/m+50uMVZ6foUM5vq89AACAaCDgBAAAEEWntm0QcHssZp74ekBudzjUpVmqV/u2w3n6+9drtf9EcA+cA3l88ka/2/5zWV+9ee2Aah2/Z8uy+T5+1887YyPW7PZ4wD/82Tk+S33Fkqy8Yk34dp3WRyAzsMRqk93P61NitfmcP6Y67C7XfWmIAadbP1rhd1urBkmKMxkVZzI65/AxhVhabN/xQo14do4+XbLXrX3hDu+MJEkym7z/yZloNumaMyuynN6ctzOoc284mBvCSGuPwlKr7v58te7+fLUzG6Vb81Q9NK6n/jG2p7Pf+T3dg+K3fLhcU9cf0jPTNmvX0QLZ7Q5ZbHa9PGubs89rv+7Q/xbsUofxkysN3C0YP0Kf3XyWVj8yMow/Xe3RsUmK0hPNXu1nd2kchdHUHGMMlgcEAAB1FwEnAACAKEpNCJxdkFcS2vwt0bZ6X7aKLRUPyMsfaq/cm+02L025kS/M0xfL9+n6d5dW+9zHApQVGtihkYxGg4Z1bxrUsXwGzU4GHDzLGcWiGRsPu60fyC7SNyv3R2k04THhm3X6bOle/e6V38J63PwSq7o/NFWd/jFFDofDKwjc/aGpOu3xGbrrs1U6EULgyTXA53Vcl0WLNbRAYE6R/1JsrhLiyv4pWGINbQ6nl2Zt086jBfrHd+vc2n0Fzxsmez/8D6RT05SQ+seK/OKK+3h5cLJdo2TdNKSTUlx+B1x0qnswe/rGw7rt4xV67dcdGv7sHF3zzhJ9tdz9Ov1u1QH9+6eyYPvQZ371GxiVpNYNkjSoc2M18CipWdfF4nxNgfzp7A7RHgIAAIBfBJwAAABqseLS0B4GR9vFry1wW7/wlIoJzs0m/w/9dh6t/vxOgQJB5Vkcc7YccWv/4+AO2j1pnFf/XUcL9MqsbW5zY1hPzh9TV79N/uDXa2vkPD+sPqD/TN3sN6PqpzUHtbEKmSxbfZQWC4cPFu52Lo98YZ46Tpii+75cI4fDoVmbKgJ3P645qP6PzwjqmLuPFqj/Y9P19NTNmrbhkDpOmKKOE6Zo8cl5i2ZtznL2LbVF5h6QaDZJkkp8BIKP5JVo2DO/6pWTmTR2u0MFJVZ1GD9ZX6+oCHh0GD9Zd3++ShabXSt8zH0278HhQY1l4Mn5iwIFS2KZaxD+v3N2SPJ9vzq3koD4op3HtMZj3qZEs/s/6Ue+MLeKo6y76tot+5Hf9XILzlrrwNxmAACg7ghPwW4AAABEhK+HwbEkt6jim/0vztwWoGf1BQoEJZi9v2f15nUDNLp3C5/9R70wT5K0eNcxfXLTWZKk8mfhcQECZ7Hi96e00o9rDnq1OxyOiGYDLNt9XHd/vlpSWbZFm4ZJ+nltpp66pK/i44xatOOY/vrZKknyGQgMxGwM33fplu0+rj+8scirfXtWviTpm5X7lZ4Up/8t2O3VZ9fRAnVsEjhT5+VZ25RbbHUGH8pd+dZi7XhqrCZ8W5E9VBpihlOvlunamJmr1g2SAvYrz3AqtngHtJ6eulm7jxXquRlb9dyMrQGP88Pqg/phtfdn6Z7zuyrNR3kzV3ef11Uvzdqm7i3SJEnWIAJOVptdcT7K9NVmHy/Z49UW5+Pzmp5o1lmdGmnxzuN+j/Xbdvd5rFyDWZK040hF8N5gkGKwKmvYxf4d253BYNDs+4apw/jJkuQzexgAACBaYusvdQAAgDqmsmf7oZa7iraWGYnO5SsGtnE+1JakzDBObJ6VV6IFhw1uATlfZfDKlc/rMe+BioyLUb0q5ktZMH6Eep2co8nVgu0V89KUZ+TUhQyne0d289le5CP4EC4Oh8MtiDNlXab++L9l+nrFfnV76BcVW2zamFn1OXrCGQj0FWzy5CvYJJXNh2Wx2eVwOGS12TXy+bl67dftyim0yOFwaPfRAn276oDf43b+xxS39VDncEqKL8tcenBM94D9yl+vA9ne1+VXK6pXXnFI1ya653zfnzFX8SfvD1Zb2bVVfo31buV9LZYL9fWoDd7yMUeVv4zMl6/qH/BYB7KLgj7v9HuGBt23LnOdE+13/VoG6BlbujUvmxeRgBMAAKhNCDgBAADUYp7fXq/NMnOK3IJKJqMx6EyEJqmhzSny+OTN+nKnSX3+PVNr92dLkiy2ioeKqx4eqdevOc25nnzyIXy7xsnaNXGsdk8a55bJ07pBkqbcPSTgOW11aA4nf+UNg53/pyr+M3WL23q+x/xkXy7fp0Av7fLdx7Vwx1G/210zRnYcyXcue865VBO6/vMXdZwwRV3++Yu2ZeXrmWlbdMpj0/XP79dr2LNzQjqWJYSHyTa7w1na7kie/znNJOmLZfskSZ8t3etsK7HaKt2vMinxJr113cCg+pZ/DsuDxeX3jwdGuwfL7hzexbkcS/fEQOL8fNjTEkKb9yqQrs3TwnasWOaaJfjIhb2iOJLwKg/YlsRgEBYAANRdBJwAAABqkQEn5zIp9+a8neowfrKW7vJfYqm2uPeL1W7rNrvd70NVT0O7BZ67xNPUDRVz5/z+1bJ5o1wznBqmxOuCvi218bHR2vnUWLfgUlVLxpXPkxHsz/T/7d13eFNlGwbwO0nTvUsHu2WUsvcoe08HDpYTxY0DVBAUUFABQVFEFPdCZSiOj7132XtD2aMtq3TRNk3O90eb05zkZLVpM3r/rsvLM95zzpsmb0jOk+d5XZlcOS+gbANOP20/J1nvWi9Ksn4l/a4ke2zZoWvicoFWh4fnJeGRb3fiTo58Hy/dyhGXtxWVHVt++BpafrAW28+YD1QZHq8PgoX523fTf2S32ja1+33nReuNjNia0bP00FVJdpS1cpyGAVoAOHz5DupNWInWH6616Xq/P9MWC55rJ9lWq1IAjk7pK2ZZWaN/HWqMSukZZqTUjgzAm33qicEpd8v6BIABMlk1xgFXPVv/draqFOjj0PO5ozMGAehAH8+ZVUBd9IMOe4LSRERERGWNASciIiIiJzIOftQymvvlf0Xz7Az+Wr7E19nrWZi+4gRuZeeXTQftYDzvyOErGagaZnkeGb0l+65g9dGUUl0/XubX/P7eXlA6KECUmVt4gzjI1/1vWJrL0srOK/3N/AOX0nHW4AavnnFmyufrpHN6FWgFyXM18vd9xcca3FBdfuQa5BjO/zPp36P4elMyXvptH25l5+Ox73da7PP5G9noNGMDOs/YgOPXMnDbTFDLnFe61y2zUl1L9tlW3u7l3/dL1kd0jLP5GuuOp+LeL7ba1a8WNcPQsmYYakb4i9t+e7atXedQexXfMDcMGDerXhx4b1Q1BADg41UYiMlzwwwnufm0Vh9LlWlZ6BkbnrsHm1e16drzHmuBED81Ph7U1Kb2nugBg7+V/nXkCfSl9NyxzCQRERF5LgaciIiIiJzI+La/r9q+m2HDvt2BeZuSMWbxQcd1ykGuZ+aidWy4ze2f+3WvTe2Om5nnJ6AoM+DpDrbfaLeVIAjILZrfyM/O58id6EpZfi41IxcD525D90822T2vyPdbz0FlJvss12BuqfFLDiM9xzTAanzotBUnxGWdADzy7Q4xgGts2eHCINat7Hz0m73FZP+YPvUkN60NDWtTA75qFb54pAUOvttbto05pz/shx+GWy4/d7OEwWRr7yX/jOwgLo/4eY9sm9WjO+Po5D4m2yuH+MJXrYJapcSa0V1wfEpfnJ8+AJVDbAsw66mLAowrj6ag7jsrxO3Bvl6YdE8DVA31w+tF843pM4Ky8+Uzg1yZVmffuJpwTwOse6OLZNvZqf2x6PlEBPl6YVTPurhokNEHAM91riVZf6V7YRnCVrHhODCpFx5uWa0EPfcMgT5e2DOhJ/ZN7OURJVH1jl4t/Ldw2vITVloSERERlR/3/3kmERERkYeIjw60+2ZYakbhfCubTl0viy6ZyMjV4LcdF5FQOQhd6kZazB6KCvKV3e6tUpbqF9mGmS+GUjIK538JtbMcmi0KdAL094w94Rfyhq+zI5P7YODcbTiTloUCbekCToY3wVceTcF9TavY2S/57edvZEvWT6dlmQQzwwO8kZN/1+y5tyffxPbkmwCAe4v6JQgCPlt7GrONsq2MPde5Fu5qtKgS6ouBzaoiwMcLp1Iz0aJmGIIMSnSF+Kkx8+EmGPPnIYvnA4BTH/SDWqVE94Ro7HqnBzaeuI60zFx8vPqUpF2/RjFWz1US1uZN++vF9rJZg+enD5Cs6+eRKQm1mSfcS6XE0x3j8LRMps/3W85h1pBmJb6mM9gbcAKA2pGBmPFQE4z96xD6NoyBUqlAm7hwHH6vMAD42Vrpa9Y44PpCl9oG+zwnyFJSnlxa8Eq6+fc9IiIiovLGDCciIiIiJzLMllk9ugt8LNy81VgI0hSU4IZmSUz65wg+WnkCT/24G19tSrbY9lhRJtKonnUl2wc2ty8IYejy7RycvZ5tsn3h7otYVTSvk7mb2KVhOB+Oj9r9P0KHB3hjVM+6GNOnHgJ9vMR5qUqb4WQ4v1VufnFW0sFL6TYdrzDI+Qvy9YIgCEiYuAIPz5OWlNx74TZmrTmFbIN5cDrUrmTTNV75Yz+uFt2gXXs8zWKwafbQZvhnZAeoVUoE+6oxpk8C6kYHoUqoH7rWi0Kwr9rkZv6gVtVxfvoAk8CMMcNATVSQLwa3ro6Xu9fFqQ/6YdmrHfFgi8KMqpI8JSF+1oOu5s77Zu94LHo+UTKfXLPqoQCAOlGB9nfGAn2Q2B5L9l9xaB/Kgz7gZJiFpM/csmRw68LX0rzHW5rse7t/grj838sdMMIgs/PUB/0Q4EFzFZHz5Gq0+HbzWZxJy3R2V4jKTVpGLoRSfh4iIqrI3P/bMhEREZEbe2dAfcRVCsD79zcEAIsBp09Wn0JqRi5WHrlWol/MO8K642ni8sxVJyEIgtkv5frH4mWUBZVSlJVVEisOy8/z9NZfh8Vltarkv+Z/uVsdk23XM/Mk5eG8yyCg5QyjesZjZNHjVRYFTUobuNx/MV1cjgwqzCi4dCsH608Uv24szS10Pav4tZGZW4Ar6XdN5n4CgOkrTuDzdafx6Hc7cSMrD9l5BVDa8bQM+LywbN6iPZcstru/WVUx2OJI643KpRny9lKiYZUQ8TnRPyWCIODOXencUhm5GrHcoOEcUgcm9bLah2AzQamXu9dFmzhp9ti8x1piZLfa+OXpNlbPa49NJ8snM9PZtEXvkf7eKvRuEI3WsWFiybuSeq5zbWwZ2w1np/ZHk2qhiAr2RfLU/jg/fUCpss6IDH25MRkfLj+OnrM2O7srROVi4e6LaDN1HT5efdLZXSEiclv8JEpERETkRNXD/bHhza54PDEWQGEpKXPmbUpG26nr8ML8fZi+4ni5lNHR6gScScsSg0qZedL5U+LGL0fc+OW4dCsHdY2yHzrHRwIAVEaRgFqVAkrcn1syc/cYK83N1jd6xyM8QFpqbPiPu5BXUHhTX61SWCwj6K68ioJ0ulIGnKYsPSYu/3vgCgRBQKcZGyRZRA0qByPMoOzhpHsaiOtXjV7ThoEqOQcupaPVB2vR6oO1CPW3XCLO0O0cDfZdvI01x1LNtnmpa22z++z1ZGJNJE/tjy1ju+HkB31RK9J6ptCfey8DAD5aWTg/y7O/7EHTyatx6HI6gMLMg8Sp65AwcSVuZuVh6aHCeahe7FrbphJqtmRB6cWE+GJMnwRUCbVvjiZr7AkSujP9uFIpFPjmiVZY/EJ7h5S5qx7uL3k/8qT5icg+aSXIFrTF/ou3xWVn/dCFqDy9+99RAMDcDZaz+ImIyLwK8hGfiIiIyD3Yervw2y3n0GH6enG9UdXgMunPG4sOoOesTZi/8yLSLQR7Os3YgDiDQFLDKsGYdE8DAIDxPdChbaqXuD9fbbR+A6A0GUgKhQJLX+mIuY+0ELcdvZohZtl4SnaTsUOX7wAAbmTZl312Ji0Lb/15COuOmwZuCnQCnv5pt8n2LvUisXJUZ3SrF4lnO8XhicSayM4rDOilZUqvP+nfozb1465GK8lCs8WDX263uH9s3wSL+23x8aCmeLZTHN67ryFUSgWqh/uXaA6w65l5WFuUXTiuKJtv0LwkZBeVLWz5wVqxrdxzYYtXutfBtnHdS3RsScmVQTw7tX+59qE86G/Ue2KwmlxDm6nryuS8SoPA6HdbzpbJNYhcSVmUZSYiqmhY2JmIiIjIhZT0R+8luYlti38OXAUATPnfUUQFWZ50PbUoWDD9wcYY2qaGuN14npZ60UGS9QebV8WS/VfQPSHKan9qRvjjws0ci21Ke7OgSqgfKof4SrYtO1T4d/BRl83f2VWM+fMQBrUqDAgKgoD0HA3CAsxnDvWctQkAsHDPJRyc1Fuyr37lYMxcZVqSplJg4evox6eKy7PlF81PZinjyJrvt54r8bHGZj7cxCHnebhlNYecp/WHxQGlY9cyMOTrJBy+cqfU14wJ9hXH5xu965WukyVwf7Oq+GTNKQDA8Sl94eftmeNLX1KPGUjkbgxfswt2X8LzXRyX+UnkipQOyD4lIqroGLonIiIiciElnaO4QGtfdoe9NFoBfxWV+DLn4KV0AKa/4jeep8W4lFTHupWKrmH9MdhS5uxWtvWye9YY91Ef5PLUDCdDf+29jGNXMxA3fjmav78Gb/992PpBAB6eJ80YOns9uyy6ZxN/o8DFjIeb4Ny0/ujTMFq2/VePtsDxKX1x6L3eOD99gBh0c1U7z90yu+/ZTrVsPs/aN7rg8XY1sej5REd0y241IvzxQpfamHhPA6vBptLOeeRM+pJ6xvPZEZXG4FaOCWhbYviKNTdfI5E7yCvQ4sX5e/HHrosW2/Ftmoio9Dz/GzMRERGRG9GUcI6Eg5flsx1KI1ejlayvtjH7RGUUrJHLOHqzdzwAYGS32uIvqG2ZH6LAqE27uDCTNidSMm3qpz2u3imcW8hH7fkfn99YfBD3frFVXP99p/zNma2nb0jWT6dlSdb/2mcaoHy0bQ2TbbYa29e2DJwXu9ZGbIR0nrAjV+5AoVAgsVaE7DHNaoTCz1uFYF/b5zVyRf+93MGuuYECfbzw/sBGaBMXXoa9smxcvwSM6BhntV3LmoVjvazKhzqCVidg86nryMjVIL9Ah3vmbMG4vw6JmaI5+VorZyCyXUywr/VGdjhy5Q5e+HUvdp+/hY9XncTFmzmSrGuGm8id/bbjIlYcScH4JZZ/RJPN92kiolLz/G/MRERERG7EMMtn0fOJUKuc91PLd22cP8fYXaNAVe2oAJM2I7vVwcY3u+LN3vXgpSz8SGocTJLzzt9HxOVKPgK8vUw/zgb5Or5q9LYzNwHAajk/T2Ec/NtyWpqllpqRi8e+32nXOfs3jsH79zcqcZ9e6loHneqazvmjp88eUSqArLwCyb7H2tUEAHSpJ1+2MdDHfSuNJ0/tj+c718KWsd3QpFqos7tTZvSB6QKt6972fvn3fXjih1149Nud2HbmBo5cycCC3ZfE/d9u5hw45DjPdrY9m9EW98zZipVHUzBoXhK+2HAGnWdukPyQwNK/f7vO3ULy9Syz+4mcbcPJNHH5WtGPiADgdnY+nv91jzj/ob3zQRIRkSkGnIiIiIhciGFpvDZx4Tj9YX+bjz18+Q66f7IRq4+mOKQvC/dcst5IRp7Rl/VqYf4mbRQKBWIrBUChUNic4WRcci8hVMDm0zdN2vVpGGNvlyu81rGmmWKG3l96TLI+euEBu6/x5aMtTcot6vVqIF/qztjnQ5vj3XsbYO+Enib79AFLpUKBdrWkGTvxRfOGxVUqDn4altcLcsHMpva1pdlYYf5qLHiunUk7lVKB8f3ro3q46TjzJPZkQjrLiiOF772Hr9wxCbwDQCUr8+AR2cP4fetOjgbrT5R8Hjw5F29Z/pGFIAhYeeQaBn+dhB6fbGLZPXJZhiWRz98ofl1/vPokVh1NxYif9yB23DLJMbaUeiYiIlMMOBERERG5kKc6xMFPrcKwNsWlx2ytkPXS73tx9no2nvt1r2R7ed8A6t9YGvCpGVF8I/znp9uYtNdnpljLcHre6HHdW1N6I+D5LrXw38sdkFhbvmwamSdX9tCQYVbJhhNp2J5sGuiz5PsnW1nc//nQ5maPe7BFVXz0UGMAQFiAN57qEIeIQB90rRcpe4xCocDu87fF9R+fai3Zf376AJyfPgCfD2uOLx9tgYPv9rbnoZSbrx5tKVlXKRVoVysCNQwCS2tGdy7vbjmNPhNS6yY3tF/6bZ/JtnfvbeCEnlBF0XTKajz90x68++8R642NGGeF6lkbbnHjl+OF+cWvdZYjI1dl7jPD9cw8s8ckTFyJOzkau681c9UJvPDrXnH+PiKiisZ9a0cQEREReaAqoX449F5vSQDgnf718cGy41aPzc4zvdHzyeqTWLTnEv57uSOiHTzfgzmVQ/wk6w+3rIYT1zLRtV4kusSbfuFXFZUNPHgpHVl5BbLlza5n5mH9iTTJNl+VtE1CTJBHlxQrS9YCSGdvZIvLL/6210JLU9XC/NCjvuUMJj9v6ZP56ZCmiAjwQae6lcwe+/79jdBpxgaT7UoFcM6gv02qhsge7+OlQv/Gla1132lC/KXZC1OKyhFuHtsNKXdycSMrD3WLMrcqAv1boitnOFkT5u/t7C6Qh3mpa218uTFZsu3npAuYbGf50gs3s603ApCWmYuoIPOfJfI0WrcuUUqeK9/ghzOCwYxkln5wo9UJ+Hv/ZQzvYH2eQUNzNxSOyV3nb6GdmbkjiYg8GTOciIiIiFyM8ZfftnG2fVmVuxE7Z/0ZpGbk4csNZ+zqQ16B5V8pJ0/tjzZx4XgisabVc/l4qfD+wEZmAwcqgxSuRu+uMtl/Ji0Lj30nnS8owKcwQNHXoCyaoxMfDMuveTq5QKCxm1l5yMzVIFdjucTM2L71JOt/PGtaBk7Oaz3qisuh/t7oHB8JhYX0vsoh8jc9lUbHuGK5PFs92KKquGwYHIsJ8UUjM4E0T6XSZzi5ccApNSPX2V0gD3N/s6om27ytZKzK0dg4N1q/z7aIy3LZ08/9uhdX0u/iSvpdk31EzmRYGtjHq/Az5Pdbz2HZ4WsWj9t/Kd3sPq1OwPoTqbiZVZwldTo1U1yWK62q9++BK/jfwavWuk1E5JYYcCIiIiJycV4q22rq3blrvuyHxs6btD1nbTK7b+fbPaBSKrDo+UQx6wIAOtSJkJ1jxpqd56TZNcY3sXrO2oSTBl/gAaBKUbChZ/0ocdvl2469wfWdTBk4by/P/Pj8w/DWGNSymsU2ey7cRsod0xvm/4zsIC6f+qAfnu1UCz0SolAlxBe73ulh89xCTasXB1Aa2xBM8VIpcW6a6RxnSgXQsEqwuO7Oz9m79zbEz0+3QfJU2+dy81T6wLSrBpxsKV3asqbludKI7KX/8YWhfK0O324+a9d5ss2U1DN2MztfXJYrg7v3wm10mL4eHaav5/w35DLyjeYWPZmSCUEQTOanlLPz7C2TbUnJNxE7bhlqv70cT/+0B70+3Szue+fv4pKWciX18gt0uJOjwWsLDuCVP/Yj10JQiojIXbnvty8iIiKiCkJtJeBkyw11e+vIX7plPnhjXJrvx+Gt8VLX2vjl6bYlKh2SZ5Qxk2PDHBCfDW4CAGgXV3wD19/b9MZbadSODMSr3etItsWUU1nC8qZSKjBzUFOLbYJ8vKBSSl+Li55PRLPqofh0SFMseak9vL2UUKuU+H54a2wf38Ni6SVjrWLDARS+3isF+th0jFwGlEKhwK8j2gIAHm9nPQPPlYX4qdElPtLk714RqWyc681ZbOlWhI2vayJbmYtzfrjcehlePZ1OwBM/7LL72v8esJydwRvp5CoKdNLPmW//fRgLd1+y6diUjFyTHxQM+3aHZP1WUSBWEATsOl8coDL+fP7P/iuIn7ACi/cWX9v4MzARkSdgcV0iIiIiF2dcIsyY8S839QyDTIZz8Dhat4QodEuIst7QjI51K+G7refE9ZtZ+QiwMAdEkK8X4qODcAbS4Feemb9DaRhn57zdP8Hh13BllQJ9cKOoVIy/jxfyjX6x3iauMEj0QHPL2VG2CPZV4/B7vcVSNyWlUADhAd44P31AqftErkOf6emqk7DrHF3Tk8gGQb7239LJzivA49/vhEYrQKkAmtcIk2QO7hjfAxtPpmHcksNmz6HVCXhz8UGL1+GIIFchVzLS0uvb2P5L6WhRw3KG6vXMPLxkNMel8Y9FRi08AACSeVnztFoA7lv6l4hIDjOciIiIiFycpYDTF480N7sv3aDE3q5ztxxyo9bRWUSA6ZxVp4zK5xkz9/cID/B2WJ/0utSTzm1ka3k4T/B6r3h0qltJXNcJgiS4Ob6f44NvQb7qUpfA81M7/jVKzqcf966a4eSqpf7Is4X62//vXqsP1mLfxXQcvnIHBy/fwU/bz0v2x4T4YmibGvjy0RZmz3E7J9/sPj1XDQ5TxVOS8o6G1QUe/HI7tiffwOnUTLPlU19bsB+7z9+WbLMly8/cj8aIiNyZXd/mvvrqKzRp0gTBwcEIDg5GYmIiVqxYIe7Pzc3FyJEjERERgcDAQDz00ENITU2VnOPixYsYMGAA/P39ERUVhTFjxqCgQFoveOPGjWjRogV8fHxQp04d/PTTTyV/hERERERuzlI5rXuaVJHdfueuBnkF0i+6liYvttXsoeYDXCVlPK/JM7/sEZeHfbPDuLnJ3+O7J1rhycSaeNjKHEQlERXkK7legHfFKRDwSvc6eH9g8RxdglHA6fFE1yhXp5/PS++AhQm+yX15KV17DidmOJE7yNVoLX4WaFC5eP67YF/5rIvYcctM5rVpVj3UpJ2LDlWqgApkMpzMGdExDsen9MXw9rGS7Y98uxO9Pt2MJ3/cLXvc9uSbJtuMA1ByyiI7n4jI2ewKOFWrVg3Tp0/H3r17sWfPHnTv3h33338/jh49CgAYPXo0/ve//2Hx4sXYtGkTrl69igcffFA8XqvVYsCAAcjPz8f27dvx888/46effsKkSZPENufOncOAAQPQrVs3HDhwAKNGjcIzzzyDVatWOeghExEREbkXXzMZG/+O7GD2mBkrT5j8atLWX1GeScsSl729lJKsppKU77HGV63CfU3lA2dJZ02/wBtnOPVsEI3J9zcyyZRylJ71i8sF+pVBhperUigUCPTxQlylAACFNw/1r6H46ED4u0jw7fvhrSXrcjd9yP2pXD7gJL+9WfVQfPN4S+x6u0f5dohIxhtWyuB9P7yVuKy08E/qyN/3Sdb/kfk84qpjlSoeaxlOL3crnK/zsyHNMPGeBvDzVuHZTrVk224+dd3m6361MRlrjqWKpYnlcA4nIvJEdn0rv/fee9G/f3/UrVsX8fHx+PDDDxEYGIgdO3bgzp07+P777zFr1ix0794dLVu2xI8//ojt27djx47CX6auXr0ax44dw/z589GsWTP069cP77//PubOnYv8/MKU7Hnz5iEuLg6ffPIJ6tevj5dffhkPP/wwPv30U8c/eiIiIiI3EBkkP9F8U5lfFOv9tvOiSYDJlhI4APDKH/vF5e+eaFUmpdOMTbm/oc1tLX1xLwvPdS6+6eDpWQxVQ/1MtunjezuSb2LMn4cAmE6E7Uz1KwfjkbY1xPVXutdxYm+orOgDTsaZm8Z0OgE5+QUW25QFczfXO9ethN4NYxAV7Cu7n6ispdzJxa9J55GdV4Blh66ZbTesTQ1UDin+N8DLUsTJBqOL5qshcjbj+SeNvdmnHvZN7IWBzauK22x5z7Yls/7ZX/ag1QdrMfK3fbL7rfWNiMgdlfhniVqtFosXL0Z2djYSExOxd+9eaDQa9OzZU2yTkJCAGjVqICkpCe3atUNSUhIaN26M6OhosU2fPn3w4osv4ujRo2jevDmSkpIk59C3GTVqlMX+5OXlIS+v+OZDRkYGAECj0UCj0Zg7rET053P0eYk8EccLke04XsiS2YOb4LVFhyTbrL1WMu9KAzPdP9mE0+/3tnqt49cyxGVvpYAhLatg4r+FGe0q6MrkNRqgLs5aUqsUVq9RnuOlXlSAuKxWCB49Rv83sh0OXLqDxFrh4uPUPzOfrDkltlMrrT9H5alyUPE8Jg82jXGpvrkCT/j3RdAVBpp0AnD5Ziaig31RoNXBqyizUaPV4YPlJ/DH7stQANgypguizATry0JevpmAvuDZ7xmeyBPGC1Dc/4e+2oYr6bk4evWOxfZT7k2QPGb9mAOAgU0r45+D5oNVcn+rrWduuP3fkKxzh/Hy5+6LZveN6xsPjUaDIG/TzzXv3Vsf7/3vuNljp95fH3/uvWxTH5Ydlh8/2bl5Lv23I8dyh/FSEfF5sY09fx+7A06HDx9GYmIicnNzERgYiL///hsNGjTAgQMH4O3tjdDQUEn76OhopKSkAABSUlIkwSb9fv0+S20yMjJw9+5d+PmZ/uoSAKZNm4bJkyebbF+9ejX8/ctmcuc1a9aUyXmJPBHHC5HtOF5Izq08wPij2/LlywEA4T4q3Moznedp+l9JME5o//t/y+FjoSpc4Q8ti6+za2cSUo8CvaoqcScfuHxoO64eLtljsCbCR4WbeQpotALmLFiO2sHSvuiF+wjiOCmv8fJMPQW0ArBtQ8UYn6tPFy9nZ6lQHHYqlHnntvj6cwW6TED/Wlm3hqW4zXHnf1+yNID+Oe44czNUCgFKAA/E6dAhWsBrScXvFQKAD35fj77Vyy8j0bB/PioBedrCMXPmzCkszz1Zbv0gx3Gf8SJ/W0f/Hn0lvXD/gt2Wb4wbv6efN3hfTb12BeYK5LzeqKDoWNN+/Ld0OVwoIZbKkCuPl+WHTT/H6CmuHcPy5cdk94UKwJBaCiw8K//BecWKFWhZSYm9N+x7kQ+ppcXSi0pkFyiwdftO3Djm2dnzZMqVx0tFxufFspycHJvb2h1wqlevHg4cOIA7d+7gzz//xJNPPolNmzbZexqHGz9+PF5//XVxPSMjA9WrV0fv3r0RHBxs4Uj7aTQarFmzBr169YJaLT+RJhEV4nghsh3HC1mSmpGLyfs2S7b1798fAPBa0mrZY/beVKHw1muxmk3bY8Gey3imQ03ERweZHJOZWwDsXC+uDx/YB37eKvQvZf9tkRJyHtNWFmbRfH7U/MfUFaO7IchbUa7jpTwev6v69NRW4K70C4Z3QAj69090Uo/kVU24hjqRAWhYxbGf/T2BJ/z7cjdfi3f2rBPXtYICWgCLzqrweL9EIClJ0n7FZRU+f956RqejLN57BdhTmAn6+bAWeH5+YWnS2nXqoj/LPLoVdxsv+s8AXkoFCgxKO1r7jGBM317vwKV0fHpkFwCgTlxNJKVdkj3uxSGFx9VpmYkBX0jH4QX/enile22brk/uyR3GyzcXkoCsTNl9Pbt3RY1w8z9Sr3wxHQvP7pLd179/fyz5dR9w44Zk+45xXdFu+kaz5xw9uAeOz9+HQ5cz0LRFS/RIiDLbljyLO4yXiojPi2301eRsYXfAydvbG3XqFH5gbtmyJXbv3o3Zs2djyJAhyM/PR3p6uiTLKTU1FTExMQCAmJgY7NolfaNOTU0V9+n/r99m2CY4ONhsdhMA+Pj4wMfHtGSCWq0usxdLWZ6byNNwvBDZjuOF5Pj5SGu892sUY/V1IjenyJBvCz+L/b3/Ks5PH2Cy//C5dMl6oJ8PlEr5X4U6Ws1KpgEwQxEB3ni5ex1EhwaIKf0cL2VP7ne3x65lutzf/eFWNaw3quDcebxY6ve9c5Nkt5fnY337n6Piso+6+Gv25fQ8t/2bV3TuMl6WvtIRJ1Iy8UDzqmj5wRqk5xT/+2gP4/YKZXFWh1KpxKs96uLzdaeNDxOPa1gt3GRfama+W/wNqfRcebxEB/vh6FX5gFOlYH+L/Q7wLS7Z+3b/BNzK1mDepmT8O7ID1Go1xvWrj02ntkiOiQkNwJxhzSVzohry9VbDW1U4vgoEhcv+3ajsuPJ4qcj4vFhmz9+m1MnNOp0OeXl5aNmyJdRqNdatK/7V2cmTJ3Hx4kUkJhb++jExMRGHDx9GWlqa2GbNmjUIDg5GgwYNxDaG59C30Z+DiIiIqCLSz1MCAENbV8eXj7Yok+s8+YP0x0HlFWwCgAALtf56N4jG3om98FSHuHLrDxVSleNrgMgT+KqL38sUHD5UxhpVDcHDLatBpVRg5WudJftuZ5uZW8wGGm3xzw12nruFl7rWhr+3hZq8MnQCS4WR893btLK43DauODD641OtEeJn+Qaq2uDzd79GlTGuXwLOTx+AptVDAQDRwb5mrlkFh9+Tz7JVKRXYc+E2AOCztaZBXCIid2dXwGn8+PHYvHkzzp8/j8OHD2P8+PHYuHEjHn30UYSEhGDEiBF4/fXXsWHDBuzduxdPPfUUEhMT0a5dOwBA79690aBBAzz++OM4ePAgVq1ahQkTJmDkyJFidtILL7yAs2fPYuzYsThx4gS+/PJLLFq0CKNHj3b8oyciIiJyE2pV8V3LEH81FB54FzPAx3zy/cyHm5ZjT8jQ2evZzu4CkVtpXiNUXC7Q8oY7lR/Dzwqz1pxC8/fl56NY90YXzHi4CZa+0hEA8Ehb0wxRjbY4s/pESiZ81SrMGtzM4vWXv9pJsn5Xo7W160RlxktZeOszsVYE6kYHitu71bOvlF2gzOfU8ABvtI4Nk23v713c/uVuxaVVDX/IcyYty64+EBG5A7tK6qWlpeGJJ57AtWvXEBISgiZNmmDVqlXo1asXAODTTz+FUqnEQw89hLy8PPTp0wdffvmleLxKpcLSpUvx4osvIjExEQEBAXjyyScxZcoUsU1cXByWLVuG0aNHY/bs2ahWrRq+++479OnTx0EPmYiIiMj9SLJMyun+5YFJvcrnQkUs/XLaS+V5ATYicqz46ECcSnX+zTsfLxUebFEVS/ZdwQtdOH8NlR+lwY9R5Mrf6dWODETtyMIb78em9IGf2vTf30Bf09tFfRvFWLx+A6M59OpGWS6VS1Qe9Jl2SiUwtm8CbmTmY2DzKjYda/jZ1NtL/jf7i19oj9hxywAA9QzmR1UpFfj9mba4q9Gierg/vthwprAfHvijMSIiQ3YFnL7//nuL+319fTF37lzMnTvXbJuaNWti+fLlFs/TtWtX7N8vX+uUiIiIqCLyNijpUS1MOq9lm7hw7Dp3y+5zbjiRhm4GExXvv3hbsj/U39v4kDKlKTAfSWNZN9eSWCvC2V0gMrH0lU5Iy8xFx482mL0xWF4+GdQUk+9riCBfzgVA5ceWG9k/DG8lWTfMwjDUvKhkGABMGFBfts3Mh5tYvJavWokr6XdRJcTXIzOzyT2IASeFAsG+asx7vKXNxxpmNdny4yfjl3n7OpUAALlF2X5qlULymZ6IyBPxXY6IiIjIDRjeqDG+kVo9zN/isR8+0AgJMaa/Mn7qp93Yfb44UPXsL3vF5YdaVCtpV0usVmSA2X1eDDi5lMqh8nMWEJWlNrHhZvcdm9IH3l5K8eZgfoFOUhKsvCkUCgabqNwpbLjDUyfStqwjhUKBpa90xIcPNMKIjvLzJw5qVd3iOaatOIEO09dznhpyKl3RPwUlCXqGBXhj9tBm+OrRFvDxMp+JP6ZPPQDAlPsbye73Vatw6L3e2D+pN5RKBXo3iBb3CZzrjIg8DANORERERG5GAekX5nfM/PJYz0upwImUTNl9+4omLRYEATey8sTtcvM5lDVzvxxtWi0EXvw1qEtpVdP8jX+isjJzkGk2xfv3N8RnQ5qJWRqG2Ro5eZw/hioWWzKc/H3M3zQ31qhqCB5tW7PU2UmzLZT3Iypr+gynklZnvr9ZVfRrXNlim5Hd6uDUB/3QJs7856NgX7X4o4jmNYrnfdpnVGGAiMjd8Zs7ERERkZsxvu8THuCN13rUNdve0g0ofQZAVl6BZLvcfA5lTa2U/2jq44S+kLyJ9zRA7wbRGNbG8q/aicpCzQjTLMjHE2MxsHlVcd3bSylmRN7VMOBEFYst99MrBfqUeT/sodHqMGv1SWw6dd3ZXSEPdOFmNmasOgmg7OdOsqeU67rjqeLyfweulkV3iIichgEnIiIiIjcj90vjED/zpZss1Zz/ePUpAMD25JuS7c6Y/0RppmxeSeanorIxomMcvnmiFefiIKcb27cezk7tL7tPXZQRWZ4l9XzVhddsWi2k3K5JZMzaDfWtb3Urp57YbsHuS/h8/Rk8+cMutygtJggCjl/LQH6B80p2ku0e+HI7rmcWZvBfuJXj5N4U23OhOKvp56QLTuwJEZHjMeBERERE5GZiI0znbHqkbQ30bhCN6Q82Ntln7QbUm4sP4lZ2vmSbjxMCTuZ0rRfp7C4QkYtRKhRmg9Sqou26crp5rdMJyNUU3nwe1Su+XK5JJMfabwGqWZnz0RH+ejHRrvYT/zkiLl83KO3rqr7feg79Zm9B/IQVzu4K2cDw8+2ZtCwn9oQ80Z27Guh0AgRBcIuAOVF5cZ07CURERERk0aLnEzHjoSZoFWtaH95XrcI3T7TC0DY1MLqn9IanSqlA9XA/s+f9c+9lhAd4S7apXWjOpA61Kzm7CxVa/8YxAIBhbcp/Xi8ic2qEm79xrr/priunez+HrtwRl73MBMGIykNZlwwDgHmPtQQAfPSQ6Q9cAKBlzXBMfUB+nzXucL/2g2XHnd0F8jCd6vJzrjs6evUOWn+wFq8s2I8nftiFfrO3IK+ApXyJAAaciIiIiNxGm7hwDG5tfe4cfYBAz0upwI/D21g85pek85J1lQvdNHWlvlREswY3w09PtcZ79zVwdleIsPC5dhjXLwF9G8aYbaO/6a4tp4hTnsFcUV5m5qIjKg/l8c9l30YxOPlBXwxpbf5HCGoLpXz1jly5g++2nJVss1amTldeUWSiMlbSoCy5jl+2X0C+Vodlh65hy+kbOJGSiX85HxcRAAaciIiIiDyO8Q+clQoF6kQFWjxm2xnpHE6BPl6O7pZN5OaXsOXGFZUdX7UKXetFwcdL5eyuEKFtrQi80KW22XJ6QPFN9/Iqb/Pqgv3lch0ia8ojwwmA1X8P5OaBNJ5T7Z45W02yhfIKtLiRlScbeNp06jqaTF6N/w4694ZuXKUAp16fPMNQgx+QbTl9w4k9oZKSe7sd++eh8u8IkQtiwImIiIjI40i/AdmbIbTitU7w83ZOcKFamD8WPtcOYf5qcZulG8tERMb0N93LKxkiNaN43hlvL75fkfMY3wAd0TEOr/WoW+79kCvL+z8bAkVHr2ag1QdrET9hBd7++7Ak8PTkD7uQlVeAV/8oDPCeu5GNa3fu2t03jVaHvRdumwTA9HQ6ATNXncDqoynI1ZiWxzIsQTxr9Um7r08EFH62ZSk991ZO8X0it8SAExEREZGHMY7P2Btwql852IG9sV/bWhFY+moncV3NElVEZAd9kFrnhAlhYiOY/UDOozC4A/pg86qYeE8D9GoQDQCIMJqrsSzJBZxuZuVbPe61BQfE5d93XsSSfZcBALvO3ZK0S8/JR7ePNyJx2nq7+/bhsuN46KvteGPRQdn9K4+mYO6GZDz3614kTFyJG1l5kv2HLqeLy5+vP2NTJuWrf+zH5+tO291X8mwsGe3eLt+2P+BNVFHw2zsRERGRh1EY/eQuO8/9JrCtHOwrLveoH+XEnhCRu9HfwyuPOZz2X7wtWY8I9CnzaxLZYsn+KwCARlVDsO6NLtg01rRkbVnxkrmRHh8TZPd53l96DIA0yAMAF2/liMv2ls78aft5AMB/B6/KvkdcTZfeRG71wVqsO54KAHjm593QaKXHpOdozF6rQKtD7Lhl+O/gVcxacwqHL9+xq6/Okl+gw8LdF3H5do71xm7kk0FNnd0FCZXB5/Xj1zKc2BMqCXOlEPMKtMjKKyjn3hC5FgaciIiIiDyMVictE2OubIycP19IdHR3SkSpVODE+31xcFJv3sAlIrvoS+qVR4LTGIP5Goa1qW6hJZHz1I4MLNe5GbPzTW+2PvnDLsSOW4bTqZl2nKdwTifjuZ7m77ggLp9KzbJ4jrwC8z+6uZ6ZZ7JNLgg14uc9eOm3vVh7PM1k38I9l2TPvfX0DQz9Zodk271fbMWGk6bncDXfbjmLt/46jI4fbbDrM6SrC/J1zvyk5hjOddZv9hYn9oQcJcjXC/UmrESjd1chm0EnqsAYcCIiIiLyMLka6c0Be2qMO7ucniFftQohBnM5ERHZongOp7KPOHkblA6LCvK10JKofPl4Oe92z6aT183u6/XpZoz4abfN52o7dZ3JtkV7LovLfT7bbPbYzaeuo8GkVfg16bzs/t92XsDd/MKA1CerTyJ23DKzmSbLD6fIbpeb52l78g089v1O7Llw22TfUz/a/tidZcfZm+Jy3XdWOLEnjuXtxDEhJ9iXn3HdWcuaYSbbMnOLg0yn7AiuE3ka13q3JSIiIqIyY3zzqV+jGMn6pHsaIKAcfwFNRFQW9EF2bTkEnLxUxRF9uTJiRM4yvl+C067doIrlH6+sO2F7lo8tpTGf+Vk+iDPy933Q6gRM/PcoAOCOUfm7OevPYOg3ScjM1WDO+jMAgH8OXLW5bwBw5Mod7L1wCyuPXANQOL/UI9/utOscrsb482LKnVwn9cSxXC3g5KN2rf6QfbJyLWcwGZc4J6pI+O5GRERE5OH0v/b/+6UO6N+4OMhkHFy6t2mVcu0XEVFZ0E/knVoON0kPGczJ4qXi12tyHaH+3k679vD2sQCANnHh5XK9tcfTsPmUaVaVYRA4dtwyNJ2y2qTNwct30Pg90+32XPuhr5Lwwvx9+GvvZew4e6vE5zJW4KRydmqj97J200yzzNyRM7P+5DAc4d5OWslgYkk9qshc692WiIiIiBxOf8OlQZVgfPloS3G7n1olaRcZxLmSiMhz/O+QfZkKpcUMJ3IlSie+HhUKBc5PH4BFz5dsXsgBTSrbfcwTP+yCziAbKjNXg9tGGU32sDTn1VMdYmW3v7H4IPy8pZ+tnkisieNT+mL20GY2XVerExA7bhlixy1DnXdW4ONVJ8V9L/++D8/8vKfMA1G2ZJW5I2+VynqjcsQMGPeVIzNPnbF9MiU1iSoKBpyIiIiIPFxYgPyvnGMrBZRzT4iIys8DzauV+TWaVAsRl1UMOJELceeX4xfDmpfouM/WnRaXS5O1BAB9GsbIzmu5Y3wPvHtvQ7NzXvobBZym3N8Ift4qJNaKAFD4vAgWyn1+ueGMZP2LovWc/AIsPXQNa4+nos47K/Dt5rMmx55OzTQpG1gScnEQubmq3I2rlbB7pG0NZ3eBSmjq8uNW23yy5lQ59ITINbnWuy0REREROVT72hFoa1TS5rdn2uK5zrXweLuaTuoVEVHZaVo9FED5lCtqX7uSuMyAE7kSpZtmT4zsVtts5seTiZY/t3y+7jQW7r5o03Xa146wuN9XrcSK1zph/8Re4ra/X2qPmBBfcdlY27hwfGMQCDIMSunLGOsE4OjVDLPXrRMVaLItv0CHLKPyXB8uP47XFuzH3XwtsvIKsOX0dfT6dDOaTlltMaAFAK8vOoDOMzaYLfnVrHqYyTa5koXuwJXfl+Ojg5zdBSqh+TuK32e+eKQ56pl5Lo9cuSO7ncjTMeBERERE5GEMa9T//mw7kxs3HepUwtv967vc5MlERI6gv7+os3LT1RG8VcXvr6eszOdAVJ5qRvg7uws2ebB5Vcn6mD4JZttOvr+RZN04mwgA3vrrMIZ8nWT1uiM6xpme/76G4nJeQWHZurAAb+yf2AtHJ/dB8xrFgRhfg7LEDxQ9hp3nbmHNsVRx+7JXOsr29Z45W832665MJlH8hBXIzjPd/u+Bq6g/aSUavbsKj3+/S9yeaWHumLTMXCzZdwUXb+Xgfwfly47q3zsHtyrOEq1UVHb5VnY+NpxIk5QvdGWG5aPlXi/Opp9bNcJMNQJyfZ3qRmLV6M6y+yyNdSJPxrsMRERERB6mTlQghrSqjpHdaju7K0RE5U4fAnLE7dC8Ai0e+HKb2fI5XiqDAP8u2zIriMrSkpfa44tHmqNhlRDrjV3ArCHNcHRyH0y+ryGOT+lrtt3mMd0AACfe74uJ9zTA0lc64piZ9jvP3bJ4zW8eb4kEg+yje5tWwTv966N1bHFGuD7gBBQGnQJk5nQ6P30Akqf2x/3Nqshex3AeLeMf/2Tkype+u3O3cHsbo+z01Ixccw/HxL4Lt5Fp5vzHDLKrDINjhvTBJJVSgdiiwKV+26B52/HUT7sxf+cFm/vjTAW6wufxtR51US3M9YKwL3apAwD8EZgbC3DBQCaRs/EdjYiIiMjDKBQKfPRwE4u/EtbTl9V7tUfdsu4WEVG50JcSc0SC09pjadh/MV1SJsuQl0GGU7OiUn5EztSiRhjuaSIfAHGGJ6yUwQMKy8092T4WfhZu3NYoCnz4qlUY0TEOjaoWBtR+e6atTf1oWi0E56cPwPnpA9C7YQyqhvph3mMt8f2TrTBnWHM827kWEmKKy2JdTb9r03lVSgVOptif3bjs0DXZ7R+vOgmgcM4mQ0O/2WHzuYf/uBuN31sNrUwWkmGJufS7Grz0216T+ZmW7L8CALhwMwfnb+YAKA6EJV/PBgD8ufeyzf1xJo228G/wqIvOl6QPNOUbBDjJveh/eDKgSWUn94TIdTDgRERERFSBvXdfQ6wc1QmjezLgRESeQZ9IYG0eE2tOp2bi7b8PW76WwUxRTybGlup6RJ7orb7Wf/xijaVSaB3qVJKUTTP21aMtcH76APz7ckeTfX0bxaBH/Whx3TAjae+F2zb3r16M6fwtU+5vaLLt7NT+4nJ1M9k22fmFwZ8jVzIQ5GuaVWWPtcdNM5gMY1B7L9zG8sMpSJi4UtLm3I3CoNL25JvithE/75GU0budk1+qvpUHrU4Qg25qlWve/mTAyXNsO3PD2V0gchmu+Y5LREREROVCpVQgISbY7ATdRETuRh8EKk24SRAE9Pp0s/irfrPtDK7Ct1EiU46YN+ffkR0s7q8W5md2X7/GJcs6eL1XvM1tu8RHwtsooPGETABaqVSgflEpP62ZgHhwUZDpzd7xODipt2ybP55tZ1O/5AJxBVr5wMbI3/chr0CLtEzzpftqvb1cXL50667LB0k0Bo/VMBvVlaiL+pVn5nkh19S1XqTJtvQcy58XACBPo8XHh1T454D8/GlEnoIBJyIiIiIiIvIcYoZTyU+RdPamybYxiw9i/QlpxoDhNXo1iAYRSTniBy11o00ziAx9NrSZ7PZ1b3Sx+1pJ47vj+ydb4ZXudWw+RqFQ4NSH/bDwuXb47olWSBrf3WxbfVzq0KV0jF9yCHkFxeXsNFodMnILS+klxARDqVTg5AfSeaq+faIV2sSFo1XNMHSqW0myb8ZDTSTrp1Iz8fm607iZlYfUjFx8tTHZbKnAZYeuYf6OixjydXHpviUvtbf4uBfudu15684Wlf8DXDjDScUMp/KUV6CVLTVpr1qVAgEAL3SxPF9uXKUAyfqqY2m4lK3AmL+O4NHvduDIlTul7guRKypdfi4RERERERGRC9Hf3hZKkeN0Ji3LZNvivZexeO9lnJ8+wGTf4FbV4O/Nr9dEjjZhQH2rbRpWCTHZFhXkg9qRgXZfr3KIHyqHmM+YsqRtrQirbfQZmJ+sOQUA+GPXJcx8uAkGtaqONceKA9r6knU+Xiqcnz4Aq4+mIMhXjcTahdf488XCYFDsuGXiMYNbV8fYvw6J6x8sOw4AWHEkBYIg4ISVuaYu3coRy+kBQLi/N6Y/2BjjlsiXFr14K8fq43Umw38DjDPQXIWXQb/OpGWiTpTl4CqVXE5+ARpMWgWgMDg7uHV1m447lZqJj1acwOhe8eLccduTC8vnqQ0y5zrHR2LzqeuSY3VGv3xZfiRFXN525ibumbMV56b1Z6UJ8jiu+Y5LREREREREVAIKB2Q4Tfr3qE3t9PNEGc7lRETmDWtTAzvf7oHmNULx1aMtrLYP9LEtkJs0vjsiArzF9fuaVilxH8vSYZmMhjF/HsLFmzniTWwA8DEqh9e7YYwYbDI06Z4GAApvdgPAO/1NA3THr2VYDTYBQFZegWS9apgfhli4Ka9SuvYtRV1R0pBapZDMz+VKDAMWPWdtdmJPPN+P286Ly2P/OoTYcctsmnfpqR93Y92JNNw/dxuAwvkd9eMpM7d4zIztU8/kWONsqnUnrpu0mbvhjE39J3Inrv2vAxEREREREZEdHDGHky20OgHzNp0tvKZr3sskcjnTHmyM6GBf/P1SB4vzK02+ryF6NYjGgy2q2XTeyiF+6Fm/uKylykXn7DGn88wNmL+juERd34YxNh33ZPtYLHyuHeY9Vhi8e7ZzrRL3wbism1qlhEKhwBtm5rOqGupb4muVJUEQkJ1XgL0XbgEANNqy/teg5Fy11J8nmrnqpMm2R7/bafW4K0VlKPXBo4OXi4PGP20/Ly7LlavUGQScpi4/Lnv+j1efstoHInfDnH8iIiIiIiLyGMUZTmV7k3HJvstiRgADTkSO9WT7WDzZPtauY3zUxTfvvVw0o8VW3l62BSJUSoVNpfxsodHqUDPCHxdu5qBR1WBx+8vd64glAA39tvMihraq6pBrO1Lc+OXO7oLN3P116gnm77iAtIxcjOoZb1Mm3Hdbzspuv5Wdb7Lt6p1cLN5zCYm1I/DNZvnjiDwRQ+lERERERETkMco6+JOr0QIAkg0mpAdL6hFZFRvhX6bnVxoMfmUFjgK/3T/B5rZ1oornucov0OHCzcJ5mUb1KM5qUigUknKFetXCSjbXVVkynkPH1akMAhy2lo8k+51JM19ScsI/R/D5+jP49+AV2f2GbyX5BTpJecr3728oLpsLVo358xBWH02V3af3S9J5i/uJ3A0DTkREREREROQxxJJ6ZZTgpL+hqXazkl1EzrL81U4Y3j4Wf73Yvtyu+dfey+V2LVfzXOfaaFEj1GKbqqF+2PpWN9zIyhO35RZoxWXD7QCwZ0JPnPmwn2RbvZig0nfWwXafv+XsLthFYRDNaOegTDWSOncj26b5sUYvPIjYccvw284Lku2Gweub2dJxUdUg6NqngfkymFOWHrN47Un/HkWBVmey/XpmnqQsH5G7YMCJiIiIiIiIPIZYUq+MZnH6fP1pAICPQckrrc70RhERFWpQJRjv3dcQEYE+ZXodwxvDV+/klum1HCUmuGzmQZo1uJnF/YNaVUO1MH+k52jEbdvO3BSXexjMhwUUBka8jOYbmrshGbdzTMuIOdOc9Wec3QW7Na0eCgCo4qJzYrm7vRdu29X+nb+PSII8KgvZkoYBwxB/tf2dM5BnNIfa7vO30PrDtaj19nKcScsq1bmJyhsDTkREREREROQx9DeASpPh5F10Y1VuHpUjVzKg1QlQKYv37TjrXr+qJ/JEujKet83R/nu5A3a83aNMzh1bKQCrR3fGhAH1ZfdrZLIpDFUKNC2hBwDj+knL9T389c6SdbAMpJgJMtaqFFDOPbFP7waFwb08DX+4UBZKMk2WYfDHUnXOkynmS/WV5poA8OWG4uBpz1mbHHYdovLAgBMRERERERF5DP29odJUoUmoXFgqat5jLWT3j1l8UFJS7+KtnJJfjIgcQmsw6FeP7uzEnlgX5OuFJtVCTbbf06Qydr3jmCBUfHQQRnSMk913tmgOuv6N5cuAKczcZX+hS23J+sVbd0vRQ8eav+OC7PbyLOVYEvps2YV7LrF8WhkoyXRuydeLM4oM59kyfnq61YsqabdMaI1Oflomq+lMWiYe/36n25WOpIqHASciIiIiIiLyGGJJvVJkO+QX/dLYW6VC27hwk/1L9l/B9cw8k+1E5DwFBqUtYyNcO6ulWVEZNWNfPNICUUGOK61mLnCk3/zRQ01KfY00F4k5mctwC/YrXamzsmaYSbvp9HUn9qTiODq5j8X998zZKi4blurUaqWvsTCjMnrLX+2EAY0r46tH5X+sYokgCPjv4FXUn7gS6Tn58PdWSfY3nbwaPWdtxpbTNzBoXpLsnE9EroIBJyIiIiIiIvIY+ltDBToBU/53DOtPpNp9Dn25KbVKgdaxpgGnOlGB+Hrz2dJ0k4gc7FZ28XxChhmIruTLR1ugRY1QTH2gsbgtsVZEufcjpCgIE+DtZfexb/aOl6x/eMALV9KdH3XyMlM7TVWSmmrl6PyN4gzZjLsaCy2pJBSQPv/v3tsAAT62v+4NXz4FRvM1RhnNwdagSjDmPtoCsRbKOHavUnyOxS8kissnUjLx6h/7cVejRbMpa3AqVZrhdMfotVHnnRU2Pwai8saAExEREREREXkM/Q+QP1l9Cj9sO4enf9qDLzeeQey4ZTiVKj/fgk4n4GRKJpYfvoZfk85DU3QStZcS+TK/Im5YJViy/smgpo59EERkt1VHi4PL5jJ7nK1/48pY8lIHVA/3F7dVD/cr9374eBVmT5Tkz/Ry97om277Zcq60XSo1H7XKeiMXdODSbXFZo2VJPUczfo1XCbVvvBkGLAsMyt492raG2WPMZdtVC/PD/TV1mP90K6we3Vnyg5YnfthlV7+IXJn9P2UgIiIiIiIiclGbTxWWJLqRVVzybsbKkwCA3p9uxvnpA0yO+WjlCdmMJW+VEj9tO2/1mg+1rFbC3hJRRTeqZzzWn7iOR9pUL5PzbxnbDQcvpyM9R4MJ/xwBUFwmrKSBuZY1w7D3QnGgpBQVTB3GuARZqL8a4/slOKk3tjP8UcN3W87iYf574lDGr3H9nFnfPdEK/zt0FR8MbASFQoFG764S2xgGmQxL6hUYBATfGVDf7DUrh8gHtV7sHAekHULbuHCo1aUv9ZhfoJOUZCRyFXxVEhERERERUYVmrjyeWqVEZJCPuK6/oZmdV1Au/SIiz1cl1A+73+mB13vXK5PzVw/3xz1NquCxdjXFbRoL87/MsGFepxe61Jas38jKN9Oy/NSKDJSs75/YC0Nam89CcRWaguIgRm2jx0ClZxxS1QdoejaIxuyhzRHkq0agUYm9RlVDxGWlQfBJW5ThVCXEF/4WylGGB3hj6SsdTcbJoJZVTdr2bhBt0+OQM3vdqRIfS1SWGHAiIiIiIiKiCuObzck2t1UogOWvdQIAdI6PxPSiG7Frj6chyLfwZtPL3eo4vpNEVKGUdwnAX3dcMNn2WLsa2DexFwa3tp5p1bN+FMb0KQ6QuUIFw28Nfjjw+zNtXbasojHDgEbr2DAn9qR08gt0+GHrOZw2U7rWVejnL7Mk2Lc4mGQ4h5OmaA4nlQ1zxDWqGoJnOsVJtsm9Jlcfs3+eSb25G2z/PENUnhhwIiIiIiIiogpj6vITkvVcjdZs21yNFiF+apyfPgC/PN1Gsi8ztzDLKaFykOM7SUR20wd/h9oQMKnoEmtFiMsd6kSgdmQA3r23IcIDvG06XqFQYKRBsL1etPMzc7aeuSEut69TyYk9sU+H2sXPxadrTzuxJ6WzcM8lTFl6DL0+3ezsrkgYx3hsySLLMshiVhmcIPVOLoDC4JotDI/dMrabTccYqmEw1xuRO2HAiYiIiIiIiCosS78ujo+WBpPu5JiWjTp3PdvhfSIi+73eKx5LX+mIDwY2cnZXXF7vhsVlvOaPaIs1o7tArbL/FuHwxMKSdXk23oAnUwEG5dzu3NU4sSels+f8rTK/xsaTabh0K8euYxRGRfV81SozLYvtv5hefLxB0OjF3/YBAFIz8owPkWX43EYF+8i2+XhQU7PH//lCosXzGwaOiVwJA05ERERERERUoeh0xXNmnEzJMNvO+MZUtTDTXxu7SdUmIo+nVCrQqGoIvEoQOKkoBjSpjCBfLwxsVjyXjEKhkJR1s4df0bx2d/PNZ4qSZU93jLPeyA2oyvgfw21nbmD4j7vRacYGu44zfGlXDfWz+bjpKwqzoVUlHBtA4XxR28d1x/Zx3eHjJR/oerhlNdSLls+Ujgr2Ndn2TMc4DG8fCwBIOnuzxH0jKkvmZzgjIiIiIiIi8kD5Wh18lYU3f3zN3ASS07VepMm2vo1iHNYvIqKy9MWw5ijQCSXKZpKjVhaep8AgiE/2sWVOIXdQ0qClrXaWMLhiGAe7kn7XbLtnOsbhu63nxPV5m5LxTKc4+NmQEWVJFRuCXPq5oaw5P30AACB23DJxW36BDt5eDLKTa+ErkoiIiIiIiDzG2/0TJOv/vdwBZ6f2l2z798AVcfkfg2Vr5Cb8rlXJ+XOXEBHZQqFQOCzYBBQHGXQCA04VXVlnOJU0pqm0sV8T7mmAI5P7SLZptDp4qco+jfmshdK8T3cozIB7pXvxnGkDm1URl6cuP152HSMqIQaciIiIiIiIyGM817m2ZF2jFaBUKrBjfA9x21t/Hcbzv+6BVicgWeZGT4ifGj8Mb2XT9cr6V91ERK5Koy3MzDiRkuXUfggGAa+m1UKc2JPSW3bomrO7IDp69Q5S7uTa1Las/y0saVDTngBroI+0EJhSobA5YOUocZUCEB3sg68fbwkAeGdAfax4rRNG94wX20x/qIm4HBkkPzcUkTMx4EREREREREQe5dcRbcTluEoBAICYEOlcCKuOpqLJe6vEdS+lAq/1qIsDk3rhwKRe6J4QXT6dJSJyU99sKSxBdvDyHaf2Q2uQ/vLufQ2d2JOSmfdYS3F55O/7JI/HWc7fyMaAz7ei3bR1uJ2dj3mbkvG/g1fFIKMxV506rTRzMAHSOaDKSveEKHH5oRZVsfPtnujTsLBcr0qpQP3KwZKAnq9ahcRaEQCAKAacyAVxDiciIiIiIiLyKIm1IhAR4I2wAG+EB3ibbZdtMNH9x4OaYmDzqlbPPaRVdSzcc8kh/SQicmcarfMDI4B0Dqn46CAn9qRk4qOlpVnP3chCnSjnPo6Dl9PF5ebvrxGXG1QOxsMtq+HpjnGS9vkFxYGom1l5iAh0jUBIaUri/bz9vHQSqDLy5aMtkDBxJQDA39u2W/WBvoXtXGUMEhly0fgzERERERERUcl4qZTYM6En1r7eRbK9X6MYs8fY+ivhqQ82RpAPf7tJROQqMu5qxGUvNyxzahwyOJPm3BKFlhy7loEpS4/hkEFACgD2XrgtLheUQYZWSc/opSz5re8vNyZD7tU0oHHlEp9Tjq9ahQ8faISOdSphUKtqNh3jXZRSZi7jjMiZGHAiIiIiIiIij6OQ+VVyhzqVzLaPiwyw6bwqpQKxlWxrS0REZW/V0RRx2R0DTmqjoMibiw85qSfFrM19lJaRJ1k3nA/R0UlBOp2A7WduSLblarTYcDINuRqtmaMK2VtSr1qYn2Rd7vAxferZdU5bPNq2JuY/0xZBvmqb2nt7MeBErosBJyIiIiIiIqoQzN14urdpFVQO8ZPdJ+fwFefOV0JE5AraxYU5uwsAgJvZ+eJyaefscQbjJJysvALndMRAZaN5D419tSnZ7D7BwQlOM1eflMwTJggC+s/egqd+3I2EiSuhs5BRdT2zODC29a1uVq8VYaEMr56ft8pqm7KmLioVmFfAgBO5HgaciIiIiIiIqEJQmfnZdX6B5V9IExGRqcZVQ8RlwdFRBjsYXlouu9XVVbHjBw/lxVpVPMMSesa0JSypd/xaBp77ZQ9OpmRKtn+1URrcunAzB2dvFGdU/bDtnNlzvjB/r7hcLczfah+igosDbYNaVoPcFEk6J77W9dQsqUcujAEnIiIiIiIiqhCUZn753qpmuF3neSKxpiO6Q0Tk1h5rW11cdmamRfMaoU67tiOY+7fJmWwJqvy+8yJe+m0v8o2ee+OAk60ZW4O/TsLqY6l44oedAIBrd+5i0Z5LJu2mLD0mWVcWBRlPpWbi0q0ccfvRq/ZnI0+5v6G4HB3sC43M6zrABeZx1JfUO3Ap3bkdIZLh/BFCREREREREVA60OvkbokPbVJfdbs6YPvXgpVTi3qaOnTiciMidRAX5iMt387XwVZdfqbHzN7Kx4+xNPNyymlhGLyEmqNyu7+k+X3faapu3/z4MAKgWdlKy3TDgNH3FCczblIxfR7RBp7qRFs+XmVsYmEotmh+q/+wtuJ2jMWm3/kSatK/rT+PBFlXR+9PNAIDz0wcU/v9Gjsmx1lQO8cMzHePw3dZzKNAJshlEwTbOs1SWfLwKx9rGk9ex7NA1DGjCzyPkOpjhRERERERERBWC4Twfek2rhdg8SbdekK8ak+5tgOY1XGP+EiIiZzCcL6m8y4x1/Xgjxi05jF+SLojl39yxnJ6r2nL6hs1tf026IFnXGrwW5hXN9fT8r3thj1+SzssGm+Sk52hw+fZdcT2vqEyul6pkrwf961qr04nZU64m2yBrbOTv+5zYEyJTzHAiIiIiIiKiCqFAZjIGf29+LSYiKgnDAI+zZrWZsvQY+jaMAQCo+LN6p7irkc6DqJOZwykn3765Eif9e9Su9vfM2SouZ+dpsWDXJbz7X/E5vnm8pc3n0gecCnQCQvydn80k59DldMm6IAgMuJLL4FsxERERERERVQiGN7zURb987lAnwlndISLyGOWc4CSx8mgKgOIyY+RcWjMvhrSM3HK5/v6LtyXBphA/NXoXBSVt4VUUcNLpBBTIlNRzBWqj6Grc+OVIvp7lpN4QSTHgRERERERERBVCTn5xCZr1b3TFtAcb47nOtZ3YIyIi96Yoym0SyinHaXvyDXyxXn5+IW8PSnHSymQJlac2ceEm2+Y+0gJf25ApJJdNDAAPfLm91P2yxYif90jW28o8FktUysLXUYFOQIGTnwdz/LxNg6s9PtnkhJ4QmWLtACIiIiIiIqoQHmlbA78kXUC3epGoHu6PYW1qOLtLRERuTYHCcnrlleH0yLc7ze7zUXtOwKlAp4NK6byMrbsyJfBqRwUgwIYytObm87qSfld2e1k7aFR+zhp93FKrE6AxEzxzNj81s/nIdXnOOzERERERERGRBQkxwTgwqRe+f7K1s7tCROQZiqaNcWZJPT0fL8+5zZmUfBPZeQXWG5aBO3c1OHzljsl2Xy+VOL+RJWWVnXVf0yqS9S8eaW7TcakZeXZdR5/hVBhwcs2SenIZTpY4O2OOKhbPeScmIiIiIiIisiLU3xtKG26YERGRdTqh8P30+LUMJ/fEvedwqhnhL1kf/uNuNHx3Fbacvl7ufZn4zxHZ7VXD/GwKOJnLcLImyNdy9tTEexpISv3FRgRgUMtqJbqWJfo5nLQuPIfT83aUA/5g6TG0+mANUstpDi0iBpyIiIiIiIiIiIioxJ76abdk/dyNbMzdcAaXbuWUWx/cOcPpn5c64IfhrUy2T/7fsXLvy7YzN2S3KxUKhPqrrR5f0hiNpVDWXy+2R2SQD3aduyVu81IpMO3BxlbPO/eRFnb1Qx9UK9AJyJYpLegKGlQJxoPNq5psl8tk+m7rOdzO0eDrTWfLo2tEDDgRERERERERERGR4/SbvRkzV51EpxkbcLWc5u7xduOAU1iAN7onRMPZCbhvLj6Im9n5svuUCtuyyAp0hRGnm1mmpezWHEvF2etZ4nquRotvNifjTFoWMnLlSwg+37kWWtYMM9ke6OMFL5USW8Z2Q8/60Vj6SkeTNr890xYDmlS22mdD+oBTTn4B8jSuGXACgFlDmplsMy4BePFmccD3hszzQVQW3PedmIiIiIiIiIiIiFxOrqb4xnf76evL5ZruXFJPz0spvVV7Ji3LTMuy8efey2b3KRS2RcOK4k3YKpMp9ewve9D9k03i+hfrz2Dq8hPoOWuTSVu9l7rWkd1eLaywDGH1cH9892QrNKoaItk/YUB9dKhTyaY+Gzp0uXD+qrXH06DRutfcR8YBp84zN4jLzpoTjCoeBpyIiIiIiIiIiIjIrfmo3f82py1zJLk6bdEcTuk5GrNt9Nk2SWdvWj1fiA1l/PTeu7cBAODhltXwTKdaNh9n6MLNbHFZn62lt+K1TiU6Z1lpZZT5pQ+Q6XQC9l64Ldm37kRaufWLKjb3fycmIiIiIiIiIiIil3U3X4t5m5JxJi2zzK7hznM46RnHm2pHBpTbtU+kZJjdN/m+hrLbH2tXw2SbrmgeoXf/O2r2fPkFhYEc46AIAIzuGS8uz3i4iezxgT5estsfbVcTi55PxAcDG5m9tjWGpRmNM5xqRwaW+Lxl4Y/n2mHhc+3EdX2G02drT+Ghr7Y7q1tUwbn/OzERERERERERERE5VVpGrrhsPJ/SlxvPYPqKE+g5a7Pd59Voddh4Mg1ZVkqCyQUv3I1x2bq6UUHQ6sqnrNv5GzmS9ZkGwZ76lYPFZcNgzy2Z+Z60OgG/7bxg8VpanQBBMH1c0cE+eLVHHUy6pwE+HtQUg1tVlz2+lplAnFqlRJu4cPiqS15eMbFWhNl9rpaAplYp0dagv/pA3rzNZ53VJSIGnIiIiIiIiIiIiKh0pq04gf6zt+BGVp7JXDJz1p8p8XnnrD+D4T/uxsNWMjau3cm1uN8drTyaggGfbymXaxkHtvy9iwNLhnEww0DR8sMpJucp0Al45+8jFq+l0eok83zppWbkQaFQ4OmOcXi4ZTWT/S1qhAIAhrSWD0Q5Qse65ud98lK59q30TaeuQ6sTxMATkTPI5x8SERERERERERER2ejv/VcAAK0+WOvQ836+7jQA4ESK5XJ81cL8HHpdZ5DL4jqRkok/dl3EsDam5escyXj+qDCDuZPqRhWXkjMMS73dPwFTl5+QHKeTyVwyVlDCrK35z7TF8WuZaF49tETH26J5jTCTbUnjuyPM37vMrukon645hehgX9l9wb5e2H3+FlrUCPOIucLIdbl2WJaIiIiIiIiIiIg8hlwptZI6NqWPuNyjfrTDzutqxi85jPM3ssv0Gj5q6W3ixNoR2DuhJ7aN645QM8EWuTmNDl2+Y/VavT/dDG0JXgf+3l5oWTMMynIOmFQO8StVmb7ycjM7H4cup8vuy8gtwKB5SfiG5faojDHgRERERERERERERHYb28TyvEpy8uwo95Wr0Vrc7+OlwtJXOmLCgPoYVoZl1lzBzey8Mj1/XtHfumXNMJyfPgAKhQIRgT6oGirNHDOME3WoY1p+bt6mZJuudzIlo+SdJbOsla/8fN1p5BVYHldEpcGAExEREREREREREdmtagDg720+82PvhJ4m2/ZfTLf5/C/M32txv0qpQKOqIXimUy2Xn1+ntE6nZuHIFevZQyWVk18YhFApLGcPNakWAgDwU6tKlfVzO1tjsm1cv4QSn49sc1ejRb0JK53dDfJgnv1OTERERERERERERGVGH6iQExHog7Wvd5Fssye7YuPJ6yXul6cZt+Qw7pmzFbey88vk/K8vOggA2HX+lsV2s4c2x2PtauC/lzuU6nprjqVK1gc0rowXutQu1TnJdlfS7zq7C+ShGHAiIiIiIiIiIiKiMlEnSjrPz/AfdzupJ57h2h3HBgqy8grw9/7LNrePCfHFBwMbo250UKmuu3DPpeLl59rhi0eal+p8ZeXVHnWd3YUysf3MDWd3gTyUl7M7QERERERERERERJ5lWJsazu6CR1IpLZe8s9ebiw5i5dEUh57TUJvYcLSvE4FTqZl4qEU1jPh5j0mbVrHhUFgp5ecso3t6ZsDpdk7ZZMoRMcOJiIiIiIiIiIiIHMpbVRxAmHRPA8m+y7dzyrs7HkPp4MCMcbDple51HHr+sX3rYVTPeHz5aEuzwTIHx9BKTf8nHtSymssGwgy907++2X1BvvL5Jj9sPV9GvaGKjgEnIiIiIiIiIiIicqj0uxpx+emOcZJ925Nv2nSONnHhAIB6pSzf5m7C/NVm95V1cGZI6+p2tT/0Xm/89FRrDGtjelzNCH+0rBkmrlcK9JE9h6sFdXaM74Efn2qNmYOaOrsrNnm2cy2z+3y8VLLbUzJy8dde20spEtmKASciIiIiIiIiIiIqtZ71o8Xljx5qItnXvEaouDz2z0M2nU+rEwAAo3vFl75zbsTby/wtW0Fw3HWy8gpMtgX5mA92yQn2VaNrvSh4q0z7POmeBpJgUqOqIfZ30gmig33RrV6Us7vhED4WXktvLD5Yjj2hioIBJyIiIiIiIiIiIiq1755sheSp/XFuWn/4qqWZFcY5LDn5psEOQ5m5Guy9cBsAoFYp8OcLiY7sqksL9jUf9Hl1wQH8tO2cQ67zxqIDptf2ky/BZs3By3dMtsmV/4sI8C7R+alkdI6MUBLZgAEnIiIiIiIiIiIicgiVUiFbIq1yiJ9kffOpGxbP88of+yXnrBbm75gOugFLFeaOX8vAe/87Vupr/LD1HFYdTZW5dsnK2x24lC5zLtN2N7PzJet1owJLdD2SWjO6s+z2QJ+SBRCJSooBJyIiIiIiIiIiIipTY/vWk6xnGMzxJGfjyevislqlhJfKteb5KUs1wq0H13aevYnXFx3Agl0XsePsTbH8oC0u387BlKWlD1pZk5aRZ7XNF4+0KPN+VAR1zcxz1i3BM0oDkvtgwImIiIiIiIiIiIhK5K0+hfMrDW5VzWK7mhEBkvU/9122+RoqpWmwqY4HZsbMH9EWfRpGY+oDja22HfLNDizZdwXjlhzG0G92YMaqE1aPEQQBGq0OHT/a4IjuWiVXNnFAk8qSdV81b0+Xhec718JLXWtjVM+6ZttYmiuMqKT4qiIiIiIiIiIiIqISGdGhJlaO6mRTkMTQrnO3bG577GqG5Ob46tGdsfSVjnZdzx10rFsJXz/eClHBvhjePhbeXkr8/VJ7JE/tj7hKARaP/XrTWavnf/aXPaj7zgqz+2tZuYYl1cP9TLYF+5nORfX+/Y0k6z5eKpM2VHrJ17Mwtm8C/L2lJfX2TeyF13sVBonb145wRtfIw7GIIxEREREREREREZWIQqFAQkxwmV6jWpgfgn3V+GxIMyiVCsSbKR/mSd67ryHe7l9fDLTdyLRens6atcfTzO6rHu6Hv0d2KPG51UrTvIYe9aNNtoUHeEuPq0ClEsvTsDY1xOUNb3bFhhNpeKRtDfiqVWLJRnvKMBLZigEnIiIiIiIiIiIiclm9GhQGLgY2r+rknpQvw6yuzDzT8nT2iB23zOy+nW/3QHSwb6nOLzfHVrCv9VvPXioW4CoLMSHFz2dcpQDEdYwT1/UlKgu05RNwytVo8f3Wc+hRP8picPp6Zh7G/XUIj7StIRusJPfAEU1EREREREREREQuS6FgFowtjly5Y7Jt38XbFoNN8x5rUepgEwB4yWQ4mXvefhjeCgBQKdAbITJl96j0FDA/ZryKAk6OzHBKSr6JM2lZsvu+3JiMmatOou9nW5BtIXA6bcVxrDuRhhE/7wEAnL+Rjf0Xbzusj1Q+GHAiIiIiIiIiIiIicnPnb2ZL1rU6AQ9+ud1s++WvdkLfRpUdcm17SuN1T4jG+ekDsGdCL4dcmwq91LW2uCzAfDBJn+GUnV+AUQv247+DV0t13XM3sjHs2x3oOWuT7P4954vna/t+6znZNvkFOizZd0Vc/zXpPLp+vBEPfLkdaRm5peoflS8GnIiIiIiIiIiIiKjMbR/X3dld8GhRQdJMpf8OXjHTslC9GAfOhWWUzTTCoIQblY83etezqZ2+/OHRqxn458BVvPrH/lJd99DldHFZLsvOMIstyEyZxTnrT0vWJ/57VFzece6WcXNyYQw4ERERERERERERUZmrEuqH355pCwAI8FZJ9uVqtBg4dxumrzjhjK55hD92XZSsX8/Ms9hen+niCIZnGtmtNt7pX99h5ybbGD6flsokqmTKHwpCycvrvbbggLh8z5ytSM/Jl+xfcSRFXJ78v2OIHbcM7/13FO2nrcMPRRlPc9afMXv+V//YX6r+UfliwImIiIiIiIiIiIjKhX9RoCkswFuyfdXRFBy4lI55m5Ix8Z8j4vZXe9Qt1/65s7/3SzOa/NQqMy2ByiGln7fJnDB/bygdGMwi2y19pSMWPZ+ISoE+Ztt4yTw3t7LzZVpal5NvOidT8nX5uZwM/bT9PK7eycWUpccgCAK61Yu02H7O+jO4kn63RH2k8iWfw0ZERERERERERETkYGpV4e/fNVqdZHuBtjiD4dcdF8TlSoHSwBTZLjJIPqi0bVx3RAQ49u+qYHzJJTSqGmK1jVzAae3xVPRqEINwG18X525k49stZ/H7zosW29mSmbQ9+SZu5Wgstpm15hRmrTmF89MH2NQ/ch4GnIiIiIiIiIiIiKhc+HjpA07SG9HhZgJLCkYyAAAPt6yGP/detuuYaSuOi8vta0dgYPOqiAzyQdVQP0d3T1JSj9XPXJt+DidDb/11GG/9ddjmgE63jzea3Xc3vziY/EvSBbPt9B79bqdkvVPdSqgW5m9SIhIA0jJyEWWhXCA5H0vqERERERERERERUbkQM5wKpBlOeRqtbHsVA04AgFqRAbLb5z7SwuwxF27miMs/P90Gg1tVR7d6UQ7vmzEBjDi5Mrk5nOxhLWvpse+LA0jv/nfUrnO/2Tsev45oi2kPNsb79zc02Z9mZV4ycj4GnIiIiIiIiIiIiKhc6LMrMvMKJDeu84wCUHo6pssAAHLz5QNy9WKCJOvbztyQbacP9JUVZqK5D7mSevbI18qPVUM3s/KQlpFr97kNT/1Yu5om+++ZsxVn0qzPEUXOw4ATERERERERERERlYusvAJx+UZWvricaybD6di1jDLvkzsI9DU3M4qAV3vUFddeW7AfK4+koONH68VtdaICy7h3wPv3NxKXlQw+ubSSPD3Tlh9H/IQViB23DO8vPWa1fcsP1qLN1HWSbXsn9MRnQ5rBT63CsDbVZY8b0KSyQT8V+GxIM5M293+xFQDwzeZkPPDlNizafQl5BfLvH1T+GHAiIiIiIiIiIiKiclE3qjgjp/WHa3E7uzDolKuRz5rokVD2JeDcQbSZeWv8vb3wcrc64vqNrHy8MH8vLt++K24b3KpamfevQZVgPNspDrUqBWBIa/lgArkGc8FdALhwM1t2+9ebzyK/KAtx/g7TuZUGt6qGMX3qmT3vm73jERHog4HNq+L4+30x7cEmiAiQztvm46VErUrS0pEDm1c1CTplF2X7TV1+AvsvpmPsX4fw6LfSeaDIeRhwIiIiIiIiIiIionKhMirn1fz9NQAgyVAwDJDER0tLxlVU5krWVQn1g7eX5Vu8T3eIK4sumXhnQAOsf7MrgnzV5XI9Kpk8M8FdAOgyc6NkPfl6FtafSLV4Pl+1Em/1TUCNcH+zbQa3Mg1CfvNEK8n6yQ/6QSlT7m9g86qSdbmKgHsu3LbYRyo/5nIxiYiIiIiIiIiIiMpc7Lhl6BwfCQAY1qYGpj3YGA80r4bMXA2qW7iJXZHIhZuGt4+16VivMp6/idxLy9gwm9oJgoAen2yy2u6rR1siItAHAxpXxit/7JdtEyWTodeyZhjG9UvA9BUnzJbYk6MTgNVHU2xuT+WLASciIiIiIiIiIiJyqs2nrgMoLKsFAIm1I5zZHZcjl+D03n0NxeWBzargnwNXy7FH5K58vFQ2tftj1yWb2nWtVxgslstOsub5zrXQtV4k6kTaN8/Yc7/utftaVD4Y3iYiIiIiIiIiIiKX4Ku27WZ4ReNtJUvJWlk9InucvZ6Ft/8+bLXd+ekDzJZ71Pt1RBuz+xQKBRJigq1m4X31aAurfdHpBKttqOzxnYiIiIiIiIiIiIhcwvFrGc7ugkvqlhAllh2Uo2bZPHKQ77acRXcbSul9OqSpTecL8/cubZfQr3Flq20W7rEtI4vKFt+JiIiIiIiIiIiIyCVsKiqtR1JqlRK/PG0+U8RchtPa1zuXVZfIQ32w7LhN7fo1sh4EAgCllQyokgj1V5tsO3Q5XbJ+/FoGYsctw9g/Dzr8+mQeA05ERERERERERETkEl7uVsfZXXBLKjM39SMDfcu5J+QOXutRt1THj+lTT7b85blp/RHk4wU/g30xIY55DT6ZWBMA0KhqMNJzNCb7m9cIk6z3m70FALBoz2WHXJ9s4+XsDhAREREREREREREBgL8P53CyxT1NpNklv++6KNtOyXQDkjG6Vzxe7Fob25NvYPf52/hqY7JNx219qxuupueidWyY7H6FQoHDk/sAAM6kZSI7T4vwgNKX1AOA1nHh+DnpAo5ckS+7WaCVzuHUqmYY9ly4DQDYf/G2SUCKyoZdbznTpk1D69atERQUhKioKAwcOBAnT56UtElJScHjjz+OmJgYBAQEoEWLFvjrr78kbW7duoVHH30UwcHBCA0NxYgRI5CVlSVpc+jQIXTq1Am+vr6oXr06ZsyYUcKHSERERERERERERO5AEKy3IVM5+VrZ7Sql48uZkWfwVavQPSEab/VNsPmYamH+aBMXDoUNZfLqRAWhafXQUvTQNv0axQAAtDqdZHuDKsHi8p27phlRVDbsCjht2rQJI0eOxI4dO7BmzRpoNBr07t0b2dnZYpsnnngCJ0+exH///YfDhw/jwQcfxODBg7F//36xzaOPPoqjR49izZo1WLp0KTZv3oznnntO3J+RkYHevXujZs2a2Lt3L2bOnIn33nsP33zzjQMeMhERERERERERETnbgMaVcX76AMm2znUjndQbz1QW8+cQOcOyQ9dMto3vl4D8gsJA042sfAgGEevo4OJSfgcupZd5/6iQXQGnlStXYvjw4WjYsCGaNm2Kn376CRcvXsTevXvFNtu3b8crr7yCNm3aoFatWpgwYQJCQ0PFNsePH8fKlSvx3XffoW3btujYsSPmzJmDBQsW4OrVqwCA3377Dfn5+fjhhx/QsGFDDB06FK+++ipmzZrlwIdOREREREREREREzqIsyr7ZNq67uK16uJ+zuuORGHAiT+HjZRrKaFcrAutOpAEAZq87jZf/KE560QeiAOCztachCAL2X7yNOzLzP5HjlGoOpzt37gAAwsPDxW3t27fHwoULMWDAAISGhmLRokXIzc1F165dAQBJSUkIDQ1Fq1atxGN69uwJpVKJnTt34oEHHkBSUhI6d+4Mb+/i+o59+vTBRx99hNu3byMszLTeYl5eHvLy8sT1jIzCWo4ajQYajWNfRPrzOfq8RJ6I44XIdhwvRLbjeCGyHccLke04Xohs54jxIuh00Gg0CFQXb/NSCByDFjzdviZ+3nERI7vESf5OL3ethS82njVpry3QQCNwIidnc9d/XzrXjcDm0zfFdWf2v1+jaPxz4KpkmxI6+HgpkVcUXFp26Bo+G1TYxzxNgdiucdVgzFl3CrPWngEAnJrSCwqFwm2fl/Jmz9+nxAEnnU6HUaNGoUOHDmjUqJG4fdGiRRgyZAgiIiLg5eUFf39//P3336hTpw6AwjmeoqKipJ3w8kJ4eDhSUlLENnFxcZI20dHR4j65gNO0adMwefJkk+2rV6+Gv79/SR+mRWvWrCmT8xJ5Io4XIttxvBDZjuOFyHYcL0S243ghsl3JxkvhLcmC21ewfPllAMAbjQGlAli3eqUDe+d5mgKY0Ro4tWczThlsz7+tAKAyab9q5Uowycl1uOq/LzPaAGN3SUMFKoWAhyql4kqKCsmZhS+i5cuXO6N7AIBjMq/xpG1bkFcg7ffy5cuRpwWOXlBCX+DNNy8ds9ZmiG0W/rsCwcW5Li77vLiKnJwcm9uWOOA0cuRIHDlyBFu3bpVsnzhxItLT07F27VpUqlQJ//zzDwYPHowtW7agcePGJb2cVePHj8frr78urmdkZKB69ero3bs3goODLRxpP41GgzVr1qBXr15Qq9XWDyCqwDheiGzH8UJkO44XIttxvBDZjuOFyHalGS8R9W9h3YnreL1nHfiqTYMkZL+AU9fxzYnCcmKDW1bFor1XAAADBvR3ZreoiDv8++Idm4JRiw6J62EBPujfvys25x1B8r7CzKL+/Z33eupZoMPXk9dKtvXu0Q1TD2yRbOvcozeaf7Besm33DWmW38S9XljwTGs0qRLo8s+LK9BXk7NFiQJOL7/8MpYuXYrNmzejWrVq4vbk5GR88cUXOHLkCBo2bAgAaNq0KbZs2YK5c+di3rx5iImJQVpamuR8BQUFuHXrFmJiYgAAMTExSE1NlbTRr+vbGPPx8YGPj4/JdrVaXWYvlrI8N5Gn4Xghsh3HC5HtOF6IbMfxQmQ7jhci25VkvHSMj0bH+Ogy6lHF5OVVfJu3Q91ICFAgPjqI72UuxpX/fVF7SUMFT3eMg1qtxoQBDSEICjzcqppT+65WA4tfSMSgeUniNl+DKXn0en22zabzDf1uN06/37vo3K77vLgCe/42dhXwFAQBL7/8Mv7++2+sX7/epOydPrVKqZSeVqVSQacrrKOYmJiI9PR07N27V9y/fv166HQ6tG3bVmyzefNmSW3ANWvWoF69erLl9IiIiIiIiIiIiIgqKqVB3Ty1SomZg5ri2c61nNgjcjcqo0jBE4mxAICwAG/MGtIM7WtXKv9OGfFSSutDeqkUJiUjb2TllWOPyJhdAaeRI0di/vz5+P333xEUFISUlBSkpKTg7t27AICEhATUqVMHzz//PHbt2oXk5GR88sknWLNmDQYOHAgAqF+/Pvr27Ytnn30Wu3btwrZt2/Dyyy9j6NChqFKlCgDgkUcegbe3N0aMGIGjR49i4cKFmD17tqRkHhERERERERERERFBctM9NSPXeR0ht5VXoJOsB3i7XrlLhVF0qVKgacUzci67Ak5fffUV7ty5g65du6Jy5crifwsXLgRQmFq1fPlyREZG4t5770WTJk3wyy+/4Oeff5bUd/ztt9+QkJCAHj16oH///ujYsSO++eYbcX9ISAhWr16Nc+fOoWXLlnjjjTcwadIkPPfccw562ERERERERERERESeQasTxOWMuwVO7Am5q00nr0vWjYM7rkAnFL/O29UKBwAYbCIXYNccToINz17dunXx119/WWwTHh6O33//3WKbJk2aYMuWLRbbEBEREREREREREVV0Bdri+7bX7tx1Yk/IXfmo7cpNcQrDknq+6sIMrB+Gt8LTP+2Rbd+8RiiOX8tArkYnu5/l9xzP9V9FRERERERERERERGRWga74hnqLGmFO7Am5K6ULZjQZa1QlRFzW97d7QjSOTekj2/7vlzpgwoAGZs/32bozju0gMeBERERERERERERE5M40BhlOg1pVc2JPyF0F+aqd3QWrlAYZTvsu3haX/b3NF3Ib2ro67m9WBYE+pm1YftLxGHAiIiIiIiIiIiIicmOGGU6uOPcOub4u8ZHO7oJd0nM0NrXzUikxe2hzHJlsmgWVo9E6ulsVHgNORERERERERERERG7MMMOJqCQSa0c4uwt2aVA5WLL+/v0NJevVwvxMjvErmvdJr0vdSo7vWAXHgBMRERERERERERGRG9PqGHCiikWtkmbyPZ4Yi3PT+ovrPl6moY/9k3rBoCofIoN8yqx/FRUDTkRERERERERERERuTGC8iRygZc0wAECYv+vP5yRHoVBgwoD6CPTxwuyhzU32+6pVODttANrEhQMABA4chzM/mxYREREREREREREREVUI3zzeEj9uO49Brao5uytW+XmrZLc/06kWnu4QB6XS/Fxm+l2MNzlehQ84abVaaDS2TTCmp9Fo4OXlhdzcXGi1nFjMU3l7e0OpZBIgEREREREREREReb6IQB+82aees7th0fQHG+OrTcn48IHGZttYCjYBgAKF+3WCwBJwDlZhA06CICAlJQXp6eklOjYmJgaXLl2CQmH5xUvuS6lUIi4uDt7e3s7uChERERERERERkVkCmKpBFcPQNjUwtE2NUp1Dn2OgEzjnkKNV2ICTPtgUFRUFf39/uwJHOp0OWVlZCAwMZAaMh9LpdLh69SquXbuGGjVqMLBIRERERERERERE5AGURfd6GaZ1vAoZcNJqtWKwKSIiwu7jdTod8vPz4evry4CTB4uMjMTVq1dRUFAAtdo9J8ojIiIiIiIiIiLP16t+NN75+wgaVgl2dleIXJ4+uUDgJE4OVyEDTvo5m/z9/Z3cE3Jl+lJ6Wq2WASciIiIiIiIiInJZUcG+OPxeb/h7V8jbvUR20U/xpGPAyeEq9DsQy6SRJXx9EBERERERERGRuwjy5Q+miWyhv+urY7zJ4VgPjoiIiIiIiIiIiIiIKgQlS+qVGQaciIiIiIiIiIiIiIioQtBXtmKGk+Mx4ERmxcbG4rPPPnN2N8zauHEjFAoF0tPTnd0VIiIiIiIiIiIiInIDnMOp7DDgRGWma9euGDVqVJmdv3379rh27RpCQkLK7BpERERERERERERE5DmKS+o5uSMeiAEnMpGfn+/sLlil0Wjg7e2NmJgYMQWSiIiIiIiIiIiIiMgSZVFUhHM4OR4DTih8YeXkF9j13918rd3HyP1nz4u6a9eueOWVVzBq1CiEhYUhOjoa3377LbKzs/HUU08hKCgIderUwYoVK8RjtFotRowYgbi4OPj5+aFevXqYPXu25LzDhw/HwIED8eGHH6JKlSqoV6+e7PW/++47hIaGYt26dQCAI0eOoF+/fggMDER0dDQef/xx3LhxQzznpk2bMHv2bCgUCigUCpw/f172vLGxsXj//fcxbNgwBAQEoGrVqpg7d66kjUKhwFdffYX77rsPAQEB+PDDD2VL6m3btg1du3aFv78/wsLC0KdPH9y+fRsAoNPpMG3aNPFv0bRpU/z55582//2JiIiIiIiIiIiIyL0pwDmcyoqXszvgCu5qtGgwaZVTrn1sSh/4e9v+NPz8888YO3Ysdu3ahYULF+LFF1/E33//jQceeABvv/02Pv30Uzz++OO4ePEi/P39odPpUK1aNSxevBgRERHYvn07nnvuOVSuXBmDBw8Wz7tu3ToEBwdjzZo1stedMWMGZsyYgdWrV6NNmzZIT09H9+7d8cwzz+DTTz/F3bt38dZbb2Hw4MFYv349Zs+ejVOnTqFRo0aYMmUKACAyMtLs45o5cybefvttTJ48GatWrcJrr72G+Ph49OrVS2zz3nvvYfr06fjss8/g5eWFs2fPSs5x4MAB9OjRA08//TRmz54NLy8vbNiwAVqtFgAwbdo0zJ8/H/PmzUPdunWxefNmPPbYY4iMjESXLl1sfg6IiIiIiIiIiIiIyD0pOIdTmWHAyc00bdoUEyZMAACMHz8e06dPR6VKlfDss88CACZNmoSvvvoKhw4dQrt27aBWqzF58mTx+Li4OCQlJWHRokWSgFNAQAC+++47eHt7m1zzrbfewq+//opNmzahYcOGAIAvvvgCzZs3x9SpU8V2P/zwA6pXr45Tp04hPj4e3t7e8Pf3R0xMjNXH1aFDB4wbNw4AEB8fj23btuHTTz+VBJweeeQRPPXUU+K6ccBpxowZaNWqFb788ktxm76/eXl5mDp1KtauXYvExEQAQK1atbB161Z8/fXXDDgRERERERERERERVQD6OZyY4eR4DDgB8FOrcGxKH5vb63Q6ZGZkIig4CEpl6aoS+qlVdrVv0qSJuKxSqRAREYHGjRuL26KjowEAaWlp4ra5c+fihx9+wMWLF3H37l3k5+ejWbNmkvM2btxYNtj0ySefIDs7G3v27EGtWrXE7QcPHsSGDRsQGBhockxycjLi4+Ptelz6IJDh+meffSbZ1qpVK4vnOHDgAAYNGiS778yZM8jJyZEEsIDC+aqaN29uV1+JiIiIiIiIiIiIyD0pizKcOIeT4zHghML5gewpa6fT6VDgrYK/t1epA072UqvVknWFQiHZptBHZ3U6AMCCBQvw5ptv4pNPPkFiYiKCgoIwc+ZMfFLYvAAAGmJJREFU7Ny5U3KegIAA2et16tQJy5Ytw6JFi8QMJADIysrCvffei48++sjkmMqVK5fswVlhro96fn5+ZvdlZWUBAJYtW4aqVatK9vn4+JS+c0RERERERERERETk8vQZTgw3OR4DTh5u27ZtaN++PV566SVxW3Jyss3Ht2nTBi+//DL69u0LLy8vvPnmmwCAFi1a4K+//kJsbCy8vORfRt7e3uL8Sdbs2LHDZL1+/fo29xMozP5at26dpISgXoMGDeDj44OLFy+yfB4RERERERERERFRRcU5nMpM+abnULmrW7cu9uzZg1WrVuHUqVOYOHEidu/ebdc52rdvj+XLl2Py5MlimbuRI0fi1q1bGDZsGHbv3o3k5GSsWrUKTz31lBhkio2Nxc6dO3H+/HncuHFDzLqSs23bNsyYMQOnTp3C3LlzsXjxYrz22mt29XP8+PHYvXs3XnrpJRw6dAgnTpzAV199hRs3biAoKAhvvvkmRo8ejZ9//hnJycnYt28f5syZg59//tmu6xARERERERERERGRe9LHmWasOu3cjnggBpw83PPPP48HH3wQQ4YMQdu2bXHz5k1JtpOtOnbsiGXLlmHChAmYM2cOqlSpgm3btkGr1aJ3795o3LgxRo0ahdDQULHM4JtvvgmVSoUGDRogMjISFy9eNHv+N954A3v27EHz5s3xwQcfYNasWejTx/Z5tQAgPj4eq1evxsGDB9GmTRskJibi33//FTOw3n//fUycOBHTpk1D/fr10bdvXyxbtgxxcXF2/z2IiIiIiIiIiIiIyP1sOJkmLucUOLEjHogl9dzIxo0bTbadP3/eZJvhZGc+Pj748ccf8eOPP0raTJs2TVz+6aefZK9nfO7OnTuLcyEBhdlTS5YsMdvf+Ph4JCUlmd1vKDg4GIsWLTK7X24Ct65du5ps79KlC7Zt2yZ7DoVCgddee83uzCkiIiIiIiIiIiIi8gzpORpxeVuqAg87sS+ehhlORERERERERERERERUIQR4q8TlTdcYInEk/jWJiIiIiIiIiIiIiKhCiA7xFZc1Oid2xAOxpB45nVxZQCIiIiIiIiIiIiIiRyvQFk/TkqtVOLEnnocZTkREREREREREREREVCEUaKVpTYev3HFSTzwPA05ERERERERERERERFQhFOgEyfrN7Hwn9cTzMOBEREREREREREREREQVwo2sPMn64r1XnNQTz8OAExERERERERERERERVQhGCU7I1Wid0xEPxIATERERERERERERERFVSJFBPs7ugsdgwImIiIiIiIiIiIiIiCqE6GBpgGlUjzpO6onnYcCJiIiIiIiIiIiIiIgqhKqhfpL1mGBfJ/XE8zDgVIEcPXoUDz30EGJjY6FQKPDZZ5+V+pz33XcfatSoAV9fX1SuXBmPP/44rl69Ktv2zJkzCAoKQmhoqMm+xYsXIyEhAb6+vmjcuDGWL18u2S8IAiZNmoTKlSvDz88PPXv2xOnTp0vdfyIiIiIiIiIiIiKqOD4b0lwMOj1Wh/M3ORIDThVITk4OatWqhenTpyMmJsYh5+zWrRsWLVqEkydP4q+//kJycjIefvhhk3YajQbDhg1Dp06dTPZt374dw4YNw4gRI7B//34MHDgQAwcOxJEjR8Q2M2bMwOeff4558+Zh586dCAgIQJ8+fZCbm+uQx0FEREREREREREREnq9GhD+2jeuO0+/3RutIwdnd8SgMOBnIzs82+19ugTSwka0x3/au5q5N5y2JvLw8vPrqq4iKioKvry86duyI3bt3i/uPHj2Ke+65B8HBwQgKCkKnTp2QnJwMAGjdujVmzpyJoUOHwsfHMROhjR49Gu3atUPNmjXRvn17jBs3Djt27IBGo5G0mzBhAhISEjB48GCTc8yePRt9+/bFmDFjUL9+fbz//vto0aIFvvjiCwCF2U2fffYZJkyYgPvvvx9NmjTBL7/8gqtXr+Kff/5xyOMgIiIiIiIiIiIiIqKS83J2B1xJ4LRAs/v61+2PZY8sE9fjv4lHTkGObNsuNbtg4/CN4nrs7FjcyLlh0k541/7o6dixY/HXX3/h559/Rs2aNTFjxgz06dMHZ86cwd27d9G5c2d07doV69evR3BwMLZt24aCggKbzz916lRMnTrVYptjx46hRo0aJttv3bqF3377De3bt4darRa3r1+/HosXL8aBAwewZMkSk+OSkpLw+uuvS7b16dNHDCadO3cOKSkp6Nmzp7g/JCQEbdu2RVJSEoYOHWrz4yMiIiIiIiIiIiIiIsdjwMmNZGdn46uvvsJPP/2Efv36AQC+/fZbrFmzBt9//z1u376NkJAQLFiwQAz4xMfH23WNF154QTYLyVCVKlUk62+99Ra++OIL5OTkoF27dli6dKm47+bNmxg+fDjmz5+P4OBg2fOlpKQgOjpasi06OhopKSnifv02c22IiIiIiIiIiIiIiMh5GHAykDU+y+w+lVIlWT/13CkEBwVDqTStSqhUSLedf+28Q/qXnJwMjUaDDh06iNvUajXatGmD48ePIyUlBZ06dZJkF9krPDwc4eHhdh0zZswYjBgxAhcuXMDkyZPxxBNPYOnSpVAoFHj22WfxyCOPoHPnziXuExERERERERERERERuTYGnAwEeAfY3lYdgADvANmAU2nOWxp+fn6lPkdJSupVqlQJlSpVQnx8POrXr4/q1atjx44dSExMxPr16/Hff//h448/BlA4H5NOp4OXlxe++eYbPP3004iJiUFqaqrkGqmpqYiJiQEA8f+pqamoXLmypE2zZs1K/ZiJiIiIiIiIiIiIiKh0GHByI7Vr14a3tze2bduGmjVrAgA0Gg12796NUaNGITs7Gz///DM0Gk2Js5xKUlLPkE6nAwDk5eUBKJyfSavVivv//fdffPTRR9i+fTuqVq0KAEhMTMS6deswatQosd2aNWuQmJgIAIiLi0NMTAzWrVsnBpgyMjKwc+dOvPjii3Y/RiIiIiIiIiIiIiIiciwGnNxIQEAAXnzxRYwZMwbh4eGoUaMGZsyYgZycHIwYMQI6nQ5z5szB0KFDMX78eISEhGDHjh1o06YN6tWrh/z8fBw7dgwAkJ+fjytXruDAgQMIDAxEnTp1ANhXUm/nzp3YvXs3OnbsiLCwMCQnJ2PixImoXbu2GCyqX7++5Jg9e/ZAqVSiUaNG4rbXXnsNXbp0wSeffIIBAwZgwYIF2LNnD7755hsAgEKhwKhRo/DBBx+gbt26iIuLw8SJE1GlShUMHDiwtH9WIiIiIiIiIiIiIiIqJQac3Mz06dOh0+nw+OOPIzMzE61atcKqVasQFhYGAFi/fj3GjBmDLl26QKVSoVmzZuKcT1evXkXz5s3Fc3388cf4+OOP0aVLF2zcuNHuvvj7+2PJkiV49913kZ2djcqVK6Nv376YMGECfHx8bD5P+/bt8fvvv2PChAl4++23UbduXfzzzz+SoNTYsWORnZ2N5557Dunp6ejYsSNWrlwJX19fu/tNRERERERERERERESOxYCTm/H19cXnn3+Ozz//XHZ/kyZNsGrVKtl9sbGxEATBYX1p3Lgx1q9fb9cxw4cPx/Dhw022Dxo0CIMGDTJ7nEKhwJQpUzBlyhR7u0lERERERERERERERGVM6ewOEBERERERERERERERkXtjwImIiIiIiIiIiIiIiIhKhQEnIiIiIiIiIiIiIiIiKhUGnIiIiIiIiIiIiIiIiKhUKnTASafTObsL5MIEQXB2F4iIiIiIiIiIiIiI3IKXszvgDN7e3lAqlbh69SoiIyPh7e0NhUJh8/E6nQ75+fnIzc2FUlmhY3YeSxAEXL9+HQqFAmq12tndISIiIiIiIiIiIiJyaRUy4KRUKhEXF4dr167h6tWrdh8vCALu3r0LPz8/uwJV5F4UCgWqVasGlUrl7K4QEREREREREREREbm0ChlwAgqznGrUqIGCggJotVq7jtVoNNi8eTM6d+7M7BcPplarGWwiIiIiIiIiIiIiIrJBhQ04ARDLpdkbNFKpVCgoKICvry8DTkREREREREREREREVOFxAiIiIiIiIiIiIiIiIiIqFQaciIiIiIiIiIiIiIiIqFQYcCIiIiIiIiIiIiIiIqJS8dg5nARBAABkZGQ4/NwajQY5OTnIyMjgHE5EVnC8ENmO44XIdhwvRLbjeCGyHccLke04Xohsx/Himvi82EYfY9HHXCzx2IBTZmYmAKB69epO7gkREREREREREREREZH7yszMREhIiMU2CsGWsJQb0ul0uHr1KoKCgqBQKBx67oyMDFSvXh2XLl1CcHCwQ89N5Gk4Xohsx/FCZDuOFyLbcbwQ2Y7jhch2HC9EtuN4cU18XmwjCAIyMzNRpUoVKJWWZ2ny2AwnpVKJatWqlek1goOD+UIkshHHC5HtOF6IbMfxQmQ7jhci23G8ENmO44XIdhwvronPi3XWMpv0LIejiIiIiIiIiIiIiIiIiKxgwImIiIiIiIiIiIiIiIhKhQGnEvDx8cG7774LHx8fZ3eFyOVxvBDZjuOFyHYcL0S243ghsh3HC5HtOF6IbMfx4pr4vDieQhAEwdmdICIiIiIiIiIiIiIiIvfFDCciIiIiIiIiIiIiIiIqFQaciIiIiIiIiIiIiIiIqFQYcCIiIiIiIiIiIiIiIqJSYcCJiIiIiIiIiIiIiIiISoUBJyIiIiIiIiIiIiIiIioVlw44TZs2Da1bt0ZQUBCioqIwcOBAnDx5UtImNzcXI0eOREREBAIDA/HQQw8hNTVV3H/w4EEMGzYM1atXh5+fH+rXr4/Zs2dLzrFkyRL06tULkZGRCA4ORmJiIlatWmW1f4IgYNKkSahcuTL8/PzQs2dPnD59WrZtXl4emjVrBoVCgQMHDlg998aNG9GiRQv4+PigTp06+OmnnyT7N2/ejHvvvRdVqlSBQqHAP//8Y/Wc5Nk4XsyPl/feew8KhULyX0JCgtXzkufieDE/XjIzMzFq1CjUrFkTfn5+aN++PXbv3m31vOS5Kup4uXbtGh555BHEx8dDqVRi1KhRJm2WLFmCVq1aITQ0FAEBAWjWrBl+/fVXq30mz8XxYn68dO3a1eTzmEKhwIABA6z2mzxTRR0vtvSH3/fJGMeL+f7w+z4Z43gx3x9nft/3hOclNjbW5P1m+vTpVs9d0e7zu3TAadOmTRg5ciR27NiBNWvWQKPRoHfv3sjOzhbbjB49Gv/73/+wePFibNq0CVevXsWDDz4o7t+7dy+ioqIwf/58HD16FO+88w7Gjx+PL774QmyzefNm9OrVC8uXL8fevXvRrVs33Hvvvdi/f7/F/s2YMQOff/455s2bh507dyIgIAB9+vRBbm6uSduxY8eiSpUqNj3uc+fOYcCAAejWrRsOHDiAUaNG4ZlnnpEMjuzsbDRt2hRz58616Zzk+ThezI8XAGjYsCGuXbsm/rd161abzk+eiePF/Hh55plnsGbNGvz66684fPgwevfujZ49e+LKlSs2XYM8T0UdL3l5eYiMjMSECRPQtGlT2Tbh4eF45513kJSUhEOHDuGpp57CU089ZdMXGvJMHC/mx8uSJUskn8WOHDkClUqFQYMG2XQN8jwVdbzY0h9+3ydjHC+W+8Pv+2SI48V8f5z5fd9TnpcpU6ZI3m9eeeUVi+etkPf5BTeSlpYmABA2bdokCIIgpKenC2q1Wli8eLHY5vjx4wIAISkpyex5XnrpJaFbt24Wr9WgQQNh8uTJZvfrdDohJiZGmDlzprgtPT1d8PHxEf744w9J2+XLlwsJCQnC0aNHBQDC/v37LV577NixQsOGDSXbhgwZIvTp00e2PQDh77//tnhOqng4XorHy7vvvis0bdrU4nmoYuN4KRwvOTk5gkqlEpYuXSpp06JFC+Gdd96xeG6qOCrKeDHUpUsX4bXXXrOpbfPmzYUJEybYfG7ybBwv5n366adCUFCQkJWVZfO5ybNVxPFiS3/4fZ/kcLwU94ff98kajpfC/rja9313fF5q1qwpfPrpp9YemkRFvM/v0hlOxu7cuQOg8NekQGFUU6PRoGfPnmKbhIQE1KhRA0lJSRbPoz+HHJ1Oh8zMTIttzp07h5SUFMm1Q0JC0LZtW8m1U1NT8eyzz+LXX3+Fv7+/9QcJICkpSXJeAOjTp4/Fx0RkjONF+phOnz6NKlWqoFatWnj00Udx8eJFm85PFQPHS+F5CwoKoNVq4evrK2nj5+fHXwmSqKKMF3sJgoB169bh5MmT6Ny5c5lcg9wPx4t533//PYYOHYqAgIAyuwa5l4o6XmzpD5Exjhdpf/h9nyzheCnsj6t933fH5wUApk+fjoiICDRv3hwzZ85EQUGBxcdZEe/zu03ASafTYdSoUejQoQMaNWoEAEhJSYG3tzdCQ0MlbaOjo5GSkiJ7nu3bt2PhwoV47rnnzF7r448/RlZWFgYPHmy2jf780dHRZq8tCAKGDx+OF154Aa1atbL6GA3PLXfejIwM3L171+bzUMXF8SIdL23btsVPP/2ElStX4quvvsK5c+fQqVMnZGZm2nwd8lwcL8XjJSgoCImJiXj//fdx9epVaLVazJ8/H0lJSbh27ZrN1yHPVZHGi63u3LmDwMBAeHt7Y8CAAZgzZw569erl8OuQ++F4MW/Xrl04cuQInnnmmTK7BrmXijxebOkPkSGOF2l/+H2fLOF4Ke6PK33fd8fnBQBeffVVLFiwABs2bMDzzz+PqVOnYuzYsRYfa0W8z+82AaeRI0fiyJEjWLBgQYnPceTIEdx///1499130bt3b9k2v//+OyZPnoxFixYhKioKAPDbb78hMDBQ/G/Lli02XW/OnDnIzMzE+PHjzbYxPO8LL7xg/4MiksHxItWvXz8MGjQITZo0QZ8+fbB8+XKkp6dj0aJFNp+DPBfHi9Svv/4KQRBQtWpV+Pj44PPPP8ewYcOgVLrNRwYqQxwvpoKCgnDgwAHs3r0bH374IV5//XVs3LjRrnOQZ+J4Me/7779H48aN0aZNmxIdT56noo4Xuf4QWcPxIh0v/L5PlnC8SMeLq3zfd8fnBQBef/11dO3aFU2aNMELL7yATz75BHPmzEFeXh4A3ucXOamUn11GjhwpVKtWTTh79qxk+7p16wQAwu3btyXba9SoIcyaNUuy7ejRo0JUVJTw9ttvm73OH3/8Ifj5+ZnUsszIyBBOnz4t/peTkyMkJyfL1s/s3Lmz8OqrrwqCIAj333+/oFQqBZVKJf4HQFCpVMITTzwhCIIgOW9qaqogCILQqVMnk7rnP/zwgxAcHCzbb3hAbUdyHI4Xy+NFr1WrVsK4ceMstiHPx/FifrxkZWUJV69eFQRBEAYPHiz079/f7OOjiqGijRdD9szhNGLECKF37942tSXPxfHymtk+Z2VlCcHBwcJnn31mtg1VLBV1vJjrjzF+3ydDHC+Wx4s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" ] @@ -1886,7 +1673,7 @@ }, { "cell_type": "code", - "execution_count": 427, + "execution_count": 66, "metadata": { "tags": [] }, @@ -1895,22 +1682,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.01, 0.002]) : [1, '-0.501%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.015, 0.002]) : [1, '-0.501%']\n", - "Max txs and PnL for ( [oc_inc, trail_inc] = [0.02, 0.002]) : [1, '-0.494%']\n" + "Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = [0.01, 0.002]) : [40, '-2.756%', '-2.756%']\n", + "Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = [0.015, 0.002]) : [26, '0.016%', '0.016%']\n", + "Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = [0.02, 0.002]) : [10, '7.848%', '7.848%']\n" ] } ], "source": [ "# range's lenght = 2*increment\n", "stk = 1000000\n", - "# oc_increments = [round(mu_ema_log_returns+ 3 *std_ema_log_returns,4), \n", - "# round(mu_ema_log_returns+ 4 *std_ema_log_returns,4),\n", - "# round(mu_ema_log_returns+ 5 *std_ema_log_returns,4)]\n", "oc_increments = [0.01, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", "trailing_increments = [0.002]#, 0.003, 0.005]#, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", "maker_fees_counter_lengths = {}\n", - "pnl_results = {}\n", + "realised_pnl_results = {}\n", + "unrealised_pnl_results = {}\n", "total_results = []\n", "# for period_n_open_close in periods_n_open_close:\n", "for oc_increment in oc_increments:\n", @@ -1921,15 +1706,14 @@ " directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, oc_increment, trailing_increment)\n", " maker_fees_counter = run_sim(stk, period, open_close, slippage, 2*oc_increment, 2*trailing_increment, directory)\n", " maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", - " # print(\"Max txs for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", - " # maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])])\n", - " # directory = \"From_%s_to_%s_open_close_at_%s/dydx_results.csv\" % (period[0], period[1], open_close)\n", " dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)\n", - " pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_stgy_pnl'][len(dydx_results)-1]\n", - " print(\"Max txs and PnL for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", + " realised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_realised_pnl'][len(dydx_results)-1]\n", + " unrealised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_realised_pnl'][len(dydx_results)-1]+dydx_results['pnl'][len(dydx_results)-1]\n", + " print(\"Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", " [maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])], \n", - " str(round(pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%'])\n", - " total_results.append([maker_fees_counter_lengths, pnl_results])" + " str(round(realised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%',\n", + " str(round(unrealised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%'])\n", + " total_results.append([maker_fees_counter_lengths, realised_pnl_results, unrealised_pnl_results])" ] }, { @@ -2045,11 +1829,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "data = historical_data" + "period = best_1_week[0]\n", + "data = historical_data.loc[period[0]:period[1]]" ] }, { @@ -2061,7 +1846,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2082,7 +1867,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -2150,7 +1935,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -2158,10 +1943,18 @@ "output_type": "stream", "text": [ "Jumps of prices (Returns):\n", - "Mean price jump: 0.048924%\n", - "Std of mean: 0.10266%\n", - "Mean of EMA price jump: 0.048924%\n", - "Std of Mean EMA: 0.052945%\n" + "Mean price jump: 0.063432%\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'std_sma_abs_returns' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJumps of prices (Returns):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_sma_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of mean:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_sma_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean of EMA price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_ema_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of Mean EMA:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_ema_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'std_sma_abs_returns' is not defined" ] } ], @@ -2175,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2183,10 +1976,18 @@ "output_type": "stream", "text": [ "Jumps of log(prices) (log_returns):\n", - "Mean price jump: 0.048926%\n", - "Std of mean: 0.102703%\n", - "Mean of EMA price jump: 0.048925%\n", - "Std of Mean EMA: 0.052947%\n" + "Mean price jump: 0.063432%\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'std_sma_abs_log_returns' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [17], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJumps of log(prices) (log_returns):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_sma_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of mean:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_sma_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean of EMA price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_ema_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of Mean EMA:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_ema_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'std_sma_abs_log_returns' is not defined" ] } ], @@ -3530,7 +3331,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -3538,10 +3339,10 @@ "output_type": "stream", "text": [ "Jumps of prices (Returns):\n", - "Mean price jump: 0.000246%\n", - "Std of mean: 0.145833%\n", - "Mean of EMA price jump: 0.000246%\n", - "Std of Mean EMA: 0.094817%\n" + "Mean price jump: -0.000718%\n", + "Std of mean: 0.091498%\n", + "Mean of EMA price jump: -0.000721%\n", + "Std of Mean EMA: 0.071179%\n" ] } ], @@ -3555,7 +3356,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -3563,10 +3364,10 @@ "output_type": "stream", "text": [ "Jumps of log(prices) (log_returns):\n", - "Mean price jump: 0.00014%\n", - "Std of mean: 0.145778%\n", - "Mean of EMA price jump: 0.00014%\n", - "Std of Mean EMA: 0.094815%\n" + "Mean price jump: -0.000759%\n", + "Std of mean: 0.091497%\n", + "Mean of EMA price jump: -0.000763%\n", + "Std of Mean EMA: 0.071178%\n" ] } ], @@ -3580,24 +3381,24 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean of EMA +-2*Std of Mean EMA: ['-0.189%', '0.19%']\n", - "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 89.016%\n", - "Mean of EMA +-3*Std of Mean EMA: ['-0.284%', '0.285%']\n", - "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 95.305%\n", - "Mean of EMA +-4*Std of Mean EMA: ['-0.379%', '0.379%']\n", - "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 97.72%\n" + "Mean of EMA +-2*Std of Mean EMA: ['-0.143%', '0.142%']\n", + "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 90.268%\n", + "Mean of EMA +-3*Std of Mean EMA: ['-0.214%', '0.213%']\n", + "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 96.637%\n", + "Mean of EMA +-4*Std of Mean EMA: ['-0.285%', '0.284%']\n", + "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 98.571%\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3618,7 +3419,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3626,16 +3427,16 @@ "output_type": "stream", "text": [ "Increments based on H1: log_r ~ N(mu_ema, sigma_ema): math.e ** (mu_ema_log_returns-std_ema_log_returns^2/2 + factor *std_ema_log_returns)\n", - "factor = 1 0.094955%\n", - "1+mu+K*sigma, K = 1 0.094955%\n", - "factor = 2 0.189905%\n", - "1+mu+K*sigma, K = 2 0.189769%\n", - "factor = 3 0.284944%\n", - "1+mu+K*sigma, K = 3 0.284584%\n", - "factor = 4 0.380074%\n", - "1+mu+K*sigma, K = 4 0.379399%\n", - "factor = 5 0.475294%\n", - "1+mu+K*sigma, K = 5 0.474213%\n" + "factor = 1 0.070414%\n", + "1+mu+K*sigma, K = 1 0.070415%\n", + "factor = 2 0.141667%\n", + "1+mu+K*sigma, K = 2 0.141592%\n", + "factor = 3 0.212971%\n", + "1+mu+K*sigma, K = 3 0.21277%\n", + "factor = 4 0.284326%\n", + "1+mu+K*sigma, K = 4 0.283948%\n", + "factor = 5 0.355732%\n", + "1+mu+K*sigma, K = 5 0.355126%\n" ] } ], @@ -3684,7 +3485,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ From e7ed069afa9cb2f7eb0d274cf03d466b608c5d40 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agustin=20Mu=C3=B1oz=20Gonzalez?= Date: Fri, 21 Oct 2022 10:14:41 -0300 Subject: [PATCH 16/16] trying oc_inc, tr_inc combinations agains user expectation --- jupyter-lab/Simulations_oc_range.ipynb | 547 +++++++++++++++++-------- 1 file changed, 365 insertions(+), 182 deletions(-) diff --git a/jupyter-lab/Simulations_oc_range.ipynb b/jupyter-lab/Simulations_oc_range.ipynb index cdebc1b..43a6e68 100644 --- a/jupyter-lab/Simulations_oc_range.ipynb +++ b/jupyter-lab/Simulations_oc_range.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -25,46 +25,46 @@ "Requirement already satisfied: matplotlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (3.6.0)\n", "Requirement already satisfied: python-binance in /home/ubuntu/cruize/env/lib/python3.10/site-packages (1.0.16)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: numpy>=1.21.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (1.23.3)\n", "Requirement already satisfied: pytz>=2020.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (2022.2.1)\n", - "Requirement already satisfied: google-api-python-client>=1.5.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (2.63.0)\n", + "Requirement already satisfied: numpy>=1.21.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pandas) (1.23.3)\n", "Requirement already satisfied: google-auth-oauthlib in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (0.5.3)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n", + "Requirement already satisfied: google-api-python-client>=1.5.5 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pygsheets) (2.63.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.0.5)\n", "Requirement already satisfied: pillow>=6.2.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (9.2.0)\n", - "Requirement already satisfied: packaging>=20.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (21.3)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n", "Requirement already satisfied: cycler>=0.10 in 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(from matplotlib) (21.3)\n", "Requirement already satisfied: six in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-binance) (1.16.0)\n", + "Requirement already satisfied: ujson in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-binance) (5.5.0)\n", + "Requirement already satisfied: requests in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-binance) (2.28.1)\n", "Requirement already satisfied: dateparser in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-binance) (1.1.1)\n", + "Requirement already satisfied: aiohttp in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-binance) (3.8.3)\n", "Requirement already satisfied: websockets in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from python-binance) (10.3)\n", + "Requirement already satisfied: google-auth-httplib2>=0.1.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-api-python-client>=1.5.5->pygsheets) (0.1.0)\n", + 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google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (4.9)\n", - "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (5.2.0)\n", "Requirement already satisfied: oauthlib>=3.0.0 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib->pygsheets) (3.2.1)\n", "Requirement already satisfied: pytz-deprecation-shim in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from tzlocal->dateparser->python-binance) (0.1.0.post0)\n", "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/ubuntu/cruize/env/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.5.5->pygsheets) (0.4.8)\n", @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": { "tags": [] }, @@ -199,7 +199,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -216,7 +215,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -225,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -434,7 +432,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -443,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -554,7 +551,7 @@ " # print(\"CAUTION: OPEN PRICE LOWER THAN open_close!\")\n", " # print(\"Difference of: \", stgy_instance.trigger_prices['open_close'] - self.market_price)\n", " self.entry_price = self.market_price\n", - " self.short_size = -aave_class_instance.collateral_eth_initial\n", + " self.short_size = -aave_class_instance.collateral_eth_initial /0.9 # We divide by 0.9 bc aave_coll = 0.9*stk but we want protection for the 100% amount\n", " # self.collateral = aave_class_instance.debt_initial\n", " self.notional = self.notional_calc()\n", " self.equity = self.equity_calc()\n", @@ -610,7 +607,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -626,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -825,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -834,7 +830,7 @@ " self.historical_data = None\n", "\n", " @staticmethod\n", - " def write_data(stgy_instance, #previous_price, last_outside, current_outside,\n", + " def write_data(stgy_instance, previous_price, last_outside, current_outside,\n", " date, file_location,\n", " sheet=False):\n", " aave_instance = stgy_instance.aave\n", @@ -857,10 +853,10 @@ " dydx_wanted_keys = [\n", " \"market_price\",\n", " \"entry_price\",\n", - " # \"short_size\",\n", + " \"short_size\",\n", " # \"collateral\",\n", " # \"notional\",\n", - " # \"equity\",\n", + " \"equity\",\n", " # \"leverage\",\n", " \"pnl\",\n", " # \"price_to_liquidation\",\n", @@ -893,8 +889,8 @@ " data_dydx.append(str(list(dydx_instance.__dict__.values())[i]))\n", " data_dydx.append(stgy_instance.open_close_range[0])\n", " data_dydx.append(stgy_instance.open_close_range[1])\n", - " # data_dydx.append(current_outside)\n", - " # data_dydx.append(last_outside)\n", + " data_dydx.append(current_outside)\n", + " data_dydx.append(last_outside)\n", " data_dydx.append(stgy_instance.trailing_stop_range[0])\n", " # data_dydx.append(stgy_instance.trigger_prices['trailing_stop'])\n", " data_dydx.append(stgy_instance.trailing_stop_range[1])\n", @@ -970,16 +966,16 @@ " \"P\",\n", " \"oc_rge_0\",\n", " \"oc_rge_1\",\n", - " # \"out\",\n", - " # \"l_out\",\n", + " \"out\",\n", + " \"l_out\",\n", " \"trail_stp_rge_0\",\n", " # \"trail_stp\",\n", " \"trail_stp_rge_1\",\n", " \"entry\",\n", - " # \"short_size\",\n", + " \"short_size\",\n", " # \"collateral\",\n", " # \"notional\",\n", - " # \"equity\",\n", + " \"equity\",\n", " # \"leverage\",\n", " \"pnl\",\n", " # \"price_to_liquidation\",\n", @@ -1134,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1317,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 62, "metadata": { "tags": [] }, @@ -1438,7 +1434,9 @@ " last_trailing_outside = False\n", " elif (market_price < stgy.trailing_stop_range[0]):\n", " last_trailing_outside = -1\n", - " \n", + " \n", + " if last_outside == False:\n", + " last_outside = 1\n", " while(i < len(stgy.historical_data)):\n", " # for i in range(initial_index, len(stgy.historical_data)):\n", " # pass\n", @@ -1477,7 +1475,7 @@ " stgy.dydx.close_short(stgy)\n", " last_outside = outside\n", " \n", - " # open_close_range action\n", + " # trailing_range action\n", " if (last_trailing_outside == 1) and (trailing_outside == -1):\n", " stgy.dydx.open_short(stgy)\n", " last_trailing_outside = trailing_outside\n", @@ -1486,26 +1484,31 @@ " elif (last_trailing_outside == -1) and (trailing_outside == 1):\n", " stgy.dydx.close_short(stgy)\n", " last_trailing_outside = trailing_outside\n", - " stgy.open_close_range = [market_price * (1-oc_increment), market_price]\n", - " stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-trailing_increment), \n", - " stgy.open_close_range[0]]\n", + " # stgy.open_close_range = [market_price * (1-oc_increment), market_price]\n", + " # stgy.trailing_stop_range = [stgy.open_close_range[0] * (1-trailing_increment), \n", + " # stgy.open_close_range[0]]\n", "\n", " i += 1\n", " # Here we identify price movent direction by comparing current price, previous price and all the triggers\n", " # and we execute all the actions involved between both (current and previous prices)\n", - " time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", + " # time_used = stgy.parameter_manager.find_scenario(stgy, market_price, previous_price, i)\n", " ############################## \n", " # We update trailing\n", - " # Everytime price crosses the lower bound, we move the trailing range\n", - " if (market_price <= stgy.trailing_stop_range[0]):\n", - " stgy.trailing_stop_range = [market_price, \n", - " market_price * (1+trailing_increment)]\n", + " # Everytime price crosses the lower bound, we move the trailing range down\n", + " if market_price <= floor:\n", + " if (market_price < stgy.trailing_stop_range[0]):\n", + " stgy.trailing_stop_range = [market_price, \n", + " market_price * (1+trailing_increment)]\n", + " # Everytime price crosses the upper bound, we move the trailing range up\n", + " elif (market_price > stgy.trailing_stop_range[1]):\n", + " stgy.trailing_stop_range = [market_price * (1-trailing_increment), \n", + " market_price]\n", " ################################\n", " # OC LOGIC\n", " # If prices goes above floor we restart oc_range\n", - " if market_price >= floor:\n", - " stgy.open_close_range = [floor * ((1+slippage)*(1+mu+2*sigma)), \n", - " floor * ((1+slippage)*(1+mu+2*sigma)) * (1+oc_increment)]\n", + " # if market_price >= floor:\n", + " # stgy.open_close_range = [floor * ((1+slippage)*(1+mu+2*sigma)), \n", + " # floor * ((1+slippage)*(1+mu+2*sigma)) * (1+oc_increment)]\n", " # trailing_outside = 1\n", " # last_trailing_outside = 1\n", " ########################\n", @@ -1527,7 +1530,7 @@ " # We write the data into the google sheet or csv file acording to sheet value\n", " # (sheet = True --> sheet, sheet = False --> csv)\n", " current_date = str(stgy.historical_data.index[i-1])\n", - " stgy.data_dumper.write_data(stgy, #previous_price, last_outside, outside,\n", + " stgy.data_dumper.write_data(stgy, previous_price, last_outside, outside,\n", " current_date, file_location,#period, open_close,\n", " sheet=False)\n", " return stgy.dydx.maker_fees_counter" @@ -1542,7 +1545,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -1553,10 +1556,12 @@ "# Normal cases 50 to 150 crosses\n", "normal_1_week = [[\"2020-05-31 00:00:00\",\"2020-06-07 00:00:00\"],240]\n", "normal_1_month = [[[\"2020-05-31 00:00:00\",\"2020-06-30 00:00:00\"],240],\n", - " [[\"2021-12-01 00:00:00\",\"2022-01-01 00:00:00\"],historical_data['close'].max()*0.8]]\n", + " [[\"2021-12-01 00:00:00\",\"2022-01-01 00:00:00\"],historical_data['close'].max()*0.8],\n", + " [[\"2019-11-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]\n", "# Worst cases 150+ crosses\n", "worst_1_week = [[\"2019-10-26 05:00:00\",\"2019-11-02 00:00:00\"],183]\n", - "worst_1_month = [[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183]\n", + "worst_1_month = [[[\"2019-10-01 03:00:00\",\"2019-11-01 00:00:00\"],183], \n", + " ]\n", "\n", "worst_3_month = [ [[\"2020-05-31 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2019-12-15 00:00:00\"],182]]\n", "worst_6_month = [ [[\"2020-02-20 00:00:00\",\"2020-09-01 00:00:00\"],240], [[\"2019-09-15 00:00:00\",\"2020-03-15 00:00:00\"],182]]\n", @@ -1565,23 +1570,23 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "396" + "100" ] }, - "execution_count": 64, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Period of Simulations\n", - "periods_n_open_close = worst_3_month[1]\n", + "periods_n_open_close = normal_1_week\n", "period = periods_n_open_close[0]\n", "p = periods_n_open_close[1]\n", "data_set = historical_data.loc[period[0]:period[1]]\n", @@ -1591,12 +1596,12 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1631,6 +1636,26 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(40.16999999999999, 182, 141.83)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "open_close-data['close'][-1], open_close, data['close'][-1]" + ] + }, { "cell_type": "code", "execution_count": 332, @@ -1673,7 +1698,35 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "def short_analyser(dydx_results):\n", + " short_profit = []\n", + " short_loss = []\n", + " for i in range(len(dydx_results)-1):\n", + " if dydx_results['short_status'][i] and not dydx_results['short_status'][i+1]:\n", + " if dydx_results['pnl'][i+1] > 0:\n", + " short_profit.append([i+1,dydx_results['pnl'][i+1]])\n", + " else:\n", + " short_loss.append([i+1,dydx_results['pnl'][i+1]])\n", + " number_profits = len(short_profit)\n", + " number_losses = len(short_loss)\n", + " profits = sum([i[1] for i in short_profit])\n", + " losses = sum([i[1] for i in short_loss])\n", + " total = profits + losses\n", + " print(\"Number of short_profits: \", number_profits)\n", + " print(\"Total profit from short_profits: \", profits)\n", + " print(\"Number of short_losses: \", number_losses)\n", + " print(\"Total profit from short_losses: \", losses)\n", + " print(\"Profits + Losses: \", total)\n", + " print(\"############################################### \\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 82, "metadata": { "tags": [] }, @@ -1682,38 +1735,141 @@ "name": "stdout", "output_type": "stream", "text": [ - "Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = [0.01, 0.002]) : [40, '-2.756%', '-2.756%']\n", - "Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = [0.015, 0.002]) : [26, '0.016%', '0.016%']\n", - "Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = [0.02, 0.002]) : [10, '7.848%', '7.848%']\n" + "##################################\n", + "We have to beat user expect of: -9431.664915397329 ---> -0.943%\n", + "##################################\n", + "Case [oc_inc, trail_inc] = [0.01, 0.01]:\n", + "Realised PnL: -90667.969 ---> -9.067%\n", + "Unrealised PnL: -98679.143 ---> -9.868%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.01, 0.015]:\n", + "Realised PnL: -54368.255 ---> -5.437%\n", + "Unrealised PnL: -62379.429 ---> -6.238%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.01, 0.02]:\n", + "Realised PnL: -88137.702 ---> -8.814%\n", + "Unrealised PnL: -96148.876 ---> -9.615%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.015, 0.01]:\n", + "Realised PnL: -32236.664 ---> -3.224%\n", + "Unrealised PnL: -32236.664 ---> -3.224%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.015, 0.015]:\n", + "Realised PnL: 4063.05 ---> 0.406%\n", + "Unrealised PnL: 4063.05 ---> 0.406%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.015, 0.02]:\n", + "Realised PnL: -42284.064 ---> -4.228%\n", + "Unrealised PnL: -42284.064 ---> -4.228%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.02, 0.01]:\n", + "Realised PnL: -32236.664 ---> -3.224%\n", + "Unrealised PnL: -32236.664 ---> -3.224%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.02, 0.015]:\n", + "Realised PnL: 4063.05 ---> 0.406%\n", + "Unrealised PnL: 4063.05 ---> 0.406%\n", + "############################################### \n", + "\n", + "Case [oc_inc, trail_inc] = [0.02, 0.02]:\n", + "Realised PnL: -52601.039 ---> -5.26%\n", + "Unrealised PnL: -52601.039 ---> -5.26%\n", + "############################################### \n", + "\n" ] } ], "source": [ "# range's lenght = 2*increment\n", "stk = 1000000\n", + "slippage = 0.0005\n", "oc_increments = [0.01, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", - "trailing_increments = [0.002]#, 0.003, 0.005]#, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", + "trailing_increments = [0.01, 3*0.005, 4*0.005]#, 2*0.005, 3*0.005, 4*0.005]#[0.0005, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01]\n", "maker_fees_counter_lengths = {}\n", "realised_pnl_results = {}\n", "unrealised_pnl_results = {}\n", "total_results = []\n", "# for period_n_open_close in periods_n_open_close:\n", + "size_eth = -1000000/data['close'][1]\n", + "user_expectation = size_eth*(data['close'][-1]-floor)\n", + "print(\"##################################\")\n", + "print(\"We have to beat user expect of: \" + str(user_expectation) + \" ---> \" + str(round(user_expectation/stk * 100,3)) + \"%\" )\n", + "print(\"##################################\")\n", "for oc_increment in oc_increments:\n", " for trailing_increment in trailing_increments:\n", " period = periods_n_open_close[0]\n", " open_close = periods_n_open_close[1]\n", - " slippage = 0.0005\n", " directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, oc_increment, trailing_increment)\n", " maker_fees_counter = run_sim(stk, period, open_close, slippage, 2*oc_increment, 2*trailing_increment, directory)\n", " maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=maker_fees_counter\n", " dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)\n", " realised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_realised_pnl'][len(dydx_results)-1]\n", " unrealised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]=dydx_results['total_realised_pnl'][len(dydx_results)-1]+dydx_results['pnl'][len(dydx_results)-1]\n", - " print(\"Max txs, Realised and Unrealised PnL for ( [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \") :\", \n", - " [maker_fees_counter_lengths[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])], \n", - " str(round(realised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%',\n", - " str(round(unrealised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%'])\n", - " total_results.append([maker_fees_counter_lengths, realised_pnl_results, unrealised_pnl_results])" + " print(\"Case [oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment]) + \":\")\n", + " print(\"Realised PnL: \" + \n", + " str(round(realised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])],3)) + \n", + " \" ---> \" +\n", + " str(round(realised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%')\n", + " print(\"Unrealised PnL: \" +\n", + " str(round(unrealised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])],3)) + \n", + " \" ---> \" +\n", + " str(round(unrealised_pnl_results[\"[oc_inc, trail_inc] = \"+str([oc_increment, trailing_increment])]/stk*100,3))+'%')\n", + " print(\"############################################### \\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "directory = \"Files/Tests/From_%s_to_%s_open_close_at_%s_[oc_incr,trail_inc]_[%s,%s]/\" % (period[0], period[1], open_close, 0.02, 0.002)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "dydx_results = pd.read_csv(directory + 'dydx_results.csv', low_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of short_profits: 2\n", + "Total profit from short_profits: 93382.74959999998\n", + "Number of short_losses: 3\n", + "Total profit from short_losses: -10958.17979999992\n", + "Total final pnl: 82424.56980000006\n" + ] + } + ], + "source": [ + "number_profits = len(short_profit)\n", + "number_losses = len(short_loss)\n", + "profits = sum([i[1] for i in short_profit])\n", + "losses = sum([i[1] for i in short_loss])\n", + "total = profits + losses\n", + "print(\"Number of short_profits: \", number_profits)\n", + "print(\"Total profit from short_profits: \", profits)\n", + "print(\"Number of short_losses: \", number_losses)\n", + "print(\"Total profit from short_losses: \", losses)\n", + "print(\"Total final pnl: \", total)" ] }, { @@ -1829,11 +1985,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "period = best_1_week[0]\n", + "period = periods_n_open_close[0]\n", "data = historical_data.loc[period[0]:period[1]]" ] }, @@ -1846,7 +2002,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1867,41 +2023,7 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Of returns\n", - "emw_returns = returns.ewm(alpha=0.8, adjust=False)\n", - "mu_sma_returns = returns.mean()\n", - "mu_ema_returns = emw_returns.mean().mean()\n", - "\n", - "std_sma_returns = returns.std()\n", - "std_ema_returns = emw_returns.std().mean()\n", - "# Others\n", - "mu_sma_abs_returns = abs(returns).mean()\n", - "returns_max = returns.max()\n", - "returns_min = returns.min()\n", - "\n", - "# Of log-returns\n", - "emw_log_returns = log_returns.ewm(alpha=0.8, adjust=False)\n", - "mu_sma_log_returns = log_returns.mean()\n", - "mu_ema_log_returns = emw_log_returns.mean().mean()\n", - "\n", - "std_sma_log_returns = log_returns.std()\n", - "std_ema_log_returns = emw_log_returns.std().mean()\n", - "\n", - "\n", - "# Others\n", - "mu_sma_abs_log_returns = abs(log_returns).mean()\n", - "log_returns_max = log_returns.max()\n", - "log_returns_min = log_returns.min()\n", - "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 279, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1935,7 +2057,27 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.038945663010967135, 0.0, 0.039724329478103115, 0.0)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs_returns_max, abs_returns_min, abs_log_returns_max, abs_log_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1943,18 +2085,10 @@ "output_type": "stream", "text": [ "Jumps of prices (Returns):\n", - "Mean price jump: 0.063432%\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'std_sma_abs_returns' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJumps of prices (Returns):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_sma_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of mean:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_sma_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean of EMA price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_ema_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of Mean EMA:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_ema_abs_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'std_sma_abs_returns' is not defined" + "Mean price jump: 0.045378%\n", + "Std of mean: 0.09795%\n", + "Mean of EMA price jump: 0.045378%\n", + "Std of Mean EMA: 0.049873%\n" ] } ], @@ -1968,7 +2102,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1976,18 +2110,10 @@ "output_type": "stream", "text": [ "Jumps of log(prices) (log_returns):\n", - "Mean price jump: 0.063432%\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'std_sma_abs_log_returns' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [17], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJumps of log(prices) (log_returns):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_sma_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of mean:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_sma_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean of EMA price jump:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(mu_ema_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStd of Mean EMA:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(std_ema_abs_log_returns\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m6\u001b[39m))\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'std_sma_abs_log_returns' is not defined" + "Mean price jump: 0.04538%\n", + "Std of mean: 0.097988%\n", + "Mean of EMA price jump: 0.045379%\n", + "Std of Mean EMA: 0.049875%\n" ] } ], @@ -2001,28 +2127,28 @@ }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean of EMA +-1*Std of Mean EMA: ['-0.004%', '0.102%']\n", - "Percentage of jumps within Mean of EMA +-1*Std of Mean EMA: 82.618%\n", - "Mean of EMA +-2*Std of Mean EMA: ['-0.057%', '0.155%']\n", - "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 89.667%\n", - "Mean of EMA +-3*Std of Mean EMA: ['-0.11%', '0.208%']\n", - "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 93.911%\n", - "Mean of EMA +-4*Std of Mean EMA: ['-0.163%', '0.261%']\n", - "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 96.261%\n", - "Mean of EMA +-5*Std of Mean EMA: ['-0.216%', '0.314%']\n", - "Percentage of jumps within Mean of EMA +-5*Std of Mean EMA: 97.631%\n" + "Mean of EMA +-1*Std of Mean EMA: ['-0.004%', '0.095%']\n", + "Percentage of jumps within Mean of EMA +-1*Std of Mean EMA: 82.785%\n", + "Mean of EMA +-2*Std of Mean EMA: ['-0.054%', '0.145%']\n", + "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 89.682%\n", + "Mean of EMA +-3*Std of Mean EMA: ['-0.104%', '0.195%']\n", + "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 93.726%\n", + "Mean of EMA +-4*Std of Mean EMA: ['-0.154%', '0.245%']\n", + "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 96.125%\n", + "Mean of EMA +-5*Std of Mean EMA: ['-0.204%', '0.295%']\n", + "Percentage of jumps within Mean of EMA +-5*Std of Mean EMA: 97.492%\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -3225,15 +3351,15 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Percentage of jumps greater than 0.05 or lower than -0.05: 0.001522%\n", - "Percentage of jumps greater than 0.1 or lower than -0.1: 0.000127%\n", + "Percentage of jumps greater than 0.05 or lower than -0.05: 0.0%\n", + "Percentage of jumps greater than 0.1 or lower than -0.1: 0.0%\n", "Percentage of jumps greater than 0.15 or lower than -0.15: 0.0%\n", "Percentage of jumps greater than 0.25 or lower than -0.25: 0.0%\n", "Percentage of jumps greater than 0.5 or lower than -0.5: 0.0%\n" @@ -3331,7 +3457,64 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Of returns\n", + "emw_returns = returns.ewm(alpha=0.8, adjust=False)\n", + "mu_sma_returns = returns.mean()\n", + "mu_ema_returns = emw_returns.mean().mean()\n", + "\n", + "std_sma_returns = returns.std()\n", + "std_ema_returns = emw_returns.std().mean()\n", + "# Others\n", + "mu_sma_abs_returns = abs(returns).mean()\n", + "returns_max = returns.max()\n", + "returns_min = returns.min()\n", + "\n", + "# Of log-returns\n", + "emw_log_returns = log_returns.ewm(alpha=0.8, adjust=False)\n", + "mu_sma_log_returns = log_returns.mean()\n", + "mu_ema_log_returns = emw_log_returns.mean().mean()\n", + "\n", + "std_sma_log_returns = log_returns.std()\n", + "std_ema_log_returns = emw_log_returns.std().mean()\n", + "\n", + "\n", + "# Others\n", + "mu_sma_abs_log_returns = abs(log_returns).mean()\n", + "log_returns_max = log_returns.max()\n", + "log_returns_min = log_returns.min()\n", + "std_ema_abs_log_returns = abs(log_returns).ewm(alpha=0.8, adjust=False).std().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.03586809796233226,\n", + " -0.038945663010967135,\n", + " 0.03523981716241664,\n", + " -0.039724329478103115)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "returns_max, returns_min, log_returns_max, log_returns_min" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -3339,10 +3522,10 @@ "output_type": "stream", "text": [ "Jumps of prices (Returns):\n", - "Mean price jump: -0.000718%\n", - "Std of mean: 0.091498%\n", - "Mean of EMA price jump: -0.000721%\n", - "Std of Mean EMA: 0.071179%\n" + "Mean price jump: -0.000161%\n", + "Std of mean: 0.107951%\n", + "Mean of EMA price jump: -0.000161%\n", + "Std of Mean EMA: 0.061815%\n" ] } ], @@ -3356,7 +3539,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -3364,10 +3547,10 @@ "output_type": "stream", "text": [ "Jumps of log(prices) (log_returns):\n", - "Mean price jump: -0.000759%\n", - "Std of mean: 0.091497%\n", - "Mean of EMA price jump: -0.000763%\n", - "Std of Mean EMA: 0.071178%\n" + "Mean price jump: -0.000219%\n", + "Std of mean: 0.107986%\n", + "Mean of EMA price jump: -0.000219%\n", + "Std of Mean EMA: 0.061817%\n" ] } ], @@ -3381,24 +3564,24 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean of EMA +-2*Std of Mean EMA: ['-0.143%', '0.142%']\n", - "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 90.268%\n", - "Mean of EMA +-3*Std of Mean EMA: ['-0.214%', '0.213%']\n", - "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 96.637%\n", - "Mean of EMA +-4*Std of Mean EMA: ['-0.285%', '0.284%']\n", - "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 98.571%\n" + "Mean of EMA +-2*Std of Mean EMA: ['-0.124%', '0.123%']\n", + "Percentage of jumps within Mean of EMA +-2*Std of Mean EMA: 87.135%\n", + "Mean of EMA +-3*Std of Mean EMA: ['-0.186%', '0.185%']\n", + "Percentage of jumps within Mean of EMA +-3*Std of Mean EMA: 93.087%\n", + "Mean of EMA +-4*Std of Mean EMA: ['-0.247%', '0.247%']\n", + "Percentage of jumps within Mean of EMA +-4*Std of Mean EMA: 96.202%\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3419,7 +3602,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -3427,16 +3610,16 @@ "output_type": "stream", "text": [ "Increments based on H1: log_r ~ N(mu_ema, sigma_ema): math.e ** (mu_ema_log_returns-std_ema_log_returns^2/2 + factor *std_ema_log_returns)\n", - "factor = 1 0.070414%\n", - "1+mu+K*sigma, K = 1 0.070415%\n", - "factor = 2 0.141667%\n", - "1+mu+K*sigma, K = 2 0.141592%\n", - "factor = 3 0.212971%\n", - "1+mu+K*sigma, K = 3 0.21277%\n", - "factor = 4 0.284326%\n", - "1+mu+K*sigma, K = 4 0.283948%\n", - "factor = 5 0.355732%\n", - "1+mu+K*sigma, K = 5 0.355126%\n" + "factor = 1 0.061598%\n", + "1+mu+K*sigma, K = 1 0.061598%\n", + "factor = 2 0.123472%\n", + "1+mu+K*sigma, K = 2 0.123415%\n", + "factor = 3 0.185384%\n", + "1+mu+K*sigma, K = 3 0.185232%\n", + "factor = 4 0.247335%\n", + "1+mu+K*sigma, K = 4 0.247049%\n", + "factor = 5 0.309324%\n", + "1+mu+K*sigma, K = 5 0.308866%\n" ] } ], @@ -3485,7 +3668,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [