This repository is a curated collection of quantitative trading strategies implemented in Python. Each notebook demonstrates a complete strategy — from data loading and signal generation to backtesting and performance evaluation — inspired by academic research, trading literature, and practitioner insights.
The goal of this library is to provide:
- Reproducible strategy implementations
- Clear, documented notebooks for learning and experimentation
- Foundations for extension, so you can adapt them to your own research and trading ideas
This is a notebook that implements the strategies described in the book "Buy The Fear, Sell The Greed" by Larry Connors.
Notebook : Buy The Fear
Source : https://www.amazon.in/Buy-Fear-Sell-Greed-Behavioral/dp/0578206501
This notebook contains the Python implementation of the rules of the Trend Following strategy by Carlo Zarattini et al., along with a few extra personal ideas
Notebook : Trend-Following by Carlo Zarattini
Source : https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5084316
Contains the implementation of the momentum RSI strategy proposed in the substack article and evaluates the signal strength and strategy performance.
Notebook : Momentum RSI in Crypto
Source : https://tradingresearchub.com/p/flipping-the-rsi-script-when-overbought?lli=1
As the title says, this is a simple pairs trading notebook. I load up the data, pick a high correlated pair, then trade the z-score of its spread long/short. Although simple, it appears to be high effective, thus I will work on the proposed next steps (see at the bottom of the notebook) when pairs trading comes up again as a theme in my backlog (soon).
Notebook : Simple Pairs Trading
A simple breakout strategy using the donchian upper channels, with a twist - we only enter if the asset is having a favorable 14-day beta to the market (total crypto mcap). We measure the market's ROC over the last 14 days, and we compare the ROC's sign with the asset's beta sign. If the are aligned (+/+ or -/-) then the token is moving towards a favorable direction.
Notebook : Regime-based Breakout in Crypto
Yet again a simple breakout system, tested on Bitcoin. Its performance and simplicity are amazing. Tested over other tokens, incl. ETH and SOL, and they all perform incredibly well, outperforming buy and hold consistently. This notebook serves as a solid foundation towards to construction of a portfolio of breakout systems in crypto (to be explored).
Notebook : Donchian Breakout on Bitcoin
A market-neutral long/short crypto strategy that ranks tokens using a composite percentile-based momentum factor. It holds long positions in the top 10 and short positions in the bottom 10 tokens, aiming to capture relative performance trends while minimizing market exposure.
Notebook : Percentile-rank Momentum Strategy
This is an extremely simple, yet robust, mean reversion system. It enters either long or short (depending on the position of the price against the 50-day SMA, which acts as a regime filter) based on the position of the 14-day Z-Score. Signals are derived from Bitcoin and tested on the top tokens by market cap, showing robustness across the board. A 2X Leveraged Portfolio of these tokens exhibits interestingly solid performance.
Notebook : Market-wide Regime-based Mean Reversion