This approach is particularly useful when dealing with datasets that have been previously cleaned, aggregated, or transformed, allowing for a streamlined visualization process. Ensure your preprocessed data adheres to the expected format as described in the provided Pydantic model documentation. For detailed information on the data structure and the Pydantic model used for preprocessing, please refer to the Data Model Documentation.
+ +The script realtime.py streams live data from the Binance WebSocket API and updates the footprint chart whenever a new one-minute candle closes.
Install the dependencies and run:
+pip install -r requirements.txt
+python realtime.py
+The chart will refresh each minute with the latest BTC/USDT data.
diff --git a/realtime.py b/realtime.py new file mode 100644 index 0000000..e52767c --- /dev/null +++ b/realtime.py @@ -0,0 +1,92 @@ +import json +import random +import string +import requests +import pandas as pd +import websocket +from OrderFlow import OrderFlowChart + +SYMBOL = 'btcusdt' +DEPTH_LIMIT = 20 +KEEP_CANDLES = 20 + +orderflow_df = pd.DataFrame(columns=['bid_size', 'price', 'ask_size', 'identifier']) +ohlc_df = pd.DataFrame(columns=['open', 'high', 'low', 'close', 'identifier']) + + +def random_id(length=5): + letters = string.ascii_letters + return ''.join(random.choice(letters) for _ in range(length)) + + +def fetch_orderbook(): + url = 'https://api.binance.com/api/v3/depth' + resp = requests.get(url, params={'symbol': SYMBOL.upper(), 'limit': DEPTH_LIMIT}) + data = resp.json() + bids = {float(p): float(q) for p, q in data.get('bids', [])} + asks = {float(p): float(q) for p, q in data.get('asks', [])} + prices = sorted(set(list(bids.keys()) + list(asks.keys())), reverse=True) + rows = [] + for price in prices: + rows.append({ + 'price': price, + 'bid_size': bids.get(price, 0.0), + 'ask_size': asks.get(price, 0.0), + }) + return pd.DataFrame(rows) + + +def on_message(ws, message): + global orderflow_df, ohlc_df + data = json.loads(message) + kline = data.get('k') + if not kline: + return + if kline.get('x'): + identifier = random_id() + ts = pd.to_datetime(kline['T'], unit='ms') + candle = pd.DataFrame({ + 'open': [float(kline['o'])], + 'high': [float(kline['h'])], + 'low': [float(kline['l'])], + 'close': [float(kline['c'])], + 'identifier': [identifier] + }, index=[ts]) + ohlc_df = pd.concat([ohlc_df, candle]) + + ob = fetch_orderbook() + ob['identifier'] = identifier + ob.index = [ts] * len(ob) + orderflow_df = pd.concat([orderflow_df, ob]) + + if len(ohlc_df) > KEEP_CANDLES: + keep_ids = ohlc_df.iloc[-KEEP_CANDLES:]['identifier'] + ohlc_df = ohlc_df.iloc[-KEEP_CANDLES:] + orderflow_df = orderflow_df[orderflow_df['identifier'].isin(keep_ids)] + + chart = OrderFlowChart(orderflow_df, ohlc_df, identifier_col='identifier') + chart.plot() + + +def on_error(ws, error): + print('WebSocket error:', error) + + +def on_close(ws, close_status_code, close_msg): + print('WebSocket closed') + + +def on_open(ws): + print('WebSocket connection opened') + + +if __name__ == '__main__': + print('Starting real-time chart...') + ws = websocket.WebSocketApp( + f'wss://stream.binance.com:9443/ws/{SYMBOL}@kline_1m', + on_message=on_message, + on_error=on_error, + on_close=on_close + ) + ws.on_open = on_open + ws.run_forever() diff --git a/requirements.txt b/requirements.txt index 2496b38..517a91f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,5 @@ numpy==1.24.1 pandas==1.5.2 plotly==5.9.0 +requests>=2.0 +websocket-client>=1.5