quant-trading-strategy-templates/templates/eurgbp-volume-proxy-divergence-5m

EURGBP Volume proxy divergence 5m

This strategy implements a volume proxy divergence approach for EURGBP on 5-minute candles using HistGradientBoostingClassifier.

Overview

• Pair: EURGBP
• Timeframe: 5m
• Model: HistGradientBoostingClassifier with feature engineering focused on volume-price divergence
• Goal: Identify divergences between price action and volume-weighted price action to predict reversals

Features Engineered

1. Returns: 1, 3, 6, 12 period returns
2. ATR-normalized candle range and close location value (from idea)
3. Volume proxy divergence:
   • Price RSI (momentum of price changes)
   • Volume-price RSI (momentum of volume-weighted price changes)
   • Volume proxy divergence (difference between the two RSIs)
   • Volume MACD, signal line, histogram
4. Distance from EMAs (from idea): 20, 50, and 200 period EMAs
5. Prior swing high and swing low distance (from idea)
6. Volume features: Z-score and ratio to moving average
7. Rolling volatility percentile (from idea): Fast/slow volatility ratio, volatility percentile

Configuration

See quant.config.json for hyperparameters:

• lookback: 100 candles for prediction
• horizon: 5 candles forward for labeling (5m timeframe)
• threshold: 0.0006 (6 pips) for ATR-normalized breakout
• min_confidence: 0.50 minimum probability for signal generation

Usage

This template follows the PyP Quant Mode contract:

def train(data, config):
    return model, metrics

def predict(model, market_data, config):
    return {"signal": "UP|DOWN|HOLD", "confidence": 0.0, "metadata": {}}

Disclaimer

Educational template only. Not financial advice. Past performance does not guarantee future results.