# 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:
```python
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.