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47 lines
1.7 KiB
Markdown
47 lines
1.7 KiB
Markdown
# EURGBP Volume proxy divergence 5m
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This strategy implements a volume proxy divergence approach for EURGBP on 5-minute candles using HistGradientBoostingClassifier.
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## Overview
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- **Pair**: EURGBP
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- **Timeframe**: 5m
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- **Model**: HistGradientBoostingClassifier with feature engineering focused on volume-price divergence
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- **Goal**: Identify divergences between price action and volume-weighted price action to predict reversals
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## Features Engineered
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1. **Returns**: 1, 3, 6, 12 period returns
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2. **ATR-normalized candle range and close location value** (from idea)
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3. **Volume proxy divergence**:
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- Price RSI (momentum of price changes)
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- Volume-price RSI (momentum of volume-weighted price changes)
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- Volume proxy divergence (difference between the two RSIs)
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- Volume MACD, signal line, histogram
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4. **Distance from EMAs** (from idea): 20, 50, and 200 period EMAs
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5. **Prior swing high and swing low distance** (from idea)
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6. **Volume features**: Z-score and ratio to moving average
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7. **Rolling volatility percentile** (from idea): Fast/slow volatility ratio, volatility percentile
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## Configuration
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See `quant.config.json` for hyperparameters:
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- `lookback`: 100 candles for prediction
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- `horizon`: 5 candles forward for labeling (5m timeframe)
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- `threshold`: 0.0006 (6 pips) for ATR-normalized breakout
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- `min_confidence`: 0.50 minimum probability for signal generation
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## Usage
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This template follows the PyP Quant Mode contract:
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```python
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def train(data, config):
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return model, metrics
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def predict(model, market_data, config):
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return {"signal": "UP|DOWN|HOLD", "confidence": 0.0, "metadata": {}}
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```
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## Disclaimer
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Educational template only. Not financial advice. Past performance does not guarantee future results. |