quant-trading-strategy-templates/templates/audusd-multi-timeframe-trend-filter-1m

AUDUSD Multi-timeframe trend filter 1m

This strategy implements a multi-timeframe trend filter approach for AUDUSD on 1-minute candles using RandomForestClassifier.

Overview

• Pair: AUDUSD
• Timeframe: 1m
• Model: RandomForestClassifier with feature engineering focused on multi-timeframe trend analysis
• Goal: Trend following strategy that filters signals across multiple timeframes (short, medium, long-term)

Features Engineered

1. Returns: 1, 3, 6, 12 period returns
2. ATR-normalized price action: Range percentage, body percentage, close position
3. Multi-timeframe trend filters:
   • Short-term: 5 EMA vs 13 EMA
   • Medium-term: 13 EMA vs 34 EMA
   • Long-term: 34 EMA vs 89 EMA
   • Price position relative to each EMA (5, 13, 34, 89)
4. Rolling volatility percentile (from idea): Fast/slow volatility ratio, volatility percentile
5. Distance from EMAs (from idea): 20, 50, and 200 period EMAs
6. Prior swing high and swing low distance (from idea)
7. Volume features: Z-score and ratio to moving average

Configuration

See quant.config.json for hyperparameters:

• lookback: 100 candles for prediction
• horizon: 1 candle forward for labeling
• threshold: 0.0004 (4 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.