zhutoutoutousan
122 lines
3.7 KiB
Markdown
122 lines
3.7 KiB
Markdown
# MetaTrader 5 ONNX EA Setup Guide
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## Quick Start
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1. **Copy Model Files to MT5**
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- Copy `models/XAUUSD_H1_model.onnx` to: `MT5_Data_Folder/MQL5/Files/models/`
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- The EA will look for the model at: `models\XAUUSD_H1_model.onnx`
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2. **Compile the EA**
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- Open `ai/ONNX_EA.mq5` in MetaEditor
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- Press F7 to compile
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- Check for any errors
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3. **Attach to Chart**
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- Open XAUUSD H1 chart in MT5
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- Drag `ONNX_EA.ex5` from Navigator to chart
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- Configure parameters (see below)
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## Model Information
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- **Model Type**: LSTM Neural Network
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- **Input**: 60 bars × 13 features
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- **Output**: Price change percentage (e.g., -0.003 = -0.3% decrease)
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- **Features**: OHLC, volume, RSI, EMA20, EMA50, ATR, price_change, high_low_ratio, volume_ma, volume_ratio
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## EA Parameters
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### ONNX Model Settings
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- **InpModelPath**: `models\\XAUUSD_H1_model.onnx` (path relative to MQL5/Files/)
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- **InpLookback**: `60` (must match training)
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- **InpUsePrediction**: `true` (enable/disable predictions)
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### Trading Settings
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- **InpLotSize**: `0.01` (start small for testing)
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- **InpMagicNumber**: `123456` (unique identifier)
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- **InpSlippage**: `3` (points)
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- **InpStopLoss**: `50` (pips)
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- **InpTakeProfit**: `100` (pips)
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### Prediction Settings
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- **InpPredictionThreshold**: `0.00005` (0.005% as decimal, minimum change to trade)
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- **InpUseConfidence**: `true` (enable confidence filter)
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- **InpMinConfidence**: `0.1` (10% minimum confidence)
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## Important Notes
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### Feature Normalization
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⚠️ **The EA uses simplified normalization that may not exactly match training.**
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For best results:
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1. The training script saves a scaler (`XAUUSD_H1_scaler.pkl`)
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2. You should implement the same MinMaxScaler logic in MQL5
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3. Or export scaler parameters (min/max) from Python and use in MQL5
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Current implementation uses:
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- OHLC: Raw values (should be normalized by scaler)
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- Volume: Divided by 1,000,000
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- RSI: Divided by 100
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- EMAs/ATR: Normalized differences
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- Price change: Percentage
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- Volume MA: Divided by 1,000,000
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### Prediction Format
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The new model predicts **price change percentage** directly:
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- Example: `-0.003` = price will decrease by 0.3%
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- Old format (absolute price) is also supported for backward compatibility
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### Testing Recommendations
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1. **Start with Strategy Tester**
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- Use Visual Mode to see predictions
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- Check Expert tab for prediction logs
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- Verify predictions make sense
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2. **Monitor Logs**
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- Check "Experts" tab for prediction values
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- Verify confidence calculations
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- Watch for any errors
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3. **Adjust Parameters**
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- If too many trades: Increase `InpPredictionThreshold` or `InpMinConfidence`
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- If no trades: Decrease thresholds
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- Adjust stop loss/take profit based on volatility
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## Troubleshooting
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### "Failed to load ONNX model"
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- Check model path is correct
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- Ensure model file exists in `MQL5/Files/models/`
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- Check file permissions
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### "Failed to prepare input data"
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- Ensure enough historical data (need 60+ bars)
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- Check indicator calculations
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- Verify symbol is XAUUSD
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### "Empty output from ONNX model"
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- Check model input shape matches (1, 60, 13)
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- Verify feature preparation matches training
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- Check ONNX runtime version compatibility
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### Predictions seem wrong
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- Feature normalization may not match training
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- Implement proper MinMaxScaler from training
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- Check feature order matches training (13 features in correct order)
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## Model Training Info
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- **Training Date**: 2026-01-06
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- **Training MAE**: 0.0013 (0.13%)
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- **Validation MAE**: 0.0018 (0.18%)
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- **Data Period**: Last 2 years
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- **Timeframe**: H1
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## Next Steps
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1. Test in Strategy Tester first
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2. Compare predictions with Python backtest
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3. Adjust parameters based on results
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4. Consider implementing proper scaler normalization
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5. Test on demo account before live trading
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