zhutoutoutousan
198 lines
4.7 KiB
Markdown
198 lines
4.7 KiB
Markdown
# Quick Start Guide - ONNX with MetaTrader 5
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Get started with ONNX machine learning models in MT5 in 5 minutes!
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## Prerequisites
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1. **MetaTrader 5** installed and running
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2. **Python 3.8+** installed
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3. **MT5 account** (demo or live) with access to historical data
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## Step 1: Install Dependencies
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```bash
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cd ai
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pip install -r requirements.txt
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```
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This installs:
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- TensorFlow/Keras for model training
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- ONNX runtime for model inference
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- MetaTrader5 Python module
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- Other required libraries
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## Step 2: Verify MT5 Connection
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Make sure MT5 is running and you're logged in. The scripts will automatically connect to MT5.
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## Step 3: Train Your First Model
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Train a price prediction model for Gold (XAUUSD):
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```bash
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python train_onnx_model.py --symbol XAUUSD --timeframe H1 --epochs 30
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```
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This will:
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- Download 2 years of historical data
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- Train an LSTM neural network
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- Save the model as `models/XAUUSD_H1_model.onnx`
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**Expected time**: 5-15 minutes depending on your hardware.
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## Step 4: Test the Model
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Make a prediction with your trained model:
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```bash
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python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD
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```
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You should see output like:
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```
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Current XAUUSD price: 2650.12345
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Making 1 prediction(s)...
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Predicted next price: 2652.54321
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Expected change: 2.41976 (0.09%)
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```
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## Step 5: Use in MetaTrader 5
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### Option A: Copy Model to MT5
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1. Copy your ONNX model to MT5's Files folder:
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```
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<MT5 Data Folder>\MQL5\Files\models\XAUUSD_H1_model.onnx
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```
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Default locations:
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- Windows: `C:\Users\<YourName>\AppData\Roaming\MetaQuotes\Terminal\<TerminalID>\MQL5\Files\`
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- Or find it: MT5 → File → Open Data Folder → MQL5 → Files
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2. Open `ONNX_EA.mq5` in MetaEditor
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3. Compile (F7)
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4. Attach to chart:
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- Model path: `models\XAUUSD_H1_model.onnx`
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- Lookback: `60` (must match training)
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- Set your trading parameters
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### Option B: Run from MetaEditor
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If you have Python integration enabled in MetaEditor:
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1. Open `train_onnx_model.py` in MetaEditor
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2. Press F7 (Compile) to run
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3. The model will be saved to the project folder
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## Common Commands
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### Train for Different Symbols
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```bash
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# EUR/USD on 15-minute charts
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python train_onnx_model.py --symbol EURUSD --timeframe M15
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# Bitcoin on 4-hour charts
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python train_onnx_model.py --symbol BTCUSD --timeframe H4
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```
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### Custom Training Parameters
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```bash
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python train_onnx_model.py \
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--symbol XAUUSD \
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--timeframe H1 \
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--lookback 100 \
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--epochs 100 \
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--batch-size 64
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```
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### Multiple Predictions
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```bash
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python predict_with_onnx.py \
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--model models/XAUUSD_H1_model.onnx \
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--symbol XAUUSD \
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--predictions 5
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```
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## Troubleshooting
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### "MT5 initialization failed"
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- ✅ Make sure MT5 is running
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- ✅ Log into your account in MT5
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- ✅ Check that the symbol exists (e.g., XAUUSD, not GOLD)
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### "No data available"
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- ✅ Ensure you have historical data downloaded in MT5
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- ✅ Check the date range (script uses last 2 years)
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- ✅ Verify symbol name is correct
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### "Failed to load ONNX model" in EA
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- ✅ Check the file path is correct
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- ✅ Ensure model is in MT5's Files folder
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- ✅ Verify the model file exists and is not corrupted
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### Model predictions seem wrong
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- ✅ Ensure `InpLookback` in EA matches training `--lookback`
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- ✅ Check that you're using the same symbol/timeframe
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- ✅ Verify feature normalization matches training
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## Next Steps
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1. **Experiment with different models**:
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- Try different lookback periods
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- Adjust network architecture
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- Add more features
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2. **Optimize trading parameters**:
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- Test different prediction thresholds
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- Tune stop loss/take profit
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- Adjust confidence levels
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3. **Backtest thoroughly**:
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- Use MT5 Strategy Tester
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- Test on different time periods
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- Analyze performance metrics
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4. **Monitor and improve**:
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- Track prediction accuracy
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- Retrain models periodically
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- Adjust based on market conditions
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## Example Workflow
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```bash
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# 1. Train model
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python train_onnx_model.py --symbol XAUUSD --timeframe H1 --epochs 50
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# 2. Test predictions
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python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD
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# 3. Copy model to MT5 Files folder
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# (Manual step)
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# 4. Compile and attach ONNX_EA.mq5 to chart
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# 5. Monitor and adjust parameters
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```
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## Tips
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- 🎯 Start with longer timeframes (H1, H4) for more stable predictions
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- 📊 Use multiple models for different market conditions
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- 🔄 Retrain models periodically (weekly/monthly)
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- ⚠️ Always test on demo account first
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- 📈 Monitor model performance and adjust parameters
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## Need Help?
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- Check the main [README.md](README.md) for detailed documentation
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- Review the MQL5 ONNX documentation: https://www.mql5.com/en/docs/onnx/onnx_prepare
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- Examine the code comments for implementation details
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Happy trading! 🚀
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