profitable-expert-advisor/ai/dummy/QUICKSTART.md

Quick Start Guide - ONNX with MetaTrader 5

Get started with ONNX machine learning models in MT5 in 5 minutes!

Prerequisites

1. MetaTrader 5 installed and running
2. Python 3.8+ installed
3. MT5 account (demo or live) with access to historical data

Step 1: Install Dependencies

cd ai
pip install -r requirements.txt

This installs:

• TensorFlow/Keras for model training
• ONNX runtime for model inference
• MetaTrader5 Python module
• Other required libraries

Step 2: Verify MT5 Connection

Make sure MT5 is running and you're logged in. The scripts will automatically connect to MT5.

Step 3: Train Your First Model

Train a price prediction model for Gold (XAUUSD):

python train_onnx_model.py --symbol XAUUSD --timeframe H1 --epochs 30

This will:

• Download 2 years of historical data
• Train an LSTM neural network
• Save the model as models/XAUUSD_H1_model.onnx

Expected time: 5-15 minutes depending on your hardware.

Step 4: Test the Model

Make a prediction with your trained model:

python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD

You should see output like:

Current XAUUSD price: 2650.12345
Making 1 prediction(s)...

Predicted next price: 2652.54321
Expected change: 2.41976 (0.09%)

Step 5: Use in MetaTrader 5

Option A: Copy Model to MT5

1. Copy your ONNX model to MT5's Files folder:

   <MT5 Data Folder>\MQL5\Files\models\XAUUSD_H1_model.onnx
   

   Default locations:

   • Windows: C:\Users\<YourName>\AppData\Roaming\MetaQuotes\Terminal\<TerminalID>\MQL5\Files\
   • Or find it: MT5 → File → Open Data Folder → MQL5 → Files

2. Open ONNX_EA.mq5 in MetaEditor

3. Compile (F7)

4. Attach to chart:

   • Model path: models\XAUUSD_H1_model.onnx
   • Lookback: 60 (must match training)
   • Set your trading parameters

Option B: Run from MetaEditor

If you have Python integration enabled in MetaEditor:

1. Open train_onnx_model.py in MetaEditor
2. Press F7 (Compile) to run
3. The model will be saved to the project folder

Common Commands

Train for Different Symbols

# EUR/USD on 15-minute charts
python train_onnx_model.py --symbol EURUSD --timeframe M15

# Bitcoin on 4-hour charts
python train_onnx_model.py --symbol BTCUSD --timeframe H4

Custom Training Parameters

python train_onnx_model.py \
    --symbol XAUUSD \
    --timeframe H1 \
    --lookback 100 \
    --epochs 100 \
    --batch-size 64

Multiple Predictions

python predict_with_onnx.py \
    --model models/XAUUSD_H1_model.onnx \
    --symbol XAUUSD \
    --predictions 5

Troubleshooting

"MT5 initialization failed"

• ✅ Make sure MT5 is running
• ✅ Log into your account in MT5
• ✅ Check that the symbol exists (e.g., XAUUSD, not GOLD)

"No data available"

• ✅ Ensure you have historical data downloaded in MT5
• ✅ Check the date range (script uses last 2 years)
• ✅ Verify symbol name is correct

"Failed to load ONNX model" in EA

• ✅ Check the file path is correct
• ✅ Ensure model is in MT5's Files folder
• ✅ Verify the model file exists and is not corrupted

Model predictions seem wrong

• ✅ Ensure InpLookback in EA matches training --lookback
• ✅ Check that you're using the same symbol/timeframe
• ✅ Verify feature normalization matches training

Next Steps

1. Experiment with different models:

   • Try different lookback periods
   • Adjust network architecture
   • Add more features

2. Optimize trading parameters:

   • Test different prediction thresholds
   • Tune stop loss/take profit
   • Adjust confidence levels

3. Backtest thoroughly:

   • Use MT5 Strategy Tester
   • Test on different time periods
   • Analyze performance metrics

4. Monitor and improve:

   • Track prediction accuracy
   • Retrain models periodically
   • Adjust based on market conditions

Example Workflow

# 1. Train model
python train_onnx_model.py --symbol XAUUSD --timeframe H1 --epochs 50

# 2. Test predictions
python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD

# 3. Copy model to MT5 Files folder
# (Manual step)

# 4. Compile and attach ONNX_EA.mq5 to chart

# 5. Monitor and adjust parameters

Tips

• 🎯 Start with longer timeframes (H1, H4) for more stable predictions
• 📊 Use multiple models for different market conditions
• 🔄 Retrain models periodically (weekly/monthly)
• ⚠️ Always test on demo account first
• 📈 Monitor model performance and adjust parameters

Need Help?

• Check the main README.md for detailed documentation
• Review the MQL5 ONNX documentation: https://www.mql5.com/en/docs/onnx/onnx_prepare
• Examine the code comments for implementation details

Happy trading! 🚀