profitable-expert-advisor/ai/btcusd1min/README.md

BTCUSD 1-Minute ONNX Model Training

This directory contains the training script for a BTCUSD price prediction model using 1-minute timeframe data from 2017 to 2026.

Requirements

pip install yfinance tensorflow scikit-learn pandas numpy tf2onnx onnx tqdm

Usage

1. Run the training script:

cd ai/btcusd1min
python main.py

Note: yfinance 1-minute data is limited to the last 7 days. For longer historical training, the script will use the most recent available data.

Configuration

The script is configured with:

• Symbol: BTCUSD
• Timeframe: M1 (1 minute)
• Lookback: 60 bars (60 minutes of history)
• Date Range: 2017-01-01 to 2026-01-01
• Model Architecture: LSTM with 3 layers (128, 64, 32 units)
• Epochs: 50 (with early stopping)
• Batch Size: 64

Output

The script will create:

• models/BTCUSD_M1_model.onnx - The trained ONNX model
• models/BTCUSD_M1_model_scaler.pkl - The MinMaxScaler used for normalization

Model Features

The model uses 13 features:

1. Open
2. High
3. Low
4. Close
5. Tick Volume
6. RSI (14 period)
7. EMA 20
8. EMA 50
9. ATR (14 period)
10. Price Change (percentage)
11. High/Low Ratio
12. Volume MA (20 period)
13. Volume Ratio

Model Output

The model predicts the price change percentage for the next bar (1 minute ahead).

Notes

• Training on 9 years of 1-minute data will take significant time and memory
• The script fetches data in 3-month chunks to manage memory
• Early stopping and learning rate reduction are enabled to prevent overfitting
• The model uses dropout (0.3) for regularization

Using the Model in MQL5

After training, copy the ONNX model to your MT5 MQL5/Files/ directory and use it in an Expert Advisor similar to the XAUUSD H1 EA.