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+# 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
+
+```bash
+pip install yfinance tensorflow scikit-learn pandas numpy tf2onnx onnx tqdm
+```
+
+## Usage
+
+1. **Run the training script:**
+```bash
+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.