zhutoutoutousan
69 lines
1.7 KiB
Markdown
69 lines
1.7 KiB
Markdown
# BTCUSD 1-Minute ONNX Model Training
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This directory contains the training script for a BTCUSD price prediction model using 1-minute timeframe data from 2017 to 2026.
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## Requirements
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```bash
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pip install yfinance tensorflow scikit-learn pandas numpy tf2onnx onnx tqdm
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```
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## Usage
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1. **Run the training script:**
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```bash
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cd ai/btcusd1min
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python main.py
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```
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**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.
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## Configuration
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The script is configured with:
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- **Symbol**: BTCUSD
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- **Timeframe**: M1 (1 minute)
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- **Lookback**: 60 bars (60 minutes of history)
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- **Date Range**: 2017-01-01 to 2026-01-01
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- **Model Architecture**: LSTM with 3 layers (128, 64, 32 units)
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- **Epochs**: 50 (with early stopping)
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- **Batch Size**: 64
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## Output
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The script will create:
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- `models/BTCUSD_M1_model.onnx` - The trained ONNX model
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- `models/BTCUSD_M1_model_scaler.pkl` - The MinMaxScaler used for normalization
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## Model Features
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The model uses 13 features:
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1. Open
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2. High
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3. Low
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4. Close
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5. Tick Volume
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6. RSI (14 period)
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7. EMA 20
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8. EMA 50
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9. ATR (14 period)
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10. Price Change (percentage)
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11. High/Low Ratio
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12. Volume MA (20 period)
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13. Volume Ratio
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## Model Output
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The model predicts the **price change percentage** for the next bar (1 minute ahead).
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## Notes
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- Training on 9 years of 1-minute data will take significant time and memory
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- The script fetches data in 3-month chunks to manage memory
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- Early stopping and learning rate reduction are enabled to prevent overfitting
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- The model uses dropout (0.3) for regularization
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## Using the Model in MQL5
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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.
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