31 lines
800 B
Markdown
31 lines
800 B
Markdown
# OHLCV Predictor - Inference (Quick Reference)
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For full instructions, see the main README.
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## Minimal usage
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```python
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from predictor import OHLCVPredictor
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predictor = OHLCVPredictor('../data/xgboost_model_all_features.json')
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predictions = predictor.predict(your_ohlcv_dataframe)
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```
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Your DataFrame needs these columns:
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- `Timestamp`, `Open`, `High`, `Low`, `Close`, `Volume`, `log_return`
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Note: If you are only running inference (not training with `main.py`), compute `log_return` first:
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```python
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import numpy as np
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df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
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```
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## Files to reuse in other projects
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- `predictor.py`
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- `custom_xgboost.py`
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- `feature_engineering.py`
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- `technical_indicator_functions.py`
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- your trained model file (e.g., `xgboost_model_all_features.json`)
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