# OHLCV Predictor - Inference (Quick Reference)

For full instructions, see the main README.

## Minimal usage

```python
from predictor import OHLCVPredictor

predictor = OHLCVPredictor('../data/xgboost_model_all_features.json')
predictions = predictor.predict(your_ohlcv_dataframe)
```

Your DataFrame needs these columns:
- `Timestamp`, `Open`, `High`, `Low`, `Close`, `Volume`, `log_return`

Note: If you are only running inference (not training with `main.py`), compute `log_return` first:

```python
import numpy as np
df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
```

## Files to reuse in other projects

- `predictor.py`
- `custom_xgboost.py`
- `feature_engineering.py`
- `technical_indicator_functions.py`
- your trained model file (e.g., `xgboost_model_all_features.json`)