OHLCVPredictor/INFERENCE_README.md

OHLCV Predictor - Inference (Quick Reference)

For full instructions, see the main README.

Minimal usage

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:

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)