ramseshk
7d7a67bd20
Tier 1 ML enhancements: - Feature engineering (37 features across 5 groups: thermal, dynamic, moisture, temporal, interaction) from NWP model output - 7 LightGBM probability models for rain/temp/wind thresholds - Temperature-scaled probabilities to prevent overconfidence on bootstrap data - MLPredictor: unified inference pipeline replacing heuristic sigmoids - Ensemble disagreement signals (composite spread → edge amplification) - Fixed calibration loop: update_calibration() now functional (EMA of errors) - record_outcome() wired for post-resolution feedback - Nautilus strategy updated: ML predictions take priority, heuristics as fallback - Historical backtest engine with Sharpe/ROI/max-DD simulation - Bootstrap training data generator from HK climate normals Run: python ml/train.py && python ml/backtest.py --edge 50
21 lines
499 B
Python
21 lines
499 B
Python
"""ML prediction pipeline for HK weather.
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Usage:
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from ml import MLPredictor
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predictor = MLPredictor()
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predictor.fetch_and_predict()
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signal = predictor.generate_signal("temp_gt_30c_24h", market_probability=45.0)
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"""
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from ml.predictor import MLPredictor
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from ml.model import ModelEnsemble, WeatherModel, TARGET_DEFINITIONS
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from ml.features import FeatureEngine
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__all__ = [
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"MLPredictor",
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"ModelEnsemble",
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"WeatherModel",
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"FeatureEngine",
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"TARGET_DEFINITIONS",
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]
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