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
18 lines
242 B
Plaintext
18 lines
242 B
Plaintext
venv/
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__pycache__/
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*.pyc
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*.pyo
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.env
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data/weights/*.npz
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data/models/*.lgb
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data/models/*_meta.json
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data/models/training_summary.json
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.opencache/
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.openmeteo_cache*
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.openmeteo_cache.sqlite
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.calibration_history.json
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logs/
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*.log
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.DS_Store
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*.sqlite
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