hk-weather-mkt

rams/hk-weather-mkt

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Author	SHA1	Message	Date
ramseshk	03f9ea2129	Add spatial features, typhoon model, ERA5 pipeline, portfolio Kelly Tier 2 enhancements: - SpatialWeatherClient: multi-station Open-Meteo fetcher for all HK locations Extracts urban heat island delta, coastal-inland gradients, wind convergence, precipitation spatial heterogeneity, composite instability index - TyphoonModel: data-driven signal probability for T1/T3/T8/T10 Climatological base rates + conditional transition probabilities Currently active T1 signal → 25% T3/24h, 10% T8/72h, 22% T8/120h ENSO modulation, active storm proximity boost, month-specific seasonality - ERA5 download/process pipeline via CDS API Downloads hourly reanalysis for HK region, processes to daily training format Output schema matches Open-Meteo for seamless feature compatibility - PortfolioKelly: correlation-aware simultaneous Kelly sizing Covariance matrix from historical outcome correlations Prevents over-betting on correlated rain/temp/wind markets Σ⁻¹ μ vector formulation, regularized inversion, independent fallback - MLPredictor updated: integrates spatial + typhoon + portfolio Kelly record_outcome feeds both calibration AND portfolio correlation matrix	2026-08-10 17:56:02 +08:00
ramseshk	7d7a67bd20	Add ML prediction pipeline — LightGBM, calibration fix, ensemble disagreement 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	2026-08-10 17:50:07 +08:00

Author

SHA1

Message

Date

ramseshk

03f9ea2129

Add spatial features, typhoon model, ERA5 pipeline, portfolio Kelly

Tier 2 enhancements:
- SpatialWeatherClient: multi-station Open-Meteo fetcher for all HK locations
  Extracts urban heat island delta, coastal-inland gradients, wind convergence,
  precipitation spatial heterogeneity, composite instability index
- TyphoonModel: data-driven signal probability for T1/T3/T8/T10
  Climatological base rates + conditional transition probabilities
  Currently active T1 signal → 25% T3/24h, 10% T8/72h, 22% T8/120h
  ENSO modulation, active storm proximity boost, month-specific seasonality
- ERA5 download/process pipeline via CDS API
  Downloads hourly reanalysis for HK region, processes to daily training format
  Output schema matches Open-Meteo for seamless feature compatibility
- PortfolioKelly: correlation-aware simultaneous Kelly sizing
  Covariance matrix from historical outcome correlations
  Prevents over-betting on correlated rain/temp/wind markets
  Σ⁻¹ μ vector formulation, regularized inversion, independent fallback
- MLPredictor updated: integrates spatial + typhoon + portfolio Kelly
  record_outcome feeds both calibration AND portfolio correlation matrix

2026-08-10 17:56:02 +08:00

ramseshk

7d7a67bd20

Add ML prediction pipeline — LightGBM, calibration fix, ensemble disagreement

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

2026-08-10 17:50:07 +08:00

2 Commits