I Luk Kim
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1e66b67c7a
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Add technical/scientific feature experiments (v6new.106-173) and v6new.122 SQS 72.4
Tier 1: Vol/RSI/BB/OBV features — sizing scalers hurt public SQS, scoring
adjustments ineffective on 28-30 trades. Only doc_quality gate lowering
(0.66→0.55) improved results (+2 trades, +0.8 SQS).
Tier 2: Hurst exponent, Shannon entropy, sector momentum — entropy bonus
CW +10.7pp but SQS equivalent (72.3 vs 72.4). Sector momentum hurt badly.
Tier 3: OU theta, gravitational pull, market temperature — all caused
large CW return drops (-80 to -103pp). Physics-based indicators don't
fit event-driven PEAD.
Best result: v6new.122 (SQS 72.4, #3 leaderboard) = v6new.29 + doc quality
gate 0.66→0.55. Single parameter change outperformed all feature engineering.
New code:
- libs/features/market_features.py: 9 new features (vol, RSI, BB, OBV,
Hurst, entropy, OU theta, gravitational pull, market temperature)
- libs/backtest/scoring.py: v12-v14 scoring models with technical gates
- libs/backtest/allocator.py: volatility + conviction size scalers
- libs/backtest/domain.py: volatility_size_scaler + conviction_boost config
- scripts/enrich_*.py: snapshot enrichment scripts
- 68 experiment configs (v6new.106-173)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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5 months ago |