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I Luk Kim 090bfa8e36 Add contrarian feature analysis + v15 scoring (v6new.174-188) ... Data analysis revealed OBV Q1 (distribution) has 56.4% WR vs Q5 51.2% — contrarian signal confirmed. Previous OBV bonus was applied in wrong direction. Corrected with v15 scoring models. Best result: v6new.185 (entropy + risk 0.058) CW 274.4% but SQS 72.2, still below v6new.122 (72.4). WFV/robustness offsets CW gains. v6new.122 confirmed as optimal under current SQS v4 formula. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>		5 months ago
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__init__.py	feat: implement Phase 4 -- event-driven backtester	5 months ago
allocator.py	Add technical/scientific feature experiments (v6new.106-173) and v6new.122 SQS 72.4	5 months ago
artifacts.py	Add score and event_type to trade blotter output	5 months ago
attention.py	Unify BacktestRunner and PaperTradingEngine trade decision logic	5 months ago
calendar.py	feat: implement Phase 4 -- event-driven backtester	5 months ago
domain.py	Add technical/scientific feature experiments (v6new.106-173) and v6new.122 SQS 72.4	5 months ago
execution.py	Add score and event_type to trade blotter output	5 months ago
manifests.py	Implement multi-engine PEAD strategy research workflow	5 months ago
metrics.py	Recalibrate public SQS and exposure-aware tracking	5 months ago
scoring.py	Add contrarian feature analysis + v15 scoring (v6new.174-188)	5 months ago
selector.py	Add attention-aware PEAD caps and promote step75	5 months ago
snapshot_store.py	Implement multi-engine PEAD strategy research workflow	5 months ago
splits.py	feat: implement Phase 4 -- event-driven backtester	5 months ago
tracker.py	Add attention client and continue PEAD research	5 months ago