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Author SHA1 Message Date
I Luk Kim 7676ef7867 Register v6new.255 on leaderboard (SQS 72.2, #9, CW 293.2%) 
Higher risk (0.069) + tighter stops (ATR 1.45) produce best CW return
but lower SQS than v6new.196 (72.6) due to WFV instability.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 5 months ago
I Luk Kim 6dee2c24d2 Add v6new.197-214: parameter grid search around v196 
Best CW return: v6new.207 (288.3%) = v196 + risk 0.060 + ATR 1.6.
Public SQS: v6new.196 (72.6) still best — v207 is 72.5.
ATR 1.6 is optimal stop distance. Sector limit increase hurts badly.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 5 months ago
I Luk Kim cc953949f1 Add v6new.196: new best SQS 72.6 (#3) — v29+entropy+risk+doc_quality 
v6new.196 = v29 base + entropy scoring (v13e) + per_trade_risk 0.058
+ doc_quality 0.50. CW return 284.4% (+21.5pp over v122), 194 trades.

Key finding: v29 base with doc_quality 0.50 (not 0.55) is the optimal
quality gate when combined with entropy scoring.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 5 months ago
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
I Luk Kim 1e66b67c7a 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>
 5 months ago
I Luk Kim 02542248b7 Add attention-aware PEAD caps and promote step75 5 months ago
I Luk Kim b507fbf499 Add attention client and continue PEAD research 5 months ago
I Luk Kim c646303423 Recalibrate public SQS and exposure-aware tracking 5 months ago
I Luk Kim 9ec0b26e10 Implement multi-engine PEAD strategy research workflow 5 months ago
I Luk Kim 2395a0c0c3 feat: PEAD mid-cap strategy + pipeline hardening + README cleanup 
- Implement PEAD 7% Long+Short strategy with mid-cap universe expansion
- Add Stock Oracle screener/company clients, text sentiment features
- Enhance backtest engine: short-side execution, walk-forward CV, MFE/MAE analysis
- Harden pipeline: sequential Oracle API calls, scoring recalibration (event_quality 65%)
- Add experiment configs for 60+ strategy variants and journal tracking
- Add review/analysis CLI tools
- Remove obsolete dev/phase0-4 design documents and analysis scripts
- Clean README to reflect only implemented features (remove unbuilt adapters/engines)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 5 months ago