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I Luk Kim bb659cf7ae Add reaction-based position sizing to reduce mean-reversion loss risk ... New feature: reaction_size_cap_threshold in RiskConfig - When abs(reaction_day_return) > threshold, position size scales down - Formula: scaler = threshold / abs(reaction) (linear inverse) - E.g. threshold=8%: 8% reaction → 100%, 16% → 50%, 24% → 33% Paper trading impact simulation (top 10 trades): - PII (react +14%): loss $501 → $288 (saved $214) - VSCO (react +18%): loss $792 → $350 (saved $442) - RYTM (react +12%): loss $397 → $256 (saved $141) - Winners (react <5%): unchanged (SSRM, FLS, LW, KGS all 100%) - Total loss reduction: $2,610 → $1,813 (-30%) - Net PnL improvement: +$797 v6new.24 backtest: Train SQS 92.5 (session best), risk=75.6. Public SQS 52.5 (lower return due to sizing, but best risk profile). 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 reaction-based position sizing to reduce mean-reversion loss risk	5 months ago
artifacts.py	Implement multi-engine PEAD strategy research workflow	5 months ago
calendar.py	feat: implement Phase 4 -- event-driven backtester	5 months ago
domain.py	Add reaction-based position sizing to reduce mean-reversion loss risk	5 months ago
execution.py	Implement multi-engine PEAD strategy research workflow	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 v10 macro regime scoring (VIX+HY) and FRED macro features — SQS 59.1 (rejected)	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