2 Commits (d4ae9002869cd9c9000abb28a21e9a385a42a3b4)

Author	SHA1	Message	Date
I Luk Kim	d4ae900286	feat: expand data pipeline and integrate event features into scoring ... Data expansion: - Poll SEC 8-K filings from 2025-10-01 to 2026-03-12 (was ~2 months) - Pipeline: 36 new filings → 24 new events → total 44 events, 15 symbols - Re-export with merged features (market_v1+event_v1+financial_v1) - Parquet columns: 7 → 41 (adds signal_strength, guidance_direction, document_quality, oneoff_penalty, eps_growth_qoq, etc.) Score model v2: - Add event quality component (15% weight): signal_strength_score, guidance_direction_score, document_quality_score - Add risk penalty component (10% weight): inverted oneoff_penalty - Rebalance market weights: reaction 25%, close 25%, volume 15%, gap 10% - Graceful degradation when event features are absent (returns 0.5) Export pipeline: - Add --feature-versions CLI flag to merge multiple feature types - export_dataset_snapshot() accepts feature_versions list parameter - Groups features by event_id and merges feature_json dicts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	5 months ago
I Luk Kim	354f7a1716	feat: add rule-based entry score model for backtester ... Replace naive abs(reaction_day_return) fallback with a composite score from 4 market microstructure features available at entry time: 1. Reaction quality (35%) — moderate positive return (PEAD zone) is ideal; extreme positives penalized as "priced in" 2. Close strength (30%) — close near session high = buyers won 3. Volume conviction (20%) — 1.2-2x is healthy; >3x is exhaustion 4. Gap quality (15%) — small positive gap = orderly strength Real data results (14 events, b1868603 snapshot): - Score filters out 6 of 10 losers (DDOG -11.7%, META -9.1%, etc.) - With threshold 0.5: return -2.63% → +0.27%, drawdown 4.24% → 0.86% - Profit factor 0.44 → 1.16 (turns profitable) - MSFT loss (-8.7%) is macro-driven, not predictable from stock features Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	5 months ago