New engines (configurable but not promoted by default):
- libs/backtest/earnings_runup.py: pre-earnings drift entry (T-7 to T-3, attention+volume z-scores)
- libs/backtest/peer_sympathy.py: peer reaction trade after leader earnings, with reaction_close variant
- libs/backtest/vol_breakout_52w.py: 52-week high volume breakout
- libs/backtest/cross_sectional_momentum.py: 12-1 momentum with VIX/SPY-50dma regime filter
Pipeline additions:
- libs/parser/event_type_normalizer.py: normalize Oracle fallback raw vocab to strategy
vocabulary (earnings_result→earnings_release, regulation_fd→guidance_update, etc.).
Wired into apps/pipeline/event_parser/main.py oracle-fallback path.
- libs/labeler/label_generator.py: preserve future entry_dates as label_status='pending'
instead of dropping as 'unavailable'.
- libs/export/snapshot_export.py: include 'pending' labels in snapshot export.
- libs/parser/rule_parser.py: harden 8-K item-code classifier against dirty input strings.
Infrastructure:
- libs/backtest/snapshot_store.py: _PRICE_FEATURE_WARMUP_DAYS 120→400 (needed for
xsmom 12-1 lookback of 273 trading days).
- apps/backtester/run.py + apps/paper_trader/backtest_sim.py: disable auto-refresh
of snapshots (user request — auto-refresh was silently rebuilding snapshots with
current code, making historical backtests irreproducible across DB mutations).
Engine config support:
- libs/backtest/domain.py: add fields for ER/PS/VolBO/xsmom engine configs.
- libs/backtest/execution.py: wire pct-trailing for EarningsRunup.
- libs/backtest/scoring.py: synthetic candidate scoring for new engines.
Configs (POC + sweeps, none promoted as active strategy):
- Phase A-E PEAD baseline comparisons (no PEAD / +ER / +xsmom / +sleeves variants)
- Phase F1-F8 silo allocation sweep (v7.356 + ER/xsmom silos 15-60%)
- xsmom_poc_v1, xsmom_v2_regime_mdd
- v7.356_plus_er_silo_05/10/15/20/25/30 ranges
- earnings_runup_* / peer_sympathy_* / vol_breakout_52w_* POCs
Tests: 16-tests-each for each new engine + event_type_normalizer tests.
Note: backtest reproducibility infrastructure remains broken — see
_backup_2026-05-10/HANDOFF.md for details on the +12,592% reference run that
cannot be reproduced after rebuilds. Followup work needed before trusting any
specific backtest number.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pre-market label_generator runs request future-dated price windows from
Stock Oracle, which correctly returns 404 because the data does not yet
exist. The labeler was swallowing this as label_status='unavailable' with
entry_date=None. Snapshot export then filtered these rows out, so live
PEAD trading silently lost candidates whose entry_dates fell on
later trading days (e.g., post-market 8-K filings late Friday → Monday
open entry). This explains today's missed RKLB/SNDK/AKAM/MNST/AMD/MRNA
even though their 8-Ks parsed correctly.
Changes:
- libs/labeler/label_generator.py: in 404/empty-bars path, when
entry_date >= today, preserve entry_date and mark label_status='pending'.
New log event label_price_pending_future_window distinguishes from real
data-unavailable failures (past dates still log label_price_unavailable).
- libs/export/snapshot_export.py: include 'pending' in the
label_status filter so today's not-yet-labeled events flow into the
live snapshot.
- apps/pipeline/label_generator/main.py: regeneration logic also
retries existing 'unavailable' rows whose entry_date is null or future
to recover events already mis-labeled in the DB.
- tests/unit/test_labeler.py: regression test reproducing the
RKLB/SNDK/AKAM failure mode and asserting label_status='pending' with
entry_date preserved.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds earnings surprise extraction to parser/features/labeler pipeline,
improves filing fetcher robustness, and extends snapshot export with
new field support.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Previously only used rows[0].keys() — columns present in later rows
(like earnings_surprise_pct from sparse features) were silently dropped.
Now collects all unique keys across all rows.
YoY earnings surprise tested: WR spread only 2.5pp (55.2% vs 52.7%).
Not actionable — YoY growth != analyst consensus surprise.
v6new.30 remains the framework optimum.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New features:
- _enrich_macro_features() in snapshot_export: adds macro_vix, macro_hy_spread from FRED
- compute_return_max_long_score_v10: +12% bonus in favorable regime (VIX>18+HY>3.25)
- _macro_regime_score(): regime-aware scoring component
Findings:
- VIX signal is statistically strong: 62.3% WR (VIX>18+HY>3.25) vs 50.8% (other)
- But scoring bonus promotes marginal trades, diluting OOS quality
- Same pattern as eps_growth, drift bonus: raw signal ≠ scoring improvement
- v6new.17 SQS 59.1 < v6new.9 SQS 63.3
v6new.9 remains best at SQS 63.3 after 17 experiments.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
I Luk Kim