Kunthawat Greethong 8db3d48ae2 [verified] P0-B registry-driven scoring + P3 PIT backtest + P4 factor-weight learning ...

P0-B (registry is the single source of truth for scoring):
- FACTORS now carries center/span normalization spec; unused hand-written
  per-theme surprise blocks in dashboard.py replaced by one registry-driven
  compute_theme_surprises() (themes.py).
- THEMES['banks'] adds bank_npl weight so NPL is genuinely blended.
- factor_value/normalize hardened against NaN/inf (finite guards).
- Board re-ranks (TRUE/GULF up, TOP->3) per registry weights; 3 new tests
  incl. 'changing a registry weight changes output'.

P3 (point-in-time backtest):
- run_backtest is now a real multi-rebalance engine (reallocates every window,
  reconciles holdings, marks to market) instead of allocate-once+break.
- Added leakage_guard (False unless a PIT score_fn is supplied), planned vs
  actual rebalances, and momentum_at() true 12-1 (skips last month, PIT).

P4 (factor-weight learning):
- weight_learning.py: cross-sectional Spearman IC, forward-return builder,
  IC aggregation + t-stat, and apply_weight_update (new = clip(old*(1+shrink*IC))).
- GET /api/v1/learning/momentum endpoint. Live result: momentum IC=0.012
  t=0.132 over 22 periods -> momentum has no reliable predictive power here.
  Macro/demographic factors blocked (no historical factor vintages yet).

Two independent review gates passed (deleg_fe6f45cd, deleg_718218f8): empty
security/logic arrays; their non-blocking suggestions applied (finite guards,
dedupe leakage_guard resolution). 226 tests pass; Vite build passes.

2026-08-27 07:12:18 +07:00

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