[verified] Cross-theme surprise normalization + historical factor store (P4 enabler)

A. Cross-theme comparability:
- compute_theme_surprises now weight-normalizes by total |weight| (weighted
  average), so every theme surprise on same [-1,1] scale regardless of factor
  count/weight (retail 0.189->0.145; auto_credit 1.0->0.64).

B. Historical factor store (enables learning macro/demographic factors):
- New factor_history.py: append-only per-factor JSONL, dedupes unchanged
  values, rejects non-finite, records every FACTORS value each scheduler run.
- scheduler.py: jobs carry fetch_module; refresh_all records factor history
  (non-fatal); added bank_npl job.
- GET /api/v1/learning/factors?min_points= reports n_points/learnable per
  factor so users see when P4 learning unlocks (validated query parsing).
- weight_learning: generic learn_factor_series() aggregator (momentum reuses).

Independent review deleg_5dd358e3 passed=true (empty security/logic arrays);
its two robustness suggestions applied (finite guard in record(), clean 400 on
bad min_points). 234 tests pass; Vite build passes.

docs/engineering-log.md

@@ -94,3 +94,5 @@
 - Fresh final M2.9 independent review `deleg_e5407553` returned a schema-valid `passed=true` verdict with empty `security_concerns` and `logic_errors`. It recorded three non-blocking test gaps and three deferred suggestions covering concurrent persistence, same-raw normalized-content mismatch cases, predecessor-reference mismatch cases, malformed manifest fixtures, and focused helper/UI coverage. No commit or push has been made.
 - M2.9 observation-integrity follow-up added RED/GREEN regressions for same-raw/different-normalized content, malformed snapshot manifest entries, observations before immutable first capture, and duplicate equal-time observations for one snapshot. The focused price suite and full backend suite pass at 125 tests; independent review `deleg_0e2282c8` returned schema-valid `passed=true` with empty `security_concerns` and `logic_errors` (suggestion: retain the new regression coverage). Process-level locking and focused helper/UI coverage remain deferred.
 - Audit + fix pass (2026-08-26, plan at `docs/audit-and-plan-2026-08-26.md`): triple-confirmed the declarative `FACTORS`/`THEMES` framework is by-passed by hand-written scoring in `dashboard._theme_surprises`, and that `/api/v1/simulation` recomputed a divergent 3-theme path. Fixed P1 (simulation reuses the canonical board via `default_scores` — live check: sim top pick PTT == top board combined 1.600), P2 (dashboard now emits `source_summary{factor_keys:11, rows:6}`; frontend shows "N ปัจจัย · M แหล่ง"), and P5-partial (removed dead `list_themes`/`Theme`/`build_theme_scores`/`_map_index` + the tests that locked them; added `backend/tests/conftest.py` so pytest needs no `PYTHONPATH`). Deferred P0 (registry-driven re-baseline) and P3/P4 (point-in-time backtest + factor-weight learning) pending explicit scope/baseline sign-off. Full backend suite: **203 tests pass**; Vite build passes. This work is own-engine review gated before commit.
+- P0-B + P3 + P4 (commit `8db3d48`, reviews `deleg_fe6f45cd` + `deleg_718218f8` both `passed=true`): the declarative FACTORS/THEMES registry is now the single source of truth (`compute_theme_surprises` reads registry; hand-written per-theme blocks removed; FACTORS carries center/span normalization spec; bank NPL wired into banks). P3 rebuilt `run_backtest` as a real multi-rebalance PIT engine (`leakage_guard`, `momentum_at` true 12-1). P4 added factor-weight learning (Spearman IC -> `apply_weight_update`) + `/api/v1/learning/momentum`. Live result: momentum IC=0.012, t=0.132 over 22 periods (no reliable predictive power in this SET50 window). 226 tests pass.
+- Follow-up (A+B): theme surprises now weight-normalized by total |weight| so cross-theme magnitudes are comparable (retail 0.189->0.145). Added append-only `FactorHistory` store (`data/factor_history/<key>.jsonl`) that records every FACTORS value each scheduler run, wired into `refresh_all` (non-fatal), plus `/api/v1/learning/factors` readiness endpoint. Macro/demographic factors start at n=1 and become learnable (P4) as history accumulates. 233 tests pass.