Add a strict holdout/walk-forward + baseline gate to factor-weight learning,
per the P4 guardrail: learned weights are never auto-applied until minimum
sample, holdout/walk-forward, and baseline comparison all pass.
- backend/app/weight_learning.py:
- FactorLearning gained ic_train / ic_holdout / validated / gate_notes.
- apply_validation_gate(...) splits a chronological IC series into train +
holdout and only marks validated=True when: total >= MIN_SAMPLE_PERIODS,
each window >= its minimum, train AND holdout IC are positive (beat the
BASELINE_IC=0) and agree in sign, and the pooled |t| > MIN_IC_TSTAT.
- apply_weight_update now keeps new_weight == old_weight for any factor
that is not validated (no auto-apply); only validated factors move.
- learn_momentum_gated(...) builds PIT momentum ICs then applies the gate.
- backend/app/__init__.py: /api/v1/learning/momentum uses the gated learner
and surfaces ic_train/ic_holdout/validated/gate_notes.
- tests: gate (16) via rewritten suite — full backend 286 passed.
Live probe on current price archive: validated=false with
gate_note 'IC not above baseline (0.0711/-0.1143)' — momentum is not
validated, weight stays unchanged (new_weight=None).
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