feat(eval): VaR breach rate metric (#12) + HMM regime detector (#13) — rq-04 prep

#12 — VaR_breach_rate_99_oos_regime_cond metric:
- internal/eval/var.go: VaRBreachRate() + kupiecPOF() + LinearProbePredict() (stdlib math only)
- internal/eval/var_test.go: 8 golden tests (zero/all breach, perfect calibration, boundary)
- cmd/eval/main.go: -metric var flag (no-leakage probe → VaR → Kupiec P)
- scripts/var_breach.py: Python equivalent with METRIC_KEY constant (13 TDD tests)
- train.py LOCKED VaR EVAL BLOCK: writes VaR_breach_rate_99_oos_regime_cond + kupiec_p to metrics.json
- Fixed bug: train.py used bare 'os' before import; now uses module-level '_os' consistently

#13 — HMM regime detector + JEPA conditioning seam:
- scripts/prepare_regime.py: GaussianHMM (diag, 3-state) on realized_vol; states sorted by mean vol
  (0=calm, 1=stressed, 2=crisis); deterministic (random_state=42); outputs eurusd_regime.parquet
- tests/test_regime.py: 11 TDD tests (dtype, states, determinism, vol sort, daily fallback)
- train.py: JEPA_ENABLE_REGIME toggle + REGIME CONDITIONING SEAM (concat baseline, agent-editable)
- requirements.txt: hmmlearn>=0.3, scikit-learn>=1.4

78 Python + all Go tests green.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

requirements.txt

@@ -6,3 +6,5 @@ numpy>=2.0
 pandas>=2.2
 pyarrow>=16
 histdata>=1.3        # histdata.com downloader (handles the tk token politely)
+hmmlearn>=0.3        # regime detector (prepare_regime.py, jepa-fx-risk#13)
+scikit-learn>=1.4    # HMM dependency