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>

scripts/var_breach.py

@@ -0,0 +1,67 @@
+"""Parametric 99% VaR breach rate + Kupiec POF p-value.
+
+Used by train.py's LOCKED VaR EVAL BLOCK to write VaR_breach_rate_99_oos_regime_cond
+to metrics.json so the autoresearch loop can optimise it.
+
+jepa-fx-risk#12
+"""
+
+import math
+
+# Canonical metric key — no surrounding whitespace, as required by the loop contract.
+METRIC_KEY = "VaR_breach_rate_99_oos_regime_cond"
+
+# Default normal 99th-percentile z-score.
+Z99 = 2.326
+
+
+def var_breach_rate(pred_vol, actual_vol, z99=Z99):
+    """Compute VaR breach rate and Kupiec POF p-value.
+
+    Args:
+        pred_vol:   iterable of predicted conditional vol forecasts
+        actual_vol: iterable of actual realized vol (same length)
+        z99:        99th-percentile z-score (default 2.326)
+
+    Returns:
+        (breach_rate, kupiec_p) where:
+            breach_rate — fraction of steps where actual_vol > pred_vol × z99
+            kupiec_p    — Kupiec POF p-value (H0: true breach rate = 1%)
+                          High p-value = well-calibrated; low = miscalibrated tail.
+    """
+    pred_v = list(pred_vol)
+    act_v = list(actual_vol)
+    n = len(pred_v)
+    if n == 0 or n != len(act_v):
+        return 0.0, 1.0
+
+    n1 = sum(1 for p, a in zip(pred_v, act_v) if a > p * z99)
+    breach_rate = n1 / n
+    p = kupiec_pvalue(n, n1)
+    return breach_rate, p
+
+
+def kupiec_pvalue(n, n1, p0=0.01):
+    """Kupiec Proportion-of-Failures likelihood ratio test.
+
+    H0: true breach probability = p0.
+    Returns P(chi²(1) > LR) using the identity P(chi²(1)>x) = erfc(sqrt(x/2)).
+    Returns 1.0 for n=0 or LR<=0 (well-calibrated / over-conservative).
+    """
+    if n == 0:
+        return 1.0
+    n0 = n - n1
+    phat = n1 / n
+
+    if n1 == 0:
+        # 0 × ln(0/p0) = 0 by convention; only n0 term contributes
+        lr = 2 * n0 * math.log((1 - phat) / (1 - p0))
+    elif n1 == n:
+        lr = 2 * n1 * math.log(phat / p0)
+    else:
+        lr = 2 * (n1 * math.log(phat / p0) + n0 * math.log((1 - phat) / (1 - p0)))
+
+    if lr <= 0:
+        return 1.0
+    # P(chi²(1) > LR) = erfc(sqrt(LR/2))
+    return math.erfc(math.sqrt(lr / 2))