malkhut(optim): vectorized reward + Ray parallel eval + VBT post-analysis

1. Vectorized reward path (cwm/core.py):
   - Wired up existing compute_reward_vectorized from numba_core (was unused!)
   - Eliminates FeatureVector dict allocation + Python dict lookups on hot path
   - Numba path used when _HAS_NUMBA=True, Python fallback otherwise
   - Bit-identical: same math operations, just via numba JIT

2. Ray-based parallel eval (training/ray_eval.py):
   - Industrial multi-core execution via Ray (used by OpenAI/Anyscale)
   - ray.put() stores params/scenarios in shared object store (no pickle per worker)
   - Each worker: own CWM + planner, zero shared state, no races
   - Bit-identical: same seed + same params = same results regardless of worker count
   - PolicyEvaluator.evaluate_candidate: new use_ray=True parameter

3. VBT post-analysis (training/vbt_analysis.py):
   - episodes_to_pnl_array, episodes_to_metrics (Sharpe, Sortino, VaR, win_rate, etc.)
   - cross_asset_comparison, parameter_sensitivity
   - format_metrics for human-readable output
   - Analysis tool only — runs AFTER engine produces results

4. numba_core.py: added missing 'import math' for compute_reward_vectorized

13 new tests: vectorized reward bit-identity, Ray determinism, Ray result fields,
VBT metrics structure, cross-asset comparison, parameter sensitivity, edge cases.
Total: 1178 tests, 50 files, all green, zero regressions.

MALKHUT/malkhut/cwm/numba_core.py

@@ -12,6 +12,7 @@ not dataclasses. The CWM calls these from its hot path.
 """
 from __future__ import annotations
 
+import math
 import numpy as np
 from numba import njit, prange