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/training/cma_trainer.py

@@ -964,8 +964,13 @@ class PolicyEvaluator:
         record_to_matrix: bool = False,
         matrix: Optional[Any] = None,
         workers: int = 0,
+        use_ray: bool = False,
     ) -> Tuple[float, list[EpisodeResult]]:
-        if workers > 1 and len(scenarios) > 1:
+        if use_ray and workers > 1 and len(scenarios) > 1:
+            from malkhut.training.ray_eval import RayEpisodeRunner
+            runner = RayEpisodeRunner(workers=workers)
+            results = runner.run_episodes(params, scenarios, rng_seed, planner_type)
+        elif workers > 1 and len(scenarios) > 1:
             from malkhut.training.parallel_eval import ParallelEpisodeRunner
             runner = ParallelEpisodeRunner(workers=workers)
             results = runner.run_episodes(params, scenarios, rng_seed, planner_type)