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jgrusewski 7d9808ecf0 fix(ml): smooth CVaR penalty, fix clip leakage, align noisy sigma, fix eval_supervised

Fixes from deep investigation audit (LOW/MEDIUM priority):

1. CVaR penalty: hard cliff (0 or 10) → smooth ramp with gradient signal
   for PSO. Formula: min(10, max(0, -cvar-0.05)*200).

2. Clip outliers leakage: data_loading.rs now computes clip bounds from
   training portion only (first 80%), then applies to full series.
   Log returns and windowed normalize are causal (no leakage).

3. Noisy sigma scheduler: hyperopt now matches conservative() defaults
   (enabled, initial=0.8, final=0.4) so hyperopt-found params
   generalize to train_best without scheduler mismatch.

4. evaluate_supervised.rs: NormStats fallback from test data (leakage)
   replaced with bail! matching evaluate_baseline.rs behavior.

5. Doc comments: stale 27D references updated to 31D (4 locations).

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

2026-03-05 21:14:31 +01:00

baseline_common

fix(ml): wire spread_cost_bps into RL training — commission + spread = total tx cost

2026-03-01 22:10:43 +01:00

cuda_test.rs

chore: clean up examples, update ML binaries and risk tests

2026-02-27 01:33:18 +01:00

download_baseline.rs

chore: clean up examples, update ML binaries and risk tests

2026-02-27 01:33:18 +01:00

evaluate_baseline.rs

feat(ml): expand DQN hyperopt search space 27D→31D + GPU-batched eval inference