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jgrusewski f39219be9e feat(ml): expand DQN hyperopt search space 27D→31D + GPU-batched eval inference

Root cause: catastrophic OOS eval (Sharpe -280) traced to 11 issues
including hardcoded training dynamics and per-bar CPU inference.

Search space (dqn.rs):
- Add warmup_ratio [0.0, 0.15] — was hardcoded to 0
- Add lr_decay_type [Constant/Linear/Cosine] — was hardcoded Constant
- Add min_epochs_before_stopping [2, 6] — was 1000 (disabled)
- Add minimum_profit_factor [1.1, 2.0] — was hardcoded 1.5
- Widen entropy_coefficient [0.01, 0.2] for 45-action factored space

GPU-batched eval (evaluate_baseline.rs):
- 1024-bar chunked inference for both DQN and PPO
- DQN: batch_greedy_actions per chunk (was per-bar select_action)
- PPO: action_probabilities + GPU argmax per chunk
- ~1000x fewer GPU kernel launches
- Add trade_sharpe_ratio for hyperopt-comparable metric

Preprocessing (preprocessing.rs):
- Add compute_clip_bounds/clip_outliers_with_bounds for leakage-free
  clipping across train/val/test splits

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

2026-03-05 20:45:13 +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