2 Commits

Author	SHA1	Message	Date
jgrusewski	26b51a4f99	feat(ml): real validation, transaction costs, and data fixes for DQN/PPO pipeline ... Replace stub validation functions with real model inference (DQN greedy, PPO act()) so early stopping optimizes actual trading performance instead of market volatility. Add transaction costs (commission + bid-ask spread) to reward computation across train/hyperopt/evaluate examples. Key changes: - Symbol filtering (--symbol ES.FUT) prevents mixing futures contracts - BTreeMap timestamp dedup handles overlapping .FUT contract bars - Return clamping (--max-bar-return) filters contract roll boundaries - Warmup offset alignment fixes feature-to-bar index mismatch - Kelly sizing: 3 stubs replaced with real data-driven implementations - Adam optimizer: BUG #14 diagnostic logging demoted to trace - TFT: varmap_mut() accessor for checkpoint loading - PPO hyperopt: with_costs() builder for tx cost configuration DQN eval (ES.FUT, 2 folds): Sharpe=11.36, MaxDD=7.42%, WinRate=33.2% Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-24 00:43:11 +01:00
jgrusewski	e3f32742fa	feat(ml): walk-forward training pipeline with real Databento data ... Fix zstd decoder in train_baseline.rs and evaluate_baseline.rs (same pattern as hyperopt adapters — branch on .dbn.zst extension). Add CLI flags for walk-forward config (train/val/test/step months), learning rate, and max-steps-per-epoch to make pipeline validation feasible. Pipeline validated end-to-end: hyperopt (5 trials, best Sharpe 2.37) → walk-forward training (4 folds, 6/1/1 month windows on ES.FUT) → evaluation (4 fold test sets, checkpoints + norm stats saved). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-23 21:48:10 +01:00