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

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