e3f32742fa
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>
14 lines
376 B
JSON
14 lines
376 B
JSON
{
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"ppo": {
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"best_objective": -10.562240600585938,
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"best_params": {
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"clip_epsilon": 0.1937704475953032,
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"entropy_coeff": 0.004575857121380468,
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"policy_learning_rate": 0.0009063277596553022,
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"value_learning_rate": 0.00008412342994353697,
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"value_loss_coeff": 1.3082900521181515
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},
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"elapsed_secs": 4.173037206,
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"trials": 6
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}
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} |