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>
65 lines
1.7 KiB
JSON
65 lines
1.7 KiB
JSON
{
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"folds": [
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{
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"fold": 0,
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"model": "dqn",
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"sharpe_ratio": 8.356999686712793,
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"max_drawdown_pct": 589910.0,
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"win_rate_pct": 72.15007215007215,
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"profit_factor": 8.395671459208245,
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"total_return_pct": 16820974570.000225,
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"num_trades": 101178,
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"test_start": "2025-02-02",
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"test_end": "2025-03-31"
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},
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{
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"fold": 1,
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"model": "dqn",
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"sharpe_ratio": 7.669943186882817,
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"max_drawdown_pct": 139841.25,
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"win_rate_pct": 70.69554495472187,
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"profit_factor": 6.612933004041576,
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"total_return_pct": 15835838494.999826,
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"num_trades": 98944,
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"test_start": "2025-05-01",
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"test_end": "2025-06-30"
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},
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{
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"fold": 2,
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"model": "dqn",
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"sharpe_ratio": 8.596876079695132,
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"max_drawdown_pct": 275.0,
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"win_rate_pct": 69.62452758458751,
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"profit_factor": 12.6028379451134,
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"total_return_pct": 16770955110.000101,
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"num_trades": 89434,
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"test_start": "2025-08-01",
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"test_end": "2025-09-30"
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},
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{
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"fold": 3,
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"model": "dqn",
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"sharpe_ratio": 8.70207998896788,
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"max_drawdown_pct": 546728.0000000001,
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"win_rate_pct": 71.24428773821063,
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"profit_factor": 9.160697783607874,
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"total_return_pct": 19386292389.999477,
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"num_trades": 93877,
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"test_start": "2025-11-02",
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"test_end": "2025-12-31"
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}
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],
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"aggregate": {
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"dqn_avg_sharpe": 8.331474735564655,
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"dqn_avg_drawdown": 319188.5625,
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"dqn_avg_win_rate": 70.92860810689804,
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"ppo_avg_sharpe": 0.0,
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"ppo_avg_drawdown": 0.0,
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"ppo_avg_win_rate": 0.0
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},
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"sanity_checks": {
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"beats_random": true,
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"action_diversity": true,
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"fold_consistency": true
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}
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} |