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Feature Iteration Run #437: zz_sylvain

Status: completed | Started: 2026-07-16 17:44:42 | Ended: 2026-07-16 17:46:13

⭐ Best Accepted Iteration

Iteration 1: k_high.pct_change(12)

baseline PF: 1.037 → modified PF: 1.062 | Cohen's d: 0.000 | p-value: 1.000

📊 Iterations

Iter Y Label Model Warm Proxy Parent FS Feature DSL DSL Valid Baseline PF Modified PF Scalar Score t-stat p-value Cohen's d Result Reason
1 — gradient_boosting — — — K_loop_iter_1_563283 k_high.pct_change(12) ✅ 1.037 1.062 0.1812 0.000 1.000 0.000 ✅ ACCEPT accepted

📄 Report Markdown

# AUTOLOOP: zz_sylvain (1 iterations)

> **Phase**: Y4.7 Option B (DSL → Python code → FeatureSet candidate)
> **Target indicator**: zz_sylvain
> **Base feature_set_id**: 7
> **Claim**: (default) Propose a new k_* feature that improves risk-adjusted returns
> **Objective mode**: scalarize
> **Metrics**: ['profit_factor', 'sharpe_ratio', 'win_rate', 'max_drawdown', 'test_accuracy']
> **Total duration**: 90.96s
> **Total accepted**: 1
> **Total rejected**: 0

## Iterations 詳情

### Iteration 1
- DSL: `k_high.pct_change(12)` (feature=K_loop_iter_1_563283)
- DSL valid: True
- FeatureSet candidate id: 446
- baseline PF: 1.037 → modified PF: 1.062
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.1812
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.037, modified=1.062, diff=0.026
  - sharpe_ratio: baseline=0.995, modified=1.675, diff=0.680
  - win_rate: baseline=0.419, modified=0.438, diff=0.019
  - max_drawdown: baseline=0.120, modified=0.118, diff=-0.003
  - test_accuracy: baseline=0.971, modified=0.970, diff=-0.000

📊 Multi-Objective Metrics

Iteration 1: K_loop_iter_1_563283

Y Label: — | Parent FS: #—

Objective mode: scalarize

Scalar score: 0.1812

MetricBaselineModifiedDiff
win_rate 0.419 0.438 0.019
max_drawdown 0.120 0.118 -0.003
sharpe_ratio 0.995 1.675 0.680
profit_factor 1.037 1.062 0.026
test_accuracy 0.971 0.970 0.000

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260717_014613.md