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Feature Iteration Run #341: zz_sylvain

Status: completed | Started: 2026-07-16 13:45:19 | Ended: 2026-07-16 13:45:52

⭐ Best Accepted Iteration

Iteration 1: k_high.pct_change(12)

baseline PF: 0.932 → modified PF: 1.135 | 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) ✅ 0.932 1.135 1.4950 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) Adding k_high_pct_change_12 improves Sharpe by 10%
> **Objective mode**: scalarize
> **Metrics**: ['profit_factor', 'sharpe_ratio', 'win_rate', 'max_drawdown', 'test_accuracy']
> **Total duration**: 32.62s
> **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: 307
- baseline PF: 0.932 → modified PF: 1.135
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 1.4950
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=0.932, modified=1.135, diff=0.203
  - sharpe_ratio: baseline=-2.226, modified=3.540, diff=5.765
  - win_rate: baseline=0.424, modified=0.427, diff=0.002
  - max_drawdown: baseline=0.173, modified=0.096, diff=-0.076
  - test_accuracy: baseline=0.921, modified=0.958, diff=0.037

📊 Multi-Objective Metrics

Iteration 1: K_loop_iter_1_563283

Y Label: — | Parent FS: #—

Objective mode: scalarize

Scalar score: 1.4950

MetricBaselineModifiedDiff
win_rate 0.424 0.427 0.002
max_drawdown 0.173 0.096 -0.076
sharpe_ratio -2.226 3.540 5.765
profit_factor 0.932 1.135 0.203
test_accuracy 0.921 0.958 0.037

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260716_214552.md