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Feature Iteration Run #1064: zz_sylvain

Status: completed | Started: 2026-07-26 23:50:25 | Ended: 2026-07-26 23:50:26

📊 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 C8 lightgbm — — #7 k_iter1_k_high_pct_change k_high.pct_change(12) ❌ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT quality_gate: canonical duplicate: k_high.pct_change
2 C8 lightgbm — — #7 k_iter2_k_close_rolling_mean k_close.rolling(20).mean() ❌ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT quality_gate: canonical duplicate: k_close.rolling.mean
3 C8 lightgbm — — #7 k_iter3_k_volume_rolling_mean k_volume / k_volume.rolling(20).mean() ❌ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT quality_gate: canonical duplicate: k_volume / k_volume.rolling.mean
4 C8 lightgbm — — #7 k_iter4_k_high_pct_change k_high.pct_change(12) ❌ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT quality_gate: canonical duplicate: k_high.pct_change
5 C8 lightgbm — — #7 k_iter5_k_close_rolling_mean k_close.rolling(20).mean() ❌ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT quality_gate: canonical duplicate: k_close.rolling.mean

📄 Report Markdown

# AUTOLOOP: zz_sylvain (5 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**: 0.34s
> **Total accepted**: 0
> **Total rejected**: 5

## Iterations 詳情

### Iteration 1
- DSL: `k_high.pct_change(12)` (feature=k_iter1_k_high_pct_change)
- DSL valid: False
- FeatureSet candidate id: None
- baseline PF: 0.000 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0000
- accepted: False, reason: quality_gate: canonical duplicate: k_high.pct_change

### Iteration 2
- DSL: `k_close.rolling(20).mean()` (feature=k_iter2_k_close_rolling_mean)
- DSL valid: False
- FeatureSet candidate id: None
- baseline PF: 0.000 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0000
- accepted: False, reason: quality_gate: canonical duplicate: k_close.rolling.mean

### Iteration 3
- DSL: `k_volume / k_volume.rolling(20).mean()` (feature=k_iter3_k_volume_rolling_mean)
- DSL valid: False
- FeatureSet candidate id: None
- baseline PF: 0.000 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0000
- accepted: False, reason: quality_gate: canonical duplicate: k_volume / k_volume.rolling.mean

### Iteration 4
- DSL: `k_high.pct_change(12)` (feature=k_iter4_k_high_pct_change)
- DSL valid: False
- FeatureSet candidate id: None
- baseline PF: 0.000 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0000
- accepted: False, reason: quality_gate: canonical duplicate: k_high.pct_change

### Iteration 5
- DSL: `k_close.rolling(20).mean()` (feature=k_iter5_k_close_rolling_mean)
- DSL valid: False
- FeatureSet candidate id: None
- baseline PF: 0.000 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0000
- accepted: False, reason: quality_gate: canonical duplicate: k_close.rolling.mean

📊 Multi-Objective Metrics

Iteration 1: k_iter1_k_high_pct_change

Y Label: C8 | Parent FS: #7

Objective mode: single

Scalar score: 0.0000

Iteration 2: k_iter2_k_close_rolling_mean

Y Label: C8 | Parent FS: #7

Objective mode: single

Scalar score: 0.0000

Iteration 3: k_iter3_k_volume_rolling_mean

Y Label: C8 | Parent FS: #7

Objective mode: single

Scalar score: 0.0000

Iteration 4: k_iter4_k_high_pct_change

Y Label: C8 | Parent FS: #7

Objective mode: single

Scalar score: 0.0000

Iteration 5: k_iter5_k_close_rolling_mean

Y Label: C8 | Parent FS: #7

Objective mode: single

Scalar score: 0.0000

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260727_075026.md