Feature Iteration Run #229: volume_price_pivot_v86_y_C0
Status: completed | Started: 2026-07-15 19:05:16 | Ended: 2026-07-15 19:05:16
📊 Iterations
| Iter | Y Label | Model | Warm | Proxy | Parent FS | Feature | Baseline PF | Modified PF | Scalar Score | Result |
|---|---|---|---|---|---|---|---|---|---|---|
| 尚無 iteration 資料 | ||||||||||
📄 Report Markdown
# AUTOLOOP: volume_price_pivot_v86_y_C0 (2 iterations)
> **Phase**: Y4.7 Option B (DSL → Python code → FeatureSet candidate)
> **Target indicator**: volume_price_pivot_v86_y_C0
> **Base feature_set_id**: None
> **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**: 0.01s
> **Total accepted**: 0
> **Total rejected**: 2
## Iterations 詳情
### Iteration 1
- DSL: `K_high.pct_change(12)` (feature=K_iter_1_K_high)
- DSL valid: True
- 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: persist_to_db failed: volume_price_pivot_v86_y_C0 沒有 feature_set_id，無法建立 candidate
### Iteration 2
- DSL: `K_close.rolling(20).mean()` (feature=K_iter_2_K_close)
- DSL valid: True
- 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: persist_to_db failed: volume_price_pivot_v86_y_C0 沒有 feature_set_id，無法建立 candidate
📁 Offline Report
Markdown backup: reports/autoloop/volume_price_pivot_v86_y_C0/20260716_030516.md