Feature Iteration Run #313: volume_price_pivot_v86_y_C8

# AUTOLOOP: volume_price_pivot_v86_y_C8 (3 iterations)

> **Phase**: Y4.7 Option B (DSL → Python code → FeatureSet candidate)
> **Target indicator**: volume_price_pivot_v86_y_C8
> **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**: 0.03s
> **Total accepted**: 0
> **Total rejected**: 3

## 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_C8 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_C8 沒有 feature_set_id，無法建立 candidate

### Iteration 3
- DSL: `k_volume / k_volume.rolling(20).mean()` (feature=k_iter_3_k_volume / k_volume)
- 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_C8 沒有 feature_set_id，無法建立 candidate