Feature Iteration Run #971: volume_price_pivot_v86_y_C0

# 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**: 2
> **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**: 9.44s
> **Total accepted**: 0
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `k_volume * k_close_position_in_hl / (k_volume.rolling(20).mean() + 1)` (feature=k_iter1_k_volume_k_close_position_in__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_close_position_in_hl / (k_volume.rolling.mean + 1)

### Iteration 2
- DSL: `i_rsi(k_close, 14) * (k_high - k_low) / k_close` (feature=k_iter2_i_rsi_k_close_k_high_k_low)
- 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: NaN ratio 1.000 exceeds 0.3