Feature Iteration Run #970: 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**: 10.26s
> **Total accepted**: 0
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `k_momentum_pct_change * obv_ensemble_3 / (1 + i_atr(k_high, k_low, k_close, 14) / k_close)` (feature=k_iter1_k_momentum_pct_chang_i_atr_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: canonical duplicate: k_momentum_pct_change * obv_ensemble_3 / (1 + i_atr(k_high,k_low,k_close) / k_close)

### Iteration 2
- DSL: `k_momentum_pct_change * vol_ratio / (1 + i_atr(k_high, k_low, k_close, 14) / k_close)` (feature=k_iter2_k_momentum_pct_chang_vol_ratio_i_atr_k_high)
- 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: safe_eval DSL: 變數未定義: vol_ratio