Feature Iteration Run #798: volume_price_pivot_v86_y_C0

# AUTOLOOP: volume_price_pivot_v86_y_C0 (4 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**: 15.28s
> **Total accepted**: 0
> **Total rejected**: 4

## Iterations 詳情

### Iteration 1
- DSL: `k_volume * i_atr(k_high, k_low, k_close, 14) / (k_close * (1 + k_body_size_pct))` (feature=k_iter_1_k_volume * i_atrk_high, k_low, k_close, 14) / k_close * 1 + k_body_size_pct)))
- 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 produces inf when ['k_close'] is zero; avoid unprotected division by these variables

### Iteration 2
- DSL: `k_volume / i_atr(k_high, k_low, k_close, 14) / (k_high - k_low + 1)` (feature=k_loop_iter_2_aa3b1d)
- DSL valid: True
- FeatureSet candidate id: 1079
- 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: min_p=1.000 ≥ 0.05, max_cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=0.000, modified=0.000, diff=0.000
  - sharpe_ratio: baseline=0.000, modified=0.000, diff=0.000
  - win_rate: baseline=0.000, modified=0.000, diff=0.000
  - max_drawdown: baseline=0.000, modified=0.000, diff=0.000
  - test_accuracy: baseline=0.000, modified=0.000, diff=0.000

### Iteration 3
- DSL: `i_macd(k_close, 12, 26, 9) * k_volume / (k_close * (1 + i_atr(k_high, k_low, k_close, 14)))` (feature=k_loop_iter_3_fdf5a8)
- DSL valid: True
- FeatureSet candidate id: 1080
- 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: min_p=1.000 ≥ 0.05, max_cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=0.000, modified=0.000, diff=0.000
  - sharpe_ratio: baseline=0.000, modified=0.000, diff=0.000
  - win_rate: baseline=0.000, modified=0.000, diff=0.000
  - max_drawdown: baseline=0.000, modified=0.000, diff=0.000
  - test_accuracy: baseline=0.000, modified=0.000, diff=0.000

### Iteration 4
- DSL: `i_rsi(k_close, 14) * k_volume / (i_atr(k_high, k_low, k_close, 14) * (1 + k_body_size_pct))` (feature=k_loop_iter_4_5eec6b)
- DSL valid: True
- FeatureSet candidate id: 1081
- 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: min_p=1.000 ≥ 0.05, max_cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=0.000, modified=0.000, diff=0.000
  - sharpe_ratio: baseline=0.000, modified=0.000, diff=0.000
  - win_rate: baseline=0.000, modified=0.000, diff=0.000
  - max_drawdown: baseline=0.000, modified=0.000, diff=0.000
  - test_accuracy: baseline=0.000, modified=0.000, diff=0.000