Feature Iteration Run #797: 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**: 17.07s
> **Total accepted**: 0
> **Total rejected**: 4

## Iterations 詳情

### Iteration 1
- DSL: `k_volume * k_close_position_in_hl / (k_volume.rolling(20).mean() + 1)` (feature=k_loop_iter_1_4d0da5)
- DSL valid: True
- FeatureSet candidate id: 1075
- 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 2
- DSL: `i_atr(k_high, k_low, k_close, 14) / (k_close.rolling(20).std() + 1e-6) * k_close_position_in_hl` (feature=k_loop_iter_2_1bc93e)
- DSL valid: True
- FeatureSet candidate id: 1076
- 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_rsi(k_close, 14) * (k_high - k_low) / (i_atr(k_high, k_low, k_close, 14) + 1e-6) * k_body_size_pct` (feature=k_loop_iter_3_ff0108)
- DSL valid: True
- FeatureSet candidate id: 1077
- 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_macd(k_close, 12, 26, 9) * k_close_position_in_hl * k_body_size_pct / (i_atr(k_high, k_low, k_close, 14) + 1e-6)` (feature=k_loop_iter_4_650ad4)
- DSL valid: True
- FeatureSet candidate id: 1078
- 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