Feature Iteration Run #795: 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**: 10.05s
> **Total accepted**: 0
> **Total rejected**: 4

## Iterations 詳情

### Iteration 1
- DSL: `(k_close - i_ema(k_close, 20)) * i_rsi(k_close, 14) / (1 + k_volume.rolling(20).mean())` (feature=k_loop_iter_1_30a07d)
- DSL valid: True
- FeatureSet candidate id: 1067
- 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: `(k_close - i_ema(k_close, 20)) / (1 + i_atr(k_high, k_low, k_close, 14))` (feature=k_loop_iter_2_4b7261)
- DSL valid: True
- FeatureSet candidate id: 1068
- 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) / (1 + i_atr(k_high, k_low, k_close, 14))` (feature=k_loop_iter_3_557e15)
- DSL valid: True
- FeatureSet candidate id: 1069
- 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 / (1 + i_atr(k_high, k_low, k_close, 14))` (feature=k_loop_iter_4_eb3ce5)
- DSL valid: True
- FeatureSet candidate id: 1070
- 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