Feature Iteration Run #911: zz_sylvain

# AUTOLOOP: zz_sylvain (5 iterations)

> **Phase**: Y4.7 Option B (DSL → Python code → FeatureSet candidate)
> **Target indicator**: zz_sylvain
> **Base feature_set_id**: 7
> **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**: 73.19s
> **Total accepted**: 0
> **Total rejected**: 5

## Iterations 詳情

### Iteration 1
- DSL: `obv_ensemble_3 * k_high.pct_change(12) * (1 - dist_to_high) / (1 + atr_norm)` (feature=k_iter1_k_high_pct_change_dist_to_high_atr_norm)
- 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: C8 leakage: ['dist_to_high']

### Iteration 2
- DSL: `k_high.pct_change() / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high) * (1 + o_div_binary_12)` (feature=k_iter2_k_high_pct_change_atr_norm_price_position)
- 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: C8 leakage: ['dist_to_high']

### Iteration 3
- DSL: `k_momentum_pct_change * vol_ratio / (1 + atr_norm)` (feature=k_loop_iter_3_34a7f3)
- DSL valid: True
- FeatureSet candidate id: 1235
- baseline PF: 1.947 → modified PF: 1.085
- t_stat: 0.000, p_value: 0.463, cohens_d: 0.243
- scalar_score: -0.4478
- accepted: False, reason: normalized_score -0.2581 <= 0.0, sharpe_delta -16.8215 < -0.1, hypothesis test not confirmed

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=1.085, diff=-0.862
  - sharpe_ratio: baseline=19.162, modified=2.341, diff=-16.821
  - win_rate: baseline=0.600, modified=0.500, diff=-0.100
  - max_drawdown: baseline=0.056, modified=0.187, diff=0.131
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.000

### Iteration 4
- DSL: `k_high.pct_change(12)` (feature=k_iter4_k_high_pct_change)
- 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_high.pct_change

### Iteration 5
- DSL: `k_close.rolling(20).mean()` (feature=k_iter5_k_close_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_close.rolling.mean