Feature Iteration Run #722: zz_sylvain

# AUTOLOOP: zz_sylvain (3 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**: 12.14s
> **Total accepted**: 0
> **Total rejected**: 3

## Iterations 詳情

### Iteration 1
- DSL: `(k_high - i_ema(k_close, 20)).pct_change(12) / (1 + hl_range)` (feature=k_iter_1_k_high - i_emak_close, 20)))
- 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 produced inf values; likely division by zero

### Iteration 2
- DSL: `(k_high - i_ema(k_close, 20)).pct_change(12) / (1 + hl_range + i_atr(k_high, k_low, k_close, 14))` (feature=k_loop_iter_2_6cee51)
- DSL valid: True
- FeatureSet candidate id: 939
- 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: p=1.000 ≥ 0.05, 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: `(k_high.pct_change(12) / (1 + atr_norm)) * (1 - price_position) * (1 - dist_to_high) * i_rsi(k_close, 14)` (feature=k_loop_iter_3_5a7335)
- DSL valid: True
- FeatureSet candidate id: 940
- 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: p=1.000 ≥ 0.05, 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