Feature Iteration Run #1329: 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**: 110.58s
> **Total accepted**: 0
> **Total rejected**: 5

## Iterations 詳情

### Iteration 1
- DSL: `` (feature=)
- DSL valid: True
- 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: propose v4.2: LLM 回應 parse_failed — reject 該輪（不靜默落 stub）。

### Iteration 2
- DSL: `` (feature=)
- DSL valid: True
- 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: propose v4.2: LLM 回應 parse_failed — reject 該輪（不靜默落 stub）。

### Iteration 3
- DSL: `length = 14
corr_val = ta.correlation(close, open, length)
atr_n = ta.atr(high, low, close, 5) / close
vol_sma = ta.sma(volume, 20)
vol_r = volume / (nz(vol_sma, ta.cum(volume) / (bar_index + 1)) + 0.000000001)
result = corr_val / (1 + atr_n) * vol_r
result` (feature=k_iter3_k_corr_open_atr_vol)
- 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: 編譯錯誤: 未知 ta. 函數/名稱: correlation (對齊 vocabulary)

### Iteration 4
- DSL: `ta.sma(close, 12)` (feature=k_iter4_ta)
- DSL valid: True
- FeatureSet candidate id: 1470
- 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: scalar_score 0.0000 <= 0.0, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.3: 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 5
- DSL: `ta.ema(close, 20)` (feature=k_iter5_ta)
- DSL valid: True
- FeatureSet candidate id: 1471
- 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: scalar_score 0.0000 <= 0.0, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.3: 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