Feature Iteration Run #834: zz_sylvain

# AUTOLOOP: zz_sylvain (6 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**: 140.97s
> **Total accepted**: 2
> **Total rejected**: 4

## Iterations 詳情

### Iteration 1
- DSL: `obv_rebased.pct_change(12) / (1 + atr_norm) * (1 - dist_to_high)` (feature=k_iter1_pct_change_atr_norm_dist_to_high)
- 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.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 + hl_range)` (feature=k_loop_iter_2_79482e)
- DSL valid: True
- FeatureSet candidate id: 1156
- baseline PF: 1.947 → modified PF: 2.466
- t_stat: 0.000, p_value: 0.012, cohens_d: 0.388
- scalar_score: 1.3886
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=2.466, diff=0.519
  - sharpe_ratio: baseline=19.162, modified=24.727, diff=5.565
  - win_rate: baseline=0.600, modified=0.636, diff=0.036
  - max_drawdown: baseline=0.056, modified=0.050, diff=-0.006
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.000

### Iteration 3
- DSL: `k_high.pct_change(12) * (1 - price_position) * (1 + hl_range) * (1 - dist_to_high) / (1 + i_macd(k_close, 12, 26, 9))` (feature=k_loop_iter_3_5a8b99)
- DSL valid: True
- FeatureSet candidate id: 1157
- baseline PF: 1.947 → modified PF: 1.459
- t_stat: 0.000, p_value: 0.343, cohens_d: 0.307
- scalar_score: -0.2186
- accepted: False, reason: min_p=0.343 ≥ 0.05

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=1.459, diff=-0.488
  - sharpe_ratio: baseline=19.162, modified=11.249, diff=-7.914
  - win_rate: baseline=0.600, modified=0.490, diff=-0.110
  - max_drawdown: baseline=0.056, modified=0.230, diff=0.174
  - test_accuracy: baseline=0.992, modified=0.981, diff=-0.012

### Iteration 4
- DSL: `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high)` (feature=k_loop_iter_6_301c87)
- DSL valid: True
- FeatureSet candidate id: 1158
- baseline PF: 1.947 → modified PF: 2.466
- t_stat: 0.000, p_value: 0.296, cohens_d: 0.211
- scalar_score: 0.6193
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=2.466, diff=0.519
  - sharpe_ratio: baseline=19.162, modified=24.727, diff=5.565
  - win_rate: baseline=0.600, modified=0.636, diff=0.036
  - max_drawdown: baseline=0.056, modified=0.050, diff=-0.006
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.000

### Iteration 5
- DSL: `Looking at the proven features, the winning structure is `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high)` with PF 2.11` (feature=k_iter5_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: safe_eval DSL: 語法錯誤: invalid syntax (<unknown>, line 1)

### Iteration 6
- DSL: `Looking at the base feature and the proven patterns, I need to apply a high-impact mutation. The proven winning structure combines `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high)`` (feature=k_iter6_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: safe_eval DSL: 語法錯誤: invalid syntax (<unknown>, line 1)