Feature Iteration Run #1788: zz_sylvain
Status: completed | Started: 2026-10-03 12:53:47 | Ended: 2026-10-03 12:55:14
📊 Iterations
| Iter | Y Label | Model | Warm | Proxy | Parent FS | Feature | Baseline PF | Modified PF | Scalar Score | Result |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | C8 |
gradient_boosting |
— | — | #1748 | k_iter1_k_rsi_divergence_atr_norm | 0.000 | 0.000 | 0.0000 | ❌ REJECT |
📄 Report Markdown
# AUTOLOOP: zz_sylvain (1 iterations)
> **Phase**: Y4.7 Option B (DSL → Python code → FeatureSet candidate)
> **Target indicator**: zz_sylvain
> **Base feature_set_id**: 1748
> **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**: 87.02s
> **Total accepted**: 0
> **Total rejected**: 1
## Iterations 詳情
### Iteration 1
- DSL: `rsi_val = ta.rsi(close, 14)
dev = rsi_val - 50
atr_pct = ta.atr(high, low, close, 14) / close
result = dev / (atr_pct + 1e-10)
result` (feature=k_iter1_k_rsi_divergence_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: max_diff=15.865156 (tol=0.001), nan_mismatch=0 | 修復中止: translator_verify round 2: 修復回應解析失敗 (ok) — 不再重試
📊 Multi-Objective Metrics
Iteration 1: k_iter1_k_rsi_divergence_atr_norm
Y Label: C8 | Parent FS: #1748
Objective mode: single
Scalar score: 0.0000
📁 Offline Report
Markdown backup: reports/autoloop/zz_sylvain/20261003_205514.md