Feature Iteration Run #1825: zz_sylvain
Status: completed | Started: 2026-10-03 16:02:29 | Ended: 2026-10-03 17:01:16
📊 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_macd_atrnorm_volratio_v3 | 0.000 | 0.000 | 0.0000 | ❌ REJECT |
| 2 | C8 |
gradient_boosting |
— | — | #1748 | k_iter2_k_atr_regime_volratio_v2 | 0.000 | 0.000 | 0.0000 | ❌ REJECT |
| 3 | C8 |
gradient_boosting |
— | — | #1748 | k_iter3_k_obv_zscore_atr_gate_v3 | 7.176 | 2.249 | -2.1185 | ❌ REJECT |
📄 Report Markdown
# AUTOLOOP: zz_sylvain (3 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**: 3525.79s
> **Total accepted**: 0
> **Total rejected**: 3
## Iterations 詳情
### Iteration 1
- DSL: `fast = ta.ema(close, 12)
slow = ta.ema(close, 26)
macd_line = fast - slow
atr_n = ta.atr(high, low, close, 5) / (close + 1e-8)
vol_sma = ta.sma(volume, 20)
vol_rel = volume / (vol_sma + 1e-8)
mom_vol = macd_line / (close + 1e-8)
result = mom_vol / (1 + atr_n) * vol_rel
result` (feature=k_iter1_k_macd_atrnorm_volratio_v3)
- 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: translator_verify: 3 輪修復仍未通過 — max_diff=inf (tol=0.001), nan_mismatch=999, error=OO exec error: "'__name__' not in globals"
### Iteration 2
- DSL: `atr_fast = ta.atr(high, low, close, 5)
atr_slow = ta.atr(high, low, close, 20)
vol_ratio_fast = atr_fast / (close + 1e-8)
vol_ratio_slow = atr_slow / (close + 1e-8)
regime = vol_ratio_fast / (vol_ratio_slow + 1e-8)
vol_sma = ta.sma(volume, 20)
vol_rel = volume / (vol_sma + 1e-8)
result = regime * vol_rel
result` (feature=k_iter2_k_atr_regime_volratio_v2)
- 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: linearly redundant with vol_ratio (|Pearson|=0.961 >= 0.9)
### Iteration 3
- DSL: `obv_val = ta.obv(close, volume)
obv_sma = ta.sma(obv_val, 20)
obv_std = ta.stdev(obv_val, 20)
obv_z = (obv_val - obv_sma) / (obv_std + 1e-8)
atr_n = ta.atr(high, low, close, 5) / (close + 1e-8)
result = obv_z / (1 + atr_n)
result` (feature=k_iter3_k_obv_zscore_atr_gate_v3)
- DSL valid: True
- FeatureSet candidate id: 1832
- baseline PF: 7.176 → modified PF: 2.249
- t_stat: 0.000, p_value: 0.029, cohens_d: 0.216
- scalar_score: -2.1185
- accepted: False, reason: normalized_score -0.2178 <= 0.0, profit_factor_delta -4.9267 < 0.25, sharpe_delta -22.2890 (-44.76%) < -10%, hypothesis test not confirmed
**Metrics summary:**
- profit_factor: baseline=7.176, modified=2.249, diff=-4.927
- sharpe_ratio: baseline=49.793, modified=27.504, diff=-22.289
- win_rate: baseline=0.667, modified=0.634, diff=-0.032
- max_drawdown: baseline=0.020, modified=0.069, diff=0.049
- test_accuracy: baseline=0.955, modified=0.983, diff=0.029
📊 Multi-Objective Metrics
Iteration 1: k_iter1_k_macd_atrnorm_volratio_v3
Y Label: C8 | Parent FS: #1748
Objective mode: single
Scalar score: 0.0000
Iteration 2: k_iter2_k_atr_regime_volratio_v2
Y Label: C8 | Parent FS: #1748
Objective mode: single
Scalar score: 0.0000
Iteration 3: k_iter3_k_obv_zscore_atr_gate_v3
Y Label: C8 | Parent FS: #1748
Objective mode: scalarize
Scalar score: -2.1185
| Metric | Baseline | Modified | Diff |
|---|---|---|---|
| win_rate | 0.667 | 0.634 | -0.032 |
| max_drawdown | 0.020 | 0.069 | 0.049 |
| sharpe_ratio | 49.793 | 27.504 | -22.289 |
| profit_factor | 7.176 | 2.249 | -4.927 |
| test_accuracy | 0.955 | 0.983 | 0.029 |
📁 Offline Report
Markdown backup: reports/autoloop/zz_sylvain/20261004_010116.md