Feature Iteration Run #827: zz_sylvain
Status: completed | Started: 2026-07-20 13:18:33 | Ended: 2026-07-20 13:21:32
⭐ Best Accepted Iteration
Iteration 2: k_high.pct_change(12) / (1 + atr_norm) * (1 - price_positio…
baseline PF: 1.286 → modified PF: 2.113 | Cohen's d: 0.478 | p-value: 0.206
📊 Iterations
| Iter | Y Label | Model | Warm | Proxy | Parent FS | Feature | Baseline PF | Modified PF | Scalar Score | Result |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | — |
gradient_boosting |
— | — | #7 | k_loop_iter_3_87349c | 1.286 | 1.892 | 0.3017 | ✅ ACCEPT |
| 2 | — |
gradient_boosting |
— | — | #7 | k_loop_iter_4_7be2e9 | 1.286 | 2.113 | 1.4199 | ✅ ACCEPT |
| 3 | — |
gradient_boosting |
— | — | #7 | k_loop_iter_3_c18d35 | 1.286 | 1.461 | 0.1039 | ✅ ACCEPT |
| 4 | — |
gradient_boosting |
— | — | #7 | k_loop_iter_4_41b5f3 | 1.286 | 2.113 | 1.4199 | ✅ ACCEPT |
📄 Report Markdown
# AUTOLOOP: zz_sylvain (4 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**: 178.74s
> **Total accepted**: 4
> **Total rejected**: 0
## Iterations 詳情
### Iteration 1
- 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_87349c)
- DSL valid: True
- FeatureSet candidate id: 1129
- baseline PF: 1.286 → modified PF: 1.892
- t_stat: 0.000, p_value: 0.471, cohens_d: 0.168
- scalar_score: 0.3017
- accepted: True, reason: accepted
**Metrics summary:**
- profit_factor: baseline=1.286, modified=1.892, diff=0.606
- sharpe_ratio: baseline=7.879, modified=19.176, diff=11.297
- win_rate: baseline=0.553, modified=0.600, diff=0.047
- max_drawdown: baseline=0.075, modified=0.054, diff=-0.021
- test_accuracy: baseline=0.986, modified=0.986, diff=0.000
### Iteration 2
- DSL: `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 + hl_range) * (1 - dist_to_high)` (feature=k_loop_iter_4_7be2e9)
- DSL valid: True
- FeatureSet candidate id: 1130
- baseline PF: 1.286 → modified PF: 2.113
- t_stat: 0.000, p_value: 0.206, cohens_d: 0.478
- scalar_score: 1.4199
- accepted: True, reason: accepted
**Metrics summary:**
- profit_factor: baseline=1.286, modified=2.113, diff=0.827
- sharpe_ratio: baseline=7.879, modified=21.700, diff=13.821
- win_rate: baseline=0.553, modified=0.596, diff=0.043
- max_drawdown: baseline=0.075, modified=0.051, diff=-0.023
- test_accuracy: baseline=0.986, modified=0.986, diff=0.000
### Iteration 3
- DSL: `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 + hl_range) * (1 - dist_to_high) * i_macd(k_close, 12, 26, 9)` (feature=k_loop_iter_3_c18d35)
- DSL valid: True
- FeatureSet candidate id: 1131
- baseline PF: 1.286 → modified PF: 1.461
- t_stat: 0.000, p_value: 0.081, cohens_d: 0.353
- scalar_score: 0.1039
- accepted: True, reason: accepted
**Metrics summary:**
- profit_factor: baseline=1.286, modified=1.461, diff=0.175
- sharpe_ratio: baseline=7.879, modified=11.792, diff=3.912
- win_rate: baseline=0.553, modified=0.560, diff=0.007
- max_drawdown: baseline=0.075, modified=0.051, diff=-0.023
- test_accuracy: baseline=0.986, modified=0.987, diff=0.001
### Iteration 4
- DSL: `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high) * (1 - return_5) * (1 + hl_range)` (feature=k_loop_iter_4_41b5f3)
- DSL valid: True
- FeatureSet candidate id: 1132
- baseline PF: 1.286 → modified PF: 2.113
- t_stat: 0.000, p_value: 0.206, cohens_d: 0.478
- scalar_score: 1.4199
- accepted: True, reason: accepted
**Metrics summary:**
- profit_factor: baseline=1.286, modified=2.113, diff=0.827
- sharpe_ratio: baseline=7.879, modified=21.700, diff=13.821
- win_rate: baseline=0.553, modified=0.596, diff=0.043
- max_drawdown: baseline=0.075, modified=0.051, diff=-0.023
- test_accuracy: baseline=0.986, modified=0.986, diff=0.000
📊 Multi-Objective Metrics
Iteration 1: k_loop_iter_3_87349c
Y Label: — | Parent FS: #7
Objective mode: scalarize
Scalar score: 0.3017
| Metric | Baseline | Modified | Diff |
|---|---|---|---|
| win_rate | 0.553 | 0.600 | 0.047 |
| max_drawdown | 0.075 | 0.054 | -0.021 |
| sharpe_ratio | 7.879 | 19.176 | 11.297 |
| profit_factor | 1.286 | 1.892 | 0.606 |
| test_accuracy | 0.986 | 0.986 | 0.000 |
Iteration 2: k_loop_iter_4_7be2e9
Y Label: — | Parent FS: #7
Objective mode: scalarize
Scalar score: 1.4199
| Metric | Baseline | Modified | Diff |
|---|---|---|---|
| win_rate | 0.553 | 0.596 | 0.043 |
| max_drawdown | 0.075 | 0.051 | -0.023 |
| sharpe_ratio | 7.879 | 21.700 | 13.821 |
| profit_factor | 1.286 | 2.113 | 0.827 |
| test_accuracy | 0.986 | 0.986 | 0.000 |
Iteration 3: k_loop_iter_3_c18d35
Y Label: — | Parent FS: #7
Objective mode: scalarize
Scalar score: 0.1039
| Metric | Baseline | Modified | Diff |
|---|---|---|---|
| win_rate | 0.553 | 0.560 | 0.007 |
| max_drawdown | 0.075 | 0.051 | -0.023 |
| sharpe_ratio | 7.879 | 11.792 | 3.912 |
| profit_factor | 1.286 | 1.461 | 0.175 |
| test_accuracy | 0.986 | 0.987 | 0.001 |
Iteration 4: k_loop_iter_4_41b5f3
Y Label: — | Parent FS: #7
Objective mode: scalarize
Scalar score: 1.4199
| Metric | Baseline | Modified | Diff |
|---|---|---|---|
| win_rate | 0.553 | 0.596 | 0.043 |
| max_drawdown | 0.075 | 0.051 | -0.023 |
| sharpe_ratio | 7.879 | 21.700 | 13.821 |
| profit_factor | 1.286 | 2.113 | 0.827 |
| test_accuracy | 0.986 | 0.986 | 0.000 |
📁 Offline Report
Markdown backup: reports/autoloop/zz_sylvain/20260720_212132.md