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Feature Iteration Run #688: zz_sylvain

Status: completed | Started: 2026-07-18 11:47:14 | Ended: 2026-07-18 11:49:21

⭐ Best Accepted Iteration

Iteration 1: k_high.pct_change(12) * i_rsi(k_close, 14) / (1 + atr_norm)…

baseline PF: 1.286 → modified PF: 1.892 | Cohen's d: 0.000 | p-value: 1.000

📊 Iterations

Iter Y Label Model Warm Proxy Parent FS Feature DSL DSL Valid Baseline PF Modified PF Scalar Score t-stat p-value Cohen's d Result Reason
1 — gradient_boosting — — #7 k_loop_iter_1_766814 k_high.pct_change(12) * i_rsi(k_close, … ✅ 1.286 1.892 3.0122 0.000 1.000 0.000 ✅ ACCEPT accepted
2 — gradient_boosting — — #7 k_loop_iter_1_75605d k_high.pct_change(5) ✅ 1.286 2.113 3.7086 0.000 1.000 0.000 ✅ ACCEPT accepted
3 — gradient_boosting — — #7 k_loop_iter_3_5653d6 k_high.pct_change(5) * i_rsi(k_close, 1… ✅ 1.286 1.404 0.5768 0.000 1.000 0.000 ✅ ACCEPT accepted

📄 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**: 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**: 126.83s
> **Total accepted**: 3
> **Total rejected**: 0

## Iterations 詳情

### Iteration 1
- DSL: `k_high.pct_change(12) * i_rsi(k_close, 14) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high) / (1 + dist_to_low) / (1 + k_consecutive_shrink)` (feature=k_loop_iter_1_766814)
- DSL valid: True
- FeatureSet candidate id: 873
- baseline PF: 1.286 → modified PF: 1.892
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 3.0122
- 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(5)` (feature=k_loop_iter_1_75605d)
- DSL valid: True
- FeatureSet candidate id: 874
- baseline PF: 1.286 → modified PF: 2.113
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 3.7086
- 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(5) * i_rsi(k_close, 14)` (feature=k_loop_iter_3_5653d6)
- DSL valid: True
- FeatureSet candidate id: 875
- baseline PF: 1.286 → modified PF: 1.404
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.5768
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.286, modified=1.404, diff=0.118
  - sharpe_ratio: baseline=7.879, modified=10.036, diff=2.157
  - win_rate: baseline=0.553, modified=0.544, diff=-0.009
  - max_drawdown: baseline=0.075, modified=0.102, diff=0.027
  - test_accuracy: baseline=0.986, modified=0.987, diff=0.001

📊 Multi-Objective Metrics

Iteration 1: k_loop_iter_1_766814

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: 3.0122

MetricBaselineModifiedDiff
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_1_75605d

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: 3.7086

MetricBaselineModifiedDiff
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_5653d6

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: 0.5768

MetricBaselineModifiedDiff
win_rate 0.553 0.544 -0.009
max_drawdown 0.075 0.102 0.027
sharpe_ratio 7.879 10.036 2.157
profit_factor 1.286 1.404 0.118
test_accuracy 0.986 0.987 0.001

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260718_194921.md