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Feature Iteration Run #438: zz_sylvain

Status: completed | Started: 2026-07-16 17:57:43 | Ended: 2026-07-16 17:59:57

⭐ Best Accepted Iteration

Iteration 2: k_volume.pct_change(10) * price_position - dist_to_high

baseline PF: 1.198 → modified PF: 1.187 | 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 — — — k_loop_iter_1_fb23f7 k_close.pct_change(3) / hl_range.shift(… ✅ 1.037 0.000 -0.7585 0.000 1.000 0.000 ❌ REJECT p=1.000 ≥ 0.05, cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']
2 — gradient_boosting — — — k_loop_iter_2_23a5a5 k_volume.pct_change(10) * price_positio… ✅ 1.198 1.187 0.0817 0.000 1.000 0.000 ✅ ACCEPT accepted
3 — gradient_boosting — — — k_loop_iter_3_d413b3 return_5 / (hl_range + atr_norm) ✅ 1.219 0.000 -2.0448 0.000 1.000 0.000 ❌ REJECT p=1.000 ≥ 0.05, cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']

📄 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**: 133.65s
> **Total accepted**: 1
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `k_close.pct_change(3) / hl_range.shift(1)` (feature=k_loop_iter_1_fb23f7)
- DSL valid: True
- FeatureSet candidate id: 447
- baseline PF: 1.037 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: -0.7585
- accepted: False, reason: p=1.000 ≥ 0.05, cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=1.037, modified=0.000, diff=-1.037
  - sharpe_ratio: baseline=0.995, modified=0.000, diff=-0.995
  - win_rate: baseline=0.419, modified=0.000, diff=-0.419
  - max_drawdown: baseline=0.120, modified=0.000, diff=-0.120
  - test_accuracy: baseline=0.971, modified=0.000, diff=-0.971

### Iteration 2
- DSL: `k_volume.pct_change(10) * price_position - dist_to_high` (feature=k_loop_iter_2_23a5a5)
- DSL valid: True
- FeatureSet candidate id: 448
- baseline PF: 1.198 → modified PF: 1.187
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0817
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.198, modified=1.187, diff=-0.012
  - sharpe_ratio: baseline=5.537, modified=5.779, diff=0.242
  - win_rate: baseline=0.443, modified=0.540, diff=0.097
  - max_drawdown: baseline=0.082, modified=0.133, diff=0.050
  - test_accuracy: baseline=0.973, modified=0.949, diff=-0.024

### Iteration 3
- DSL: `return_5 / (hl_range + atr_norm)` (feature=k_loop_iter_3_d413b3)
- DSL valid: True
- FeatureSet candidate id: 449
- baseline PF: 1.219 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: -2.0448
- accepted: False, reason: p=1.000 ≥ 0.05, cohens_d=0.000 ≤ 0.3, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.4: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=1.219, modified=0.000, diff=-1.219
  - sharpe_ratio: baseline=5.847, modified=0.000, diff=-5.847
  - win_rate: baseline=0.461, modified=0.000, diff=-0.461
  - max_drawdown: baseline=0.187, modified=0.000, diff=-0.187
  - test_accuracy: baseline=0.970, modified=0.000, diff=-0.970

📊 Multi-Objective Metrics

Iteration 1: k_loop_iter_1_fb23f7

Y Label: — | Parent FS: #—

Objective mode: scalarize

Scalar score: -0.7585

Failed constraints: sharpe_ratio >= 1.0: got 0.0000, win_rate >= 0.4: got 0.0000

MetricBaselineModifiedDiff
win_rate 0.419 0.000 -0.419
max_drawdown 0.120 0.000 -0.120
sharpe_ratio 0.995 0.000 -0.995
profit_factor 1.037 0.000 -1.037
test_accuracy 0.971 0.000 -0.971

Iteration 2: k_loop_iter_2_23a5a5

Y Label: — | Parent FS: #—

Objective mode: scalarize

Scalar score: 0.0817

MetricBaselineModifiedDiff
win_rate 0.443 0.540 0.097
max_drawdown 0.082 0.133 0.050
sharpe_ratio 5.537 5.779 0.242
profit_factor 1.198 1.187 -0.012
test_accuracy 0.973 0.949 -0.024

Iteration 3: k_loop_iter_3_d413b3

Y Label: — | Parent FS: #—

Objective mode: scalarize

Scalar score: -2.0448

Failed constraints: sharpe_ratio >= 1.0: got 0.0000, win_rate >= 0.4: got 0.0000

MetricBaselineModifiedDiff
win_rate 0.461 0.000 -0.461
max_drawdown 0.187 0.000 -0.187
sharpe_ratio 5.847 0.000 -5.847
profit_factor 1.219 0.000 -1.219
test_accuracy 0.970 0.000 -0.970

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260717_015957.md