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Feature Iteration Run #723: zz_sylvain

Status: completed | Started: 2026-07-18 14:14:10 | Ended: 2026-07-18 14:15:45

⭐ Best Accepted Iteration

Iteration 1: k_high.pct_change(12) * (1 + hl_range)

baseline PF: 1.286 → modified PF: 2.113 | 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_705c0a k_high.pct_change(12) * (1 + hl_range) ✅ 1.286 2.113 3.7086 0.000 1.000 0.000 ✅ ACCEPT accepted
2 — random_forest — — #7 k_loop_iter_3_4e9c71 k_high.pct_change(12) / (1 + atr_norm) … ✅ 1.286 0.000 -2.5760 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']
3 — gradient_boosting — — #7 k_loop_iter_3_3d40a1 k_high.pct_change(12) * (1 + hl_range) … ✅ 1.286 2.113 3.7086 0.000 1.000 0.000 ✅ ACCEPT accepted
4 — random_forest — — #7 k_loop_iter_2_301c87 k_high.pct_change(12) / (1 + atr_norm) … ✅ 1.286 0.000 -2.5760 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 (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**: 94.94s
> **Total accepted**: 2
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `k_high.pct_change(12) * (1 + hl_range)` (feature=k_loop_iter_1_705c0a)
- DSL valid: True
- FeatureSet candidate id: 941
- 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 2
- DSL: `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high) * vol_ratio` (feature=k_loop_iter_3_4e9c71)
- DSL valid: True
- FeatureSet candidate id: 942
- baseline PF: 1.286 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: -2.5760
- 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=1.286, modified=0.000, diff=-1.286
  - sharpe_ratio: baseline=7.879, modified=0.000, diff=-7.879
  - win_rate: baseline=0.553, modified=0.000, diff=-0.553
  - max_drawdown: baseline=0.075, modified=0.000, diff=-0.075
  - test_accuracy: baseline=0.986, modified=0.000, diff=-0.986

### Iteration 3
- DSL: `k_high.pct_change(12) * (1 + hl_range) * (1 - dist_to_high)` (feature=k_loop_iter_3_3d40a1)
- DSL valid: True
- FeatureSet candidate id: 943
- 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 4
- DSL: `k_high.pct_change(12) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_high)` (feature=k_loop_iter_2_301c87)
- DSL valid: True
- FeatureSet candidate id: 944
- baseline PF: 1.286 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: -2.5760
- 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=1.286, modified=0.000, diff=-1.286
  - sharpe_ratio: baseline=7.879, modified=0.000, diff=-7.879
  - win_rate: baseline=0.553, modified=0.000, diff=-0.553
  - max_drawdown: baseline=0.075, modified=0.000, diff=-0.075
  - test_accuracy: baseline=0.986, modified=0.000, diff=-0.986

📊 Multi-Objective Metrics

Iteration 1: k_loop_iter_1_705c0a

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 2: k_loop_iter_3_4e9c71

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: -2.5760

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

MetricBaselineModifiedDiff
win_rate 0.553 0.000 -0.553
max_drawdown 0.075 0.000 -0.075
sharpe_ratio 7.879 0.000 -7.879
profit_factor 1.286 0.000 -1.286
test_accuracy 0.986 0.000 -0.986

Iteration 3: k_loop_iter_3_3d40a1

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 4: k_loop_iter_2_301c87

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: -2.5760

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

MetricBaselineModifiedDiff
win_rate 0.553 0.000 -0.553
max_drawdown 0.075 0.000 -0.075
sharpe_ratio 7.879 0.000 -7.879
profit_factor 1.286 0.000 -1.286
test_accuracy 0.986 0.000 -0.986

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260718_221545.md