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Feature Iteration Run #840: zz_sylvain

Status: completed | Started: 2026-07-23 05:17:44 | Ended: 2026-07-23 05:20:06

⭐ Best Accepted Iteration

Iteration 2: k_high.pct_change(12) * (1 + hl_range) / (1 + atr_norm) * (…

baseline PF: 1.947 → modified PF: 2.466 | Cohen's d: 0.388 | p-value: 0.012

📊 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_9de057 k_momentum_pct_change / (1 + i_atr(k_hi… ✅ 1.947 1.243 -0.3081 0.000 0.386 0.344 ❌ REJECT min_p=0.386 ≥ 0.05
2 — gradient_boosting — — #7 k_loop_iter_2_04b6ed k_high.pct_change(12) * (1 + hl_range) … ✅ 1.947 2.466 1.3886 0.000 0.012 0.388 ✅ ACCEPT accepted
3 — gradient_boosting — — #7 k_loop_iter_3_4a1a9a k_momentum_pct_change - o_div_amplitude… ✅ 1.947 0.775 -3.3916 0.000 0.278 0.577 ❌ REJECT min_p=0.278 ≥ 0.05, constraints: ['sharpe_ratio >= 1.0: got -7.5544']

📄 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**: 142.44s
> **Total accepted**: 1
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `k_momentum_pct_change / (1 + i_atr(k_high, k_low, k_close, 14)) - o_div_amplitude_12` (feature=k_loop_iter_1_9de057)
- DSL valid: True
- FeatureSet candidate id: 1184
- baseline PF: 1.947 → modified PF: 1.243
- t_stat: 0.000, p_value: 0.386, cohens_d: 0.344
- scalar_score: -0.3081
- accepted: False, reason: min_p=0.386 ≥ 0.05

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=1.243, diff=-0.704
  - sharpe_ratio: baseline=19.162, modified=7.645, diff=-11.517
  - win_rate: baseline=0.600, modified=0.489, diff=-0.111
  - max_drawdown: baseline=0.056, modified=0.126, diff=0.070
  - test_accuracy: baseline=0.992, modified=0.979, diff=-0.014

### Iteration 2
- DSL: `k_high.pct_change(12) * (1 + hl_range) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_low)` (feature=k_loop_iter_2_04b6ed)
- DSL valid: True
- FeatureSet candidate id: 1185
- baseline PF: 1.947 → modified PF: 2.466
- t_stat: 0.000, p_value: 0.012, cohens_d: 0.388
- scalar_score: 1.3886
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=2.466, diff=0.519
  - sharpe_ratio: baseline=19.162, modified=24.727, diff=5.565
  - win_rate: baseline=0.600, modified=0.636, diff=0.036
  - max_drawdown: baseline=0.056, modified=0.050, diff=-0.006
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.000

### Iteration 3
- DSL: `k_momentum_pct_change - o_div_amplitude_12 * (1 + atr_norm) * (1 - price_position)` (feature=k_loop_iter_3_4a1a9a)
- DSL valid: True
- FeatureSet candidate id: 1186
- baseline PF: 1.947 → modified PF: 0.775
- t_stat: 0.000, p_value: 0.278, cohens_d: 0.577
- scalar_score: -3.3916
- accepted: False, reason: min_p=0.278 ≥ 0.05, constraints: ['sharpe_ratio >= 1.0: got -7.5544']

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=0.775, diff=-1.172
  - sharpe_ratio: baseline=19.162, modified=-7.554, diff=-26.717
  - win_rate: baseline=0.600, modified=0.424, diff=-0.176
  - max_drawdown: baseline=0.056, modified=0.131, diff=0.075
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.001

📊 Multi-Objective Metrics

Iteration 1: k_loop_iter_1_9de057

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: -0.3081

MetricBaselineModifiedDiff
win_rate 0.600 0.489 -0.111
max_drawdown 0.056 0.126 0.070
sharpe_ratio 19.162 7.645 -11.517
profit_factor 1.947 1.243 -0.704
test_accuracy 0.992 0.979 -0.014

Iteration 2: k_loop_iter_2_04b6ed

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: 1.3886

MetricBaselineModifiedDiff
win_rate 0.600 0.636 0.036
max_drawdown 0.056 0.050 -0.006
sharpe_ratio 19.162 24.727 5.565
profit_factor 1.947 2.466 0.519
test_accuracy 0.992 0.992 0.000

Iteration 3: k_loop_iter_3_4a1a9a

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: -3.3916

Failed constraints: sharpe_ratio >= 1.0: got -7.5544

MetricBaselineModifiedDiff
win_rate 0.600 0.424 -0.176
max_drawdown 0.056 0.131 0.075
sharpe_ratio 19.162 -7.554 -26.717
profit_factor 1.947 0.775 -1.172
test_accuracy 0.992 0.992 -0.001

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260723_132006.md