跳到主要內容

Feature Iteration Run #497: zz_sylvain

Status: completed | Started: 2026-07-17 17:18:57 | Ended: 2026-07-17 17:19:58

📊 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_iter_1_k_body_size_pct / hl_range * i_rsik_close, 14) - 50 k_body_size_pct / hl_range * i_rsi(k_cl… ❌ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT safe_eval DSL produces inf when ['hl_range'] is zero; avoid unprotected division by these variables
2 — gradient_boosting — — — k_loop_iter_2_c45cac (k_close - i_ema(k_close, 20)) / i_atr(… ✅ 1.286 1.238 -0.3428 0.000 1.000 0.000 ❌ REJECT p=1.000 ≥ 0.05, cohens_d=0.000 ≤ 0.3

📄 Report Markdown

# AUTOLOOP: zz_sylvain (2 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**: 60.73s
> **Total accepted**: 0
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `k_body_size_pct / hl_range * i_rsi(k_close, 14) - 50` (feature=k_iter_1_k_body_size_pct / hl_range * i_rsik_close, 14) - 50)
- DSL valid: False
- FeatureSet candidate id: None
- baseline PF: 0.000 → modified PF: 0.000
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: 0.0000
- accepted: False, reason: safe_eval DSL produces inf when ['hl_range'] is zero; avoid unprotected division by these variables

### Iteration 2
- DSL: `(k_close - i_ema(k_close, 20)) / i_atr(k_high, k_low, k_close, 14)` (feature=k_loop_iter_2_c45cac)
- DSL valid: True
- FeatureSet candidate id: 543
- baseline PF: 1.286 → modified PF: 1.238
- t_stat: 0.000, p_value: 1.000, cohens_d: 0.000
- scalar_score: -0.3428
- accepted: False, reason: p=1.000 ≥ 0.05, cohens_d=0.000 ≤ 0.3

**Metrics summary:**
  - profit_factor: baseline=1.286, modified=1.238, diff=-0.048
  - sharpe_ratio: baseline=7.879, modified=6.648, diff=-1.231
  - win_rate: baseline=0.553, modified=0.435, diff=-0.118
  - max_drawdown: baseline=0.075, modified=0.100, diff=0.025
  - test_accuracy: baseline=0.986, modified=0.975, diff=-0.011

📊 Multi-Objective Metrics

Iteration 1: k_iter_1_k_body_size_pct / hl_range * i_rsik_close, 14) - 50

Y Label: — | Parent FS: #—

Objective mode: single

Scalar score: 0.0000

Iteration 2: k_loop_iter_2_c45cac

Y Label: — | Parent FS: #—

Objective mode: scalarize

Scalar score: -0.3428

MetricBaselineModifiedDiff
win_rate 0.553 0.435 -0.118
max_drawdown 0.075 0.100 0.025
sharpe_ratio 7.879 6.648 -1.231
profit_factor 1.286 1.238 -0.048
test_accuracy 0.986 0.975 -0.011

📁 Offline Report

Markdown backup: reports/autoloop/zz_sylvain/20260718_011958.md