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Feature Iteration Run #1380: volume_price_pivot_v86_y_C0

Status: completed | Started: 2026-09-30 03:02:58 | Ended: 2026-09-30 03:03:51

📊 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 C0 gradient_boosting — — #2 k_iter1_ta_5ba8 ta.sma(close, 12) ✅ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT normalized_score 0.0000 <= 0.0, profit_factor_delta 0.0000 < 0.25, hypothesis test not confirmed, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.3: got 0.0000']
2 C0 gradient_boosting — — #2 k_iter2_ta_95f0 ta.ema(close, 20) ✅ 0.000 0.000 0.0000 0.000 1.000 0.000 ❌ REJECT normalized_score 0.0000 <= 0.0, profit_factor_delta 0.0000 < 0.25, hypothesis test not confirmed, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.3: got 0.0000']

📄 Report Markdown

# AUTOLOOP: volume_price_pivot_v86_y_C0 (2 iterations)

> **Phase**: Y4.7 Option B (DSL → Python code → FeatureSet candidate)
> **Target indicator**: volume_price_pivot_v86_y_C0
> **Base feature_set_id**: 2
> **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**: 52.50s
> **Total accepted**: 0
> **Total rejected**: 2

## Iterations 詳情

### Iteration 1
- DSL: `ta.sma(close, 12)` (feature=k_iter1_ta_5ba8)
- DSL valid: True
- FeatureSet candidate id: 1517
- 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: normalized_score 0.0000 <= 0.0, profit_factor_delta 0.0000 < 0.25, hypothesis test not confirmed, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.3: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=0.000, modified=0.000, diff=0.000
  - sharpe_ratio: baseline=0.000, modified=0.000, diff=0.000
  - win_rate: baseline=0.000, modified=0.000, diff=0.000
  - max_drawdown: baseline=0.000, modified=0.000, diff=0.000
  - test_accuracy: baseline=0.000, modified=0.000, diff=0.000

### Iteration 2
- DSL: `ta.ema(close, 20)` (feature=k_iter2_ta_95f0)
- DSL valid: True
- FeatureSet candidate id: 1518
- 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: normalized_score 0.0000 <= 0.0, profit_factor_delta 0.0000 < 0.25, hypothesis test not confirmed, constraints: ['sharpe_ratio >= 1.0: got 0.0000', 'win_rate >= 0.3: got 0.0000']

**Metrics summary:**
  - profit_factor: baseline=0.000, modified=0.000, diff=0.000
  - sharpe_ratio: baseline=0.000, modified=0.000, diff=0.000
  - win_rate: baseline=0.000, modified=0.000, diff=0.000
  - max_drawdown: baseline=0.000, modified=0.000, diff=0.000
  - test_accuracy: baseline=0.000, modified=0.000, diff=0.000

📊 Multi-Objective Metrics

Iteration 1: k_iter1_ta_5ba8

Y Label: C0 | Parent FS: #2

Objective mode: scalarize

Scalar score: 0.0000

Failed constraints: sharpe_ratio >= 1.0: got 0.0000, win_rate >= 0.3: got 0.0000

MetricBaselineModifiedDiff
win_rate 0.000 0.000 0.000
max_drawdown 0.000 0.000 0.000
sharpe_ratio 0.000 0.000 0.000
profit_factor 0.000 0.000 0.000
test_accuracy 0.000 0.000 0.000

Iteration 2: k_iter2_ta_95f0

Y Label: C0 | Parent FS: #2

Objective mode: scalarize

Scalar score: 0.0000

Failed constraints: sharpe_ratio >= 1.0: got 0.0000, win_rate >= 0.3: got 0.0000

MetricBaselineModifiedDiff
win_rate 0.000 0.000 0.000
max_drawdown 0.000 0.000 0.000
sharpe_ratio 0.000 0.000 0.000
profit_factor 0.000 0.000 0.000
test_accuracy 0.000 0.000 0.000

📁 Offline Report

Markdown backup: reports/autoloop/volume_price_pivot_v86_y_C0/20260930_110351.md