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Feature Iteration Run #838: zz_sylvain

Status: completed | Started: 2026-07-23 04:59:14 | Ended: 2026-07-23 05:03:17

⭐ Best Accepted Iteration

Iteration 2: k_high.pct_change(12) * (1 + hl_range) * (1 - dist_to_high)…

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_ab1574 i_macd(k_close, 12, 26, 9) * (1 - dist_… ✅ 1.947 1.895 -0.1020 0.000 0.337 0.330 ❌ REJECT min_p=0.337 ≥ 0.05
2 — gradient_boosting — — #7 k_loop_iter_2_d32d51 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_4d300c k_high.pct_change(12) * (1 + hl_range) … ✅ 1.947 2.481 1.3042 0.000 0.046 0.281 ✅ ACCEPT accepted
4 — gradient_boosting — — #7 k_loop_iter_4_af91f2 k_high.pct_change(12) * (1 + hl_range) … ✅ 1.947 2.466 1.3886 0.000 0.012 0.388 ✅ ACCEPT accepted
5 — gradient_boosting — — #7 k_loop_iter_5_59c5f5 k_high.pct_change(12) * (1 + hl_range) … ✅ 1.947 1.230 -0.3192 0.000 0.552 0.311 ❌ REJECT min_p=0.552 ≥ 0.05
6 — gradient_boosting — — #7 k_loop_iter_6_1f5681 k_momentum_pct_change * (1 - dist_to_hi… ✅ 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 (6 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**: 242.41s
> **Total accepted**: 3
> **Total rejected**: 3

## Iterations 詳情

### Iteration 1
- DSL: `i_macd(k_close, 12, 26, 9) * (1 - dist_to_high) / (1 + atr_norm)` (feature=k_loop_iter_1_ab1574)
- DSL valid: True
- FeatureSet candidate id: 1175
- baseline PF: 1.947 → modified PF: 1.895
- t_stat: 0.000, p_value: 0.337, cohens_d: 0.330
- scalar_score: -0.1020
- accepted: False, reason: min_p=0.337 ≥ 0.05

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=1.895, diff=-0.053
  - sharpe_ratio: baseline=19.162, modified=18.205, diff=-0.957
  - win_rate: baseline=0.600, modified=0.562, diff=-0.037
  - max_drawdown: baseline=0.056, modified=0.056, diff=0.000
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.000

### Iteration 2
- DSL: `k_high.pct_change(12) * (1 + hl_range) * (1 - dist_to_high) / (1 + atr_norm) * (1 - price_position)` (feature=k_loop_iter_2_d32d51)
- DSL valid: True
- FeatureSet candidate id: 1176
- 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_high.pct_change(12) * (1 + hl_range) * (1 - dist_to_high) / (1 + atr_norm) * (1 - price_position) * i_rsi(k_close, 14)` (feature=k_loop_iter_3_4d300c)
- DSL valid: True
- FeatureSet candidate id: 1177
- baseline PF: 1.947 → modified PF: 2.481
- t_stat: 0.000, p_value: 0.046, cohens_d: 0.281
- scalar_score: 1.3042
- accepted: True, reason: accepted

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=2.481, diff=0.534
  - sharpe_ratio: baseline=19.162, modified=24.452, diff=5.290
  - win_rate: baseline=0.600, modified=0.567, diff=-0.033
  - max_drawdown: baseline=0.056, modified=0.056, diff=0.000
  - test_accuracy: baseline=0.992, modified=0.992, diff=-0.000

### Iteration 4
- DSL: `k_high.pct_change(12) * (1 + hl_range) * (1 - dist_to_high) / (1 + atr_norm) * (1 - price_position) * (1 - dist_to_low)` (feature=k_loop_iter_4_af91f2)
- DSL valid: True
- FeatureSet candidate id: 1178
- 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 5
- DSL: `k_high.pct_change(12) * (1 + hl_range) * (1 - dist_to_high) * i_rsi(k_close, 14) / (1 + i_atr(k_high, k_low, k_close, 14))` (feature=k_loop_iter_5_59c5f5)
- DSL valid: True
- FeatureSet candidate id: 1179
- baseline PF: 1.947 → modified PF: 1.230
- t_stat: 0.000, p_value: 0.552, cohens_d: 0.311
- scalar_score: -0.3192
- accepted: False, reason: min_p=0.552 ≥ 0.05

**Metrics summary:**
  - profit_factor: baseline=1.947, modified=1.230, diff=-0.717
  - sharpe_ratio: baseline=19.162, modified=7.251, diff=-11.911
  - win_rate: baseline=0.600, modified=0.500, diff=-0.100
  - max_drawdown: baseline=0.056, modified=0.202, diff=0.146
  - test_accuracy: baseline=0.992, modified=0.981, diff=-0.011

### Iteration 6
- DSL: `k_momentum_pct_change * (1 - dist_to_high) / (1 + atr_norm) - o_div_amplitude_12` (feature=k_loop_iter_6_1f5681)
- DSL valid: True
- FeatureSet candidate id: 1180
- 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_ab1574

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: -0.1020

MetricBaselineModifiedDiff
win_rate 0.600 0.563 -0.037
max_drawdown 0.056 0.056 0.000
sharpe_ratio 19.162 18.205 -0.957
profit_factor 1.947 1.895 -0.053
test_accuracy 0.992 0.992 0.000

Iteration 2: k_loop_iter_2_d32d51

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_4d300c

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: 1.3042

MetricBaselineModifiedDiff
win_rate 0.600 0.567 -0.033
max_drawdown 0.056 0.056 0.000
sharpe_ratio 19.162 24.452 5.290
profit_factor 1.947 2.481 0.534
test_accuracy 0.992 0.992 0.000

Iteration 4: k_loop_iter_4_af91f2

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 5: k_loop_iter_5_59c5f5

Y Label: — | Parent FS: #7

Objective mode: scalarize

Scalar score: -0.3192

MetricBaselineModifiedDiff
win_rate 0.600 0.500 -0.100
max_drawdown 0.056 0.202 0.146
sharpe_ratio 19.162 7.251 -11.911
profit_factor 1.947 1.230 -0.717
test_accuracy 0.992 0.981 -0.011

Iteration 6: k_loop_iter_6_1f5681

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_130317.md