example_hyperparameter_report/example_analysis.md

⚙️ Strategy Configuration

Parameter	Value	Description
strategy	Bollinger_w10_std1.5	Trading strategy name
symbol	EURUSD	Trading instrument
account_name	FUNDEDNEXT_STLR2_6K	Trading account identifier
bar_type	tick	Bar type (tick/volume/time/dollar/...)
bar_size	M1	Bar timeframe
price	mid_price	Price type (bid/ask/mid)
target_lookback	20	Target calculation lookback periods
profit_target	1	Profit target in risk multiples
stop_loss	2	Stop loss in risk multiples
max_holding_period	{"days": 1}	Maximum holding period
min_ret	0	Minimum return threshold
vertical_barrier_zero	True	Vertical barrier at zero crossing
filter_as_series	False	Filter as time series

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📊 Hyperparameter Tuning Analysis Report

Generated on: 2026-05-03 15:37:00

🎯 Executive Summary

Key Findings

• Best Model Performance: 0.7984 ± 0.0101
• Risk Level: LOW

✅ Excellent: Model shows high consistency across validation folds.

📈 Main Visualization

Hyperparameter Analysis Overview

📊 Performance Overview

Total Models Evaluated: 36
Performance Range: 0.7984 - 0.6039
Average Performance: 0.7040 ± 0.0599

🏆 Top Models Comparison

Rank	Mean Score	Std Score	Fit Time (s)	Efficiency
1	0.7984	0.0101	2.35	0.34
2	0.7977	0.0418	2.05	0.39
3	0.7970	0.0172	2.71	0.29
4	0.7970	0.0232	3.77	0.21
5	0.7963	0.0150	2.10	0.38

🛡️ Stability Analysis

Models meeting stability threshold: 23

Best Stable Model

• Score: 0.7984
• Standard Deviation: 0.0101

⏱️ Time-Efficiency Analysis

Training Time Statistics

• Fastest Model: 0.62s
• Slowest Model: 4.92s
• Average Time: 3.01s
• Median Time: 3.20s

📊 Hyperparameter Trends

Parameter Impact Analysis

n_estimators

Value	Mean Score	Score Std	Count	Avg Time (s)
200	0.7277	0.0559	12	2.91
50	0.6963	0.0684	12	3.36
100	0.6881	0.0514	12	2.76

max_depth

Value	Mean Score	Score Std	Count	Avg Time (s)
7	0.7207	0.0473	9	3.05
3	0.7136	0.0549	9	3.08
10	0.6928	0.0821	9	2.34
5	0.6889	0.0536	9	3.57

min_samples_split

Value	Mean Score	Score Std	Count	Avg Time (s)
2	0.7239	0.0555	12	2.88
5	0.7026	0.0469	12	3.23
10	0.6857	0.0730	12	2.93

🎯 Model Selection Recommendations

Final Recommendation

Recommended Hyperparameters

n_estimators = 50
max_depth = 10
min_samples_split = 10

🔍 Specific Insights

Model Architecture Analysis

Overall Performance: EXCELLENT
Stability Rating: HIGH

Best Model Details

• max_depth: 10
• n_estimators: 50
• mean_test_score: 0.7984
• Standard Deviation: 0.0101
• Training Time: 2.35s

💼 Practical Trading Implications

Performance Expectations

• Expected Win Rate: ~79.8%
• Performance Consistency: High
• Risk Assessment: LOW

Trading Strategy Considerations

✅ Stable Strategy Detected

• Can consider standard position sizing
• Strategy likely to perform consistently
• Lower monitoring frequency acceptable

📋 Detailed Results

Complete Results (Top 10 Models)

Rank	mean_test_score	std_test_score	mean_fit_time	n_estimators	max_depth	min_samples_split
1	0.7984	0.0101	2.35s	50	10	10
2	0.7977	0.0418	2.05s	200	7	10
3	0.7970	0.0172	2.71s	200	10	10
4	0.7970	0.0232	3.77s	50	3	2
5	0.7963	0.0150	2.10s	200	10	2
6	0.7807	0.0244	1.68s	100	3	2
7	0.7559	0.0396	2.35s	200	7	2
8	0.7552	0.0329	2.88s	200	5	2
9	0.7551	0.0322	4.60s	50	7	5
10	0.7414	0.0369	2.02s	50	3	5

📚 Appendix

A. Glossary

• Mean Test Score: Average performance across CV folds
• Std Test Score: Standard deviation across CV folds
• Mean Fit Time: Average training time per model
• Stability Threshold: Maximum acceptable std (default: 0.03)
• Efficiency Score: Performance per unit of training time

B. Analysis Methodology

1. Cross-Validation: Typically 5-fold stratified CV
2. Scoring Metric: mean_test_score
3. Hyperparameter Search: GridSearch/RandomizedSearch
4. Stability Analysis: Models with std ≤ 0.03 considered stable
5. Time Efficiency: Pareto frontier analysis

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Report generated by Hyperparameter Analysis Module