Backtest Analysis Skill

Analyze a Jupyter notebook containing algorithmic trading backtest results and generate a comprehensive summary report.

Analysis Steps

1. •

   Version Control Information

   • •Run git status to check current state
   • •Run git log -1 --format="%H %ci" for latest commit hash and date
   • •Note any uncommitted changes
2. •

   Read the Notebook

   • •Use Read tool to load the specified .ipynb file
   • •Parse cells for code, markdown, and outputs
3. •

   Extract Key Information

   Model/Strategy Details:

   • •Strategy name, type, and configuration
   • •Key hyperparameters
   • •Training and testing period information

   Date Coverage:

   • •Backtest period (start, end, duration)

   Performance Metrics:

   • •Monetary results: returns, capital, drawdowns, trade statistics
   • •Statistical analysis: risk metrics, benchmark comparisons, distributions
   • •Extract whatever metrics are available in the notebook
4. •

   Generate Report

Output a structured markdown report:

# Backtest Analysis Report

**Notebook:** [filename]
**Generated:** [date]
**Git Commit:** [hash] ([date])
**Uncommitted Changes:** [yes/no]

## Strategy
[Name and brief description]

**Configuration:**
- [Key parameters]

## Period
- **Dates:** [start] to [end] ([duration])

## Performance

| Metric | Value | Benchmark |
|--------|-------|-----------|
| Total Return | X% | X% |
| Annualized Return | X% | X% |
| Max Drawdown | X% | X% |
| Sharpe Ratio | X.XX | X.XX |
| Win Rate | X% | - |
| Total Trades | X | - |

## Risk Metrics
| Metric | Value |
|--------|-------|
| Volatility | X% |
| Alpha | X% |
| Beta | X.XX |

## Key Findings
- [Notable observations]
- [Strengths and weaknesses]

## Concerns/Recommendations
- [Any issues or suggestions]

Instructions

• •Extract all available metrics from the notebook
• •Mark unavailable metrics as "N/A"
• •Provide brief analysis, not just data
• •Flag unusual results or potential issues
• •Keep report concise but comprehensive