analyzing-backtests
Analyzes algorithmic trading backtest results from Jupyter notebooks and generates summary reports. Use when the user wants to analyze or summarize backtest notebooks.
Best use case
analyzing-backtests is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyzes algorithmic trading backtest results from Jupyter notebooks and generates summary reports. Use when the user wants to analyze or summarize backtest notebooks.
Teams using analyzing-backtests should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-backtests/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-backtests Compares
| Feature / Agent | analyzing-backtests | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyzes algorithmic trading backtest results from Jupyter notebooks and generates summary reports. Use when the user wants to analyze or summarize backtest notebooks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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: ```markdown # 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
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