analyzing-backtests

This Skill analyzes Jupyter notebooks containing algorithmic trading backtests and extracts key metrics like returns, drawdown, Sharpe ratio, and win rate to produce a structured Markdown summary report. It parses code, markdown, and cell outputs to capture strategy performance and risk data, then organizes findings into sections covering strategy, period, performance, risk metrics, and recommendations.

The analysis extracts performance metrics including returns, capital, drawdowns, Sharpe ratio, win rate, number of trades, volatility, alpha, and beta. Unavailable metrics are marked as N/A in the report.

Yes, the Skill reads .ipynb files directly and parses all code cells, markdown sections, and output data to extract backtest results. It captures version control state and processes the notebook end-to-end without requiring manual data extraction.

The Markdown report includes sections on strategy name and configuration, date coverage, performance metrics, risk metrics, key findings from the backtest results, and actionable recommendations based on the analysis.

You need a Jupyter notebook (.ipynb) containing your algorithmic trading backtest results with strategy configuration, date coverage, and performance or risk metrics present in code, markdown, or cell outputs.