backtesting-analysis

To compare and rank trading strategy backtests, you can compute key performance metrics like returns, drawdown, and win rate across historical data. This Skill uses pandas and numpy to evaluate multiple strategy variants and generates a ranked report for deployment selection.

Backtesting performance is evaluated using metrics such as returns, drawdown, and win rate computed via numpy and scipy. The Skill calculates these quantitative metrics across historical data to provide clear insights and rank strategy effectiveness for deployment.

Yes, executing backtests and analyzing results requires integration with the OpenAlgo backtest engine and AITRAPP adapter. The Skill orchestrates backtests through these platforms while using a Python stack for data handling and metrics computation.

Yes, you can export backtest ranking reports to CSV or JSON formats. After computing performance metrics and ranking strategies over historical data, the Skill generates these report files to streamline strategy review and deployment decisions.

To run a backtest over a specific date range, execute the ranking script with your desired parameters. The Skill orchestrates the backtest via the OpenAlgo stack, collects the results, and automatically generates a ranked report for review.

Strategy backtesting and metrics computation require a Python stack including pandas, numpy, and scipy for data handling and mathematical calculations. The Skill also depends on pyyaml, structlog, httpx, and pydantic-settings for configuration and operational tasks.