backtesting

backtesting

Purpose

This skill enables OpenClaw to perform backtesting on historical financial data, simulating trading strategies to measure metrics like returns, drawdowns, and Sharpe ratio. Use it to validate strategies before live trading, identifying potential flaws in logic or risk exposure.

When to Use

Apply this skill when developing or refining trading strategies, such as evaluating a moving average crossover on stock data. Use it for quantitative analysis in algorithmic trading, portfolio optimization, or risk assessment, especially when historical data is available and strategies need empirical validation.

Key Capabilities

• Simulate trades using historical price data from CSV, JSON, or API sources.
• Compute performance metrics including total return, volatility, maximum drawdown, and risk-adjusted returns.
• Support for common indicators like SMA, RSI, and Bollinger Bands via integrated libraries.
• Handle multiple assets or portfolios, with options for transaction costs, slippage, and position sizing.
• Output results in JSON format for easy parsing and visualization.

Usage Patterns

To backtest a strategy, provide a Python script defining the strategy logic, historical data source, and parameters. Always set the API key via environment variable $OPENCLAW_API_KEY before running. For CLI, use flags to specify inputs; for API, send a POST request with a JSON payload. Validate data integrity first by checking for missing values or incorrect timestamps. Run tests iteratively, adjusting parameters based on initial results.