Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger wit

Backtesting Trading Strategies Overview Simulate trading strategies against historical data. Results describe a historical simulation, not expected future performance. The framework includes 8 built in strategies, the metrics listed below, and parameter optimization. Key Features: 8 pre built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum) Performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown) Parameter grid search optimization Equity curve visualization Trade by trade analysis Prerequisites Before invoking this skill, the user installs the required dependencies in the Python environment used for the simulation: Optional for advanced features: Instructions 1. Fetch historical data (cached to ${CLAUDE SKILL DIR}/data/ for reuse): 2. Run a backtest with default or custom parameters: 3. Analyze results saved to ${CLAUDE SKILL DIR}/reports/ includes summary.txt (performance metrics), trades.csv (trade log), equity.csv (equity curve data), and chart.png (visual equity curve). 4. Optimize parameters via grid search to find the best combination: Output Performance Metrics Metric Description Total Return Overall percentage gain/loss CAGR Compound annual growth rate Sharpe Ratio Historical risk adjusted return Sortino Ratio Downside risk adjusted return Calmar Ratio Return divided by max drawdown Risk Metrics Metric Description Max Drawdown Largest peak to trough decline VaR (95%) Value at Risk at 95% confidence CVaR (95%) Expected loss beyond VaR Volatility Annualized standard deviation Trade Statistics Metric Description Total Trades Number of round trip trades Win Rate Percentage of profitable trades Profit Factor Gross profit divided by gross loss; N/A with no observed losses Expectancy Expected value per trade Example Output Figure free layout; actual values depend on the input series, fees, slippage, strategy and parameters. Do not present these placeholders as measured returns. Profit factor is unavailable when no losing trades were observed, including a zero trade run or a single winning/breakeven trade. A single losing trade yields zero gross profit divided by its loss; this is not evidence of strategy quality. Zero/one trade samples cannot establish performance, and optimization is an in sample historical comparison rather than a forecast. Supported Strategies Strategy Description Key Parameters sma crossover Simple moving average crossover fast period , slow period ema crossover Exponential MA crossover fast period , slow period rsi reversal RSI overbought/oversold period , overbought , oversold macd MACD signal line crossover fast , slow , signal bollinger bands Mean reversion on bands period , std dev breakout Price breakout from range lookback , threshold mean reversion Return to moving average period , z threshold momentum Rate of change momentum period , threshold Configuration Create ${CLAUDE SKILL DIR}/config/settings.yaml : Error Handling Use Read to inspect [ references/errors.md ](references/errors.md) for common issues and solutions. Examples See [ references/examples.md ](references/examples.md) for detailed usage examples including: Multi asset comparison Walk forward analysis Parameter optimization workflows Files File Purpose scripts/backtest.py Main backtesting engine scripts/fetch data.py Historical data fetcher scripts/strategies.py Strategy definitions scripts/metrics.py Performance calculations scripts/optimize.py Parameter optimization Resources [yfinance](https://github.com/ranaroussi/yfinance) Yahoo Finance data [TA Lib](https://ta lib.org/) Technical analysis library [QuantStats](https://github.com/ranaroussi/quantstats) Portfolio analytics

backtesting-trading-strategies