backtesting-trading-strategies

Backtest trading strategies against historical data with performance metrics and parameter optimization. Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization. Key Features: Install required dependencies: Optional for advanced features: Create ${CLAUDE_SKILL_DIR}/config/settings.yaml : See ${CLAUDE_SKILL_DIR}/references/errors.md for common issues and solutions. See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed usage examples including: - Includes 8 pre-built strategies (SMA, EMA, RSI, MACD, Bollinger Bands, Breakout, Mean Reversion, Momentum) for crypto and traditional assets - Calculates comprehensive metrics: Sharpe, Sortino, Calmar ratios, max drawdown, VaR, volatility, win rate, and profit factor - Supports parameter grid search optimization to find best strategy combinations - Generates equity curves, trade logs, and performance summaries saved to reports directory - Requires pandas, numpy, yfinance; optional ta-lib and scipy for advanced analysis - 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum) - Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown) - Parameter grid search optimization - Equity curve visualization -...