backtesting-trading-strategies

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: - 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 - Trade-by-trade analysis - Fetch historical data (cached to ${CLAUDE_SKILL_DIR}/data/ for reuse): python ${CLAUDE_SKILL_DIR} /scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d - Run a backtest with default or custom parameters: python ${CLAUDE_SKILL_DIR} /scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y python ${CLAUDE_SKILL_DIR} /scripts/backtest.py \ --strategy rsi_reversal \ --symbol ETH-USD \ --period 1y \ --capital 10000 \ # 10000: 10 seconds in ms --params '{"period": 14, "overbought": 70, "oversold": 30}' - Analyze results saved to ${CLAUDE_SKILL_DIR}/reports/ -- includes *_summary.txt...