backtesting-trading-strategies

# Backtesting Trading Strategies ## Overview 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:** - 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 ## Prerequisites Install required dependencies: ```bash set -euo pipefail pip install pandas numpy yfinance matplotlib ``` Optional for advanced features: ```bash set -euo pipefail pip install ta-lib scipy scikit-learn ``` ## Instructions 1. Fetch historical data (cached to `${CLAUDE_SKILL_DIR}/data/` for reuse): ```bash python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d ``` 2. Run a backtest with default or custom parameters: ```bash python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \ --strategy rsi_reversal \ --symbol ETH-USD \ --period 1y \ --capital 10000 \ # 10000: 10 seconds in ms --params '{"period": 14, "overbought": 70, "oversold": 30}' ``` 3. Analyze results saved to `${CLAUDE_SKILL_DIR}/reports/` -- includes `*_summary.txt` (perfo...