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: set -euo pipefail pip install pandas numpy yfinance matplotlib Optional for advanced features: set -euo pipefail pip install ta-lib scipy scikit-learn Instructions 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 (performance metrics), _trades.csv (trade log), _equity.csv (equity curve data), and _chart.png (visual equity curve). Optimize parameters via grid search to find the best combination: python ${CLAUDE_SKILL_DIR} /scripts/optimize.py \ --strategy sma_crossover \ --symbol BTC-USD \ --period 1y \ --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'

HTTP 200 OK

Output Performance Metrics Metric Description Total Return Overall percentage gain/loss CAGR Compound annual growth rate Sharpe Ratio Risk-adjusted return (target: >1.5) 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 Expectancy Expected value per trade Example Output ================================================================================ BACKTEST RESULTS: SMA CROSSOVER BTC-USD | [start_date] to [end_date] ================================================================================ PERFORMANCE | RISK Total Return: +47.32% | Max Drawdown: -18.45% CAGR: +47.32% | VaR (95%): -2.34% Sharpe Ratio: 1.87 | Volatility: 42.1% Sortino Ratio: 2.41 | Ulcer Index: 8.2

TRADE STATISTICS Total Trades: 24 | Profit Factor: 2.34 Win Rate: 58.3% | Expectancy: $197.17 Avg Win: $892.45 | Max Consec. Losses: 3 ================================================================================ 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 : data : provider : yfinance cache_dir : ./data backtest : default_capital : 10000

10000: 10 seconds in ms

commission : 0.001

0.1% per trade

slippage : 0.0005

0.05% slippage

risk : max_position_size : 0.95 stop_loss : null

Optional fixed stop loss

take_profit : null

Optional fixed take profit

Error Handling See ${CLAUDE_SKILL_DIR}/references/errors.md for common issues and solutions. Examples See ${CLAUDE_SKILL_DIR}/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 - Yahoo Finance data TA-Lib - Technical analysis library QuantStats - Portfolio analytics