SKILL.md
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: