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

1. Fetch historical data (cached to ${CLAUDESKILLDIR}/data/ for reuse):

   python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d

1. 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}'

1. Analyze results saved to ${CLAUDESKILLDIR}/reports/ -- includes summary.txt (performance metrics), trades.csv (trade log), equity.csv (equity curve data), and chart.png (visual equity curve).
2. 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

Risk Metrics

/backtest-strategy

Testing moving average strategy:
- Symbol: BTC/USDT
- Period: 1 year
- Capital: $10,000
- Parameters: 50/200 MA