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:

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 ${CLAUDE_SKILL_DIR}/​data/ for reuse):

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

2. 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}'
   

3. 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).

4. 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

Trade Statistics

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

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

Resources

• yfinance - Yahoo Finance data
• TA-Lib - Technical analysis library
• QuantStats - Portfolio analytics