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

1. 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}'

1. 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).
2. Optimize parameters via grid search to find the best combination:

bash 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:

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