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: ```bash pip install pandas numpy yfinance matplotlib ``` Optional for advanced features: ```bash pip install ta-lib scipy scikit-learn ``` ## Instructions ### Step 1: Fetch Historical Data ```bash python {baseDir}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d ``` Data is cached to `{baseDir}/data/{symbol}_{interval}.csv` for reuse. ### Step 2: Run Backtest Basic backtest with default parameters: ```bash python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y ``` Advanced backtest with custom parameters: ```bash # Example: backtest with specific date range python {baseDir}/scripts/backtest.py \ --strategy rsi_reversal \ --symbol ETH-USD \ --period 1y \ --capital 10000 \ --params '{"period": 14, "overbought": 70, "oversold": 30}' ``` ### Step 3: Analyze Results Results are saved to `{baseDir}/reports/` including: - `*_summary.txt` - Performance metrics - `*_trades.csv` - Trade log - `*_equity.csv` - Equity curve data - `*_chart.png` - Visual equity curve ### Step 4: Optimize Parameters Find optimal parameters via grid search: ```bash python {baseDir}/scripts/optimize.py \ --strategy sma_crossover \ --symbol BTC-USD \ --period 1y \ --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}' ``` ## 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 `{baseDir}/config/settings.yaml`: ```yaml data: provider: yfinance cache_dir: ./data backtest: default_capital: 10000 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 `{baseDir}/references/errors.md` for common issues and solutions. ## Examples See `{baseDir}/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](https://github.com/ranaroussi/yfinance) - Yahoo Finance data - [TA-Lib](https://ta-lib.org/) - Technical analysis library - [QuantStats](https://github.com/ranaroussi/quantstats) - Portfolio analytics