--- name: backtesting-trading-strategies description: | Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals". allowed-tools: Read, Write, Edit, Grep, Glob, Bash(python:*) version: 2.0.0 author: Jeremy Longshore license: MIT ---

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:

pip install pandas numpy yfinance matplotlib

Optional for advanced features:

pip install ta-lib scipy scikit-learn

Instructions

Step 1: Fetch Historical Data

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:

python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y

Advanced backtest with custom parameters:

# 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:

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:

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