Using Backtest
Master the Backtest tool to validate your pairs trading strategies using historical data.
🚀 Quick Start Guide
1. Understanding Backtesting
Backtesting is a powerful method to test trading strategies using historical data before risking real money.
📊 What Backtesting Does:
- • Strategy Validation: Test your trading logic on historical data
- • Performance Measurement: Quantify returns, risk, and drawdowns
- • Parameter Optimization: Find optimal settings for your strategy
- • Risk Assessment: Understand potential losses and volatility
🎯 Backtesting Benefits:
- • Risk-Free Testing: No real money at risk during testing
- • Data-Driven Decisions: Base strategies on historical evidence
- • Strategy Refinement: Identify and fix strategy weaknesses
- • Confidence Building: Trade with proven strategy performance
2. Setting Up Your Backtest
Configure your backtest parameters to match your trading strategy and risk tolerance.
📈 Stock Pair Selection:
- • Stock A & Stock B: Choose from available stocks in your database
- • Data Availability: Ensure both stocks have sufficient historical data
- • Correlation: Select pairs that historically move together
- • Liquidity: Consider trading volume and bid-ask spreads
📅 Date Range Selection:
- • From Date: Start of your testing period (default: 2021-01-01)
- • To Date: End of your testing period (default: today)
- • Recommended Duration: At least 1-2 years for reliable results
- • Market Cycles: Include both bull and bear market periods
3. Configuring Strategy Parameters
Fine-tune your trading strategy parameters for optimal performance and risk management.
⚙️ Core Strategy Parameters:
Days for calculating Z-scores (default: 60)
Threshold for opening trades (default: 2.5)
Threshold for closing trades (default: 1.5)
🛡️ Risk Management Parameters:
Maximum days to hold a trade (default: 15)
Maximum loss percentage (default: -10%)
Profit target percentage (default: 10%)
4. Running Your Backtest
Execute your backtest and monitor the simulation process to generate performance results.
▶️ Execution Steps:
- 1. Verify all parameters are set correctly
- 2. Ensure both stocks have data for the selected date range
- 3. Click the "Run Backtest" button
- 4. Wait for the analysis to complete
- 5. Review the generated results and trade history
📊 What Happens During Backtest:
- • Data Processing: Historical prices are aligned and processed
- • Z-Score Calculation: Rolling Z-scores computed for each day
- • Trade Simulation: Entry/exit signals trigger virtual trades
- • Performance Tracking: P&L, drawdowns, and statistics calculated
5. Understanding Backtest Results
Learn how to interpret the backtest results to evaluate strategy performance and make improvements.
📈 Performance Metrics:
Overall strategy performance percentage
Percentage of profitable trades
Mean profit/loss per trade
Largest peak-to-trough decline
📋 Trade History:
- • Entry/Exit Dates: When trades were opened and closed
- • Trade Type: LONG (buy ratio) or SHORT (sell ratio)
- • Entry/Exit Z-Scores: Z-score values at trade points
- • Exit Reason: Why the trade was closed (target, stop, time)
- • Trade P&L: Profit or loss for each individual trade
Strategy Optimization
Use backtest results to refine your strategy parameters and improve performance.
🔧 Parameter Tuning:
- • Entry Thresholds: Adjust Z-score levels for better entry timing
- • Exit Thresholds: Optimize profit taking and loss cutting
- • Lookback Periods: Test different calculation windows
- • Risk Parameters: Fine-tune stops and targets
📊 Performance Analysis:
- • Identify Patterns: Look for common exit reasons
- • Risk Assessment: Evaluate drawdown patterns
- • Market Conditions: Analyze performance in different periods
- • Strategy Validation: Confirm strategy works across time
💡 Best Practices & Tips
🎯 Backtesting Strategy:
- • Test strategies across multiple market cycles
- • Use out-of-sample data for final validation
- • Consider transaction costs and slippage
- • Avoid overfitting to historical data
⚡ Performance Optimization:
- • Focus on risk-adjusted returns, not just total returns
- • Monitor drawdowns and recovery periods
- • Test parameter sensitivity and robustness
- • Consider market regime changes
🔄 Continuous Improvement:
- • Regularly re-run backtests with new data
- • Document successful parameter combinations
- • Compare performance across different pairs
- • Integrate backtest results with live trading
🎯 What's Next?
Now that you understand how to use the Backtest tool, validate your strategies and optimize your trading approach.