Backtesting
Test your strategies on historical data before risking real money using QuantsEdge's Strategy Lab.
What is Backtesting?
Backtesting simulates how your strategy would have performed using past market data.
How It Works
Choose a strategy and time period
System replays historical price data
Strategy generates buy/sell signals
Simulated trades execute
Performance calculated
Why Backtest?
Validate Logic - Does the strategy actually work? Are the rules sound? Set Expectations - Realistic return expectations, typical drawdowns, win rate Optimize Parameters - Find best settings, compare variations, refine approach Build Confidence - See strategy in action, understand behavior, trust before going live
Running a Backtest
1. Access Strategy Lab
Navigate to: Strategy Lab → Your Bot → Backtest
2. Configure Parameters
Time Period: Recent data (3 months), Longer validation (6-12 months), Multiple conditions (1-2 years) Starting Capital: Use your planned trading capital (e.g., $10,000) Trading Pair: Must match your bot's target (e.g., BTC-USD) Fees: Include realistic trading fees (default: 0.05%)
3. Run the Test
Click "Start Backtest" - Processing time: 30-90 seconds
What happens: Loads historical data, runs strategy logic, simulates order fills, calculates P&L and metrics
4. Review Results
Understanding Results
Key Metrics
Total Return - Overall profit/loss percentage. Good: 10-20% over 6 months, Excellent: 20%+, Warning: 50%+ might be over-optimized
Win Rate - Percentage of winning trades. Good: 50%+, Note: High win rate doesn't guarantee profitability
Profit Factor - Gross profits ÷ Gross losses. Good: Above 1.5, Excellent: Above 2.0, Warning: Below 1.3
Max Drawdown - Worst peak-to-valley decline. Acceptable: Under 20%, Good: Under 15%, Warning: Over 25%
Total Trades - Number of trades executed. Too few: <20, Good: 30-100, Too many: >200 (overtrading)
Performance Chart
Equity Curve - Shows capital growth over time. Look for: Steady upward trend, recovers from drawdowns, no single trade creates all profit
Red flags: Flat for long periods, large drawdowns, all profit from 1-2 trades
Interpreting Results
Good Backtest Signs
Positive total return (10%+), profit factor above 1.5, max drawdown under 20%, consistent performance over time, multiple winning periods, reasonable trade frequency
Warning Signs
Over-Optimization: Perfect 90%+ win rate, huge returns (100%+), almost no drawdown, won't work live Curve Fitting: All profit from one period, different results in different periods, too good to be true Insufficient Data: Only 5-10 trades, too short time period, need more testing
What If Results Are Bad?
Negative returns: Try different parameters, test different strategy type, current market might not suit strategy
Don't abandon immediately:
Some strategies work only in certain conditions
Test on different time periods
Understand why it lost
Backtest Best Practices
Time Period Selection
Include Multiple Conditions: Bull markets, bear markets, sideways markets, high and low volatility
Walk-Forward Testing
Better validation method: Backtest on Period 1, optimize parameters, test on Period 2. If still profitable → good sign, if not → was over-fit
Out-of-Sample Testing
Reserve data for validation: Backtest on 70% of your data for optimization, then test on the remaining 30% for validation. If validation fails, your strategy is over-optimized.
Realistic Settings
Include All Costs: Factor in trading fees (0.05% typical), slippage (0.1-0.2%), and funding rates for perpetuals to get accurate results.
Use Realistic Fills: Market orders should assume some slippage, while limit orders should use conservative fill assumptions based on market conditions.
Common Backtest Mistakes
Testing Only Bull Markets: Only backtesting during rising prices gives misleading results. Test across different market conditions including bear markets, sideways markets, and high volatility periods.
Over-Optimizing Parameters: Testing 100 parameter combinations and choosing the best one leads to overfitting. Test only 3-5 logical variations maximum to avoid curve-fitting.
Ignoring Drawdowns: Only looking at total return is dangerous. Maximum drawdown is as important as returns and shows the real risk of your strategy.
Too-Short Time Periods: Backtesting only 2-4 weeks isn't sufficient. Use a minimum of 3 months, preferably 6-12 months of data for reliable results.
Assuming Perfect Execution: Expecting exact entry/exit prices is unrealistic. Always include realistic costs and slippage in your backtests.
After Backtesting
If Results Are Good
Next step: Paper Trading - Don't go straight to live trading. Paper trade for 1-2 weeks minimum to verify live behavior matches your backtest, build confidence, then deploy small live positions.
If Results Are Mixed
Options: Adjust parameters carefully, try different time periods, test a different strategy entirely, or combine with other strategies for diversification.
Don't: Keep tweaking until "perfect," skip paper trading, or risk real money yet.
If Results Are Bad
Don't force it: Sometimes strategies simply don't work for certain markets. Try a completely different approach or accept that some ideas don't work in practice.
Better to know now than after losing money
Backtest Limitations
What Backtesting Can't Tell You
Future Market Conditions
Past performance ≠ future results
Markets change
New conditions emerge
Psychological Factors
How you'll handle real losses
Emotional discipline
Stress of real money
Exact Live Performance
Slippage varies
Liquidity changes
Technical issues can occur
Black Swan Events
Extreme market crashes
Exchange outages
Unprecedented volatility
Why You Still Need Paper Trading
Backtesting is essential but not sufficient:
Paper trading tests real-time execution
Reveals current market behavior
Builds psychological readiness
Final validation before live
Never skip paper trading.
Quick Checklist
Before Running Backtest
After Backtest Results
Next Steps
Passed Backtesting? Move to paper trading to validate in real-time. Paper Trading Guide →
Want to Optimize? Learn advanced parameter tuning. Strategy Optimization →
Analyze Deeply Understand performance metrics. Performance Analysis →
Backtesting is your first defense against bad strategies. Take it seriously, but remember it's only the first step in validation.
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