Courses / Technical Analysis Essentials

Backtesting Properly

Validating your strategy with historical data

Backtesting applies your trading rules to historical data to measure past performance.

Proper backtesting process: 1. Define clear, objective rules (no subjective interpretation) 2. Start from the oldest data and move forward 3. Don't peek ahead — decide entries/exits in real-time simulation 4. Record every trade: entry, exit, result, notes 5. Test across different market conditions (trending, ranging) 6. Minimum 200 trades for statistical significance

Metrics to track: - Win rate: What percentage of trades are winners? - Average R:R: Average reward relative to risk - Max drawdown: Largest peak-to-trough decline - Profit factor: Gross profit / gross loss (need > 1) - Sharpe ratio: Risk-adjusted return

Common pitfalls: - Curve fitting: Over-optimizing for historical data - Look-ahead bias: Using information not available at the time - Survivorship bias: Only testing on assets that still exist

Pro tip: Split your data: use 70% for testing and hold back 30% for out-of-sample validation.

Common mistake: Adjusting rules until backtest shows 95% win rate. Better: This is curve fitting. Simple rules that work on unseen data beat complex over-optimized systems..

Key takeaways

• 200+ trades minimum for statistical significance
• Track win rate, R:R, drawdown, profit factor
• Avoid curve fitting and look-ahead bias
• Test across different market conditions

Quiz

• Curve fitting in backtesting means: Answer: Over-optimizing rules to fit past data. Curve fitting = tweaking rules excessively to match historical data, destroying future performance.

Educational content, not financial advice.