The Complete Guide to Backtesting Strategies

Backtesting is essential for validating trading strategies. Learn proper methodology to get accurate results and avoid costly mistakes in live trading.

What is Backtesting?

Backtesting simulates how a trading strategy would have performed on historical data. It helps you:

• Validate strategy profitability before risking real money
• Understand maximum drawdowns and risk
• Optimize parameters for better performance
• Build confidence in your approach

Proper Backtesting Methodology

1. Sufficient Data Sample

Test on at least 2-3 years of data including different market conditions (bull, bear, sideways).

2. Include Transaction Costs

Always account for:

• Commission fees (0.1% - 0.3% per trade)
• Bid-ask spreads
• Slippage (1-5 ticks depending on liquidity)

3. Realistic Position Sizing

Use position sizes that match your actual trading capital. Don't backtest with unrealistic leverage.

4. Out-of-Sample Testing

Reserve 20-30% of data for forward testing after optimization.

Key Metrics to Evaluate

Metric	Good Value	What It Measures
Win Rate	>50%	Percentage of winning trades
Profit Factor	>1.5	Gross profit / Gross loss
Max Drawdown	<20%	Largest peak-to-trough decline
Sharpe Ratio	>1.0	Risk-adjusted returns
Total Trades	>100	Statistical significance

Common Backtesting Mistakes

❌ Look-Ahead Bias

Using future information in your calculations. Ensure indicators only use past/current data.

❌ Survivorship Bias

Testing only on stocks/assets that still exist today, ignoring delisted companies.

❌ Overfitting (Curve Fitting)

Optimizing too many parameters until backtest looks perfect. This creates strategies that fail in live trading.

❌ Ignoring Market Regimes

Strategies that work in bull markets may fail in bear markets. Test across all conditions.

Walk-Forward Analysis

Professional approach to validation:

1. Optimize on Period 1 (in-sample)
2. Test on Period 2 (out-of-sample)
3. Re-optimize on Period 2
4. Test on Period 3 (out-of-sample)
5. Repeat...

This simulates real-world periodic re-optimization.

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