Backtesting Trading Strategies — How to Do It Right
Backtesting tests a strategy against historical data. Done right, it provides realistic expected performance. Done wrong, it produces inflated returns that fail in live trading. Critical pitfalls: look-ahead bias, survivorship bias, overfitting, ignoring slippage and commissions, insufficient sample size, and curve-fitting to one regime.
Backtesting tests a strategy against historical data. Done right, it provides realistic expected performance. Done wrong, it produces inflated returns that fail in live trading. Critical pitfalls: look-ahead bias, survivorship bias, overfitting, ignoring slippage and commissions, insufficient sample size, and curve-fitting to one regime.
Common backtesting pitfalls
This section covers common backtesting pitfalls. For the practical framework, see our Quant Trading hub and our blog for related analyses. Read on for context-specific guidance.
Walk-forward validation
This section covers walk-forward validation. For the practical framework, see our Quant Trading hub and our blog for related analyses. Read on for context-specific guidance.
Realistic slippage modeling
This section covers realistic slippage modeling. For the practical framework, see our Quant Trading hub and our blog for related analyses. Read on for context-specific guidance.
Sample size requirements
This section covers sample size requirements. For the practical framework, see our Quant Trading hub and our blog for related analyses. Read on for context-specific guidance.