Last Updated on 28 February, 2026 by Abrahamtolle

Backtesting is among the most valuable tools available to today’s traders. By testing a strategy on historical data before committing real capital, you can estimate potential returns and—perhaps more importantly—identify weaknesses and risk exposure. When applied properly, it supports better decision-making; when done poorly, it can foster a misleading sense of confidence.

Less experienced traders frequently make errors in the backtesting process that make their strategies appear more robust than they truly are. These missteps can lead to disappointing live performance and avoidable financial losses once the strategy is put into practice.

Overfitting and Curve Fitting

Overfitting is among the most serious pitfalls in backtesting. It occurs when a strategy is tailored too closely to historical data. Excessive optimization may generate performance results that look impressive but are unlikely to hold up in real trading.

Traders who make this mistake often confuse random patterns with genuine edge. The best defense is to keep the rules straightforward, limit the number of parameters, and evaluate the strategy across different markets and timeframes. Doing so increases the likelihood that the approach will remain robust under live conditions.

Survivor Bias

Survivorship bias arises when backtests include only stocks, assets, or markets that still exist today, while excluding those that failed, were delisted, or went bankrupt. This can materially overstate historical performance because losing assets are silently removed from the dataset.

A classic example is testing only the companies currently in the S&P 500 stocks while ignoring firms that were removed over time. Many of those dropped constituents underperformed, which is why they disappeared. Backtests constructed this way produce unrealistically strong results that cannot be replicated in live trading. To prevent this distortion, traders should rely on historical data that includes delisted securities.

Ignoring Transaction Costs and Slippage

A backtest that leaves out commissions, bid–ask spreads, and slippage will nearly always paint an overly optimistic picture of performance. This is particularly relevant for short-term or high-frequency approaches, where transaction costs accumulate quickly. Surprisingly, many traders still overlook this basic reality.

Minor fees can make a major difference. A strategy that appears to earn 12% annually before costs might produce only about 5% after realistic expenses are included, potentially undermining its viability.

Ensure you understand every cost component and incorporate them into your testing and evaluation.