Backtesting is where strategy dreams meet reality. A brilliant idea can fail backtesting, or worse, pass backtesting falsely. The most dangerous backtest is one that tells you a bad strategy works. Avoiding these pitfalls requires discipline and skepticism.
The Overfitting Trap
Overfitting occurs when a strategy fits the historical data too perfectly. More parameters mean more flexibility to fit noise. A strategy with 50 optimization parameters can fit noise in the data perfectly, then fail immediately on new data.
Combat overfitting through out-of-sample testing. Never optimize on data you'll use to evaluate performance. Split data chronologically: optimization period, validation period, out-of-sample period. The out-of-sample period should never inform parameter selection.
Look-Ahead Bias and Data Integrity
Look-ahead bias is the most insidious error. Using information that wouldn't be available at decision time inflates backtest returns. For example, using closing price from day N to make decisions at the open of day N.
Strict data discipline prevents this error. Use only information available at the decision point. If you decide at market open, use only previous day's close and earlier data. Be paranoid about data timing.
Survivorship Bias
Backtesting on current index constituents introduces survivorship bias. Companies that failed weren't in the index yesterday, so you never trade them. But real investors at that time could have.
Use historical index constituents, not current ones. Include delisted companies. This is harder but essential for realistic backtest results. The universe available to you should match the universe available then.
Transaction Costs and Slippage
The gap between backtest and live performance often comes from underestimating costs. Bid-ask spreads, commissions, and market impact are real. Optimistic backtest assumptions don't survive real trading.
Use conservative cost assumptions. Account for market impact based on position size and liquidity. If unsure, overestimate costs. A strategy that works with realistic costs is more valuable than one that doesn't.
Statistical Significance and Multiple Testing
If you test 1,000 strategies, one will pass backtesting by pure chance. Multiple testing inflation is real. A 95% confidence strategy tested 20 times has a 64% chance of at least one false positive.
Use proper statistical frameworks. Apply Bonferroni corrections or other multiple testing adjustments. Better yet: pre-register hypotheses before testing. This forces honest statistical thinking.
Educational content only. Not investment advice.