Your Backtest Can Pass for the Wrong Reason: 5 Python Checks Before Deployment

A backtest may appear flawless on the surface, but underlying issues can cause it to fail in real-world trading. The key is to scrutinize the results closely before deploying the strategy. Five crucial checks can expose potential problems.

First, assess whether later data could alter earlier signals. Run the strategy on the full dataset, then exclude the final 20% and rerun. Compare the signals from both runs. Identical signals confirm the strategy's invariance. Deviations may indicate issues like global normalization, centered windows, backward fills, or feature selection that depend on the entire dataset.

Second, check for suspicious alignment between the signal and future returns. For each row, determine the return that was available when the signal was generated. Analyze the correlation between the signal and return, the correlation at different future offsets, cumulative forward returns, and the number of observations supporting each result. A high correlation could signal information leakage, but not always. Extreme correlations warrant investigation, not automatic acceptance or dismissal.

Third, ensure performance holds up under chronological holdouts. Use sequential train/test splits with gaps between them to mimic real-world conditions. Evaluate the strategy's performance on earlier evidence, then purge a time gap before assessing later results. Repeat this over expanding windows. If performance is heavily dependent on a single period, the results may be overly optimistic.

Fourth, factor in trading costs when calculating turnover. Costs affect gross returns and should be subtracted from net returns. Declare the cost per unit of position change and adjust the net return accordingly. Turnover can be estimated as the absolute difference between current and previous positions. The precise cost model depends on the trading venue and strategy.

Lastly, identify if a small subset of observations drives most of the backtest results. Sort the P&L contributions for each observation and calculate the percentage contributed by the largest few. If a few trades account for nearly all the profit, the strategy's robustness may be overstated. While this doesn't automatically invalidate the strategy, it warrants further scrutiny of the concentration effect.