LookaheadBias

Lookahead bias is a data leakage problem in which information from the future is used to train or evaluate a model, producing performance estimates that would not be achievable in real time. It occurs when the data pipeline exposes outcomes or other data that would not be available at the moment a decision is made.

In practice, lookahead bias often shows up during backtesting, forecasting, or machine learning with time series. Common sources include using future prices or outcomes to inform current decisions, computing indicators with data that spans beyond the current time, or labeling data with outcomes that are only known after the fact. It can also arise during data preparation when features are derived from the entire data set rather than using a time-ordered split.

The main consequence is inflated or unrealistic performance estimates. Models may appear to perform well in backtests or validation but fail in live deployment due to the leakage of information that would not be available in real time. This can lead to overfitting, misestimation of risk, and poor long-term outcomes when the model is used in practice.

Prevention generally involves rigorous time-aware data handling. Key practices include using time-based train/validation/test splits, employing rolling-origin or walk-forward validation, deriving features only from information available up to the decision time, and ensuring backtests simulate realistic execution constraints and costs. Clear separation of training data from future data, and explicit checks for leakage, help reduce lookahead bias. See also data leakage, backtesting bias, and time-series cross-validation.