Time Series Feature Leakage Quant Interview Guide
Time series feature leakage quant interview guide for future data, revised data, window endpoints, examples, prevention, and validation.
Candidates building or critiquing time-ordered model inputs.
Leakage uses information too early
Feature leakage occurs when a model input contains information that would not have been available at prediction time. In time series, this often comes from future windows or revised data.
Window endpoints are common traps
A rolling statistic must end before the decision timestamp. Including the current target period, even accidentally, can make a backtest look much better than live performance.
Concrete example
A feature based on the full-day high cannot be used for a trade decision made at midday unless that high was already known. Otherwise the model is seeing the future.
Prevention is procedural
Use point-in-time data, explicit timestamp checks, lagged joins, and validation that mirrors deployment. Leakage prevention should be built into the research workflow.
Common mistakes
Candidates often check only the target label. Every feature, transformation, and join needs timing discipline, especially when data is revised or published with delay.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.