Lookahead Bias Quant Interview Guide
Lookahead bias quant interview guide for future information, feature timing, time-ordered data, backtests, detection, and prevention.
Candidates discussing time-ordered data and backtest validity.
Lookahead bias uses future information
Lookahead bias appears when a model or backtest uses information that would not have been available at the decision time.
Timing is the core issue
A feature can be valid in general but invalid if its timestamp, release delay, or revision history makes it unavailable when used.
Concrete example
Using end-of-day data to decide a trade supposedly made earlier that day can create lookahead if the data was not known yet.
Prevent with time discipline
Use point-in-time data, clear cutoffs, and validation that respects chronology. State the timing assumption explicitly before trusting the result.
Common mistakes
Candidates often check train/test split but ignore feature availability. Time order matters for every input, not only labels.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.