Feature Selection Quant Interview Guide
Feature selection quant interview guide for candidate features, filters, regularization, stability, multiple testing, examples, and checks.
Candidates building signals from noisy candidate variables.
Feature selection controls noise
Quant candidates often have many possible features and weak signals. Selection should reduce noise while avoiding data snooping and unstable choices.
Start with economic and data logic
A feature should have a plausible reason to help, be available at decision time, and have stable measurement. Purely in-sample filters are risky.
Concrete example
A volume-based feature may look predictive during one regime. Test whether its effect survives different periods, assets, and reasonable transformations.
Regularization can help
L1 penalties, tree-based screening, stability selection, and simple correlation checks can support selection, but each needs validation.
Common mistakes
Candidates often select features after looking at the test set. Keep the test set clean or the final performance estimate becomes optimistic.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.