ML Backtesting Quant Interview Guide
ML backtesting quant interview guide for temporal splits, leakage, costs, turnover, benchmarks, robustness, and example answers.
Candidates evaluating machine learning signals with historical data.
Backtesting turns predictions into decisions
A machine learning validation should show not only prediction quality but also how predictions would drive trades, positions, or risk decisions.
Respect time and availability
Train only on past data, use features available at the decision time, and apply the same rules that would exist in deployment.
Concrete example
A daily model can be trained through month end, scored on the next month, traded with lagged execution assumptions, then rolled forward.
Costs and turnover matter
A model with attractive gross signal can be unusable after transaction costs, slippage, borrow, fees, or excessive turnover.
Common mistakes
Candidates often report validation loss only. For quant use, connect model quality to a realistic decision rule and compare against simple baselines.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.