Time Series Python Quant Interview Guide
Time series Python quant interview guide for timestamps, sorting, resampling, rolling features, leakage, examples, and validation.
Candidates manipulating timestamped data and rolling features.
Sort and timestamp before modeling
Time-series code should sort by time, define the decision timestamp, and preserve what was known then. Incorrect ordering can invalidate the whole result.
Rolling features need endpoints
A rolling mean or volatility feature should use only past data for a forward decision. Including the current or future target period creates leakage.
Concrete example
When predicting tomorrow, a 20-day rolling feature should be shifted so the target day is not included in its own input window.
Resampling changes meaning
Daily, hourly, and event-based resampling can change count, price, and volume interpretation. State aggregation rules such as last, sum, mean, or max.
Common mistakes
Candidates often compute correct-looking rolling columns with wrong timing. The timestamp and shift are as important as the formula.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.