What you will learn
- Point-in-time construction
- Scaling within training data
- Missingness
- Feature leakage
Point-in-time construction
Features should be constructed from data available at prediction time. Revised releases, finalized bars and full-period statistics can leak later information even when the feature name sounds historical.
Scaling within training data
Fit scaling and other learned preprocessing within the training portion. Apply the fitted transformation unchanged to the evaluation portion. A pipeline helps organize this procedure but still needs correct temporal splits and input definitions.
Missingness
Missingness may carry information, but its treatment must be deliberate. An imputer fitted across the full dataset can leak distribution information. Record whether missing values reflect closures, outages or unavailable releases.
Feature leakage
Feature leakage can be subtle: a post-trade tag, final holding period or outcome-dependent selection field may encode the answer. Audit the provenance and availability of every input, not only obvious future-price columns.
Worked example
A model predicts whether a trade wins using its final holding duration. That duration depends on the exit path and was not known at entry, so the apparently strong feature is unavailable for the stated prediction.
Try it yourself
Design a feature audit table for name, source, transformation, availability time and allowed prediction use.
Show the worked solution
For each feature, show how it is produced and when it is knowable. Reject or redesign the final-duration feature for entry-time prediction. Keep the audit with the model version so later additions receive the same review.
Apply this to your course project
Compare a simple baseline with one model using a frozen temporal evaluation plan.
Keep the calculation inputs, assumptions and decisions with your work. Practical exercises are self-reviewed; the scored knowledge checks assess the questions shown, not an independent certification of practical competence.