Feature Engineering for Trading

Difficulty intermediate

Overview

Feature engineering transforms raw market data into predictive signals for machine learning models. Quality features matter more than model choice.

Avoiding Overfitting

Technique	Purpose
Purged cross-validation	Prevents information leakage
Feature importance	Remove irrelevant features
Regularization	Penalize complexity
Out-of-sample testing	Validate on unseen data
Walk-forward analysis	Test in chronological order

Best Practices

1. Economic Rationale — Every feature should have a reason
2. Avoid Look-Ahead — Never use future data
3. Stationarity — Transform non-stationary features
4. Normalize — Scale features for ML algorithms
5. Handle Missing Data — Impute or drop carefully
6. Test Stability — Features should be robust over time
7. Document Everything — Track feature performance

Next Steps

• Model Selection — Choosing ML algorithms
• Backtesting Frameworks — Testing ML strategies
• Factor Investing — Traditional factors vs ML