About the Factors & Features category

If you are familiar with ML, you may agree that feature engineering tends to be the most important ingredient for improving predictions. The same holds true for trading with a twist: on the one hand, there is decades of research into factors that drive asset returns and quantitative methods to predict prices; on the other hand, new data sources and ML create new opportunities and require re-evaluating established perspectives.

This category focuses on topics, code examples and new ideas related to the chapter Financial Feature Engineering: How to research Alpha Factors, including:

  • Which categories of factors exist, why they work, and how to measure them,
  • Creating alpha factors using NumPy, pandas, and TA-Lib,
  • How to de-noise data using wavelets and the Kalman filter,
  • Using Zipline to test individual and multiple alpha factors,
  • How to use Alphalens to evaluate predictive performance.