Engineering alpha factors that predict returns
Based on a conceptual understanding of key factor categories, their rationale, and popular metrics, a key task is to identify new factors that may better capture the risks embodied by the return drivers laid out previously, or to find new ones. In either case, it will be important to compare the performance of innovative factors to that of known factors to identify incremental signal gains.
Key tools that facilitate the transformation of data into factors include the Python libraries for numerical computing, NumPy and pandas, as well as the Python wrapper around the specialized library for technical analysis, TA-Lib. Alternatives include the expression alphas developed in Zura Kakushadze's 2016 paper, 101 Formulaic Alphas, and implemented by the alphatools library. In addition, the Quantopian platform provides a large number of built-in factors to speed up the research process.
To apply one or more factors to an investment...