Factor Engine: A Python Library for Systematic Financial Factor Computation and Analysis
Ata Keskin
TL;DR
Factor Engine tackles the need for a high-performance, transparent tool to compute financial factors from panel data in Python. It introduces a modular, decorator-based API that defers lag creation and uses as-of joins to ensure time-series correctness, enabling scalable factor computation with Polars integration. The paper demonstrates the framework via MispricingFactors, achieving high agreement with a Stata reference and enabling single-factor and ensemble ML backtests for trading strategy development. Its design promises practical impact by bridging academic factor research with scalable, reproducible workflows suitable for quantitative finance research and application.
Abstract
Factor Engine is a high-performance, open-source Python library designed for the systematic computation and analysis of financial factors. Built around a modular and extensible API that leverages Python decorators, Factor Engine enables users to define custom factors with ease and integrates seamlessly with the modern data science ecosystem. To assess its practical effectiveness, we compare the mispricing factors computed by Factor Engine to those generated using a reference Stata implementation, finding that both approaches yield highly similar results and comparable performance in backtesting analyses. Furthermore, we experimentally apply these factors within machine learning workflows for trading strategy development, illustrating their practical utility and potential for quantitative finance research.