Factor Engine: A Python Library for Systematic Financial Factor Computation and Analysis

Paper: arXiv 2602.14138

Authors: Ata Keskin

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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 5.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a software engineering framework and Python library (Factor Engine) designed for efficient financial factor engineering rather than novel theoretical mathematics. While it lacks heavy mathematical derivations, it demonstrates solid empirical utility by replicating Stata-based mispricing factors and integrating them into machine learning workflows.

Research Flowchart

  flowchart TD
    A[Research Goal: Develop & Validate High-Performance Python 'Factor Engine'] --> B[Inputs: Financial Market & Accounting Data]
    B --> C[Methodology: Modular Python API with Decorators]
    C --> D[Computation: Systematic Factor Generation]
    D --> E[Validation: Compare vs. Stata Reference & Backtesting]
    D --> F[Application: Integrate Factors into ML Trading Workflows]
    E & F --> G[Outcomes: Highly Similar Results to Stata & Enhanced Quant Research]