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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.14138 |
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| _version_ | 1866912906265755648 |
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| author | Keskin, Ata |
| author_facet | Keskin, Ata |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14138 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Factor Engine: A Python Library for Systematic Financial Factor Computation and Analysis Keskin, Ata Computational Finance 91G15, 91G60 D.2.13; J.4 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. |
| title | Factor Engine: A Python Library for Systematic Financial Factor Computation and Analysis |
| topic | Computational Finance 91G15, 91G60 D.2.13; J.4 |
| url | https://arxiv.org/abs/2602.14138 |