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Shai-am: A Machine Learning Platform for Investment Strategies

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  • Jonghun Kwak
  • Jungyu Ahn
  • Jinho Lee
  • Sungwoo Park

Abstract

The finance industry has adopted machine learning (ML) as a form of quantitative research to support better investment decisions, yet there are several challenges often overlooked in practice. (1) ML code tends to be unstructured and ad hoc, which hinders cooperation with others. (2) Resource requirements and dependencies vary depending on which algorithm is used, so a flexible and scalable system is needed. (3) It is difficult for domain experts in traditional finance to apply their experience and knowledge in ML-based strategies unless they acquire expertise in recent technologies. This paper presents Shai-am, an ML platform integrated with our own Python framework. The platform leverages existing modern open-source technologies, managing containerized pipelines for ML-based strategies with unified interfaces to solve the aforementioned issues. Each strategy implements the interface defined in the core framework. The framework is designed to enhance reusability and readability, facilitating collaborative work in quantitative research. Shai-am aims to be a pure AI asset manager for solving various tasks in financial markets.

Suggested Citation

  • Jonghun Kwak & Jungyu Ahn & Jinho Lee & Sungwoo Park, 2022. "Shai-am: A Machine Learning Platform for Investment Strategies," Papers 2207.00436, arXiv.org.
    • Handle: RePEc:arx:papers:2207.00436
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    References listed on IDEAS

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    1. Xiao Yang & Weiqing Liu & Dong Zhou & Jiang Bian & Tie-Yan Liu, 2020. "Qlib: An AI-oriented Quantitative Investment Platform," Papers 2009.11189, arXiv.org.
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