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The finance industry has adopted machine learning (ML) as a form ofquantitative research to support better investment decisions, yet there areseveral challenges often overlooked in practice. (1) ML code tends to beunstructured and ad hoc, which hinders cooperation with others. (2) Resourcerequirements and dependencies vary depending on which algorithm is used, so aflexible and scalable system is needed. (3) It is difficult for domain expertsin traditional finance to apply their experience and knowledge in ML-basedstrategies unless they acquire expertise in recent technologies. This paperpresents Shai-am, an ML platform integrated with our own Python framework. Theplatform leverages existing modern open-source technologies, managingcontainerized pipelines for ML-based strategies with unified interfaces tosolve the aforementioned issues. Each strategy implements the interface definedin the core framework. The framework is designed to enhance reusability andreadability, facilitating collaborative work in quantitative research. Shai-amaims to be a pure AI asset manager for solving various tasks in financialmarkets.

Shai-am: A Machine Learning Platform for Investment Strategies