论文标题
Shai-AM：用于投资策略的机器学习平台
Shai-am: A Machine Learning Platform for Investment Strategies
论文作者
论文摘要
金融行业已采用机器学习（ML）作为定量研究的一种形式来支持更好的投资决策，但是在实践中经常忽略了一些挑战。 （1）ML代码倾向于非结构化和临时性，这阻碍了与他人的合作。 （2）资源需求和依赖项因使用哪种算法而有所不同，因此需要灵活且可扩展的系统。 （3）传统金融领域专家很难在基于ML的策略中运用其经验和知识，除非他们获得了最近的技术专业知识。本文介绍了Shai-Am，这是一个与我们自己的Python框架集成的ML平台。该平台利用现有的现代开源技术，管理基于ML的策略的容器化管道，并具有统一的接口来解决上述问题。每种策略都实现了核心框架中定义的接口。该框架旨在增强可重复性和可读性，从而促进定量研究中的协作工作。 Shai-Am的目标是成为纯粹的AI资产经理，以解决金融市场中的各种任务。
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.
