Dynamic datasets and market environments for financial reinforcement learning

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Pub Date : 2024-02-26 DOI:10.1007/s10994-023-06511-w
Xiao-Yang Liu, Ziyi Xia, Hongyang Yang, Jiechao Gao, Daochen Zha, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
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Abstract

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present an updated version of FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The codes are available at https://github.com/AI4Finance-Foundation/FinRL-Meta

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金融强化学习的动态数据集和市场环境
由于动态数据集的独特性,金融市场对于深度强化学习来说是一个特别具有挑战性的领域。由于金融数据信噪比低、历史数据的幸存者偏差和模型过拟合等主要因素,为训练金融强化学习(FinRL)代理构建高质量的市场环境非常困难。在本文中,我们介绍了 FinRL-Meta 的更新版本,这是一个以数据为中心、可公开访问的库,可将来自真实市场的动态数据集处理成健身房风格的市场环境,并一直由 AI4Finance 社区积极维护。首先,我们遵循 DataOps 范式,通过自动数据整理管道提供了数百种市场环境。其次,我们提供自制示例并转载热门研究论文,作为用户设计新交易策略的垫脚石。我们还将库部署在云平台上,以便用户可视化自己的结果,并通过社区竞赛评估相对性能。第三,我们提供了数十个 Jupyter/Python 演示,并将其整理成课程和文档网站,为快速发展的社区提供服务。这些代码可在 https://github.com/AI4Finance-Foundation/FinRL-Meta
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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