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 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 open-source codes for the data curation pipeline are available at https://github.com/AI4Finance-Foundation/FinRL-Meta

翻译：金融市场由于其动态数据集的独特特征，成为深度强化学习特别具有挑战性的试验场。构建高质量的市场环境用于训练金融强化学习代理面临诸多困难，主要原因包括金融数据的低信噪比、历史数据的生存偏差以及模型过拟合。本文提出FinRL-Meta，这是一个以数据为中心且开放可访问的库，它将来自真实市场的动态数据集处理为gym风格的市场环境，并由AI4Finance社区持续维护。首先，遵循DataOps范式，我们通过自动化数据整理流程提供数百个市场环境。其次，我们提供自研示例并复现热门研究论文，作为用户设计新交易策略的基石。此外，我们将该库部署在云平台上，使用户能够通过社区竞赛可视化自身结果并评估相对性能。最后，我们提供数十个按课程组织的Jupyter/Python演示和文档网站，以服务于快速发展的社区。数据整理流程的开源代码可在https://github.com/AI4Finance-Foundation/FinRL-Meta获取。