论文标题
FINRL-META：数据驱动的金融强化学习的市场环境和基准
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
论文作者
论文摘要
金融是深入加强学习的特别困难游乐场。但是，由于三个主要因素，即财务数据的信噪比低，历史数据的幸存者偏见以及模型过于适当的阶段。在本文中，我们提出了一个公开访问的Finrl-Meta库，该图书馆已由AI4FINANCE社区积极维护。首先，遵循DataOps范式，我们将通过自动管道提供数百个市场环境，该管道从现实世界中收集动态数据集并将其处理为健身房式的市场环境。其次，我们将流行论文复制为用户设计新交易策略的垫脚石。我们还在云平台上部署了库，以便用户可以可视化自己的结果并通过社区竞争评估相对绩效。第三，Finrl-Meta提供了成千上万的Jupyter/Python演示，这些演示组织为课程和文档网站，为快速发展的社区提供服务。 Finrl-Meta可在以下网址找到：https：//github.com/ai4finance-foundation/finrl-meta
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage. In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we will provide hundreds of market environments through an automatic pipeline that collects dynamic datasets from real-world markets and processes them into gym-style market environments. Second, we reproduce popular 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, FinRL-Meta provides tens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. FinRL-Meta is available at: https://github.com/AI4Finance-Foundation/FinRL-Meta
