FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Abstract
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
Keywords
Cite
@article{arxiv.2211.03107,
title = {FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning},
author = {Xiao-Yang Liu and Ziyi Xia and Jingyang Rui and Jiechao Gao and Hongyang Yang and Ming Zhu and Christina Dan Wang and Zhaoran Wang and Jian Guo},
journal= {arXiv preprint arXiv:2211.03107},
year = {2022}
}
Comments
NeurIPS 2022 Datasets and Benchmarks. 36th Conference on Neural Information Processing Systems Datasets and Benchmarks Track