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FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

Author

Listed:
  • Xiao-Yang Liu
  • Hongyang Yang
  • Jiechao Gao
  • Christina Dan Wang

Abstract

Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely \textit{to decide where to trade, at what price} and \textit{what quantity}, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, \textit{full-stack framework, customization, reproducibility} and \textit{hands-on tutoring}. Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor's risk-aversion. Moreover, serving as practitioners' stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc.

Suggested Citation

  • Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
    • Handle: RePEc:arx:papers:2111.09395
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    References listed on IDEAS

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    1. Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.
    2. Bekiros, Stelios D., 2010. "Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets," European Journal of Operational Research, Elsevier, vol. 202(1), pages 285-293, April.
    3. Mao Guan & Xiao-Yang Liu, 2021. "Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach," Papers 2111.03995, arXiv.org, revised Dec 2021.
    4. Wenhang Bao & Xiao-yang Liu, 2019. "Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis," Papers 1906.11046, arXiv.org.
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    8. Fischer, Thomas G., 2018. "Reinforcement learning in financial markets - a survey," FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
    10. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Xiao-Yang Liu & Jingyang Rui & Jiechao Gao & Liuqing Yang & Hongyang Yang & Zhaoran Wang & Christina Dan Wang & Jian Guo, 2021. "FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance," Papers 2112.06753, arXiv.org, revised Mar 2022.
    2. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
    3. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
    4. Mao Guan & Xiao-Yang Liu, 2021. "Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach," Papers 2111.03995, arXiv.org, revised Dec 2021.

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