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MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management

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  • Zhenhan Huang
  • Fumihide Tanaka

Abstract

Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the ever-changing markets. These approaches are rigid and unscalable to accommodate the varying number of assets of portfolios and increasing need for heterogeneous data. Also, RL agents in the existing systems are ad-hoc trained and hardly reusable for different portfolios. To confront the above problems, a modular design is desired for the systems to be compatible with reusable asset-dedicated agents. In this paper, we propose a multi-agent RL-based system for PM (MSPM). MSPM involves two types of asynchronously-updated modules: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). An EAM is an information-generating module with a DQN agent, and it receives heterogeneous data and generates signal-comprised information for a particular asset. An SAM is a decision-making module with a PPO agent for portfolio optimization, and it connects to EAMs to reallocate the assets in a portfolio. Trained EAMs can be connected to any SAM at will. With its modularized architecture, the multi-step condensation of volatile market information, and the reusable design of EAM, MSPM simultaneously addresses the two challenges in RL-based PM: scalability and reusability. Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation by its outperformance over five baselines in terms of accumulated rate of return (ARR), daily rate of return, and Sortino ratio. MSPM improves ARR by at least 186.5% compared to CRP, a widely-used PM strategy. To validate the indispensability of EAM, we back-test and compare MSPMs on four portfolios. EAM-enabled MSPMs improve ARR by at least 1341.8% compared to EAM-disabled MSPMs.

Suggested Citation

  • Zhenhan Huang & Fumihide Tanaka, 2021. "MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management," Papers 2102.03502, arXiv.org, revised Feb 2022.
    • Handle: RePEc:arx:papers:2102.03502
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    References listed on IDEAS

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    1. Mih�ly Ormos & Andr�s Urb�n, 2013. "Performance analysis of log-optimal portfolio strategies with transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 13(10), pages 1587-1597, October.
    2. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    3. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    4. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
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    Cited by:

    1. Zhenhan Huang & Fumihide Tanaka, 2023. "A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management," Papers 2307.01599, arXiv.org.
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    3. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.

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