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Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning

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Listed:
  • Hengxi Zhang
  • Zhendong Shi
  • Yuanquan Hu
  • Wenbo Ding
  • Ercan E. Kuruoglu
  • Xiao-Ping Zhang

Abstract

Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this context, reinforcement learning (RL), which operates on a reward-centric mechanism for optimal control, has surfaced as a potentially effective solution to the intricate financial decision-making conundrums presented. This paper delves into the fusion of two established financial trading strategies, namely the constant proportion portfolio insurance (CPPI) and the time-invariant portfolio protection (TIPP), with the multi-agent deep deterministic policy gradient (MADDPG) framework. As a result, we introduce two novel multi-agent RL (MARL) methods, CPPI-MADDPG and TIPP-MADDPG, tailored for probing strategic trading within quantitative markets. To validate these innovations, we implemented them on a diverse selection of 100 real-market shares. Our empirical findings reveal that the CPPI-MADDPG and TIPP-MADDPG strategies consistently outpace their traditional counterparts, affirming their efficacy in the realm of quantitative trading.

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  • Hengxi Zhang & Zhendong Shi & Yuanquan Hu & Wenbo Ding & Ercan E. Kuruoglu & Xiao-Ping Zhang, 2023. "Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning," Papers 2303.11959, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2303.11959
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    References listed on IDEAS

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    1. Huanming Zhang & Zhengyong Jiang & Jionglong Su, 2021. "A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management," Papers 2103.11455, arXiv.org.
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