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

Hengxi Zhang, Zhendong Shi, Yuanquan Hu, Wenbo Ding, Ercan E. Kuruoglu +1 more
3/15/2023
q-fin.TRcs.LGcs.MA

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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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Cite this paper

@article{zhang2023optimizing,
  title  = {Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning},
  author = {Hengxi Zhang and Zhendong Shi and Yuanquan Hu and Wenbo Ding and Ercan E. Kuruoglu and Xiao-Ping Zhang},
  year   = {2023},
  eprint = {2303.11959},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.11959v2}
}

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