Optimizing Trading Strategies in Quantitative Markets Using Multi-Agent Reinforcement Learning
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.
BibTeX
@inproceedings{icassp2024_optimizingtradin,
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},
booktitle = {ICASSP 2024},
year = {2024}
}