Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
This work addresses the problem of optimizing trading strategies for quantitative finance practitioners, but it is incremental as it builds on existing methods.
The paper tackled the challenge of profit-driven stock trading in quantitative markets by combining established financial strategies (CPPI and TIPP) with multi-agent reinforcement learning, resulting in novel methods that consistently outperformed traditional counterparts on 100 real-market shares.
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.