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

Paper: arXiv 2303.11959

Authors: 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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative integration of classical portfolio protection strategies (CPPI and TIPP) with multi-agent deep deterministic policy gradient (MADDPG) algorithms. The empirical testing on 100 real-market stocks shows promising results, though reproducibility is somewhat constrained by the lack of explicit code links.

Research Flowchart

  flowchart TD
    A[Research Goal: Optimize Quantitative Trading via Multi-Agent RL] --> B[Input Data: 100 Real-Market Shares]
    B --> C[Methodology: Integrate Financial Strategies with MADDPG]
    C --> D[Develop Models: CPPI-MADDPG & TIPP-MADDPG]
    D --> E[Computational Execution & Empirical Backtesting]
    E --> F[Findings: Proposed Models Consistently Outperform Traditional Strategies]