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

arXiv: https://arxiv.org/abs/2303.11959 · HTML: https://mlsanity.com/paper/2303.11959/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series](https://mlsanity.com/paper/1909.10801/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization](https://mlsanity.com/paper/2510.07943/) (2025)
- [Near Optimal Convergence to Coarse Correlated Equilibrium in General-Sum Markov Games](https://mlsanity.com/paper/2511.02157/) (2025)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation](https://mlsanity.com/paper/2505.03586/) (2025)
- [Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading](https://mlsanity.com/paper/2512.02227/) (2025)
- [Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading](https://mlsanity.com/paper/2607.02864/) (2026)

## Abstract (arXiv)

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.