Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior

2024-03-28 · Zhiyuan Yao, Zheng Li, Matthew Thomas, Ionut Florescu

Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets. However, traditional rule-based market simulators often fall short in accurately capturing the dynamic behavior of market participants, particularly in response to external market impact events or changes in the behavior of other participants. In this study, we explore an agent-based simulation framework employing reinforcement learning (RL) agents. We present the implementation details of these RL agents and demonstrate that the simulated market exhibits realistic stylized facts observed in real-world markets. Furthermore, we investigate the behavior of RL agents when confronted with external market impacts, such as a flash crash. Our findings shed light on the effectiveness and adaptability of RL-based agents within the simulation, offering insights into their response to significant market events.

📄 PDF Abstract BibTeX arXiv:2403.19781

Code (0)

등록된 구현이 없습니다.

Tasks

Reinforcement Learning (RL)

Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation

2020-06-10 · Michaël Karpe, Jin Fang, Zhongyao Ma, Chen Wang

Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving…

Multi-agent Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning (RL)

Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning

2023-03-03 · Thomas Wolgast, Astrid Nieße

Energy market rules should incentivize market participants to behave in a market and grid conform way. However, they can also provide incentives for undesired and unexpected strategies if the market design is flawed. Mul…

Model-based Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

INTAGS: Interactive Agent-Guided Simulation

2023-09-04 · Song Wei, Andrea Coletta, Svitlana Vyetrenko, Tucker Balch

In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production, to avoid unexpected losses in t…

Algorithmic Trading Causal Inference Decision Making Generative Adversarial Network+2

Reinforcement Learning for Market Making in a Multi-agent Dealer Market

2019-11-14 · Sumitra Ganesh, Nelson Vadori, Mengda Xu, Hua Zheng 외

Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulati…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology

2023-02-28 · Zijian Shi, John Cartlidge

Modern financial exchanges use an electronic limit order book (LOB) to store bid and ask orders for a specific financial asset. As the most fine-grained information depicting the demand and supply of an asset, LOB data i…