Reinforcement Learning Revolutionizes Market Insights with Adaptive Simulations

Table of Links

Part 1: Abstract & Introduction

Part 2: Important Concepts

Part 3: System Description

Part 4: Agents & Simulation Details

Part 5: Experiment Design

Part 6: Continual Learning

Part 7: Experiment Results

Part 8: Market and Agent Responsiveness to External Events

Part 9: Conclusion & References

Part 10: Additional Simulation Results

Part 11: Simulation Configuration

Abstract

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.

Introduction

Modern financial markets serve as vehicles for setting up-to-date prices, thus shaping economic landscapes worldwide. Thus understanding how markets react to external and internal events is crucial for investors and regulators. Designing a proper financial market simulator can answer “what if” questions and help market participants make informed decisions in a fast-paced and volatile market. An extensive body of literature exists dedicated to methods of simulating market behavior [1, 2, 3, 4, 5, 6, 7]. Among these papers, agent-based market simulators stand out due to their ability to emulate dynamics of the real-world markets. Conventional agent-based systems use rule-based agents, e.g., [8, 9]. These systems have issues when calibrating to real markets and fail to capture realistic market dynamics. This limitation arises from the rigid, hard-coded nature of rule-based agents, which prevents them from adapting to changing market conditions. In contrast, agents which are capable of learning have the ability to optimize their goals by learning from the environment and the behavior of other agents. This adaptability closely mirrors the behavior of real-world investors, enhancing the realism of the simulation.

Recently, we have seen several successful applications of machine learning techniques in financial problems such as portfolio management [10, 11], credit rating [12, 13], and order execution [14, 15]. Reinforcement learning (RL) is an important class of machine learning methods, where agents are capable of learning optimal strategies without knowing the underlying environment dynamics [16]. Recently, several papers have been published that use RL agents to construct a simulation environment for financial markets. Lussange et al. [7] model a market using hundreds of RL agents, each solving a simplified investment problem. However, the participants of real-world stock markets have different goals and use complex strategies. Ideally, we should let these agents learn to trade in a highly realistic environment where each agent optimizes its own utility. Our proposed simulation framework allows agents to learn complex strategies. In another direction, [6, 17] formulate RL-based market makers and liquidity takers and use them to simulate a dealer market. Our work extends this idea to simulate a complete continuous double auction stock market using complex RL-based agents.

In this study, we propose a simulation framework with only a small group of representative RL agents. We compare this system with one composed of rule-based zero-intelligence agents as well as with real market data. The results obtained using the RL agents’ system are comparable with real data. Further, we show that the system is capable of adapting to changing market conditions.