Scalable Agent-Based Modeling for Complex Financial Market Simulations

TL;DR

This paper introduces a scalable, distributed agent-based simulation platform for complex financial markets, capable of modeling multiple assets and realistic trading mechanisms, capturing key market properties without historical data fitting.

Contribution

It presents the first scalable framework supporting multiple assets, parallel decision-making, and continuous double auction in real-time financial market simulations.

Findings

Captures known statistical properties of real markets

Supports multiple assets and heterogeneous agents

Operates efficiently with distributed computing

Abstract

In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and leverages distributed computing to scale the number and complexity of simulated agents. Heterogeneous agents make decisions in parallel, and their orders are processed through a realistic, continuous double auction matching engine. We present a baseline model implementation and show that it captures several known statistical properties of real financial markets (i.e., stylized facts). Further, we demonstrate these results without fitting models to historical financial data. Thus, this framework could be used for direct applications such as human-in-the-loop machine learning or to explore theoretically exciting questions about market microstructure's role in forming the statistical regularities of real markets. To the best of our knowledge, this study is the first to implement multiple assets, parallel agent decision-making, a continuous double auction mechanism, and intelligent agent types in a scalable real-time environment.