Scalable Agent-Based Modeling for Complex Financial Market Simulations

ArXiv ID: 2312.14903 “View on arXiv”

Authors: Unknown

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.

Keywords: Agent-based modeling, Continuous double auction, Market microstructure, Multi-asset simulation, Distributed computing, General Financial Markets (Multi-asset)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper demonstrates high mathematical complexity through its use of sophisticated stochastic processes, statistical analysis of stylized facts, and complex system dynamics. It shows substantial empirical rigor by implementing a realistic continuous double auction matching engine, running large-scale simulations with distributed computing, and validating results against known market properties without historical data fitting, making it backtest-ready and implementation-heavy.

  flowchart TD
    A["Research Goal<br>Develop Scalable Agent-Based<br>Financial Market Simulator"] --> B["Key Methodology"]
    B --> C["Computational Processes"]
    C --> D["Key Findings"]
    
    B --> B1["Heterogeneous Agent Design<br>Multiple Asset Classes"]
    B --> B2["Distributed Computing<br>Parallel Decision-Making"]
    B --> B3["Continuous Double Auction<br>Matching Engine"]
    
    C --> C1["Simulate Large-Scale<br>Multi-Asset Markets"]
    C --> C2["Process Orders in Real-Time<br>without Historical Data"]
    
    D --> D1["Captures Stylized Facts<br>of Real Markets"]
    D --> D2["Scalable Framework for<br>ML & Market Microstructure Research"]