  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.
