  We present our Agent-Based Market Microstructure Simulation (ABMMS), an
Agent-Based Financial Market (ABFM) that captures much of the complexity
present in the US National Market System for equities (NMS). Agent-Based models
are a natural choice for understanding financial markets. Financial markets
feature a constrained action space that should simplify model creation, produce
a wealth of data that should aid model validation, and a successful ABFM could
strongly impact system design and policy development processes. Despite these
advantages, ABFMs have largely remained an academic novelty. We hypothesize
that two factors limit the usefulness of ABFMs. First, many ABFMs fail to
capture relevant microstructure mechanisms, leading to differences in the
mechanics of trading. Second, the simple agents that commonly populate ABFMs do
not display the breadth of behaviors observed in human traders or the trading
systems that they create. We investigate these issues through the development
of ABMMS, which features a fragmented market structure, communication
infrastructure with propagation delays, realistic auction mechanisms, and more.
As a baseline, we populate ABMMS with simple trading agents and investigate
properties of the generated data. We then compare the baseline with
experimental conditions that explore the impacts of market topology or
meta-reinforcement learning agents. The combination of detailed market
mechanisms and adaptive agents leads to models whose generated data more
accurately reproduce stylized facts observed in actual markets. These
improvements increase the utility of ABFMs as tools to inform design and policy
decisions.
