  Most economic theories typically assume that financial market participants
are fully rational individuals and use mathematical models to simulate human
behavior in financial markets. However, human behavior is often not entirely
rational and is challenging to predict accurately with mathematical models. In
this paper, we propose \textbf{A}gent-based \textbf{S}imulated
\textbf{F}inancial \textbf{M}arket (ASFM), which first constructs a simulated
stock market with a real order matching system. Then, we propose a large
language model based agent as the stock trader, which contains the profile,
observation, and tool-learning based action module. The trading agent can
comprehensively understand current market dynamics and financial policy
information, and make decisions that align with their trading strategy. In the
experiments, we first verify that the reactions of our ASFM are consistent with
the real stock market in two controllable scenarios. In addition, we also
conduct experiments in two popular economics research directions, and we find
that conclusions drawn in our \model align with the preliminary findings in
economics research. Based on these observations, we believe our proposed ASFM
provides a new paradigm for economic research.
