  An artificial stock market is established based on multi-agent . Each agent
has a limit memory of the history of stock price, and will choose an action
according to his memory and trading strategy. The trading strategy of each
agent evolves ceaselessly as a result of self-teaching mechanism. Simulation
results exhibit that large events are frequent in the fluctuation of the stock
price generated by the present model when compared with a normal process, and
the price returns distribution is L\'{e}vy distribution in the central part
followed by an approximately exponential truncation. In addition, by defining a
variable to gauge the "evolvement complexity" of this system, we have found a
phase cross-over from simple-phase to complex-phase along with the increase of
the number of individuals, which may be a ubiquitous phenomenon in multifarious
real-life systems.
