In this paper, an adaptive system is proposed which attempts to combine together the approaches of studies of historical data and researches of multi-agent artificial market by evolving a double auction market model with diversity of different traders. The purpose of this research is to construct an artiflcial market which is more close to realistic one and more practical for future researches. The model with heterogeneous agents and the environment with which agents and market interact is complicated but controllable by data mining the optimal proportion of the different agents at the input to the market that generates an output which can fit historical data curve. The simulation results suggest that the system performance is close to the expecting values in the testing with adequate training in advance.
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