In this study we rethought realistic settings of an artificial market from a viewpoint of long memory process. With the aim of analyzing the mechanism of the stock price change, we construct an artificial stock market composed of multiple agents whose investment strategies are represented by tree-shaped programs. The market is optimized by using a Genetic Programming so that the change of its stock price resembles that of "real" stock market statistically. In order to perform an efficient optimization and to analyze agents' behavior easily, we use ADG; Automatically Defined Groups previously proposed. We show experimentally that complex changes like real market appears in the proposed artificial market. It is purpose to establish more realistic settings.