Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used conjunction with computational, mathematical, and modeling methodologies simulate technical trading. Fuzzy logic may using linear, geometric, dynamic, integer programming. These techniques, when combined logic, help decision-maker arrive at a better solution while still facing some degree ambiguity uncertainty. The moving average method useful metric that give trade recommendations aid investors further. While signals inform purchase sell, simple provides no such information. In this research, we suggest approach intensity signals, measured terms volume, determined by rule. propose rules, more resistant decision-making mistakes, mitigate uncertainty inherent conventional indicators method.