Features extraction based on particle swarm optimization for high frequency financial data

A novel stock trading system is developed in this paper. The trading system combines particle swarm optimization based clustering method and basic financial rules to discover the potential features of high frequency financial data. By analyzing the robustness of PSO based trading system and comparing with the classical buy and hold trading policy, the empirical study gives evidences that the newly proposed trading system can be used as a decision support system for stock investors.