A Dynamic Stock Trading System Using GQTS And Moving Average In The U.S. Stock Market

Evolutionary algorithms or metaheuristic methods are common approaches applied in highly complex optimization problems, such as stock trading. In this paper we employ a novel approach for using a dynamic trading system with a technical indicator called the moving average (MA). Moreover, we utilize an improved metaheuristic algorithm, the global-best guided quantum-inspired tabu search algorithm (GQTS), which has a fast and stable feature to efficiently search for the optimal trading strategy of MA. Our approach employs the sliding window technique to avoid overfitting problem. In addition, we propose year-on-year sliding windows and 2-phase sliding window to adapt to the phenomenon of an economic cycle in the investment period and to efficiently solve stock trading problems. The experimental environment is the United States stock market. The experiment result shows that combine GQTS and MA can find better strategies that outperform other normal strategies. The performance of remuneration indicates that the trading system is enhanced with a year-on-year sliding window and 2-phase sliding window.