Can PSO Improve TA-Based Trading Systems?

In this paper, we propose and apply a methodology to improve the performances of trading systems based on Technical Indicators. As far as the methodology is concerned, we take into account a simple trading system and optimize its parameters - namely, the various time window lengths - by the metaheuristic known as Particle Swarm Optimization. The use of a metaheuristic is justied by the fact that the involved optimization problem is complex (it is nonlinear, nondifferentiable and integer). Therefore, the use of exact solution methods could be extremely time-consuming for practical purposes. As regards the applications, we consider the daily closing prices of eight important stocks of the Italian stock market from January 2, 2001, to April 28, 2017. Generally, the performances achieved by trading systems with optimized parameters values are better than those with standard settings. This indicates that parameter optimization can play an important role.

In this paper, we propose and apply a methodology to improve the performances of trading systems based on Technical Indicators. As far as the methodology is concerned, we take into account a simple trading system and optimize its parameters—namely, the various time window lengths—by the metaheuristic known as Particle Swarm Optimization. The use of a metaheuristic is justified by the fact that the involved optimization problem is complex (it is nonlinear, nondifferentiable and integer). Therefore, the use of exact solution methods could be extremely time-consuming for practical purposes. As regards the applications, we consider the daily closing prices of eight important stocks of the Italian stock market from January 2, 2001, to April 28, 2017. Generally, the performances achieved by trading systems with optimized parameters values are better than those with standard settings. This indicates that parameter optimization can play an important role.