Optimization of Moving Averages as Trend Indicators of a Stock Market Asset with Particle Swarm Optimization Algorithm

Predicting price movements in the stock market has been a relevant topic, which has attracted the attention of many investors for years. One of the ways to predict future price trends is by making use of technical indicators, among which we can highlight moving averages as one of the most widely used indicators. One of the most important aspects when elaborating a strategy based on moving averages is the choice of the number of periods to consider in the calculation of the average, it would be interesting to have some method that would be able to find the best values in order to optimize the strategy. In this paper we are going to propose a method to optimize a strategy based on moving averages, specifically we are going to use an algorithm known as Particle Swarm Optimization, to try to find the best combination of periods of the moving averages, with the aim of maximizing the profits obtained. The performance of the strategy based on optimized moving averages will be evaluated on some stocks of companies belonging to the NASDAQ-100.