Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics

Market timing, one of the core deciding when to buy or sell an asset of interest on a financial market. Market timing strategies can be built by using a collection of components or functions that process market context and return a recommendation on the course of action to take. In this chapter, we revisit the work presented in [20] on the application of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to the issue of market timing while using a novel approach for training and testing called Trend Representative Testing. We provide more details on the process of building trend representative datasets, as well as, introduce a new PSO variant with a different approach to pruning. Results show that the new pruning procedure is capable of reducing solution length while not adversely affecting the quality of the solutions in a statistically significant manner.