Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing

Market Timing is the capacity of deciding when to buy or sell a given asset on a financial market. Market Timing strategies are usually composed of components that process market context and return a recommendation whether to buy or sell. The main issues with composing market timing strategies are twofold: (i) selecting the signal generating components; and (ii) tuning their parameters. In previous work, researchers usually attempt to either tune the parameters of a set of components or select amongst a number of components with predetermined parameter values. In this paper, we approach market timing as one integrated problem and propose to solve it with two variants of Particle Swarm Optimization (PSO). We compare the performance of PSO against a Genetic Algorithm (GA), the most widely used metaheuristic in the domain of market timing. We also propose the use of trend representative testing to circumvent the issue of overfitting commonly associated with step-forward testing. Results show PSO to be competitive with GA, and that trend representative testing is an effective method of exposing strategies to various market conditions during training and testing.