Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic algorithms to optimise the time to make stock market investment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An Introduction to Evolutionary Computation in Finance
IEEE Computational Intelligence Magazine
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Automatic trading methods, such as algorithmic trading, are important issues in recent financial markets. Various approaches have been proposed in this context. We compare some genotype coding methods of technical indicators and their parameters to acquire stock trading strategy using genetic algorithms (GAs) in this paper. In previous related works, the locus-based representation was widely employed for encoding technical indicators on chromosomes in GAs, and the direct coding was also widely adopted for encoding the parameters of the indicators. However, we show that these conventional methods are not so effective for the GA search. Therefore, we propose a new genotype coding methods, namely the allele-based indirect representation. We examine the performance of the proposed and conventional coding methods in stock trading for twenty companies in the first section of the Tokyo Stock Exchange for recent ten years. In our empirical results, the allele-based indirect representation is superior to the other ones both on the cumulative profits and the computational costs.