Abstract
This paper deals with the development of a method for generating input and output signals in the Spanish stock market. It is based on the application of set of simple trading rules optimized by genetic programming. To this aim we use the HeuristicLab software. To evaluate the performance of our method we make a comparison with other traditional methods such as Buy & Hold and Simple Moving Averages Crossover. We study three different market scenarios: bull market, bear market and sideways market. Empirical test series show that market global behavior has a great influence on the results of each method and that strategies based on genetic programming perform best in the sideways market.
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Luengo, S., Winkler, S., Barrero, D.F., Castaño, B. (2015). Optimization of Trading Rules for the Spanish Stock Market by Genetic Programming. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_60
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DOI: https://doi.org/10.1007/978-3-319-19066-2_60
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