Abstract
Genetic algorithms are an application of evolutionary models derived and used in solving various problems of modelling, programming and optimisation. In this paper, we developed an optimisation methodology using the free Indicore SDK 2.0 software to increase the quality and performance of the results of autotrading robots programmed in the LUA 5.1 language with various parameters optimisable in number and range, by dividing the overall process optimisation into various sub phases. This methodology was applied to an autotrading robot built on the basis of the Moving Average Convergence Divergence technique for the currency pair of EUR/USD. This application was for a time scale of 1 h, during a period of annual in-sample optimisation between 2001 and 2007. We then tested this algorithm by applying the optimal configuration yielded by this process to an out-of-sample phase spanning 2008 to August 2011. The results show that the optimal configuration yielded by the optimisation methodology could be used as a tool to increase the quality of autotrading robots, because, in addition to producing positive results in the optimisation phase, the technique improves performance and behaviour when applied in the testing phase.
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Alonso-Gonzalez, A., Almenar-Llongo, V., Peris-Ortiz, M. (2014). Optimisation Methodology Based on Genetic Algorithms to Increase the Quality and Performance in Autotrading Robots. In: Peris-Ortiz, M., Álvarez-García, J. (eds) Action-Based Quality Management. Springer, Cham. https://doi.org/10.1007/978-3-319-06453-6_12
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