Abstract: During last 50 years, the markets have been object of study. Technical and fundamental indicators have been used to try to predict the behavior of the market and then execute buying or selling orders. Neural networks are currently being used with good results although they can be useless after a period of time. This paper proposes an algorithm that combines bioinspired techniques to maximize the hits in the prediction rates. The proposal shown in this paper relies in an ANN to achieve these goals. The differential factors of this approach are the election of the ANN structure with grammatical swarm and the training process through the use of HydroPSO. Also a grammatical swarm algorithm is used to generate trading rules, this method shows better results than the first approach. This combination of techniques provides an automatic way to define the most suitable bioinspired model for the instrument in our analysis.

Keywords: Trading strategies, Technical indicators, Grammatical Swarm, Neural networks.

ACM Classification Keywords: F.1.1 Theory of Computation - Models of Computation, I.2.6 Artificial Intelligence.

Link:

INTELLIGENT TRADING SYSTEMS

Luis F. de Mingo, Nuria Gómez Blas, Alberto Arteta

http://www.foibg.com/ijita/vol22/ijita22-01-p02.pdf