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dc.contributor.advisorLópez-Valverde, Francisco 
dc.contributor.advisorLuna Valero, Francisco
dc.contributor.authorBerutich Lindquist, José Manuel
dc.contributor.otherLenguajes y Ciencias de la Computaciónen_US
dc.date.accessioned2018-03-07T11:39:12Z
dc.date.available2018-03-07T11:39:12Z
dc.date.issued2017-05-25
dc.identifier.urihttps://hdl.handle.net/10630/15353
dc.description.abstractGAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions.en_US
dc.language.isoengen_US
dc.publisherUMA Editorialen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgoritmos genéticos - Tesis doctoralesen_US
dc.subject.otherAlgorithmic Tradingen_US
dc.subject.otherFinanceen_US
dc.subject.otherRobust Optimizationen_US
dc.subject.otherGenetic Algorithmen_US
dc.subject.otherGenetic Programmingen_US
dc.titleRobust optimization of algorithmic trading systemsen_US
dc.typeinfo:eu-repo/semantics/doctoralThesisen_US
dc.centroE.T.S.I. Informáticaen_US


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