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Title: Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution
Authors: Gabrielsson, Patrick
Johansson, Ulf
König, Rikard
Department: University of Borås. School of Business and IT
Issue Date: 2014
Citation: IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK
Pages: 473-480
Publisher: IEEE
Media type: text
Publication type: conference paper, peer reviewed
Keywords: Grammatical evolution
High-frequency trading
Subject Category: Subject categories::Engineering and Technology::Computer and Information Science::Computer Science
Subject categories::Social Sciences::Computer and Information Science::Computer and Information Science::Computer Science
Research Group: CSL@BS
Area of Research: Machine learning
Data mining
Strategic Research Area: none
Abstract: Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution (GE). In this paper, we explore the possibility of evolving transparent entry- and exit trading strategies for the E-mini S&P 500 index futures market in a high-frequency trading environment using grammatical evolution. We compare the performance of models incorporating risk into their calculations with models that do not. Our empirical results suggest that profitable, risk-averse, transparent trading strategies for the E-mini S&P 500 can be obtained using grammatical evolution together with technical indicators.
Description: Best paper award.
DOI: 10.1109/CIFEr.2014.6924111
Sustainable development: -
Appears in Collections:Konferensbidrag / Conference papers (Informatics)

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