Profit refiner of futures trading using clustering algorithm

Authors:
Yen-Tseng Hsu;Hui-Fen Hung;Jerome Yeh;Ming-Chung Liu
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Lowering psychological pressure of investors and increasing the futures trading profit are the main purposes of this paper. First of all, this study aims to transfer profit curve (PC) generated by a non-AI-based trading strategy into technical indices, and enable clustering of high-low points of PC to display high-low point signals through some AI-based methods such as Grey Clustering, SOM and K-mean. Next, it attempts to close the transaction with high-point signal, and then open a position at low-point one, thus constructing three groups of profit refiners: GCR (Grey Clustering Refiner), SOMR (SOM Refiner) and KMR (K-Mean Refiner). Finally, the features of these refiners are analyzed to evaluate the test results using some performance indices. SOMR could improve the profit to the greatest possible extent, followed by GCR and KMR; on the other hand, KMR could lower psychological pressure, followed by SOMR and GCR. As a whole, three groups of refiners can really improve the profit and alleviate psychological burden of investors.