Research Area:  Machine Learning

Abstract:

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.

Keywords:  
Forex
Trading Expert System
Evidential Reasoning
Machine Learning
Deep Learning

Author(s) Name:  Ludmila Dymova,Pavel Sevastjanov and Krzysztof Kaczmarek

Publisher name:  ELSEVIER

DOI:  10.1016/j.eswa.2015.12.028

Volume Information:   Volume 51, 1 June 2016, Pages 1-13

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0957417415008301