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International Conference on Computational Collective Intelligence

ICCCI 2019: Computational Collective Intelligence pp 122–132Cite as

Classification of the Symbolic Financial Data on the Forex Market

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11684))

Abstract

A symbolic representation for any data can be used as a tool for reducing the irrelevant noise. Any methods of reducing noise are extremely useful in the field of financial data, where a good trading signal is crucial to achieving the profits for the long term trading approach. In this article, we use the concept of symbolic representation to transform the market situation described as a time series of successive price changes into the simplified representation of this situation. Every element of such symbolic representation is further treated as an attribute in the decision table. On the basis of historical data transformed in the same manner, we try to identify the market situations leading to the increase in the instrument value on the forex market.

We use a set of well-known classifiers built and trained with the use of historical data. Finally, we use these classifiers to estimate the possible efficiency of the present market situation. There is no need to exactly identify the quality of the signal. We are interested in price direction rather than the exact price of the instrument, thus we use the concept of fuzzy accuracy. Fuzzy accuracy allows us to properly classify objects belonging not only for the actual decision class but also for the neighboring decision classes.

The presented approach is verified with the use of the large set of data collected from the forex market.

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Author information

Authors and Affiliations

  1. Faculty of Informatics and Communication, Department of Knowledge Engineering, University of Economics, 1 Maja 50, 40-287, Katowice, Poland

    Jan Kozak, Przemysław Juszczuk & Krzysztof Kania

Authors
  1. Jan Kozak
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  2. Przemysław Juszczuk
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  3. Krzysztof Kania
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Corresponding author

Correspondence to Przemysław Juszczuk .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l'Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l'Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l'Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Kozak, J., Juszczuk, P., Kania, K. (2019). Classification of the Symbolic Financial Data on the Forex Market. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-28374-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28373-5

  • Online ISBN: 978-3-030-28374-2

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