Analyzing performance of high frequency currency rates prediction model using linear kernel SVR on historical data

Chanakya Serjam, Akito Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We analyze the performance of various models constructed using linear kernel SVR and trained on historical bid data for high frequency currency trading. The bid tick data is converted into equally spaced (1 min) data. Different values for the number of training samples, number of features, and the length of the timeframes are used when conducting the experiments. These models are used to conduct simulated currency trading in the following year. We record the profits, hit ratios and number of trades executed from using these models. Our results indicate it is possible to obtain a profit as well as good hit ratio from a linear model trained only on historical data under certain pre-defined conditions. On examining the parameters for the linear models generated, we observe that a large number of models have all co-efficient values as negative while giving profit and good hit ratio, suggesting a simple yet effective trading strategy.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
EditorsSatoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
PublisherSpringer Verlag
Pages498-507
Number of pages10
ISBN (Print)9783319544717
DOIs
Publication statusPublished - 2017
Event9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan
Duration: 2017 Apr 32017 Apr 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10191 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
CountryJapan
CityKanazawa
Period17/4/317/4/5

Keywords

  • Currency prediction
  • High frequency limit order book
  • Machine learning
  • Support vector regression (SVR)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Serjam, C., & Sakurai, A. (2017). Analyzing performance of high frequency currency rates prediction model using linear kernel SVR on historical data. In S. Tojo, L. M. Nguyen, N. T. Nguyen, & B. Trawinski (Eds.), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings (pp. 498-507). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10191 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-54472-4_47