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

Chanakya Serjam, Akito Sakurai

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルIntelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
編集者Satoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
出版者Springer Verlag
ページ498-507
ページ数10
ISBN(印刷物)9783319544717
DOI
出版物ステータスPublished - 2017
イベント9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan
継続期間: 2017 4 32017 4 5

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10191 LNAI
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
Japan
Kanazawa
期間17/4/317/4/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Serjam, C., & Sakurai, A. (2017). Analyzing performance of high frequency currency rates prediction model using linear kernel SVR on historical data. : S. Tojo, L. M. Nguyen, N. T. Nguyen, & B. Trawinski (版), 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); 巻数 10191 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-54472-4_47