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Machine Intelligence and Big Data in Industry pp 161–168Cite as

Application of Machine Learning Algorithms for Bitcoin Automated Trading

Part of the Studies in Big Data book series (SBD,volume 19)

Abstract

The aim of this paper is to compare and analyze different approaches to the problem of automated trading on the Bitcoin market. We compare simple technical analysis method with more complex machine learning models. Experimental results showed that the performance of tested algorithms is promising and that Bitcoin market is still in its youth, and further market opportunities can be found. To the best of our knowledge, this is the first work that tries to investigate applying machine learning methods for the purpose of creating trading strategies on the Bitcoin market.

Keywords

  • Bitcoin Market
  • Complex Machine Learning Models
  • Exponential Moving Average (EMA)
  • Maximum Drawdown
  • Bitcoin Exchange

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Kamil Żbikowski

Authors
  1. Kamil Żbikowski
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Correspondence to Kamil Żbikowski .

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Editors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Dominik Ryżko

  2. Institute of Computer Science, Warsaw University of Technology, Abo, Poland

    Piotr Gawrysiak

  3. Institute of Computer Science, Warsaw University of Technology, Warszawa, Poland

    Marzena Kryszkiewicz

  4. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybiński

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Żbikowski, K. (2016). Application of Machine Learning Algorithms for Bitcoin Automated Trading. In: Ryżko, D., Gawrysiak, P., Kryszkiewicz, M., Rybiński, H. (eds) Machine Intelligence and Big Data in Industry. Studies in Big Data, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-30315-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-30315-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30314-7

  • Online ISBN: 978-3-319-30315-4

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