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Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

Author

Listed:
  • Takuya Shintate

    () (Graduate School of Arts and Sciences, International Christian University, Osawa 3-10-2, Mitaka, Tokyo 181-8585, Japan)

  • Lukáš Pichl

    () (Graduate School of Arts and Sciences, International Christian University, Osawa 3-10-2, Mitaka, Tokyo 181-8585, Japan)

Abstract

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading.

Suggested Citation

  • Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(1), pages 1-15, January.
  • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:17-:d:199465
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    References listed on IDEAS

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    1. repec:eee:ecolet:v:164:y:2018:i:c:p:109-111 is not listed on IDEAS
    2. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, arXiv.org.
    3. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jun 2019.
    4. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
    5. repec:eee:ecolet:v:167:y:2018:i:c:p:81-85 is not listed on IDEAS
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    Cited by:

    1. repec:gam:jjrfmx:v:12:y:2019:i:2:p:54-:d:219095 is not listed on IDEAS

    More about this item

    Keywords

    cryptocurrency; metric learning; classification framework; time series; trend prediction;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • E - Macroeconomics and Monetary Economics
    • F2 - International Economics - - International Factor Movements and International Business
    • F3 - International Economics - - International Finance
    • G - Financial Economics

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