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

Takuya Shintate and Lukáš Pichl
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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

JRFM, 2019, vol. 12, issue 1, 1-15

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.

Keywords: cryptocurrency; metric learning; classification framework; time series; trend prediction (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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