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.
Highlights
Machine learning (ML) methods adapted from among deep learning algorithms have been recently applied to financial time series prediction with a number of publications in computer science journals (Greff et al 2017; Fe-Fei et al 2003; Zhang et al 2018), as well as in economics and finance journals (Koutmos 2018; Kristoufek 2018)
There is a gap in the existing literature, which is pronounced in the uncovered field of the applications of machine learning methods for time series to cryptocurrency trading data
We proposed a new trend prediction classification learning method and showed that it performed well in the domain where taking the non-stationarity assumption was quite fair
Summary
Machine learning (ML) methods adapted from among deep learning algorithms have been recently applied to financial time series prediction with a number of publications in computer science journals (Greff et al 2017; Fe-Fei et al 2003; Zhang et al 2018), as well as in economics and finance journals (Koutmos 2018; Kristoufek 2018). The application of deep learning techniques faces a difficult trade-off: deep learning algorithms require a large number of data samples to learn from, implying in practice high-frequency data, such as minute-sampled trade records, whereas the training patterns over long periods are not always stationary, meaning varying patterns may be extracted from different segments of the training dataset. Some empirical results (Mäkinen et al 2018; Sirignano and Cont 2018; Zhang et al 2018) with deep learning algorithms showed that there might be a universal price formulation for the deterministic part of trading behavior to some degree, which implies financial data at high frequency exhibit some stylized facts and could posses learnable patterns that are stationary over long time periods. The works in (Graves et al 2014; Koch 2015; Santoro et al 2016; Vinyals et al 2016) showed how to deploy deep learning algorithms for such purposes in various applications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
