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Deep learning and technical analysis in cryptocurrency market

Author

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  • Goutte, Stéphane
  • Le, Hoang-Viet
  • Liu, Fei
  • von Mettenheim, Hans-Jörg

Abstract

A large number of modern practices in financial forecasting rely on technical analysis, which involves several heuristics techniques of price charts visual pattern recognition as well as other technical indicators. In this study, we aim to investigate the potential use of those technical information (candlestick information as well as technical indicators) as inputs for machine learning models, especially the state-of-the-art deep learning algorithms, to generate trading signals. To properly address this problem, empirical research is conducted which applies several machine learning methods to 5 years of Bitcoin hourly data from 2017 to 2022. From the result of our study, we confirm the potential of trading strategies using machine learning approaches. We also find that among several machine learning models, deep learning models, specifically the recurrent neural networks, tend to outperform the others in time-series prediction.

Suggested Citation

  • Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323001824
    DOI: 10.1016/j.frl.2023.103809
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    Cited by:

    1. Hulusi Mehmet Tanrikulu & Hakan Pabuccu, 2024. "The Effect of Data Types' on the Performance of Machine Learning Algorithms for Financial Prediction," Papers 2404.19324, arXiv.org.
    2. Riahi, Rabeb & Bennajma, Amel & Jahmane, Abderrahmane & Hammami, Helmi, 2024. "Investing in cryptocurrency before and during the COVID-19 crisis: Hedge, diversifier or safe haven?," Research in International Business and Finance, Elsevier, vol. 67(PB).
    3. Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024. "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers 2405.11431, arXiv.org, revised Jun 2024.

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