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Deep Learning And Technical Analysis In Cryptocurrency Market

Author

Listed:
  • Stéphane Goutte

    (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [Ile-de-France] - Institut de Recherche pour le Développement)

  • Viet Hoang Le

    (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [Ile-de-France] - Institut de Recherche pour le Développement)

  • Fei Liu

    (IPAG Business School)

  • Hans-Jörg Mettenheim, Von

    (IPAG Business School)

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

  • Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Deep Learning And Technical Analysis In Cryptocurrency Market," Working Papers halshs-03917333, HAL.
    • Handle: RePEc:hal:wpaper:halshs-03917333
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03917333v1
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    References listed on IDEAS

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    1. Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Kevin Rink, 2025. "The role of technical chart patterns in the early Bitcoin market: intraday evidence from the Mt.Gox transaction dataset," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-67, December.
    2. 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.
    3. 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).
    4. Grudniewicz, Jan & Ślepaczuk, Robert, 2023. "Application of machine learning in algorithmic investment strategies on global stock markets," Research in International Business and Finance, Elsevier, vol. 66(C).
    5. 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.
    6. Yichen Luo & Yebo Feng & Jiahua Xu & Paolo Tasca & Yang Liu, 2025. "LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management," Papers 2501.00826, arXiv.org, revised Jan 2025.
    7. Jos'e 'Angel Islas Anguiano & Andr'es Garc'ia-Medina, 2025. "Technical Analysis Meets Machine Learning: Bitcoin Evidence," Papers 2511.00665, arXiv.org.
    8. Jinseong Park & Hyungjin Ko & Jaewook Lee, 2025. "Modeling Asset Price Process: An Approach for Imaging Price Chart with Generative Diffusion Models," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 349-375, July.

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