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Deep Learning And Technical Analysis In Cryptocurrency Market

Author

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  • Stéphane Goutte

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

  • Viet Hoang Le

    (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [France-Nord] - 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-03917333
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    References listed on IDEAS

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