DEEP LEARNING AND TECHNICAL ANALYSIS IN CRYPTOCURRENCY MARKET

Stéphane Goutte, Viet Hoang Le, Fei Liu () and Hans-Jörg Mettenheim, Von ()
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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

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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.

Keywords: Bitcoin Technical Analysis Machine Learning Deep Learning Convolutional Neural Networks Recurrent Neural Network; Bitcoin; Technical Analysis; Machine Learning; Deep Learning; Convolutional Neural Networks; Recurrent Neural Network (search for similar items in EconPapers)
Date: 2023-01-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-pay
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03917333v1
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Journal Article: Deep learning and technical analysis in cryptocurrency market (2023)
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