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Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

By
Ahmed EL YOUSSEFI ,
Ahmed EL YOUSSEFI

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Abdelaaziz HESSANE ,
Abdelaaziz HESSANE

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Imad ZEROUAL ,
Imad ZEROUAL

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Yousef FARHAOUI ,
Yousef FARHAOUI

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Abstract

In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows

How to Cite

1.
EL YOUSSEFI A, HESSANE A, Zeroual I, FARHAOUI Y. Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Mar. 11 [cited 2024 Apr. 25];3:638. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/638

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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