Leveraging Machine Learning for Optimal Trade Entry Point Identification in the Cryptocurrency Market

Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems

Abulfazl Yavari ¹ , Hasan Aama ² , Seyyed Mohammad R. Hashemi ³

1 - Assistant Professor, Faculty of Computer Engineering and IT, Payam-e Noor University, Tehran, Iran
2 - Assistant Professor, Faculty of Management Economics and Accounting, Payam-e Noor University, Tehran, Iran
3 - Assistant Professor, Faculty of Computer Engineering, Skill National University, Tehran, Iran

Received: 2024-04-13 Accepted : 2024-06-01 Published : 2024-06-21

Keywords: Cryptocurrency market, market forecasting, artificial neural network, support vector machine, nearest neighbor, technical analysis,

Abstract :

Introduction: In the domain of financial forecasting, machine learning (ML) models have garnered significant attention in recent years. One prominent application lies in the cryptocurrency market, where intelligent trading bots facilitate a substantial portion of daily transactions.

Method: This paper investigates the efficacy of ML-based methods for identifying optimal trade entry points. Specifically, we employ the Relative Strength Index (RSI) and Simple Moving Average (SMA) indicators to extract a set of trend and crossover features. Subsequently, these features are utilized to train multilayer neural network, support vector machine, and nearest neighbor learning models. The performance of each model is then evaluated using digital currency market data encompassing the first seven months of 2023 for a variety of cryptocurrencies.

Results: Our findings demonstrate that, firstly, the extracted features exhibit promising efficacy. Secondly, the nearest neighbor model achieved the highest profitability during the evaluation period compared to the other investigated models.

Discussion: In the research conducted in this field, technical indicators are often used directly in market forecasting but in the proposed method of this article, instead of directly using the values of the indicators as the input of the classification methods, trend behaviors and their intersections have been used. In the continuation of this research, it is possible to determine the best exit points from the transaction with the help of machine learning and the use of volume indicators in the learning process.

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