Machine Learning Technique to Predict the Right Buying and Selling for EUR_USD


  • Mohamed EL Mahjouby Student, Faculty of Sciences, USMBA, Fez-30000, Morocco
  • Younes Manzali Student, Faculty of Sciences, USMBA, Fez–30000, Morocco
  • Mohamed Taj Bennani Professor, Faculty of Sciences, USMBA, Fez–30000, Morocco
  • Mohamed Lamrini Professor, Faculty of Sciences, USMBA, Fez–30000, Morocco
  • Mohamed EL FAR Professor, Faculty of Sciences, USMBA, Fez–30000, Morocco


Technical indicators, Machine learning, Classification, Adaptive boosting classifier, Decision tree classifier


Predicting foreign exchange movements is an extensively studied and widely notable domain in finance. They have many studies using machine learning for the exchange market. This research explores and uses machine-learning techniques such as gradient boosting, random forest, bagging, extreme gradient boosting classifier, adaptive boosting, gaussian naïve, decision tree, and logistic regression and combines the adaptive boosting classifier with a base estimator decision tree. The goal of this combination is to forecast the optimal moments for purchasing and selling the euro against the dollar currency pair. This method entails suggesting the inclusion of 21 technical indicators into the training dataset to enhance the precision of the methodologies and our approach. The objective of this enhancement is to predict upcoming instances of buying and selling the currency pair euro against the dollar. The set of four metrics involves accuracy and measurements within the area under the receiver-operating characteristic curve, utilized for comparing multiple machine-learning models and assessing the effectiveness of various classification models. Analysis of the experiment demonstrates that our method achieves higher accuracy when compared to the decision tree classifier and other models, which obtained an accuracy of 0.763.


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How to Cite

Mahjouby, M. E. ., Manzali, Y. ., Bennani, M. T. ., Lamrini, M. ., & FAR, M. E. . (2024). Machine Learning Technique to Predict the Right Buying and Selling for EUR_USD. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 439–445. Retrieved from



Research Article