Forex Price Movement Prediction Using Stacking Machine Learning Models

Abstract

Forex is an attractive choice for investors who admire any making profit challenges in the fluctuating market. But on the other hand, it means investors can lose money at the same time. Many investors look for ways to reduce the risks by finding price movement prediction tools. Therefore, this paper proposes the Stacking Machine Learning Models to predict the future price direction to help investors to decide and plan strategies. We experimented with comparing baseline models to evaluate the accuracy performance. In addition, we improve the accuracy performance using Technical Analysis and Fibonacci Retracements to gain an accuracy of 90%.