Unlocking Forex Market Trends: Advanced Predictive Modeling with Tree Ensembles

Abstract In this study, the author constructed a draft model to assess and predict the fluctuations of the foreign exchange (Forex) market using the Tree Ensembles ensemble learning method, combining three main models: Random Forest, Gradient Boosting Machines (GBM), and XGBoost. The author focused on applying predictive indicators of Forex trading to develop techniques to help investors detect growth trends through market buy and sell prices. As a result, the model provided accurate predictions of market trends, ensuring stability and high accuracy, surpassing other machine learning methods currently being applied. Particularly, the forecasting method from the model demonstrated the ability to handle various types of data flexibly, including numerical and text data, without requiring excessive preprocessing. This opens the door for applying the model to real-life situations where data is often diverse and complex. In summary, this study not only provides an effective method for predicting Forex market fluctuations but also suggests significant potential for the development of assessment and prediction models in the currency field in the future.