Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 295-315
Abstract
This research presents a predictive model for determining the next-day price direction of EUR/USD in the Binary Options market. The study utilizes technical indicators and price data over a 10,000-day span, collected from TradingView, and applies machine learning techniques particularly an ensemble classification framework combining CNN, LSTM, SVM, and XGBoost models. A total of 23 features were engineered from candlestick data and popular indicators such as RSI, MACD, ATR, and EMA. Statistical analysis ensured data quality and distribution symmetry. Model performance was evaluated using accuracy, F1 score, and ROC-AUC metrics. The resulting ensemble model outperformed individual models in predictive accuracy and stability. This research contributes to the development of automated trading systems and serves as a foundation for further work in financial time series forecasting using machine learning.