Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

Foreign Exchange, commonly called Forex, is a form of investment in the non-real sector great demand. Forex marketplace that specializes foreign exchange trading. Technology advancements have made it easy to monitor conditions real time and present them an easyto - understand graphical form. As result, predictions are closely related investment, starting from market sentiment economic technical matters. One Artificial Intelligence methods can be used classifying Support Vector Machine (SVM). SVM machine learning classification method based on Structural Risk Minimization (SRM) principle find best hyperplane separates two classes input space determines decision function by minimizing empirical risk. This study candlestick patterns predict chart movements using (SVM) method. The purpose this was measure accuracy making so assist traders decisions forex level obtained data results reached 90.72% with precision 87.69%. With relatively good accuracy, candlesticks indicate current trend’s direction.