Thesis Type: Doctorate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Graduate School of Natural and Applied Sciences, Graduate School of Natural and Applied Sciences, Turkey
Approval Date: 2017
Student: Mustafa Onur Özorhan
Co-Supervisor: ONUR TOLGA ŞEHİTOĞLU, İSMAİL HAKKI TOROSLUAbstract:
This thesis presents a method to predict the direction and magnitude of movement of currency pairs in the foreign exchange market. The method uses clustering and classification methods with a combination of two dimensional chart patterns, processed price data and technical indicator data. The input data is adapted to each trading day with a moving time-frame. The accuracy of the prediction models are tested across several different currency pairs. The experimental results suggest that using two dimensional chart patterns mixed with processed price data and the Zigzag technical indicator improves overall performance and adapting the input data to each trading period results in increased accuracy and profits. The predictions should be applicable in real world, since trading concepts such as spreads, swap commissions and leverages are taken into account.