Deep Learning for Financial Time-Series Data Analytics: An Image Processing Based Approach

Abstract

Forex or Foreign Exchange is the largest financial market with a huge amount of daily trading volume. Traditionally, the tool or strategy that Forex traders usually used is divided into Fundamental Analysis and Technical Analysis. Presently, the world computational technological advance, such as Artificial Intelligence plays a significant role in the financial domain. The various existing research applies Machine Learning and Deep Learning to develop powerful models that can be used as a tool for traders in order to develop their trading tools or strategies. However, most existing models are developed targeting for the stock market. There are still lag of research that applies the modern Machine Learning or Deep Learning for predicting the movement of the price in the Forex market. In this paper, we propose a novel predicting model based on Deep Convolutional Neural Network that can be effectively used as a tool in order to make the profits for Forex traders. We evaluate the performance of the proposed CNN model from two perspectives. The first perspective is to evaluate the accuracy of the prediction and the second perspective is to evaluate the ability to make profits. The experimental results show that in term of accuracy of the prediction, our proposed CNN model provide the accuracy up to approximately 77%. This result is similar to the C5.0 algorithm, which is a rule-based algorithm of Machine Learning. The results are different in a trivial faction. In terms of financial perspective, the proposed CNN model performs well as it produces approximately $69K for one and a half year (from January 2017 to September 2018).