Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks (CNNs) to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. Second, we use a CNN, which is a type of deep learning, to train our trading model. Third, we evaluate the model's performance in terms of the accuracy of classification. The experimental results show that if the strategy is clear enough to make the images obviously distinguishable the CNN model can predict the prices of a financial asset. Hence, our approach can help devise trading strategies and help clients automatically obtain personalized trading strategies.
REF choose the convolutional neural network to process the trading data.
Predict Forex Trend via Convolutional Neural Networks
Journal of Intelligent Systems
Mathematics Computer Science Economics