Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach - Sezer, Omer Berat; Ozbayoglu, Ahmet Murat (Applied soft computing, 2018)
•We converted 1-D financial technical analysis data to 2-D images for classification.•We used 2-D deep convolutional neural network for trend forecasting.•We propose a robust algorithmic trading model that works in any market condition.•To best of our knowledge, 2-D CNN with TA has not been used for financial trading before.•Model outperformed Buy & Hold, RSI, MA, LSTM, MLP over long time periods. Computational intelligence techniques for financial trading systems have always been quite popular. In the last decade, deep learning models start getting more attention, especially within the image processing community. In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D images, 15 different technical indicators each with different parameter selections are utilized. Each indicator instance generates data for a 15 day period. As a result, 15 × 15 sized 2-D images are constructed. Each image is then labeled as Buy, Sell or Hold depending on the hills and valleys of the original time series. The results indicate that when compared with the Buy & Hold Strategy and other common trading systems over a long out-of-sample period, the trained model provides better results for stocks and ETFs.