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Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

Omer Berat Sezer*, Ahmet Murat Ozbayoglu

Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, 06560 Turkey

* Corresponding Author: Omer Berat Sezer,

Intelligent Automation & Soft Computing 2020, 26(2), 323-334.


Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.


Cite This Article

O. B. Sezer and A. M. Ozbayoglu, "Financial trading model with stock bar chart image time series with deep convolutional neural networks," Intelligent Automation & Soft Computing, vol. 26, no.2, pp. 323–334, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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