@Article{2018.100000065,
AUTHOR = {Omer Berat Sezer, Ahmet Murat Ozbayoglu},
TITLE = {Financial Trading Model with Stock Bar Chart Image Time Series with Deep  Convolutional Neural Networks},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {323--334},
URL = {http://www.techscience.com/iasc/v26n2/39939},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.31209/2018.100000065}
}