  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.
