Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4031
Title: Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks
Authors: Sezer, Ömer Berat
Özbayoğlu, Ahmet Murat
Keywords: Algorithmic Trading
Computational Intelligence
Convolutional Neural Networks
Deep Learning
Financial Forecasting
Stock Market
Issue Date: 2020
Publisher: Tech Science Press
Source: Sezer, O. B., and Ozbayoglu, A. M. (2019). Financial trading model with stock bar chart image time series with deep convolutional neural networks. arXiv preprint arXiv:1903.04610.
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.
URI: https://hdl.handle.net/20.500.11851/4031
http://autosoftjournal.net/paperShow.php?paper=100000065
ISSN: 10798587
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

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