Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach

By Omer Berat Sezer, A. Ozbayoglu. 2018

387 citations recorded by the source corpus.

Abstract 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.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research, Model Architectures, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “In some studies [188, 189, 193, 199, 219], innovative transformations of financial time”
Deep Learning for Financial Applications : A Survey (2020, methodology): “[58] also proposed a novel technique that converts financial time series data that consisted of technical analysis indicator outputs to 2-dimensional images and classified these images using CNN to determine the trading signals.”
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, methodology): “Sezer et al. [27] proposed a novel algorithmic trading model CNN-TA (Convolutional Neural Network with Technical Analysis) using a 2-”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…al., 2018; Al-Thelaya et al., 2019; de A. Arajo et al., 2019; Cao et al., 2019; Cao & Wang, 2019; Ding & Qin, 2019; Lee et al., 2019; Li et al., 2019b; Sachdeva et al., 2019; Sethia & Raut, 2019; Tang et al., 2019), or between -1 and 1 (Ticknor, 2013; Zhang et al., 2017b; Sezer & Ozbayoglu, 2018).”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “Examples are to apply DNNs such as RNN, LSTM, GAN, CNN and regression neural networks or combine some of them with classic models such as time series regression, probabilistic modeling or classic learners to model financial time series for market movement trend and price forecasting, change detectio”
Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting (2019, methodology): “Indeed, it has been proved in previous studies that CNN based classification models start emerging as the best performance achievers in various applications, outperforming classical intelligence methods like SVM [27].”