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

Sezer, Ömer Berat; Özbayoğlu, A. Murat

doi:10.31209/2018.100000065

Cited by 28 publications

(27 citation statements)

References 21 publications

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“…The author of [188] proposed a method that used CNN with Gramian Angular Field (GAF), Moving Average Mapping (MAM), Candlestick with converted image data. In [189], a novel method, CNN with feature imaging was proposed for the prediction of the buy/sell/hold positions of the Exchange-Traded Funds (ETFs)' prices and Dow30 stocks' prices. The authors of [190] proposed a method that uses Empirical Mode Decomposition and Factorization Machine based Neural Network (EMD2FNN) models to forecast the stock close prices' direction accurately.…”

Section: Trend Forecastingmentioning

confidence: 99%

“…Even though some 1-D representations exist, the 2-D implementation for CNN was more common, mostly inherited through image recognition applications of CNN from computer vision implementations. In some studies [188,189,193,199,219], innovative transformations of financial time series data into an image-like representation has been adapted and impressive performance results have been achieved. As a result, CNN might increase its share of interest for financial time series forecasting in the next few years.…”

Section: Trend Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Trend Forecastingmentioning

confidence: 99%

Section: Trend Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

View full text Add to dashboard Cite

show abstract

“…In their study, Sezer and Özbayoğlu (2019) suggested a non-traditional approach for stock prediction using the convolutional neural network to determine the ''Buy'', ''Sell'' and ''Hold'' scenarios directly over 2-D stock bar chart views without presenting any additional time series related to the basic stock [22].…”

Section: Related Workmentioning

confidence: 99%

YOLO Object Recognition Algorithm and “Buy-Sell Decision” Model Over 2D Candlestick Charts

2020

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Earning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a ''Buy-Sell'' object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa İstanbul (BIST) between 2000-2018 were used. The model, which can make a ''Buy-Sell'' decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the ''Buy-Sell'' decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85% while a 100% profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.

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“…In the doctoral thesis study, deep learning approaches were developed for index prediction in Dow Jones (USA) stock exchange. Sezer (2019) has divided the time series data set of Dow Jones shares into 5 years of training and 1 year of testing. Within the scope of the thesis, 4 different approaches are proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Algoritmik İşlemler İçin Derin Öğrenme Tabanlı Regresyon Yaklaşımı: BİST30 Örneği

SANTUR

2020

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Today, one of the common uses of artificial intelligence is financial markets. In these markets, which are known as stock market, making price predictions for the future using machine learning and deep learning, making the rise and fall forecasts of indices, sectors and stocks are the main approaches used in this field. In the near future in the financial markets, artificial intelligence based software robots are expected to operate instead of people. For this purpose, learning models are developed by using trend and stock price movements. Validation studies such as accuracy, error value and portfolio simulation are performed to demonstrate the performance of the developed models. In this study, a regression model using deep learning was developed to make adaptive buy-sell operations on the time series consisting of closing prices using data from Borsa İstanbul (BIST). The 2006-2015 range of the BIST30 index was used for training, the 2015-2018 range was used for testing, and the model portfolio value gained 39% on the test data for 694 trading days and the trend direction was estimated with 82% accuracy.

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

Cited by 28 publications

References 21 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

YOLO Object Recognition Algorithm and “Buy-Sell Decision” Model Over 2D Candlestick Charts

Algoritmik İşlemler İçin Derin Öğrenme Tabanlı Regresyon Yaklaşımı: BİST30 Örneği

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