Comparison of Stock “Trading” Decision Support Systems Based on Object Recognition Algorithms on Candlestick Charts

Abstract

The fundamental purpose of every investor making investments in financial fields, is to make profit by buying an investment instrument at a low price and selling the same at a higher price. In this study, within the framework of the aforementioned standpoint, an effective “Trading” decision support model was designed, which can be used for stock market analyses, parity analyses, index analyses, and for the stock analyses of other stock exchanges, briefly for all investment instruments for which candlestick charts are created. An innovative model design was achieved through a bilateral perspective with both financial and scientific aspects by designing these models that operated on a pattern detection basis. The study incorporated the use of 2D candlestick charts of the BIST stocks. The charts were labeled in two separate data sets. Initially, 10,000 pieces of data were labeled on 550 2D candlestick charts, which were trained with YoloV3 Data Group-1 (DG-1). Subsequently, the data set was increased to 20,000 pieces. Out of this set of 20,000 labeled data prepared, 10,000 labeled data were picked randomly. The newly-created set of 10,000 labeled data was named DG-2, which was trained with the YoloV3, YoloV4, Faster R-CNN, SDD algorithms. An assessment was made regarding the performance results obtained following the trainings implemented for these four chosen algorithms. For the aforementioned assessment, three different scenarios were created, and out of all these scenarios, the YoloV3 DG-2 algorithm, which was trained with an improved data set, was observed to be most successful one. As a result of the comparative test scenarios, the YoloV3 DG-2 model achieved a pattern recognition success of 98%. On the other hand, it was also observed to have achieved a prediction success of 100%, while bringing in a return by 89.94%, regarding the object class detected. In addition, no additional parameters were used in this observed gain success. Consequently, the YoloV3 DG-2, determined as the final model, could be implemented as a decision support model for all investment instruments for which a candlestick chart can be created.

Keywords

• Deep learning
• CNN
• object recognition
• object detection
• finance
• candlestick chart