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

Abstract

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.

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation

“…Our previous work [ 21 ] proposed a deep convolutional neural network for stock market prediction based on candlestick charts for two different stock markets (Taiwan50 and Indo10). In 2020, Birogul [ 25 ] employed a real-time object detection system (YOLO) to recognize buy–sell objects inside 2D candlestick charts; from these buy–sell objects, the trader can make their decision on the stock. Not long after that, Hung [ 24 ] proposed a deep predictor framework for price movement based on candlestick charts.…”

“…Many researchers have applied deep learning to the question of stock market prediction. There are several approaches for stock market prediction, such as analyzing indicators of historical time series data [ 16 , 17 , 18 , 19 , 20 ], or using candlestick chart converted from historical data [ 21 , 22 , 23 , 24 , 25 , 26 ], or analyzing the social media [ 27 , 28 , 29 , 30 , 31 , 32 ], or analyzing the financial news [ 33 , 34 , 35 , 36 ]. However, using a single classifier may not achieve maximum performance compared with using combined classifiers.…”

Point Cloud Reduction and Denoising Based on Optimized Downsampling and Bilateral Filtering

“…The burden is increased by the huge amount of point cloud data. A series of algorithms [17][18][19][20][21][22] is adopted without reducing the feature data. Noise is recognized and feature data is retained.…”

Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning

“…Orquin et al [34] tested the efficiency of EUR/USD pair taking only into consid-eration the candlestick charts of the prices. Birogul et al [35] used the famous YOLO (You Only Look Once) object detector to detect the patterns in candlestick charts in order to generate Buy/Sell signals for a stock. Fengqian et al [36] proposed a novel technique to generate trading strategies on single stocks using candlestick charts.…”