Duy Trong Nguyen
ABSTRACT
Technical analysis is a chart-based method, from price movements and trading volume of stocks to analyze movements to make trend predictions and make real buying and selling decisions. Pattern-based technical analysis is one of the most effective for stock market volatility. The problem analysts often face is that looking for patterns wastes time and effort with thousands of stock symbols. This research aims to apply object detection techniques to analyze and recognize chart patterns, thus evaluating the accuracy of price action candlesticks. However, image data of the patterns on the candlestick chart is too scarce. We built an image dataset consisting of four patterns: Head and Shoulder, reverse Head and Shoulders, Double Top, and Double Bottom. Candlestick charts' distinctive shape makes it challenging to discern precise patterns, and segmentation has been used in the data processing section to reduce candlestick chart noise.
Moreover, data collection also encountered the problem of time and effort. So the method to generate variable data uses possible patterns to enrich the data set. The experiments reveal that performance in detecting patterns is described later in this article.
- Lovrić, Miodrag; Milanović, Marina; Stamenković, Milan .2014. Algoritmic methods for segmentation of time series: An overview. In Journal of Contemporary Economic and Business Issues.ISSN 1857-9108.Google Scholar
- Jamaloodeen, Mohamed; Heinz, Adrian; and Pollacia, Lissa .2018. A Statistical Analysis of the Predictive Power of Japanese Candlesticks. Journal of International & Interdisciplinary Business Research: Vol. 5 , Article 5.Google Scholar
- Max Jönsson. 2016. The Predictive Power of Candlestick Patterns. In Department of Economics NEKH01.Google Scholar
- Zhu M., Atri S., Yegen E. 2016. Are candlestick trading strategies effective in certain stocks with distinct features. In Pacific-Basin Finance Journal, 37, 116-127.Google ScholarCross Ref
- Nguyen, N. 2016. Stock Price Prediction using Hidden Markov Model. Editorial Express, March, 21.Google Scholar
- Henrique BM, Sobreiro VA, Kimura H. Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl. 2019; 124:226–51.Google Scholar
- Zhang Y, Wu L. Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst Appl. 2009; 36(5):8849–54.Google Scholar
- Xinyu Guo, Xun Liang, Xiang Li. 2007. A Stock Pattern Recognition Algorithm Based on Neural Networks. Third International Conference on Natural Computation .ICNC.Google ScholarDigital Library
- Moghaddam AH, Moghaddam MH, Esfandyari MJJoEF, Science A. 2016.Stock market index prediction using artificial neural network.89–93.Google Scholar
- Krauss C, Do XA, Huck N. 2017. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur J Oper Res. 259(2):689–702.Google ScholarCross Ref
- C. L. Jan.2018.An effective financial statements fraud detection model for the sustainable development of financial markets: evidence from Taiwan. Sustainability ,vol. 10, no. 2 p. 513.Google ScholarCross Ref
- Gupta D, Pratama M, Ma Z, Li J, Prasad M.2019. Financial time series forecasting using twin support vector regression. PLoS ONE. 14(3).Google Scholar
- Kusuma, R. M. I., Ho, T. T., Kao, W. C., Ou, Y. Y., & Hua, K. L. (2019). Using deep learning neural networks and candlestick chart representation to predict stock market. arXiv preprint arXiv:1903.12258.Google Scholar
- Liang Q, Rong W, Zhang J, Liu J, Xiong Z, editors .2017. Restricted Boltzmann machine based stock market trend prediction. International Joint Conference on Neural Networks (IJCNN).IEEE.Google Scholar
- Yaohu Lin, Shancun Liu, Haijun Yang. 2020. Improving stock trading decisions based on pattern recognition using machine learning technology. PMID: 34358269.Google Scholar
- James Chen, LLC .2015.Double Bottom Definition.In Investopedia.Google Scholar
- Chung, Fu-Lai 2004. An evolutionary approach to pattern-based time series segmentation. In IEEE transactions on evolutionary computation.8.5, pp. 471-489.Google Scholar
- Aleksey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao .2020.YOLOv4: Optimal Speed and Accuracy of Object Detection. In ArXiv. ArXiv:2004.10934.Google Scholar
- Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao .2020. Scaled-YOLOv4: Scaling Cross Stage Partial Network. In ArXiv. ArXiv:2011.08036.Google Scholar
- Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun. 2021 YOLOX: Exceeding YOLO Series in 2021. In ArXiv. ArXiv:2107.08430.Google Scholar
- MixingTan,QuocV.Le. 2020 .EfficientDet:Scalable and Efficient Object Detection. In ArXiv .ArXiv:1911.09070.Google Scholar
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier,Alexander Kirillov, ,SergeyZagoruyko. 2020. End-to-End Object Detection with Transformers. In ArXiv .ArXiv:2005.12872v3.Google Scholar
- Yufeng Han,Yang Liu, Guofu Zhou and Yingzi Zhu.2021.Technical Analysis in the Stock Market: A Review. In SSRN Journal. 10.2139.Google Scholar
- Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao .2021.You Only Learn One Representation: Unified Network for Multiple Tasks. In ArXiv. ArXiv: 2105.04206Google Scholar
- MixingTan, QuocV.Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In ArXiv.ArXiv: 1905.11946.Google Scholar
- Jun-Hao Chen, Yun-Cheng Tsai.2020. Encoding candlesticks as images for pattern classification using convolutional neural networks. Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.Google Scholar
- Yu-Chia Hsu.2021.Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes.Appl. Sci. 2021, 11(14), 6594 .Google Scholar
- SERDAR BIROGUL, GÜNAY TEMÜR , AND UTKU KOSE. 2020. YOLO Object Recognition Algorithm and "Buy-Sell Decision" Model Over 2D Candlestick Charts. IEEE.Google Scholar
- Celal Buğra Kaya, Alperen Yılmaz, Gizem Nur Uzun, Zeynep Hilal Kilimci. 2020. Stock Pattern Classification from Charts using Deep Learning Algorithms. In 8 th International Symposium on Innovative Technologies in Engineering and Science.Google Scholar
- Zhang N, Lin A, Shang P.2017. Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Phys A Stat Mech its Appl. 477:161–73.Google Scholar
- Fischer T, Krauss C. 2018.Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 270(2):654–69.Google ScholarCross Ref
- Bao W, Yue J, Rao Y.2017. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE. 12(7):e0180944.Google ScholarCross Ref
- Qiu J, Wang B, Zhou C. 2020. Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE. 15(1).Google Scholar
Index Terms
- Object Detection Approach for Stock Chart Patterns Recognition in Financial Markets
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