CREATION OF A NEURAL NETWORK ALGORITHM FOR AUTOMATED COLLECTION AND ANALYSIS OF STATISTICS OF EXCHANGE QUOTES GRAPHICS
Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved. Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions. The process of summarizing the information received from financial markets still requires physical attention and labor, which increases the risks associated primarily with the human factor and corresponding errors. An algorithm has been developed and tested for the automated collection of statistics from graphs of stock quotes, including data on the development and context of various figures (patterns) of technical analysis, as well as an improved adaptation and tracking system for the trend. The modeling process, analysis and the results of applying the analysis algorithm and statistics collection are presented. The developed algorithm works in conjunction with the previously created neural network pattern detector, which allows to automatically search for the exact boundaries of technical analysis figures of various sizes, analyze the context in front of them and play the patterns. This makes it possible to obtain important statistics that allow one to determine the degree of confidence in emerging patterns, taking into account their type, context, and other factors. In terms of accuracy and efficiency, the developed algorithm meets the existing challenges in the financial markets and can significantly increase the efficiency of the trader or investor through the automated processing of graphic and visual data. The created solution is universal in nature and can be applied to any capital market, regardless of the location and nature of the assets placed. The results can be used both to improve the accuracy of existing trading strategies, and for the analytical work of financial market participants. The use of new technologies for statistical processing of information can significantly improve the accuracy of investment and trade decisions
Skuratov, V., Kuzmin, K., Nelin, I., Sedankin, M. (2019). Application of a convolutional neural network to create a detector of technical analysis figures on exchange quotes charts. EUREKA: Physics and Engineering, 6, 50–56. doi: https://doi.org/10.21303/2461-4262.2019.001055
Dash, R., Dash, P. K. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science, 2 (1), 42–57. doi: https://doi.org/10.1016/j.jfds.2016.03.002
Chiang, W.-C., Enke, D., Wu, T., Wang, R. (2016). An adaptive stock index trading decision support system. Expert Systems with Applications, 59, 195–207. doi: https://doi.org/10.1016/j.eswa.2016.04.025
Priya, S. R., Arabinda, S. (2019). Statistical Analysis of Stock Prices of Selected Companies in Construction Industry. Advances in Management, 12 (1), 39–47.
De Souza, M. J. S., Ramos, D. G. F., Pena, M. G., Sobreiro, V. A., Kimura, H. (2018). Examination of the profitability of technical analysis based on moving average strategies in BRICS. Financial Innovation, 4 (1). doi: https://doi.org/10.1186/s40854-018-0087-z
Bulkovski, T. N. (2009). Polnaya entsiklopediya graficheskih modeley. Moscow: Smart book.
Korczak, J., Hernes, M. (2017). Deep Learning for Financial Time Series Forecasting in A-Trader System. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. doi: https://doi.org/10.15439/2017f449
Battres-Estrada, G. (2015). Deep Learning for Multivariate Financial Time Series. KTH royal institute of technology Diva.
Khare, K., Darekar, O., Gupta, P., Attar, V. Z. (2017). Short term stock price prediction using deep learning. 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). doi: https://doi.org/10.1109/rteict.2017.8256643
Maknickienė, N., Maknickas, A. (2012). Application of Neural Network for Forecasting of Exchange Rates and Forex Trading. The 7th International Scientific Conference “Business and Management 2012”. Selected Papers. doi: https://doi.org/10.3846/bm.2012.017
Sezer, O. B., Ozbayoglu, M., Dogdu, E. (2017). A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters. Procedia Computer Science, 114, 473–480. doi: https://doi.org/10.1016/j.procs.2017.09.031
Galeshchuk, S., Mukherjee, S. (2017). Deep networks for predicting direction of change in foreign exchange rates. Intelligent Systems in Accounting, Finance and Management, 24 (4), 100–110. doi: https://doi.org/10.1002/isaf.1404
Nikou, M., Mansourfar, G., Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26 (4), 164–174. doi: https://doi.org/10.1002/isaf.1459
Vochozka, M., Horák, J., Šuleř, P. (2019). Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate. Journal of Risk and Financial Management, 12 (2), 76. doi: https://doi.org/10.3390/jrfm12020076
Parot, A., Michell, K., Kristjanpoller, W. D. (2019). Using Artificial Neural Networks to forecast Exchange Rate, including VAR‐VECM residual analysis and prediction linear combination. Intelligent Systems in Accounting, Finance and Management, 26 (1), 3–15. doi: https://doi.org/10.1002/isaf.1440
Banga, J. S., Brorsen, B. W. (2019). Profitability of alternative methods of combining the signals from technical trading systems. Intelligent Systems in Accounting, Finance and Management, 26 (1), 32–45. doi: https://doi.org/10.1002/isaf.1442
Yang, F., Wang, M. (2020). A review of systematic evaluation and improvement in the big data environment. Frontiers of Engineering Management, 7 (1), 27–46. doi: https://doi.org/10.1007/s42524-020-0092-6
Lahmiri, S. (2020). A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra-day data. Intelligent Systems in Accounting, Finance and Management. doi: https://doi.org/10.1002/isaf.1470
León, C., Machado, C., Murcia, A. (2015). Assessing Systemic Importance With a Fuzzy Logic Inference System. Intelligent Systems in Accounting, Finance and Management, 23 (1-2), 121–153. doi: https://doi.org/10.1002/isaf.1371
Bulashev, S. V. (2003). Statistika dlya treyderov. Moscow: Kompaniya Sputnik+, 245.
Abstract views: 37 PDF Downloads: 17
Copyright (c) 2020 Victor Skuratov, Konstantin Kuzmin , Igor Nelin, Mikhail Sedankin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the CREATIVE COMMONS copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons Attribution License, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.