espEconomy: strategy and practiceEconomy: strategy and practice1997-99672663-550XИнститут экономики10.51176/1997-9967-2023-3-268-283esp-1122Research ArticleФИНАНСЫ И УПРАВЛЕНЧЕСКИЙ УЧЕТ, БУХГАЛТЕРСКИЙ УЧЕТFINANCE AND MANAGEMENT ACCOUNTING, ACCOUNTINGМетодологическая основа и опыт применения data mining методов в торговлеMethodological Basis and Experience of Using Data Mining Methods in TradeКайыпТ. Д.KaiypD. T.

ул. К. Сатпаева 2, Z19A0K6, Астана

Diana T. Kaiyp

2 K. Satpayev Str., Z19A0K6, Astana

kayp.di@bk.ruhttps://orcid.org/0000-0002-3987-9353ЖартыбаеваМ. Г.ZhartybayevaM. G.

PhD

ул. К. Сатпаева 2, Z19A0K6, Астана

Makpal G. Zhartybayeva – Ph.D.

2 K. Satpayev Str., Z19A0K6, Astana

makkenskii@mail.ruhttps://orcid.org/0000-0002-3726-8882ОралбековаЖ. О.OralbekovaZh. O.

PhD

ул. К. Сатпаева 2, Z19A0K6, Астана

Zhanar O. Oralbekova – Ph.D.

2 K. Satpayev Str., Z19A0K6, Astana

oralbekova@bk.ruЕвразийский национальный университет Л.Н. ГумилеваКазахстанL.N. Gumilyov Eurasian national universityKazakhstan202304102023183268283Copyright © Кайып Т.Д., Жартыбаева М.Г., Оралбекова Ж.О., 20232023Кайып Т.Д., Жартыбаева М.Г., Оралбекова Ж.О.Kaiyp D.T., Zhartybayeva M.G., Oralbekova Z.O.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://esp.ieconom.kz/jour/article/view/1122

В статье исследован процесс data mining для получения полезной информации из данных. Рассмотрена возможность использования данных методов на практике в финансовой сфере. Поскольку финансовая деятельность тесно связана с социальной жизнью, использование data mining методов играет важную роль в анализе и прогнозировании финансового рынка в современную эпоху больших данных. Однако из-за различий в опыте исследователей разных дисциплин непросто использовать data mining методы при анализе финансовых данных. Поэтому создание методологической базы для практического применения data mining методов при анализе финансовых данных является актуальным вопросом. Цель данной статьи — создать методологическую базу для использования data mining методов для эффективной торговли. При обработке данных о продукте использовались априорные методы и методы визуализации, а также описывалась их реализация на практике. В результате были созданы сценарии компьютерных приложений как образец практической реализации алгоритмов этих методов. Построение количественной торговой стратегии требует сначала статистического анализа информации на рынке, а затем тестирования количественной модели на собранных данных. В этом исследовании была разработана количественная торговая система, основанная на data mining методах. В качестве основного инструмента разработки используется веб-платформа Jupyter, и были разработаны 3 ядра: отбор количественных данных, тестирование стратегии на данных, анализ временных рядов и визуализация. Разработанная система поддерживает модули для принятия простых торговых решений.

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

экономикаторговлястратегияпрактикаdata miningфинансыфинансовая сфераКазахстанEconomyTradeStrategyPracticeData MiningFinanceFinancial SectorKazakhstanРабота выполнена в рамках проекта AP09058557, финансируемого за счет гранта МНВО РКThe work was carried out within the framework of project AP09058557, funded by a grant from the MSHE of the Republic of KazakhstanReferencesAggarwal, C. C., & Han, J. (2014). Frequent Pattern Mining. International Publishing Switzerland. Springer.Aggarwal, C. C., & Han, J. (2014). Frequent Pattern Mining. International Publishing Switzerland. Springer.Ahmed, S. R. (2004). Applications of data mining in retail business. In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. (Vol. 2, pp. 455-459). IEEE. https://doi.org/10.1109/ITCC.2004.1286695Ahmed, S. R. (2004). Applications of data mining in retail business. In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. (Vol. 2, pp. 455-459). IEEE. https://doi.org/10.1109/ITCC.2004.1286695Borodin, A., Panaedova, G., Frumina, S., Kairbekuly, A., & Shchegolevatykh, N. (2021). Modeling the business environment of an energy holding in the formation of a financial strategy. Energies, 14(23), 8107-8125. https://doi.org/10.3390/en14238107Borodin, A., Panaedova, G., Frumina, S., Kairbekuly, A., & Shchegolevatykh, N. (2021). Modeling the business environment of an energy holding in the formation of a financial strategy. Energies, 14(23), 8107-8125. https://doi.org/10.3390/en14238107Desai, B., & Ramageri, B. (2013). Role of data mining in retail sector. International Journal on Computer Science and Engineering, 5(1), 47-50.Desai, B., & Ramageri, B. (2013). Role of data mining in retail sector. International Journal on Computer Science and Engineering, 5(1), 47-50.Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques. Third edition, Elsevier.Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques. Third edition, Elsevier.Hand, D., Mannila, H., & Smyth, P. (2001). Principles of Data Mining. The MIT Press, Massachusetts Institute of Technology.Hand, D., Mannila, H., & Smyth, P. (2001). Principles of Data Mining. The MIT Press, Massachusetts Institute of Technology.Hand, D. J. (2009). Mining the past to determine the future: Problems and possibilities. International Journal of Forecasting, 25(3), 441–451. https://doi.org/10.1016/j.ijforecast.2008.09.004Hand, D. J. (2009). Mining the past to determine the future: Problems and possibilities. International Journal of Forecasting, 25(3), 441–451. https://doi.org/10.1016/j.ijforecast.2008.09.004Kashwan, K. R., & Velu, C. M. (2013). Customer Segmentation Using Clustering and Data Mining Techniques. International Journal of Computer Theory and Engineering, 5(6), 856-861. https://doi.org/10.7763/IJCTE.2013.V5.811Kashwan, K. R., & Velu, C. M. (2013). Customer Segmentation Using Clustering and Data Mining Techniques. International Journal of Computer Theory and Engineering, 5(6), 856-861. https://doi.org/10.7763/IJCTE.2013.V5.811Krishnamoorthy, M., & Karthikeyan, R. (2022). Pattern mining algorithms for data streams using itemset. Measurement: Sensors. 24, 100421-100430. https://doi.org/10.1016/j.measen.2022.100421Krishnamoorthy, M., & Karthikeyan, R. (2022). Pattern mining algorithms for data streams using itemset. Measurement: Sensors. 24, 100421-100430. https://doi.org/10.1016/j.measen.2022.100421Li, B., Ma, Z., & Wang, T. (2021). Find trade patterns in Сhina’s stock markets using data mining agents. Studies in Computational Intelligence, 897, 171-179. https://doi.org/10.1007/978-3-030-49728-6_11Li, B., Ma, Z., & Wang, T. (2021). Find trade patterns in Сhina’s stock markets using data mining agents. Studies in Computational Intelligence, 897, 171-179. https://doi.org/10.1007/978-3-030-49728-6_11Li, Q., Li, S., Zhang, S., Hu, J., & Hu, J. (2019). A Review of Text Corpus-Based Tourism Big Data Mining. Applied Sciences, 9(16), 3300. https://doi.org/10.3390/app9163300Li, Q., Li, S., Zhang, S., Hu, J., & Hu, J. (2019). A Review of Text Corpus-Based Tourism Big Data Mining. Applied Sciences, 9(16), 3300. https://doi.org/10.3390/app9163300Liu, Y., Cao, J., & Zhang, Q. (2022). The product marketing model of the economic zone by the sensor big data mining algorithm. Sustainable Computing: Informatics and Systems, 36, 100820. https://doi.org/10.1016/j.suscom.2022.100820Liu, Y., Cao, J., & Zhang, Q. (2022). The product marketing model of the economic zone by the sensor big data mining algorithm. Sustainable Computing: Informatics and Systems, 36, 100820. https://doi.org/10.1016/j.suscom.2022.100820Ma, T., Fraser-Mackenzie, P.A.F., Sung, M., Kansara, A.P., & Johnson J.E.V. (2022). Are the least successful traders those most likely to exit the market? A survival analysis contribution to the efficient market debate. European Journal of Operational Research, 299 (1), 330-345. https://doi.org/10.1016/j.ejor.2021.08.050Ma, T., Fraser-Mackenzie, P.A.F., Sung, M., Kansara, A.P., & Johnson J.E.V. (2022). Are the least successful traders those most likely to exit the market? A survival analysis contribution to the efficient market debate. European Journal of Operational Research, 299 (1), 330-345. https://doi.org/10.1016/j.ejor.2021.08.050Mikalef, P., Pappas, I.O., Krogstie, J., & Giannakos, M. (2018). Big Data Analytics Capabilities: A Systematic Literature Review and Research Agenda. Information Systems and e-Business Management, 16, 547–578. https://doi.org/10.1007/s10257-017-0362-yMikalef, P., Pappas, I.O., Krogstie, J., & Giannakos, M. (2018). Big Data Analytics Capabilities: A Systematic Literature Review and Research Agenda. Information Systems and e-Business Management, 16, 547–578. https://doi.org/10.1007/s10257-017-0362-yNielsen, F. A. (2015). Data Mining with Python (Working draft). [cited March 30, 2023]. Available at: https://www.webpages.uidaho.edu/~stevel/504/Data%20Mining%20with%20Python.pdfNielsen, F. A. (2015). Data Mining with Python (Working draft). [cited March 30, 2023]. Available at: https://www.webpages.uidaho.edu/~stevel/504/Data%20Mining%20with%20Python.pdfNussbaumer, K. C. (2015). Storytelling with data: a data visualization guide for business professionals. Wiley Publishing inc.Nussbaumer, K. C. (2015). Storytelling with data: a data visualization guide for business professionals. Wiley Publishing inc.Pandin, C. R. M., & Fahrudin, R. (2019). Application of data mining for Indonesian products export in South Korea using clustering: Indonesia Trade Promotion Center Busan. IOP Conference Series: Materials Science and Engineering, 662(2), 022053. https://doi.org/10.1088/1757-899X/662/2/022053Pandin, C. R. M., & Fahrudin, R. (2019). Application of data mining for Indonesian products export in South Korea using clustering: Indonesia Trade Promotion Center Busan. IOP Conference Series: Materials Science and Engineering, 662(2), 022053. https://doi.org/10.1088/1757-899X/662/2/022053Shu, X., & Ye, Y. (2022). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2022.102817Shu, X., & Ye, Y. (2022). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2022.102817Srividya, K., Sowjanya, A. M., & Kumar, T. A. (2017). Sentiment Analysis of Facebook Data Using Naïve Bayes Classifier. International Journal of Computer Science and Information Security, 15 (1), 179–186.Srividya, K., Sowjanya, A. M., & Kumar, T. A. (2017). Sentiment Analysis of Facebook Data Using Naïve Bayes Classifier. International Journal of Computer Science and Information Security, 15 (1), 179–186.Singh, S., & Yassine, A. (2018). Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting. Energies, 11(2), 452- 478. https://doi.org/10.3390/en11020452Singh, S., & Yassine, A. (2018). Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting. Energies, 11(2), 452- 478. https://doi.org/10.3390/en11020452Sun, L. (2022). Research on Mining Balanced Competition Strategy in Financial Market Based on Computer Data Mining Method. Mobile Information Systems, 6202890. https://doi.org/10.1155/2022/6202890Sun, L. (2022). Research on Mining Balanced Competition Strategy in Financial Market Based on Computer Data Mining Method. Mobile Information Systems, 6202890. https://doi.org/10.1155/2022/6202890Vercellis, C. (2015). Business intelligence: data mining and optimization for decision making. Wiley Publishing inc.Vercellis, C. (2015). Business intelligence: data mining and optimization for decision making. Wiley Publishing inc.van Nguyen, T., Zhou, L., Chong, A. Y. L., Li, B., & Pu, X. (2020). Predicting customer demand for remanufactured products: A data-mining approach. European Journal of Operational Research, 281(3), 543- 558. https://doi.org/10.1016/j.ejor.2019.08.015van Nguyen, T., Zhou, L., Chong, A. Y. L., Li, B., & Pu, X. (2020). Predicting customer demand for remanufactured products: A data-mining approach. European Journal of Operational Research, 281(3), 543- 558. https://doi.org/10.1016/j.ejor.2019.08.015Wang, F., Li, M., Mei, Y., & Li, W. (2020). Time Series Data Mining: A Case Study with Big Data Analytics Approach. IEEE Access, 8, 14322–14328. https://doi.org/10.1109/ACCESS.2020.2966553Wang, F., Li, M., Mei, Y., & Li, W. (2020). Time Series Data Mining: A Case Study with Big Data Analytics Approach. IEEE Access, 8, 14322–14328. https://doi.org/10.1109/ACCESS.2020.2966553

The authors declare that there are no conflicts of interest present.