A SYSTEM ANALYSIS METHODOLOGY FOR TRADING FINANCIAL ASSETS, USING TECHNICAL INDICATORS IN MACHINE LEARNING MODELS
DOI:
https://doi.org/10.32689/maup.it.2025.3.23Keywords:
machine learning, technical indicators, cryptocurrency market, classification tasksAbstract
The article is devoted to the development of a nine-step system analysis methodology for trading financial assets. This methodology includes the stages of preparing data for analysis, building mathematical models, and analyzing test results. A feature of the methodology is the use of such technical indicators as Bollinger bands, the stochastic oscillator, and the parabolic stop and reversal Indicator. The goal. Develop a systematic analysis methodology for trading financial assets.The methodology. Based on the proposed system analysis methodology, a computer program was developed. Using this program, a number of computational experiments were conducted on real statistical data, which allowed us to compare the useof such technical financial market indicators as Bollinger bands, stochastic oscillator, and parabolic stop and reversal indicatorin the development of machine learning models for forecasting price dynamics.The scientific novelty. A step-by-step methodology for system analysis for trading financial assets is presented. The proposedmethodology is implemented in the form of a computer program. The analysis and comparison of the use of various technicalindicators of the financial market on real statistical data is performed.Conclusions. It was found that when using various technical indicators for a mathematical model in the form of a random forest, the best forecasting results are shown by the stochastic oscillator, followed by the Bollinger Bands according to the obtained modeling results, and the worst result was provided by the model using the stop and reverse indicator.
References
Комп’ютерна програма “ML technical indicators for Crypto”. URL: https://github.com/oterentiev/ml-techical-indicators-for-crypto (date of access: 12.08.2025).
Bollinger J. A. Bollinger on Bollinger Bands. McGraw Hill, 2001. 227 p. ISBN-13: 978-0071373685.
Deep A., Monico C., Shirvani A., Rachev S., Fabozzi F. Assessing the Impact of Technical Indicators on Machine Learning Models for Stock Price Prediction. 2024. 22 p. URL: https://arxiv.org/html/2412.15448v1 (date of access: 12.08.2025).
Lakhwan D., Dave A. Determining the most efficient technical indicator of investing in financial markets based on trends, volume, momentum and volatility. Myśl Ekonomiczna i Polityczna. No. 3 Vol. 70. 2020 P. 64–137. URL: https://bazekon.uek.krakow.pl/rekord/171628292 (date of access: 14.08.2025).
Mostafavi S. M., Hooman A. R. Key technical indicators for stock market prediction. Machine Learning with Applications. Volume 20, June 2025. 16 p. Online ISSN: 2666-8270.
Murphy J. J. Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance, 1999. 576 p. ISBN-13: 978-0735200661
Optuna: A hyperparameter optimization framework. URL: https://optuna.readthedocs.io/en/stable/ (date of access: 15.08.2025).
Paik C., Choi J., Vaquero I. U. Algorithm-based low-frequency trading using a stochastic oscillator, Williams %R, and trading volume for the S&P 500. Journal of Risk and Financial Management. Vol. 17 No.11 Art. 501. 2024. 20 p. DOI: http://dx.doi.org/10.3390/jrfm17110501
Prasetijo A. B., Saputro T. A., Windasari I. P., Windarto Y. E. Buy/sell signal detection in stock trading with Bollinger Bands and Parabolic SAR: With web application for proofing trading strategy. Proceedings of the 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Piscataway NJ, 2017. P. 41–44. DOI: http://dx.doi.org/10.1109/ICITACEE.2017.8257672
Precious Metal Market Summary. URL: https://www.grandviewresearch.com/industry-analysis/precious-metals-market (date of access: 16.07.2025).
Roussi R. YFinance – Yahoo! Finance Market Data Downloader. URL: https://ranaroussi.github.io/yfinance/ (date of access: 16.07.2025).
Scornet E., Biau G., Vert J.-P. Consistency of Random Forests. Annals of Statistics. Vol. 43, No. 4. 2015. 1716–1741 p. DOI: http://dx.doi.org/10.1214/15-AOS1321
Sharpe W. F. The Sharpe Ratio. Journal of Portfolio Management. Vol. 21, No. 1. 1994. P. 49–58. DOI: http://dx.doi.org/10.3905/jpm.1994.409501
UK – London Stock Exchange Market Capitalization. URL: https://en.macromicro.me/series/4227/uk-london-market-cap?utm_source=chatgpt.com (date of access: 16.07.2025).
Wilder W. New Concepts in Technical Trading Systems Hardcover. Trend Research, 1978. 141 p. ISBN-13 : 978-0894590276
Zhang J., Cai K., Wen J. A survey of deep learning applications in cryptocurrency. iScience. vol. 27, 2024. 40 p. URL: https://doi.org/10.1016/j.isci.2023.108509 (date of access: 12.08.2025).
