COMPARISON OF SYSTEMS OF FORECASTING THE DIRECTION OF CHANGES IN THE EXCHANGE RATE OF A FINANCIAL INSTRUMENT USING SIMPLE AND EXPONENTIAL MOVING AVERAGES

Authors

DOI:

https://doi.org/10.31732/2663-2209-2022-70-61-75

Keywords:

trading system, technical analysis indicator, moving average, simple average, exponential average, currency pair, exchange operation

Abstract

The range of actual research points to the fact that some technical analysis indicators have predictive power, and therefore trading strategies based on them have some applied value. This work examines some topical issues of the development and use of a trader's trading system, which relies on signals generated by indicators of technical analysis, particularly exponential and simple moving averages, in deciding to execute a stock transaction. The works of modern researchers, which describe approaches to using these indicators, are analyzed. In this context, the purpose of the study is to analyze the impact of exponential moving average settings and their combinations on the profitability of the trading system, as well as to compare the results of such a system with the results of a strategy built on simple moving averages and a combination of simple and exponential moving averages. Based on this, tasks that are solved using slides of this kind were formed. The technique of selecting technical indicators and their settings when creating a trader's trading system is proposed. Several variants of the formation and interpretation of the signal regarding the subsequent change in the rate of the financial asset generated by such a system are considered. The article also discusses the criteria for comparing strategies at the testing stage. The results of using different techniques were calculated and compared, and the optimal ones were determined according to the selected selection criteria. Trading simulations were performed for the EUR/USD currency pair, using weekly quotes from 1999 to 2023, based on which the optimal combination of sliders for use in the trading system was determined. It was noted that the strategy, based on exponential moving averages, needs additional optimization. Options for possible optimization and the corresponding tools that can be used are indicated. Based on the research results, it was concluded that the proposed approach to developing a trader's trading system could be used to perform actual exchange operations.

Downloads

Download data is not yet available.

Author Biography

Vadym Savchenko, KROK University

Postgraduate student, KROK University, Kyiv

References

Пилипченко, О., Кузьмінський, В., & Чумаченко, О. (2021). Використання методів технічного аналізу для прогнозування ринку криптовалют. Вчені записки Університету «КРОК», 4(64), 28–35. URL: https://doi.org/10.31732/2663-2209-2021-64-28-35 (Дата звернення: 01.05.2023)

Бакай Є. І. Модель прийняття рішень для фінансових часових рядів на основі пари середніх з використанням оцінки різних часових вимірів / Є. І. Бакай, В. В. Кабачий, Р. В. Маслій // Вісник Вінницького політехнічного інституту. - 2017. - № 1. - С. 70-77. - URL: http://nbuv.gov.ua/UJRN/vvpi_2017_1_13. (Дата звернення: 04.05.2023)

ДЯЧЕНКО, Ю. А. Розвиток методів прогнозування динаміки біржових цін на сільськогосподарські товари. 2018. URL: http://surl.li/gbous (Дата звернення: 03.03.2023)

KHAND, Salma, et al. The Performance of Exponential Moving Average, Moving Average Convergence-Divergence, Relative Strength Index and Momentum Trading Rules in the Pakistan Stock Market. Indian Journal of Science and Technology, 2019, 12: 26. URL: http://surl.li/irnvk (Дата звернення: 03.05.2023)

RESTA, Marina; PAGNOTTONI, Paolo; DE GIULI, Maria Elena. Technical analysis on the bitcoin market: trading opportunities or investors’ pitfall?. Risks, 2020, 8.2: 44. URL: https://pdfs.semanticscholar.org/8f8f/96b4089c651f92053a57b49d2cfb430fbe9e.pdf (Дата звернення: 03.05.2023)

COCCO, Luisanna; TONELLI, Roberto; MARCHESI, Michele. An agent-based artificial market model for studying the bitcoin trading. IEEE Access, 2019, 7: 42908-42920. URL: http://surl.li/irnvz (Дата звернення: 03.05.2023)

ZAKAMULIN, Valeriy; GINER, Javier. Trend following with momentum versus moving averages: A tale of differences. Quantitative Finance, 2020, 20.6: 985-1007. URL: https://www.tandfonline.com/doi/full/10.1080/14697688.2020.1716057 (Дата звернення: 04.05.2023)

PASPANTHONG, Art; TANTIVASADAKARN, Nick; VITHAYAPALERT, Will. Machine learning in intraday stock trading. Computer Science Department, Stanford University, 2019. URL: cs229.stanford.edu/proj2019spr/report/28.pdf (Дата звернення: 04.05.2023)

HUSHANI, Phillip. Using Autoregressive Modelling and Machine Learning for Stock Market Prediction and Trading. URL: http://surl.li/irnwm (Дата звернення: 04.05.2023)

ALONSO-MONSALVE, Saúl, et al. Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 2020, 113250. URL: https://drive.google.com/file/d/199OnPesR4lUrVDgKiy0714iIbJuK8Huf/view (Дата звернення: 04.05.2023)

HOSEINZADE, Ehsan; HARATIZADEH, Saman. CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 2019, 129: 273-285. URL: http://surl.li/irnxi (Дата звернення: 04.05.2023)

LAI, Chun Yuan; CHEN, Rung-Ching; CARAKA, Rezzy Eko. Prediction stock price based on different index factors using LSTM. In: 2019 International conference on machine learning and cybernetics (ICMLC). IEEE, 2019. p. 1-6. URL: http://surl.li/irnxm (Дата звернення: 05.05.2023)

PRAMUDYA, Rommy. Technical analysis to determine buying and selling signal in stock trade. International Journal of Finance & Banking Studies (2147-4486), 2020, 9.1: 58-67. URL: https://www.ssbfnet.com/ojs/index.php/ijfbs/article/view/666/539 (Дата звернення: 05.05.2023)

SONKIYA, Priyank; BAJPAI, Vikas; BANSAL, Anukriti. Stock price prediction using BERT and GAN. arXiv preprint arXiv:2107.09055, 2021. URL: http://surl.li/irnxv (Дата звернення: 06.05.2023)

Moving averages: simple and exponential. ChartSchool. [Електронний ресурс] URL: https://school.stockcharts.com/doku.php?id=technical_indicators:moving_averages (Дата звернення: 07.05.2023)

HUANG, Zhe; MARTIN, Franck. Pairs trading strategies in a cointegration framework: back-tested on CFD and optimized by profit factor. Applied Economics, 2019, 51.22: 2436-2452. URL: http://surl.li/gboww (Дата звернення: 07.05.2023)

QuantifiedStrategies. Trading System And Strategy Performance Metrics [Електронний ресурс]. URL: https://www.quantifiedstrategies.com/trading-strategy-and-system-performance-metrics/ (Дата звернення: 07.05.2023)

ATAS. Core mathematics for Forex traders. Part 2. URL: https://atas.net/trading-preparation/funds-management/core-mathematics-for-forex-traders-part-2/ (Дата звернення: 07.05.2023)

САВЧЕНКО, Вадим. ПРОГНОЗУВАННЯ НАПРЯМУ ЗМІН КУРСУ ФІНАНСОВОГО ІНСТРУМЕНТУ З ВИКОРИСТАННЯМ ПРОСТИХ КОВЗНИХ СЕРЕДНІХ. Вчені записки Університету «КРОК», 2023, 1 (69): 38-51. URL: http://snku.krok.edu.ua/index.php/vcheni-zapiski-universitetu-krok/article/view/566/590 (Дата звернення: 09.05.2023)

О. В. Раєвнєва, І. В. Аксьонова, О. І. Бровко. Статистика: навч. посіб. / за заг. ред. О. В. Раєвнєвої. Харків: ХНЕУ ім. С. Кузнеця, 2019. – 389 с. URL: http://:repository.hneu.edu.ua/bitstream/123456789/24523/1/2019%20-%20Раєвнєва%20О%20В.pdf (Дата звернення: 20.05.2023)

Published

2023-06-30

How to Cite

Savchenko, V. (2023). COMPARISON OF SYSTEMS OF FORECASTING THE DIRECTION OF CHANGES IN THE EXCHANGE RATE OF A FINANCIAL INSTRUMENT USING SIMPLE AND EXPONENTIAL MOVING AVERAGES. Science Notes of KROK University, (2(70), 61–75. https://doi.org/10.31732/2663-2209-2022-70-61-75