FORECASTING THE DIRECTION OF CHANGES IN THE EXCNANGE RATE OF A FINANCIAL INSTRUMENT USING SIMPLE MOVING AVERAGES

Authors

DOI:

https://doi.org/10.31732/2663-2209-2022-69-38-51

Keywords:

trading system, technical analysis indicator, moving average, currency pair, arbitrage

Abstract

Own previous research points to the fact that a range of technical analysis indicators have a certain predictive power, and therefore trading rules and strategies, based on these indicators, have a certain practical value. This article examines some actual issues of the developing and functioning of a trading system, using technical analysis indicators – moving averages. The approaches offered by modern researchers to the use of these indicators in forecasting changes in the financial asset exchange rate are analyzed. In this context, the purpose of the study is to analyze the impact of moving average settings and their combinations on the performance of the trading system, which is based on their use for forecasting the direction of changes in the financial instrument exchange rate. Based on this, the tasks that are solved, using moving averages, were formed. An option for organizing work on developing a trader's trading system is offered. Various options for the formation and interpretation of the trading signal, generated by the system, are considered. The article also pays attention to the criteria for comparing strategies at the testing stage. The results of using different settings options and combinations of indicators are presented and compared, the optimal ones are determined according to the given selection criteria. Trading simulations were made on the example of the EUR/USD currency pair, using data for the period from 1999 to 2023, based on which the optimal combination of technical indicators was selected for real settings of the trading system. The understanding that a trading system, based on moving averages, requires additional optimization is noted separately. The areas of possible optimization are indicated, as well as the tools that can be used for that purpose, while the emphasis is placed on the tools that are available to the retail trader. It is concluded that the proposed approach to developing of a trading system can be used to perform real arbitrage operations.

Downloads

Download data is not yet available.

Author Biography

Vadym Savchenko, KROK University

Postgraduate student, KROK University, Kyiv, Ukraine

References

ISLAM, Md Saiful; HOSSAIN, Emam. Foreign exchange currency rate prediction using a GRU-LSTM hybrid network. Soft Computing Letters, 2021, 3: 100009. URL: http://surl.li/gbove.

SOBREIRO, Vinicius Amorim, et al. The profitability of moving average trading rules in BRICS and emerging stock markets. The North American Journal of Economics and Finance, 2016, 38: 86-101. URL: http://surl.li/gbovl.

SALISU, Afees A.; GUPTA, Rangan; OGBONNA, Ahamuefula E. A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data. International Journal of Finance & Economics, 2022, 27.1: 384-400. URL: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ijfe.2158

YILDIRIM, Deniz Can; TOROSLU, Ismail Hakkı; FIORE, Ugo. Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financial Innovation, 2021, 7: 1-36. URL: http://surl.li/gbovr.

Moving averages: simple and exponential. URL: http://surl.li/gbovx.

SIM, Hyun Sik, et al. Is deep learning for image recognition applicable to stock market prediction?. Complexity, 2019, 2019. URL: https://www.hindawi.com/journals/complexity/2019/4324878/

DIACHENKO, Y.A. Rozvytok metodiv prohnozuvannia dynamiky birzhovych tsin na silskogospodarski tovary. 2018. URL: http://surl.li/gbous.pdf

KUO, Shu-Yu; CHOU, Yao-Hsin. Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms. IEEE Access, 2021, 9: 140383-140396. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9565877

Dzhusov A. A. Osobennosti primeneniya indikatora "skolzyashchaya srednyaya" dlya povisheniya effektivnosti investirovaniya / A. А. Dzhusov // Visnyk Dnipropetrovskoho universytetu. Seriia : Menedzhment innovatsii. - 2015. - Т. 23, vyp. 4. - s. 42-48. - URL: http://nbuv.gov.ua/UJRN/vdumi_2015_23_4_8

Bakai Ye. I. Model pryiniattia rishen dlia finansovykh chasovykh riadiv na osnovi pary serednikh z vykorystanniam otsinky riznykh chasovykh vymiriv / Ye. I. Bakai, V. V. Kabachyi, R. V. Maslii // Visnyk Vinnytskoho politekhnichnoho instytutu. - 2017. - № 1. - S. 70-77. - URL: http://nbuv.gov.ua/UJRN/vvpi_2017_1_13

Ostrovska K. Yu. Doslidzhennia tekhnichnykh indykatoriv dlia optymalnoi stratehii birzhevoho rynku z vykorystanniam Python ta biblioteky Ta-lib / K. Yu. Ostrovska, N. O. Kyslova, O. O. Holovatskyi // Systemni tekhnolohii. - 2018. - Vyp. 5. - С. 71-80. - URL: http://nbuv.gov.ua/UJRN/st_2018_5_11

GROBYS, Klaus; AHMED, Shaker; SAPKOTA, Niranjan. Technical trading rules in the cryptocurrency market. Finance Research Letters, 2020, 32: 101396. https://www.sciencedirect.com/science/article/pii/S1544612319308852

LI, Yuming; NI, Pin; CHANG, Victor. Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing, 2020, 102.6: 1305-1322. URL: http://surl.li/gbowk

HARI, Yulius; DEWI, Lily Puspa. Forecasting system approach for stock trading with relative strength index and moving average indicator. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2018, 10.2-3: 25-29. URL: https://core.ac.uk/download/pdf/270215518.pdf

AYCEL, Üzeyir; SANTUR, Yunus. A new moving average approach to predict the direction of stock movements in algorithmic trading. Journal of New Results in Science, 2022, 11.1: 13-25. URL: https://web.archive.org/web/20220511052359id_/https://dergipark.org.tr/en/download/article-file/1912987

Moving Average. Справка по MetaTrader 5. URL: https://www.metatrader5.com/ru/terminal/help/indicators/trend_indicators/ma

ZHANG, Zezheng; KHUSHI, Matloob. Ga-mssr: Genetic algorithm maximizing sharpe and sterling ratio method for robotrading. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. p. 1-8. URL: https://arxiv.org/ftp/arxiv/papers/2008/2008.09471.pdf

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

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

ATAS. Core mathematics for Forex traders. Part 2. URL: http://surl.li/gboxx.

Published

2023-03-30

How to Cite

Savchenko, V. (2023). FORECASTING THE DIRECTION OF CHANGES IN THE EXCNANGE RATE OF A FINANCIAL INSTRUMENT USING SIMPLE MOVING AVERAGES. Science Notes of KROK University, (1(69), 38–51. https://doi.org/10.31732/2663-2209-2022-69-38-51