IMPLEMENTATION OF AN EFFECTIVE METHOD OF TECHNICAL ANALYSIS FOR DETERMINING THE MARKET TREND ON THE METATRADER 5 PLATFORM

  • T.N. Kondratieva Don State Technical University
  • I.F. Razveeva Don State Technical University
  • G.S. Onore Don State Technical University
  • E. R. Muntyan Southern Federal University
Keywords: Graphical analysis, technical analysis, multimarket platform, MetaTrader 5, Forex, algorithmic trading, innovative trading, trading strategy, technical indicator

Abstract

The article discusses the possibilities of using effective methods of technical analysis to determine
the trend direction of financial assets on the multi-market platform MetaTrader 5.
The advantages and expediency of using technical analysis methods in various areas of financial
markets are shown by examples of works by Russian and foreign authors. In order to determine
the direction and strength of the trend, the insufficiency of the use of such technical indicators as
Bollinger bands (BB), MACD, MA, EMA, constructed according to various laws of the distribution
of prices of financial assets, is justified. Graphical analysis tools are considered as a special case
of technical analysis, characterized by special visibility and visualization of the process under
study. The article provides examples of technical analysis figures, shows them to belong to the
basic types of graphical analysis. The concept of a modern trading strategy – innovative trading is
considered, a comparative analysis of the trading strategy, its opportunities and risks is carried out. The paper describes the developed software module in the form of a trading indicator on the
MetaTrader 5 platform in Meta Quotes Language 5 (MQL5) in combination with a script in the
high-level Python language for algotrading using technical analysis methods. The indicator is
based on the construction of support and resistance lines to determine the direction and strength
of the trend. An algorithm for determining the direction and strength of the trend with technical
analysis tools has been developed, which as a result will improve the accuracy of the trend direction
forecast by integrating a technical indicator into the system. For the experimental part of the
process, a financial instrument was used – the EUR/USD currency pair, the total study period was
six months. As a result, the algorithm worked correctly in real time without human intervention for
twenty-four weeks. Based on the test results, it is possible to judge the prospects of research in this
area for solving a large range of applied problems of financial mathematics and analytics.
The application of the created indicator can be extended to any trading operations on various
financial instruments and their combinations.

References

1. Hernández Martínez Robert. Automated Investing. Algorithmic Trading in Financial Markets:
Expert Advisor (EA), 2022.
2. Kondrat’ieva T.N., Muntyan E.R., Razveeva I.F. Realizatsiya torgovogo sovetnika dlya
mul'tirynochnoy platformy MetaTrader 5 [Implementation of a trading advisor for the
MetaTrader 5 multi-market platform]. Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU.
Engineering Sciences], 2022, No. 4, pp. 157-169.
3. Eremin V.V. Vybor tekhnicheskogo indikatora dlya rossiyskogo fondovogo rynka [The choice
of a technical indicator for the Russian stock market]. Alleya nauki [Alley of Science], 2020,
Vol. 1, No 11(50), pp. 97-105.
4. Kondrat’ieva T.N. Kompleksnyy podkhod analiza sovremennykh indikatorov finansovogo
rynka [An integrated approach to the analysis of modern indicators of the financial market].
Obozrenie prikladnoy i promyshlennoy matematiki [Review of Applied and Industrial Mathematics],
2015, Vol. 22, No 5, pp. 589-590.
5. Kondrat’ieva T.N., Triputa I.G., Prudnikov S.E. Optimizatsiya i testirovanie sovetnika v
klientskom terminale MetaTrader 5 [Optimization and testing of the Expert Advisor in the
MetaTrader 5 client terminal], Sovremennye naukoemkie tekhnologii [Modern high-tech technologies],
2018, No 12-1, pp. 77-83.
6. Irwin S.H.; Park C.-H. What do we know about the profitability of technical analysis?,
J. Econ. Surv., 2007, 21, pp. 786-826.
7. Market Technicians Association. CMT Level I 2016: An Introduction to Technical Analysis;
JohnWiley & Sons: Hoboken, NJ, USA, 2015.
8. Sevryugin Yu.V., Soshin N.A. Klassifikatsiya avtomatizirovannykh birzhevykh torgovykh
strategit - torgovyh robotov [Classification of automated exchange trading strategies - trading
robots]. Aktual'nye problemy i perspektivy razvitiya ekonomiki: rossiyskij i zarubezhnyy opyt
[Actual problems and prospects for the development of the economy: Russian and foreign experience],
2022, No. 2 (40), pp. 7-11.
9. Vizgunov A.N., Goldengorin B.I., Zamaraev V.A., Kalyagin V.A., Koldanov A.P.,
Koldanov P.A., Pardalos P.M. Primenenie rynochnykh grafov k analizu fondovogo rynka
[Application of market graphs to stock market analysis]. Zhurnal NEA [NEA Journal], 2012,
No. 3 (15), pp. 66-81.
10. Vizgunov A.N., Trifonov Yu.V. Primenenie modeli grafa dohodnostey dlya analiza fondovogo
rynka [Application of the yield graph model for stock market analysis]. Vestnik
Nizhegorodskogo universiteta im. N.I. Lobachevskogo [Bulletin of the Nizhny Novgorod University
named after N.I. Lobachevsky], 2013, No. 6 (1), pp. 285-289.
11. Muntyan E.R. Realizatsiya nechetkoy modeli vzaimodeystviya ob"ektov slozhnykh
tekhnicheskikh sistem na osnove grafov [Implementation of a fuzzy model of interaction between
objects in complex technical systems based on graphs], Programmnye produkty i
sistemy [Software & Systems], 2019, Vol. 32, No. 3, pp. 411-418. DOI: 10.15827/0236-
235X.127.411-418.
12. Muntyan E.R. Ispol'zovanie nechetkikh GH-modeley dlya predstavleniya slozhnykh
tekhnicheskikh sistem [Using fuzzy GH-models to represent the complex technical systems],
Informatizatsiya i svyaz' [Informatization and communication], 2020, No. 3, pp. 55-60. DOI:
1034219/.2078-8320-2021-12-3-55-60.
13. Vereshun V.D., Kolodenkova A.E., Muntyan E.R. Integrated approach to modeling the objects
of complex technical systems, Lecture Notes in Networks and Systems, 2022, Vol. 330, LNNS,
pp. 199-204. DOI: 10.1007/978-3-030-87178-9_20.
14. Kondrat’ieva T.N. Predskazanie tendentsiy razvitiya riskovykh aktivov pri pomoshchi polos
Bollindzhera dlya rossiyskogo finansovogo rynka [Prediction of trends in the development of
risky assets using Bollinger bands for the Russian financial market], Obozrenie prikladnoy i
promyshlennoy matematiki [Review of Applied and Industrial Mathematics], 2014, Vol. 21,
No. 1, pp. 63-64.
15. Yanyun Yao, Shangzhen Cai & Huimin Wang. Are technical indicators helpful to investors in
china’s stock market? A study based on some distribution forecast models and their combinations,
Economic Research-Ekonomska Istraživanja, 2022, 35:1, 2668-2692. DOI: 10.1080/
1331677X.2021.1974921.
16. Han Yufeng, Liu Yang, Zhou Guofu and Zhu Yingzi. Technical Analysis in the Stock Market:
A Review (May 21, 2021). Available at SSRN: https://ssrn.com/abstract=3850494.
17. Nazário R.T.F., Silva J.L., Sobreiro V.A., Kimura H. A literature review of technical analysis
on financial asset markets. Q. Rev. Econ. Financ. 2017, 66, pp, 115-126.
18. Kondrat’ieva T.N., Rveveeva I.F. Avtomaticheskaya torgovaya sistema v klientskom terminale
MetaTrader 5 [Automated trading system in the MetaTrader 5 client terminal], Sovremennye
naukoemkie tekhnologii [Modern high technologies], 2018, No. 10, pp. 56-60.
19. Belokonskaya E.G., Kalyagin I.I. Razvitie instrumental'nykh sredstv algotreydinga na osnove
primeneniya neyronnykh setey [Development of tools for algorithmic trading based on the use
of neural networks]. Izvestiya vysshikh uchebnykh zavedeniy. Seriya: Ekonomika, finansy i
upravlenie proizvodstvom [News of higher educational institutions. Series: Economics, finance
and production management], 2017, No. 1 (31), pp. 53-56.
20. Zhang Chao, Zhang Yihuang, Cucuringu Mihai and Qian Zhongmin. Volatility Forecasting
with Machine Learning and Intraday Commonality (May 2022).
21. Kondratieva T., Prianishnikova L., Razveeva I. Machine learning for algorithmic trading. E3S
Web of Conferences, Moscow, 2020, pp. 01019. DOI 10.1051/e3sconf/202022401019.
22. Kondrat’ieva T.N. Prognozirovanie tendentsiy finansovykh vremennykh ryadov s
pomoshch'yu neyronnoy seti LSTM [Forecasting trends of financial time series using the
LSTM neural network]. Internet-zhurnal “Naukovedenie” [Online journal of “Science Studies”],
2017, Vol. 9, No. 4, pp. 61.
23. Chen W. Comparative study on volatility prediction effect of Shanghai Composite Index Based
on deep learning. Stat. Inf. Forum, 2018, 2018, 33, 99-106.
24. Lei B., Zhang B., Song Y. Volatility Forecasting for High-Frequency Financial Data Based on
Web Search Index and Deep Learning Model. Mathematics, 2021, 9 (4), 320 p. Available at:
https://doi.org/ 10.3390/math9040320.
Published
2023-02-17
Section
SECTION I. MODELS AND METHODS OF INFORMATION PROCESSING