IDEAS home Printed from https://ideas.repec.org/a/aip/access/v2y2021i3p222-233.html
   My bibliography  Save this article

Digitalization of data analysis tools as the key for success in the online trading markets

Author

Listed:
  • Andrii ROSKLADKA

    (Kyiv National University of Trade and Economics, Kyiv, Ukraine)

  • Roman BAIEV

    (Kyiv National University of Trade and Economics, Kyiv, Ukraine)

Abstract

Digitalization is the process, which takes places in the contemporary world, severe competition in all markets, growing demand for new and easy technologies, changing software user preferences, and much more. For the past decades of years, the share of online forex trades that had place in the world accounted for more than 90% of all forex deals. This article discusses the main points of the online trading, the prospects of this industry and key factors that may affect the industry; also, the crucial attention was paid to the online trading tools segment, as a part of the immense market in general and as a tool in the portfolio of every modern online trading specialist. The online commerce industry is one of the largest and fastest growing sectors of the world economy. It creates jobs and assets, makes people richer and provides truly equal access for all. An analysis of existing online trading tools, such as the MT4 platform, was conducted. The main characteristics of online trade in European countries and the problems of its development in Ukraine are studied. The main components of trade in the supply area were studied in detail. Based on the research of the authors, the algorithmic rules of the automated trading system (expert advisor) were described and its software implementation is created. Similar automated systems are especially helpful when specialists need to work with many charts and timeframes at once, because the system might indicate the proper market situation better, and the system won’t have any human-biased feelings, just pure strategies and algorithms.

Suggested Citation

  • Andrii ROSKLADKA & Roman BAIEV, 2021. "Digitalization of data analysis tools as the key for success in the online trading markets," Access Journal, Access Press Publishing House, vol. 2(3), pages 222-233, September.
    • Handle: RePEc:aip:access:v:2:y:2021:i:3:p:222-233
    DOI: 10.46656/access.2021.2.3(2)
    as

    Download full text from publisher

    File URL: https://journal.access-bg.org/journalfiles/journal/issue-2-3-2021/digitalization_of_data_analysis_tools_as_the_key_for_success.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.46656/access.2021.2.3(2)?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    2. Almeida, Marco Pollo & Paixão, Rafael S. & Ramos, Pedro L. & Tomazella, Vera & Louzada, Francisco & Ehlers, Ricardo S., 2020. "Bayesian non-parametric frailty model for dependent competing risks in a repairable systems framework," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Loy, Adam & Hofmann, Heike, 2014. "HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i05).
    4. Maximilian Böck & Martin Feldkircher & Florian Huber, 2020. "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R," Globalization Institute Working Papers 395, Federal Reserve Bank of Dallas.
    5. Guo, Jia & Zhou, Bu & Zhang, Jin-Ting, 2018. "Testing the equality of several covariance functions for functional data: A supremum-norm based test," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 15-26.
    6. Gregor Kastner & Sylvia Fruhwirth-Schnatter & Hedibert Freitas Lopes, 2016. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models," Papers 1602.08154, arXiv.org, revised Jul 2017.
    7. Aaron T L Lun & Hervé Pagès & Mike L Smith, 2018. "beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-15, May.
    8. Shen, Yunyi & Olson, Erik R. & Van Deelen, Timothy R., 2021. "Spatially explicit modeling of community occupancy using Markov Random Field models with imperfect observation: Mesocarnivores in Apostle Islands National Lakeshore," Ecological Modelling, Elsevier, vol. 459(C).
    9. Zhe Yu & Raquel Prado & Erin Burke Quinlan & Steven C. Cramer & Hernando Ombao, 2016. "Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 549-563, April.
    10. Kenneth A. Flagg & Andrew Hoegh & John J. Borkowski, 2020. "Modeling Partially Surveyed Point Process Data: Inferring Spatial Point Intensity of Geomagnetic Anomalies," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 186-205, June.
    11. Lu Chen & Balgobin Nandram, 2023. "Bayesian Logistic Regression Model for Sub-Areas," Stats, MDPI, vol. 6(1), pages 1-23, January.
    12. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
    13. Xiaolei Wang & Tomasz Wo'zniak, 2025. "Bayesian Analyses of Structural Vector Autoregressions with Sign, Zero, and Narrative Restrictions Using the R Package bsvarSIGNs," Papers 2501.16711, arXiv.org.
    14. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
    15. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    16. Wim J. van der Linden & Luping Niu & Seung W. Choi, 2024. "A Two-Level Adaptive Test Battery," Journal of Educational and Behavioral Statistics, , vol. 49(5), pages 730-752, October.
    17. François Bachoc & Marc G Genton & Klaus Nordhausen & Anne Ruiz-Gazen & Joni Virta, 2020. "Spatial blind source separation," Biometrika, Biometrika Trust, vol. 107(3), pages 627-646.
    18. Dirk Eddelbuettel & James Joseph Balamuta, 2018. "Extending R with C++: A Brief Introduction to Rcpp," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 28-36, January.
    19. Fischer, Daniel & Oja, Hannu, 2015. "Mann-Whitney Type Tests for Microarray Experiments: The R Package gMWT," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i09).
    20. Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Structural Vector Autoregressions with Non-Centred Stochastic Volatility," Papers 2404.11057, arXiv.org, revised Oct 2025.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aip:access:v:2:y:2021:i:3:p:222-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mariana Petrova (email available below). General contact details of provider: https://access-bg.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.