ACS Applied Computer Science

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THE POTENTIAL FOR REAL-TIME TESTING OF HIGH-FREQUENCY TRADING STRATEGIES THROUGH A DEVELOPED TOOL DURING VOLATILE MARKET CONDITIONS

Mantas VAITONIS, Konstantinas KOROVKINAS
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The purpose of this paper was to investigate in practice the possibility of using evolutionary algorithms to solve the traveling salesman problem on a real example. The goal was achieved by developing an original implementation of the evolutionary algorithm in Python, and by preparing an example of the traveling salesman problem in the form of a directed graph representing Polish voivodship cities. As part of the work an application in Python was written. It provides a user interface which allows to set selected parameters of the evolutionary algorithm and solve the prepared problem. The results are presented in both text and graphical form. The correctness of the evolu¬tionary algorithm's operation and the implementation was confirmed by performed tests. A large number of tested solutions (2500) and the analysis of the obtained results allowed for a conclusion that an optimal (relatively suboptimal) solution was found.

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  • APA 7th style
Vaitions, M., & Korovkinas, K. (2023). The potential for real-time testing of high-frequency trading strategies through a developed tool during volatile market conditions. Applied Computer Science, 19(2), 63-81. https://doi.org/10.35784/acs-2023-15
  • Chicago style
Vaitions, Mantas, and Konstantinas Korovkinas. "The potential for real-time testing of high-frequency trading strategies through a developed tool during volatile market conditions." Applied Computer Science 19, no. 2 (2023): 63-81.
  • IEEE style
M. Vaitions and K.  Korovkinas, "The potential for real-time testing of high-frequency trading strategies through a developed tool during volatile market conditions," Applied Computer Science, vol. 19, no. 2, pp.63-81, 2023, doi: 10.35784/acs-2023-15.
  • Vancouver style
Vaitions M, Korovkinas K. The potential for real-time testing of high-frequency trading strategies through a developed tool during volatile market conditions. Applied Computer Science. 2023;19(2):63-81.

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