Vol. 3 No. 5 (2025), Articles
Vol. 3 No. 5 (2025)

SCALABLE ALGORITHMS FOR HIGH-FREQUENCY TRADING DATA ANALYSIS: FROM TRADITIONAL INDEXING TO MODERN STREAM PROCESSING

Articles
Published 2025-05-12
R.T. Gaipnazarov
Tashkent University of Applied Sciences, Department of “Computer Engineering” Teacher1
F.A. Jo‘raboyev
Tashkent University of Applied Sciences, Department of “Computer Engineering” Teacher
A.A.Bahromov
Tashkent University of Applied Sciences, Department of “Computer Engineering” Teacher3
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Keywords

high-frequency trading, scalable algorithms, stream processing, data indexing, real-time analytics, Python, SQL

How to Cite

SCALABLE ALGORITHMS FOR HIGH-FREQUENCY TRADING DATA ANALYSIS: FROM TRADITIONAL INDEXING TO MODERN STREAM PROCESSING. (2025). MEDICINE, PEDAGOGY AND TECHNOLOGY: THEORY AND PRACTICE, 3(5), 145-153. https://universalpublishings.com/~niverta1/index.php/mpttp/article/view/11731
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Abstract

High-frequency trading (HFT) systems generate and process vast volumes of financial data in real time, demanding scalable and efficient algorithms for data analysis. This study presents a comparative investigation of classical indexing techniques and modern stream processing algorithms for HFT data analysis

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References

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