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

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Originally published in MEDICINE, PEDAGOGY AND TECHNOLOGY: THEORY AND PRACTICE; Vol. 3 No. 5 (2025): MEDICINE, PEDAGOGY AND TECHNOLOGY: THEORY AND PRACTICE; 145-153.
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R.T., Gaipnazarov and F.A., Jo‘raboyev and A.A.Bahromov (2025) SCALABLE ALGORITHMS FOR HIGH-FREQUENCY TRADING DATA ANALYSIS: FROM TRADITIONAL INDEXING TO MODERN STREAM PROCESSING. MEDICINE, PEDAGOGY AND TECHNOLOGY: THEORY AND PRACTICE; Vol. 3 No. 5 (2025): MEDICINE, PEDAGOGY AND TECHNOLOGY: THEORY AND PRACTICE; 145-153.

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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

Item Type: Article
Additional Information: Imported from Medicine, Pedagogy and Technology: Theory and Practice
SWORD Depositor: Admin User
Depositing User: Admin User
Date Deposited: 30 Sep 2026 22:01
Last Modified: 30 Sep 2026 22:01
URI: https://universalpublishings.uz/id/eprint/19001

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