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

Аннотация

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. We examine the limitations of traditional database indexing (e.g., B-trees, hash indexes) in the context of low-latency, high-throughput environments and contrast them with contemporary stream processing frameworks and probabilistic data structures. Using synthetic and real-world trading datasets, we implement and benchmark representative algorithms in Python and SQL, evaluating their performance in terms of latency, throughput, and scalability. Our results demonstrate that while classical methods remain effective for batch analytics and historical queries, modern stream processing approaches-such as windowed aggregation, approximate counting, and event-driven architectures-offer significant advantages for real-time HFT applications. The findings provide actionable insights for practitioners and researchers developing next- generation financial analytics platforms. 145 https://universalpublishings.com МЕДИЦИНА, ПЕДАГОГИКА И ТЕХНОЛОГИЯ: ТЕОРИЯ И ПРАКТИКА Researchbib Impact factor: 13.14/2024 SJIF 2024 = 5.444 Том 3, Выпуск 05, Мая Keywords: high-frequency trading, scalable algorithms, stream processing, data indexing, real-time analytics, Python, SQL