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Toward Real-Time High-Frequency Stock Monitoring System Using Node.js

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Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) (SoCPaR 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 614))

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Abstract

Investing in stocks is a very popular financial management for modern people. Accurate data is aided for investor to make better decisions. But there are some challenges to design such a real-time system to present high-Frequent data. This paper introduced a solution toward real-time high-frequency stock monitoring system. The architecture, process and implementation of this system are presented respectively. This system is better in real-time monitoring, distributed services, high degree of concurrency, and interactive user experience using cache, load balancing, and CDN techniques. It is evaluated that Node.js performed well with advantages of its single thread event-driven mode and asynchronous I/O when it was in the environment of high concurrency.

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Acknowledgments

This work was supported by the Teaching Research Project of University of Jinan (J1524), the Project of Cooperative Education of The Ministry of Education (201601018009), the School-Enterprise Cooperation Project of 2017 Tencent Education Reform of Innovation and Entrepreneurship, the Science and Technology Program of University of Jinan (XKY1734), the Open Project Joint Funding of Information Science and Engineering School of Linyi University and Discipline Team of Intelligent Logistics and Information Engineering (LDXX2017KF155), the Shandong Provincial Natural Science Foundation (ZR201702170261), the Shandong Provincial Key R&D Program (2015GGX106007 & 2016ZDJS01A12), and the Project of Shandong Province Higher Educational Science and Technology Program (J16LN13).

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Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Hao Qu, Kun Ma, Zhe Yang & Xuewei Niu

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China

    Kun Ma

  3. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    Zhe Yang

  4. Machines Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, 98071, USA

    Ajith Abraham

Authors
  1. Hao Qu
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  2. Kun Ma
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  3. Zhe Yang
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  4. Xuewei Niu
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  5. Ajith Abraham
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Corresponding author

Correspondence to Kun Ma .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. VIT University, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. School of Engineering, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  4. Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Qu, H., Ma, K., Yang, Z., Niu, X., Abraham, A. (2018). Toward Real-Time High-Frequency Stock Monitoring System Using Node.js. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-60618-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60617-0

  • Online ISBN: 978-3-319-60618-7

  • eBook Packages: EngineeringEngineering (R0)

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