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Parallelizing High-Frequency Trading using GPGPU

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Abstract

The world of trading and market has evolved greatly. With the aid of technology, traders and trading establishments use trading platforms to perform various transactions. They are able to utilize several effective algorithms to analyse the market data and identify the key points required to carry out a successful trading operation. High-frequency trading (HFT) platforms are capable of such operations and are used by traders, investors and establishments to make their operations easier and faster. To accommodate high processing and high frequency of transactions, we integrate the concept of parallelism and combine the processing power of GPU using general-purpose graphics processing unit (GPGPU) to enhance the speedup of the system. High processing power without involving further costs in hardware upgradation is our approach. Methods of deep learning and machine learning also add a feature to provide help or assistance for several traders using this platform.

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Informed Consent

This work was carried out towards enhancing the speedup and promoting better high-frequency trading by combining parallelism and GPU processing. A deep learning model, namely long short-term memory (LSTM), has been used in combination with GPGPU to enhance the performance of this HFT system and the performance is reported.

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Acknowledgements

The authors wish to express their sincere thanks to the management of VIT for permitting to take up this work.

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Not Applicable.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India

    Aditya Anil, Ashwin Sudha Arun, Lalitha Ramchandar & A. Balasundaram

  2. Center for Cyber Physical Systems, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India

    A. Balasundaram

Authors
  1. Aditya Anil
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  2. Ashwin Sudha Arun
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  3. Lalitha Ramchandar
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  4. A. Balasundaram
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Correspondence to A. Balasundaram.

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Anil, A., Arun, A.S., Ramchandar, L. et al. Parallelizing High-Frequency Trading using GPGPU. Natl. Acad. Sci. Lett. 44, 465–470 (2021). https://doi.org/10.1007/s40009-021-01064-9

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  • DOI: https://doi.org/10.1007/s40009-021-01064-9

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