[期刊论文][Article]


Parallelizing High-Frequency Trading using GPGPU

作   者:
Aditya Anil;Ashwin Sudha Arun;Lalitha Ramchandar;A. Balasundaram;

出版年:2021

页     码:465 - 470
出版社:Springer Nature


摘   要:

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.



关键字:

High-frequency trading; General-purpose graphics processing unit; Deep learning; Machine learning


所属期刊
National Academy Science Letters
ISSN: 0250-541X
来自:Springer Nature