Parallelizing High-Frequency Trading using GPGPU

被引：0

作者：

Anil, Aditya ^{[1
]}

Arun, Ashwin Sudha ^{[1
]}

Ramchandar, Lalitha ^{[1
]}

Balasundaram, A. ^{[1
,2
]}

机构：

[1] Vellore Inst Technol VIT, Sch Comp Sci & Engn, Chennai, Tamil Nadu, India

[2] Vellore Inst Technol VIT, Ctr Cyber Phys Syst, Chennai, Tamil Nadu, India

来源：

NATIONAL ACADEMY SCIENCE LETTERS-INDIA | 2021年 / 44卷 / 05期

关键词：

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

D O I：

10.1007/s40009-021-01064-9

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

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.

引用

页码：465 / 470

页数：6

共 6 条

[1]

Andersen A.C., 2012, A novel algorithmic trading framework applying evolution and machine learning for portfolio optimization

[2]

Brook Matthew, 2013, 2013 IEEE 15th Conference on Business Informatics (CBI), P101, DOI 10.1109/CBI.2013.23

[3]

Ghorpade J, 2012, ARXIV PREPRINT ARXIV

[4]

Jia XY, 2018, 2018 IEEE 4TH INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE 2018), P49, DOI 10.1109/CCSSE.2018.8724810

[5] Deep Robust Reinforcement Learning for Practical Algorithmic Trading [J].

Li, Yang ;

Zheng, Wanshan ;

Zheng, Zibin .

IEEE ACCESS, 2019, 7 :108014-108022

[6]

Wang Z, 2015, EAST CHINA NORM UNIV, V1, P1

← 1 →