Parallel computing model based on python in quantitative analysis

Qiutong Li; Yuechen Yang; Xiaona Kang

doi:10.1117/12.2639265

17 May 2022 Parallel computing model based on python in quantitative analysis

Qiutong Li, Yuechen Yang, Xiaona Kang

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 1225916 (2022) https://doi.org/10.1117/12.2639265
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

The calculation of serious technique index and using historical data is an important cornerstone for back test of trading strategy. It is a critical step that how to complete this calculation and test in determine the ultimate success of quantitative analysis. On the basis of python multiprocessing library, this paper proposes and implements a multi-process parallel computing model which is light-weight and easy-using. This model abstracts the multi-process concurrent computing as a base class which is used for all kinds of index calculation and history back tests to be independent operated. It could reduce the coupling between various strategies calculation and make full use of the CPU resources that effectively improve the computing speed of back testing strategy as well as provide a reliable guarantee for quantitative analysis.

Citation Download Citation

Qiutong Li, Yuechen Yang, and Xiaona Kang "Parallel computing model based on python in quantitative analysis", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 1225916 (17 May 2022); https://doi.org/10.1117/12.2639265

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available