<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid development of quantitative trading business in the field of investment, quantitative trading platform is becoming an important tool for numerous investing users to participate in quantitative trading. In using the platform, return time of backtesting historical data is a key factor that influences user experience. In the aspect of optimising data access time, cache management is a critical link. Research work on cache management has achieved many referential results. However, quantitative trading platform has its special demands. (1) Data access of users has overlapping characteristics for time-series data. (2) This platform uses a wide variety of caching devices with heterogeneous performance. To address the above problems, a cache management approach adapting quantitative trading platform is proposed. It not only merges the overlapping data in the cache to save space but also places data into multi-level caching devices driven by user experience. Our extensive experiments demonstrate that the proposed approach could improve user experience up to >50% compared with the benchmark algorithms.</p>
</abstract>

With the rapid development of quantitative trading business in the field of investment, quantitative trading platform is becoming an important tool for numerous investing users to participate in quantitative trading. In using the platform, return time of backtesting historical data is a key factor that influences user experience. In the aspect of optimising data access time, cache management is a critical link. Research work on cache management has achieved many referential results. However, quantitative trading platform has its special demands. (1) Data access of users has overlapping characteristics for time-series data. (2) This platform uses a wide variety of caching devices with heterogeneous performance. To address the above problems, a cache management approach adapting quantitative trading platform is proposed. It not only merges the overlapping data in the cache to save space but also places data into multi-level caching devices driven by user experience. Our extensive experiments demonstrate that the proposed approach could improve user experience up to >50% compared with the benchmark algorithms.

Multi-level cache management of quantitative trading platform

School of Computer Science and Technology

Shandong University of Finance and Economics

Information Technology Department, Faculty of Computing and Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Multi-level cache management of quantitative trading platform"}

With the rapid development of quantitative trading business in the field of investment, quantitative trading platform is becoming an important tool for...

Multi-level cache management of quantitative trading platform

Publié en ligne: 10 nov. 2021

Pages: 249 - 260

Reçu: 02 mars 2021

Accepté: 22 juil. 2021

DOI: https://doi.org/10.2478/amns.2021.2.00045

Mots clés
multi-level cache, cache placement, cached data merging, user experience function, quantitative trading platform

© 2021 Zou Lida et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Multi-level cache management of quantitative trading platform

Publié en ligne: 10 nov. 2021

Pages: 249 - 260

Reçu: 02 mars 2021

Accepté: 22 juil. 2021

DOI: https://doi.org/10.2478/amns.2021.2.00045

Mots clésmulti-level cache, cache placement, cached data merging, user experience function, quantitative trading platform

© 2021 Zou Lida et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
multi-level cache, cache placement, cached data merging, user experience function, quantitative trading platform