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Detecting trading trends in financial tick data : the DEBS 2022 grand challenge

Title data

Frischbier, Sebastian ; Tahir, Jawad ; Doblander, Christoph ; Hormann, Arne ; Mayer, Ruben ; Jacobsen, Hans-Arno:
Detecting trading trends in financial tick data : the DEBS 2022 grand challenge.
In: Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems. - New York : Association for Computing Machinery , 2022 . - pp. 132-138
ISBN 978-1-4503-9308-9

Abstract in another language

The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry. The GC 2022 edition focuses on real-time complex event processing of high-volume tick data provided by Infront Financial Technology GmbH. The goal of the challenge is to efficiently compute specific trend indicators and detect patterns in these indicators like those used by real-life traders to decide on buying or selling in financial markets. The data set Trading Data used for benchmarking contains 289 million tick events from approximately 5500+ financial instruments that had been traded on the three major exchanges Amsterdam (NL), Paris (FR), and Frankfurt am Main (GER) over the course of a full week in 2021. The data set is made publicly available. In addition to correctness and performance, submissions must explicitly focus on reusability and practicability. Hence, participants must address specific nonfunctional requirements and are asked to build upon open-source platforms. This paper describes the required scenario and the data set Trading Data, defines the queries of the problem statement, and explains the enhancements made to the evaluation platform Challenger that handles data distribution, dynamic subscriptions, and remote evaluation of the submissions.

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Data Systems > Chair Data Systems - Univ.-Prof. Dr. Ruben Mayer
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 26 Apr 2023 10:54
Last Modified: 26 Apr 2023 10:54