IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2206.13237.html
   My bibliography  Save this paper

The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data

Author

Listed:
  • Sebastian Frischbier
  • Jawad Tahir
  • Christoph Doblander
  • Arne Hormann
  • Ruben Mayer
  • Hans-Arno Jacobsen

Abstract

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.

Suggested Citation

  • Sebastian Frischbier & Jawad Tahir & Christoph Doblander & Arne Hormann & Ruben Mayer & Hans-Arno Jacobsen, 2022. "The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data," Papers 2206.13237, arXiv.org.
  • Handle: RePEc:arx:papers:2206.13237
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2206.13237
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sebastian Frischbier & Mario Paic & Alexander Echler & Christian Roth, 2019. "Managing the Complexity of Processing Financial Data at Scale -- an Experience Report," Papers 1908.03206, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      NEP fields

      This paper has been announced in the following NEP Reports:

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2206.13237. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.