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

TL;DR

This paper presents the DEBS 2022 Grand Challenge focused on real-time detection of trading trends in high-volume financial tick data, emphasizing efficiency, reusability, and practical applicability in a competitive setting.

Contribution

It introduces a large-scale benchmark dataset, defines specific trend detection queries, and details enhancements to the evaluation platform for real-time processing of financial data.

Findings

Benchmark dataset with 289 million tick events available publicly

Participants demonstrated real-time trend detection capabilities

Evaluation platform supports dynamic subscriptions and remote evaluation

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.