Detecting technical trading patterns in financial data with Apache Flink

Le résumé fourni par la source

The DEBS Grand Challenge is an annual competition in which participants strive to build the fastest and most scalable distributed and event-based systems that solve a practical problem. For the year 2022, the challenge focuses on real-time complex event processing of real-world high-volume financial trading data. The goal is to efficiently compute specific trend indicators and detect patterns that would assist real-life traders in deciding to buy or sell on the financial markets. This paper aims to solve the above challenge using Apache Flink [1] - an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation.

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