This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business.
Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item. One such additional piece of information (which we use as an example in this post) is the sentiment of the news.
Refinitiv Data (RD) Libraries provide a comprehensive set of interfaces for uniform access to the Refinitiv Data Catalogue. The library offers multiple layers of abstraction providing different styles and programming techniques suitable for all developers, from low-latency, real-time access to batch ingestions of Refinitiv data.
In this post, we present a prototype AWS architecture that ingests our news feeds using RD Libraries and enhances them with machine learning (ML) model predictions using Amazon SageMaker,

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