AI Case Study: Implementing a realtime Financial News Agent - part 1: Overview

Image of a frustrated trader which is sitting in front of multiple screens. The amount of information overwhelms him

The Problem

24H a day 7 days a week - Financial Markets never sleep.

How can you as a human keep up with a constantly evolving stream of information? In an era marked by rapid changes and overwhelming data, filtering out the noise to focus on what truly matters is a real challenge.

Moreover, with endless updates pouring in from every corner of our digital world, the pressure to discern reliable insights intensifies. As information cascades relentlessly, it becomes ever more difficult to determine which voices are essential and which are merely background noise.

The Solution

In this tutorial we build a Open Source Realtime Market Tracker which may help you in your daily market research.

Visualization of an AI market analysis system showing multiple data streams being processed in real-time

Structure of the agentic system (broad view)

Inputs:

Audio
Video
Text on screen

Reasoning Agent:

Reason over the news
Assign sentiment to the news
Assign a confidence score to the sentiment
collects knowledge about events, entities, etc.

Outputs:

Summary of the news
Summary of current narratives
Alert the user on significant events

Knowledge Base:

Collects knowledge about events, entities, etc.

Used Technologies

Python
OpenCV
Face Recognition
OpenAI Whisper

Interactive Animation of the the System Structure:

Zoom in to see the details:

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Structure of this Tutorial

We will build this system in stages On the end of each stage there will be a fully working prototype with the Scope of the stage This will make it easy to implement the stages by yourself.

Here is an overview of all Articles