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Project 03 / AI + financial data

AI Stock Agent

Many sources. A clearer research picture.

Contribution

Project development

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Project documentation Source code
Study 03 / Abstract interpretationIndependent traces converge into an ordered field: different sources, one research surface.

01 / Context

The starting point.

Researching a publicly traded company means moving between different kinds of information. AI Stock Agent brings stock prices, news, and financial metrics into one research workflow.

02 / Approach

How the pieces connect.

A LangChain agent connects the research process to external financial data. A Flask interface makes the resulting analysis accessible through the browser.

  1. 01Prices, news, metrics
  2. 02LangChain agent
  3. 03Company research
  4. 04Flask interface
A conceptual view of the research workflow. The artwork represents signals, not actual market prices or investment results.

03 / In focus

Connecting data to an interface

The stack combines Python, LangChain, OpenAI, and Polygon.io, with Flask and Bootstrap providing the web interface. The emphasis is on bringing separate sources into an experience that can be explored.

  • Python
  • Flask
  • LangChain
  • OpenAI
  • Polygon.io
  • Bootstrap

04 / Outcome

What came together.

The result is an AI-assisted company research tool with a web interface. Source and setup details are available in the repository.

Read the project documentation
Next connection / 04Ask Eddie