Build a live market context agent for stock screening
A commenter paired a custom RAG agent with a live database to keep stock research and screening tied to current market data.
- What they did
- They said standard ChatGPT lagged behind the market and made it hard to compare metrics across names. To fix that, they put together their own retrieval agent on top of a live database so it could answer research questions with up-to-date market context. They used it for chat-style analysis and stock screening.
- How it went
- The main benefit was fresher context for evaluating stocks. They did not share performance stats, accuracy figures, or a detailed implementation, and the setup details for the database and retrieval process were left unspecified.
- Tips
- If you try this, decide first what data you will keep current and how often it should refresh. Build in a way to show the source behind each answer, and pause for approval before any action that would submit, send, or change data.
Try it yourself with Claude Code
Help me create a simple retrieval agent for current stock screening. Use [market data source or database] and pull in recent prices, key fundamentals, and news for [tickers, sector, or watchlist]. Let me ask questions such as: which names show improving earnings revisions this week, or which holdings have the strongest momentum right now? Show the supporting sources with each answer, and stop to ask for my approval before you run anything that would submit, send, delete, or change data. When you are done, I want to be able to run the query from [terminal/app] and review the result with citations.
Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.
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