How to Build Your Own Agentic Investment Team with Phidata

Author(s): Yaksh Birla

Originally published on Towards AI.

Create a multi-agent framework that puts super-powered financial analysis in your pocket.

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A common challenge when valuing equities is: processing vast amounts of information from multiple sources to make informed investment decisions.

Traditional approaches involve:

Gathering financial data from various platforms.Reading through multiple reports, news and other articles.Building and maintaining complex spreadsheet models.Synthesizing this information into something actionable.

Holding and managing all of these inputs all at once often starts looking like this:

It’s no secret, AI stands as one of the most viable solutions, making it easy to process large datasets efficiently.

However, most large language models (LLMs) available to the public today still can’t perform detailed deep dives into real-time stock data with much accuracy.

ChatGPT and Claude have context date cutoffs.Perplexity is great for real-time information but limited when it comes to analytical tasks.ChatGPT Search is still underwhelming.

For detailed research and equity analysis, we need something more precise and able to work with real-time information and structured datasets.

In fact, what if we could combine it all? What if we could leverage AI agents that can navigate the news, Google searches, financial datasets and coding tasks all in one system?

A… Read the full blog for free on Medium.

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Published via Towards AI