Solving a Portfolio Analysis Problem with LangGraph and Python

Author(s): Can Demir

Originally published on Towards AI.

In financial markets, analysts often evaluate an investment by looking at multiple perspectives: price trends, technical indicators, financial fundamentals, and sometimes market sentiment. Traditionally, performing such a fundamental analysis is a manual, time-consuming process. However, with the power of Artificial Intelligence (AI) and Large Language Models (LLMs), much of this analysis can be automated.

In this tutorial, we’ll build a portfolio analysis agent using Python and the LangChain ecosystem’s LangGraph framework. LangGraph provides a structured way to coordinate multiple LLM-based agents or “chains” in a well-defined workflow. By leveraging this framework, we can orchestrate a single agent that gathers data from various sources (prices, technical indicators, fundamentals) and synthesizes a coherent, actionable analysis. This approach mirrors how a hedge fund might use a portfolio manager agent who delegates to specialized analysts — think of fundamental, technical, and sentiment experts — then aggregates their findings.

We’ll walk through the entire solution, from environment setup and data ingestion to LangGraph configuration, agent creation, data flow logic, and — ultimately — interpreting results. The tutorial assumes familiarity with Python, the LangChain ecosystem, and basic financial concepts.

Imagine wanting to analyze a single stock (or an entire portfolio) to determine if it’s a wise investment. A comprehensive… Read the full blog for free on Medium.

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Published via Towards AI