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Learn to build a comprehensive financial market analysis tool using intelligent agents in this tutorial that demonstrates the integration of Model Context Protocol (MCP), smolagents, FastAPI, and Streamlit to create a professional-grade investment platform. Discover how to leverage Large Language Models to orchestrate investment tools and transform raw market data into actionable insights through five distinct analysis types: technical analysis with four investment strategies for single stocks, market scanning for comparing multiple stocks simultaneously, fundamental analysis for interpreting financial statements, multi-sector analysis for portfolio diversification, and combined technical-fundamental analysis for building complete investment theses. Explore two types of AI agents including the traditional ToolCallingAgent that uses JSON to call tools sequentially, and the advanced CodeAgents that write Python code to invoke tools with loops and variables. Master the implementation of a modern web interface that delivers these analytical capabilities while understanding how AI-powered systems can interpret financial data and generate professional investment reports for better market decision-making.
