MCP 101 Tutorial: Build Your Own Modular AI Agent for Stock Investment Insights

Author(s): Lorentz Yeung

Originally published on Towards AI.

MCP 101 Tutorial: Build Your Own Modular AI Agent for Stock Investment Insights

Welcome to this MCP 101 tutorial! A brief context of the project I started building AI systems for stock investment in 2021 with a sentiment analysis-based trading system powered by XGBoost. That system analyzed market sentiment from news, social media, and financial reports to predict stock movements and generate trading signals. It combined natural language processing (NLP) for sentiment scoring, machine learning for pattern recognition, and backtesting for strategy validation.

This tutorial demonstrates the setup and use of a modular AI system named MCP (Multi-Component Protocol) that integrates advancements in AI orchestration for stock investment. The article covers the framework’s components, necessary software installations, and practical applications. It also highlights the features of a flexible AI agent capable of executing real-time market queries and arithmetic operations. Further instructions for running the code and enhancing the system with additional functionalities, such as sentiment analysis and XGBoost integration, are provided to encourage contributions and experimentation from the community.

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Published via Towards AI