Building Financial AI Agents with RAG and Connors RSI

Financial data analysis is evolving rapidly with the emergence of artificial intelligence agents and modern retrieval systems. This text-based course guides you through developing specialized AI agents capable of analyzing market data, evaluating quantitative indicators like Connors RSI, and referencing financial documents with high accuracy. You will transition from fundamental concepts to building functional retrieval-augmented generation pipelines tailored for finance. Through clear explanations and practical written exercises, you will explore how combining quantitative rules with generative intelligence creates powerful decision-support tools. What you'll learn: - Understand foundational principles of quantitative finance and technical indicators including Connors RSI - Implement retrieval-augmented generation architectures using modern vector storage patterns - Design autonomous AI agents that analyze financial datasets and synthesize textual reports - Apply prompt engineering techniques to ensure precise, grounded financial reasoning - Integrate structured market data with unstructured financial filings for comprehensive analysis - Practice backtesting and evaluating indicator-driven strategies through structured code examples The course begins with essential financial terminology, indicator mathematics, and AI agent architectures before advancing to pipeline integration and strategy testing. Designed for beginners in AI engineering and financial data science, no prior background in quantitative finance is required. Start reading today to build your own intelligent financial analysis tools.