Feb 24
Loading video player...
📄 YouTube Description (SEO-Optimized) 🚀 In this video, I build a full end-to-end AI Stock Analysis Bot using CrewAI, GPT-4, Streamlit, and real market data. This is not a toy project — it’s a production-ready, multi-agent AI system that performs: ✅ Stock price performance analysis ✅ Advanced risk & volatility metrics (Sharpe, Sortino, Drawdown, CAGR) ✅ Fundamental analysis using real financial data ✅ Price anomaly detection using quantitative methods ✅ Buy / Hold / Sell signal generation ✅ Per-stock AI memory (long-term insights per ticker) ✅ Natural language Q&A with context awareness The system uses multiple AI agents (Stock Analyst, Risk Analyst, Fundamental Analyst, Anomaly Analyst) orchestrated via CrewAI, combined with quantitative finance logic and LLMs for explainability. 🧠 What You’ll Learn How to design a multi-agent AI system How to combine LLMs + quantitative finance How to build AI systems with memory Real-world agent orchestration patterns Using Streamlit to ship AI apps fast Avoiding hallucinations with data-grounded AI 🛠️ Tech Stack Used Python CrewAI (Agent orchestration) OpenAI GPT models Streamlit yFinance Pandas & NumPy Quantitative finance metrics Session-based memory design 🎯 Who This Video Is For AI / ML Engineers GenAI Developers Quant & Finance Engineers Data Scientists Anyone building real AI products, not demos ⚠️ Disclaimer: This project is for educational purposes only and not financial advice. #ArtificialIntelligence #StockMarket #GenerativeAI #MachineLearning #Python #CrewAI #AgenticAI #LLM #OpenAI #AIEngineering #MultiAgentSystems #QuantFinance #StockAnalysis #AlgorithmicTrading #FinTech #EquityResearch #Streamlit #DataScience #PythonProjects #BuildInPublic #AIProjects