📊 Stock Price Prediction with Agentic AI: Complete Guide

🚀 What You'll Learn

By the end of this series, you'll have a complete understanding of:

• Setting up your environment for AI development
• Building data pipelines to collect and preprocess financial data
• Implementing machine learning models for stock prediction
• Using LSTM networks for sequential data analysis
• Integrating LLMs for sentiment analysis and event prediction
• Creating an Agentic AI system with LangChain

We'll cover everything from the basics to advanced techniques, with practical examples and code snippets.

🔧 Part 1: Environment Setup

• Python environment, Jupyter, FastAPI setup
• PostgreSQL DB and schema setup
• Robinhood integration (optional)

📰 Part 2: Data Collection Pipelines

• Finnhub API: News & Sentiment
• Yahoo/Alpha Vantage: OHLC, Financials
• FRED API: Economic Indicators
• Google Trends: Market Fear & Momentum
• AAII Investor Sentiment: Lagging Indicator
• Earnings Calendar (Next 14 Days)

📈 Part 3: Machine Learning Models (XGBoost, Random Forest)

• Technical Indicators (RSI, MACD, ADX)
• Sentiment Scores
• Economic, Earnings, and News Inputs
• Train/Test Split and Feature Importance

⏳ Part 4: LSTM for Sequential Stock Prediction

• Daily OHLCV + Indicator Sequences
• LSTM Model Setup & Evaluation
• Compare with Tree-Based Models

🧠 Part 5: LLM-Based Sentiment Analysis

• FinBERT, OpenAI, Gemini, and Ollama Mistral
• Integrating with FastAPI & PostgreSQL
• Impact Scoring + Noise Filtering

🤖 Part 6: Agentic AI with LangChain

• Stock Router Agent with Tool Usage
• Event Prediction: Bullish Breakout, Bearish Pressure, Volume Spike, Earnings Run-Up
• Build a Chat-Like Experience
• Expose APIs for Angular/Streamlit UI