Stock Price Prediction with Agentic AI: Complete Guide Coming up Soon

This is a multi-part blog series that will walk you through building an end-to-end AI-powered system for stock price prediction. From data acquisition and model building to integrating LLMs and deploying an Agentic AI system using LangChain, this series has everything you need to start your journey in intelligent market analysis.

What You'll Learn

• Setting up AI environments • Building data pipelines • ML models for prediction • LSTM usage for sequences • LLMs for sentiment/event prediction • Agentic AI system with LangChain

Part 1: Environment Setup

• Python, Jupyter, FastAPI • PostgreSQL schema • Optional Robinhood integration

Part 2: Data Pipelines

• Finnhub: News & Sentiment • Yahoo/Alpha: OHLC & Financials • FRED: Macro Indicators • Google Trends & AAII Sentiment • Earnings Calendar insights

Part 3: ML Models (XGBoost, RF)

• RSI, MACD, ADX • Sentiment and Macro signals • Feature engineering & importance

Part 4: LSTM for Sequences

• OHLCV + indicator input • LSTM model setup • Compare tree-based vs. LSTM

Part 5: LLM Sentiment Analysis

• FinBERT, OpenAI, Gemini, Mistral • API integration + PostgreSQL • Impact scoring, noise filtering

Part 6: Agentic AI System

• Stock Router Agent • Event prediction: breakouts, pressure, volume, earnings • Chat-like frontend with APIs

Tags

AI, Stock Prediction, LangChain

Coming Soon

Each part will be published weekly with code, visuals, and interactive demos.

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