Uploaded April 2026 | Updated September 2026, 1 week ago
Have you ever looked at stock charts and wondered how the daily news actually impacts the price action? In this Python tutorial, we build an AI tool that doesn't just look at historical stock numbers—it actively scrapes the latest financial headlines and uses advanced reasoning to predict short-term market moves!
Using the blazing-fast Groq API and the Llama 3.3 70b model, we write a Python script from scratch in JupyterLab. We'll pull live news and price data via yfinance, calculate risk metrics with pandas, and visualize the history with plotly. Finally, we feed everything into our AI model to generate a clear, reasoned prediction on whether a stock is bullish, bearish, or neutral based on current real-world events.
What You Will Learn
• Live Data Scraping: How to extract the latest news headlines and historical closing prices using yfinance.
• Financial Math in Python: Calculating a stock's annualized volatility using pandas to give the AI context on market risk.
• AI Integration: Connecting to the Groq API using LangChain to process complex financial text.
• Prompt Engineering: Structuring the prompt so the AI provides reasoned context and confidence levels, not just a blind guess.
🔗 Get the Code:
Download the JupyterLab Notebook (.ipynb) and the Python Script (.py):
gist.github.com/LittleWebHut/3d0ebbfb3036917c09dff01423fcbbb8
Disclaimer: I am not a financial advisor, and this is not financial advice. Code in this video is provided for educational purposes only. External APIs and libraries may update or change over time, so the exact output or behavior may vary slightly when you run it. Always test the code in your own environment and refer to the latest documentation. Always do your own research before investing.
Have you ever looked at stock charts and wondered how the daily news actually impacts the price action? In this Python tutorial, we build an AI tool that doesn't just look at historical stock numbers—it actively scrapes the latest financial headlines and uses advanced reasoning to predict short-term market moves!
Using the blazing-fast Groq API and the Llama 3.3 70b model, we write a Python script from scratch in JupyterLab. We'll pull live news and price data via yfinance, calculate risk metrics with pandas, and visualize the history with plotly. Finally, we feed everything into our AI model to generate a clear, reasoned prediction on whether a stock is bullish, bearish, or neutral based on current real-world events.
What You Will Learn
• Live Data Scraping: How to extract the latest news headlines and historical closing prices using yfinance.
• Financial Math in Python: Calculating a stock's annualized volatility using pandas to give the AI context on market risk.
• AI Integration: Connecting to the Groq API using LangChain to process complex financial text.
• Prompt Engineering: Structuring the prompt so the AI provides reasoned context and confidence levels, not just a blind guess.
🔗 Get the Code:
Download the JupyterLab Notebook (.ipynb) and the Python Script (.py):
gist.github.com/LittleWebHut/3d0ebbfb3036917c09dff01423fcbbb8
Disclaimer: I am not a financial advisor, and this is not financial advice. Code in this video is provided for educational purposes only. External APIs and libraries may update or change over time, so the exact output or behavior may vary slightly when you run it. Always test the code in your own environment and refer to the latest documentation. Always do your own research before investing.
