Build a Custom ETF with AI (Python Tutorial)

This video shows you how to build a conceptual AI-generated thematic ETF in Python and run a simplified backtest of it against the S&P 500 index. The tutorial shows you how to search for companies within niche subsectors of the economy with Perplexity Sonar through the OpenAI Python library, as well as how to pull financial stock data from Stooq.

Because the portfolio is assembled with today’s information, the backtest contains look-ahead and survivorship bias, meaning historical results will look better than what was likely attainable in real time. Treat the dashboard as an idea-discovery tool, not investment or financial advice, and explore how different prompts and shorter look-back windows affect the simulated performance.

Important Disclaimer
This video is for informational and educational purposes only. It does NOT provide financial, investment, trading, or any other professional advice. Past performance is NOT a guarantee or predictor of future actual trading results. You are solely responsible for your trading decisions.


****Beta Test Preview****
Want to run backtests directly in Google Sheets without any code?
Check out the FREE Stock Backtester Google Sheets Add On:
https://stockbacktester.com/


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*Resources*
Why Stooq over yfinance?    • Need a Yahoo Finance alternative? Use Stoo...  
How to setup Perplexity Sonar API Key: https://docs.perplexity.ai/guides/get...
How to deploy a Streamlit app to the Streamlit Community Cloud:    • Build a Python Web App with Streamlit  

*Full Code*
https://deepcharts.substack.com/p/bui...

*Chapters*
0:00 Intro and Demo
0:42 Warning: Look-Ahead Bias in Backtesting
1:12 AI Tech Stack
2:35 Setting up specialized Python Environment
2:46 Library Imports + Configuration
3:29 Main Functions
5:35 Setting up the Streamlit UI
7:20 Running a Streamlit Dashboard Locally
7:35 Two Additional Notes