From Web Scraping to SQL: Automating Index Holdings with Python
In this video, we walk step-by-step through how to programmatically collect index holdings data (like the S&P 500) using Python—the same type of data that professional platforms charge thousands (or millions) for.
You’ll learn how to:
Scrape index holdings data from the web using Selenium
Parse and clean messy HTML using BeautifulSoup
Normalize and validate the data
Store the results in a SQL database for long-term use
Build a reusable pipeline you can extend to other indices (Russell, MSCI, sector ETFs, etc.)
This is a real-world data engineering workflow designed for finance professionals, quants, and developers who want full control over their data.
🧠 Who this video is for
Aspiring quant researchers
Data engineers working in finance
Equity analysts who want reproducible data pipelines
Anyone tired of paying for basic index holdings data
🛠️ Tech stack used
Python
Selenium
BeautifulSoup
SQL (database-agnostic)
📂 Code & Resources
All code used in this video is available on GitHub
Feel free to fork the repo, experiment, and adapt it to your own data pipeline.
Timestamps:
00:00 – Introduction
01:36 – The iShares Website
04:09 – Scraping index holdings with Selenium
09:35 – Cleaning and validating the data with BeautifulSoup
19:20 – Visualizing the data using plotly
20:40 – Storing data in SQL with sqlite3
25:06 – Final thoughts