How to scrape finance market data from FRED using the Minexa.ai extension

FRED publishes hundreds of commodity price series, all publicly accessible, all updated regularly. The problem is that the data sits inside a category listing page that was built for browsing, not for export. Getting it into a spreadsheet means either copying rows one by one or building something custom. Neither option scales.

This guide shows how to extract structured commodity price data from fred.stlouisfed.org using the Minexa.ai Chrome extension, no code required.

Watch the full tutorial first

The video below walks through the entire extraction from start to finish on the FRED commodity prices page.

https://www.youtube.com/watch?v=Vj2lLpBAWMA

What the extracted data looks like

Each row in the output corresponds to one entry in the FRED listing. Here are two labelled examples from the extracted dataset:

Row 1 - WTI crude oil header row

• commodity_prices: Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma

• series_link: /series/DCOILWTICO

• popularity_bar_spans: popularity-bar-span-DCOILWTICO (87% popular)

Row 2 - WTI detail row with frequency variants

• commodity_prices: Dollars per Barrel, Not Seasonally Adjusted

• indicator_code: DCOILWTICO

• commodity_code: MCOILWTICO

• commodity_code_2: WCOILWTICO

• series_link: /series/MCOILWTICO

• unit_aria_label: unitLinkDCOILWTICO

The commodity_prices_description field returns a traversable array of tag-typed objects per listing, encoding the series name, relative path, and DOM popularity bar identifiers. The indicator_code field surfaces the primary FRED series code, while series_link gives you the relative path to the default series view for that commodity.

Step-by-step extraction walkthrough

Open the Minexa.ai home page in Chrome. This is your starting point before navigating to FRED.

Navigate to the FRED commodity prices category page at fred.stlouisfed.org/categories/32217. The page lists all commodity price series available in the database.

Open the Minexa.ai extension popup. Click 'I'm on the right page' to confirm the target URL and let detection begin.

Minexa.ai detects the pagination setup automatically. Review what it found and click Continue.

Choose whether to scrape the list only, or the list plus linked detail pages for each series. For a broad commodity overview, the single list option covers the key fields.

Select simple scraping mode for standard extraction, or advanced if you need custom interaction steps before data loads.

Minexa.ai highlights the full data container automatically. Confirm the selection and click 'Create scraper'.

All extracted data points appear with next/previous navigation so you can review every column before running the full job.

Once the job completes, export your dataset to Excel, Google Sheets, or JSON directly from the results view.

The scraper configuration is saved automatically. The next time you run it against the same FRED category page, extraction starts immediately without repeating any setup steps.

Ready to pull FRED commodity data into your own workflow? Install the Minexa.ai extension and run your first extraction in a few minutes.

If you work with financial data from government sources more broadly, the post on scraping tax and accounting data from ATO covers a related extraction workflow worth reading alongside this one.