Bloomberg News Scraper

Copying Bloomberg stories into a spreadsheet by hand does not scale, and the page markup changes often enough to break a quick script. Paste a list of article links and this scraper hands back a tidy row per story: the headline, the full body text, who wrote it, when it ran, the topic tags, and the lead photo. Feed it two URLs or a few hundred and pipe the clean output straight into your research, monitoring, or NLP workflow.

What you get

One row per article, with the same column layout every time. Anything the page does not expose comes back as null, so your sheet or database stays rectangular. Each record covers four groups:

• Story — articleTitle, seoTitle, summaryText, articleBody, storyId, urlSlug, readMinutes
• Credits — bylineText, writerName, writerTwitter
• Classification — sectionName, categoryList, topicTags, paywalled
• Media and dates — imageLink, captionText, photoCredit, publishedDate, updatedDate, collectedAt, plus articleUrl and errorMessage

Quick start

1. Open the actor and click Try for free to bring up the input form.
2. Paste one or more Bloomberg article links into Article URLs (use the full /news/articles/... address — query strings are stripped for you).
3. Set a Results limit to cap the run and raise Timeout (seconds) if pages load slowly.
4. Press Start, then download the dataset as JSON, CSV, Excel, or XML when the run finishes.

Use cases

• Market and equity research — pull a batch of stories on a sector or ticker and run sentiment or topic analysis over the whole set
• News monitoring — refresh a fixed list of article links on a schedule and watch the updatedDate field for edits
• Editorial and competitive intelligence — filter by sectionName, topicTags, or writerName to see what Bloomberg is covering and who is covering it
• Data journalism — collect authorship and publication patterns across hundreds of stories for an investigation
• NLP and dataset building — assemble a clean, structured corpus of financial reporting to train or evaluate models
• Internal dashboards — feed the structured rows into Slack alerts, BigQuery, or a reporting dashboard

Input

Field	Type	Required	Description
articleUrls	array of strings	Yes	Bloomberg article links to scrape. Each must contain the /news/articles/... path; query parameters are removed automatically.
resultsLimit	integer	No	Largest number of articles a single run will collect, counted across all URLs. Default 40 (1–1000).
timeoutSeconds	integer	No	Seconds to wait on each request before it is treated as failed. Default 45 (5–120); raise it for slow pages.

Example input

{
    "articleUrls": [
        "https://www.bloomberg.com/news/articles/2026-04-10/fed-officials-signal-patience-on-rate-cuts-amid-sticky-inflation",
        "https://www.bloomberg.com/news/articles/2026-04-11/nvidia-supplier-ramps-output-as-ai-chip-demand-keeps-climbing"
    ],
    "resultsLimit": 40,
    "timeoutSeconds": 45
}

Output

Each input URL becomes one row, and every field is always present — values the page does not expose come back as null so the dataset stays rectangular. A row that fails to scrape still appears, with errorMessage set and the rest left empty.

Example output

{
    "articleUrl": "https://www.bloomberg.com/news/articles/2026-04-11/nvidia-supplier-ramps-output-as-ai-chip-demand-keeps-climbing",
    "storyId": "TFGSTUKK3NYD00",
    "articleTitle": "Nvidia Supplier Ramps Output as AI Chip Demand Keeps Climbing",
    "seoTitle": "Nvidia Supplier Ramps Output as AI Chip Demand Keeps Climbing",
    "bylineText": "Debby Wu",
    "writerName": "Debby Wu",
    "writerTwitter": "debbywu1",
    "publishedDate": "2026-04-11T01:30:56.367Z",
    "updatedDate": "2026-04-11T03:42:27.089Z",
    "summaryText": "A key Nvidia supplier said it is lifting production through the rest of the year as orders for AI accelerators show no sign of cooling.",
    "articleBody": "A key Nvidia supplier said it is lifting production through the rest of the year as orders for AI accelerators show no sign of cooling...",
    "imageLink": "https://assets.bwbx.io/images/users/iqjWHBFdfxIU/itJ0yPa0NDcg/v0/-1x-1.webp",
    "captionText": "A semiconductor fabrication line.",
    "photoCredit": "Photographer: I-Hwa Cheng/Bloomberg",
    "sectionName": "technology",
    "categoryList": "technology",
    "topicTags": "Semiconductors, AI, Nvidia, Supply Chain, Asia",
    "paywalled": false,
    "readMinutes": 2.5,
    "urlSlug": "2026-04-11/nvidia-supplier-ramps-output-as-ai-chip-demand-keeps-climbing",
    "collectedAt": "2026-04-11T05:12:00.000Z",
    "errorMessage": null
}

Output fields

Field	Type	Description
articleUrl	string	Canonical web address of the story
storyId	string	Bloomberg internal SUID code for the piece
articleTitle	string	Primary headline shown on the story
seoTitle	string	Headline variant tuned for search engines
bylineText	string	Author credit line printed on the article
writerName	string	Full name of the lead author
writerTwitter	string	Twitter handle of the lead author, minus the @
publishedDate	string	ISO 8601 UTC timestamp of first publication
updatedDate	string	ISO 8601 UTC timestamp of the latest edit
summaryText	string	Lede or short summary line for the article
articleBody	string	Full plain-text of the story; empty when paywalled
imageLink	string	Address of the lead (lede) photo
captionText	string	Caption attached to the lead photo
photoCredit	string	Photographer or agency credited for the lead photo
sectionName	string	Bloomberg desk: markets, technology, politics, wealth, and similar
categoryList	string	Section categories as a comma-separated string
topicTags	string	Content tag names as a comma-separated string
paywalled	boolean	True when the story sits behind a subscription
readMinutes	number	Rough reading time in minutes, to one decimal
urlSlug	string	Date and slug portion of the URL
collectedAt	string	ISO 8601 UTC timestamp of when the row was captured
errorMessage	string	Reason a story failed; null on success

Tips for best results

• Use full article links. The path has to contain /news/articles/ — section or topic landing pages are skipped with a warning in the log.
• Cap test runs with resultsLimit. Set it to 5–10 while you confirm the columns fit your pipeline, then raise it for the real batch.
• Raise timeoutSeconds to about 60 if you see timeout errors on long stories or during busy periods.
• Split very large jobs. For more than a couple hundred links, several runs of 100–200 are steadier than one giant run.
• Check paywalled before reading articleBody. Metadata such as headline, author, and tags still comes back on subscriber-only stories, but the body may be empty.
• Failures don't stop the run. A bad link lands in the dataset with errorMessage filled in, and the scraper moves on to the next URL.

How can I use Bloomberg news data?

How can I use the Bloomberg News Scraper to build a financial news dataset? Paste a batch of Bloomberg article links and each one returns the full body text, headline, author, publish date, section, and tags as a single row. Export the lot as JSON or CSV and you have a clean corpus ready for sentiment scoring, topic modeling, or any text-mining project — no manual copy-paste.

How can I track Bloomberg coverage of a company or market over time? Keep a running list of article URLs for the topic you follow and re-run the scraper on a schedule. Filter rows by topicTags, sectionName, or writerName to see how the coverage shifts, and watch updatedDate to catch stories Bloomberg has revised after publishing.

How can I pull Bloomberg article text and metadata for research? Drop in one link or several. For each story you get the headline, summary, full text, reading time, lead image, and the credit line in one structured record — enough context for academic work, data journalism, or feeding a downstream news pipeline.

How can I export Bloomberg articles to CSV or Excel? Run the scraper, then open the Storage tab and download the dataset in JSON, CSV, Excel, or XML. Every article keeps the same column order, so the file opens cleanly in Sheets, Excel, or a pandas DataFrame.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.