Bloomberg Scraper | News Articles and Full Text avatar

Bloomberg Scraper | News Articles and Full Text

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Bloomberg Scraper | News Articles and Full Text

Bloomberg Scraper | News Articles and Full Text

Try for free

Bloomberg scraper for news articles by keyword, ticker, section, topic or date. Each article comes with its full text, AI summary, authors, brand, tickers, companies, topics, publish times, lead image and audio link. 20 articles in 6 seconds, 27 fields each, in 2026. No Bloomberg account needed.

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from $0.64 / 1,000 articles

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SilentFlow

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Bloomberg News Scraper | Articles, Full Text, AI Summaries and Tickers by Keyword, Section or Date (2026)

Bloomberg articles, 27 fields each, from any search.

From SilentFlow. Creators of Facebook Search Scraper and JD Products Scraper (#1 for โ€œjd scraperโ€ in Apify Store search), with 940,000+ combined lifetime runs.

Turn Bloomberg news into a clean dataset: find articles by keyword, ticker, section, topic, link or date, and get each one read in full with its authors, times, tickers and topics. 20 articles in 7 seconds in our test.

Why choose this actor?

Still pasting Bloomberg links into a spreadsheet one by one? Need every story about a company, but only have a ticker? Want last month's coverage of a theme for a backtest, not just today's front page?

  • Search Bloomberg by keyword or ticker, no links needed. Type nvidia, TSLA:US or federal reserve and the actor finds the stories for you. In our test, Bloomberg's search returned 21 readable articles for "nvidia" and the actor read 20 of them in full in 7 seconds.
  • Pull a whole date window from the archive. Set a start and end date and leave everything else empty: the actor reads every Bloomberg article published in that window, newest first. For 1 to 3 October 2026 it listed 640 articles; the archive goes back to 1991 and holds about 7,000 articles a month.
  • Get the gist and the audio, not just the text. Bloomberg's AI summary bullets came back on 16 of 20 rows of our keyword test, and the audio narration link on 19 of 20, so you can triage, quote or listen without opening a single page. Section fronts work the same way: pick Markets, Technology, Crypto or any of 19 sections.

Try Bloomberg Scraper free

Try your first article

Paste this input and press Start. It searches Bloomberg for "nvidia" and reads the 20 most relevant articles in full.

{
    "searchQueries": ["nvidia"],
    "maxItems": 20
}

The Apify Free plan is enough to try it: a run like this one finishes in seconds and gives you a table you can download as JSON, CSV or Excel.

How it works

Copy to your AI assistant

Paste this block into Claude, ChatGPT or Cursor so it knows how to use this actor:

You have access to the Bloomberg Scraper on Apify: silentflow/bloomberg-scraper


What it does: finds Bloomberg news articles by keyword, ticker, section, topic, link
or publication date, then reads each article in full. One dataset row per article.


Input (all optional, but give at least one keyword, link, section or date):
- searchQueries (array of strings): keywords, company names or tickers such as "nvidia", "TSLA:US".
  Bloomberg returns its 25 most relevant stories per keyword.
- startUrls (array of {"url": "..."}): Bloomberg article links, section fronts
  (https://www.bloomberg.com/markets), topic pages (https://www.bloomberg.com/topics/hedge-funds)
  or quote pages (https://www.bloomberg.com/quote/NVDA:US, read as the news about that ticker).
- sections (array of strings): latest, markets, economics, technology, ai, politics, wealth,
  crypto, opinion, green, businessweek, pursuits, citylab, industries, deals, india,
  fixed-income, commodities, work-shift.
- dateFrom (string): "2026-10-01" or a span back from now such as "7 days". UTC.
- dateTo (string): last day kept, included, "2026-10-31".
  Dates filter every source. Dates alone (no keyword, link or section) read the archive, newest first.
- maxItems (integer, default 100): total articles for the run.


Output per article (28 fields):
- id (string, Bloomberg story id), url (string, permanent article link), title (string),
  type (article | feature | opinion | newsletter)
- summary (string or null), aiSummary (array of strings or null), bodyText (string or null,
  paragraphs separated by blank lines), section (string or null), brand (string),
  topics (array of strings), tickers (array of strings), companies (array of {ticker, name}),
  contentTags (array of {id, type, score})
- byline (string), authors (array of strings)
- wordCount (integer), audioDurationSeconds (integer or null)
- publishedAt, updatedAt (RFC 3339 UTC strings), region (AMER | EMEA | APAC | Global)
- imageUrl, imageCaption, imageCredit, audioUrl (strings or null)
- isPremium (boolean, subscriber-only story: details but no text), hasFullText (boolean),
  foundBy (string: "keyword: nvidia", "section: Markets", "archive"...), scrapedAt (RFC 3339)


Use apify-client for Python or JavaScript, or the Apify API, to run it and read the dataset.

How to scrape Bloomberg news articles

Get every Bloomberg story about a company

One keyword, the stories Bloomberg ranks most relevant, each read in full.

{
    "searchQueries": ["nvidia"],
    "maxItems": 25
}

Follow the Markets and Technology fronts

Pick sections from the list. Each section gives the stories Bloomberg shows on that front today, from about 15 on Deals to over 100 on India. In our test, Markets listed 34 articles.

{
    "sections": ["markets", "technology"],
    "maxItems": 60
}

Track several tickers over the last week

Keywords and tickers combine with a date window: only the articles published in the last 7 days are kept.

{
    "searchQueries": ["TSLA:US", "AAPL:US", "private credit"],
    "dateFrom": "7 days",
    "maxItems": 75
}

Input parameters

ParameterTypeDefaultWhat it does
searchQueriesarray of stringsnone (prefilled with nvidia)Keywords, company names, people or tickers. Bloomberg returns its 25 most relevant stories per keyword.
startUrlsarray of linksnoneBloomberg article links, section fronts, topic pages and quote pages.
sectionsarray of section namesnoneSection fronts to read, from Latest to Work Shift (19 in total).
dateFromdate or spannoneKeep articles published on or after this day, such as 2026-10-01 or 7 days.
dateTodate or spannoneKeep articles published on or before this day, included.
maxItemsinteger100Total articles for the run, across every keyword, link, section and date.
  • Articles: https://www.bloomberg.com/news/articles/..., /news/features/..., /opinion/articles/..., /news/newsletters/.... Tracking parameters such as ?srnd= are dropped.
  • Section fronts: https://www.bloomberg.com/markets, /technology, /ai, /crypto, /markets/fixed-income and every other section of the list.
  • Topic pages: https://www.bloomberg.com/topics/hedge-funds, /topics/artificial-intelligence. A topic gives about 40 stories, the articles among them are read.
  • Quote pages: https://www.bloomberg.com/quote/NVDA:US is read as a search for that ticker.
  • Video and podcast pages have no text to read: the run log names them and moves on.

Dates

dateFrom and dateTo take a day (2026-10-01), a full timestamp, or a span back from now (24 hours, 7 days, 3 months, 1 year). Times are UTC. With a keyword, link or section, the dates filter what those sources list. With dates alone, the actor reads the archive of that window, newest first, as deep as maxItems allows.

What data can you extract from Bloomberg?

Every row is one article, with 28 fields grouped the way you read a story:

  • Identity: id (Bloomberg's own story id, stable across runs and the best key to de-duplicate), url (the permanent article link), title, type (article, feature, opinion or newsletter).
  • Content: summary (the dek when Bloomberg gives one), aiSummary (Bloomberg's AI summary bullets), bodyText (the full text, one paragraph per block, sub-headings and list items kept), section, brand, topics.
  • Markets: tickers (every Bloomberg ticker attached to the story), companies (each listed company the text links to, with its ticker and name), contentTags (Bloomberg's subject tags: companies, people, places, topics and commodities, each with a relevance score, highest first).
  • Authors: byline as printed, and authors split into names.
  • Measures: wordCount, audioDurationSeconds.
  • Time and place: publishedAt, updatedAt in RFC 3339 UTC, region where the story was written (AMER, EMEA, APAC or Global).
  • Media: imageUrl of the lead picture (or the first picture of the story), imageCaption, imageCredit, audioUrl of the narrated version.
  • Meta: isPremium, hasFullText, foundBy (which keyword, section, topic, link or archive found the row), scrapedAt.

Absent values are null, never an empty string, so filters and joins behave.

Output example

Text fields are shortened here.

{
    "id": "TNZNEQN3N09F00",
    "url": "https://www.bloomberg.com/news/articles/2026-11-09/morgan-stanley-to-buy-fintech-vinyl-equity-in-wealth-expansion",
    "title": "Morgan Stanley to Buy Fintech Vinyl Equity in Wealth Expansion",
    "type": "article",
    "summary": "One-sentence dek of the article, as Bloomberg shows it under the headline.",
    "aiSummary": [
        "First AI summary bullet.",
        "Second AI summary bullet.",
        "Third AI summary bullet."
    ],
    "bodyText": "First paragraph of the article...\n\nSecond paragraph...\n\n(418 words in total)",
    "section": "Banking",
    "brand": "finance",
    "topics": ["FinTech", "Wealth Management"],
    "tickers": ["MS:US"],
    "companies": [
        { "ticker": "MS:US", "name": "Morgan Stanley" }
    ],
    "contentTags": [
        { "id": "MS:US", "type": "Company", "score": 13.001 },
        { "id": "finance", "type": "Classification", "score": 13.001 },
        { "id": "US", "type": "Region", "score": 13.001 }
    ],
    "byline": "Hannah Levitt and Dylan Sloan",
    "authors": ["Hannah Levitt", "Dylan Sloan"],
    "wordCount": 418,
    "audioDurationSeconds": 150,
    "publishedAt": "2026-11-09T13:45:00Z",
    "updatedAt": "2026-11-09T13:45:00Z",
    "region": "AMER",
    "imageUrl": "https://assets.bwbx.io/images/users/iqjWHBFdfxIU/iDWatK0IZiZM/v0/-1x-1.jpg",
    "imageCaption": "Caption of the lead picture.",
    "imageCredit": "Photographer: Sean Zanni/Patrick McMullan/Getty Images",
    "audioUrl": "https://assets.bwbx.io/s3/readings/TNZNEQN3N09F/40c334c4f547f2d9560679fcf631f366.09ab9ebd-ec99-4117-b2bb-e4fadbee1a1b.mp3",
    "isPremium": false,
    "hasFullText": true,
    "foundBy": "topic: fintech",
    "scrapedAt": "2026-11-09T14:22:07Z"
}

Two views are ready in the dataset: Articles (headline, authors, section, published, words, tickers, link) and Full text (headline, summary, AI summary, article text, topics, link).

Advanced usage

Build a dated corpus for a backtest

Dates alone read the archive. This pulls up to 2,000 articles of the third quarter of 2026, newest first, with their text, tickers and tags, ready for sentiment or event studies.

{
    "dateFrom": "2026-07-01",
    "dateTo": "2026-09-30",
    "maxItems": 2000
}

Watch a portfolio every morning

Schedule this input daily in Apify. Each run keeps only the last 24 hours, so you get the fresh Bloomberg coverage of your holdings and nothing twice from the window.

{
    "searchQueries": ["NVDA:US", "MSFT:US", "JPM:US", "XOM:US"],
    "dateFrom": "24 hours",
    "maxItems": 100
}

Feed a headline channel from section fronts

Read the Latest front with Markets and Crypto, and send the dataset to Slack, a Google Sheet or your BI tool with an Apify integration. Stories listed by two sections are delivered once.

{
    "sections": ["latest", "markets", "crypto"],
    "maxItems": 120
}

Paste a few articles you already have, add a topic page and a theme keyword. Every source is read in order and the run total stays at maxItems.

{
    "startUrls": [
        { "url": "https://www.bloomberg.com/news/articles/2026-11-09/morgan-stanley-to-buy-fintech-vinyl-equity-in-wealth-expansion" },
        { "url": "https://www.bloomberg.com/topics/hedge-funds" },
        { "url": "https://www.bloomberg.com/quote/TSLA:US" }
    ],
    "searchQueries": ["private credit"],
    "maxItems": 80
}

Ground an AI assistant in Bloomberg's coverage of a theme

Pull a month of one topic, then chunk bodyText by paragraph and keep aiSummary, tickers and publishedAt as metadata in your vector store.

{
    "startUrls": [{ "url": "https://www.bloomberg.com/topics/artificial-intelligence" }],
    "searchQueries": ["artificial intelligence"],
    "dateFrom": "1 month",
    "maxItems": 150
}

FAQ

What does this actor extract from Bloomberg? News articles. Each row has the headline, the full text, Bloomberg's AI summary bullets when the story has them, authors, publish and update times, section, topics, tickers, the companies the text links to, subject tags, word count, the lead picture with its caption and credit, and the narrated audio link.

Do I need a Bloomberg account or a Terminal? No. The actor reads what Bloomberg serves to any reader, without signing in. It does not give Terminal data such as prices or filings: it is a news actor.

What about subscriber-only stories? Some Bloomberg stories, mostly paid newsletters, are reserved for subscribers. The actor respects that: their row still ships with the headline, authors, times, topics and tickers, with isPremium: true, and bodyText and aiSummary left null. In our 30-article archive test, 1 story was subscriber-only and 29 came with their full text.

How many articles does a keyword give? Bloomberg's search returns its 25 most relevant stories per keyword, recent ones first; videos among them are skipped, so expect 15 to 25 articles (21 for "nvidia" and 16 for "TSLA:US" in our tests). For more depth, add related keywords and tickers, or use a date window, which reads the full archive.

How far back can I go? To 1991. A date window with no keyword reads every article published in it, newest first, about 7,000 a month (7,053 in October 2026). Set maxItems to the volume you need.

Can I run several keywords, sections and links at once? Yes. All sources run in one go, in the order links, keywords, sections. An article found by two sources is delivered once, and maxItems is the total for the run.

How fresh is the data? Every run reads Bloomberg live. Section fronts change through the day; updatedAt tells you when a story was last edited, and id is Bloomberg's own story id, so you can match a story across runs and update its row instead of adding a second one.

Which links stay valid? url is Bloomberg's permanent article address. imageUrl and audioUrl point to files in Bloomberg's media library, which Bloomberg controls: if you archive results, keep your own copy of the media you need.

Why is section or summary sometimes null? Bloomberg does not set them on every story. Rows found from a section front or a topic page carry the section label and dek that page shows; rows found by keyword or link carry what the story itself gives.

What is Bloomberg? Bloomberg is a global business and financial news organization, publishing several thousand stories a month on markets, companies, economics, politics, technology and wealth, alongside its Businessweek, Green, CityLab and Pursuits brands.

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Support

Need something this actor does not do yet? We ship features fast.

  • Feature requests go straight to our backlog
  • Enterprise needs? We do custom integrations and high-volume plans
  • Pricing questions? Check the Monetization tab on the actor page

Response time: usually under 24 hours.

This actor extracts publicly available data from Bloomberg. It does not bypass any login, paywall or CAPTCHA: stories reserved for subscribers are delivered without their text. Bloomberg's articles are copyrighted works, and users are responsible for complying with Bloomberg's terms of service, with copyright law, and with applicable data protection laws (GDPR, CCPA, and PIPL where relevant). Rows carry journalists' names and the names of people tagged in stories, which can be personal data: store and process them accordingly. Use the text for research, monitoring and analysis, and check your rights before republishing it. The data returned is informational; verify it before using it for regulated decisions.

Bloomberg Scraper by SilentFlow: Bloomberg's news, searchable, dated and structured.

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