Bloomberg News Scraper

Bloomberg News Scraper collects recent Bloomberg headlines from public section feeds and returns structured article records for analysts, builders, and AI agents. It accepts section names, keyword filters, ticker filters, a recency window, and a maximum article count. Each returned record represents one Bloomberg article and includes the headline, summary when present, canonical URL, authors, section, tickers, image metadata, publish time when available, and scrape timestamp. The run also produces a machine-readable summary in the default key-value store, which makes it easy to track what was selected and what was written.

Best fit and connected workflows

This Actor fits workflows that need current Bloomberg coverage in a clean dataset format:

• market watchlists that track specific tickers across Bloomberg sections
• newsroom or research pipelines that summarize recent business, technology, or politics coverage
• agent workflows that classify, route, or alert on recent headlines through Apify MCP
• dashboards and newsletters that refresh from public Bloomberg section feeds on a schedule

For related content inside Apify, workflow connections are available only when verified related Actors are supplied. No related Actor transfer has been verified for this Actor.

Practical scenario

A research analyst starts with a watchlist of AAPL, NVDA, and TSLA, then selects the technology and markets sections with a 24-hour window. The run returns article records that include the headline, summary, authors, section, tickers, published time, and canonical URL. The analyst uses the tickers and summary fields to decide which stories deserve a deeper read, then opens the url for the original Bloomberg article page and shares the record in an internal briefing.

Input

Use one or more Bloomberg sections, optionally narrow the feed with keywords or tickers, and set a recency window and article cap.

Field	Type	Default	Description
sections	array of strings	["markets"]	Bloomberg sections to pull through the public news index. Options include markets, technology, politics, business, economics, wealth, green, crypto, pursuits, industries, citylab, equality, and businessweek.
searchKeywords	array of strings	[]	Case-insensitive keywords matched against the headline or indexed summary, such as AI, Fed, or oil.
tickers	array of strings	[]	Ticker symbols matched against the headline or indexed summary, such as AAPL, IREN, or TSLA.
sinceHours	integer	0	Recency window in hours. 0 returns currently indexed results.
maxItems	integer	100	Hard cap on scraped articles across selected sections. Each returned article counts as one billable result.
proxyConfiguration	object	Apify proxy off	Optional Apify proxy configuration. The public news index usually works without a proxy.

Focused JSON example

{
  "sections": ["markets", "technology"],
  "searchKeywords": ["AI", "Fed"],
  "tickers": ["AAPL", "NVDA"],
  "sinceHours": 24,
  "maxItems": 50,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Output

The default dataset contains one record per scraped article.

Field	Type	Description
title	string	Bloomberg article headline from the public news index.
summary	string or null	Indexed Bloomberg article summary or dek when present.
url	string	Canonical Bloomberg article URL.
authors	array of strings	Author names when exposed by the upstream index.
publishedAt	string or null	Publish timestamp in ISO 8601 format when available.
section	string	Bloomberg section used to discover the record.
tickers	array of strings	Input ticker symbols matched in the headline or indexed summary.
imageUrl	string or null	Lead image URL when present.
imageCaption	string or null	Caption or media description for the lead image when present.
guid	string	Stable unique identifier for the article, usually the Bloomberg URL.
scrapedAt	string	Timestamp when the Actor captured the item.

Illustrative dataset record

{
  "title": "Stocks Rise as Traders Weigh Fed Rate Path",
  "summary": "Markets moved higher as investors assessed the latest economic data.",
  "url": "https://www.bloomberg.com/news/articles/2026-06-25/example-story",
  "authors": ["Bloomberg News"],
  "publishedAt": "2026-06-25T13:00:00.000Z",
  "section": "markets",
  "tickers": ["NMS:AAPL"],
  "imageUrl": "https://assets.bwbx.io/images/users/example.jpg",
  "imageCaption": "Traders work on the floor of an exchange.",
  "guid": "https://www.bloomberg.com/news/articles/2026-06-25/example-story",
  "scrapedAt": "2026-06-25T19:19:05.527Z"
}

The run summary is also written to the default key-value store as OUTPUT and RUN_SUMMARY. It includes the selected sections, filters, feed counts, dataset item count, charged article count, estimated event cost, and terminal status.

How it works

This Actor reads Bloomberg articles from a public news-index feed and keeps Bloomberg-sourced items with canonical Bloomberg article URLs. It covers 13 Bloomberg sections and applies optional keyword, ticker, and recency filters before writing dataset items. The live contract also records truthful terminal outcomes and charge-after-write ordering, which aligns billed article events with returned records.

Pricing

This Actor uses Pay per event plus Apify platform usage. The pricing model charges once for Actor start and once for each returned Bloomberg article. Use the live Pricing tab in the Apify Console for the current platform and event pricing details.

An example in words: a run that returns ten articles is billed for one Actor start event and ten article-scraped events, plus any platform usage associated with the run.

Use with AI agents (MCP)

This Actor is usable through Apify MCP as a tool that returns structured Bloomberg news records from public section feeds. The exact Actor identity is khadinakbar/bloomberg-news-scraper.

Pull recent Bloomberg articles from the markets and technology sections, filter for AI and Fed mentions, and return records with title, summary, tickers, publishedAt, and url.

The tool output is the default dataset, so an agent can read each article record as a machine-friendly source item. Provenance comes from Bloomberg's public section index, and scope is limited to the selected sections plus any keyword, ticker, and recency filters. Pagination is expressed through maxItems, which sets the article cap for the run. Cost guidance is direct: each returned article counts as one billable result, so narrower inputs generally reduce the total event count.

Run via the Apify API

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor('khadinakbar/bloomberg-news-scraper').call({
  sections: ['markets', 'technology'],
  searchKeywords: ['AI'],
  tickers: ['AAPL'],
  sinceHours: 24,
  maxItems: 20,
});

const datasetId = run.defaultDatasetId;
const { items } = await client.dataset(datasetId).listItems();

console.log(items);

Best results and outcome guidance

Start with one or two sections that match the topic you need, then add keywords or tickers to tighten the feed. Use sinceHours when you want a recent slice of Bloomberg coverage, and lower maxItems when you are testing a new workflow. The most useful records usually combine title, summary, tickers, publishedAt, and url, with imageUrl and imageCaption adding context when present. When the feed is broader than expected, narrow the section list first, then add keyword or ticker filters.

Continue the workflow

• Then use CoinMarketCap Scraper | Crypto Market Data to extend Bloomberg News Scraper with a neighboring market research source when the brief calls for Coinmarketcap data.
• Then use Yahoo Finance Scraper — Quotes, Financials, History, Options to extend Bloomberg News Scraper with a neighboring market research source when the brief calls for Yahoo data.

Design note

I found that the live dataset contract marks title, url, authors, section, tickers, guid, and scrapedAt as required fields, which makes each record dependable for downstream parsing.

FAQ

Is this Actor suitable for topic-based Bloomberg monitoring?

Yes. Section selection, keyword filtering, ticker filtering, and recency filtering make it well suited for topic-specific monitoring across Bloomberg's public section feeds.

What makes one returned record meaningful?

One record represents one Bloomberg article discovered in the selected public section feed. It is useful as a single unit for alerting, summarization, classification, or manual review.

Can I use this Actor in an Apify MCP workflow?

Yes. This Actor is MCP-ready and returns structured dataset items that an MCP client can consume as tool output.

Where does the article URL point?

Each record includes the canonical Bloomberg article URL, which is also used as the stable guid in the dataset schema.

How should I handle image fields?

Treat imageUrl and imageCaption as contextual metadata. They are present when the upstream index exposes them and omitted or null when unavailable.

Responsible use

This Actor collects publicly indexed Bloomberg headlines, summaries, canonical links, and metadata. Use the output in accordance with Bloomberg's terms and applicable law. The Actor returns article-level metadata and indexed summary text, not the full body of Bloomberg articles.