How to Collect Bloomberg Headlines for a News Watchlist

If your daily routine starts with checking Bloomberg topic pages, a structured reading list can save you from collecting the same links by hand. The Bloomberg Category News Scraper returns headlines and article metadata from category URLs, ready to export from Apify.

I maintain this Actor. Its documented output includes article links, authors, labels, and timestamps. It does not provide full article bodies or a way around subscription access. The workflow below builds a watchlist of stories to read, rather than a full-text news archive.

Decide which question your watchlist should answer

A narrow topic is easier to review than a broad stream of market news. For example, start with Bloomberg's AI category if you want to follow funding announcements and company developments. Add another category only when it contributes stories you actually need.

Each row is a story surfaced by a category page. A recent collection time does not mean every story was published recently, and requesting a category is not a date-range search through Bloomberg's archive.

Keep these distinctions in mind when choosing the tool:

Collect ten stories from one category

Open the Actor on Apify, sign in, and use the JSON view of Input:

{
  "searchUrls": ["https://www.bloomberg.com/ai"],
  "maxItemsPerUrl": 10
}

Despite the name searchUrls, these are category URLs. Use a category page here, not an individual article URL. The cap is per URL: two category URLs with maxItemsPerUrl: 10 allow up to 20 returned records before any downstream deduplication.

Click Start, inspect the status and log, and open the resulting dataset. Check that headlines have working article links and that their topics match your category. A limit of ten does not guarantee ten new stories or ten stories published today.

This abbreviated record comes from the historical sample used in the earlier guide:

{
  "headline": "OpenAI Co-Founder Sutskever's Startup Is Fundraising at $30 Billion-Plus Valuation",
  "byline": "Kate Clark",
  "id": "SRUC0QDWLU6800",
  "label": "AI",
  "publishedAt": "2025-02-17T20:30:31.679Z",
  "updatedAt": "2025-02-17T20:30:31.678Z",
  "type": "article"
}

The 2025 publication date is deliberate: this is an output example, not a fresh news claim. publishedAt identifies publication time, while updatedAt describes the source's reported update time. Store your own collection time separately so you can tell when your system first saw the story.

Pricing as of September 6, 2026: the configured result event is $0.002, plus an Actor-start charge of $0.00005 per GB of allocated memory, with a minimum of one event. Ten saved records correspond to $0.02 in result event fees before the start charge. Confirm current pricing before scheduling frequent checks.

Make the CSV useful on the first day

Export the dataset as CSV and retain id, headline, byline, url, label, publishedAt, and updatedAt. Add collectedAt with the run time, plus two columns for your review: relevant and notes.

Sort by publication time, not the order in which rows happen to arrive. Read the headlines and mark which belong in your watchlist. A simple keyword filter can help narrow a long list, but it will miss synonyms and can include unrelated stories. Review the matches before relying on them.

If you collect overlapping categories, deduplicate by article id. Keep the category associations in a separate column or table if they matter. The same article appearing on two category pages is one story with two placements, not two independent reports.

Open the article link to read the source. Metadata helps you decide what to read; it cannot support a summary of an article you have not accessed.

On later runs, separate new stories from updates

Save the input as an Apify task once the first export looks right. Choose an interval that matches how often you will use the reading list, then retain each run's dataset or copy it into your own store.

For every collected record:

1. Look up its article ID in your previous records.
2. If the ID is new, add it to the next reading list and record when you first saw it.
3. If the ID already exists, compare updatedAt and the headline. Flag changes separately from new articles.
4. Keep the previous values when an audit trail matters.

A story disappearing from the next category snapshot is not evidence that Bloomberg deleted it. Category pages change their selection. Likewise, repeated appearances should not trigger a fresh alert every time.

If a category returns empty results, open that category manually, check the URL, and inspect the run log before increasing the schedule frequency. If it returns fewer stories than expected, distinguish the page's current selection from the amount you hoped to collect. A larger item cap cannot create archive access.

Where the metadata stops helping

Headline-only sentiment is sentiment about a headline. It can omit qualifications, quotations, and context in the article. If your project needs full-text analysis, confirm an appropriate source and access rights before building that part of the pipeline.

Similarly, a premium flag is metadata, not permission to reuse content or proof that every reader can open an article. Keep your watchlist linked to the original reporting and handle any further use according to the access and permissions you have.

The Bloomberg Node.js example provides a separate starting point for code integration. Keep the same ID-based deduplication when moving beyond a spreadsheet.

Questions before you run

Does this scraper return full Bloomberg articles?

The documented output is headline and article metadata from category pages. It does not establish full article-body extraction or subscription bypass.

Can I request all articles between two dates?

The current input accepts category URLs and an item limit per URL. It has no date-range archive input.

How should I avoid duplicate news alerts?

Store article IDs between runs. Alert on previously unseen IDs, and handle changes to an existing article as updates rather than new stories.