The useful question on a markets desk is never how many articles. It is which instrument was written about, when, and what the price did afterwards.
ScrapeIt runs the collector as a managed service. You name the sections, symbols or sectors; we build the pipeline, run it on your cadence and hand back CSV, JSON, Excel or a push into your warehouse, with ticker associations as rows and timestamps precise to the minute.
Cadence is set against the trading session rather than the clock: frequent through market hours and around scheduled releases, slower overnight. That is both cheaper and more useful than a flat interval.
We collect what the site renders publicly and pace requests. CNBC journalism is copyrighted and the terms restrict automated collection and reuse, so the dataset is built for analysis rather than republication. It is coverage data, not market data and not investment advice; if your use case is regulated, take it to your own counsel and compliance team before the project starts.
The article record covers canonical URL, headline, summary, section and subsection, byline, publication timestamp, last updated timestamp, item type and the article identifier.
Ticker association is the field that makes this source worth collecting. Symbols attached to a story are delivered as rows rather than a string, each with the exchange where it resolves, so a story about three companies produces three joinable rows instead of one unusable cell.
Both timestamps are kept apart, and on a markets source the gap between them matters more than usual: a story updated repeatedly through a session is following a developing move, and that is a different event from a piece published once.
Front and section observations record what was promoted, in what order and when. On a markets front, promotion through a trading session is itself a signal about what the desk considered material.
Then the usual context: topic tags where published, publicly rendered body text and word count, outbound links, video duration where the item is a video, and the collection timestamp on every row.
Quote pages exist per symbol and respond, which makes it possible to collect the site's own view of an instrument alongside the coverage attached to it. What we do not do is present that as market data: for prices, use a market data provider with a licence, and use this source for the coverage.
Earnings clusters need deliberate handling. Volume spikes hard around reporting dates, and the mix shifts towards preview and reaction pieces. Typing content and keeping precise timestamps lets a client model those windows separately instead of letting them distort a full year trend.
Joining to price data is the standard extension and the reason most clients buy this source. Coverage rows carry the symbol and the minute; price series come from wherever the client licenses them; the join is on symbol and time. We build that join rather than handing over two datasets and a problem.
Historical backfill is practical because article URLs are stable and section pagination reaches back. It runs as a one time job separate from the ongoing feed.
CNBC is a business and markets newsroom, and its structure reflects that. Stories are attached to companies and instruments, quote pages exist per symbol and respond directly, and the section tree is organised around markets, investing, technology and economy rather than around a general news desk.
That attachment is the reason to collect this source rather than a general outlet. Elsewhere you have to infer which company a story is about from the text. Here the association is published, which turns a media dataset into something that joins cleanly to financial data.
Discovery is straightforward: a news sitemap covers recent items and responds, section fronts carry ordered selections, and quote pages exist per symbol. Article pages carry structured markup with headline, publisher and timestamps.
Volume is high and skewed by the trading day. Coverage clusters around market open, earnings releases and macro announcements, which matters for any time series: a naive daily count reads the trading calendar rather than the news.
Get a Quote
Developers
Customers worldwide
Pages extracted
Hours saved for our clients
€199 / one-time
setup fee - included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Media data and market data live in different systems, and joining them is usually the hardest part of any event study. Company names are ambiguous, the same firm appears under several forms, and inferring an instrument from prose is error prone in exactly the cases that matter.
On this source the link is published. A story carries the symbols it is about, so coverage attaches to an instrument without inference. That makes the questions people actually have answerable: how much coverage preceded a move, whether attention clustered before or after an earnings release, which competitor in a sector gets written about more and when.
The second reason is timing precision. On a markets source, the minute matters. A story published two minutes before a move and one published an hour after are different facts entirely, and both timestamps plus a collection timestamp are needed to say which you are holding.
The third is the trading calendar. Coverage volume is shaped by market hours, earnings season and macro release dates, and a daily count that ignores that is measuring the calendar. Timestamped rows let a client normalise against the session rather than against the day.
The fourth caution is one we state plainly: this is published journalism, not a market signal product. It tells you what was written and when. Anything further is your model, and the responsibility for it is yours.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, watches it through earnings seasons and site changes, and repairs it before your series develops a hole.
You see a sample first, in your format, over the symbols or sectors you actually track, with ticker associations already resolved so you can test the join against your own price data.
Yes, and that is the main reason to collect this source rather than a general outlet. The association is published rather than inferred from prose, so coverage attaches to an instrument without guesswork. We deliver symbols as rows, not a string, so a story about three companies produces three joinable rows.
That is the standard project. Coverage rows carry the symbol and the minute; the price series comes from wherever you license it; the join is on symbol and time. We build the join rather than handing over two datasets and leaving you to reconcile them.
No. We can collect what the site publishes on its quote pages, but we do not present that as market data and would not recommend using it that way. For prices use a market data provider with a licence; use this source for what it is, which is coverage.
By typing content and keeping precise timestamps, so preview and reaction pieces around reporting dates can be modelled as their own window. Untreated, earnings season distorts a full year trend and a daily count ends up measuring the trading calendar rather than the news.
No, and we say so plainly. It is published journalism with timestamps and ticker associations: what was written, about which instrument, and when. Anything beyond that is your model and your responsibility. If your use case is regulated, take it to your compliance team before the project starts.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
