The Best Financial Data Providers in the Worldand how to build your own datasets

Financial data is the raw material behind almost every serious market decision: which equities to hold, how to price risk, when a sector is turning. The firms that supply that data have spent decades building coverage, accuracy, and the terminals and feeds that traders, analysts, and risk teams rely on. Knowing who they are, and what each one is genuinely strong at, is the first step in choosing where to spend a data budget.

This guide profiles the leading financial data providers in the world, describing each one fairly: what it is known for, the kinds of data it carries, and who it suits. Then it turns to alternative data and how teams build their own financial datasets by collecting public web sources, where a collection tool fits into that workflow. By the end you will understand both the established vendors and the build-your-own path that increasingly runs alongside them.

What makes a strong financial data provider

Before comparing names, it helps to know the dimensions that separate one provider from another. The same handful of factors come up whether you are an investment bank, a fintech startup, or a research desk.

• Coverage and asset classes. Equities, fixed income, foreign exchange, commodities, derivatives, and increasingly crypto. The broader and deeper the coverage, including history for backtesting, the more a provider can anchor a whole workflow.
• Accuracy and timeliness. In markets a small delay or error is expensive. Real-time feeds, clean reference data, and reliable corporate-actions handling are what institutions pay for.
• Delivery and integration. A desktop terminal, a feed, or an API with SDKs and documentation. How the data reaches your systems shapes how usable it is day to day.
• Analytics and content. Many providers bundle research, news, ratings, and modeling tools on top of the raw numbers, which is often the real reason a desk standardizes on one.

The providers below are the established leaders. The order follows the long-standing market view of who anchors the institutional data landscape, starting with the two terminals most trading floors are built around.

The best financial data providers in the world

Bloomberg

The Bloomberg Terminal is the closest thing the industry has to a standard. It combines real-time and historical market data across equities, fixed income, foreign exchange, and commodities with news, analytics, messaging, and execution tools in a single environment. For institutional traders, portfolio managers, and analysts, it is often the default workspace rather than just a data source.

Bloomberg is known for breadth and depth in one place: market data, a respected newsroom, and proprietary analytics, plus programmatic access for firms that want to pull data into their own systems. It suits banks, asset managers, and hedge funds that need comprehensive coverage and are willing to pay for an all-in-one professional platform.

Refinitiv (LSEG)

Refinitiv, now part of the London Stock Exchange Group, is the other heavyweight in institutional market data and the most direct alternative to Bloomberg. Its Eikon and Workspace platforms deliver wide coverage across asset classes, alongside news, analytics, and risk and compliance content. It carries one of the longest-running and most widely cited historical datasets in the industry.

Refinitiv is known for global coverage, deep reference and pricing data, and strong content for risk and regulatory workflows. It suits large financial institutions, trading desks, and compliance teams that want an established, broad platform and value its long data history and integrations with professional tooling.

S&P Global Market Intelligence

S&P Global Market Intelligence brings together company financials, credit ratings, economic indicators, and a growing body of alternative and ESG data. Its strength is fundamental and reference data: detailed company information, sector analysis, and the credit and risk content that S&P Global is historically known for.

It is known for rich fundamentals, credit and ratings insight, and customizable risk and analysis tools, with API access for teams that want to pull data into their own models. It suits investment research, corporate strategy, credit, and risk functions that need depth on companies and economies rather than only fast-moving price ticks.

Moody's

Moody's is best known as one of the major credit rating agencies, and its data and analytics arm extends that into credit risk, economic research, and company data. Through its ratings and its analytics products, it provides the credit assessments, risk models, and economic forecasting that lenders, insurers, and fixed-income investors rely on.

Moody's is known above all for credit ratings and credit-risk analytics, supported by economic data and modeling tools. It suits banks, insurers, asset managers, and corporates focused on credit exposure, counterparty risk, and fixed-income analysis, where its ratings and risk content are a long-standing reference point.

FactSet

FactSet is an integrated platform that pulls together market data, company fundamentals, analytics, and portfolio tools, with a reputation for clean data integration and responsive client support. Rather than competing purely on raw breadth, it focuses on connecting many data sources into consistent, analysis-ready workflows.

It is known for strong data integration, portfolio analytics, and a flexible API and feed model that let teams blend FactSet data with their own. It suits buy-side analysts, portfolio managers, and research teams who want a well-integrated analytics environment and value support and data consistency across equities, fixed income, and fundamentals.

Morningstar

Morningstar is widely recognized for investment research and data on funds, equities, and managed products, including its independent ratings and analyst research. It serves both professional and individual-investor audiences, and its fund and portfolio data is a common reference across wealth management.

Morningstar is known for fund and equity research, independent ratings, and portfolio analytics, with data feeds and APIs for firms that build on top of it. It suits wealth managers, financial advisors, fund analysts, and platforms that need trusted research and managed-product data alongside market prices.

Xignite

Xignite is a cloud-based market-data provider built around APIs, offering market data as a service so teams can access prices and reference data without standing up heavy infrastructure. It has long been associated with the fintech and developer audience that wants data delivered cleanly over modern APIs.

It is known for cloud-delivered market-data APIs, breadth of catalog, and developer-friendly integration across languages. It suits fintech startups, app builders, and enterprises that prefer to consume market data programmatically and scale usage up or down rather than commit to a full terminal.

Alpha Vantage

Alpha Vantage is a developer-focused provider of market-data APIs covering equities, foreign exchange, cryptocurrencies, and technical indicators, with a free tier that has made it popular for prototypes and smaller projects. It is lighter than the institutional platforms above and aimed squarely at individual developers and small teams.

It is known for an accessible API, a free entry point, and coverage that spans stocks, FX, crypto, and technical indicators. It suits individual traders, students, indie developers, and startups building tools or testing strategies who need straightforward programmatic data without enterprise commitments.

Summary of the providers

A compact view of who each provider is for and the kinds of data it centers on.

Alternative data and building your own datasets

The providers above sell structured, vetted datasets. Alongside them, an entire field of alternative data has grown up: signals that do not arrive in a neat feed but can be assembled from public sources on the open web. News flow and sentiment, regulatory filings, product prices and availability, job postings, app reviews, and marketplace listings all carry information that moves ahead of, or fills gaps in, traditional data.

Building a dataset from these sources is a different exercise from subscribing to a feed. You decide what signal you want, identify the public pages that carry it, collect that content reliably and repeatedly, then clean and structure it into something you can analyze. This is the path many quant and research teams take when they want a proprietary edge, and it is a well-documented one: see how teams approach web scraping in hedge-fund workflows and price intelligence for concrete patterns.

Common public sources that feed a homegrown financial dataset include:

• Financial news and market sites. Headlines, earnings coverage, and macroeconomic commentary that can be tracked over time for sentiment and event detection.
• Company pages and regulatory filings. Investor-relations pages and public disclosures that hold earnings, guidance, and corporate-action details.
• Marketplaces and pricing pages. Public product prices, stock levels, and listings that act as a proxy for demand, inflation, or company performance.
• Public price and crypto data. Exchange rates and token performance published openly across many sites.

The hard part is rarely the analysis. It is the collection: public pages render with JavaScript, change structure, rate-limit, and block automated requests, and a financial dataset is only useful if the collection runs reliably day after day. That is the layer a collection tool handles.

Once the raw pages are flowing in, the work shifts to structuring them. Web-collected content is messy by nature, so deduplicating, normalizing, and shaping it into consistent records is what turns a pile of HTML into a dataset a model or analyst can use. Our guides on cleaning scraped data for AI and ML and how AI data extraction works cover that step in depth. Treated this way, alternative data does not replace the established providers; it complements them, giving you signals they do not package and a view you control end to end.

Scraping responsibly

Building your own financial dataset from public sources comes with responsibilities. Respect each site's terms of service and its robots.txt directives, focus on publicly available information rather than anything behind a login you are not entitled to, and keep request rates reasonable so you do not strain the servers you depend on. Financial data also carries licensing and compliance considerations, so be clear on how any data you collect can be used and redistributed. Collection tools that rotate IPs and throttle politely help you stay a good citizen while you gather.

Frequently Asked Questions (FAQs)

What is the best financial data provider?

There is no single best provider, only the best fit for your work. Bloomberg and Refinitiv lead for broad institutional coverage and analytics, S&P Global and Moody's for fundamentals and credit, FactSet and Morningstar for analytics and research, and Xignite and Alpha Vantage for developer-friendly APIs. Match the provider to the asset classes, depth, and delivery method you need.

What is the difference between a financial data provider and a collection tool like Crawlbase?

A financial data provider sells structured, vetted datasets such as prices, fundamentals, and ratings. A collection tool like Crawlbase does not sell financial data at all; it gathers public web pages reliably so you can build your own dataset. The providers give you finished feeds, while a collection layer helps you assemble alternative data yourself.

What are the three types of financial data?

Financial data is commonly grouped into price data (stock prices, indices, exchange rates), fundamental data (financial statements, earnings, ratios), and alternative data (non-traditional signals such as web sentiment, filings, pricing, and listings). The established providers focus on the first two, while alternative data is often assembled from public web sources.

Where can I get financial data for free?

Several providers offer free tiers or limited access, and Alpha Vantage in particular is popular for free programmatic data covering equities, foreign exchange, and crypto. Public web sources such as news sites, company pages, and marketplaces are also freely available, which is why many teams collect and structure them into their own datasets rather than relying only on paid feeds.

Can I build my own financial dataset by scraping public sources?

Yes. Teams routinely build proprietary datasets from public news, filings, prices, and listings to capture alternative-data signals the standard vendors do not package. The main challenge is reliable collection from pages that render with JavaScript, change structure, and block automated requests, which is where a collection tool like the Crawlbase Crawling API helps, followed by cleaning and structuring the results.

Is it legal to collect public financial data from the web?

Collecting publicly available data is widely practiced, but you should respect each site's terms of service and robots.txt, avoid anything behind a login you are not entitled to, and keep request rates reasonable. Financial data also carries licensing and compliance considerations, so be clear on how the data you gather can be used and redistributed before you build on it.

TA

Thomas Adewale

Technical Writer · Crawlbase

Technical writer at Crawlbase covering proxy networks, rotation strategy, and the plumbing behind reliable crawling at scale.