quandl
QuandlQuandl is a Data Platform
We host data from hundreds of publishers on a single easy-to-use website. We make it easy for data users to get the data they need in the format they want.
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Sharadar is an independent research and analytics firm founded in 2013. Sharadar specializes in extraction, standardization and organization of financial data from company filings. We combine people, software and rigorous review processes to generate accurate, professional grade data for professional investors and analysts.
Leading Provider of stock market data and research solutions for online brokerages, clearing firms, banks, media portals, public companies, and financial service corporations.
Zacks Investment Research has been a leading authority on North American earnings estimates since 1979. Zacks data is trusted by almost all major financial data and media companies including Bloomberg, CapitalIQ, Morningstar, the Wall Street Journal, Nasdaq, USAToday, ShareBuilder, AOL, MSN and more.
Stevens Analytics uses industry-leading technology, robust process control, and decades of financial markets expertise to create comprehensive, accurate and timely databases for sale at a price far below other data vendors.
OptionWorks® Research is produced by The Applied Research Company, a registered Commodity Trading Advisor and member of the US National Futures Association. It is the product of more than 20 years of research and development in option pricing and modeling. The ARC’s consulting clients include exchanges, brokers, proprietary trading firms, and software vendors. Thousands of traders worldwide use ARC-designed products daily.
Quantcha is a financial software and services company focused on equity and option investing. In addition to data feeds, Quantcha provides a suite of tools for searching, filtering, and analyzing stock market investments.
DataYes is a fast-growing financial information management and investment management services company. It is led by a group of experts in the fields of finance and leading edge computer technology. Using Big Data, Cloud and Mobile Computing, DataYes provides investors with an innovative first-class platform for global financial services. Currently DataYes has over 200 employees in Shanghai headquarter and San Jose offices and is serving more than 150 institutional clients in China.
Borrell works with more than 700 media properties, Internet "pure-play" companies, investment analysts, and industry vendors. This work focuses on helping companies understand and capitalize on the evolving media landscape, and to grow revenues exponentially rather than incrementally. Borrell’s employees and analysts have worked in pure-play Internet companies, run retail businesses, managed new media divisions for large companies, and sold local advertising. They have hands-on experience that provides an understanding of the business that other consultants rarely offer. Borrell tracks and forecasts advertising, promotions, and digital services spending across all business types for all U.S. market definitions.
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Quandl offers a single point of access to external financial and economic databases. While most of them are free to use, the company charges a premium subscription to access all databases. Its Application Program Interface (API) integrates external databases into users' current workflow with an instant activation. Company website: quandl.com
- Worldwide datasets are available from organizations including exchanges, private firms, central banks, statistical agencies, international organizations and universities. The financial data includes:
- Equities
- Fixed income and currencies
- Commodities
- Futures and options
- Alternative investments
- Financial estimates
- Fundamentals
- Economics, demography and more
- There are limited amount of databases for non-equity asset classes
- Data can be retrieved using API requests, provided users know the Quandl codes.
- API requests:
- API keys should always be included.
- Requests without an API key can be accepted with a limit of 50 calls per day.
- All API requests must be made using HTTPS.
- The Quandl API supports cross-origin resource sharing.
- Metadata and data can be returned using different API requests.
- A list of databases can also be downloaded with the respective metadata.
- All data can be downloaded in a premium database in a single call, by appending data to the request.
- Quandl Codes:
- Every database on Quandl has a unique string identifier called the database_code.
- Every dataset on Quandl belongs to only one database. Hence, every dataset has a database_code and a dataset_code associated with it. Both of these are required to identify the dataset. The combination of database_code and dataset_code is called the Quandl code.
- CSV, JSON and XML:
- CSV requests will only return data.
- Both JSON and XML will return data and parameters.
- Users can customize the dataset object being returned by adding various optional parameters.
- Data retrieval methods and parameters
- Quick start guide
- Up to 59 analytical tools are supported by Quandl to retrieve the financial data:
- API, R, Python, Excel and Ruby.
- Eviews, Matlab, Stata, Mathematica, etc.
- C#, C/C++, Java, .NET, etc.
- A full list of available formats is available here.
We chose Quandl because it was fast and easy to get the basics going (EOD via Symbol). We're on a Ruby platform, and a gem is availabe from Quandl (and others):
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[https://github.com/quandl/quandl-ruby] <--- pre-release version for new v3 API
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[https://github.com/quandl/quandl_client] <---This is the old deprecated client
the quandl_client is out of date and requires you to manually specify the HTTPS version of the API URL (see line 2 of quandl_initializer.rb)
The gem has the advantage that you never need to see the raw data from their API. Caveats: The old version isn't updated, and the new one is of unknown quality and recency.
We're not updating to the new one because we don't want to have to make a separate call for each symbol we're intetested in (i.e. all of them). Also they don't have CUSIP data.
Quandl has a different way of thinking about datasets that you might expect. For example, for EOD (end of day) price data, a dataset is available for AAPL. Another, separate dataset is available for eod data for MSFT.
What this means is that if you want to get prices for many securities for each day, you need to pull from each of those databases. To get eod prices for 20 securites you need to pull from 20 databases.
Quandl is not oriented around CUSIP identifiers. It is oriented around ticker symbols. There are no CUSIP identifiers in any of the datasets (this is according to Quandl help desk in spring '15). Also, Quandl does not have a security master.
We have found Quandl support to be responsive and helpful.