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This is the official documentation for Nasdaq Data Link's Python Package. The package can be used to interact with the latest version of theNasdaq Data Link's RESTful API. This package is compatible with python v3.7+.

The installation process varies depending on your python version and system used. However in most cases the following should work:

pip install nasdaq-data-link

Alternatively on some systems python3 may use a different pip executable and may need to be installed via an alternate pip command. For example:

pip3 install nasdaq-data-link

By default, SSL verification is enabled. To bypass SSL verification (not recommended), simply:

nasdaqdatalink.ApiConfig.verify_ssl=False

You may use environment variables to configure the Data Link SDK to avoid anyinline boilerplate.

If you wish to store your API as an environment variable, you can do so by settingNASDAQ_DATA_LINK_API_KEY. If set, NASDAQ_DATA_LINK_API_KEY will take precedence over the API Key file mentioned below.

The default configuration file location is~/.nasdaq/data_link_apikey. Theclient will attempt to load this file if it exists. Note: if the file existsand empty, a ValueError will be thrown.

Since 1.0.1, thenasdaq-data-link module will attempt to autoload your API Key. If you prefer to store it in another location, you mustexplicitly callread_key() with a custom path. See below:

importnasdaqdatalinknasdaqdatalink.read_key(filename="/data/.corporatenasdaqdatalinkapikey")

There are two methods for retrieving data in Python: the Quick method and the Detailed method. The latter is more suitable to application programming. Both methods work with Nasdaq Data Link's two types of data structures: time-series (dataset) data and non-time series (datatable).

The following quick call can be used to retrieve a dataset:

importnasdaqdatalinkdata=nasdaqdatalink.get('NSE/OIL')

This example finds all data points for the datasetNSE/OIL and stores them in a pandas dataframe. You can then view the dataframe with data.head().

A similar quick call can be used to retrieve a datatable:

importnasdaqdatalinkdata=nasdaqdatalink.get_table('ZACKS/FC',ticker='AAPL')

This example retrieves all rows forZACKS/FC whereticker='AAPL' and stores them in a pandas dataframe. Similarly you can then view the dataframe with data.head().

Note that in both examples if anapi_key has not been set you may receive limited or sample data. You can find more details on these quick calls and others in ourQuick Method Guide.

Currently, Nasdaq Data Link debug logging is limited in scope. However, to enable debuglogs you can use the following snippet.

importnasdaqdatalinkimportlogginglogging.basicConfig()# logging.getLogger().setLevel(logging.DEBUG)  # optionally set level foreverything.Usefultoseedependencydebuginfoaswell.data_link_log=logging.getLogger("nasdaqdatalink")data_link_log.setLevel(logging.DEBUG)

Our API can provide more than just data. It can also be used to search and provide metadata or to programmatically retrieve data. For these more advanced techniques please follow ourDetailed Method Guide.

If you wish to work on local development please clone/fork the git repo and usepip install -r requirements.txt to setup the project.

We recommend the following tools for testing any changes:

• nose for running tests.
• tox for testing against multiple versions of python.
• flake8 for syntax checking.
• virtualenv for use with tox virtualization.

The following are instructions for running our tests:

1. Make sure a version of 3.x is installed locally in your system. To avoid permission issues on OSX we recommend installing the packages from:https://www.python.org/downloads/
2. Installvirtualenv andtox using:pip install tox virtualenv
3. Run following command (you may notice slow performance the first time):python setup.py install
4. Run the following command to test the plugin in all versions of python we support:tox

Once you have all required packages installed, you can run tests locally with:

Running all tests locally

python-Walwayssetup.py-qtest

Running an individual test

python-munittesttest.[testfilename].[classname].[individualtestname]`

Example:

python-munittest-vtest.test_datatable.ExportDataTableTest.test_download_get_file_info

We would suggest downloading the data in raw format in the highest frequency possible and performing any data manipulationin pandas itself.

Seethis link for more information about timeseries in pandas.

To release the package, you can follow the instructions on thispage

• Nasdaq Data Link
• Nasdaq Data Link Tools
• API Docs

MIT License