This is the official documentation for Nasdaq Data Link's Python Package. The package can be used to interact with the latest version of the Nasdaq 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-linkAlternatively 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| Option | Explanation | Example |
|---|---|---|
| api_key | Your access key | tEsTkEy123456789 |
| use_retries | Whether API calls which return statuses in retry_status_codes should be automatically retried |
True |
| number_of_retries | Maximum number of retries that should be attempted. Only used if use_retries is True |
5 |
| max_wait_between_retries | Maximum amount of time in seconds that should be waited before attempting a retry. Only used if use_retries is True |
8 |
| retry_backoff_factor | Determines the amount of time in seconds that should be waited before attempting another retry. Note that this factor is exponential so a retry_backoff_factor of 0.5 will cause waits of [0.5, 1, 2, 4, etc]. Only used if use_retries is True |
0.5 |
| retry_status_codes | A list of HTTP status codes which will trigger a retry to occur. Only used if use_retries is True |
[429, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511] |
By default, SSL verification is enabled. To bypass SSL verification (not recommended), simply:
nasdaqdatalink.ApiConfig.verify_ssl = FalseYou may use environment variables to configure the Data Link SDK to avoid any inline boilerplate.
| Env | Description |
|---|---|
| NASDAQ_DATA_LINK_API_KEY | The SDK will configure itself to use the given API Key |
| NASDAQ_DATA_LINK_BASE_DOMAIN | The SDK will configure itself to use the provided domain |
If you wish to store your API as an environment variable, you can do so by setting NASDAQ_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. The
client will attempt to load this file if it exists. Note: if the file exists
and empty, a ValueError will be thrown.
Since 1.0.1, the nasdaq-data-link module will attempt to autoload your API Key. If you prefer to store it in another location, you must
explicitly call read_key() with a custom path. See below:
import nasdaqdatalink
nasdaqdatalink.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:
import nasdaqdatalink
data = nasdaqdatalink.get('NSE/OIL')This example finds all data points for the dataset NSE/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:
import nasdaqdatalink
data = nasdaqdatalink.get_table('ZACKS/FC', ticker='AAPL')This example retrieves all rows for ZACKS/FC where ticker='AAPL' and stores them in a pandas dataframe. Similarly you can then view the dataframe with data.head().
Note that in both examples if an api_key has not been set you may receive limited or sample data. You can find more details on these quick calls and others in our Quick Method Guide.
Currently, Nasdaq Data Link debug logging is limited in scope. However, to enable debug logs you can use the following snippet.
import nasdaqdatalink
import logging
logging.basicConfig()
# logging.getLogger().setLevel(logging.DEBUG) # optionally set level for
everything. Useful to see dependency debug info as well.
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 our Detailed Method Guide.
If you wish to work on local development please clone/fork the git repo and use pip 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:
- 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/
- Install
virtualenvandtoxusing:pip install tox virtualenv - Run following command (you may notice slow performance the first time):
python setup.py install - 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 -W always setup.py -q testRunning an individual test
python -m unittest test.[test file name].[class name].[individual test name]`Example:
python -m unittest -v test.test_datatable.ExportDataTableTest.test_download_get_file_infoWe would suggest downloading the data in raw format in the highest frequency possible and performing any data manipulation in pandas itself.
See this link for more information about timeseries in pandas.
To release the package, you can follow the instructions on this page
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