|Build Status|
Unofficial Intrinio API client for Python. It gives easy access to financial data.
Install this package by using the pip tool:
.. code:: bash
pip install intrinio
Before retrieving data using the package the API username and password has to be configured, either by setting the username and password attributes of the intrinio package:
.. code:: python
import intrinio
intrinio.client.username = 'USERNAME_FROM_INTRINIO'
intrinio.client.password = 'PASSWORD_FROM_INTRINIO'
Or by setting the system environment variables:
Install the intrinio package, import it and set username and password as above first.
Get prices starting at 2016-01-01 for Apple:
.. code:: python
intrinio.prices('AAPL', start_date='2016-01-01')
Get yearly fundamentals including PE ratio, net debt, total capital and over 100 other variables for Apple:
.. code:: python
intrinio.financials('AAPL')
Get company information about Google:
.. code:: python
intrinio.companies('GOOG')
Get company information about Google using the low level get function:
.. code:: python
intrinio.get('companies', identifier='GOOG')
Get cik, lei, name and ticker of companies with "Bank" in their company name:
.. code:: python
intrinio.companies(query='Bank')
Screen stocks with PE higher than 10:
.. code:: python
intrinio.screener('pricetoearnings~gt~10')
There are a high- and low level functions used to access the Intrinio API.
The high level functions are mostly simple wrappers of the get function that retrieves all data with optional parameters to filter the data. They might also do some data conversion like for example the prices endpoint where the date column is used as the index for the Pandas DataFrame.
For more information about available endpoints and their parameters, see
Intrinio API documentation at: Intrinio Docs <http://docs.intrinio.com/>__
Low level functions
- get(endpoint, \*\*parameters):
Get complete dataset from an endpoint using optional query
parameters.
Args:
::
endpoint: Intrinio endpoint, for example: companies
parameters: Optional query parameters
Returns: Dataset as a Pandas DataFrame
- get\_page(endpoint, page\_number=1, page\_size=None, \*\*parameters):
Get a dataset page from an endpoint using optional query parameters.
Args:
::
endpoint: Intrinio endpoint, for example: companies
page_number: Optional page number where 1 is first page (default 1)
page_size: Optional page size (default max page size for the endpoint)
parameters: Optional query parameters
Returns: Dataset page as a Pandas DataFrame with an additional
total\_pages attribute
High level functions
companies(identifier=None, query=None):
Get companies with optional filtering using parameters.
Args:
::
identifier: Identifier for the legal entity or a security associated
with the company: TICKER SYMBOL | FIGI | OTHER IDENTIFIER
query: Search of company name or ticker symbol
Returns: Dataset as a Pandas DataFrame
securities(identifier=None, query=None, exch_symbol=None):
Get securities with optional filtering using parameters.
Args:
::
identifier: Identifier for the legal entity or a security associated
with the company: TICKER SYMBOL | FIGI | OTHER IDENTIFIER
query: Search of security name or ticker symbol
exch_symbol: Exchange symbol
Returns: Dataset as a Pandas DataFrame
indices(identifier=None, query=None, type=None):
Get indices with optional filtering using parameters.
Args:
::
identifier: Intrinio symbol associated with the index
query: Search of index name or symbol
type: Type of indices: stock_market | economic | sic
Returns: Dataset as a Pandas DataFrame
prices(identifier, start_date=None, end_date=None, frequency='daily', sort_order='desc'):
Get historical stock market prices or indices.
Args:
::
identifier: Stock market symbol or index
start_date: Start date of prices (default no filter)
end_date: Last date (default today)
frequency: Frequency of prices: daily (default) | weekly | monthly |
quarterly | yearly
sort_order: Order of prices: asc | desc (default)
Returns: Dataset as a Pandas DataFrame
news(identifier):
Get news for a company.
Args:
::
identifier: stock market ticker symbol associated with the company's
common stock. If the company is foreign, use the stock exchange
code, followed by a colon, then the ticker.
Returns: Dataset as a Pandas DataFrame
financials(identifier, type='FY', statement='calculations'):
Get standardized fundamental data for a company.
Args:
::
identifier: stock market ticker symbol associated with the company's
common stock. If the company is foreign, use the stock exchange
code, followed by a colon, then the ticker.
type: Period type: FY (default) | QTR | TTM | YTD
statement: Type of fundamental data: calculations (default) |
income_statement | balance_sheet | cash_flow_statement
Returns: Dataset as a Pandas DataFrame
financials_period(identifier, fiscal_year, fiscal_period='FY', statement='calculations'):
Get standardized fundamental data for a single period for a company.
Args:
::
fiscal_year: Year
fiscal_period: FY (default) | Q1 | Q2 | Q3 | Q4 | Q1TTM | Q2TTM | Q3TTM
| Q2YTD | Q3YTD
identifier: stock market ticker symbol associated with the company's
common stock. If the company is foreign, use the stock exchange
code, followed by a colon, then the ticker.
statement: Type of fundamental data: calculations (default) |
income_statement | balance_sheet | cash_flow_statement
Returns: Dataset as a Pandas DataFrame
fundamentals(identifier, type='FY', statement='calculations'):
Get available periods with standardized fundamental data for a company.
Args:
::
identifier: stock market ticker symbol associated with the company's
common stock. If the company is foreign, use the stock exchange
code, followed by a colon, then the ticker.
type: Period type: FY (default) | QTR | TTM | YTD
statement: Type of fundamental data: calculations (default) |
income_statement | balance_sheet | cash_flow_statement
Returns: Dataset as a Pandas DataFrame
screener(conditions, order_column=None, order_direction=None, primary_only=None, logic=None):
Find securities that meet a list of conditions.
Args:
::
conditions: Comma-separated list of conditions. Each condition
consists of three or four elements separated by tildes (~):
Data_tag~Operator~Value~Label(Optional)
order_column: A data tag by which to order the results
order_direction: Order of the results: asc (default) | desc
primary_only: Return only primary securities (excluding special
securities such as preferred shares)
logic: How the conditions are applied using AND by default
Returns: List of tickers that meet the conditions as a Pandas DataFrame
The tests will cache API requests in ~/.cache/intrinio to make the tests go faster and avoid excessive API traffic. It is not recommended to use caching for non-testing purposes, it should only be used for tests.
Run the tests using pytest in the root directory of the project:
.. code:: bash
py.test
Or run the runtests script to also generate a coverage report (saved to tmp/).
.. code:: bash
bin/runtests
.. |Build Status| image:: https://travis-ci.org/nhedlund/intrinio.svg?branch=master :target: https://travis-ci.org/nhedlund/intrinio
FAQs
Intrinio API client
We found that intrinio demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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