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Intrinio API client

Project description

Build Status

Unofficial Intrinio API client for Python. It gives easy access to financial data.

Setup

Install this package by using the pip tool:

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:

import intrinio
intrinio.client.username = 'USERNAME_FROM_INTRINIO'
intrinio.client.password = 'PASSWORD_FROM_INTRINIO'

Or by setting the system environment variables:

  • INTRINIO_USERNAME
  • INTRINIO_PASSWORD

Quick start

Get prices starting at 2016-01-01 for Apple:

import intrinio
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:

import intrinio
intrinio.financials('AAPL')

Get company information about Google:

import intrinio
intrinio.companies('GOOG')

Get company information about Google using the low level get function:

import intrinio
intrinio.get('companies', identifier='GOOG')

Get cik, lei, name and ticker of companies with “Bank” in their company name:

import intrinio
intrinio.companies(query='Bank')

Usage

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.

Low level functions

For more information about available endpoints and their parameters, see Intrinio API documentation at: http://docs.intrinio.com/

  • 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

Tests

Run the tests using pytest in the root directory of the project:

py.test

Or run the runtests script to also generate a coverage report (saved to tmp/).

bin/runtests

Version history

  • 0.1: Initial version

License

  • MIT License

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