intrinio 0.2.1

Intrinio API client

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

Install the intrinio package, import it and set username and password as above first.

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

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:

intrinio.financials('AAPL')

Get company information about Google:

intrinio.companies('GOOG')

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

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

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

intrinio.companies(query='Bank')

Screen stocks with PE higher than 10:

intrinio.screener('pricetoearnings~gt~10')

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.

For more information about available endpoints and their parameters, see Intrinio API documentation at: Intrinio Docs

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

Tests

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:

py.test

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

bin/runtests

Version history

• See CHANGELOG.md

License

• MIT License