Unofficial Intrinio API client for Python. It gives easy access to financial data.
Setup
Install this package by using the pip tool:
pip install intrinioBefore 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 symbolReturns: 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 symbolReturns: 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_statementReturns: 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_statementReturns: 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_statementReturns: 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 defaultReturns: 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.testOr run the runtests script to also generate a coverage report (saved to tmp/).
bin/runtestsVersion history
See CHANGELOG.md
License
MIT License
1 maintainer
Release files for intrinio 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| intrinio-0.2.1.tar.gz | 7.2 kB | Details |
Release files / intrinio-0.2.1.tar.gz
| Download URL | intrinio-0.2.1.tar.gz |
|---|---|
| Size | 7.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a42af826de4873df38079d32eb77ea314a128d50c8af4f03d1a138dd07d84057
|
|
BLAKE2b-256 checksum How to use checksums |
3cbb71afeb08e8f779f7f4eba3145290a3d6a545746a5ef89c96a1e1adec5df9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
