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=============
Tiingo Python
=============
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Tiingo is a financial data platform that makes high quality financial tools available to all. Tiingo has a REST and Real-Time Data API, which this library helps you to access. Presently, the API includes support for the following endpoints:
* Stock Market Ticker Closing Prices + Metadata. Data includes full distribution details and is validated using a proprietary EOD Price Engine.
* Curated news from top financial news sources + blogs. Stories are tagged with topic tags and relevant stock tickers by Tiingo's algorithms.
Usage
--------
First, install the library from PyPi:
.. code-block:: shell
pip install tiingo
If you prefer to receive your results in ``pandas DataFrame`` or ``Series`` format, and you do not already have pandas installed, install it as an optional dependency:
.. code-block:: shell
pip install tiingo[pandas]
Next, initialize your client. It is recommended to use an environment
variable to initialize your client for convenience.
.. code-block:: python
from tiingo import TiingoClient
# Set TIINGO_API_KEY in your environment variables in your .bash_profile, OR
# pass a dictionary with 'api_key' as a key into the TiingoClient.
client = TiingoClient()
Alternately, you may use a dictionary to customize/authorize your client.
.. code-block:: python
config = {}
# To reuse the same HTTP Session across API calls (and have better performance), include a session key.
config['session'] = True
# If you don't have your API key as an environment variable,
# pass it in via a configuration dictionary.
config['api_key'] = "MY_SECRET_API_KEY"
# Initialize
client = TiingoClient(config)
Now you can use ``TiingoClient`` to make your API calls. (Other parameters are available for each endpoint beyond what is used in the below examples, inspect the docstring for each function for details.).
.. code-block:: python
# Get Ticker
ticker_metadata = client.get_ticker_metadata("GOOGL")
# Get latest prices, based on 3+ sources as JSON, sampled weekly
ticker_price = client.get_ticker_price("GOOGL", frequency="weekly")
# Get historical GOOGL prices from August 2017 as JSON, sampled daily
historical_prices = client.get_ticker_price("GOOGL",
fmt='json',
startDate='2017-08-01',
endDate='2017-08-31',
frequency='daily')
# Check what tickers are available, as well as metadata about each ticker
# including supported currency, exchange, and available start/end dates.
tickers = client.list_stock_tickers()
# Get news articles about given tickers or search terms from given domains
articles = client.get_news(tickers=['GOOGL', 'AAPL'],
tags=['Laptops'],
sources=['washingtonpost.com'],
startDate='2017-01-01',
endDate='2017-08-31')
To receive results in ``pandas`` format, use the ``get_dataframe()`` method:
.. code-block:: python
#Get a pd.DataFrame of the price history of a single symbol (default is daily):
ticker_history = client.get_dataframe("GOOGL")
#The method returns all of the available information on a symbol, such as open, high, low, close,
#adjusted close, etc. This page in the tiingo api documentation lists the available information on each
#symbol: https://api.tiingo.com/docs/tiingo/daily#priceData.
#Frequencies and start and end dates can be specified similarly to the json method above.
#Get a pd.Series of only one column of the available response data by specifying one of the valid the
#'metric_name' parameters:
ticker_history = client.get_dataframe("GOOGL", metric_name='adjClose')
#Get a pd.DataFrame for a list of symbols for a specified metric_name (default is adjClose if no
#metric_name is specified):
ticker_history = client.get_dataframe(['GOOGL', 'AAPL'],
frequency='weekly',
metric_name='volume',
startDate='2017-01-01',
endDate='2018-05-31')
Further Docs
--------
* Official Tiingo Documentation: https://api.tiingo.com
* `tiingo-python` Documentation: https://tiingo-python.readthedocs.io.
Features
--------
* Easy programmatic access to Tiingo API
* Reuse requests session across API calls for better performance
* On most methods, pass in `fmt="object"` as a keyword to have your responses come back as `NamedTuples`, which should have a lower memory impact than regular Python dictionaries.
Roadmap:
--------
* Client-side validation of tickers
* Data validation of returned responses
* Case insensitivity for ticker names
* More documentation / code examples
Feel free to file a PR that implements any of the above items.
Related Projects:
--------
* Riingo_ : Client for Tiingo in the R Programming Language
.. _Riingo: https://github.com/business-science/riingo
Credits
---------
* Many thanks to Rishi Singh for creating Tiingo.
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage