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

If you'd like to try this library before installing, click below to open a folder of online runnable examples.

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First, install the library from PyPi:

.. code-block:: shell

pip install tiingo

If you prefer to receive your results in

pandas DataFrame

Series

.. 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 TIINGOAPIKEY in your environment variables in your .bashprofile, OR # pass a dictionary with 'apikey' 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['apikey'] = "MYSECRETAPIKEY"

# Initialize client = TiingoClient(config)

Now you can use

TiingoClient

.. code-block:: python

# Get Ticker tickermetadata = client.getticker_metadata("GOOGL")

# Get latest prices, based on 3+ sources as JSON, sampled weekly tickerprice = client.getticker_price("GOOGL", frequency="weekly")

# Get historical GOOGL prices from August 2017 as JSON, sampled daily historicalprices = client.getticker_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.liststocktickers()

# 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')

# Get definitions for fields available in the fundamentals-api, ticker is # optional definitions = getfundamentalsdefinitions('GOOGL')

# Get fundamentals which require daily-updated (like marketCap). A start- # and end-date can be passed. If omited, will get all available data. fundamentalsdaily = CLIENT.getfundamentals_daily('GOOGL', startDate='2020-01-01', endDate='2020-12-31')

# Get fundamentals based on quarterly statements. Accepts time-range like # daily-fundamentals. asReported can be set to get the data exactly like # it was reported to SEC. Set to False if you want to get data containing # corrections fundamentalsstmnts = CLIENT.getfundamentals_statements('GOOGL', startDate='2020-01-01', endDate='2020-12-31', asReported=True)

To receive results in

pandas

get_dataframe()

.. code-block:: python

#Get a pd.DataFrame of the price history of a single symbol (default is daily): tickerhistory = client.getdataframe("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 #'metricname' parameters: tickerhistory = client.getdataframe("GOOGL", metricname='adjClose')

#Get a pd.DataFrame for a list of symbols for a specified metricname (default is adjClose if no #metricname is specified): tickerhistory = client.getdataframe(['GOOGL', 'AAPL'], frequency='weekly', metric_name='volume', startDate='2017-01-01', endDate='2018-05-31')

You can specify any of the end of day frequencies (daily, weekly, monthly, and annually) or any intraday frequency for both the

get_ticker_price

get_dataframe

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

.. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _

audreyr/cookiecutter-pypackage