Tiingo Python

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Tiingo is a financial data platform making high quality financial tools available to all. Tiingo has a REST and Real-Time Data API, which this library helps you access. The API includes support for these 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.

  • Fundamentals data, including definitions, daily data, and quarterly statements for tickers

Usage

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

Launch Binder

First, install the library from PyPi:

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:

pip install tiingo[pandas]

Next, initialize your client. It is recommended to use an environment variable to initialize your client for convenience.

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.

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.).

# 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')

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

# Get fundamentals which require daily-updated (like marketCap). A start-
# and end-date can be passed. If omited, will get all available data.
fundamentals_daily = client.get_fundamentals_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
fundamentals_stmnts = client.get_fundamentals_statements('GOOGL',
                                                         startDate='2020-01-01',
                                                         endDate='2020-12-31',
                                                         asReported=True)

To receive results in pandas format, use the get_dataframe() method:

#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')

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 and get_dataframe methods. Weekly frequencies resample to the end of day on Friday, monthly frequencies resample to the last day of the month, and annually frequencies resample to the end of day on 12-31 of each year. The intraday frequencies are specified using an integer followed by “Min” or “Hour”, for example “30Min” or “1Hour”.

Cryptocurrency

# You can obtain cryptocurrency metadata using the following method.
# NOTE: Crypto symbol MUST be encapsulated in brackets as a Python list!

client.get_crypto_metadata(['BTCUSD'], fmt='json')

#You can obtain top-of-book cryptocurrency quotes from the ``get_crypto_top_of_book()`` method.
# NOTE: Crypto symbol MUST be encapsulated in brackets as a Python list!

crypto_price = client.get_crypto_top_of_book(['BTCUSD'])``

# You can obtain historical Cryptocurrency price quotes from the get_crypto_price_history() method.
# NOTE: Crypto symbol MUST be encapsulated in brackets as a Python list!

client.get_crypto_price_history(tickers = ['BTCUSD'], startDate='2020-12-2',
                                endDate='2020-12-3', resampleFreq='1Hour')

Websockets Support

from tiingo import TiingoWebsocketClient

def cb_fn(msg):

    # Example response
    # msg = {
    #   "service":"iex" # An identifier telling you this is IEX data.
    #   The value returned by this will correspond to the endpoint argument.
    #
    #   # Will always return "A" meaning new price quotes. There are also H type Heartbeat msgs used to keep the connection alive
    #   "messageType":"A" # A value telling you what kind of data packet this is from our IEX feed.
    #
    #   # see https://api.tiingo.com/documentation/websockets/iex > Response for more info
    #   "data":[] # an array containing trade information and a timestamp
    #
    # }

    print(msg)

subscribe = {
        'eventName':'subscribe',
        'authorization':'API_KEY_GOES_HERE',
        #see https://api.tiingo.com/documentation/websockets/iex > Request for more info
        'eventData': {
            'thresholdLevel':5
      }
}

# any logic should be implemented in the callback function (cb_fn)
TiingoWebsocketClient(subscribe,endpoint="iex",on_msg_cb=cb_fn)

Further Docs

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.

Credits

  • Many thanks to Rishi Singh for creating Tiingo.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.