Python REST Client for interacting with the Tiingo Financial Data API
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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:
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 DataFrameor
Seriesformat, 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 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
TiingoClientto 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 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
pandasformat, use the
get_dataframe()method:
.. 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_priceand
get_dataframemethods. 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".
tiingo-pythonDocumentation: https://tiingo-python.readthedocs.io.
fmt="object"as a keyword to have your responses come back as
NamedTuples, which should have a lower memory impact than regular Python dictionaries.
Feel free to file a PR that implements any of the above items.
.. _Riingo: https://github.com/business-science/riingo
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