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Project description

Thinknum

As companies move their business operations to the Internet, new data trails are being created that can provide unique insights on these companies. Thinknum Alternative Data indexes all of these data trails in one platform, providing investors with critical data points that others miss.

Installation

pip install thinknum

Query

Import library.

from thinknum import Query

To authenticate, you must first obtain a client_id and client_secret from your assigned Thinknum account manager. Your client_secret must not be shared or exposed via publicly accessible resources (such as browser client-side scripting).

q = Query(
    client_id='Your client id',
    client_secret='Your client secret'
)

You will get a list of datasets, each of which has the dataset id and its display_name.

q.get_dataset_list()

You will get dataset's metadata.

q.get_dataset_metadata(dataset_id='job_listings')

It's possible to limit the dataset list to a specific ticker by specific a "ticker" query parameter. For example, getting all datasets available for Apple Inc:

q.get_ticker_dataset_list(query='nasdaq:aapl')

You can search for tickers.

q.get_ticker_list(query="tesla")

You can also search for tickers of particular dataset

q.get_ticker_list(query="tesla", dataset_id='job_listings')

You can retrieve data for specific dataset and tickers with various filters. For example:

q.add_ticker('nasdaq:lulu') # Add ticker
q.add_filter(
    column='as_of_date',
    type='>=',
    value=["2020-01-05"]
)  # Add filter
q.add_sort(
    column='as_of_date',
    order='asc'
)   # Add Sort
q.get_data(dataset_id='job_listings')    # Retrieve data

You can also specify start and limit. The default values are 1 and 100000.

q.get_data(dataset_id='job_listings', start=1, limit=1000)

Sometimes you only need get aggregated results for a dataset. In such cases you can retrieve them through the addGroup and addAggregation functions.

q.add_ticker('nasdaq:lulu') # Add ticker
q.add_group(column='as_of_date') # Add group
q.add_aggregation(
    column='dataset__entity__entity_ticker__ticker__ticker',
    type='count'
)   # Add aggregation
q.add_sort(
    column='as_of_date',
    order='asc'
)   # Add sort
q.get_data(dataset_id='job_listings')

There a few functions that you can apply to queries to gather even more insight into the data. You can retrieve a listing of the available functions in a dataset with the getDatasetMetadata function. For example, there is nearby function for store dataset.

q.add_ticker('nasdaq:lulu')
q.add_function(
    function='nearby',
    parameters={
        "dataset_type": "dataset",
        "dataset": "store",
        "tickers":["nyse:ua"],
        "entities": [],
        "distance": 5,
        "is_include_closed": False
    }
)
q.get_data(dataset_id='store')

Also, you can apply nearest function to store dataset like the following code.

q.add_ticker('nasdaq:lulu')
q.add_function(
    function='nearest',
    parameters={
        "dataset_type": "dataset",
        "dataset": "store",
        "tickers":["nyse:ua"],
        "entities": [],
        "ranks": [1],
        "is_include_closed": False
    }
)
q.get_data(dataset_id='store')

History

Import library.

from thinknum import History

Like the Query library, you must authenticate to utilize History library.

h = History(
    client_id='Your client id',
    client_secret='Your client secret'
)

To retrieve a list of available history for a dataset:

h.get_history_list(dataset_id='store')

You can view the metadata for the historical file:

h.get_history_metadata(
    dataset_id='store',
    history_date='2020-03-09'
)

To download a CSV of the historical data:

h.download(
    dataset_id='store',
    history_date='2020-03-09'
)

You can specify download path:

h.download(
    dataset_id='store',
    history_date='2020-03-09', 
    download_path='/Users/sangwonseo/Downloads'
)

For more details about Library or API

Please visit https://docs.thinknum.com/docs

If you are interested in Thinknum

Please request demo at https://www.thinknum.com/demo/

If you have any questions

Please email at customersuccess@thinknum.com

License

MIT

Project details


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