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You will be able to use Thinknum API by using python package.

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'
)

The default timeout is 180 seconds. If you need to change timeout seconds, you can configure it with the timeout argument.

q = Query(
    client_id='Your client id',
    client_secret='Your client secret',
    timeout=300
)

The timeout error won't happen if you set it to None.

q = Query(
    client_id='Your client id',
    client_secret='Your client secret',
    timeout=None
)

If you need to use a proxy, you can configure it with the proxies argument.

proxies = {
  "http": "http://10.10.1.10:3128",
  "https": "http://10.10.1.10:1080",
}

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

Requests can ignore verifying the SSL certficate if you set verify to False. By default, verify is set to True.

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

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. To retrieve data lulu's job listings in 2020, an example request is:

q.add_ticker('nasdaq:lulu') # Add ticker
q.add_filter(
    column='as_of_date',
    type='>=',
    value=["2020-01-01"]
)
q.add_filter(
    column='as_of_date',
    type='<=',
    value=["2020-12-31"]
)
q.add_sort(
    column='as_of_date',
    order='asc'
)   # Add Sort
q.get_data(dataset_id='job_listings')    # Retrieve data

You can retrieve data with OR filters. To retrieve lulu's job listings which title has sales or description has sales in 2020, an example request is:

q.add_ticker('nasdaq:aapl') # Add ticker
q.add_filter(
    column='as_of_date',
    type='>=',
    value=["2020-01-01"]
)
q.add_filter(
    column='as_of_date',
    type='<=',
    value=["2020-12-31"]
)
root_condition = q.add_condition(
    match='any'
)
q.add_filter(
    column='title',
    type='...',
    value='sales',
    condition=root_condition
)
q.add_filter(
    column='description',
    type='...',
    value='sales',
    condition=root_condition
)
q.get_data(dataset_id='job_listings')    # Retrieve data

You can retrieve data with more complicated filters. To retrieve lulu's sales job in 2020 or marketing job in 2021:

q.add_ticker('nasdaq:aapl') # Add ticker
q.add_filter(
    column='as_of_date',
    type='>=',
    value=["2020-01-01"]
)
q.add_filter(
    column='as_of_date',
    type='<=',
    value=["2020-12-31"]
)
root_condition = q.add_condition(
    match='any',
)
c1 = q.add_condition(
    match='all',
    condition=root_condition
)
q.add_filter(
    column='title',
    type='...',
    value='sales',
    condition=c1
)
q.add_filter(
    column='as_of_date',
    type='>=',
    value=["2020-01-01"],
    condition=c1
)
q.add_filter(
    column='as_of_date',
    type='<=',
    value=["2020-12-31"],
    condition=c1
)

c2 = q.add_condition(
    match='all',
    condition=root_condition
)
q.add_filter(
    column='title',
    type='...',
    value='marketing',
    condition=c2
)
q.add_filter(
    column='as_of_date',
    type='>=',
    value=["2021-01-01"],
    condition=c2
)
q.add_filter(
    column='as_of_date',
    type='<=',
    value=["2021-12-31"],
    condition=c2
)
q.get_data(dataset_id='job_listings')    # Retrieve data

Please note that the maximum depth of condition is two.

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')

Also, you can apply sales function to Car Inventory dataset like the following code.

q.add_ticker('nyse:kmx')
q.add_function(
    function='sales',
    parameters={
        "lookahead_day_count": 2,
        "start_date": "2020-01-01",
        "end_date": "2020-01-07"
    }
)
q.get_data(dataset_id='car_inventory')

Also, you can reset entire query.

q.reset_query()

Also, you can reset tickers.

q.reset_tickers()

Also, you can reset filters.

q.reset_filters()

Also, you can reset functions.

q.reset_functions()

Also, you can reset groups.

q.reset_groups()

Also, you can reset aggregations.

q.reset_aggregations()

Also, you can reset sorts.

q.reset_sorts()

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'
)

If you need to use a proxy, you can configure it with the proxies argument.

proxies = {
  "http": "http://10.10.1.10:3128",
  "https": "http://10.10.1.10:1080",
}

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

Requests can ignore verifying the SSL certficate if you set verify to False. By default, verify is set to True.

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

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'
)

License

MIT

For more details

https://pypi.org/project/thinknum/