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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.
If you want to raise an exception when data is truncated, you can add except_on_truncate as True. It is False by default.
q.get_data(
dataset_id='job_listings',
start=1,
limit=1000,
except_on_truncate=True
)
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'
)
You can see download url of the historical data:
h.get_url(
dataset_id='store',
history_date='2020-03-09'
)
License
MIT
For more details
Updated about 1 year ago