This library is designed to be a wrapper for the Ready Signal API: http://app.readysignal.com
Please direct all questions and/or recommendations to support@readysignal.com
- Syntax Note
- Available Features List
- Show Features Data
- Show Features Data Detailed
- Features Data Output
The Ready Signal API Python library can be found here: https://pypi.org/project/readysignal/
pip install readysignal
Your access token and signal ID can be found on your "Manage Signal" page within the Output information.
Your signal ID is also visible within the URL of the "Manage Signal" page:
...readysignal.com/signal/SIGNAL_ID/manage
import readysignal as rs
access_token = "your access token"
signal_id = 0 # this is your unique signal id numberIt is optional to include proxy URL(s) in your functions. The setup for proxy URL(s) is shown below, and it must be in a dictionary format. If username nand password are required too, an example is shown below:
# no username and password required
proxy_info = {
"http": "http://your_proxy_address:your_proxy_port",
"https": "https://your_proxy_address:your_proxy_port"
}
# username and password required
proxy_info2 = {
"http": "http://username:password@your_proxy_address:your_proxy_port",
"https": "https://username:password@your_proxy_address:your_proxy_port"
}If a proxy URL is needed, then it will be included as an argument in every function used. The parameter name and an example is shown below:
rs.list_signals(access_token, proxy_dict=proxy_info)Using your access_token, you can list all signals and metadata that are associated with your Ready Signal account.
rs.list_signals(access_token){'data': [{
'id': 0,
'name': 'Signal Name',
'description': 'Signal Description',
'desired_geo_grain': 'Country',
'desired_time_grain': 'Month',
'start_at': '11/01/2019',
'end_at': '01/31/2020',
'created_at': '07/23/2020',
'updated_at': '07/28/2020',
'deleted_at': None,
'company': {
'id': 0,
'chargebee_id': 'ID Hash',
'name': 'Company Name',
'plan_id': 0,
'plan_value_id': 0,
'subscription_status': 'active',
'created_at': '2020-07-23 13:20:17',
'updated_at': '2020-08-03 11:33:50',
'need_listining_webhook': 0,
'addon_users': None,
'notify_after_processing': 0
},
'user': {
'id': 0,
'company_id': 0,
'name': 'Name',
'email': 'Email',
'email_verified_at': None,
'type': 'owner',
'active': 'yes',
'reactivate': 'no',
'created_at': '2020-07-23 13:20:17',
'updated_at': '2020-07-23 13:20:17',
'deleted_at': None
},
'industry': {
'id': 1,
'name': 'Automotive',
'created_at': '2020-04-10 23:21:38',
'updated_at': '2020-04-10 23:21:38',
'deleted_at': None
},
'analysis_type': {
'id': 3,
'name': 'Classification',
'created_at': '2020-04-10 23:21:45',
'updated_at': '2020-04-10 23:21:45',
'deleted_at': None
},
'output': {
'json': 'https://app.readysignal.com/api/signals/0/output?format=json'},
'links': {
'self': 'https://app.readysignal.com/signal/0/manage',
'manage': 'https://app.readysignal.com/signal/0/manage'
}
},
{
'id': 1
...
}
]
}Using your access_token and your signal_id you can view the details of a specific signal.
# get signal details
rs.get_signal_details(access_token, signal_id){'data': {
'id': 0,
'name': 'Signal Name',
'description': 'Signal Description',
'desired_geo_grain': 'Country',
'desired_time_grain': 'Month',
'start_at': '11/01/2019',
'end_at': '01/31/2020',
'created_at': '07/23/2020',
'updated_at': '07/28/2020',
'deleted_at': None,
'company': {
'id': 0,
'chargebee_id': 'ID Hash',
'name': 'Company Name',
'plan_id': 0,
'plan_value_id': 0,
'subscription_status': 'active',
'created_at': '2020-07-23 13:20:17',
'updated_at': '2020-08-03 11:33:50',
'need_listining_webhook': 0,
'addon_users': None,
'notify_after_processing': 0
},
'user': {
'id': 0,
'company_id': 0,
'name': 'Name',
'email': 'Email',
'email_verified_at': None,
'type': 'owner',
'active': 'yes',
'reactivate': 'no',
'created_at': '2020-07-23 13:20:17',
'updated_at': '2020-07-23 13:20:17',
'deleted_at': None
},
'industry': {
'id': 1,
'name': 'Automotive',
'created_at': '2020-04-10 23:21:38',
'updated_at': '2020-04-10 23:21:38',
'deleted_at': None
},
'analysis_type': {
'id': 3,
'name': 'Classification',
'created_at': '2020-04-10 23:21:45',
'updated_at': '2020-04-10 23:21:45',
'deleted_at': None
},
'output': {
'json': 'https://app.readysignal.com/api/signals/0/output?format=json'},
'links': {
'self': 'https://app.readysignal.com/signal/0/manage',
'manage': 'https://app.readysignal.com/signal/0/manage'
}
}
}There are three different ways to receive your signal output:
- JSON
- Pandas DataFrame
- CSV export
# get signal data as json
rs.get_signal(access_token, signal_id){'current_page': 1,
'data': [
{
'start': '2019-11-01',
'end': '2019-11-30',
'country': 'United States of America',
'actual-snow-fall': '160205.00000000000000000000',
'resident-population-by-state': '9826013.22000000000000000000',
'actual-snow-cover': '3.56158109943317700000',
'population-total': '308745538.00000000000000000000',
'population-total-transformed': 17,571.156421818115497622280458798
},
{
'start': '2019-12-01',
'end': '2019-12-31',
'country': 'United States of America',
'actual-snow-fall': '219691.00000000000000000000',
'resident-population-by-state': '10153546.99400000000000000000',
'actual-snow-cover': '8.28314041638492200000',
'population-total': '308745538.00000000000000000000',
'population-total-transformed': 17,571.156421818115497622280458798
},
{
'start': '2020-01-01',
'end': '2020-01-31',
'country': 'United States of America',
'actual-snow-fall': '220869.00000000000000000000',
'resident-population-by-state': 10159386.99400000000000000000,
'actual-snow-cover': '10.69758409714073700000',
'population-total': '308745538.00000000000000000000',
'population-total-transformed': 17,571.156421818115497622280458798
}
],
'first_page_url': 'https://app.readysignal.com/api/signals/0/output?page=1',
'from': 1,
'last_page': 1,
'last_page_url': 'https://app.readysignal.com/api/signals/0/output?page=1',
'next_page_url': None,
'path': 'https://app.readysignal.com/api/signals/0/output',
'per_page': 1000,
'prev_page_url': None,
'to': 3,
'total': 3}# get signal data as Pandas DataFrame
rs.get_signal_pandas(access_token, signal_id) start ... population-total-transformed
0 2019-11-01 ... 17,571.156421818115497622280458798
1 2019-12-01 ... 18,109.798447234298274239287429023
2 2020-01-01 ... 18,732.472983479821748127047902849
# send signal data to csv file
file_name = "test_signal.csv"
rs.signal_to_csv(access_token, signal_id, file_name)USE WITH CAUTION
Use your access_token and signal_id to delete a signal
rs.delete_signal(access_token, signal_id)Creates a signal using your own data and the Auto Discover feature.
Please check Ready Signal site for tips on how to format your data.
Currently only available at the "State" or "Country" geo grain and the “Month” or “Day” time grain.
Use a file name OR Pandas DataFrame.
Set create_custom_features=0 to prevent Ready Signal from storing the input target data for future use.
rs.auto_discover(access_token, geo_grain, date_grain, filename=None, df=None, create_custom_features=1)A note on parameter syntax:
access_token: a string of users access tokenbank_name: a string of the country name of the bankfeature: a list containing the feature(s) to use. Regardless of if it is just 1 feature or many, it MUST be put in a liststart_date: a string in the format of 'YYY-MM-DD'end_date: a string in the format of 'YYY-MM-DD'
Using your access_token and bank_name, you can view all the features and an overview of their data that can be used with the feature specific functions.
rs.get_features_list(access_token, bank_name){'data': [{'feature_id': 317,
'slug_name': 'Bonos (0 - 3 years) Maturity at 12/07/2023',
'feature_name': 'Bonos (0 - 3 years) Maturity at 12/07/2023',
'product_name': 'Bonos',
'provider_name': 'Bank Of Mexico',
'geo_grain': 'Country',
'geo_grain_delimitation': 'MEXICO',
'date_grain': 'Day'},
{'feature_id': 318,
'slug_name': 'Bonos (0 - 3 years) Maturity at 09/05/2024',
'feature_name': 'Bonos (0 - 3 years) Maturity at 09/05/2024',
'product_name': 'Bonos',
'provider_name': 'Bank Of Mexico',
'geo_grain': 'Country',
'geo_grain_delimitation': 'MEXICO',
'date_grain': 'Day'},
{'feature_id': 319
...
}
]
}Using your access_token, bank_name, and a feature list containing the feature id(s), see an overview of the data for those specific features.
Reminder: Regardless of the number of features (1 or many), they must be in a list.
feat_list = [317, 318]
rs.show_feature(access_token, bank_name, feat_list){317: {'feature_id': 317,
'feature_name': 'Bonos (0 - 3 years) Maturity at 12/07/2023',
'product_name': 'Bonos',
'provider_name': 'Bank Of Mexico',
'geo_grain': 'Country',
'date_grain': 'Day',
'data_notes': None,
'units': 'Millions of pesos',
'available_through': '2023-09-26',
'published_at': None},
318: {'feature_id': 318,
'feature_name': 'Bonos (0 - 3 years) Maturity at 09/05/2024',
'product_name': 'Bonos',
'provider_name': 'Bank Of Mexico',
'geo_grain': 'Country',
'date_grain': 'Day',
'data_notes': None,
'units': 'Millions of pesos',
'available_through': '2023-09-26',
'published_at': None}}Using your access_token, bank_name, and a feature list containing the feature id(s), see in depth data for those specific features.
feat_list = [317]
rs.show_feature_detailed(access_token, bank_name, feat_list){317: {'name': 'Bonos (0 - 3 years) Maturity at 12/07/2023',
'short_name': 'Bonos (0 - 3 years) Maturity at 12/07/2023',
'geo_grain': 'Country',
'geo_grain_label': 'COUNTRY',
'geo_grain_delimitation': 'MEXICO',
'date_grain': 'Day',
'date_grain_label': 'Daily',
'licence': 'Public Domain',
'units': 'Millions of pesos',
'reporting_lag': '1 Day',
'first_date': None,
'description': '',
'citation': None,
'why_use': None,
'state_lvl_data_set_exist': 'No',
'is_state_lvl_the_same': 'No',
'allow_grain_transformation_by_date': 'Yes',
'allow_grain_transformation_by_population': 'No',
'parent_feature_id': None,
'product': {'id': 104,
'name': 'Bonos',
'created_at': None,
'updated_at': None,
'deleted_at': None,
'provider': {'id': 41,
'name': 'Bank Of Mexico',
'publisher_id': 7,
'created_at': '2022-07-14 14:06:07',
'updated_at': '2022-07-14 14:06:07',
'deleted_at': None}},
'categories': [{'id': 1,
'name': 'Economic',
'created_at': '2020-04-10T23:22:58.000000Z',
'updated_at': '2020-04-10T23:22:58.000000Z',
'deleted_at': None,
'sub_categories': [{'id': 1,
'name': 'Banking',
'category_id': 1,
'created_at': '2020-04-10 23:22:58',
'updated_at': '2020-04-10 23:22:58',
'deleted_at': None},
{'id': 2,
...
}
{'id': 18,
'name': 'Interest Rates',
'category_id': 1,
'created_at': '2020-04-10 23:23:06',
'updated_at': '2020-04-10 23:23:06',
'deleted_at': None}]}],
'deleted_at': None,
'created_at': '2023-07-10T17:40:50.000000Z',
'updated_at': '2023-09-19T18:02:05.000000Z'}
}There are two different ways to receive your feature(s) data:
- JSON
- Pandas DataFrame
You will need your access_token, bank_name, feature list of feature ids along with a start_date and end_date indicating the date range of the features.
Reminder: Regardless of the number of features (1 or many), they must be in a list.
Reminder: start_date and end_date must be in the format of 'YYY-MM-DD'.
# get feature data as json
feat_list = [317]
start_date = '2021-01-01'
end_date = '2021-12-31'
rs.get_feature_data(access_token, bank_name, feat_list, start_date, end_date){'data': [{'Trade Date': '01/04/2021',
'Security Type': 'Bonos',
'Maturity Date': '07 dic 2023',
'Volume': '0.00000000000000000000',
'Maturity Bucket': '0_3',
'Last Updated': '09/26/2023'},
{'Trade Date': '01/05/2021',
'Security Type': 'Bonos',
'Maturity Date': '07 dic 2023',
'Volume': '100.00000000000000000000',
'Maturity Bucket': '0_3',
'Last Updated': '09/26/2023'},
...
{'Trade Date': '12/31/2021',
'Security Type': 'Bonos',
'Maturity Date': '07 dic 2023',
'Volume': '0.00000000000000000000',
'Maturity Bucket': '0_3',
'Last Updated': '09/26/2023'}
]
}# get feature data as Pandas DataFrame
rs.get_feature_data_pandas(access_token, bank_name, feat_list, start_date, end_date) Trade Date ... Last Updated
0 01/04/2021 ... 09/26/2023
1 01/05/2021 ... 09/26/2023
2 01/06/2021 ... 09/26/2023