Package: fds.sdk.FactSetETF
FactSet ETF client library for PythonFactSet ETF data feeds provide complete and accurate security, fund and reference data across the universe of exchange-traded products. Data is sourced from ETF providers across the globe and includes more than 100 unique data points, resulting in comprehensive coverage to help you evaluate and construct ETFs, analyze potential trades, and perform fund research.FactSet Reference and Analytics data uses FactSet's FCS, which categorizes exchange-traded products using a rules-based system that is derived from seven core classifications. This system evaluates the asset class, economic development level, and geographical region as described in each fund's prospectus and marketing materials. ETF exposure details are presented on successively granular levels-  category, then focus, and then niche.Moreover, FactSet ETF Reference data captures over 100 unique data points and provides market-specific data if you wish to solely focus on U.S., Canadian, Australian, New Zealand, Singapore, Hong Kong or European exchanges. All data points are grouped in one of two levels, either as a Fund or as a Listing. However, FactSet ETF Analytics data is primarily available for U.S.-domiciled ETFs. A subset of reference data items are provided for European-domiciled funds. For a full breakout of regional availability and history, see Formula Definitions and Calculations.This Python package is automatically generated by theOpenAPI Generatorproject:API version: 1.0.0Package version: 1.0.7Build package: org.openapitools.codegen.languages.PythonClientCodegenFor more information, please visithttp://www.factset.com/apiRequirementsPython >= 3.7InstallationPoetrypoetryaddfds.sdk.utilsfds.sdk.FactSetETF==1.0.7pippipinstallfds.sdk.utilsfds.sdk.FactSetETF==1.0.7UsageGenerate authentication credentials.Setup Python environment.Install and activate python 3.7+. If you're usingpyenv:pyenvinstall3.9.7
pyenvshell3.9.7(optional)Install poetry.Install dependencies.Run the following:fromfds.sdk.utils.authenticationimportConfidentialClientimportfds.sdk.FactSetETFfromfds.sdk.FactSetETF.apiimportdata_items_apifromfds.sdk.FactSetETF.modelsimport*fromdateutil.parserimportparseasdateutil_parserfrompprintimportpprint# See configuration.py for a list of all supported configuration parameters.# Examples for each supported authentication method are below,# choose one that satisfies your use case.# (Preferred) OAuth 2.0: FactSetOAuth2# See https://github.com/FactSet/enterprise-sdk#oauth-20# for information on how to create the app-config.json file## The confidential client instance should be reused in production environments.# See https://github.com/FactSet/enterprise-sdk-utils-python#authentication# for more information on using the ConfidentialClient classconfiguration=fds.sdk.FactSetETF.Configuration(fds_oauth_client=ConfidentialClient('/path/to/app-config.json'))# Basic authentication: FactSetApiKey# See https://github.com/FactSet/enterprise-sdk#api-key# for information how to create an API key# configuration = fds.sdk.FactSetETF.Configuration(#     username='USERNAME-SERIAL',#     password='API-KEY'# )# Enter a context with an instance of the API clientwithfds.sdk.FactSetETF.ApiClient(configuration)asapi_client:# Create an instance of the API classapi_instance=data_items_api.DataItemsApi(api_client)# NOTE: The parameter variable defined below is just an example and may potentially contain non valid values. So please replace this with valid values.category=Category("BENCHMARK_DETAILS")# Category | The available categories that can be used to select collections of metrics for use within the ETF endpoints. |category|description| |---|---| |BENCHMARK_DETAILS|Details surrounding the underlying Benchmark Id and Segment Banchmark| |CLASSIFICATION|FactSet Fund Classification Codes and Names, across Asset Class, Broad  Geography, Fund Categories, Focus, Niche, and more.| |COSTS_FEES|Expenses and Fees such as capital gains, expense ratio, management fees, and more.| |COUNTERPARTY|Credit and Swap Counterparty details| |CREATE_REDEEM|Creation and Redemption Sizes| |DESCRIPTIVE|General Descriptive information such as name, objectives, issuer details, launch dates, website, and more.| |DISTRIBUTIONS|Dividend Dates, Dividend Treatmetns, Min/Max Cap Gains| |DOCUMENTATION|Details surrounding reporting information.| |GEARING|Leverage factors, inverse flags, and more.| |HEDGE|Hedging Information| |RISK|CIFSC Risk Ratings| |SERVICE_PROVIDERS|Distributors, issuers, and Advisor details| |STATUS|Actively Managed Flags| |STRATEGY|Segment Codes, selection criteria, strategy codes, weighting schemes, and lending details.| |STRUCTURE|ETF Type, backing codes, synthetic types, ucits compliance, legal structures, and more.| |TAX|Tax Types, distribution takes, K1 Flags, and more.|  (optional)try:# Available ETF metrics# example passing only required values which don't have defaults set# and optional valuesapi_response=api_instance.get_etf_metrics(category=category)pprint(api_response)exceptfds.sdk.FactSetETF.ApiExceptionase:print("Exception when calling DataItemsApi->get_etf_metrics:%s\n"%e)# # Get response, http status code and response headers# try:#     # Available ETF metrics#     api_response, http_status_code, response_headers = api_instance.get_etf_metrics_with_http_info(category=category)#     pprint(api_response)#     pprint(http_status_code)#     pprint(response_headers)# except fds.sdk.FactSetETF.ApiException as e:#     print("Exception when calling DataItemsApi->get_etf_metrics: %s\n" % e)# # Get response asynchronous# try:#     # Available ETF metrics#     async_result = api_instance.get_etf_metrics_async(category=category)#     api_response = async_result.get()#     pprint(api_response)# except fds.sdk.FactSetETF.ApiException as e:#     print("Exception when calling DataItemsApi->get_etf_metrics: %s\n" % e)# # Get response, http status code and response headers asynchronous# try:#     # Available ETF metrics#     async_result = api_instance.get_etf_metrics_with_http_info_async(category=category)#     api_response, http_status_code, response_headers = async_result.get()#     pprint(api_response)#     pprint(http_status_code)#     pprint(response_headers)# except fds.sdk.FactSetETF.ApiException as e:#     print("Exception when calling DataItemsApi->get_etf_metrics: %s\n" % e)Using PandasTo convert an API response to a Pandas DataFrame, it is necessary to transform it first to a dictionary.importpandasaspdresponse_dict=api_response.to_dict()['data']simple_json_response=pd.DataFrame(response_dict)nested_json_response=pd.json_normalize(response_dict)DebuggingThe SDK uses the standard libraryloggingmodule.SettingdebugtoTrueon an instance of theConfigurationclass sets the log-level of related packages toDEBUGand enables additional logging in PythonsHTTP Client.Note: This prints out sensitive information (e.g. the full request and response). Use with care.importloggingimportfds.sdk.FactSetETFlogging.basicConfig(level=logging.DEBUG)configuration=fds.sdk.FactSetETF.Configuration(...)configuration.debug=TrueConfigure a ProxyYou can pass proxy settings to the Configuration class:proxy: The URL of the proxy to use.proxy_headers: a dictionary to pass additional headers to the proxy (e.g.Proxy-Authorization).importfds.sdk.FactSetETFconfiguration=fds.sdk.FactSetETF.Configuration(# ...proxy="http://secret:password@localhost:5050",proxy_headers={"Custom-Proxy-Header":"Custom-Proxy-Header-Value"})Custom SSL CertificateTLS/SSL certificate verification can be configured with the following Configuration parameters:ssl_ca_cert: a path to the certificate to use for verification inPEMformat.verify_ssl: setting this toFalsedisables the verification of certificates.
Disabling the verification is not recommended, but it might be useful during
local development or testing.importfds.sdk.FactSetETFconfiguration=fds.sdk.FactSetETF.Configuration(# ...ssl_ca_cert='/path/to/ca.pem')Documentation for API EndpointsAll URIs are relative tohttps://api.factset.com/contentClassMethodHTTP requestDescriptionDataItemsApiget_etf_metricsGET/factset-etf/v1/metricsAvailable ETF metricsReferenceApiget_etf_reference_dataGET/factset-etf/v1/referenceReturn reference data for an ETF.ReferenceApiget_etf_reference_data_for_listPOST/factset-etf/v1/referenceFetch Reference Data for a large list of ETF securities.Documentation For ModelsCategoriesCategoryErrorResponseErrorResponseSubErrorsEtfReferenceDataEtfReferenceDataRequestEtfReferenceDataResponseIdsMetricMetricsMetricsResponseDocumentation For AuthorizationFactSetApiKeyType: HTTP basic authenticationFactSetOAuth2Type: OAuthFlow: applicationAuthorization URL:Scopes: N/ANotes for Large OpenAPI documentsIf the OpenAPI document is large, imports in fds.sdk.FactSetETF.apis and fds.sdk.FactSetETF.models may fail with a
RecursionError indicating the maximum recursion limit has been exceeded. In that case, there are a couple of solutions:Solution 1:
Use specific imports for apis and models like:from fds.sdk.FactSetETF.api.default_api import DefaultApifrom fds.sdk.FactSetETF.model.pet import PetSolution 2:
Before importing the package, adjust the maximum recursion limit as shown below:import sys
sys.setrecursionlimit(1500)
import fds.sdk.FactSetETF
from fds.sdk.FactSetETF.apis import *
from fds.sdk.FactSetETF.models import *ContributingPlease refer to thecontributing guide.CopyrightCopyright 2022 FactSet Research Systems IncLicensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.