fds.sdk.FactSetEstimates 1.1.3

FactSet Estimates client library for Python

FactSet Estimates client library for Python

Gain access to 20+ years of comprehensive estimates and statistics of over 250+ estimated metrics, including financial statement items, product segments, geosegments, and industry metrics. FactSet's consensus estimates are aggregated from a wide base of over 800+ contributors and cover over 19,000 active companies across 90+ countries. Data returned can be accessed on the data frequencies based on quarterly, fiscal years, and calendar years. FactSet Estimates updates on a real time basis intraday (every 5 minutes). Updating times vary based on earning season vs. non-earning season but the goal is to have the data available to the client within a few hours that FactSet receives updated information. Often times updates times can be much faster as FactSet has always been known as one of the fastest estimate providers in the market.

This Python package is automatically generated by the OpenAPI Generator project:

• API version: 2.5.0
• Package version: 1.1.3
• Build package: org.openapitools.codegen.languages.PythonClientCodegen

Requirements

• Python >= 3.7

Installation

Poetry

poetry add fds.sdk.utils fds.sdk.FactSetEstimates==1.1.3

pip

pip install fds.sdk.utils fds.sdk.FactSetEstimates==1.1.3

Usage

1. Generate authentication credentials.
2. Setup Python environment.

   1. Install and activate python 3.7+. If you're using pyenv:

      pyenv install 3.9.7
      pyenv shell 3.9.7
      

   2. (optional) Install poetry.

3. Install dependencies.
4. Run the following:

[!IMPORTANT] The parameter variables defined below are just examples and may potentially contain non valid values. Please replace them with valid values.

Example Code

from fds.sdk.utils.authentication import ConfidentialClient

import fds.sdk.FactSetEstimates
from fds.sdk.FactSetEstimates.api import broker_detail_api
from fds.sdk.FactSetEstimates.models import *
from dateutil.parser import parse as dateutil_parser
from pprint import pprint

# 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 class
configuration = fds.sdk.FactSetEstimates.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.FactSetEstimates.Configuration(
#     username='USERNAME-SERIAL',
#     password='API-KEY'
# )

# Enter a context with an instance of the API client
with fds.sdk.FactSetEstimates.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = broker_detail_api.BrokerDetailApi(api_client)
    ids = ["AAPL-USA"] # [str] | Security or Entity identifiers. FactSet Identifiers, tickers, CUSIP and SEDOL are accepted input. <p>***ids limit** =  3000 per request*</p> * Make Note - id limit of 3000 for defaults, otherwise the service is limited to a 30 second duration. This can be reached when increasing total number of metrics requested and depth of history. * 
    metrics = ["SALES"] # [str] | Requested metrics. Use the /metrics endpoint to return a list of available estimate items. Note, the number of metrics you are allowed to supply is limited to 1 for now. **Top 10** most used metrics are **EPS, SALES, DPS, EBITDA,EBIT, PRICE_TGT, CFPS, BPS, NET_INC, and ASSETS**.  For more details, visit [Online Assistant Page #15034](https://oa.apps.factset.com/pages/15034). 
    start_date = "2019-07-30" # str | Start date for point in time of estimates expressed in YYYY-MM-DD format. (optional)
    end_date = "2020-07-30" # str | End date for point in time of estimates expressed in YYYY-MM-DD format. (optional)
    frequency = "D" # str | Controls the frequency of the data returned.   * **D** = Daily   * **W** = Weekly, based on the last day of the week of the start date.   * **AM** = Monthly, based on the start date (e.g., if the start date is June 16, data is displayed for June 16, May 16, April 16 etc.).         * **AQ** = Quarterly, based on the start date.   * **AY** = Actual Annual, based on the start date.   (optional) if omitted the server will use the default value of "D"
    periodicity = "ANN" # str | The periodicity for the estimates requested, allowing you to fetch Quarterly, Semi-Annual, and Annual Estimates.   * **ANN** - Annual   * **QTR** - Quarterly   * **SEMI** - Semi-Annual   (optional) if omitted the server will use the default value of "ANN"
    include_all = False # bool | Include All filter is used to identify included and excluded broker details from the consensus   By default the service would return only the brokers included in the consensus-   * **TRUE** = Returns all the brokers included and excluded in the consensus   * **FALSE** = Returns only the broker details included in the consensus     (optional) if omitted the server will use the default value of False
    fiscal_period_start = "2019" # str | Fiscal period start expressed in absolute date formats. Date that will fall back to most recent completed period during resolution.   * **Fiscal Quarter-end** - YYYY/FQ (e.g., 2019/1F, 2019/2F, 2019/3F, 2019/4F)   * **Fiscal Year-end** - YYYY (e.g. 2019)  (optional)
    fiscal_period_end = "2020" # str | Fiscal period start expressed in absolute date formats. Date that will fall back to most recent completed period during resolution.   * **Fiscal Quarter-end** - YYYY/FQ (e.g., 2019/1F, 2019/2F, 2019/3F, 2019/4F)   * **Fiscal Year-end** - YYYY (e.g. 2019)  (optional)
    currency = "USD" # str | Currency code for adjusting the data. Use 'ESTIMATE' as input value for the values in Estimate Currency. For a list of currency ISO codes, visit [Online Assistant Page #1470](https://oa.apps.factset.com/pages/1470). (optional)

    try:
        # Estimates detail data for fixed fiscal periods
        # example passing only required values which don't have defaults set
        # and optional values
        api_response = api_instance.get_fixed_detail(ids, metrics, start_date=start_date, end_date=end_date, frequency=frequency, periodicity=periodicity, include_all=include_all, fiscal_period_start=fiscal_period_start, fiscal_period_end=fiscal_period_end, currency=currency)

        pprint(api_response)
    except fds.sdk.FactSetEstimates.ApiException as e:
        print("Exception when calling BrokerDetailApi->get_fixed_detail: %s\n" % e)

    # # Get response, http status code and response headers
    # try:
    #     # Estimates detail data for fixed fiscal periods
    #     api_response, http_status_code, response_headers = api_instance.get_fixed_detail_with_http_info(ids, metrics, start_date=start_date, end_date=end_date, frequency=frequency, periodicity=periodicity, include_all=include_all, fiscal_period_start=fiscal_period_start, fiscal_period_end=fiscal_period_end, currency=currency)


    #     pprint(api_response)
    #     pprint(http_status_code)
    #     pprint(response_headers)
    # except fds.sdk.FactSetEstimates.ApiException as e:
    #     print("Exception when calling BrokerDetailApi->get_fixed_detail: %s\n" % e)

    # # Get response asynchronous
    # try:
    #     # Estimates detail data for fixed fiscal periods
    #     async_result = api_instance.get_fixed_detail_async(ids, metrics, start_date=start_date, end_date=end_date, frequency=frequency, periodicity=periodicity, include_all=include_all, fiscal_period_start=fiscal_period_start, fiscal_period_end=fiscal_period_end, currency=currency)
    #     api_response = async_result.get()


    #     pprint(api_response)
    # except fds.sdk.FactSetEstimates.ApiException as e:
    #     print("Exception when calling BrokerDetailApi->get_fixed_detail: %s\n" % e)

    # # Get response, http status code and response headers asynchronous
    # try:
    #     # Estimates detail data for fixed fiscal periods
    #     async_result = api_instance.get_fixed_detail_with_http_info_async(ids, metrics, start_date=start_date, end_date=end_date, frequency=frequency, periodicity=periodicity, include_all=include_all, fiscal_period_start=fiscal_period_start, fiscal_period_end=fiscal_period_end, currency=currency)
    #     api_response, http_status_code, response_headers = async_result.get()


    #     pprint(api_response)
    #     pprint(http_status_code)
    #     pprint(response_headers)
    # except fds.sdk.FactSetEstimates.ApiException as e:
    #     print("Exception when calling BrokerDetailApi->get_fixed_detail: %s\n" % e)

Using Pandas

To convert an API response to a Pandas DataFrame, it is necessary to transform it first to a dictionary.

import pandas as pd

response_dict = api_response.to_dict()['data']

simple_json_response = pd.DataFrame(response_dict)
nested_json_response = pd.json_normalize(response_dict)

Debugging

The SDK uses the standard library logging module.

Setting debug to True on an instance of the Configuration class sets the log-level of related packages to DEBUG and enables additional logging in Pythons HTTP Client.

Note: This prints out sensitive information (e.g. the full request and response). Use with care.

import logging
import fds.sdk.FactSetEstimates

logging.basicConfig(level=logging.DEBUG)

configuration = fds.sdk.FactSetEstimates.Configuration(...)
configuration.debug = True

Configure a Proxy

You 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).

import fds.sdk.FactSetEstimates

configuration = fds.sdk.FactSetEstimates.Configuration(
    # ...
    proxy="http://secret:password@localhost:5050",
    proxy_headers={
        "Custom-Proxy-Header": "Custom-Proxy-Header-Value"
    }
)

Custom SSL Certificate

TLS/SSL certificate verification can be configured with the following Configuration parameters:

• ssl_ca_cert: a path to the certificate to use for verification in PEM format.
• verify_ssl: setting this to False disables the verification of certificates. Disabling the verification is not recommended, but it might be useful during local development or testing.

import fds.sdk.FactSetEstimates

configuration = fds.sdk.FactSetEstimates.Configuration(
    # ...
    ssl_ca_cert='/path/to/ca.pem'
)

Documentation for API Endpoints

All URIs are relative to https://api.factset.com/content

Class	Method	HTTP request	Description
BrokerDetailApi	get_fixed_detail	GET /factset-estimates/v2/fixed-detail	Estimates detail data for fixed fiscal periods
BrokerDetailApi	get_fixed_detail_for_list	POST /factset-estimates/v2/fixed-detail	Estimates detail data for fixed fiscal periods
BrokerDetailApi	get_rolling_detail	GET /factset-estimates/v2/rolling-detail	FactSet estimates detail data for rolling fiscal periods
BrokerDetailApi	get_rolling_detail_for_list	POST /factset-estimates/v2/rolling-detail	FactSet estimates detail data for rolling fiscal periods
ConsensusApi	get_fixed_consensus	GET /factset-estimates/v2/fixed-consensus	Retrieves consensus estimates for a requested list of ids and fixed fiscal periods
ConsensusApi	get_fixed_consensus_for_list	POST /factset-estimates/v2/fixed-consensus	FactSet consensus estimates for fixed fiscal periods
ConsensusApi	get_rolling_consensus	GET /factset-estimates/v2/rolling-consensus	Retrieves consensus estimates for a requested list of ids and rolling fiscal periods.
ConsensusApi	get_rolling_consensus_for_list	POST /factset-estimates/v2/rolling-consensus	Retrieves consensus estimates for a requested list of ids and rolling fiscal periods
DataItemsApi	get_estimate_metrics	GET /factset-estimates/v2/metrics	Available Estimate metrics
DataItemsApi	get_estimate_metrics_for_list	POST /factset-estimates/v2/metrics	Available Estimate metrics or ratios.
RatingsApi	get_consensus_ratings	GET /factset-estimates/v2/consensus-ratings	Ratings consensus estimates to fetch Buy, Overweight, Hold, Underweight, and Sell.
RatingsApi	get_consensus_ratings_for_list	POST /factset-estimates/v2/consensus-ratings	Ratings consensus estimates to fetch Buy, Overweight, Hold, Underweight, and Sell.
RatingsApi	get_detail_ratings	GET /factset-estimates/v2/detail-ratings	Broker Detail estimates to fetch Buy, Overweight, Hold, Underweight, and Sell.
RatingsApi	get_detail_ratings_for_list	POST /factset-estimates/v2/detail-ratings	Broker Detail estimates to fetch Buy, Overweight, Hold, Underweight, and Sell.
SegmentsApi	get_segments	GET /factset-estimates/v2/segments	Retrieves product & geographic segment estimates for a requested list of ids and fiscal periods
SegmentsApi	get_segments_for_list	POST /factset-estimates/v2/segments	Retrieves product segment estimates for a requested list of ids and fiscal periods
SurpriseApi	get_surprise	GET /factset-estimates/v2/surprise	Surprise estimates for rolling fiscal periods
SurpriseApi	get_surprise_for_list	POST /factset-estimates/v2/surprise	Surprise estimates for rolling fiscal periods

Documentation For Models

• Category
• ConsensusEstimate
• ConsensusRatings
• ConsensusRatingsRequest
• ConsensusRatingsResponse
• ConsensusResponse
• DetailEstimate
• DetailRatings
• DetailRatingsRequest
• DetailRatingsResponse
• DetailResponse
• ErrorResponse
• ErrorResponseSubErrors
• FixedConsensusRequest
• FixedDetailRequest
• Frequency
• Ids
• Metric
• Metrics
• MetricsRequest
• MetricsResponse
• Periodicity
• PeriodicityDetail
• PeriodicitySurprise
• RollingConsensusRequest
• RollingDetailRequest
• SegmentIds
• SegmentType
• SegmentsEstimate
• SegmentsRequest
• SegmentsResponse
• Statistic
• Subcategory
• Surprise
• SurpriseRequest
• SurpriseResponse

Documentation For Authorization

FactSetApiKey

• Type: HTTP basic authentication

FactSetOAuth2

• Type: OAuth
• Flow: application
• Authorization URL:
• Scopes: N/A

Notes for Large OpenAPI documents

If the OpenAPI document is large, imports in fds.sdk.FactSetEstimates.apis and fds.sdk.FactSetEstimates.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.FactSetEstimates.api.default_api import DefaultApi
• from fds.sdk.FactSetEstimates.model.pet import Pet

Solution 2: Before importing the package, adjust the maximum recursion limit as shown below:

import sys
sys.setrecursionlimit(1500)
import fds.sdk.FactSetEstimates
from fds.sdk.FactSetEstimates.apis import *
from fds.sdk.FactSetEstimates.models import *

Contributing

Please refer to the contributing guide.

Copyright

Copyright 2022 FactSet Research Systems Inc

Licensed 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 at

http://www.apache.org/licenses/LICENSE-2.0

Unless 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.