Analyst Search
Implementation details
Class names
| Model name | Parameters class | Data class |
|---|---|---|
AnalystSearch | AnalystSearchQueryParams | AnalystSearchData |
Import Statement
from openbb_core.provider.standard_models.analyst_search import (
AnalystSearchData,
AnalystSearchQueryParams,
)
Parameters
- standard
- benzinga
analyst_name: Union[str, list[str]]
Analyst names to return. Omitting will return all available analysts. Multiple items allowed for provider(s): benzinga.
• Optional: True
firm_name: Union[str, list[str]]
Firm names to return. Omitting will return all available firms. Multiple items allowed for provider(s): benzinga.
• Optional: True
analyst_name: Union[str, list[str]]
Analyst names to return. Omitting will return all available analysts. Multiple items allowed for provider(s): benzinga.
• Optional: True
firm_name: Union[str, list[str]]
Firm names to return. Omitting will return all available firms. Multiple items allowed for provider(s): benzinga.
• Optional: True
analyst_ids: Union[str, list[str]]
list of analyst IDs to return. Multiple items allowed for provider(s): benzinga.
• Optional: True
firm_ids: Union[str, list[str]]
Firm IDs to return. Multiple items allowed for provider(s): benzinga.
• Optional: True
limit: int
Number of results returned. Limit 1000.
• Default: 100
• Optional: True
page: int
Page offset. For optimization, performance and technical reasons, page offsets are limited from 0 - 100000. Limit the query results by other parameters such as date.
• Default: 0
• Optional: True
fields: Union[str, list[str]]
Fields to include in the response. See https://docs.benzinga.io/benzinga-apis/calendar/get-ratings to learn about the available fields. Multiple items allowed for provider(s): benzinga.
• Optional: True
Data
- standard
- benzinga
last_updated: datetime
Date of the last update.
firm_name: str
Firm name of the analyst.
name_first: str
Analyst first name.
name_last: str
Analyst last name.
name_full: str
Analyst full name.
last_updated: datetime
Date of the last update.
firm_name: str
Firm name of the analyst.
name_first: str
Analyst first name.
name_last: str
Analyst last name.
name_full: str
Analyst full name.
analyst_id: str
ID of the analyst.
firm_id: str
ID of the analyst firm.
smart_score: float
A weighted average of the total_ratings_percentile, overall_avg_return_percentile, and overall_success_rate
overall_success_rate: float
The percentage (normalized) of gain/loss ratings that resulted in a gain overall.
overall_avg_return_percentile: float
The percentile (normalized) of this analyst's overall average return per rating in comparison to other analysts' overall average returns per rating.
total_ratings_percentile: float
The percentile (normalized) of this analyst's total number of ratings in comparison to the total number of ratings published by all other analysts
total_ratings: int
Number of recommendations made by this analyst.
overall_gain_count: int
The number of ratings that have gained value since the date of recommendation
overall_loss_count: int
The number of ratings that have lost value since the date of recommendation
overall_average_return: float
The average percent (normalized) price difference per rating since the date of recommendation
overall_std_dev: float
The standard deviation in percent (normalized) price difference in the analyst's ratings since the date of recommendation
gain_count_1m: int
The number of ratings that have gained value over the last month
loss_count_1m: int
The number of ratings that have lost value over the last month
average_return_1m: float
The average percent (normalized) price difference per rating over the last month
std_dev_1m: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last month
smart_score_1m: float
A weighted average smart score over the last month.
success_rate_1m: float
The percentage (normalized) of gain/loss ratings that resulted in a gain over the last month
gain_count_3m: int
The number of ratings that have gained value over the last 3 months
loss_count_3m: int
The number of ratings that have lost value over the last 3 months
average_return_3m: float
The average percent (normalized) price difference per rating over the last 3 months
std_dev_3m: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last 3 months
smart_score_3m: float
A weighted average smart score over the last 3 months.
success_rate_3m: float
The percentage (normalized) of gain/loss ratings that resulted in a gain over the last 3 months
gain_count_6m: int
The number of ratings that have gained value over the last 6 months
loss_count_6m: int
The number of ratings that have lost value over the last 6 months
average_return_6m: float
The average percent (normalized) price difference per rating over the last 6 months
std_dev_6m: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last 6 months
gain_count_9m: int
The number of ratings that have gained value over the last 9 months
loss_count_9m: int
The number of ratings that have lost value over the last 9 months
average_return_9m: float
The average percent (normalized) price difference per rating over the last 9 months
std_dev_9m: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last 9 months
smart_score_9m: float
A weighted average smart score over the last 9 months.
success_rate_9m: float
The percentage (normalized) of gain/loss ratings that resulted in a gain over the last 9 months
gain_count_1y: int
The number of ratings that have gained value over the last 1 year
loss_count_1y: int
The number of ratings that have lost value over the last 1 year
average_return_1y: float
The average percent (normalized) price difference per rating over the last 1 year
std_dev_1y: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last 1 year
smart_score_1y: float
A weighted average smart score over the last 1 year.
success_rate_1y: float
The percentage (normalized) of gain/loss ratings that resulted in a gain over the last 1 year
gain_count_2y: int
The number of ratings that have gained value over the last 2 years
loss_count_2y: int
The number of ratings that have lost value over the last 2 years
average_return_2y: float
The average percent (normalized) price difference per rating over the last 2 years
std_dev_2y: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last 2 years
smart_score_2y: float
A weighted average smart score over the last 3 years.
success_rate_2y: float
The percentage (normalized) of gain/loss ratings that resulted in a gain over the last 2 years
gain_count_3y: int
The number of ratings that have gained value over the last 3 years
loss_count_3y: int
The number of ratings that have lost value over the last 3 years
average_return_3y: float
The average percent (normalized) price difference per rating over the last 3 years
std_dev_3y: float
The standard deviation in percent (normalized) price difference in the analyst's ratings over the last 3 years
smart_score_3y: float
A weighted average smart score over the last 3 years.
success_rate_3y: float
The percentage (normalized) of gain/loss ratings that resulted in a gain over the last 3 years