def get_universe_factor_exposure(
self,
start_date: dt.date,
end_date: dt.date = None,
assets: DataAssetsRequest = DataAssetsRequest(UniverseIdentifier.gsid,
[]),
format: ReturnFormat = ReturnFormat.DATA_FRAME
) -> Union[List[Dict], pd.DataFrame]:
""" Retrieve universe factor exposure data for existing risk model
:param start_date: start date for data request
:param end_date: end date for data request
:param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request
:param format: which format to return the results in
:return: factor exposure for assets requested """
results = GsFactorRiskModelApi.get_risk_model_data(
model_id=self.id,
start_date=start_date,
end_date=end_date,
assets=assets,
measures=[
Measure.Universe_Factor_Exposure, Measure.Asset_Universe
],
limit_factors=False).get('results')
universe = assets.universe if assets.universe else results[0].get(
'assetData').get('universe')
factor_exposure = build_asset_data_map(results, universe,
'factorExposure')
if format == ReturnFormat.DATA_FRAME:
factor_exposure = pd.DataFrame.from_dict(
{(i, j): factor_exposure[i][j]
for i in factor_exposure.keys()
for j in factor_exposure[i].keys()},
orient='index')
return factor_exposure
def get_residual_variance(
self,
start_date: dt.date,
end_date: dt.date = None,
assets: DataAssetsRequest = DataAssetsRequest(UniverseIdentifier.gsid,
[]),
format: ReturnFormat = ReturnFormat.DATA_FRAME
) -> Union[List[Dict], pd.DataFrame]:
""" Retrieve residual variance data for existing risk model
:param start_date: start date for data request
:param end_date: end date for data request
:param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request
:param format: which format to return the results in
:return: residual variance for assets requested """
results = GsFactorRiskModelApi.get_risk_model_data(
model_id=self.id,
start_date=start_date,
end_date=end_date,
assets=assets,
measures=[Measure.Residual_Variance, Measure.Asset_Universe],
limit_factors=False).get('results')
universe = assets.universe if assets.universe else results[0].get(
'assetData').get('universe')
residual_variance = build_asset_data_map(results, universe,
'residualVariance')
if format == ReturnFormat.DATA_FRAME:
residual_variance = pd.DataFrame(residual_variance)
return residual_variance
