def factor_covariance(risk_model_id: str,
factor_name_1: str,
factor_name_2: str,
*,
source: str = None,
real_time: bool = False,
request_id: Optional[str] = None) -> pd.Series:
"""
Covariance time-series between two factors in a risk model
:param risk_model_id: risk model entity
:param factor_name_1: first factor name
:param factor_name_2: second factor name
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:param request_id: server request id
:return: Time-series of covariances between the two factors across available risk model dates
"""
factor_1 = Factor.get(risk_model_id, factor_name_1)
factor_2 = Factor.get(risk_model_id, factor_name_2)
covariance_curve = factor_1.covariance(factor_2,
DataContext.current.start_date,
DataContext.current.end_date,
ReturnFormat.JSON)
return __format_plot_measure_results(covariance_curve,
QueryType.COVARIANCE)
def factor_zscore(asset: Asset,
risk_model_id: str,
factor_name: str,
*,
source: str = None,
real_time: bool = False,
request_id: Optional[str] = None) -> pd.Series:
"""
Asset factor exposure (in the form of z-scores) for a factor using specified risk model
:param asset: asset object loaded from security master
:param risk_model_id: requested risk model id
:param factor_name: requested factor name
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:param request_id: service request id, if any
:return: Time-series of asset factor exposure across available risk model dates
"""
risk_model = FactorRiskModel.get(risk_model_id)
factor = Factor.get(risk_model_id, factor_name)
gsid = asset.get_identifier('GSID')
# Query risk model data
query_results = risk_model.get_data(
measures=[
Measure.Factor_Name, Measure.Universe_Factor_Exposure,
Measure.Asset_Universe
],
start_date=DataContext.current.start_time,
end_date=DataContext.current.end_time,
assets=DataAssetsRequest(identifier=UniverseIdentifier.gsid,
universe=[gsid])).get('results', [])
# Get the factor data from query results
z_scores = {}
for result in query_results:
exposures = result.get('assetData', {}).get('factorExposure', [])
if exposures:
z_scores[result['date']] = exposures[0].get(factor.id)
return __format_plot_measure_results(z_scores, QueryType.FACTOR_EXPOSURE)
def factor_volatility(risk_model_id: str,
factor_name: str,
*,
source: str = None,
real_time: bool = False,
request_id: Optional[str] = None) -> pd.Series:
"""
Volatility timeseries for a factor in a risk model
:param risk_model_id: risk model entity
:param factor_name: factor name
:param source: name of function caller
:param real_time: whether to retrieve intra-day data instead of EOD
:param request_id: server request id
:return: Time-series of a factor's volatility across available risk model dates
"""
factor = Factor.get(risk_model_id, factor_name)
volatility = factor.volatility(DataContext.current.start_date,
DataContext.current.end_date,
ReturnFormat.JSON)
return __format_plot_measure_results(volatility,
QueryType.VOLATILITY,
multiplier=100)
def factor_performance(risk_model_id: str,
factor_name: str,
*,
source: str = None,
real_time: bool = False,
request_id: Optional[str] = None) -> pd.Series:
"""
Factor returns as a price time-series for a factor in a risk model
:param risk_model_id: risk model entity
:param factor_name: factor name
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:param request_id: server request id
:return: Time-series of factor returns as a price series across available risk model dates
"""
factor = Factor.get(risk_model_id, factor_name)
factor_returns = factor.returns(DataContext.current.start_date,
DataContext.current.end_date,
ReturnFormat.JSON)
factor_return_timeseries = pd.Series(factor_returns)
return prices(factor_return_timeseries, 100)