FactorRiskReport

class FactorRiskReport(risk_model_id=None, fx_hedged=True, benchmark_id=None, report_id=None, name=None, position_source_id=None, position_source_type=None, report_type=None, earliest_start_date=None, latest_end_date=None, latest_execution_time=None, status=ReportStatus.new, percentage_complete=None, **kwargs)[source]

Historical analyses on both the risk and attribution of a portfolio or asset to various factors determined by the specified risk model

Methods

__init__(risk_model_id=None, fx_hedged=True, benchmark_id=None, report_id=None, name=None, position_source_id=None, position_source_type=None, report_type=None, earliest_start_date=None, latest_end_date=None, latest_execution_time=None, status=ReportStatus.new, percentage_complete=None, **kwargs)[source]

Historical analyses on both the risk and attribution of a portfolio or asset to various factors determined by the specified risk model

Parameters:

  • risk_model_id (Optional[str]) – risk model ID

  • fx_hedged (bool) – if position source is FX hedged

  • benchmark_id (Optional[str]) – optional benchmark asset ID to include in results

  • report_id (Optional[str]) – Marquee report ID

  • name (Optional[str]) – report name

  • position_source_id (Optional[str]) – position source ID

  • position_source_type (Union[str, PositionSourceType, None]) – position source (i.e. ‘Portfolio’, ‘Asset’, or ‘Hedge’)

  • report_type (Union[str, ReportType, None]) – report type (i.e. ‘Asset Factor Risk’ or ‘Portfolio Factor Risk’)

  • earliest_start_date (Optional[date]) – start date of report

  • latest_end_date (Optional[date]) – end date of report

  • latest_execution_time (Optional[datetime]) – date of the latest execution

  • status (Union[str, ReportStatus]) – status of of report (i.e. ‘ready’, ‘executing’, or ‘done’)

  • percentage_complete (Optional[float]) – percent of the report that is complete

Examples

>>> risk_report = FactorRiskReport(
>>>     risk_model_id='RISKMODELID',
>>>     fx_hedged=True,
>>>     benchmark_id=benchmark.get_marquee_id(),
>>>     position_source_id='PORTFOLIOID',
>>>     position_source_type=PositionSourceType.Portfolio
>>> )
delete()

Delete a report from Marquee

classmethod from_target(report)[source]
classmethod get(report_id, **kwargs)[source]

Get a factor risk report from the unique report identifier

Parameters:

report_id (str) – Marquee report ID

Returns:

returns a FactorRiskReport object that correlates to the Marquee report

get_annual_risk(factor_names=None, start_date=None, end_date=None, currency=None)[source]

Get historical annual risk

Parameters:

  • factor_names (Optional[List[str]]) – optional list of factor names; must be from the following: “Factor”, “Specific”, “Total”

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Return type:

DataFrame

Returns:

a Pandas DataFrame with the results

get_benchmark_id()[source]
Return type:

str

Returns:

the unique Marquee identifier of the benchmark associated with the factor risk report

get_daily_risk(factor_names=None, start_date=None, end_date=None, currency=None)[source]

Get historical daily risk

Parameters:

  • factor_names (Optional[List[str]]) – optional list of factor names; must be from the following: “Factor”, “Specific”, “Total”

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Return type:

DataFrame

Returns:

a Pandas DataFrame with the results

get_ex_ante_var(confidence_interval=95.0, start_date=None, end_date=None, currency=None)[source]

Get ex-ante Value at Risk as defined by the risk model

Parameters:

  • confidence_interval (float) – the VaR confidence interval as a percent

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Return type:

DataFrame

Returns:

a Pandas DataFrame with the results

get_factor_exposure(mode=FactorRiskResultsMode.Portfolio, factor_names=None, factor_categories=None, start_date=None, end_date=None, currency=None, unit=FactorRiskUnit.Notional)[source]

Get historical factor exposure

Parameters:

  • mode (FactorRiskResultsMode) – results mode; defaults to the portfolio level

  • factor_names (Optional[List[str]]) – optional list of factor names; defaults to all of them

  • factor_categories (Optional[List[str]]) – optional list of factor categories; defaults to all of them

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Param:

unit: return the results in terms of notional or percent (defaults to notional)

Return type:

DataFrame

Returns:

a Pandas DataFrame with the results

get_factor_pnl(mode=FactorRiskResultsMode.Portfolio, factor_names=None, factor_categories=None, start_date=None, end_date=None, currency=None, unit=FactorRiskUnit.Notional)[source]

Get historical factor PnL

Parameters:

  • mode (FactorRiskResultsMode) – results mode; defaults to the portfolio level

  • factor_names (Optional[List[str]]) – optional list of factor names; defaults to all of them

  • factor_categories (Optional[List[str]]) – optional list of factor categories; defaults to all of them

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Param:

unit: return the results in terms of notional or percent (defaults to notional)

Return type:

DataFrame

Returns:

a Pandas DataFrame with the results

get_factor_proportion_of_risk(factor_names=None, factor_categories=None, start_date=None, end_date=None, currency=None)[source]

Get historical factor proportion of risk

Parameters:

  • factor_names (Optional[List[str]]) – optional list of factor names; defaults to all of them

  • factor_categories (Optional[List[str]]) – optional list of factor categories; defaults to all of them

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Return type:

DataFrame

Returns:

a Pandas DataFrame with the results

get_most_recent_job()

Retrieve the most current report job

get_results(mode=FactorRiskResultsMode.Portfolio, factors=None, factor_categories=None, start_date=None, end_date=None, currency=None, return_format=ReturnFormat.DATA_FRAME, unit=FactorRiskUnit.Notional)[source]

Get the raw results associated with the factor risk report

Parameters:

  • mode (FactorRiskResultsMode) – results mode; defaults to the portfolio level

  • factors (Optional[List[str]]) – optional list of factors; defaults to all of them

  • factor_categories (Optional[List[str]]) – optional list of factor categories; defaults to all of them

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

  • return_format (ReturnFormat) – return format; defaults to a Pandas DataFrame, but can be manually

set to ReturnFormat.JSON :param: unit: return the results in terms of notional or percent (defaults to notional) :rtype: Union[Dict, DataFrame] :return: risk report results

Examples

>>> factor_and_total_results = risk_report.get_results(
>>>     factors=['Factor', 'Specific'],
>>>     start_date=dt.date(2022, 1, 1),
>>>     end_date=dt.date(2021, 1, 1)
>>> )
>>> print(factor_and_total_results)
get_risk_model_id()[source]
Return type:

str

Returns:

the ID of the risk model associated with the factor risk report

get_table(mode, factors=None, factor_categories=None, date=None, start_date=None, end_date=None, unit=None, currency=None, order_by_column=None, order_type=None, return_format=ReturnFormat.DATA_FRAME)[source]

Get the results associated with the factor risk report formatted for the asset level table on the interface

Parameters:

  • mode (FactorRiskTableMode) – tables mode

  • factors (Optional[List[str]]) – optional list of factors to filter by; defaults to all

  • factor_categories (Optional[List[str]]) – optional list of factor categories to filter by; defaults to all

  • date (Optional[date]) – date for modes requiring snapshot data (defaults to the latest available date)

  • start_date (Optional[date]) – start date for modes requiring date range (defaults to 1 month before the end date)

  • end_date (Optional[date]) – end date for modes requiring date range (defaults to the latest available date)

  • unit (Optional[FactorRiskUnit]) – return the results in terms of notional or percent (defaults to notional)

  • currency (Optional[Currency]) – currency

  • order_by_column (Optional[str]) – column to order the rows by (defaults to name)

  • order_type (Optional[OrderType]) – order ascending or descending (defaults to ascending)

Return type:

Union[Dict, DataFrame]

Returns:

risk report table at asset level

Examples

>>> pnl_table = risk_report.get_table(
>>>     mode=FactorRiskTableMode.Pnl,
>>>     start_date=risk_report.earliest_start_date,
>>>     end_date=risk_report.latest_end_date
>>> )
>>> display(pd.DataFrame(pnl_table))
get_view(mode, factor=None, factor_category=None, start_date=None, end_date=None, currency=None, unit=FactorRiskUnit.Notional)[source]

Get the results associated with the factor risk report as seen on the Marquee user interface

Parameters:

  • mode (FactorRiskViewsMode) – views mode

  • factor (Optional[str]) – optional factor name

  • factor_category (Optional[str]) – optional factor category

  • start_date (Optional[date]) – start date

  • end_date (Optional[date]) – end date

  • currency (Optional[Currency]) – currency

Param:

unit: return the results in terms of notional or percent (defaults to notional)

Return type:

Dict

Returns:

risk report results

Examples

>>> category_table = risk_report.get_view(
>>>     mode=FactorRiskViewsMode.Risk,
>>>     start_date=risk_report.latest_end_date,
>>>     end_date=risk_report.latest_end_date,
>>>     unit=FactorRiskUnit.Notional
>>> ).get('factorCategoriesTable')
>>> category_df = pd.DataFrame(category_table).filter(items=[
>>>     'name',
>>>     'proportionOfRisk',
>>>     'marginalContributionToRiskPercent',
>>>     'relativeMarginalContributionToRisk',
>>>     'exposure',
>>>     'avgProportionOfRisk'
>>> ])
>>> display(category_df)
run(start_date=None, end_date=None, backcast=False, is_async=True)

Run a report with the given date range

Parameters:

  • start_date (Optional[date]) – start date (optional)

  • end_date (Optional[date]) – end date (optional)

  • backcast (bool) – set to true if the report should be backcasted; defaults to false

  • is_async (bool) – return immediately (true) or wait for results (false); defaults to true

save()

Create a report in Marquee if it doesn’t exist. Update the report if it does.

schedule(start_date=None, end_date=None, backcast=None)

Schedule a report with the given date range

Parameters:

  • start_date (Optional[date]) – start date (optional)

  • end_date (Optional[date]) – end date (optional)

  • backcast (Optional[bool]) – set to true if the report should be backcasted

set_position_source(entity_id)

Set position source type and position source ID