gs_quant.markets.baskets.Basket

class Basket(gs_asset=None, **kwargs)

Basket which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets

__init__(gs_asset=None, **kwargs)

Methods

__init__([gs_asset])
add_factor_risk_report(risk_model_id, fx_hedged)	Create and schedule a new factor risk report for your basket
cancel_rebalance()	Cancel the most recent rebalance submission
clone()	Retrieve a clone of an existing basket
create()	Create a new custom basket in Marquee
delete_factor_risk_report(risk_model_id)	Delete an existing factor risk report for your basket
entity_type()	rtype: EntityType
get(identifier, **kwargs)	Fetch an existing basket
get_all_esg_data([measures, cards, ...])	Get all ESG Data :type measures: Optional[List[ESGMeasure]] :param measures: list of ESG Measures to include in results :type cards: Optional[List[ESGCard]] :param cards: list of ESG Cards to include in results :type pricing_date: Optional[date] :param pricing_date: optional pricing date; defaults to last previous business day :type benchmark_id: Optional[str] :param benchmark_id: optional benchmark asset ID to include in results :rtype: Dict :return: a dictionary of results
get_all_thematic_exposures([start_date, ...])	Deprecated since version 0.9.110: Please use the same function using the ThematicReport class
get_bottom_five_thematic_exposures([...])	Deprecated since version 0.9.110: Please use the same function using the ThematicReport class
get_carbon_analytics([benchmark_id, ...])	rtype: Dict
get_carbon_attribution_table(benchmark_id[, ...])	rtype: DataFrame
get_carbon_coverage([reporting_year, ...])	rtype: DataFrame
get_carbon_emissions([currency, ...])	rtype: DataFrame
get_carbon_emissions_allocation([...])	rtype: DataFrame
get_carbon_sbti_netzero_coverage([...])	rtype: DataFrame
get_close_price_for_date(date)	rtype: Series
get_close_prices([start, end])	Get close price series
get_corporate_actions([start, end, ca_type])	Retrieve corporate actions for a basket across a date range
get_data_coordinate(measure[, dimensions, ...])	rtype: DataCoordinate
get_data_series(measure[, dimensions, ...])	Get asset series
get_details()	Get basket details
get_entitlements()
get_entity()	rtype: Optional[Dict]
get_esg_bottom_ten(measure[, pricing_date])	Get entity constituents with the ten lowest ESG percentile values :type measure: ESGMeasure :param measure: ESG Measure :type pricing_date: Optional[date] :param pricing_date: optional pricing date; defaults to last previous business day :rtype: DataFrame :return: a Pandas DataFrame with results
get_esg_by_region(measure[, pricing_date])	Get breakdown of entity by region, along with the weighted average score of the compositions in each region :type measure: ESGMeasure :param measure: ESG Measure :type pricing_date: Optional[date] :param pricing_date: optional pricing date; defaults to last previous business day :rtype: DataFrame :return: a Pandas DataFrame with results
get_esg_by_sector(measure[, pricing_date])	Get breakdown of entity by sector, along with the weighted average score of the compositions in each sector :type measure: ESGMeasure :param measure: ESG Measure :type pricing_date: Optional[date] :param pricing_date: optional pricing date; defaults to last previous business day :rtype: DataFrame :return: a Pandas DataFrame with results
get_esg_quintiles(measure[, pricing_date])	Get breakdown of entity by weight in each percentile quintile for requested ESG measure :type measure: ESGMeasure :param measure: ESG Measure :type pricing_date: Optional[date] :param pricing_date: optional pricing date; defaults to last previous business day :rtype: DataFrame :return: a Pandas DataFrame with results
get_esg_summary([pricing_date])	rtype: DataFrame
get_esg_top_ten(measure[, pricing_date])	Get entity constituents with the ten highest ESG percentile values :type measure: ESGMeasure :param measure: ESG Measure :type pricing_date: Optional[date] :param pricing_date: optional pricing date; defaults to last previous business day :return: a Pandas DataFrame with results
get_factor_risk_report([risk_model_id, ...])	rtype: FactorRiskReport
get_factor_risk_reports([fx_hedged, tags])	rtype: List[FactorRiskReport]
get_fundamentals([start, end, period, ...])	Retrieve fundamentals data for a basket across a date range
get_hloc_prices([start, end, interval_frequency])	Get high, low, open, close (hloc) prices
get_identifier(id_type[, as_of])	Get asset identifier
get_identifiers([as_of])	Get asset identifiers
get_latest_close_price()	rtype: float
get_latest_position_set([position_type])	rtype: PositionSet
get_latest_rebalance_data()	Retrieve the most recent rebalance data for a basket
get_latest_rebalance_date()	Retrieve the most recent rebalance date for a basket
get_live_date()	Retrieve basket's live date
get_marquee_id()
get_position_dates()	rtype: Tuple[date, ...]
get_position_set_for_date(date[, position_type])	rtype: PositionSet
get_position_sets([start, end, position_type])	rtype: List[PositionSet]
get_positions_data([start, end, fields, ...])
get_rebalance_approval_status()	Retrieve the most recent rebalance submission's approval status
get_reports([tags])	rtype: List[Report]
get_status_of_reports([tags])	rtype: DataFrame
get_thematic_beta(basket_identifier[, ...])	rtype: DataFrame
get_thematic_breakdown(date, basket_id)	Get a by-asset breakdown of a portfolio or basket's thematic exposure to a particular flagship basket on a particular date :type date: date :param date: date :type basket_id: str :param basket_id: GS flagship basket's unique Marquee ID :rtype: DataFrame :return: a Pandas DataFrame with results
get_thematic_exposure(basket_identifier[, ...])	rtype: DataFrame
get_thematic_report([tags])	rtype: ThematicReport
get_top_five_thematic_exposures([...])	Deprecated since version 0.9.110: Please use the same function using the ThematicReport class
get_type()	Retrieve basket type
get_unique_entity_key()	rtype: EntityKey
get_url()	Retrieve url to basket's product page in Marquee
poll_report(report_id[, timeout, step])	rtype: ReportStatus
poll_status([timeout, step])	Polls the status of the basket's most recent create/edit/rebalance report
update()	Update your custom basket
update_positions(position_sets[, net_positions])
upload_position_history(position_sets)	Upload basket composition history

Attributes

allow_ca_restricted_assets	Allow basket to have constituents that will not be corporate action adjusted in the future
allow_limited_access_assets	Allow basket to have constituents that GS has limited access to
clone_parent_id	Marquee Id of the source basket, in case basket composition is sourced from another marquee basket
currency	Denomination of the basket
data_dimension
default_backcast	If basket should be backcasted using the current composition
description	Free text description of basket
divisor	Divisor to be applied to the overall position set
entitlements	Basket entitlements
flagship	If the basket is flagship (internal only)
hedge_id	Marquee Id of the source hedge, in case current basket composition is sourced from marquee hedge
id
include_price_history	Include full price history when publishing to Bloomberg
initial_price	Initial price the basket it should start ticking at
name	Display name of the basket (must be <= 24 characters)
parent_basket	Ticker of the source basket, in case current basket composition is sourced from another marquee basket
position_set	Information of constituents associated with the basket
positioned_entity_type
publish_to_bloomberg	If the basket should be published to Bloomberg
publish_to_factset	If the basket should be published to Factset
publish_to_reuters	If the basket should be published to Reuters
return_type	Determines the index calculation methodology with respect to dividend reinvestment
reweight	To reweight positions if input weights don't add up to 1
target_notional	Target notional for the position set
ticker	Associated 8-character basket identifier
weighting_strategy	Strategy used to price the position set