def close_market_date(location: Optional[Union[PricingLocation, str]] = None,
date: Optional[dt.date] = None) -> dt.date:
from .core import PricingContext
if date is None:
return prev_business_date(PricingContext.current.pricing_date,
calendars=location.value if isinstance(location, PricingLocation) else location) \
if PricingContext.current.pricing_date == dt.date.today() else PricingContext.current.pricing_date
elif date > PricingContext.current.pricing_date:
raise ValueError(
'Market data date cannot be greater than pricing date')
else:
return date
def close_market_date(location: Optional[Union[PricingLocation, str]] = None,
date: Optional[dt.date] = None) -> dt.date:
"""Determine market data date based on current location (to infer calendar)
and current pricing date
:param location: location
:param date: pricing date
:return: close market date
"""
from .core import PricingContext
date = date or PricingContext.current.pricing_date
if date == dt.date.today():
# Don't use the calendars argument here as external users do not (yet) have access to that dataset
date = prev_business_date(date)
return date
def close_market_date(location: Optional[Union[PricingLocation, str]] = None,
date: Optional[dt.date] = None) -> dt.date:
"""Determine market data date based on current location (to infer calendar)
and current pricing date
:param location: location
:param date: pricing date
:return: close market date
"""
from .core import PricingContext
date = date or PricingContext.current.pricing_date
if date == dt.date.today():
date = prev_business_date(date,
calendars=location.value if isinstance(
location, PricingLocation) else location)
return date
def _market(self, date: dt.date, location: str) -> CloseMarket:
date = prev_business_date(date) if date == dt.date.today() else date
return CloseMarket(location=location, date=date, check=False)
def get_flagships_constituents(
fields: [str] = [],
basket_type: List[BasketType] = BasketType.to_list(),
asset_class: List[AssetClass] = [AssetClass.Equity],
region: List[Region] = None,
styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,
start: dt.date = None,
end: dt.date = None,
) -> pd.DataFrame:
"""
Retrieve flagship baskets constituents
:param fields: Fields to retrieve in addition to mqid, name, ticker, region, basket type, \
styles, live date, and asset class
:param basket_type: Basket type(s)
:param asset_class: Asset class (defaults to Equity)
:param region: Basket region(s)
:param styles: Basket style(s)
:param start: Start date for which to retrieve constituents (defaults to previous business day)
:param end: End date for which to retrieve constituents (defaults to previous business day)
:return: flagship baskets constituents
**Usage**
Retrieve flagship baskets constituents
**Examples**
Retrieve a list of flagship baskets constituents
>>> from gs_quant.markets.indices_utils import *
>>>
>>> get_flagships_constituents()
**See also**
:func:`get_flagships_with_assets` :func:`get_flagships_performance` :func:`get_flagship_baskets`
"""
start = start or prev_business_date()
end = end or prev_business_date()
fields = list(
set(fields).union(
set([
'id', 'name', 'ticker', 'region', 'type', 'styles', 'liveDate',
'assetClass'
])))
query = dict(fields=fields,
type=basket_type,
asset_class=asset_class,
is_pair_basket=[False],
flagship=[True])
if region is not None:
query.update(region=region)
if styles is not None:
query.update(styles=styles)
basket_data = GsAssetApi.get_many_assets_data_scroll(**query,
limit=2000,
scroll='1m')
basket_map = {get(basket, 'id'): basket for basket in basket_data}
coverage = GsDataApi.get_coverage(
dataset_id=IndicesDatasets.GSCB_FLAGSHIP.value,
fields=['type', 'bbid'],
include_history=True)
cbs, rbs = [], []
for b in coverage:
_id = get(b, 'assetId')
_type = get(b, 'type')
if _id in list(basket_map.keys()):
start_date = dt.datetime.strptime(b['historyStartDate'],
'%Y-%m-%d').date()
start_date = start_date if start < start_date else start
if _type == BasketType.CUSTOM_BASKET.value:
data = GsDataApi.query_data(
query=DataQuery(where=dict(assetId=_id),
startDate=start_date,
endDate=end),
dataset_id=IndicesDatasets.GSBASKETCONSTITUENTS.value)
basket_map[_id].update(constituents=data)
cbs.append(basket_map[_id])
elif _type == BasketType.RESEARCH_BASKET.value:
data = GsDataApi.query_data(
query=DataQuery(where=dict(assetId=_id),
startDate=start_date,
endDate=end),
dataset_id=IndicesDatasets.GIRBASKETCONSTITUENTS.value)
basket_map[_id].update(constituents=data)
rbs.append(basket_map[_id])
return pd.DataFrame(cbs + rbs)
def get_flagships_performance(
fields: [str] = [],
basket_type: List[BasketType] = BasketType.to_list(),
asset_class: List[AssetClass] = [AssetClass.Equity],
region: List[Region] = None,
styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,
start: dt.date = None,
end: dt.date = None,
) -> pd.DataFrame:
"""
Retrieve performance data for flagship baskets
:param fields: Fields to retrieve in addition to bbid, mqid, name, region, basket type, \
styles, live date, and asset class
:param basket_type: Basket type(s)
:param asset_class: Asset class (defaults to Equity)
:param region: Basket region(s)
:param styles: Basket style(s)
:param start: Date for which to retrieve pricing (defaults to previous business day)
:param end: Date for which to retrieve pricing (defaults to previous business day)
:return: pricing data for flagship baskets
**Usage**
Retrieve performance data for flagship baskets
**Examples**
Retrieve performance data for flagship Asia custom baskets
>>> from gs_quant.markets.indices_utils import *
>>>
>>> get_flagships_performance(basket_type=[BasketType.CUSTOM_BASKET], region=[Region.ASIA])
**See also**
:func:`get_flagships_with_assets` :func:`get_flagship_baskets` :func:`get_flagships_constituents`
"""
start = start or prev_business_date()
end = end or prev_business_date()
fields = list(
set(fields).union(
set([
'name', 'region', 'type', 'flagship', 'isPairBasket', 'styles',
'liveDate', 'assetClass'
])))
coverage = GsDataApi.get_coverage(
dataset_id=IndicesDatasets.GSCB_FLAGSHIP.value, fields=fields)
basket_regions = [] if region is None else [r.value for r in region]
basket_styles = [] if styles is None else [s.value for s in styles]
basket_types = [b_type.value for b_type in basket_type]
baskets_map = {}
for basket in coverage:
if get(basket, 'flagship') is False or get(basket, 'isPairBasket') is True or \
region is not None and get(basket, 'region') not in basket_regions or \
get(basket, 'type') not in basket_types or \
get(basket, 'assetClass') not in [a.value for a in asset_class] or \
styles is not None and not any(s in get(basket, 'styles', []) for s in basket_styles):
continue
baskets_map[get(basket, 'assetId')] = basket
response = GsDataApi.query_data(
query=DataQuery(where=dict(assetId=list(baskets_map.keys())),
startDate=start,
endDate=end),
dataset_id=IndicesDatasets.GSCB_FLAGSHIP.value)
performance = []
for basket in response:
basket_data = baskets_map[get(basket, 'assetId')]
basket_data.update(closePrice=get(basket, 'closePrice'))
basket_data.update(date=get(basket, 'date'))
performance.append(basket_data)
return pd.DataFrame(performance)
def closing_market_date(location: Optional[Union[PricingLocation, str]] = None) -> dt.date:
return prev_business_date(dt.date.today(),
calendars=location.value if isinstance(location, PricingLocation) else location)
def get_flagships_constituents(
fields: [str] = [],
basket_type: List[BasketType] = BasketType.to_list(),
asset_class: List[AssetClass] = [AssetClass.Equity],
region: List[Region] = None,
styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,
start: dt.date = None,
end: dt.date = None,
) -> pd.DataFrame:
"""
Retrieve flagship baskets constituents
:param fields: Fields to retrieve in addition to mqid, name, ticker, region, basket type, \
styles, live date, and asset class
:param basket_type: Basket type(s)
:param asset_class: Asset class (defaults to Equity)
:param region: Basket region(s)
:param styles: Basket style(s)
:param start: Start date for which to retrieve constituents (defaults to previous business day)
:param end: End date for which to retrieve constituents (defaults to previous business day)
:return: flagship baskets constituents
**Usage**
Retrieve flagship baskets constituents
**Examples**
Retrieve a list of flagship baskets constituents
>>> from gs_quant.markets.indices_utils import *
>>>
>>> get_flagships_constituents()
**See also**
:func:`get_flagships_with_assets` :func:`get_flagships_performance` :func:`get_flagship_baskets`
"""
start = start or prev_business_date()
end = end or prev_business_date()
basket_fields = list(
set(fields).union(
set([
'id', 'name', 'ticker', 'region', 'type', 'styles', 'liveDate',
'assetClass'
])))
fields = list(set(fields).union(set(['id', 'name'])))
query = dict(fields=basket_fields,
type=basket_type,
asset_class=asset_class,
is_pair_basket=[False],
flagship=[True])
if region is not None:
query.update(region=region)
if styles is not None:
query.update(styles=styles)
coverage_results = ThreadPoolManager.run_async([
partial(GsAssetApi.get_many_assets_data_scroll,
**query,
limit=2000,
scroll='1m'),
partial(GsDataApi.get_coverage,
dataset_id=IndicesDatasets.GSCB_FLAGSHIP.value,
fields=['type', 'bbid'],
include_history=True)
])
basket_data, coverage = coverage_results[0], coverage_results[1]
basket_map = {get(basket, 'id'): basket for basket in basket_data}
tasks, results = [], []
for b in coverage:
tasks.append(
partial(__get_constituents_data,
basket=b,
basket_map=basket_map,
start=start,
end=end,
fields=fields))
tasks = [
tasks[i * 50:(i + 1) * 50] for i in range((len(tasks) + 50 - 1) // 50)
]
for task in tasks:
results += ThreadPoolManager.run_async(task)
sleep(1)
return pd.DataFrame([r for r in results if r is not None])
