def __init__(self,
start: Optional[Union[int, dt.date]] = None,
end: Optional[Union[int, dt.date]] = None,
calendars: Union[str, Tuple] = (),
dates: Optional[Iterable[dt.date]] = None,
is_async: bool = False,
is_batch: bool = False,
use_cache: bool = False,
visible_to_gs: bool = False,
csa_term: str = None,
market_data_location: Optional[str] = None,
timeout: Optional[int] = None,
show_progress: Optional[bool] = False):
"""
A context for producing valuations over multiple dates
:param start: start date
:param end: end date (defaults to today)
:param calendars: holiday calendars
:param dates: a custom iterable of dates
:param is_async: return immediately (True) or wait for results (False) (defaults to False)
:param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.
It can be used with is_async=True|False (defaults to False)
:param use_cache: store results in the pricing cache (defaults to False)
:param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)
:param csa_term: the csa under which the calculations are made. Default is local ccy ois index
:param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)
**Examples**
>>> from gs_quant.instrument import IRSwap
>>>
>>> ir_swap = IRSwap('Pay', '10y', 'DKK')
>>> with HistoricalPricingContext(10):
>>> price_f = ir_swap.price()
>>>
>>> price_series = price_f.result()
"""
super().__init__(is_async=is_async,
is_batch=is_batch,
use_cache=use_cache,
visible_to_gs=visible_to_gs,
csa_term=csa_term,
market_data_location=market_data_location,
timeout=timeout,
show_progress=show_progress)
if start is not None:
if dates is not None:
raise ValueError('Must supply start or dates, not both')
if end is None:
end = dt.date.today()
self.__date_range = tuple(
date_range(start, end, calendars=calendars))
elif dates is not None:
self.__date_range = tuple(dates)
else:
raise ValueError('Must supply start or dates')
def __init__(self,
start: Optional[Union[int, dt.date]] = None,
end: Optional[Union[int, dt.date]] = None,
calendars: Union[str, Tuple] = (),
dates: Optional[Iterable[dt.date]] = None,
is_async: bool = False,
is_batch: bool = False,
use_cache: bool = False):
"""
A context for producing valuations over multiple dates
:param start: start date
:param end: end date (defaults to today)
:param calendars: holiday calendars
:param dates: a custom iterable of dates
:param is_async: return immediately (True) or wait for results (False). Defaults to False
:param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.
It can be used with is_async=True|False
:param use_cache: store results in the pricing cache
**Examples**
>>> from gs_quant.instrument import IRSwap
>>>
>>> ir_swap = IRSwap('Pay', '10y', 'DKK')
>>> with HistoricalPricingContext(10):
>>> price_f = ir_swap.price()
>>>
>>> price_series = price_f.result()
"""
super().__init__(is_async=is_async,
is_batch=is_batch,
use_cache=use_cache)
self.__calc_dates = None
if start is not None:
if dates is not None:
raise ValueError('Must supply start or dates, not both')
if end is None:
end = dt.date.today()
self.__date_range = tuple(
date_range(start, end, calendars=calendars))
elif dates is not None:
self.__date_range = tuple(dates)
else:
raise ValueError('Must supply start or dates')