def inflation_swap_rate(asset: Asset,
swap_tenor: str,
index_type: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
clearing_house: tm_rates._ClearingHouse = None,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day Zero Coupon Inflation Swap curves across major currencies.
:param asset: asset object loaded from security master
:param swap_tenor: relative date representation of expiration date e.g. 1m
:param index_type: benchmark type e.g. UKRPI
:param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for
spot starting swaps, 'imm1' or 'frb1'
:param clearing_house: Example - "LCH", "EUREX", "JSCC", "CME"
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
df = _get_inflation_swap_data(asset=asset,
swap_tenor=swap_tenor,
index_type=index_type,
forward_tenor=forward_tenor,
clearing_house=clearing_house,
source=source,
real_time=real_time,
query_type=QueryType.SWAP_RATE)
series = ExtendedSeries() if df.empty else ExtendedSeries(df['swapRate'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def vol_swap_strike(asset: Asset,
expiry_tenor: str,
strike_type: str = None,
location: PricingLocation = None,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day FX Vol Swaps volatilities across major crosses.
:param asset: asset object loaded from security master
:param expiry_tenor: relative date representation of expiration date e.g. 1m
:param strike_type: option type (e.g. Put, Call or Straddle )
:param location: Example - "TKO", "LDN", "NYC"
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: curve of vol swap strike
"""
df = _get_fx_vol_swap_data(asset=asset,
expiry_tenor=expiry_tenor,
strike_type=strike_type,
location=location,
source=source,
real_time=real_time,
query_type=QueryType.STRIKE_VOL)
series = ExtendedSeries() if df.empty else ExtendedSeries(df['strikeVol'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def test_swaption_vol2_return__empty_data():
replace = Replacer()
df = ExtendedSeries()
replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = "GBP"
replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [
"MADWG3WHCKNE1DJA", "MAH6JK3TZJJGFQ65"]
replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [
dt.date(2019, 1, 2), dt.date(2019, 1, 5)]
replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df
actual = tm_rates.swaption_vol(Currency("GBP", name="GBP"))
assert_series_equal(ExtendedSeries(), actual)
replace.restore()
def test_swaption_vol_smile2_returns_no_data():
replace = Replacer()
df = ExtendedSeries()
replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = "GBP"
replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [
"MADWG3WHCKNE1DJA", "MAH6JK3TZJJGFQ65"]
replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [
dt.date(2020, 1, 2), dt.date(2020, 1, 2)]
replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df
with DataContext('2019-01-01', '2025-01-01'):
actual = tm_rates.swaption_vol_smile(Currency("GBP", name="GBP"), '3m', '10y')
assert_series_equal(ExtendedSeries(), actual)
replace.restore()
def test_swaption_swaption_vol_term2_returns_empty():
replace = Replacer()
df = ExtendedSeries()
replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = "GBP"
replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [
"MADWG3WHCKNE1DJA", "MAH6JK3TZJJGFQ65"]
replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [
dt.date(2020, 1, 2), dt.date(2020, 1, 2)]
replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df
with DataContext('2019-01-01', '2025-01-01'):
actual = tm_rates.swaption_vol_term(Currency("GBP", name="GBP"), tm.SwaptionTenorType.SWAP_MATURITY, '5y', 0)
assert_series_equal(ExtendedSeries(), actual, check_names=False)
replace.restore()
def implied_volatility_new(asset: Asset,
expiry_tenor: str,
strike: str,
option_type: str = None,
expiration_location: str = None,
location: PricingLocation = None,
premium_payment_date: str = None,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day FX vanilla implied volatilities across major crosses.
:param asset: asset object loaded from security master
:param expiry_tenor: relative date representation of expiration date e.g. 1m
:param strike: option strike
:param option_type: option type (e.g. Put, Call or Straddle )
:param expiration_location: location indicating the time of the option expiry
:param location: Example - "TKO", "LDN", "NYC"
:param premium_payment_date: payment date of the premium Example: Fwd Settle vs Spot
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
df = _get_fxo_data(asset=asset,
expiry_tenor=expiry_tenor,
strike=strike,
option_type=option_type,
expiration_location=expiration_location,
location=location,
premium_payment_date=premium_payment_date,
source=source,
real_time=real_time,
query_type=QueryType.IMPLIED_VOLATILITY)
series = ExtendedSeries() if df.empty else ExtendedSeries(
df['impliedVolatility'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def inflation_swap_term(asset: Asset,
index_type: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
pricing_date: Optional[GENERIC_DATE] = None,
clearing_house: tm_rates._ClearingHouse = None,
location: PricingLocation = None,
*,
source: str = None,
real_time: bool = False,
request_id: Optional[str] = None) -> Series:
"""
Forward term structure of GS end-of-day inflation swaps.
:param asset: asset object loaded from security master
:param index_type: benchmark type e.g. UKRPI
:param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for
spot starting swaps, 'imm1' or 'frb1'
:param pricing_date: YYYY-MM-DD or relative date
:param clearing_house: Example - "LCH", "EUREX", "JSCC", "CME"
:param location: Example - "TKO", "LDN", "NYC"
: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: inflation swap forward term curve
"""
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in CURRENCY_TO_INDEX_BENCHMARK.keys():
raise NotImplementedError(
'Data not available for {} inflation swap rates'.format(
currency.value))
if location is None:
location = tm_rates._default_pricing_location(currency)
else:
location = PricingLocation(location)
calendar = location.value
if pricing_date is not None and pricing_date in list(
GsCalendar.get(calendar).holidays):
raise MqValueError(
'Specified pricing date is a holiday in {} calendar'.format(
calendar))
start, end = _range_from_pricing_date(calendar, pricing_date)
with DataContext(start, end):
df = _get_inflation_swap_data(asset=asset,
swap_tenor=None,
index_type=index_type,
forward_tenor=forward_tenor,
clearing_house=clearing_house,
source=source,
real_time=real_time,
query_type=QueryType.SWAP_RATE,
location=location,
allow_many=True,
request_id=request_id)
if df.empty:
series = ExtendedSeries(dtype=float)
else:
latest = df.index.max()
# TODO: add forward tenor to latest. Technically series index should be latest + forwardTenor + terminationTenor
# TODO: but that would make it hard to compare term structure btwn different forwardTenors
# TODO: may be implemented some day when plot can handle different x-axes
# TODO: As-is, this is consistent with other swap term measures axis handling
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
biz_day = _get_custom_bd(calendar)
df['expirationDate'] = df['terminationTenor'].apply(
_get_term_struct_date, args=(latest, biz_day))
df = df.set_index('expirationDate')
df.sort_index(inplace=True)
df = df.loc[DataContext.current.start_date:DataContext.current.
end_date]
series = ExtendedSeries(
dtype=float) if df.empty else ExtendedSeries(df['swapRate'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
if series.empty: # Raise descriptive error if no data returned + historical date context
check_forward_looking(None, source, 'inflation_swap_term')
return series
