def basis(asset: Asset,
termination_tenor: str,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day cross-currency basis swap spread.
:param asset: asset object loaded from security master
:param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: cross-currency basis swap spread curve
"""
if real_time:
raise NotImplementedError('realtime basis not implemented')
basis_mqid = convert_asset_for_rates_data_set(
asset, RatesConversionType.CROSS_CURRENCY_BASIS)
_logger.debug('where tenor=%s', termination_tenor)
q = GsDataApi.build_market_data_query(
[basis_mqid],
QueryType.BASIS,
where=FieldFilterMap(tenor=termination_tenor),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['basis']
def zc_inflation_swap_rate(asset: Asset,
termination_tenor: str,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day zero coupon inflation swap break-even rate.
:param asset: asset object loaded from security master
:param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: zero coupon inflation swap break-even rate curve
"""
if real_time:
raise NotImplementedError(
'realtime zc_inflation_swap_rate not implemented')
infl_rate_benchmark_mqid = convert_asset_for_rates_data_set(
asset, RatesConversionType.INFLATION_BENCHMARK_RATE)
_logger.debug('where tenor=%s', termination_tenor)
q = GsDataApi.build_market_data_query(
[infl_rate_benchmark_mqid],
QueryType.INFLATION_SWAP_RATE,
where=FieldFilterMap(tenor=termination_tenor),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['inflationSwapRate']
def excess_returns(
price_series: pd.Series,
benchmark_or_rate: Union[Asset, Currency, float],
*,
day_count_convention=DayCountConvention.ACTUAL_360) -> pd.Series:
if isinstance(benchmark_or_rate, float):
er = [price_series.iloc[0]]
for j in range(1, len(price_series)):
fraction = day_count_fraction(price_series.index[j - 1],
price_series.index[j],
day_count_convention)
er.append(er[-1] + price_series.iloc[j] -
price_series.iloc[j - 1] *
(1 + benchmark_or_rate * fraction))
return pd.Series(er, index=price_series.index)
if isinstance(benchmark_or_rate, Currency):
try:
marquee_id = SharpeAssets[benchmark_or_rate.value].value
except KeyError:
raise MqValueError(f"unsupported currency {benchmark_or_rate}")
else:
marquee_id = benchmark_or_rate.get_marquee_id()
with DataContext(price_series.index[0], price_series.index[-1]):
q = GsDataApi.build_market_data_query([marquee_id], QueryType.SPOT)
df = GsDataApi.get_market_data(q)
if df.empty:
raise MqValueError(f'could not retrieve risk-free rate {marquee_id}')
df = df[~df.index.duplicated(
keep='first')] # handle bad data (duplicate rows)
return excess_returns_pure(price_series, df['spot'])
def excess_returns(
price_series: pd.Series,
benchmark_or_rate: Union[Asset, Currency, float],
*,
day_count_convention=DayCountConvention.ACTUAL_360) -> pd.Series:
if isinstance(benchmark_or_rate, float):
er = [price_series.iloc[0]]
for j in range(1, len(price_series)):
fraction = day_count_fraction(price_series.index[j - 1],
price_series.index[j],
day_count_convention)
er.append(er[-1] + price_series.iloc[j] -
price_series.iloc[j - 1] *
(1 + benchmark_or_rate * fraction))
return pd.Series(er, index=price_series.index)
if isinstance(benchmark_or_rate, Currency):
try:
marquee_id = SharpeAssets[benchmark_or_rate.value].value
except KeyError:
raise MqValueError(f"unsupported currency {benchmark_or_rate}")
else:
marquee_id = benchmark_or_rate.get_marquee_id()
with DataContext(price_series.index[0], price_series.index[-1]):
q = GsDataApi.build_market_data_query([marquee_id], QueryType.SPOT)
df = GsDataApi.get_market_data(q)
curve, bench_curve = align(price_series, df['spot'], Interpolate.INTERSECT)
e_returns = [curve.iloc[0]]
for i in range(1, len(curve)):
multiplier = 1 + curve.iloc[i] / curve.iloc[
i - 1] - bench_curve.iloc[i] / bench_curve.iloc[i - 1]
e_returns.append(e_returns[-1] * multiplier)
return pd.Series(e_returns, index=curve.index)
def vol_term(asset: Asset,
strike_reference: SkewReference,
relative_strike: Real,
pricing_date: Optional[GENERIC_DATE] = None,
*,
source: str = None,
real_time: bool = False) -> pd.Series:
"""
Volatility term structure. Uses most recent date available if pricing_date is not provided.
:param asset: asset object loaded from security master
:param strike_reference: reference for strike level
:param relative_strike: strike relative to reference
:param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: volatility term structure
"""
if real_time:
raise NotImplementedError(
'realtime forward term not implemented') # TODO
if strike_reference != SkewReference.NORMALIZED:
relative_strike /= 100
start, end = _range_from_pricing_date(asset.exchange, pricing_date)
with DataContext(start, end):
_logger.debug('where strikeReference=%s, relativeStrike=%s',
strike_reference.value, relative_strike)
where = FieldFilterMap(strikeReference=strike_reference.value,
relativeStrike=relative_strike)
q = GsDataApi.build_market_data_query([asset.get_marquee_id()],
QueryType.IMPLIED_VOLATILITY,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
return pd.Series()
latest = df.index.max()
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
cbd = _get_custom_bd(asset.exchange)
df = df.assign(expirationDate=df.index + df['tenor'].map(_to_offset) +
cbd - cbd)
df = df.set_index('expirationDate')
df.sort_index(inplace=True)
df = df.loc[DataContext.current.start_date:DataContext.current.end_date]
return df['impliedVolatility'] if not df.empty else pd.Series()
def _get_inflation_swap_data(
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,
query_type: QueryType = QueryType.SWAP_RATE) -> pd.DataFrame:
if real_time:
raise NotImplementedError(
'realtime inflation swap data not implemented')
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))
index_type = _check_inflation_index_type(currency, index_type)
clearing_house = tm_rates._check_clearing_house(clearing_house)
defaults = _get_inflation_swap_leg_defaults(currency, index_type)
if not (tm_rates._is_valid_relative_date_tenor(swap_tenor)):
raise MqValueError('invalid swap tenor ' + swap_tenor)
forward_tenor = tm_rates._check_forward_tenor(forward_tenor)
fixed_rate = 'ATM'
kwargs = dict(type='InflationSwap',
asset_parameters_termination_date=swap_tenor,
asset_parameters_index=defaults['index_type'],
asset_parameters_fixed_rate=fixed_rate,
asset_parameters_clearing_house=clearing_house.value,
asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name)
rate_mqid = _get_tdapi_inflation_rates_assets(**kwargs)
_logger.debug(
f'where asset= {rate_mqid}, swap_tenor={swap_tenor}, index={defaults["index_type"]}, '
f'forward_tenor={forward_tenor}, pricing_location={defaults["pricing_location"].value}, '
f'clearing_house={clearing_house.value}, notional_currency={currency.name}'
)
q = GsDataApi.build_market_data_query([rate_mqid],
query_type,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return df
def average_implied_variance(asset: Asset,
tenor: str,
strike_reference: EdrDataReference,
relative_strike: Real,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
Historic weighted average implied variance for the underlying assets of an equity index.
:param asset: asset object loaded from security master
:param tenor: relative date representation of expiration date e.g. 1m
:param strike_reference: reference for strike level
:param relative_strike: strike relative to reference
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: average implied variance curve
"""
if real_time:
raise NotImplementedError(
'realtime average_implied_variance not implemented')
if strike_reference == EdrDataReference.DELTA_PUT:
relative_strike = abs(100 - relative_strike)
relative_strike = relative_strike / 100
delta_types = (EdrDataReference.DELTA_CALL, EdrDataReference.DELTA_PUT)
strike_ref = "delta" if strike_reference in delta_types else strike_reference.value
_logger.debug('where tenor=%s, strikeReference=%s, relativeStrike=%s',
tenor, strike_ref, relative_strike)
mqid = asset.get_marquee_id()
where = FieldFilterMap(tenor=tenor,
strikeReference=strike_ref,
relativeStrike=relative_strike)
q = GsDataApi.build_market_data_query([mqid],
QueryType.AVERAGE_IMPLIED_VARIANCE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['averageImpliedVariance']
def vol_smile(asset: Asset,
tenor: str,
strike_reference: VolSmileReference,
pricing_date: Optional[GENERIC_DATE] = None,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
Volatility smile of an asset implied by observations of market prices.
:param asset: asset object loaded from security master
:param tenor: relative date representation of expiration date e.g. 1m
:param strike_reference: reference for strike level
:param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: implied volatility smile
"""
if real_time:
raise NotImplementedError('realtime vol_smile not implemented')
mqid = asset.get_marquee_id()
start, end = _range_from_pricing_date(asset.exchange, pricing_date)
with DataContext(start, end):
q = GsDataApi.build_market_data_query(
[mqid],
QueryType.IMPLIED_VOLATILITY,
where=FieldFilterMap(tenor=tenor,
strikeReference=strike_reference.value),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
return Series
latest = df.index.max()
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
vols = df['impliedVolatility'].values
strikes = df['relativeStrike'].values
return Series(vols, index=strikes)
def implied_correlation(asset: Asset,
tenor: str,
strike_reference: EdrDataReference,
relative_strike: Real,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
Correlation of an asset implied by observations of market prices.
:param asset: asset object loaded from security master
:param tenor: relative date representation of expiration date e.g. 1m
:param strike_reference: reference for strike level
:param relative_strike: strike relative to reference
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: implied correlation curve
"""
if real_time:
raise NotImplementedError(
'realtime implied_correlation not implemented')
if strike_reference == EdrDataReference.DELTA_PUT:
relative_strike = abs(100 - relative_strike)
relative_strike = relative_strike / 100
delta_types = (EdrDataReference.DELTA_CALL, EdrDataReference.DELTA_PUT)
strike_ref = "delta" if strike_reference in delta_types else strike_reference.value
_logger.debug('where tenor=%s, strikeReference=%s, relativeStrike=%s',
tenor, strike_ref, relative_strike)
mqid = asset.get_marquee_id()
where = FieldFilterMap(tenor=tenor,
strikeReference=strike_ref,
relativeStrike=relative_strike)
q = GsDataApi.build_market_data_query([mqid],
QueryType.IMPLIED_CORRELATION,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['impliedCorrelation']
def midcurve_vol(asset: Asset,
expiration_tenor: str,
forward_tenor: str,
termination_tenor: str,
relative_strike: float,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day implied normal volatility for midcurve vol matrices.
:param asset: asset object loaded from security master
:param expiration_tenor: relative date representation of expiration date on the option e.g. 3m
:param forward_tenor: relative date representation of swap's start date after option expiry e.g. 2y
:param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y
:param relative_strike: strike level relative to at the money e.g. 10
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: midcurve implied normal volatility curve
"""
if real_time:
raise NotImplementedError('realtime midcurve_vol not implemented')
_logger.debug('where expiry=%s, forwardTenor=%s, tenor=%s, strike=%s',
expiration_tenor, forward_tenor, termination_tenor,
relative_strike)
rate_benchmark_mqid = convert_asset_for_rates_data_set(
asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)
q = GsDataApi.build_market_data_query([rate_benchmark_mqid],
QueryType.MIDCURVE_VOL,
where=FieldFilterMap(
expriy=expiration_tenor,
forward_tenor=forward_tenor,
tenor=termination_tenor,
strike=relative_strike),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['midcurveVol']
def _get_swap_data(asset: Asset, swap_tenor: str, benchmark_type: BenchmarkType = None, floating_rate_tenor: str = None,
forward_tenor: Optional[GENERIC_DATE] = None, clearing_house: _ClearingHouse = None,
source: str = None, real_time: bool = False,
query_type: QueryType = QueryType.SWAP_RATE) -> pd.DataFrame:
if real_time:
raise NotImplementedError('realtime swap_rate not implemented')
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP', 'CHF', 'SEK']:
raise NotImplementedError('Data not available for {} swap rates'.format(currency.value))
_check_benchmark_type(currency, benchmark_type)
clearing_house = _check_clearing_house(clearing_house)
defaults = _get_swap_leg_defaults(currency, benchmark_type, floating_rate_tenor)
if not (re.fullmatch('(\\d+)([bdwmy])', swap_tenor) or re.fullmatch('(frb[1-9])', forward_tenor)):
raise MqValueError('invalid swap tenor ' + swap_tenor)
if not re.fullmatch('(\\d+)([bdwmy])', defaults['floating_rate_tenor']):
raise MqValueError('invalid floating rate tenor ' + defaults['floating_rate_tenor'] + ' for index: ' +
defaults['benchmark_type'])
forward_tenor = check_forward_tenor(forward_tenor)
csaTerms = currency.value + '-1'
fixed_rate = 'ATM'
kwargs = dict(type='Swap', asset_parameters_termination_date=swap_tenor,
asset_parameters_floating_rate_option=defaults['benchmark_type'],
asset_parameters_fixed_rate=fixed_rate, asset_parameters_clearing_house=clearing_house.value,
asset_parameters_floating_rate_designated_maturity=defaults['floating_rate_tenor'],
asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name, pricing_location=defaults['pricing_location'].value)
rate_mqid = _convert_asset_for_mdapi_swap_rates(**kwargs)
_logger.debug('where asset= %s, swap_tenor=%s, benchmark_type=%s, floating_rate_tenor=%s, forward_tenor=%s, '
'pricing_location=%s', rate_mqid, swap_tenor, defaults['benchmark_type'],
defaults['floating_rate_tenor'], forward_tenor, defaults['pricing_location'].value)
where = dict(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query([rate_mqid], query_type, where=where, source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return df
def spread_option_vol(asset: Asset,
expiration_tenor: str,
long_tenor: str,
short_tenor: str,
relative_strike: float,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day implied normal volatility for spread option vol matrices.
:param asset: asset object loaded from security master
:param expiration_tenor: relative date representation of expiration date on the option e.g. 3m
:param long_tenor: relative date representation of the instrument's tenor date e.g. 1y
:param short_tenor: relative date representation of the instrument's tenor date e.g. 1y
:param relative_strike: strike level relative to at the money e.g. 10
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: spread option implied normal volatility curve
"""
if real_time:
raise NotImplementedError('realtime spread_option_vol not implemented')
rate_benchmark_mqid = convert_asset_for_rates_data_set(
asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)
_logger.debug('where expiry=%s, longTenor=%s, shortTenor=%s, strike=%s',
long_tenor, short_tenor, expiration_tenor, relative_strike)
q = GsDataApi.build_market_data_query([rate_benchmark_mqid],
QueryType.SPREAD_OPTION_VOL,
where=FieldFilterMap(
expriy=expiration_tenor,
longTenor=long_tenor,
shortTenor=short_tenor,
strike=relative_strike),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['spreadOptionVol']
def fci(country_id: str,
measure: _FCI_MEASURE = _FCI_MEASURE.FCI,
*,
source: str = None,
real_time: bool = False,
request_id: Optional[str] = None) -> pd.Series:
"""
Daily Financial Conditions Index (FCI) for each of the world's large economies and many smaller ones,
as well as aggregate FCIs for regions.
:param country_id: id of country/region
:param measure: FCI metric to retrieve
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:param request_id: server request id
:return: FCI metric value
"""
if real_time:
raise NotImplementedError('real-time FCI data is not available')
type_ = QueryType(inflection.titleize(measure.value))
if (measure == _FCI_MEASURE.REAL_FCI
or measure == _FCI_MEASURE.REAL_TWI_CONTRIBUTION):
ds = Dataset('FCI')
df = ds.get_data(geographyId=country_id)
if (measure == _FCI_MEASURE.REAL_FCI):
measure = 'realFCI'
else:
measure = 'realTWIContribution'
series = ExtendedSeries(dtype=float) if (
measure not in df.columns) else ExtendedSeries(df[measure])
series.dataset_ids = ('FCI', )
return series
q = GsDataApi.build_market_data_query([country_id],
query_type=type_,
source=source,
real_time=real_time)
df = _market_data_timed(q, request_id)
return _extract_series_from_df(df, type_, True)
def _get_fx_vol_swap_data(asset: Asset, expiry_tenor: str, strike_type: str = None,
location: PricingLocation = None,
source: str = None, real_time: bool = False,
query_type: QueryType = QueryType.STRIKE_VOL) \
-> pd.DataFrame:
if real_time:
raise NotImplementedError(
'realtime inflation swap data not implemented')
cross = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)
if cross not in FX_VOL_SWAP_DEFAULTS:
raise NotImplementedError(
'Data not available for {} FX Vol Swaps'.format(cross))
kwargs = dict(
asset_class='FX',
type='VolatilitySwap',
expiry_tenor=expiry_tenor,
asset_parameters_pair=cross,
# asset_parameters_strike_vol=strike_type
)
fxv_mqid = _get_tdapi_fxo_assets_vol_swaps(**kwargs)
if location is None:
pricing_location = PricingLocation.NYC
else:
pricing_location = PricingLocation(location)
where = dict(pricingLocation=pricing_location.value)
q = GsDataApi.build_market_data_query([fxv_mqid],
query_type,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return df
def swaption_atm_forward_rate(asset: Asset,
expiration_tenor: str,
termination_tenor: str,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day at-the-money forward rate for swaption vol matrices.
:param asset: asset object loaded from security master
:param expiration_tenor: relative date representation of expiration date on the option e.g. 3m
:param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swaption at-the-money forward rate curve
"""
if real_time:
raise NotImplementedError(
'realtime swaption_atm_forward_rate not implemented')
rate_benchmark_mqid = convert_asset_for_rates_data_set(
asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)
_logger.debug('where expiry=%s, tenor=%s', expiration_tenor,
termination_tenor)
q = GsDataApi.build_market_data_query([rate_benchmark_mqid],
QueryType.ATM_FWD_RATE,
where=FieldFilterMap(
expiry=expiration_tenor,
tenor=termination_tenor,
strike=0),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['atmFwdRate']
def swap_rate_2(asset: Asset,
swap_tenor: str,
benchmark_type: BenchmarkType = None,
floating_rate_tenor: str = None,
forward_tenor: str = 'Spot',
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day Fixed-Floating interest rate swap (IRS) 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 benchmark_type: benchmark type e.g. LIBOR
:param floating_rate_tenor: floating index rate
:param forward_tenor: relative date representation of forward starting point eg: '1y' or 'Spot' for
spot starting swaps
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
if real_time:
raise NotImplementedError('realtime swap_rate not implemented')
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP', 'CHF', 'SEK']:
raise NotImplementedError(
'Data not available for {} swap rates'.format(currency.value))
if benchmark_type is not None and \
benchmark_type.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value].keys():
raise MqValueError('%s is not supported for %s', benchmark_type,
currency.value)
defaults = _get_swap_leg_defaults(currency, benchmark_type,
floating_rate_tenor)
if not re.fullmatch('(\\d+)([bdwmy])', swap_tenor):
raise MqValueError('invalid swap tenor ' + swap_tenor)
if not re.fullmatch('(\\d+)([bdwmy])', floating_rate_tenor):
raise MqValueError('invalid floating rate tenor ' +
floating_rate_tenor)
if forward_tenor is None or forward_tenor == 'Spot':
forward_tenor = '0b'
elif not re.fullmatch('(\\d+)([bdwmy])', forward_tenor):
raise MqValueError('invalid forward tenor ' + forward_tenor)
clearing_house = 'LCH'
csaTerms = currency.value + '-1'
fixed_rate = 'ATM'
pay_or_receive = 'Receive'
kwargs = dict(
type='Swap',
asset_parameters_termination_date=swap_tenor,
asset_parameters_floating_rate_option=defaults['benchmark_type'],
asset_parameters_fixed_rate=fixed_rate,
asset_parameters_clearing_house=clearing_house,
asset_parameters_floating_rate_designated_maturity=defaults[
'floating_rate_tenor'],
asset_parameters_effective_date=forward_tenor,
asset_parameters_pay_or_receive=pay_or_receive,
asset_parameters_notional_currency=currency.name,
pricing_location=defaults['pricing_location'])
rate_mqid = _convert_asset_for_mdapi_swap_rates(**kwargs)
_logger.debug(
'where swap_tenor=%s, benchmark_type=%s, floating_rate_tenor=%s, forward_tenor=%s, '
'pricing_location=%s', swap_tenor, defaults['benchmark_type'],
defaults['floating_rate_tenor'], forward_tenor,
defaults['pricing_location'])
where = FieldFilterMap(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query([rate_mqid],
QueryType.SWAP_RATE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['swapRate']
def _get_inflation_swap_data(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,
query_type: QueryType = QueryType.SWAP_RATE,
location: PricingLocation = None,
allow_many=False,
request_id: Optional[str] = None) -> pd.DataFrame:
if real_time:
raise NotImplementedError(
'realtime inflation swap data not implemented')
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))
index_type = _check_inflation_index_type(currency, index_type)
clearing_house = tm_rates._check_clearing_house(clearing_house)
defaults = _get_inflation_swap_leg_defaults(currency, index_type)
if not (tm_rates._is_valid_relative_date_tenor(swap_tenor)):
raise MqValueError('invalid swap tenor ' + swap_tenor)
forward_tenor = tm_rates._check_forward_tenor(forward_tenor)
fixed_rate = 'ATM'
kwargs = dict(asset_class='Rates',
type='InflationSwap',
asset_parameters_termination_date=swap_tenor,
asset_parameters_index=defaults['index_type'],
asset_parameters_fixed_rate=fixed_rate,
asset_parameters_clearing_house=clearing_house.value,
asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name)
rate_mqids = _get_tdapi_inflation_rates_assets(allow_many=allow_many,
**kwargs)
if location is None:
pricing_location = tm_rates._default_pricing_location(currency)
else:
pricing_location = PricingLocation(location)
pricing_location = tm_rates._pricing_location_normalized(
pricing_location, currency)
where = dict(pricingLocation=pricing_location.value)
entity_ids = [rate_mqids
] if not isinstance(rate_mqids, list) else rate_mqids
_logger.debug(
f'where asset= {rate_mqids}, swap_tenor={swap_tenor}, index={defaults["index_type"]}, '
f'forward_tenor={forward_tenor}, pricing_location={pricing_location.value}, '
f'clearing_house={clearing_house.value}, notional_currency={currency.name}, '
f'request_id={request_id}')
q = GsDataApi.build_market_data_query(entity_ids,
query_type,
where=where,
source=source,
real_time=real_time)
_logger.debug(f'q: {q}, request_id: {request_id}')
df = _market_data_timed(q)
return df
def basis_swap_term_structure(asset: Asset, spread_benchmark_type: BenchmarkType = None, spread_tenor: str = None,
reference_benchmark_type: BenchmarkType = None, reference_tenor: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
clearing_house: _ClearingHouse = None,
pricing_date: Optional[GENERIC_DATE] = None,
*, source: str = None, real_time: bool = False, ) -> Series:
"""
GS end-of-day Floating-Floating interest rate swap (IRS) term structure across major currencies.
:param asset: asset object loaded from security master
:param spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR
:param spread_tenor: relative date representation of expiration date of spread leg e.g. 1m
:param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR
:param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m
: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 pricing_date: YYYY-MM-DD or relative date
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
if real_time:
raise NotImplementedError('realtime basis_swap_rate not implemented')
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP']:
raise NotImplementedError('Data not available for {} basis swap rates'.format(currency.value))
clearing_house = _check_clearing_house(clearing_house)
for benchmark_type in [spread_benchmark_type, reference_benchmark_type]:
_check_benchmark_type(currency, benchmark_type)
# default benchmark types
legs_w_defaults = dict()
legs_w_defaults['spread'] = _get_swap_leg_defaults(currency, spread_benchmark_type, spread_tenor)
legs_w_defaults['reference'] = _get_swap_leg_defaults(currency, reference_benchmark_type, reference_tenor)
for key, leg in legs_w_defaults.items():
if not re.fullmatch('(\\d+)([bdwmy])', leg['floating_rate_tenor']):
raise MqValueError('invalid floating rate tenor ' + leg['floating_rate_tenor'] + ' index: ' +
leg['benchmark_type'])
forward_tenor = check_forward_tenor(forward_tenor)
calendar = legs_w_defaults['spread']['pricing_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))
csaTerms = currency.value + '-1'
kwargs = dict(type='BasisSwap', asset_parameters_payer_rate_option=legs_w_defaults['spread']['benchmark_type'],
asset_parameters_payer_designated_maturity=legs_w_defaults['spread']['floating_rate_tenor'],
asset_parameters_receiver_rate_option=legs_w_defaults['reference']['benchmark_type'],
asset_parameters_receiver_designated_maturity=legs_w_defaults['reference']['floating_rate_tenor'],
asset_parameters_clearing_house=clearing_house.value, asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name,
pricing_location=legs_w_defaults['spread']['pricing_location'].value)
assets = GsAssetApi.get_many_assets(**kwargs)
if len(assets) == 0:
raise MqValueError('Specified arguments did not match any asset in the dataset')
else:
rate_mqids = [asset.id for asset in assets]
asset_string = ''
for mqid in rate_mqids:
asset_string = asset_string + ',' + mqid
_logger.debug('assets returned %s', asset_string)
_logger.debug('where spread_benchmark_type=%s, spread_tenor=%s, reference_benchmark_type=%s, '
'reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',
legs_w_defaults['spread']['benchmark_type'], legs_w_defaults['spread']['floating_rate_tenor'],
legs_w_defaults['reference']['benchmark_type'], legs_w_defaults['reference']['floating_rate_tenor'],
forward_tenor, legs_w_defaults['spread']['pricing_location'].value)
start, end = _range_from_pricing_date(calendar, pricing_date)
with DataContext(start, end):
where = dict(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query(rate_mqids, QueryType.BASIS_SWAP_RATE, where=where,
source=source, real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
series = ExtendedSeries()
else:
latest = df.index.max()
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
business_day = _get_custom_bd(calendar)
df = df.assign(expirationDate=df.index + df['terminationTenor'].map(_to_offset) + business_day - business_day)
df = df.set_index('expirationDate')
df.sort_index(inplace=True)
df = df.loc[DataContext.current.start_date: DataContext.current.end_date]
series = ExtendedSeries() if df.empty else ExtendedSeries(df['basisSwapRate'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def basis_swap_spread(asset: Asset, swap_tenor: str = '1y',
spread_benchmark_type: BenchmarkType = None, spread_tenor: str = None,
reference_benchmark_type: BenchmarkType = None, reference_tenor: str = None,
forward_tenor: Optional[GENERIC_DATE] = None, clearing_house: _ClearingHouse = None, *,
source: str = None, real_time: bool = False, ) -> Series:
"""
GS end-of-day Floating-Floating interest rate swap (IRS) 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 spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR
:param spread_tenor: relative date representation of expiration date of paying leg e.g. 1m
:param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR
:param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m
: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
"""
if real_time:
raise NotImplementedError('realtime basis_swap_rate not implemented')
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP']:
raise NotImplementedError('Data not available for {} basis swap rates'.format(currency.value))
clearing_house = _check_clearing_house(clearing_house)
for benchmark_type in [spread_benchmark_type, reference_benchmark_type]:
_check_benchmark_type(currency, benchmark_type)
if not (re.fullmatch('(\\d+)([bdwmy])', swap_tenor) or re.fullmatch('(frb[1-9])', forward_tenor)):
raise MqValueError('invalid swap tenor ' + swap_tenor)
# default benchmark types
legs_w_defaults = dict()
legs_w_defaults['spread'] = _get_swap_leg_defaults(currency, spread_benchmark_type, spread_tenor)
legs_w_defaults['reference'] = _get_swap_leg_defaults(currency, reference_benchmark_type, reference_tenor)
for key, leg in legs_w_defaults.items():
if not re.fullmatch('(\\d+)([bdwmy])', leg['floating_rate_tenor']):
raise MqValueError('invalid floating rate tenor ' + leg['floating_rate_tenor'] + ' index: ' +
leg['benchmark_type'])
forward_tenor = check_forward_tenor(forward_tenor)
csaTerms = currency.value + '-1'
kwargs = dict(type='BasisSwap', asset_parameters_termination_date=swap_tenor,
asset_parameters_payer_rate_option=legs_w_defaults['spread']['benchmark_type'],
asset_parameters_payer_designated_maturity=legs_w_defaults['spread']['floating_rate_tenor'],
asset_parameters_receiver_rate_option=legs_w_defaults['reference']['benchmark_type'],
asset_parameters_receiver_designated_maturity=legs_w_defaults['reference']['floating_rate_tenor'],
asset_parameters_clearing_house=clearing_house.value, asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name,
pricing_location=legs_w_defaults['spread']['pricing_location'].value)
rate_mqid = _convert_asset_for_mdapi_swap_rates(**kwargs)
_logger.debug('where asset=%s, swap_tenor=%s, spread_benchmark_type=%s, spread_tenor=%s, '
'reference_benchmark_type=%s, reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',
rate_mqid, swap_tenor, legs_w_defaults['spread']['benchmark_type'],
legs_w_defaults['spread']['floating_rate_tenor'],
legs_w_defaults['reference']['benchmark_type'], legs_w_defaults['reference']['floating_rate_tenor'],
forward_tenor, legs_w_defaults['spread']['pricing_location'].value)
where = dict(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query([rate_mqid], QueryType.BASIS_SWAP_RATE, where=where, source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
series = ExtendedSeries() if df.empty else ExtendedSeries(df['basisSwapRate'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def bucketize_price(asset: Asset,
price_method: str,
price_component: str,
bucket: str = '7x24',
granularity: str = 'daily',
*,
source: str = None,
real_time: bool = True) -> pd.Series:
"""'
Bucketized Elec Historical Clears
:param asset: asset object loaded from security master
:param price_method: price method between LMP and MCP: Default value = LMP
:param price_component: price type among totalPrice, energy, loss and congestion: Default value = totalPrice
:param bucket: bucket type among '7x24', 'peak', 'offpeak', '2x16h' and '7x8': Default value = 7x24
:param granularity: daily or monthly: default value = daily
:param source: name of function caller: default source = None
:param real_time: whether to retrieve intraday data instead of EOD: default value = True
:return: Bucketized Elec Historical Clears
"""
# create granularity indicator
if granularity.lower() in ['daily', 'd']:
granularity = 'D'
elif granularity.lower() in ['monthly', 'm']:
granularity = 'M'
else:
raise ValueError('Invalid granularity: ' + granularity +
'. Expected Value: daily or monthly.')
start_date, end_date = DataContext.current.start_date, DataContext.current.end_date
where = FieldFilterMap(priceMethod=price_method,
priceComponent=price_component)
with DataContext(start_date, end_date + datetime.timedelta(days=2)):
q = GsDataApi.build_market_data_query([asset.get_marquee_id()],
QueryType.PRICE,
where=where,
source=source,
real_time=True)
df = _market_data_timed(q)
_logger.debug('q %s', q)
# TODO: get timezone info from Asset
# default frequency definition
df = df.tz_convert('US/Eastern')
peak_start = 7
peak_end = 23
weekends = [5, 6]
bbid = Asset.get_identifier(asset, AssetIdentifier.BLOOMBERG_ID)
if bbid.split(" ")[0] in ['MISO', 'CAISO', 'ERCOT', 'SPP']:
df = df.tz_convert('US/Central')
peak_start = 6
peak_end = 22
if bbid.split(" ")[0] == 'CAISO':
df = df.tz_convert('US/Pacific')
weekends = [6]
start_time, end_time = pd.to_datetime(
start_date), pd.to_datetime(end_date) + datetime.timedelta(hours=23)
df['month'] = df.index.month
df['date'] = df.index.date
df['day'] = df.index.dayofweek
df['hour'] = df.index.hour
holidays = NercCalendar().holidays(start=start_date, end=end_date).date
# checking missing data points
ref_hour_range = pd.date_range(start_time,
end_time,
freq='1h',
tz='US/Eastern')
missing_hours = ref_hour_range[~ref_hour_range.isin(df.index)]
missing_dates = np.unique(missing_hours.date)
missing_months = np.unique(missing_hours.month)
# drop dates and months which have missing data
df = df.loc[(~df['date'].isin(missing_dates))]
if granularity == 'M':
df = df.loc[(~df['month'].isin(missing_months))]
# TODO: get frequency definition from SecDB
if bucket.lower() == '7x24':
pass
# offpeak: 11pm-7am & weekend & holiday
elif bucket.lower() == 'offpeak':
df = df.loc[df['date'].isin(holidays) | df['day'].isin(weekends) |
(~df['date'].isin(holidays) & ~df['day'].isin(weekends) &
((df['hour'] < peak_start)
| (df['hour'] > peak_end - 1)))]
# peak: 7am to 11pm on weekdays
elif bucket.lower() == 'peak':
df = df.loc[(~df['date'].isin(holidays)) & (~df['day'].isin(weekends))
& (df['hour'] > peak_start - 1)
& (df['hour'] < peak_end)]
# 7x8: 11pm to 7am
elif bucket.lower() == '7x8':
df = df.loc[(df['hour'] < peak_start) | (df['hour'] > peak_end - 1)]
# 2x16h: weekends & holidays
elif bucket.lower() == '2x16h':
df = df.loc[((df['date'].isin(holidays)) | df['day'].isin(weekends))
& ((df['hour'] > peak_start - 1)
& (df['hour'] < peak_end))]
else:
raise ValueError('Invalid bucket: ' + bucket +
'. Expected Value: peak, offpeak, 7x24, 7x8, 2x16h.')
df = df['price'].resample(granularity).mean()
df.index = df.index.date
df = df.loc[start_date:end_date]
return df
def basis_swap_term_structure(
asset: Asset,
spread_benchmark_type: BenchmarkType = None,
spread_tenor: str = None,
reference_benchmark_type: BenchmarkType = None,
reference_tenor: str = None,
forward_tenor: str = 'Spot',
pricing_date: Optional[GENERIC_DATE] = None,
*,
source: str = None,
real_time: bool = False,
) -> Series:
"""
GS end-of-day Floating-Floating interest rate swap (IRS) term structure across major currencies.
:param asset: asset object loaded from security master
:param spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR
:param spread_tenor: relative date representation of expiration date of spread leg e.g. 1m
:param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR
:param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m
:param forward_tenor: relative date representation of forward starting point eg: '1y' or 'Spot' for spot
starting swaps
:param pricing_date: YYYY-MM-DD or relative date
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
if real_time:
raise NotImplementedError('realtime basis_swap_rate not implemented')
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP']:
raise NotImplementedError(
'Data not available for {} basis swap rates'.format(
currency.value))
for benchmark_type in [spread_benchmark_type, reference_benchmark_type]:
if benchmark_type is not None and \
benchmark_type.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value].keys():
raise MqValueError('%s is not supported for %s', benchmark_type,
currency.value)
for floating_rate_tenor in [spread_tenor, reference_tenor]:
if not re.fullmatch('(\\d+)([bdwmy])', floating_rate_tenor):
raise MqValueError('invalid floating rate tenor ' +
floating_rate_tenor)
if forward_tenor == '0b' or forward_tenor is None or forward_tenor == 'Spot':
forward_tenor = '0d'
elif not re.fullmatch('(\\d+)([bdwmy])', forward_tenor):
raise MqValueError('invalid forward tenor ' + forward_tenor)
# default benchmark types
legs_w_defaults = dict()
legs_w_defaults['spread'] = _get_swap_leg_defaults(currency,
spread_benchmark_type,
spread_tenor)
legs_w_defaults['reference'] = _get_swap_leg_defaults(
currency, reference_benchmark_type, reference_tenor)
csaTerms = currency.value + '-1'
clearing_house = 'LCH'
kwargs = dict(
type='BasisSwap',
asset_parameters_payer_rate_option=legs_w_defaults['spread']
['benchmark_type'],
asset_parameters_payer_designated_maturity=legs_w_defaults['spread']
['floating_rate_tenor'],
asset_parameters_receiver_rate_option=legs_w_defaults['reference']
['benchmark_type'],
asset_parameters_receiver_designated_maturity=legs_w_defaults[
'reference']['floating_rate_tenor'],
asset_parameters_clearing_house=clearing_house,
asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name,
pricing_location=legs_w_defaults['spread']['pricing_location'])
assets = GsAssetApi.get_many_assets(**kwargs)
if len(assets) == 0:
raise MqValueError(
'Specified arguments did not match any asset in the dataset')
else:
rate_mqids = [asset.id for asset in assets]
_logger.debug(
'where spread_benchmark_type=%s, spread_tenor=%s, reference_benchmark_type=%s, '
'reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',
legs_w_defaults['spread']['benchmark_type'],
legs_w_defaults['spread']['floating_rate_tenor'],
legs_w_defaults['reference']['benchmark_type'],
legs_w_defaults['reference']['floating_rate_tenor'], forward_tenor,
legs_w_defaults['spread']['pricing_location'])
start, end = _range_from_pricing_date(assets[0].exchange, pricing_date)
with DataContext(start, end):
where = FieldFilterMap(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query(rate_mqids,
QueryType.BASIS_SWAP_RATE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
return pd.Series()
latest = df.index.max()
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
business_day = _get_custom_bd(asset.exchange)
df = df.assign(expirationDate=df.index +
df['terminationTenor'].map(_to_offset) + business_day -
business_day)
df = df.set_index('expirationDate')
df.sort_index(inplace=True)
df = df.loc[DataContext.current.start_date:DataContext.current.end_date]
return df['basisSwapRate'] if not df.empty else pd.Series()
def basis_swap_spread(
asset: Asset,
swap_tenor: str = '1y',
spread_benchmark_type: BenchmarkType = None,
spread_tenor: str = None,
reference_benchmark_type: BenchmarkType = None,
reference_tenor: str = None,
forward_tenor: str = 'Spot',
*,
source: str = None,
real_time: bool = False,
) -> Series:
"""
GS end-of-day Floating-Floating interest rate swap (IRS) 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 spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR
:param spread_tenor: relative date representation of expiration date of paying leg e.g. 1m
:param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR
:param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m
:param forward_tenor: relative date representation of forward starting point eg: '1y' or 'Spot' for Spot
Starting swap
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
if real_time:
raise NotImplementedError('realtime basis_swap_rate not implemented')
currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP']:
raise NotImplementedError(
'Data not available for {} basis swap rates'.format(
currency.value))
for benchmark_type in [spread_benchmark_type, reference_benchmark_type]:
if benchmark_type is not None and \
benchmark_type.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value].keys():
raise MqValueError('%s is not supported for %s', benchmark_type,
currency.value)
if not re.fullmatch('(\\d+)([bdwmy])', swap_tenor):
raise MqValueError('invalid swap tenor ' + swap_tenor)
for floating_rate_tenor in [spread_tenor, reference_tenor]:
if not re.fullmatch('(\\d+)([bdwmy])', floating_rate_tenor):
raise MqValueError('invalid floating rate tenor ' +
floating_rate_tenor)
# default benchmark types
legs_w_defaults = dict()
legs_w_defaults['spread'] = _get_swap_leg_defaults(currency,
spread_benchmark_type,
spread_tenor)
legs_w_defaults['reference'] = _get_swap_leg_defaults(
currency, reference_benchmark_type, reference_tenor)
if forward_tenor == '0b' or forward_tenor is None or forward_tenor == 'Spot':
forward_tenor = '0d'
elif not re.fullmatch('(\\d+)([bdwmy])', forward_tenor):
raise MqValueError('invalid forward tenor ' + forward_tenor)
csaTerms = currency.value + '-1'
clearing_house = 'LCH'
kwargs = dict(
type='BasisSwap',
asset_parameters_termination_date=swap_tenor,
asset_parameters_payer_rate_option=legs_w_defaults['spread']
['benchmark_type'],
asset_parameters_payer_designated_maturity=legs_w_defaults['spread']
['floating_rate_tenor'],
asset_parameters_receiver_rate_option=legs_w_defaults['reference']
['benchmark_type'],
asset_parameters_receiver_designated_maturity=legs_w_defaults[
'reference']['floating_rate_tenor'],
asset_parameters_clearing_house=clearing_house,
asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name,
pricing_location=legs_w_defaults['spread']['pricing_location'])
rate_mqid = _convert_asset_for_mdapi_swap_rates(**kwargs)
_logger.debug(
'where swap_tenor=%s, spread_benchmark_type=%s, spread_tenor=%s, reference_benchmark_type=%s, '
'reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',
swap_tenor, legs_w_defaults['spread']['benchmark_type'],
legs_w_defaults['spread']['floating_rate_tenor'],
legs_w_defaults['reference']['benchmark_type'],
legs_w_defaults['reference']['floating_rate_tenor'], forward_tenor,
legs_w_defaults['spread']['pricing_location'])
where = FieldFilterMap(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query([rate_mqid],
QueryType.BASIS_SWAP_RATE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['basisSwapRate']
def skew(asset: Asset,
tenor: str,
strike_reference: SkewReference,
distance: Real,
*,
location: str = 'NYC',
source: str = None,
real_time: bool = False) -> Series:
"""
Difference in implied volatility of equidistant out-of-the-money put and call options.
:param asset: asset object loaded from security master
:param tenor: relative date representation of expiration date e.g. 1m
:param strike_reference: reference for strike level (for equities)
:param distance: distance from at-the-money option
:param location: location at which a price fixing has been taken (for FX assets)
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: skew curve
"""
if real_time:
raise MqValueError('real-time skew not supported')
if strike_reference in (SkewReference.DELTA, None):
b = 50
elif strike_reference == SkewReference.NORMALIZED:
b = 0
else:
b = 100
kwargs = {}
if strike_reference in (SkewReference.DELTA, None):
# using delta call strikes so X DP is represented as (100 - X) DC
q_strikes = [100 - distance, distance, b]
else:
q_strikes = [b - distance, b + distance, b]
if asset.asset_class == AssetClass.FX:
q_strikes = _to_fx_strikes(q_strikes)
kwargs['location'] = location
column = 'deltaStrike' # should use SkewReference.DELTA for FX
else:
assert asset.asset_class == AssetClass.Equity
if not strike_reference:
raise MqTypeError('strike reference required for equities')
if strike_reference != SkewReference.NORMALIZED:
q_strikes = [x / 100 for x in q_strikes]
kwargs['strikeReference'] = strike_reference.value
column = 'relativeStrike'
kwargs[column] = q_strikes
_logger.debug('where tenor=%s and %s', tenor, kwargs)
where = FieldFilterMap(tenor=tenor, **kwargs)
q = GsDataApi.build_market_data_query([asset.get_marquee_id()],
QueryType.IMPLIED_VOLATILITY,
where=where,
source=source)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
return pd.Series()
curves = {k: v for k, v in df.groupby(column)}
if len(curves) < 3:
raise MqValueError('skew not available for given inputs')
series = [curves[qs]['impliedVolatility'] for qs in q_strikes]
return (series[0] - series[1]) / series[2]
def _get_fxo_data(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,
query_type: QueryType = QueryType.IMPLIED_VOLATILITY) \
-> pd.DataFrame:
if real_time:
raise NotImplementedError(
'realtime inflation swap data not implemented')
cross = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)
if cross not in FX_DEFAULTS.keys():
raise NotImplementedError(
'Data not available for {} FX Vanilla options'.format(cross))
defaults = _get_fxo_defaults(cross)
if not (tm_rates._is_valid_relative_date_tenor(expiry_tenor)):
raise MqValueError('invalid expiry ' + expiry_tenor)
if expiration_location is None:
# expirationtime = defaults["expirationTime"]
_ = defaults["expirationTime"]
else:
# expirationtime = expiration_location
_ = expiration_location
if premium_payment_date is None:
premium_date = defaults["premiumPaymentDate"]
else:
premium_date = premium_payment_date
if option_type == "Put":
call_ccy = defaults["over"]
put_ccy = defaults["under"]
else:
call_ccy = defaults["under"]
put_ccy = defaults["over"]
kwargs = dict(
asset_class='FX',
type='Option',
asset_parameters_call_currency=call_ccy,
asset_parameters_put_currency=put_ccy,
asset_parameters_expiration_date=expiry_tenor,
# asset_parameters_expiration_time=expirationtime,
asset_parameters_option_type=option_type,
asset_parameters_premium_payment_date=premium_date,
asset_parameters_strike_price_relative=strike,
)
rate_mqid = _get_tdapi_fxo_assets(**kwargs)
if location is None:
pricing_location = PricingLocation.NYC
else:
pricing_location = PricingLocation(location)
where = dict(pricingLocation=pricing_location.value)
# _logger.debug(f'where asset= {rate_mqid}, swap_tenor={swap_tenor}, index={defaults["index_type"]}, '
# f'forward_tenor={forward_tenor}, pricing_location={pricing_location.value}, '
# f'clearing_house={clearing_house.value}, notional_currency={currency.name}')
q = GsDataApi.build_market_data_query([rate_mqid],
query_type,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return df
def swap_term_structure(asset: Asset,
benchmark_type: BenchmarkType = None,
floating_rate_tenor: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
pricing_date: Optional[GENERIC_DATE] = None,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day Fixed-Floating interest rate swap (IRS) term structure across major currencies.
:param asset: asset object loaded from security master
:param benchmark_type: benchmark type e.g. LIBOR
:param floating_rate_tenor: floating index rate
: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 source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate term structure
"""
if real_time:
raise NotImplementedError('realtime swap_rate not implemented')
currency = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)
currency = CurrencyEnum(currency)
if currency.value not in ['JPY', 'EUR', 'USD', 'GBP', 'CHF', 'SEK']:
raise NotImplementedError(
'Data not available for {} swap rates'.format(currency.value))
clearing_house = 'LCH'
_check_benchmark_type(currency, benchmark_type)
forward_tenor = check_forward_tenor(forward_tenor)
defaults = _get_swap_leg_defaults(currency, benchmark_type,
floating_rate_tenor)
if not re.fullmatch('(\\d+)([bdwmy])', defaults['floating_rate_tenor']):
raise MqValueError('invalid floating rate tenor ' +
defaults['floating_rate_tenor'] + ' for index: ' +
defaults['benchmark_type'])
calendar = defaults['pricing_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))
csaTerms = currency.value + '-1'
fixed_rate = 'ATM'
kwargs = dict(
type='Swap',
asset_parameters_floating_rate_option=defaults['benchmark_type'],
asset_parameters_fixed_rate=fixed_rate,
asset_parameters_clearing_house=clearing_house,
asset_parameters_floating_rate_designated_maturity=defaults[
'floating_rate_tenor'],
asset_parameters_effective_date=forward_tenor,
asset_parameters_notional_currency=currency.name,
pricing_location=defaults['pricing_location'].value)
assets = GsAssetApi.get_many_assets(**kwargs)
if len(assets) == 0:
raise MqValueError(
'Specified arguments did not match any asset in the dataset')
else:
rate_mqids = [asset.id for asset in assets]
asset_string = ''
for mqid in rate_mqids:
asset_string = asset_string + ',' + mqid
_logger.debug('assets returned %s', asset_string)
_logger.debug(
'where benchmark_type=%s, floating_rate_tenor=%s, forward_tenor=%s, '
'pricing_location=%s', defaults['benchmark_type'],
defaults['floating_rate_tenor'], forward_tenor,
defaults['pricing_location'].value)
start, end = _range_from_pricing_date(calendar, pricing_date)
with DataContext(start, end):
where = FieldFilterMap(csaTerms=csaTerms)
q = GsDataApi.build_market_data_query(rate_mqids,
QueryType.SWAP_RATE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
return pd.Series()
latest = df.index.max()
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
business_day = _get_custom_bd(calendar)
df = df.assign(expirationDate=df.index +
df['terminationTenor'].map(_to_offset) + business_day -
business_day)
df = df.set_index('expirationDate')
df.sort_index(inplace=True)
df = df.loc[DataContext.current.start_date:DataContext.current.end_date]
return df['swapRate'] if not df.empty else pd.Series()
def _get_crosscurrency_swap_data(
asset1: Asset,
asset2: Asset,
swap_tenor: str,
rateoption_type: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
clearing_house: tm_rates._ClearingHouse = None,
source: str = None,
real_time: bool = False,
query_type: QueryType = QueryType.SWAP_RATE,
location: PricingLocation = None) -> pd.DataFrame:
if real_time:
raise NotImplementedError(
'realtime swap_rate not implemented for anything but rates')
currency1 = CurrencyEnum(
asset1.get_identifier(AssetIdentifier.BLOOMBERG_ID))
currency2 = CurrencyEnum(
asset2.get_identifier(AssetIdentifier.BLOOMBERG_ID))
if currency1.value not in CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK.keys():
raise NotImplementedError(
'Data not available for {} crosscurrency swap rates'.format(
currency1.value))
if currency2.value not in CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK.keys():
raise NotImplementedError(
'Data not available for {} crosscurrency swap rates'.format(
currency2.value))
rateoption_type1 = _check_crosscurrency_rateoption_type(
currency1, rateoption_type)
rateoption_type2 = _check_crosscurrency_rateoption_type(
currency2, rateoption_type)
if rateoption_type1 != rateoption_type2:
raise MqValueError(
'The two currencies do not both support the rate Option type ' +
rateoption_type)
rateoption_type = rateoption_type1
clearing_house = tm_rates._check_clearing_house(clearing_house)
defaults1 = _get_crosscurrency_swap_leg_defaults(currency1,
rateoption_type)
defaults2 = _get_crosscurrency_swap_leg_defaults(currency2,
rateoption_type)
if not (tm_rates._is_valid_relative_date_tenor(swap_tenor)):
raise MqValueError('invalid swap tenor ' + swap_tenor)
if defaults1["pricing_location"] == PricingLocation.NYC:
default_location = defaults2["pricing_location"]
currency = currency2
else:
default_location = defaults1["pricing_location"]
currency = currency1
if location is None:
pricing_location = PricingLocation(default_location)
else:
pricing_location = PricingLocation(location)
pricing_location = tm_rates._pricing_location_normalized(
pricing_location, currency)
where = dict(pricingLocation=pricing_location.value)
forward_tenor = tm_rates._check_forward_tenor(forward_tenor)
fixed_rate = 'ATM'
kwargs = dict(
asset_class='Rates',
type='XccySwapMTM',
asset_parameters_termination_date=swap_tenor,
asset_parameters_effective_date=forward_tenor,
asset_parameters_payer_spread=fixed_rate,
# asset_parameters_payer_currency=defaults1['currency'].value,
asset_parameters_payer_rate_option=defaults1['rateOption'],
# asset_parameters_payer_designated_maturity=defaults1['designatedMaturity'],
# asset_parameters_receiver_currency=defaults2['currency'].value,
asset_parameters_receiver_rate_option=defaults2['rateOption'],
# asset_parameters_receiver_designated_maturity=defaults2['designatedMaturity'],
asset_parameters_clearing_house=clearing_house.value,
pricing_location=pricing_location)
rate_mqid = _get_tdapi_crosscurrency_rates_assets(**kwargs)
_logger.debug(
f'where asset= {rate_mqid}, swap_tenor={swap_tenor}, forward_tenor={forward_tenor}, '
f'payer_currency={defaults1["currency"].value}, payer_rate_option={defaults1["rateOption"]}, '
f'payer_designated_maturity={defaults1["designatedMaturity"]}, '
f'receiver_currency={defaults2["currency"].value}, receiver_rate_option={defaults2["rateOption"]}, '
f'receiver_designated_maturity={defaults2["designatedMaturity"]}, '
f'clearing_house={clearing_house.value}, pricing_location={pricing_location.value}'
)
q = GsDataApi.build_market_data_query([rate_mqid],
query_type,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return df
def swap_rate(asset: Asset,
tenor: str,
benchmark_type: BenchmarkType = None,
floating_index: str = None,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
GS end-of-day Fixed-Floating interest rate swap (IRS) curves across major currencies.
:param asset: asset object loaded from security master
:param tenor: relative date representation of expiration date e.g. 1m
:param benchmark_type: benchmark type e.g. LIBOR
:param floating_index: floating index rate
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
if real_time:
raise NotImplementedError('realtime swap_rate not implemented')
currency = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)
currency = Currency(currency)
# default benchmark types
if benchmark_type is None:
if currency == Currency.EUR:
benchmark_type = BenchmarkType.EURIBOR
elif currency == Currency.SEK:
benchmark_type = BenchmarkType.STIBOR
else:
benchmark_type = BenchmarkType.LIBOR
over_nights = [BenchmarkType.OIS]
# default floating index
if floating_index is None:
if benchmark_type in over_nights:
floating_index = '1d'
else:
if currency in [Currency.USD]:
floating_index = '3m'
elif currency in [
Currency.GBP, Currency.EUR, Currency.CHF, Currency.SEK
]:
floating_index = '6m'
mdapi_divider = " " if benchmark_type in over_nights else "-"
mdapi_floating_index = BenchmarkType.OIS.value if benchmark_type is BenchmarkType.OIS else floating_index
mdapi = currency.value + mdapi_divider + mdapi_floating_index
rate_mqid = GsAssetApi.map_identifiers(GsIdType.mdapi, GsIdType.id,
[mdapi])[mdapi]
_logger.debug('where tenor=%s, floatingIndex=%s', tenor, floating_index)
q = GsDataApi.build_market_data_query([rate_mqid],
QueryType.SWAP_RATE,
where=FieldFilterMap(tenor=tenor),
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['swapRate']
def basis_swap_term_structure(
asset: Asset,
spread_benchmark_type: str = None,
spread_tenor: str = None,
reference_benchmark_type: str = None,
reference_tenor: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
clearing_house: _ClearingHouse = None,
pricing_date: Optional[GENERIC_DATE] = None,
*,
source: str = None,
real_time: bool = False,
) -> Series:
"""
GS end-of-day Floating-Floating interest rate swap (IRS) term structure across major currencies.
:param asset: asset object loaded from security master
:param spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR
:param spread_tenor: relative date representation of expiration date of spread leg e.g. 1m
:param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR
:param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m
: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 pricing_date: YYYY-MM-DD or relative date
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: swap rate curve
"""
if real_time:
raise NotImplementedError('realtime basis_swap_rate not implemented')
kwargs = _get_basis_swap_kwargs(
asset=asset,
spread_benchmark_type=spread_benchmark_type,
spread_tenor=spread_tenor,
reference_benchmark_type=reference_benchmark_type,
reference_tenor=reference_tenor,
forward_tenor=forward_tenor,
clearing_house=clearing_house)
calendar = kwargs['pricing_location']
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))
rate_mqids = _get_mdapi_rates_assets(**kwargs)
_logger.debug('assets returned %s', ', '.join(rate_mqids))
_logger.debug(
'where spread_benchmark_type=%s, spread_tenor=%s, reference_benchmark_type=%s, '
'reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',
kwargs['asset_parameters_payer_rate_option'],
kwargs['asset_parameters_payer_designated_maturity'],
kwargs['asset_parameters_receiver_rate_option'],
kwargs['asset_parameters_receiver_designated_maturity'],
kwargs['asset_parameters_effective_date'], kwargs['pricing_location'])
start, end = _range_from_pricing_date(calendar, pricing_date)
with DataContext(start, end):
where = dict(csaTerms=kwargs['asset_parameters_notional_currency'] +
'-1')
q = GsDataApi.build_market_data_query(rate_mqids,
QueryType.BASIS_SWAP_RATE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
series = ExtendedSeries()
else:
latest = df.index.max()
_logger.info('selected pricing date %s', latest)
df = df.loc[latest]
business_day = _get_custom_bd(calendar)
df = df.assign(expirationDate=df.index +
df['terminationTenor'].map(_to_offset) + business_day -
business_day)
df = df.set_index('expirationDate')
df.sort_index(inplace=True)
df = df.loc[DataContext.current.start_date:DataContext.current.
end_date]
series = ExtendedSeries() if df.empty else ExtendedSeries(
df['basisSwapRate'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def basis_swap_spread(
asset: Asset,
swap_tenor: str = '1y',
spread_benchmark_type: str = None,
spread_tenor: str = None,
reference_benchmark_type: str = None,
reference_tenor: str = None,
forward_tenor: Optional[GENERIC_DATE] = None,
clearing_house: _ClearingHouse = None,
*,
source: str = None,
real_time: bool = False,
) -> Series:
"""
GS end-of-day Floating-Floating interest rate swap (IRS) 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 spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR
:param spread_tenor: relative date representation of expiration date of paying leg e.g. 1m
:param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR
:param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m
: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
"""
if real_time:
raise NotImplementedError('realtime basis_swap_rate not implemented')
if not (re.fullmatch('(\\d+)([bdwmy])', swap_tenor)
or re.fullmatch('(frb[1-9])', forward_tenor)):
raise MqValueError('invalid swap tenor ' + swap_tenor)
kwargs = _get_basis_swap_kwargs(
asset=asset,
spread_benchmark_type=spread_benchmark_type,
spread_tenor=spread_tenor,
reference_benchmark_type=reference_benchmark_type,
reference_tenor=reference_tenor,
forward_tenor=forward_tenor,
clearing_house=clearing_house)
kwargs['asset_parameters_termination_date'] = swap_tenor
rate_mqid = _get_mdapi_rates_assets(**kwargs)
_logger.debug(
'where asset=%s, swap_tenor=%s, spread_benchmark_type=%s, spread_tenor=%s, '
'reference_benchmark_type=%s, reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',
rate_mqid, swap_tenor, kwargs['asset_parameters_payer_rate_option'],
kwargs['asset_parameters_payer_designated_maturity'],
kwargs['asset_parameters_receiver_rate_option'],
kwargs['asset_parameters_receiver_designated_maturity'],
kwargs['asset_parameters_effective_date'], kwargs['pricing_location'])
where = dict(csaTerms=kwargs['asset_parameters_notional_currency'] + '-1')
q = GsDataApi.build_market_data_query([rate_mqid],
QueryType.BASIS_SWAP_RATE,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
series = ExtendedSeries() if df.empty else ExtendedSeries(
df['basisSwapRate'])
series.dataset_ids = getattr(df, 'dataset_ids', ())
return series
def implied_volatility(asset: Asset,
tenor: str,
strike_reference: VolReference,
relative_strike: Real,
*,
source: str = None,
real_time: bool = False) -> Series:
"""
Volatility of an asset implied by observations of market prices.
:param asset: asset object loaded from security master
:param tenor: relative date representation of expiration date e.g. 1m
:param strike_reference: reference for strike level
:param relative_strike: strike relative to reference
:param source: name of function caller
:param real_time: whether to retrieve intraday data instead of EOD
:return: implied volatility curve
"""
if asset.asset_class == AssetClass.FX:
# no ATM support yet
if relative_strike == 50 and strike_reference in (
VolReference.DELTA_CALL, VolReference.DELTA_PUT):
delta_strike = 'DN'
else:
if strike_reference == VolReference.DELTA_CALL:
delta_strike = f'{relative_strike}DC'
elif strike_reference == VolReference.DELTA_PUT:
delta_strike = f'{relative_strike}DP'
elif strike_reference == VolReference.FORWARD:
if relative_strike == 100:
delta_strike = 'ATMF'
else:
raise MqValueError(
'Relative strike must be 100 for Forward strike reference'
)
elif strike_reference == VolReference.SPOT:
if relative_strike == 100:
delta_strike = 'ATMS'
else:
raise MqValueError(
'Relative strike must be 100 for Spot strike reference'
)
else:
raise MqValueError('strikeReference: ' +
strike_reference.value +
' not supported for FX')
loc_string = 'NYC'
_logger.debug('where tenor=%s, deltaStrike=%s, location=%s', tenor,
delta_strike, loc_string)
where = FieldFilterMap(tenor=tenor,
deltaStrike=delta_strike,
location=loc_string)
q = GsDataApi.build_market_data_query([asset.get_marquee_id()],
QueryType.IMPLIED_VOLATILITY,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
if df.empty:
reversed_cross = _reverse_cross(asset.name)
q = GsDataApi.build_market_data_query(
[reversed_cross.get_marquee_id()],
QueryType.IMPLIED_VOLATILITY,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
else:
if strike_reference == VolReference.DELTA_PUT:
relative_strike = abs(100 - relative_strike)
relative_strike = relative_strike if strike_reference == VolReference.NORMALIZED else relative_strike / 100
ref_string = "delta" if strike_reference in (
VolReference.DELTA_CALL,
VolReference.DELTA_PUT) else strike_reference.value
_logger.debug('where tenor=%s, strikeReference=%s, relativeStrike=%s',
tenor, ref_string, relative_strike)
where = FieldFilterMap(tenor=tenor,
strikeReference=ref_string,
relativeStrike=relative_strike)
q = GsDataApi.build_market_data_query([asset.get_marquee_id()],
QueryType.IMPLIED_VOLATILITY,
where=where,
source=source,
real_time=real_time)
_logger.debug('q %s', q)
df = _market_data_timed(q)
return Series() if df.empty else df['impliedVolatility']