def bootstrap_term_structure(self, interpolator=LogLinear):
tolerance = 1.0e-15
settings = Settings()
calendar = JointCalendar(UnitedStates(), UnitedKingdom())
# must be a business day
eval_date = self._eval_date
settings.evaluation_date = eval_date
settlement_days = self._params.settlement_days
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
ts = PiecewiseYieldCurve[BootstrapTrait.Discount,
interpolator].from_reference_date(
settlement_date,
self._rate_helpers,
DayCounter.from_name(
self._termstructure_daycount),
accuracy=tolerance)
self._term_structure = ts
self._discount_term_structure = YieldTermStructure()
self._discount_term_structure.link_to(ts)
self._forecast_term_structure = YieldTermStructure()
self._forecast_term_structure.link_to(ts)
return ts
def _set_evaluation_date(self, dt_obs):
if not isinstance(dt_obs, Date):
dt_obs = pydate_to_qldate(dt_obs)
settings = Settings()
calendar = JointCalendar(UnitedStates(), UnitedKingdom())
# must be a business day
eval_date = calendar.adjust(dt_obs)
settings.evaluation_date = eval_date
self._eval_date = eval_date
return eval_date
def make_rate_helper(label, rate, dt_obs, currency='USD'):
"""
Wrapper for deposit and swaps rate helpers makers
For Swaps: assume USD swap fixed rates vs. 6M Libor
TODO: make this more general
"""
if(currency.upper() != 'USD'):
raise Exception("Only supported currency is USD.")
rate_type, tenor, period = _parse_rate_label(label)
if not isinstance(dt_obs, Date):
dt_obs = pydate_to_qldate(dt_obs)
settings = Settings()
calendar = JointCalendar(UnitedStates(), UnitedKingdom())
# must be a business day
eval_date = calendar.adjust(dt_obs)
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
end_of_month = True
if((rate_type == 'SWAP') & (period == 'Y')):
liborIndex = Libor(
'USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360()
)
spread = SimpleQuote(0)
fwdStart = Period(0, Days)
helper = SwapRateHelper.from_tenor(rate,
Period(tenor, Years),
calendar, Annual,
Unadjusted, Thirty360(),
liborIndex, spread, fwdStart)
elif((rate_type == 'LIBOR') & (period == 'M')):
helper = DepositRateHelper(rate, Period(tenor, Months),
settlement_days,
calendar, ModifiedFollowing,
end_of_month,
Actual360())
else:
raise Exception("Rate type %s not supported" % label)
return (helper)
def bootstrap_term_structure(self, interpolator='loglinear'):
tolerance = 1.0e-15
settings = Settings()
calendar = JointCalendar(UnitedStates(), UnitedKingdom())
# must be a business day
eval_date = self._eval_date
settings.evaluation_date = eval_date
settlement_days = self._params.settlement_days
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
ts = PiecewiseYieldCurve(
'discount', interpolator, settlement_date, self._rate_helpers,
DayCounter.from_name(self._termstructure_daycount), tolerance)
self._term_structure = ts
self._discount_term_structure = YieldTermStructure(relinkable=True)
self._discount_term_structure.link_to(ts)
self._forecast_term_structure = YieldTermStructure(relinkable=True)
self._forecast_term_structure.link_to(ts)
return ts
def get_term_structure(df_libor, dtObs):
settings = Settings()
# libor as fixed in London, but cash-flows are determined according to
# US calendar, hence the need to combine both holidays lists
calendar = JointCalendar(UnitedStates(), UnitedKingdom())
# must be a business day
eval_date = calendar.adjust(dateToDate(dtObs))
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
depositData = [[1, Months, 'Libor1M'], [3, Months, 'Libor3M'],
[6, Months, 'Libor6M']]
swapData = [[1, Years, 'Swap1Y'], [2, Years,
'Swap2Y'], [3, Years, 'Swap3Y'],
[4, Years, 'Swap4Y'], [5, Years, 'Swap5Y'],
[7, Years, 'Swap7Y'], [10, Years, 'Swap10Y'],
[30, Years, 'Swap30Y']]
rate_helpers = []
end_of_month = True
for m, period, label in depositData:
tenor = Period(m, Months)
rate = df_libor.get_value(dtObs, label)
helper = DepositRateHelper(float(rate / 100), tenor, settlement_days,
calendar, ModifiedFollowing, end_of_month,
Actual360())
rate_helpers.append(helper)
endOfMonth = True
liborIndex = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360())
spread = SimpleQuote(0)
fwdStart = Period(0, Days)
for m, period, label in swapData:
rate = df_libor.get_value(dtObs, label)
helper = SwapRateHelper.from_tenor(rate / 100., Period(m, Years),
calendar, Annual, Unadjusted,
Thirty360(), liborIndex, spread,
fwdStart)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = term_structure_factory('discount', 'loglinear', settlement_date,
rate_helpers, ts_day_counter, tolerance)
return ts
def setUp(self):
self.calendar = UnitedKingdom()
today = Date(25, 11, 2009)
evaluation_date = self.calendar.adjust(today)
Settings().evaluation_date = evaluation_date
day_counter = ActualActual()
rpi_schedule = Schedule.from_rule(Date(20, 7,
2007), Date(20, 11, 2009),
Period(1, Months), self.calendar,
ModifiedFollowing)
self.cpi_ts = ZeroInflationTermStructure()
self.yts = FlatForward(evaluation_date, 0.05, day_counter)
self.ii = UKRPI(False, self.cpi_ts)
fix_data = [
206.1, 207.3, 208.0, 208.9, 209.7, 210.9, 209.8, 211.4, 212.1,
214.0, 215.1, 216.8, 216.5, 217.2, 218.4, 217.7, 216, 212.9, 210.1,
211.4, 211.3, 211.5, 212.8, 213.4, 213.4, 213.4, 214.4
]
for date, data in zip(rpi_schedule, fix_data):
self.ii.add_fixing(date, data)
dates = [
Date(25, 11, 2010),
Date(25, 11, 2011),
Date(26, 11, 2012),
Date(25, 11, 2013),
Date(25, 11, 2014),
Date(25, 11, 2015),
Date(25, 11, 2016),
Date(25, 11, 2017),
Date(25, 11, 2018),
Date(25, 11, 2019),
Date(25, 11, 2021),
Date(25, 11, 2024),
Date(26, 11, 2029),
Date(27, 11, 2034),
Date(25, 11, 2039),
Date(25, 11, 2049),
Date(25, 11, 2059)
]
rates = [
3.0495, 2.93, 2.9795, 3.029, 3.1425, 3.211, 3.2675, 3.3625, 3.405,
3.48, 3.576, 3.649, 3.751, 3.77225, 3.77, 3.734, 3.714
]
observation_lag = Period('2M')
self.helpers = [ZeroCouponInflationSwapHelper(
SimpleQuote(r / 100),
observation_lag,
maturity, self.calendar, ModifiedFollowing, day_counter, self.ii, self.yts) \
for maturity, r in zip(dates, rates)]
base_zero_rate = rates[0] / 100
self.cpi_ts.link_to(
PiecewiseZeroInflationCurve(Interpolator.Linear, evaluation_date,
self.calendar, day_counter,
observation_lag, self.ii.frequency,
self.ii.interpolated, base_zero_rate,
self.helpers))
class TestCPIBond(unittest.TestCase):
def setUp(self):
self.calendar = UnitedKingdom()
today = Date(25, 11, 2009)
evaluation_date = self.calendar.adjust(today)
Settings().evaluation_date = evaluation_date
day_counter = ActualActual()
rpi_schedule = Schedule.from_rule(Date(20, 7,
2007), Date(20, 11, 2009),
Period(1, Months), self.calendar,
ModifiedFollowing)
self.cpi_ts = ZeroInflationTermStructure()
self.yts = FlatForward(evaluation_date, 0.05, day_counter)
self.ii = UKRPI(False, self.cpi_ts)
fix_data = [
206.1, 207.3, 208.0, 208.9, 209.7, 210.9, 209.8, 211.4, 212.1,
214.0, 215.1, 216.8, 216.5, 217.2, 218.4, 217.7, 216, 212.9, 210.1,
211.4, 211.3, 211.5, 212.8, 213.4, 213.4, 213.4, 214.4
]
for date, data in zip(rpi_schedule, fix_data):
self.ii.add_fixing(date, data)
dates = [
Date(25, 11, 2010),
Date(25, 11, 2011),
Date(26, 11, 2012),
Date(25, 11, 2013),
Date(25, 11, 2014),
Date(25, 11, 2015),
Date(25, 11, 2016),
Date(25, 11, 2017),
Date(25, 11, 2018),
Date(25, 11, 2019),
Date(25, 11, 2021),
Date(25, 11, 2024),
Date(26, 11, 2029),
Date(27, 11, 2034),
Date(25, 11, 2039),
Date(25, 11, 2049),
Date(25, 11, 2059)
]
rates = [
3.0495, 2.93, 2.9795, 3.029, 3.1425, 3.211, 3.2675, 3.3625, 3.405,
3.48, 3.576, 3.649, 3.751, 3.77225, 3.77, 3.734, 3.714
]
observation_lag = Period('2M')
self.helpers = [ZeroCouponInflationSwapHelper(
SimpleQuote(r / 100),
observation_lag,
maturity, self.calendar, ModifiedFollowing, day_counter, self.ii, self.yts) \
for maturity, r in zip(dates, rates)]
base_zero_rate = rates[0] / 100
self.cpi_ts.link_to(
PiecewiseZeroInflationCurve(Interpolator.Linear, evaluation_date,
self.calendar, day_counter,
observation_lag, self.ii.frequency,
self.ii.interpolated, base_zero_rate,
self.helpers))
def test_clean_price(self):
notional = 1000000.0
fixed_rates = [0.1]
fixed_day_count = Actual365Fixed()
fixed_calendar = self.calendar
fixed_index = self.ii
contractObservationLag = Period(3, Months)
observationInterpolation = InterpolationType.Flat
settlement_days = 3
growth_only = True
baseCPI = 206.1
fixed_schedule = Schedule.from_rule(Date(2, 10,
2007), Date(2, 10, 2052),
Period(6, Months), fixed_calendar,
Unadjusted, Rule.Backward)
cpi_bond = CPIBond(settlement_days, notional, growth_only, baseCPI,
contractObservationLag, fixed_index,
observationInterpolation, fixed_schedule,
fixed_rates, fixed_day_count, ModifiedFollowing)
engine = DiscountingBondEngine(self.yts)
cpi_bond.set_pricing_engine(engine)
set_coupon_pricer(cpi_bond.cashflows, CPICouponPricer(self.yts))
storedPrice = 383.01816406
calculated = cpi_bond.clean_price
self.assertAlmostEqual(storedPrice, calculated)
def setUp(self):
self.calendar = UnitedKingdom()
today = Date(25, 11, 2009)
evaluation_date = self.calendar.adjust(today)
Settings().evaluation_date = evaluation_date
day_counter = ActualActual()
rpi_schedule = Schedule.from_rule(Date(20, 7, 2007),
Date(20, 11, 2009),
Period(1, Months),
self.calendar,
ModifiedFollowing)
self.cpi_ts = ZeroInflationTermStructure()
self.yts = FlatForward(evaluation_date, 0.05, day_counter)
self.ii = UKRPI(False, self.cpi_ts)
fix_data = [206.1, 207.3, 208.0, 208.9, 209.7, 210.9,
209.8, 211.4, 212.1, 214.0, 215.1, 216.8,
216.5, 217.2, 218.4, 217.7, 216,
212.9, 210.1, 211.4, 211.3, 211.5,
212.8, 213.4, 213.4, 213.4, 214.4]
for date, data in zip(rpi_schedule, fix_data):
self.ii.add_fixing(date, data)
dates = [Date(25, 11, 2010),
Date(25, 11, 2011),
Date(26, 11, 2012),
Date(25, 11, 2013),
Date(25, 11, 2014),
Date(25, 11, 2015),
Date(25, 11, 2016),
Date(25, 11, 2017),
Date(25, 11, 2018),
Date(25, 11, 2019),
Date(25, 11, 2021),
Date(25, 11, 2024),
Date(26, 11, 2029),
Date(27, 11, 2034),
Date(25, 11, 2039),
Date(25, 11, 2049),
Date(25, 11, 2059)]
rates = [3.0495,
2.93,
2.9795,
3.029,
3.1425,
3.211,
3.2675,
3.3625,
3.405,
3.48,
3.576,
3.649,
3.751,
3.77225,
3.77,
3.734,
3.714]
observation_lag = Period('2M')
self.helpers = [ZeroCouponInflationSwapHelper(
SimpleQuote(r / 100),
observation_lag,
maturity, self.calendar, ModifiedFollowing, day_counter, self.ii) \
for maturity, r in zip(dates, rates)]
base_zero_rate = rates[0] / 100
self.cpi_ts.link_to(
PiecewiseZeroInflationCurve(Interpolator.Linear,
evaluation_date, self.calendar, day_counter,
observation_lag, self.ii.frequency, self.ii.interpolated,
base_zero_rate, self.yts, self.helpers))
class TestCPIBond(unittest.TestCase):
def setUp(self):
self.calendar = UnitedKingdom()
today = Date(25, 11, 2009)
evaluation_date = self.calendar.adjust(today)
Settings().evaluation_date = evaluation_date
day_counter = ActualActual()
rpi_schedule = Schedule.from_rule(Date(20, 7, 2007),
Date(20, 11, 2009),
Period(1, Months),
self.calendar,
ModifiedFollowing)
self.cpi_ts = ZeroInflationTermStructure()
self.yts = FlatForward(evaluation_date, 0.05, day_counter)
self.ii = UKRPI(False, self.cpi_ts)
fix_data = [206.1, 207.3, 208.0, 208.9, 209.7, 210.9,
209.8, 211.4, 212.1, 214.0, 215.1, 216.8,
216.5, 217.2, 218.4, 217.7, 216,
212.9, 210.1, 211.4, 211.3, 211.5,
212.8, 213.4, 213.4, 213.4, 214.4]
for date, data in zip(rpi_schedule, fix_data):
self.ii.add_fixing(date, data)
dates = [Date(25, 11, 2010),
Date(25, 11, 2011),
Date(26, 11, 2012),
Date(25, 11, 2013),
Date(25, 11, 2014),
Date(25, 11, 2015),
Date(25, 11, 2016),
Date(25, 11, 2017),
Date(25, 11, 2018),
Date(25, 11, 2019),
Date(25, 11, 2021),
Date(25, 11, 2024),
Date(26, 11, 2029),
Date(27, 11, 2034),
Date(25, 11, 2039),
Date(25, 11, 2049),
Date(25, 11, 2059)]
rates = [3.0495,
2.93,
2.9795,
3.029,
3.1425,
3.211,
3.2675,
3.3625,
3.405,
3.48,
3.576,
3.649,
3.751,
3.77225,
3.77,
3.734,
3.714]
observation_lag = Period('2M')
self.helpers = [ZeroCouponInflationSwapHelper(
SimpleQuote(r / 100),
observation_lag,
maturity, self.calendar, ModifiedFollowing, day_counter, self.ii) \
for maturity, r in zip(dates, rates)]
base_zero_rate = rates[0] / 100
self.cpi_ts.link_to(
PiecewiseZeroInflationCurve(Interpolator.Linear,
evaluation_date, self.calendar, day_counter,
observation_lag, self.ii.frequency, self.ii.interpolated,
base_zero_rate, self.yts, self.helpers))
def test_clean_price(self):
notional = 1000000.0;
fixed_rates = [0.1]
fixed_day_count = Actual365Fixed()
fixed_calendar = self.calendar
fixed_index = self.ii
contractObservationLag = Period(3, Months);
observationInterpolation = InterpolationType.Flat;
settlement_days = 3
growth_only = True
baseCPI = 206.1
fixed_schedule = Schedule.from_rule(Date(2, 10, 2007),
Date(2, 10, 2052),
Period(6, Months),
fixed_calendar,
Unadjusted,
Rule.Backward)
cpi_bond = CPIBond(settlement_days, notional, growth_only,
baseCPI, contractObservationLag, fixed_index,
observationInterpolation, fixed_schedule,
fixed_rates, fixed_day_count, ModifiedFollowing)
engine = DiscountingBondEngine(self.yts)
cpi_bond.set_pricing_engine(engine)
storedPrice = 383.01816406
calculated = cpi_bond.clean_price
self.assertAlmostEqual(storedPrice, calculated)