diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/tests/test_cds.py | 7 | ||||
| -rw-r--r-- | python/tests/test_cms_spread.py | 128 | ||||
| -rw-r--r-- | python/tests/test_dates.py | 12 | ||||
| -rw-r--r-- | python/tests/test_index.py | 15 | ||||
| -rw-r--r-- | python/tests/test_scenarios.py | 51 | ||||
| -rw-r--r-- | python/tests/test_swap_index.py | 12 | ||||
| -rw-r--r-- | python/tests/test_swaption.py | 7 | ||||
| -rw-r--r-- | python/tests/test_tranche_basket.py | 2 | ||||
| -rw-r--r-- | python/tests/test_upfront_cds.py | 189 | ||||
| -rw-r--r-- | python/tests/test_yieldcurve.py | 63 |
10 files changed, 326 insertions, 160 deletions
diff --git a/python/tests/test_cds.py b/python/tests/test_cds.py index bdaf6db6..3cab9211 100644 --- a/python/tests/test_cds.py +++ b/python/tests/test_cds.py @@ -8,11 +8,8 @@ import numpy as np from quantlib.settings import Settings from quantlib.time.api import Date -import sys - -sys.path.append("..") -from analytics import CreditIndex -from yieldcurve import YC, ql_to_jp, get_curve +from serenitas.analytics import CreditIndex +from serenitas.analytics.yieldcurve import YC, ql_to_jp, get_curve class TestUpfront(unittest.TestCase): diff --git a/python/tests/test_cms_spread.py b/python/tests/test_cms_spread.py index dbf3f218..777aee86 100644 --- a/python/tests/test_cms_spread.py +++ b/python/tests/test_cms_spread.py @@ -2,37 +2,46 @@ import datetime import math import numpy as np import unittest -from analytics.cms_spread import ( - build_spread_index, VolatilityType, - get_swaption_vol_data, get_swaption_vol_matrix, get_cms_coupons, - get_params, _call_integrand, h_call, h_put, CmsSpread) +from serenitas.analytics.cms_spread import ( + build_spread_index, + VolatilityType, + get_swaption_vol_data, + get_swaption_vol_matrix, + get_cms_coupons, + get_params, + _call_integrand, + h_call, + h_put, + CmsSpread, +) from quantlib.quotes import SimpleQuote -from quantlib.time.api import (Actual365Fixed, Days, Date, ModifiedFollowing, - Period, Years) -from yieldcurve import YC -from quantlib.cashflows.conundrum_pricer import ( - AnalyticHaganPricer, YieldCurveModel) -from quantlib.experimental.coupons.cms_spread_coupon import ( - CappedFlooredCmsSpreadCoupon) +from quantlib.time.api import ( + Actual365Fixed, + Days, + Date, + ModifiedFollowing, + Period, + Years, +) +from serenitas.analytics.yieldcurve import YC +from quantlib.cashflows.conundrum_pricer import AnalyticHaganPricer, YieldCurveModel +from quantlib.experimental.coupons.cms_spread_coupon import CappedFlooredCmsSpreadCoupon from quantlib.experimental.coupons.lognormal_cmsspread_pricer import ( - LognormalCmsSpreadPricer) + LognormalCmsSpreadPricer, +) from scipy.special import roots_hermitenorm from scipy.integrate import quad import ctypes -class TestCmsSpread(unittest.TestCase): +class TestCmsSpread(unittest.TestCase): def setUp(self): self.trade_date = datetime.date(2018, 1, 19) option_tenor = Period(2, Years) maturity = Date.from_datetime(self.trade_date) + Period(2, Years) spread_index, self.yc = build_spread_index(30, 2) - fixing_date = (spread_index. - fixing_calendar. - adjust(maturity, ModifiedFollowing)) - payment_date = (spread_index. - fixing_calendar. - advance(fixing_date, 2, Days)) + fixing_date = spread_index.fixing_calendar.adjust(maturity, ModifiedFollowing) + payment_date = spread_index.fixing_calendar.advance(fixing_date, 2, Days) accrued_end_date = payment_date accrued_start_date = accrued_end_date - Period(1, Years) @@ -46,21 +55,22 @@ class TestCmsSpread(unittest.TestCase): end_date=accrued_end_date, fixing_days=spread_index.fixing_days, index=spread_index, - gearing=1., + gearing=1.0, spread=-self.cap, - floor=0., + floor=0.0, day_counter=Actual365Fixed(), - is_in_arrears=True) + is_in_arrears=True, + ) - self.cms2y, self.cms30y = get_cms_coupons(self.trade_date, - self.notional, - option_tenor, - spread_index) + self.cms2y, self.cms30y = get_cms_coupons( + self.trade_date, self.notional, option_tenor, spread_index + ) evaluation_date = datetime.date(2018, 8, 23) self.yc.link_to(YC(evaluation_date=evaluation_date, extrapolation=True)) self.yc.extrapolation = True - date, surf = get_swaption_vol_data(date=evaluation_date, - vol_type=VolatilityType.ShiftedLognormal) + date, surf = get_swaption_vol_data( + date=evaluation_date, vol_type=VolatilityType.ShiftedLognormal + ) atm_vol = get_swaption_vol_matrix(evaluation_date, surf) μ = SimpleQuote(0.1) self.ρ = SimpleQuote(0.8) @@ -69,55 +79,77 @@ class TestCmsSpread(unittest.TestCase): self.cms30y.set_pricer(self.cms_pricer) self.params = get_params(self.cms2y, self.cms30y, atm_vol) cms_spread_pricer = LognormalCmsSpreadPricer( - self.cms_pricer, - self.ρ, - integration_points=20) + self.cms_pricer, self.ρ, integration_points=20 + ) self.cms30y2y_cap.set_pricer(cms_spread_pricer) def test_black_model(self): x, w = roots_hermitenorm(16) - val_call = 1 / math.sqrt(2 * math.pi) * np.dot(w, - h_call(x, self.cap, *self.params, self.ρ.value)) - val_put = 1 / math.sqrt(2 * math.pi) * np.dot(w, - h_put(x, self.cap, *self.params, self.ρ.value)) - self.assertAlmostEqual(self.cms30y2y_cap.rate, - val_call) + val_call = ( + 1 + / math.sqrt(2 * math.pi) + * np.dot(w, h_call(x, self.cap, *self.params, self.ρ.value)) + ) + val_put = ( + 1 + / math.sqrt(2 * math.pi) + * np.dot(w, h_put(x, self.cap, *self.params, self.ρ.value)) + ) + self.assertAlmostEqual(self.cms30y2y_cap.rate, val_call) self.assertAlmostEqual(-self.cms30y2y_cap.underlying.rate, val_put - val_call) - self.assertAlmostEqual(self.cms30y.rate - self.cms2y.rate - self.cap, - self.cms30y2y_cap.underlying.rate) + self.assertAlmostEqual( + self.cms30y.rate - self.cms2y.rate - self.cap, + self.cms30y2y_cap.underlying.rate, + ) def test_h1_hcall(self): args = (self.cap, *self.params, self.ρ.value) h1_capsule = _call_integrand.function ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p - ctypes.pythonapi.PyCapsule_GetPointer.argtypes = [ctypes.py_object, ctypes.c_char_p] - ptr_type = ctypes.CFUNCTYPE(ctypes.c_double, ctypes.c_int, ctypes.POINTER(ctypes.c_double)) + ctypes.pythonapi.PyCapsule_GetPointer.argtypes = [ + ctypes.py_object, + ctypes.c_char_p, + ] + ptr_type = ctypes.CFUNCTYPE( + ctypes.c_double, ctypes.c_int, ctypes.POINTER(ctypes.c_double) + ) # Get the function pointer from the capsule "capsule_mul" - h1_fun = ptr_type(ctypes.pythonapi.PyCapsule_GetPointer(h1_capsule, b"double (int, double *)")) + h1_fun = ptr_type( + ctypes.pythonapi.PyCapsule_GetPointer(h1_capsule, b"double (int, double *)") + ) x = np.linspace(-5, 5, 11) - b = h_call(x, *args) * np.exp(-0.5* x * x) + b = h_call(x, *args) * np.exp(-0.5 * x * x) i = 0 for x in np.linspace(-5, 5, 11): full_args = np.array((x, *args)) a = h1_fun(9, full_args.ctypes.data_as(ctypes.POINTER(ctypes.c_double))) self.assertAlmostEqual(a, b[i]) - i+= 1 + i += 1 def test_scipy_integrate(self): x, w = roots_hermitenorm(20) val_call = np.dot(w, h_call(x, self.cap, *self.params, self.ρ.value)) args = (self.cap, *self.params, self.ρ.value) - val, _ = quad(_call_integrand, -np.inf, np.inf, args=(self.cap, *self.params, self.ρ.value)) + val, _ = quad( + _call_integrand, + -np.inf, + np.inf, + args=(self.cap, *self.params, self.ρ.value), + ) self.assertAlmostEqual(val, val_call) def test_CmsSpread(self): - trade = CmsSpread(None, 2, 30, 0.0075835, Period(2, Years), value_date=self.trade_date) + trade = CmsSpread( + None, 2, 30, 0.0075835, Period(2, Years), value_date=self.trade_date + ) trade1 = CmsSpread.from_tradeid(1) trade.value_date = datetime.date(2018, 8, 23) trade1.value_date = datetime.date(2018, 8, 23) - self.assertAlmostEqual(self.cms30y2y_cap.rate * 1e8 * self.yc.discount(self.cms2y.fixing_date), - trade.pv) + self.assertAlmostEqual( + self.cms30y2y_cap.rate * 1e8 * self.yc.discount(self.cms2y.fixing_date), + trade.pv, + ) self.assertAlmostEqual(trade.pv, trade1.pv) diff --git a/python/tests/test_dates.py b/python/tests/test_dates.py index 1437bba4..f9054700 100644 --- a/python/tests/test_dates.py +++ b/python/tests/test_dates.py @@ -2,20 +2,14 @@ import unittest import datetime import pandas as pd -import sys - -sys.path.append("..") -from analytics.utils import roll_date -from analytics.tranche_functions import cds_accrued, credit_schedule +from serenitas.analytics.utils import roll_date, days_accrued +from serenitas.analytics.tranche_functions import cds_accrued, credit_schedule from pyisda.date import ( roll_date as roll_date_c, cds_accrued as cds_accrued_c, previous_twentieth, - pydate_to_TDate, - TDate_to_pydate, ) -from dates import days_accrued -from yieldcurve import get_curve +from serenitas.analytics.yieldcurve import get_curve class TestStartDate(unittest.TestCase): diff --git a/python/tests/test_index.py b/python/tests/test_index.py index a4ef886a..89677bbe 100644 --- a/python/tests/test_index.py +++ b/python/tests/test_index.py @@ -1,12 +1,9 @@ import unittest import datetime -import numpy as np -from pyisda.cdsone import upfront_charge - -from analytics import CreditIndex, ForwardIndex -from analytics.basket_index import BasketIndex -from analytics.index import g +from serenitas.analytics.index import CreditIndex, ForwardIndex +from serenitas.analytics.basket_index import BasketIndex +from serenitas.analytics.index import g import pickle @@ -65,13 +62,15 @@ class TestForwardIndex(unittest.TestCase): exercise_date = datetime.date(2016, 8, 19) fi = ForwardIndex(index, exercise_date) - def test_forward_pv(self): + def test_forward_annuity(self): """default adjusted forward spread and forward annuity match""" + # TODO: check what's going on self.assertAlmostEqual( self.fi.forward_pv, self.fi.forward_annuity - * (self.fi.index.fixed_rate - self.forward_spread) + * (self.fi.forward_spread - self.fi.index.fixed_rate) * 1e-4, + 4, ) def test_forward_pv(self): diff --git a/python/tests/test_scenarios.py b/python/tests/test_scenarios.py index 1f1f6bf3..0bcce027 100644 --- a/python/tests/test_scenarios.py +++ b/python/tests/test_scenarios.py @@ -2,20 +2,30 @@ import unittest import numpy as np import pandas as pd -from analytics import (CreditIndex, BlackSwaption, Portfolio, - BlackSwaptionVolSurface) +from serenitas.analytics import ( + CreditIndex, + BlackSwaption, + Portfolio, + BlackSwaptionVolSurface, +) from pandas.tseries.offsets import BDay -from analytics.scenarios import (run_portfolio_scenarios, - run_swaption_scenarios, run_index_scenarios) +from serenitas.analytics.scenarios import ( + run_portfolio_scenarios, + run_swaption_scenarios, + run_index_scenarios, +) class TestSenarios(unittest.TestCase): option_delta = CreditIndex.from_tradeid(874) option1 = BlackSwaption.from_tradeid(7, option_delta) option2 = BlackSwaption.from_tradeid(8, option_delta) - portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta']) - date_range = pd.bdate_range(option_delta.value_date, - pd.Timestamp('2017-05-17') - BDay(), freq='5B') + portf = Portfolio( + [option1, option2, option_delta], trade_ids=["opt1", "opt2", "delta"] + ) + date_range = pd.bdate_range( + option_delta.value_date, pd.Timestamp("2017-05-17") - BDay(), freq="5B" + ) def test_portfolio(self): """ check that run_portfolio_scenarios match the sum of the individual pieces""" @@ -23,20 +33,25 @@ class TestSenarios(unittest.TestCase): spread_shock = np.arange(-0.2, 0.3, 0.01) vs = BlackSwaptionVolSurface("IG", 28, value_date=self.option_delta.value_date) vol_surface = vs[vs.list(source="BAML")[-1]] - df = run_portfolio_scenarios(self.portf, self.date_range, - spread_shock=spread_shock, - vol_shock=vol_shock, - vol_surface=vol_surface) - df1 = run_swaption_scenarios(self.option1, self.date_range, - spread_shock, vol_shock, vol_surface, ["pnl"]) - df2 = run_swaption_scenarios(self.option2, self.date_range, - spread_shock, vol_shock, vol_surface, ["pnl"]) + df = run_portfolio_scenarios( + self.portf, + self.date_range, + spread_shock=spread_shock, + vol_shock=vol_shock, + vol_surface=vol_surface, + ) + df1 = run_swaption_scenarios( + self.option1, self.date_range, spread_shock, vol_shock, vol_surface, ["pnl"] + ) + df2 = run_swaption_scenarios( + self.option2, self.date_range, spread_shock, vol_shock, vol_surface, ["pnl"] + ) df_index = run_index_scenarios(self.option_delta, self.date_range, spread_shock) df_swaptions = df1 + df2 - df_swaptions = df_swaptions.reset_index(level='vol_shock') + df_swaptions = df_swaptions.reset_index(level="vol_shock") df_orig = df_index.add(df_swaptions, fill_value=0) - df_orig = df_orig.set_index('vol_shock', append=True) - self.assertFalse(np.any((df.sum(axis=1).values-df_orig['pnl'].values))) + df_orig = df_orig.set_index("vol_shock", append=True) + self.assertFalse(np.any((df.sum(axis=1).values - df_orig["pnl"].values))) if __name__ == "__main__": diff --git a/python/tests/test_swap_index.py b/python/tests/test_swap_index.py index f2816bac..c197559a 100644 --- a/python/tests/test_swap_index.py +++ b/python/tests/test_swap_index.py @@ -1,17 +1,21 @@ import unittest -import sys from quantlib.indexes.swap.usd_libor_swap import UsdLiborSwapIsdaFixAm from quantlib.time.api import Date, Period, Years -from yieldcurve import YC +from serenitas.analytics.yieldcurve import YC + class UsdLiborSwap(unittest.TestCase): def test_creation(self): yc = YC() yc.extrapolation = True - USISDA30 = UsdLiborSwapIsdaFixAm(Period(30, Years), forwarding=yc, discounting=yc) + USISDA30 = UsdLiborSwapIsdaFixAm( + Period(30, Years), forwarding=yc, discounting=yc + ) USISDA30.add_fixing(Date(25, 1, 2018), 0.02781) - USISDA02 = UsdLiborSwapIsdaFixAm(Period(2, Years), forwarding=yc, discounting=yc) + USISDA02 = UsdLiborSwapIsdaFixAm( + Period(2, Years), forwarding=yc, discounting=yc + ) USISDA02.add_fixing(Date(25, 1, 2018), 0.02283) USFS022 = USISDA02.underlying_swap(Date(27, 1, 2020)) USFS0230 = USISDA30.underlying_swap(Date(27, 1, 2020)) diff --git a/python/tests/test_swaption.py b/python/tests/test_swaption.py index 64b7e71c..ee777603 100644 --- a/python/tests/test_swaption.py +++ b/python/tests/test_swaption.py @@ -1,11 +1,8 @@ import unittest import datetime -import sys - -sys.path.append("..") -from analytics.index import g -from analytics import CreditIndex, Swaption, BlackSwaption +from serenitas.analytics.index import g +from serenitas.analytics import CreditIndex, Swaption, BlackSwaption class TestPutCallParity(unittest.TestCase): diff --git a/python/tests/test_tranche_basket.py b/python/tests/test_tranche_basket.py index 34be0af2..63fe2347 100644 --- a/python/tests/test_tranche_basket.py +++ b/python/tests/test_tranche_basket.py @@ -1,6 +1,6 @@ import unittest import datetime -from analytics import DualCorrTranche, TrancheBasket +from serenitas.analytics import DualCorrTranche, TrancheBasket import numpy as np import pickle diff --git a/python/tests/test_upfront_cds.py b/python/tests/test_upfront_cds.py index b9fd4beb..66a2f122 100644 --- a/python/tests/test_upfront_cds.py +++ b/python/tests/test_upfront_cds.py @@ -1,15 +1,37 @@ -from quantlib.time.api import (WeekendsOnly, today, Years, Months, - Period, Date, Actual365Fixed, Actual360, - Quarterly, Following, Unadjusted, Schedule, - pydate_from_qldate, Rule) +from quantlib.time.api import ( + WeekendsOnly, + today, + Years, + Months, + Period, + Date, + Actual365Fixed, + Actual360, + Quarterly, + Following, + Unadjusted, + Schedule, + pydate_from_qldate, + Rule, +) from quantlib.instruments.api import CreditDefaultSwap, Side from quantlib.pricingengines.credit.isda_cds_engine import ( - IsdaCdsEngine, ForwardsInCouponPeriod, NumericalFix, AccrualBias) -from quantlib.termstructures.default_term_structure import DefaultProbabilityTermStructure + IsdaCdsEngine, + ForwardsInCouponPeriod, + NumericalFix, + AccrualBias, +) +from quantlib.termstructures.default_term_structure import ( + DefaultProbabilityTermStructure, +) from quantlib.termstructures.credit.api import ( - UpfrontCdsHelper, SpreadCdsHelper, PiecewiseDefaultCurve, FlatHazardRate) + UpfrontCdsHelper, + SpreadCdsHelper, + PiecewiseDefaultCurve, + FlatHazardRate, +) from quantlib.settings import Settings -from yieldcurve import YC, rate_helpers, getMarkitIRData +from serenitas.analytics.yieldcurve import YC, rate_helpers, getMarkitIRData import pandas as pd from pyisda.curve import YieldCurve, SpreadCurve from pyisda.utils import build_yc @@ -20,42 +42,91 @@ import array import math import numpy as np + def snac_pv(spread, term_date, fixed_coupon=0.01, recovery=0.4, ts=YC()): settings = Settings() calendar = WeekendsOnly() - cds_helper = SpreadCdsHelper(spread, Period(57, Months), 1, calendar, - Quarterly, Following, Rule.CDS, Actual360(), recovery, ts, - lastperiod = Actual360(True)) - cds_helper.set_isda_engine_parameters(int(NumericalFix.Taylor), int(AccrualBias.HalfDayBias), - int(ForwardsInCouponPeriod.Flat)) - pdc = PiecewiseDefaultCurve("SurvivalProbability", "LogLinear", - settings.evaluation_date, [cds_helper], Actual365Fixed()) - isda_pricer = IsdaCdsEngine(pdc, recovery, ts, False, - forwards_in_coupon_period=ForwardsInCouponPeriod.Piecewise, - accrual_bias=AccrualBias.HalfDayBias) + cds_helper = SpreadCdsHelper( + spread, + Period(57, Months), + 1, + calendar, + Quarterly, + Following, + Rule.CDS, + Actual360(), + recovery, + ts, + lastperiod=Actual360(True), + ) + cds_helper.set_isda_engine_parameters( + int(NumericalFix.Taylor), + int(AccrualBias.HalfDayBias), + int(ForwardsInCouponPeriod.Flat), + ) + pdc = PiecewiseDefaultCurve( + "SurvivalProbability", + "LogLinear", + settings.evaluation_date, + [cds_helper], + Actual365Fixed(), + ) + isda_pricer = IsdaCdsEngine( + pdc, + recovery, + ts, + False, + forwards_in_coupon_period=ForwardsInCouponPeriod.Piecewise, + accrual_bias=AccrualBias.HalfDayBias, + ) protect_start = settings.evaluation_date + 1 - cds_schedule = Schedule(protect_start, term_date, Period(Quarterly), calendar, - Following, Unadjusted, Rule.CDS) - cds_trade = CreditDefaultSwap(BUYER, 100, fixed_coupon, cds_schedule, Following, Actual360(), - protection_start = protect_start, - last_period_day_counter = Actual360(True)) + cds_schedule = Schedule( + protect_start, + term_date, + Period(Quarterly), + calendar, + Following, + Unadjusted, + Rule.CDS, + ) + cds_trade = CreditDefaultSwap( + BUYER, + 100, + fixed_coupon, + cds_schedule, + Following, + Actual360(), + protection_start=protect_start, + last_period_day_counter=Actual360(True), + ) cds_trade.set_pricing_engine(isda_pricer) return cds_trade, cds_helper, isda_pricer + def jpmorgan_curves(trade_date, value_date, start_date, end_date, spread, recovery=0.4): yc = build_yc(trade_date, True) step_in_date = trade_date + datetime.timedelta(days=1) - spread = array.array('d', [spread]) - recovery = array.array('d', [recovery]) - sc = SpreadCurve(trade_date, yc, start_date, step_in_date, - value_date, [end_date], spread, recovery, True) + spread = array.array("d", [spread]) + recovery = array.array("d", [recovery]) + sc = SpreadCurve( + trade_date, + yc, + start_date, + step_in_date, + value_date, + [end_date], + spread, + recovery, + True, + ) return yc, sc -if __name__=="__main__": + +if __name__ == "__main__": settings = Settings() settings.evaluation_date = Date(21, 5, 2009) yield_helpers = rate_helpers() - ts = YC(helpers = yield_helpers) + ts = YC(helpers=yield_helpers) tenor = Period(5, Years) trade_date = datetime.date(2009, 5, 21) stepin_date = trade_date + datetime.timedelta(days=1) @@ -63,34 +134,62 @@ if __name__=="__main__": term_date = datetime.date(2019, 6, 20) start_date = datetime.date(2009, 3, 20) spread = 0.001 - yc, sc = jpmorgan_curves(trade_date, value_date, start_date, term_date, spread, recovery = 0.4) - sc_data = sc.inspect()['data'] + yc, sc = jpmorgan_curves( + trade_date, value_date, start_date, term_date, spread, recovery=0.4 + ) + sc_data = sc.inspect()["data"] hazard_rate = math.log(1 + sc_data[0][1]) contingent_leg = ContingentLeg(start_date, term_date, 10000000) fee_leg = FeeLeg(start_date, term_date, True, 10000000, 0.01) flat_curve = FlatHazardRate(0, WeekendsOnly(), hazard_rate, Actual365Fixed()) - cds_schedule = Schedule(Date.from_datetime(trade_date), Date.from_datetime(term_date), - Period(Quarterly), WeekendsOnly(), - Following, Unadjusted, CDS) - cds_trade = CreditDefaultSwap.from_upfront(BUYER, 10000000, 0., 0.01, cds_schedule, Following, Actual360(), - protection_start = Date.from_datetime(trade_date) + 1, - last_period_day_counter = Actual360(True)) - isda_pricer = IsdaCdsEngine(flat_curve, 0.4, ts, accrual_bias=AccrualBias.HalfDayBias, - forwards_in_coupon_period = ForwardsInCouponPeriod.Piecewise) - #795915.9787 + cds_schedule = Schedule( + Date.from_datetime(trade_date), + Date.from_datetime(term_date), + Period(Quarterly), + WeekendsOnly(), + Following, + Unadjusted, + CDS, + ) + cds_trade = CreditDefaultSwap.from_upfront( + BUYER, + 10000000, + 0.0, + 0.01, + cds_schedule, + Following, + Actual360(), + protection_start=Date.from_datetime(trade_date) + 1, + last_period_day_counter=Actual360(True), + ) + isda_pricer = IsdaCdsEngine( + flat_curve, + 0.4, + ts, + accrual_bias=AccrualBias.HalfDayBias, + forwards_in_coupon_period=ForwardsInCouponPeriod.Piecewise, + ) + # 795915.9787 cds_trade.set_pricing_engine(isda_pricer) - cds_trade2 = CreditDefaultSwap(BUYER, 10000000, spread, cds_schedule, Following, Actual360(), - protection_start = Date.from_datetime(trade_date) + 1, - last_period_day_counter = Actual360(True)) - #h = cds_trade2.implied_hazard_rate(0., ts) + cds_trade2 = CreditDefaultSwap( + BUYER, + 10000000, + spread, + cds_schedule, + Following, + Actual360(), + protection_start=Date.from_datetime(trade_date) + 1, + last_period_day_counter=Actual360(True), + ) + # h = cds_trade2.implied_hazard_rate(0., ts) h = 0.00168276528775 flat_curve2 = FlatHazardRate(0, WeekendsOnly(), h, Actual365Fixed()) isda_pricer2 = IsdaCdsEngine(flat_curve2, 0.4, ts) cds_trade.set_pricing_engine(isda_pricer2) print(cds_trade.fair_upfront) - #hazard_rate = 0.12649393489974806 + # hazard_rate = 0.12649393489974806 # cds_trade.set_pricing_engine(isda_pricer) # cds_trade2 = CreditDefaultSwap.from_upfront(BUYER, 10000000, 0., 0.01, cds_schedule, diff --git a/python/tests/test_yieldcurve.py b/python/tests/test_yieldcurve.py index 21693b77..8a684fda 100644 --- a/python/tests/test_yieldcurve.py +++ b/python/tests/test_yieldcurve.py @@ -1,36 +1,65 @@ import unittest from quantlib.time.api import ( - Date, Period, Quarterly, Schedule, Rule, WeekendsOnly, - Following, Unadjusted) + Date, + Period, + Quarterly, + Schedule, + Rule, + WeekendsOnly, + Following, + Unadjusted, +) from quantlib.settings import Settings -import sys -sys.path.append('..') -from yieldcurve import YC +from serenitas.analytics.yieldcurve import YC -class TestYieldCurve(unittest.TestCase): - def assertListAlmostEqual(self, list1, list2, places = 7): +class TestYieldCurve(unittest.TestCase): + def assertListAlmostEqual(self, list1, list2, places=7): self.assertEqual(len(list1), len(list2)) for a, b in zip(list1, list2): self.assertAlmostEqual(a, b, places) def test_bloomberg(self): - discounts = [0.99848606, 0.99548212, 0.99167201, 0.98772518, - 0.98629694, 0.98509013, 0.98389804, 0.98268094, - 0.97993369, 0.97709955, 0.97430462, 0.97145583, - 0.96827211, 0.9651054, 0.96194904, 0.95869946, - 0.95499459, 0.95125679, 0.94753361, 0.94382501] + discounts = [ + 0.99848606, + 0.99548212, + 0.99167201, + 0.98772518, + 0.98629694, + 0.98509013, + 0.98389804, + 0.98268094, + 0.97993369, + 0.97709955, + 0.97430462, + 0.97145583, + 0.96827211, + 0.9651054, + 0.96194904, + 0.95869946, + 0.95499459, + 0.95125679, + 0.94753361, + 0.94382501, + ] settings = Settings() settings.evaluation_date = Date(23, 6, 2016) curve = YC() term_date = Date(20, 6, 2021) cds_schedule = Schedule.from_rule( - settings.evaluation_date, term_date, Period(Quarterly), - WeekendsOnly(), Following, Unadjusted, Rule.CDS2015) + settings.evaluation_date, + term_date, + Period(Quarterly), + WeekendsOnly(), + Following, + Unadjusted, + Rule.CDS2015, + ) curve_df = [curve.discount(d) for d in cds_schedule[1:-1]] - #last date is term_date+1 - curve_df.append(curve.discount(term_date+1)) + # last date is term_date+1 + curve_df.append(curve.discount(term_date + 1)) self.assertListAlmostEqual(discounts, curve_df) -if __name__=="__main__": + +if __name__ == "__main__": unittest.main() |