[Quantlib-users] Problem with ql.VanillaSwap [OU]

[Chilamakuri, P. <Chi...@eb...>]

OFFICIAL USE

Having issues instantiating a Vanilla swap using ql.VanillaSwap. I read online and tried ql.Actual365Fixed() as well as q.Actual365Fixed(q.Actual365Fixed.Standard). Not sure if the daycounter is the problem or the EUR3M_index. I also tried index_curve_handle and no luck. My goal is to get the par swap rate.

I cannot seem to find a resolution and no idea exactly which argument to ql.ValillaSwap has the problem:

Exception has occurred: TypeError Wrong number or type of arguments for overloaded function 'new_VanillaSwap'. Possible C/C++ prototypes are: VanillaSwap::VanillaSwap(Swap::Type,Real,Schedule const &,Rate,DayCounter const &,Schedule const &,ext::shared_ptr< IborIndex > const &,Spread,DayCounter const &,boost::optional< bool >) VanillaSwap::VanillaSwap(Swap::Type,Real,Schedule const &,Rate,DayCounter const &,Schedule const &,ext::shared_ptr< IborIndex > const &,Spread,DayCounter const &) File "C:\Pavani\aValidations\AMC_SwapSingleCcy_Bermudan\PythonCode\ASwapSingleCcyBermPricer_v2\swap.py", line 136, in get_par_swap_rate ir_swap = ql.VanillaSwap(ql.VanillaSwap.Receiver, notional, fixed_schedule, File



My code below:

def get_par_swap_rate(self, exp_date, market_index: YieldCurve, market_disc: YieldCurve, calendar):
    ql_mat_dates = []
    maturity_date = exp_date + relativedelta(years=1)
    schedule_builder = build_ql_schedule()
    fixed_tenor = "1Y"
    fixed_calendar = "TAR"
    fixed_convention = "MODFOLLOWING"
    fixed_schedule = schedule_builder.build_schedule(exp_date,
                                                maturity_date,
                                                fixed_tenor,
                                                fixed_calendar,
                                                fixed_convention,
                                                fixed_convention,
                                                date_generation = ql.DateGeneration.Forward,
                                                end_of_month = False)
    floating_tenor = "3M"
    floating_calendar = "TAR"
    floating_convention = "MODFOLLOWING"

    floating_schedule = schedule_builder.build_schedule(exp_date,
                                                maturity_date,
                                                floating_tenor,
                                                floating_calendar,
                                                floating_convention,
                                                floating_convention,
                                                date_generation = ql.DateGeneration.Forward,
                                                end_of_month = False)


    notional = 50000000
    currency = 'EUR'
    fixed_rate = 0.00192
    # fixed_rate = 0.025
    fixed_leg_daycount = ql.Actual365Fixed()
    float_spread = -0.0033
    float_leg_daycount = ql.Actual360()

    startDateList = exp_date.year, exp_date.month, exp_date.day
    # parse python datetime.date into QuantLib date format
    ql_start_date = ql.Date(startDateList[2], startDateList[1], startDateList[0])

    # set up index_curve in QuantLib
    index_tenors = market_index.tenors
    for tnr in index_tenors:
        period = ql.Period(tnr, ql.Days)
        ql_mat_date = ql_start_date + period
        ql_mat_dates.append(ql_mat_date)
    index_rates = market_index.rates
    ql_day_count = ql.Actual365Fixed(ql.Actual365Fixed.Standard)
    ql_calendar = ql.TARGET()
    interpolation = ql.Linear()
    compounding = ql.Compounded
    compounding_frequency = ql.Daily
    index_term_structure = ql.ZeroCurve(  ql_mat_dates, index_rates, ql_day_count,
                                    ql_calendar, interpolation,
                                    compounding, compounding_frequency)
    index_curve_handle = ql.YieldTermStructureHandle(index_term_structure)

    # set up index_curve in QuantLib
    ql_mat_dates.clear()
    discount_tenors = market_disc.tenors
    for tnr in discount_tenors:
        period = ql.Period(tnr, ql.Days)
        ql_mat_date = ql_start_date + period
        ql_mat_dates.append(ql_mat_date)
    disc_curve_rates = market_disc.rates
    ql_day_count = ql.Actual365Fixed(ql.Actual365Fixed.Standard)
    ql_calendar = ql.TARGET()
    interpolation = ql.Linear()
    compounding = ql.Compounded
    compounding_frequency = ql.Daily
    disc_term_structure = ql.ZeroCurve(ql_mat_dates, disc_curve_rates, ql_day_count,
                                    ql_calendar, interpolation,
                                    compounding, compounding_frequency)
    disc_curve_handle = ql.YieldTermStructureHandle(disc_term_structure)

    EUR3M_index = ql.Euribor3M(index_curve_handle)
    libor3M_index = ql.USDLibor(ql.Period(3, ql.Months), index_curve_handle)
    #swap = ql.VanillaSwap(
    # ql.VanillaSwap.Payer, 10000.0,
    # fixed_schedule, 0.02, ql.Thirty360(ql.Thirty360.BondBasis),
    # floating_schedule, index, 0.0, ql.Actual360()
    # )
    ir_swap = ql.VanillaSwap(ql.VanillaSwap.Receiver, notional, fixed_schedule,
                             fixed_rate, fixed_leg_daycount, floating_schedule,
                             EUR3M_index, float_spread, float_leg_daycount)
    swap_engine = ql.DiscountingSwapEngine(disc_curve_handle)
    ir_swap.setPricingEngine(swap_engine)
    par_swap_rate = ir_swap.fairRate()
    return par_swap_rate


Best Regards,
Pav
Phone: +44(0)7769003342


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