How to use the quantlib.time.api.Actual365Fixed function in QuantLib

def df_to_zero_curve(rates, settlement_date, daycounter=Actual365Fixed()):
    """ Converts a pandas data frame into a QL zero curve. """

    dates = [pydate_to_qldate(dt) for dt in rates.index]
    dates.insert(0, pydate_to_qldate(settlement_date))

    # arbitrarily extend the curve a few years to provide flat
    # extrapolation
    dates.append(dates[-1] + 365 * 2)

    values = rates.values.tolist()
    values.insert(0, values[0])
    values.append(values[-1])

    return ZeroCurve(dates, values, daycounter)

offset = 366

todays_date = today() - offset
settlement_date = todays_date + 2

settings.evaluation_date = todays_date

# options parameters
option_type = Put
underlying = 36
strike = 40
dividend_yield = 0.00
risk_free_rate = 0.06
volatility = 0.20
maturity = settlement_date + 363
daycounter = Actual365Fixed()

underlyingH = SimpleQuote(underlying)

# bootstrap the yield/dividend/vol curves
flat_term_structure = FlatForward(
    reference_date=settlement_date,
    forward=risk_free_rate,
    daycounter=daycounter
)

flat_dividend_ts = FlatForward(
    reference_date=settlement_date,
    forward=dividend_yield,
    daycounter=daycounter
)

reference_date=settlement_date,
        forward=0.06,
        daycounter=Actual365Fixed()
    )

    # option parameters
    exercise = AmericanExercise(
        earliest_exercise_date=settlement_date,
        latest_exercise_date=Date(17, May, 1999)
    )
    payoff = PlainVanillaPayoff(Put, 40.0)

    # market data
    underlying = SimpleQuote(36.0)
    volatility = BlackConstantVol(todays_date, TARGET(), 0.20,
                                  Actual365Fixed())
    dividend_yield = FlatForward(
        reference_date=settlement_date,
        forward=0.00,
        daycounter=Actual365Fixed()
    )

    # report
    header = '%19s' % 'method' + ' |' + \
        ' |'.join(['%17s' % tag for tag in ['value',
                                            'estimated error',
                                            'actual error']])
    print()
    print(header)
    print('-' * len(header))

    refValue = None

def dfToZeroCurve(df_rates, dtSettlement, daycounter=Actual365Fixed()):
    """
    Convert a panda data frame into a QL zero curve
    """

    dates = [dateToQLDate(dt) for dt in df_rates.index]
    dates.insert(0, dateToQLDate(dtSettlement))
    dates.append(dates[-1] + 365 * 2)
    vx = list(df_rates.values)
    vx.insert(0, vx[0])
    vx.append(vx[-1])
    return ZeroCurve(dates, vx, daycounter)

)

    cds_1y = CreditDefaultSwap(
        SELLER, nominal, quoted_spreads[2], cds_schedule, Following,
        Actual365Fixed()
    )

    cds_schedule = Schedule(
        todays_date, maturities[3], Period(Quarterly), calendar,
        termination_date_convention=Unadjusted,
        date_generation_rule=TwentiethIMM
    )

    cds_2y = CreditDefaultSwap(
        SELLER, nominal, quoted_spreads[3], cds_schedule, Following,
        Actual365Fixed()
    )

    cds_3m.set_pricing_engine(engine);
    cds_6m.set_pricing_engine(engine);
    cds_1y.set_pricing_engine(engine);
    cds_2y.set_pricing_engine(engine);

    print("Repricing of quoted CDSs employed for calibration: ")
    print("3M fair spread: {}".format(cds_3m.fair_spread))
    print("   NPV:         ", cds_3m.net_present_value)
    print("   default leg: ", cds_3m.default_leg_npv)
    print("   coupon leg:  ", cds_3m.coupon_leg_npv)

    print("6M fair spread: {}".format(cds_6m.fair_spread))
    print("   NPV:         ", cds_6m.net_present_value)
    print("   default leg: ", cds_6m.default_leg_npv)

tenors = [Period(i, Months) for i in [3, 6, 12, 24]]
    maturities = [
        calendar.adjust(todays_date + tenors[i], Following) for i in range(4)
    ]
    instruments = []
    for i in range(4):
        helper = SpreadCdsHelper(
            quoted_spreads[i], tenors[i], 0, calendar, Quarterly,
            Following, TwentiethIMM, Actual365Fixed(), recovery_rate, ts_curve
        )

        instruments.append(helper)

    # Bootstrap hazard rates
    hazard_rate_structure = PiecewiseDefaultCurve.from_reference_date(
            ProbabilityTrait.HazardRate, Interpolator.BackwardFlat, todays_date, instruments, Actual365Fixed()
    )

    #vector > hr_curve_data = hazardRateStructure->nodes();

    #cout << "Calibrated hazard rate values: " << endl ;
    #for (Size i=0; i

if __name__ == '__main__':

    #*********************
    #***  MARKET DATA  ***
    #*********************
    calendar = TARGET()

    todays_date = Date(15, May, 2007)
    # must be a business day
    todays_date = calendar.adjust(todays_date)

    Settings.instance().evaluation_date = todays_date

    # dummy curve
    ts_curve = FlatForward(
        reference_date=todays_date, forward=0.01, daycounter=Actual365Fixed()
    )

    # In Lehmans Brothers "guide to exotic credit derivatives"
    # p. 32 there's a simple case, zero flat curve with a flat CDS
    # curve with constant market spreads of 150 bp and RR = 50%
    # corresponds to a flat 3% hazard rate. The implied 1-year
    # survival probability is 97.04% and the 2-years is 94.18%

    # market
    recovery_rate = 0.5
    quoted_spreads = [0.0150, 0.0150, 0.0150, 0.0150 ]
    tenors = [Period(i, Months) for i in [3, 6, 12, 24]]
    maturities = [
        calendar.adjust(todays_date + tenors[i], Following) for i in range(4)
    ]
    instruments = []

# option parameters
    exercise = AmericanExercise(
        earliest_exercise_date=settlement_date,
        latest_exercise_date=Date(17, May, 1999)
    )
    payoff = PlainVanillaPayoff(Put, 40.0)

    # market data
    underlying = SimpleQuote(36.0)
    volatility = BlackConstantVol(todays_date, TARGET(), 0.20,
                                  Actual365Fixed())
    dividend_yield = FlatForward(
        reference_date=settlement_date,
        forward=0.00,
        daycounter=Actual365Fixed()
    )

    # report
    header = '%19s' % 'method' + ' |' + \
        ' |'.join(['%17s' % tag for tag in ['value',
                                            'estimated error',
                                            'actual error']])
    print()
    print(header)
    print('-' * len(header))

    refValue = None

    def report(method, x, dx=None):
        e = '%.4f' % abs(x - refValue)
        x = '%.5f' % x

# curve with constant market spreads of 150 bp and RR = 50%
    # corresponds to a flat 3% hazard rate. The implied 1-year
    # survival probability is 97.04% and the 2-years is 94.18%

    # market
    recovery_rate = 0.5
    quoted_spreads = [0.0150, 0.0150, 0.0150, 0.0150 ]
    tenors = [Period(i, Months) for i in [3, 6, 12, 24]]
    maturities = [
        calendar.adjust(todays_date + tenors[i], Following) for i in range(4)
    ]
    instruments = []
    for i in range(4):
        helper = SpreadCdsHelper(
            quoted_spreads[i], tenors[i], 0, calendar, Quarterly,
            Following, TwentiethIMM, Actual365Fixed(), recovery_rate, ts_curve
        )

        instruments.append(helper)

    # Bootstrap hazard rates
    hazard_rate_structure = PiecewiseDefaultCurve.from_reference_date(
            ProbabilityTrait.HazardRate, Interpolator.BackwardFlat, todays_date, instruments, Actual365Fixed()
    )

    #vector > hr_curve_data = hazardRateStructure->nodes();

    #cout << "Calibrated hazard rate values: " << endl ;
    #for (Size i=0; i