Python中的QuantLib cpibond债券示例 我试图让官方C++ cPIBOND实例在Python中工作。原始示例如下：对于scala，如下所示：

当我运行我尝试的操作时，会出现以下错误：

运行时错误：第1次迭代：在第1个活动工具、到期时失败 2010年9月1日，参考日期2009年9月1日：第二站： 2009年9月1日英国RPI修复缺失

以下是我的尝试：

import QuantLib as ql
calendar = ql.UnitedKingdom()
dayCounter = ql.ActualActual();
convention = ql.ModifiedFollowing

today = ql.Date(20, 11, 2009)
evaluationDate = calendar.adjust(today)
ql.Settings.instance().setEvaluationDate(evaluationDate)        
yTS = ql.YieldTermStructureHandle(ql.FlatForward(evaluationDate, 0.05, dayCounter))

from_date = ql.Date(20, ql.July, 2007);
to_date   = ql.Date(20, ql.November, 2009);
tenor = ql.Period(1, ql.Months)
rpiSchedule = ql.Schedule(from_date, to_date, tenor, calendar, 
                               convention, convention,
                               ql.DateGeneration.Backward, False)
cpiTS = ql.RelinkableZeroInflationTermStructureHandle()
inflationIndex = ql.UKRPI(False, cpiTS)
fixData = [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, 214.4,213.4, 214.4]
dte_fixings=[dtes for dtes in rpiSchedule]
print len(dte_fixings)
print len(fixData)
#must be the same length
inflationIndex.addFixings(dte_fixings, fixData) 
observationLag = ql.Period(2, ql.Months)
zciisData =[( ql.Date(25, ql.November, 2010), 3.0495 ),
              ( ql.Date(25, ql.November, 2011), 2.93 ),
              ( ql.Date(26, ql.November, 2012), 2.9795 ),
              ( ql.Date(25, ql.November, 2013), 3.029 ),
              ( ql.Date(25, ql.November, 2014), 3.1425 ),
              ( ql.Date(25, ql.November, 2015), 3.211 ),
              ( ql.Date(25, ql.November, 2016), 3.2675 ),
              ( ql.Date(25, ql.November, 2017), 3.3625 ),
              ( ql.Date(25, ql.November, 2018), 3.405 ),
              ( ql.Date(25, ql.November, 2019), 3.48 ),
              ( ql.Date(25, ql.November, 2021), 3.576 ),
              ( ql.Date(25, ql.November, 2024), 3.649 ),
              ( ql.Date(26, ql.November, 2029), 3.751 ),
              ( ql.Date(27, ql.November, 2034), 3.77225),
              ( ql.Date(25, ql.November, 2039), 3.77 ),
              ( ql.Date(25, ql.November, 2049), 3.734 ),
              ( ql.Date(25, ql.November, 2059), 3.714 )]

lRates=[rtes/100.0 for rtes in zip(*zciisData)[1]]
baseZeroRate = lRates[0]

zeroSwapHelpers = [ql.ZeroCouponInflationSwapHelper(a[1]/100,observationLag,
    a[0], calendar, convention, dayCounter, inflationIndex) for a in zciisData]


cpiTS.linkTo(ql.PiecewiseZeroInflation(          
  evaluationDate, calendar, dayCounter, observationLag, 
  inflationIndex.frequency(), inflationIndex.interpolated(), 
  baseZeroRate,
  yTS, zeroSwapHelpers, 1.0e-12, ql.Linear()))


notional = 1000000


fixedRates = [0.1]    

fixedDayCounter = ql.Actual365Fixed()
fixedPaymentConvention = ql.ModifiedFollowing
fixedPaymentCalendar = ql.UnitedKingdom()
contractObservationLag = ql.Period(3, ql.Months)
observationInterpolation = ql.CPI.Flat
settlementDays = 3
growthOnly = True

baseCPI = 206.1
startDate = ql.Date(2, 10, 2007)
endDate = ql.Date(2, 10, 2052)

fixedSchedule = ql.Schedule(startDate, endDate, 
                  ql.Period(6, ql.Months), fixedPaymentCalendar,
                  ql.Unadjusted,
                  ql.Unadjusted,
                  ql.DateGeneration.Backward, False)

bond = ql.CPIBond(settlementDays, notional, growthOnly,
                       baseCPI, contractObservationLag,
                       inflationIndex, observationInterpolation,
                       fixedSchedule, fixedRates, fixedDayCounter, 
                       fixedPaymentConvention)

bondEngine=ql.DiscountingBondEngine(yTS)
bond.setPricingEngine(bondEngine)
print bond.NPV() 
print bond.cleanPrice()

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我的大部分问题是，我发现很难掌握对象是如何组合在一起的。

让上述示例正常工作：

import QuantLib as ql
import datetime as dt
calendar = ql.UnitedKingdom()
dayCounter = ql.ActualActual();
convention = ql.ModifiedFollowing
lag = 3

today = ql.Date(5,3,2008)
evaluationDate = calendar.adjust(today)
issue_date = calendar.advance(evaluationDate,-1, ql.Years)
maturity_date = ql.Date(2,9,2052)
fixing_date = calendar.advance(evaluationDate,-lag, ql.Months)

ql.Settings.instance().setEvaluationDate(evaluationDate)        
yTS = ql.YieldTermStructureHandle(ql.FlatForward(evaluationDate, 0.05, dayCounter))

tenor = ql.Period(1, ql.Months)

from_date = ql.Date(20, ql.July, 2007);
to_date   = ql.Date(20, ql.November, 2009);
rpiSchedule = ql.Schedule(from_date, to_date, tenor, calendar, 
                               convention, convention,
                               ql.DateGeneration.Backward, False)

# this is the going to be holder the inflation curve.
cpiTS = ql.RelinkableZeroInflationTermStructureHandle()
inflationIndex = ql.UKRPI(False, cpiTS)
fixData = [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]
dte_fixings=[dtes for dtes in rpiSchedule]
print len(fixData)
print len(dte_fixings[:len(fixData)])
#must be the same length
#inflationIndex.addFixings(dte_fixings[:len(fixData)], fixData) 
#Current CPI level
#last observed rate
fixing_rate = 214.4


inflationIndex.addFixing(fixing_date, fixing_rate)

observationLag = ql.Period(lag, ql.Months)
zciisData =[( ql.Date(25, ql.November, 2010), 3.0495 ),
              ( ql.Date(25, ql.November, 2011), 2.93 ),
              ( ql.Date(26, ql.November, 2012), 2.9795 ),
              ( ql.Date(25, ql.November, 2013), 3.029 ),
              ( ql.Date(25, ql.November, 2014), 3.1425 ),
              ( ql.Date(25, ql.November, 2015), 3.211 ),
              ( ql.Date(25, ql.November, 2016), 3.2675 ),
              ( ql.Date(25, ql.November, 2017), 3.3625 ),
              ( ql.Date(25, ql.November, 2018), 3.405 ),
              ( ql.Date(25, ql.November, 2019), 3.48 ),
              ( ql.Date(25, ql.November, 2021), 3.576 ),
              ( ql.Date(25, ql.November, 2024), 3.649 ),
              ( ql.Date(26, ql.November, 2029), 3.751 ),
              ( ql.Date(27, ql.November, 2034), 3.77225),
              ( ql.Date(25, ql.November, 2039), 3.77 ),
              ( ql.Date(25, ql.November, 2049), 3.734 ),
              ( ql.Date(25, ql.November, 2059), 3.714 )]

#lRates=[rtes/100.0 for rtes in zip(*zciisData)[1]]
#baseZeroRate = lRates[0]

zeroSwapHelpers = [ql.ZeroCouponInflationSwapHelper(rate/100,observationLag,
    date, calendar, convention, dayCounter, inflationIndex) for date,rate in zciisData]


# the derived inflation curve
jj=ql.PiecewiseZeroInflation(          
                              evaluationDate, calendar, dayCounter, observationLag, 
                              inflationIndex.frequency(), inflationIndex.interpolated(), 
                              zciisData[0][1],#baseZeroRate,
                              yTS, zeroSwapHelpers, 1.0e-12, ql.Linear())

cpiTS.linkTo(jj)

notional = 1000000

fixedRates = [0.1]    

fixedDayCounter = ql.Actual365Fixed()
fixedPaymentConvention = ql.ModifiedFollowing
fixedPaymentCalendar = ql.UnitedKingdom()
contractObservationLag = ql.Period(3, ql.Months)
observationInterpolation = ql.CPI.Flat
settlementDays = 3
growthOnly = False

baseCPI = 206.1


fixedSchedule = ql.Schedule(issue_date,
                  maturity_date, 
                  ql.Period(ql.Semiannual),
                  fixedPaymentCalendar,
                  ql.Unadjusted,
                  ql.Unadjusted,
                  ql.DateGeneration.Backward,
                  False)

bond = ql.CPIBond(settlementDays,
                    notional,
                    growthOnly,
                    baseCPI,
                    contractObservationLag,
                    inflationIndex,
                    observationInterpolation,
                    fixedSchedule,
                    fixedRates,
                    fixedDayCounter, 
                    fixedPaymentConvention)

#bond2= ql.QuantLib.C

bondEngine=ql.DiscountingBondEngine(yTS)
bond.setPricingEngine(bondEngine)
print bond.NPV() 
print bond.cleanPrice()
compounding = ql.Compounded
yield_rate = bond.bondYield(fixedDayCounter,compounding,ql.Semiannual)
y_curve = ql.InterestRate(yield_rate,fixedDayCounter,compounding,ql.Semiannual)
##Collate results
print "Clean Price:", bond.cleanPrice()
print "Dirty Price:", bond.dirtyPrice()
print "Notional:", bond.notional()
print "Yield:", yield_rate
print "Accrued Amount:", bond.accruedAmount()
print "Settlement Value:", bond.settlementValue()

#suspect there's more to this for TIPS
print "Duration:", ql.BondFunctions.duration(bond,y_curve)
print "Convexity:", ql.BondFunctions.convexity(bond,y_curve)
print "Bps:", ql.BondFunctions.bps(bond,y_curve)
print "Basis Point Value:", ql.BondFunctions.basisPointValue(bond,y_curve)
print "Yield Value Basis Point:", ql.BondFunctions.yieldValueBasisPoint(bond,y_curve)

print "NPV:", bond.NPV()

# get the cash flows:
#cf_list=[(cf.amount(),cf.date()) for cf in bond.cashflows()]

def to_datetime(d):
    return dt.datetime(d.year(),d.month(), d.dayOfMonth())

for cf in bond.cashflows():
    try:
        amt=cf.amount()
        rte=jj.zeroRate(cf.date())
        zc=yTS.zeroRate(cf.date(),fixedDayCounter,compounding,ql.Semiannual).rate()
    except:
        amt=0
        rte=0
        zc=0
    print to_datetime(cf.date()),amt,rte,zc

问题似乎是通货膨胀指数对象需要一个日期，而不是多个索引点。我的假设是它将引出最新的有效点

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定价者的工作方式是通过通货膨胀率曲线期限结构zciisData增加实际息票。因此，其结果成为名义未来现金流。为了定价，债券定价者只需按名义期限结构对这些债券进行贴现。我添加了一些额外的代码来打印确定的现金流和“增长因子”，然后是贴现率。

您从哪里获得此

fixedRates=[0.1]

？它是债券的真实息票。因此，息票支付将等于：票面价值*通货膨胀系数*息票率