QuantLib 金融计算——自己动手封装 Python 接口（3）
概述
承接《自己动手封装 Python 接口（2）》中留下的问题，即封装 QuantLibEx 中的几个期限结构模型。
如何封装源代码？
与前一篇文章中的情况不同，要封装的程序不是已经编译好的库文件，而是 C++ 源代码。
SWIG 可以从源代码的层面封装 C++ 接口，一方面要提供头文件，告知 SWIG 类、函数等的声明；另一方面要提供源文件，让 SWIG 知道方法的实现，SWIG 会自动对源文件进行编译，并最终链接到生成的 Python 接口中。
实践
幸运的是 QuantLibEx 中几个 NS 型期限结构模型的构造函数没有引入新的类型，所以“最小功能集合”没有变。
要封装这几个模型，只需对 fittedbondcurve.i 和 setup.py 稍加修改。在 fittedbondcurve.i 中编写接口代码，在 setup.py 添加头文件路径和几个源文件就可以了。
六个 NS 型期限结构模型的参数估计
把《收益率曲线之构建曲线（5）》中的 C++ 代码翻译成 Python，验证封装后的接口是否可用。
import QuantLibEx as qlx
print(qlx.__version__)
bondNum = 16
cleanPrice = [100.4941, 103.5572, 104.4135, 105.0056, 99.8335, 101.25, 102.3832, 97.0053,
99.5164, 101.2435, 104.0539, 101.15, 96.1395, 91.1123, 122.0027, 92.4369]
priceHandle = [qlx.QuoteHandle(qlx.SimpleQuote(p)) for p in cleanPrice]
issueYear = [1999, 1999, 2001, 2002, 2003, 1999, 2004, 2005,
2006, 2007, 2003, 2008, 2005, 2006, 1997, 2007]
issueMonth = [qlx.February, qlx.October, qlx.January, qlx.January, qlx.May, qlx.January, qlx.January, qlx.April,
qlx.April, qlx.September, qlx.January, qlx.January, qlx.January, qlx.January, qlx.July, qlx.January]
issueDay = [22, 22, 4, 9, 20, 15, 15, 26, 21, 17, 15, 8, 14, 11, 10, 12]
maturityYear = [2009, 2010, 2011, 2012, 2013, 2014, 2014, 2015,
2016, 2017, 2018, 2019, 2020, 2021, 2027, 2037]
maturityMonth = [qlx.July, qlx.January, qlx.January, qlx.July, qlx.October, qlx.January, qlx.July, qlx.July,
qlx.September, qlx.September, qlx.January, qlx.March, qlx.July, qlx.September, qlx.July, qlx.March]
maturityDay = [15, 15, 4, 15, 20, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15]
issueDate = []
maturityDate = []
for i in range(bondNum):
issueDate.append(
qlx.Date(issueDay[i], issueMonth[i], issueYear[i]))
maturityDate.append(
qlx.Date(maturityDay[i], maturityMonth[i], maturityYear[i]))
couponRate = [
0.04, 0.055, 0.0525, 0.05, 0.038, 0.04125, 0.043, 0.035,
0.04, 0.043, 0.0465, 0.0435, 0.039, 0.035, 0.0625, 0.0415]
# 配置 helper
frequency = qlx.Annual
dayCounter = qlx.Actual365Fixed(qlx.Actual365Fixed.Standard)
paymentConv = qlx.Unadjusted
terminationDateConv = qlx.Unadjusted
convention = qlx.Unadjusted
redemption = 100.0
faceAmount = 100.0
calendar = qlx.Australia()
today = calendar.adjust(qlx.Date(30, qlx.January, 2008))
qlx.Settings.instance().evaluationDate = today
bondSettlementDays = 0
bondSettlementDate = calendar.advance(
today,
qlx.Period(bondSettlementDays, qlx.Days))
instruments = []
maturity = []
for i in range(bondNum):
bondCoupon = [couponRate[i]]
schedule = qlx.Schedule(
issueDate[i],
maturityDate[i],
qlx.Period(frequency),
calendar,
convention,
terminationDateConv,
qlx.DateGeneration.Backward,
False)
helper = qlx.FixedRateBondHelper(
priceHandle[i],
bondSettlementDays,
faceAmount,
schedule,
bondCoupon,
dayCounter,
paymentConv,
redemption)
maturity.append(dayCounter.yearFraction(
bondSettlementDate, helper.maturityDate()))
instruments.append(helper)
accuracy = 1.0e-6
maxEvaluations = 5000
weights = qlx.Array()
# 正则化条件
l2Ns = qlx.Array(4, 0.5)
guessNs = qlx.Array(4)
guessNs[0] = 4 / 100.0
guessNs[1] = 0.0
guessNs[2] = 0.0
guessNs[3] = 0.5
l2Sv = qlx.Array(6, 0.5)
guessSv = qlx.Array(6)
guessSv[0] = 4 / 100.0
guessSv[1] = 0.0
guessSv[2] = 0.0
guessSv[3] = 0.0
guessSv[4] = 0.2
guessSv[5] = 0.15
l2Asv = qlx.Array(6, 0.5)
guessAsv = qlx.Array(6)
guessAsv[0] = 4 / 100.0
guessAsv[1] = 0.0
guessAsv[2] = 0.0
guessAsv[3] = 0.0
guessAsv[4] = 0.2
guessAsv[5] = 0.3
l2Bc = qlx.Array(5, 0.5)
guessBc = qlx.Array(5)
guessBc[0] = 4 / 100.0
guessBc[1] = 0.0
guessBc[2] = 0.0
guessBc[3] = 0.0
guessBc[4] = 0.2
l2Bl = qlx.Array(5, 0.5)
guessBl = qlx.Array(5)
guessBl[0] = 4 / 100.0
guessBl[1] = 0.0
guessBl[2] = 0.0
guessBl[3] = 0.5
guessBl[4] = 0.5
optMethod = qlx.LevenbergMarquardt()
# 拟合方法
nsf = qlx.NelsonSiegelFitting(
weights, optMethod, l2Ns)
svf = qlx.SvenssonFitting(
weights, optMethod, l2Sv)
asvf = qlx.AdjustedSvenssonFitting(
weights, optMethod, l2Asv)
dlf = qlx.DieboldLiFitting(
0.5, weights, optMethod)
bcf = qlx.BjorkChristensenFitting(
weights, optMethod, l2Bc)
blf = qlx.BlissFitting(
weights, optMethod, l2Bl)
tsNelsonSiegel = qlx.FittedBondDiscountCurve(
bondSettlementDate,
instruments,
dayCounter,
nsf,
accuracy,
maxEvaluations,
guessNs,
1.0)
tsSvensson = qlx.FittedBondDiscountCurve(
bondSettlementDate,
instruments,
dayCounter,
svf,
accuracy,
maxEvaluations,
guessSv)
tsAdjustedSvensson = qlx.FittedBondDiscountCurve(
bondSettlementDate,
instruments,
dayCounter,
asvf,
accuracy,
maxEvaluations,
guessAsv)
tsDieboldLi = qlx.FittedBondDiscountCurve(
bondSettlementDate,
instruments,
dayCounter,
dlf,
accuracy,
maxEvaluations)
tsBjorkChristensen = qlx.FittedBondDiscountCurve(
bondSettlementDate,
instruments,
dayCounter,
bcf,
accuracy,
maxEvaluations,
guessBc)
tsBliss = qlx.FittedBondDiscountCurve(
bondSettlementDate,
instruments,
dayCounter,
blf,
accuracy,
maxEvaluations,
guessBl)
print("NelsonSiegel Results: ", tsNelsonSiegel.fitResults().solution())
print("Svensson Results: ", tsSvensson.fitResults().solution())
print("AdjustedSvensson Results: ", tsAdjustedSvensson.fitResults().solution())
print("DieboldLi Results: ", tsDieboldLi.fitResults().solution())
print("BjorkChristensen Results: ", tsBjorkChristensen.fitResults().solution())
print("Bliss Results: ", tsBliss.fitResults().solution())
NelsonSiegel Results: [ 0.0500803; -0.0105414; -0.0303842; 0.456529 ]
Svensson Results: [ 0.0431095; -0.00716036; -0.0340932; 0.0391339; 0.228995; 0.117208 ]
AdjustedSvensson Results: [ 0.0506269; -0.0116339; 0.0029305; -0.0135686; 0.179066; 0.267767 ]
DieboldLi Results: [ 0.0496643; -0.00879931; -0.0329267 ]
BjorkChristensen Results: [ 0.0508039; -0.0555185; 0.0115282; 0.0415581; 0.227838 ]
Bliss Results: [ 0.0500892; -0.0106013; -0.0315605; 0.513831; 0.456329 ]
所得结果和《收益率曲线之构建曲线（5）》中的完全一致。
