def __init__(self, start, underlyings):
myScheduler = Scheduler()
myDelays = []
freqs = ['3M', '6M', '1Y', '3Y']
for i in range(0, len(freqs)):
myDelays.append(myScheduler.extractDelay(freqs[i]))
AAA = {}
for i in range(0, len(freqs)):
vas = MC_Vasicek_Sim(x=AAAx[freqs[i]],
datelist=[start, myDelays[i] + start],
t_step=1 / 365.,
simNumber=500)
AAA[freqs[i]] = vas.getLibor()[0].loc[myDelays[i] + start]
BBB = {
'3M':
MC_Vasicek_Sim(x=BBBx[freqs[0]],
datelist=[start, myDelays[0] + start],
t_step=1 / 365.,
simNumber=500).getLibor()[0].loc[myDelays[0] +
start]
}
self.probs = {'AAA': AAA, 'BBB': BBB}
self.underlyings = underlyings
class CorporateRates(object):
def __init__(self):
self.OIS = []
self.filename = WORKING_DIR + '/CorpData.dat'
self.corporates = []
self.ratings = {
'AAA': "BAMLC0A1CAAA",
'AA': "BAMLC0A2CAA",
'A': "BAMLC0A3CA",
'BBB': "BAMLC0A4CBBB",
'BB': "BAMLH0A1HYBB",
'B': "BAMLH0A2HYB",
'CCC': "BAMLH0A3HYC"
}
self.corpSpreads = {}
self.corporates = pd.DataFrame()
self.Qcorporates = pd.DataFrame() # survival function for corporates
self.tenors = []
self.unPickleMe(file=self.filename)
self.myScheduler = Scheduler()
self.myVasicek = MC_Vasicek_Sim()
self.R = 0.4
def getCorporatesFred(self, trim_start, trim_end):
fred = Fred(api_key=FRED_API_KEY)
curr_trim_end = trim_start
if (self.corporates.size != 0):
self.trim_start = self.corporates['OIS'].index.min().date()
curr_trim_end = self.corporates['OIS'].index.max().date()
if trim_end <= curr_trim_end:
self.trim_end = curr_trim_end
return self.corporates
self.trim_start = trim_start
self.trim_end = trim_end
self.OIS = OIS(trim_start=trim_start, trim_end=trim_end)
self.datesAll = self.OIS.datesAll
self.datesAll.columns = [x.upper() for x in self.datesAll.columns]
self.datesAll.index = self.datesAll.DATE
self.OISData = self.OIS.getOIS()
for i in np.arange(len(self.OISData.columns)):
freq = self.OISData.columns[i]
self.tenors.append(self.myScheduler.extractDelay(freq=freq))
for rating in self.ratings.keys():
index = self.ratings[rating]
try:
corpSpreads = 1e-2 * (fred.get_series(
index,
observation_start=trim_start,
observation_end=trim_end).to_frame())
corpSpreads.index = [x.date() for x in corpSpreads.index[:]]
corpSpreads = pd.merge(left=self.datesAll,
right=corpSpreads,
left_index=True,
right_index=True,
how="left")
corpSpreads = corpSpreads.fillna(method='ffill').fillna(
method='bfill')
corpSpreads = corpSpreads.drop("DATE", axis=1)
self.corpSpreads[rating] = corpSpreads.T.fillna(
method='ffill').fillna(method='bfill').T
except Exception as e:
print(e)
print(index, " not found")
self.corpSpreads = pd.Panel.from_dict(self.corpSpreads)
self.corporates = {}
self.OISData.drop('DATE', axis=1, inplace=True)
ntenors = np.shape(self.OISData)[1]
for rating in self.ratings:
try:
tiledCorps = np.tile(self.corpSpreads[rating][0],
ntenors).reshape(np.shape(self.OISData))
self.corporates[rating] = pd.DataFrame(
data=(tiledCorps + self.OISData.values),
index=self.OISData.index,
columns=self.OISData.columns)
except:
print("Error in addition of Corp Spreads")
self.corporates['OIS'] = self.OISData
self.corporates = pd.Panel(self.corporates)
return self.corporates
def getCorporateData(self, rating, datelist=None):
# This method gets a curve for a given date or date list for a given rating (normally this will be just a date).
# It returns a dict of curves read directly from the corporate rates created by getCorporatesFred.
# Derive delays from self.corporates[rating].columns
myDelays = self.myScheduler.extractDelay(
freq=list(self.corporates[rating].columns))
if datelist is None:
return
outCurve = {}
for day in datelist:
# Create curves
# ..............
# ..............
# add curve to outcurve dict
outCurve[day] = myCurve
return outCurve
def getCorporateQData(self, rating, datelist=None, R=0.4):
self.R = R
if datelist is None:
return
outCurve = {}
for day in datelist:
# Create Q curves using q-tilde equation
# ..............
# ..............
outCurve[day] = myCurve
return outCurve
def pickleMe(self):
data = [self.corporates, self.corpSpreads]
with open(self.filename, "wb") as f:
pickle.dump(len(data), f)
for value in data:
pickle.dump(value, f)
def unPickleMe(self, file):
data = []
if (os.path.exists(file)):
with open(file, "rb") as f:
for _ in range(pickle.load(f)):
data.append(pickle.load(f))
self.corporates = data[0]
self.corpSpreads = data[1]
def saveMeExcel(self, whichdata, fileName):
try:
df = pd.DataFrame(whichdata)
except:
df = whichdata
df.to_excel(fileName)
class CouponBond(object):
def __init__(self, fee, start,coupon, maturity, freq, referencedate, observationdate,notional):
self.fee = fee
self.coupon=coupon
self.start = start
self.maturity = maturity
self.freq= freq
self.referencedate = referencedate
self.observationdate = observationdate
self.myScheduler = Scheduler()
self.delay = self.myScheduler.extractDelay(freq=freq)
self.getScheduleComplete()
self.ntimes=len(self.datelist)
self.referencedate = referencedate
self.pvAvg=0.0
self.cashFlows = DataFrame()
self.cashFlowsAvg = []
self.yieldIn = 0.0
self.notional=notional
def getScheduleComplete(self):
self.datelist = self.myScheduler.getSchedule(start=self.start,end=self.maturity,freq=self.freq,referencedate=self.referencedate)
fullset = list(sorted(list(set(self.datelist)
.union([self.referencedate])
.union([self.start])
.union([self.maturity])
.union([self.observationdate])
)))
return fullset,self.datelist
def setLibor(self,libor):
self.libor = libor/libor.loc[self.referencedate]
self.ntimes = np.shape(self.datelist)[0]
self.ntrajectories = np.shape(self.libor)[1]
self.ones = np.ones(shape=[self.ntrajectories])
def getExposure(self, referencedate):
if self.referencedate!=referencedate:
self.referencedate=referencedate
self.getScheduleComplete()
deltaT= np.zeros(self.ntrajectories)
if self.ntimes==0:
pdzeros= pd.DataFrame(data=np.zeros([1,self.ntrajectories]), index=[referencedate])
self.pv=pdzeros
self.pvAvg=0.0
self.cashFlows=pdzeros
self.cashFlowsAvg=0.0
return self.pv
for i in range(1,self.ntimes):
deltaTrow = ((self.datelist[i]-self.datelist[i-1]).days/365)*self.ones
deltaT = np.vstack ((deltaT,deltaTrow) )
self.cashFlows= self.coupon*deltaT
principal = self.ones
if self.ntimes>1:
self.cashFlows[-1:]+= principal
else:
self.cashFlows = self.cashFlows + principal
if(self.datelist[0]<= self.start):
self.cashFlows[0]=-self.fee*self.ones
if self.ntimes>1:
self.cashFlowsAvg = self.cashFlows.mean(axis=1)*self.notional
else:
self.cashFlowsAvg = self.cashFlows.mean() * self.notional
pv = self.cashFlows*self.libor.loc[self.datelist]
self.pv = pv.sum(axis=0)*self.notional
self.pvAvg = np.average(self.pv)*self.notional
return self.pv
def getPV(self,referencedate):
self.getExposure(referencedate=referencedate)
return self.pv/self.libor.loc[self.observationdate]
def getLiborAvg(self, yieldIn, datelist):
self.yieldIn = yieldIn
time0 = datelist[0]
# this function is used to calculate exponential single parameter (r or lambda) Survival or Libor Functions
Z = np.exp(-self.yieldIn * pd.DataFrame(np.tile([(x - time0).days / 365.0 for x in self.datelist], reps=[self.ntrajectories,1]).T,index=self.datelist))
return Z
def getYield(self,price):
# Fit model to curve data
yield0 = 0.05 * self.ones
self.price = price
self.yieldIn = self.fitModel2Curve(x=yield0)
return self.yieldIn
def fitModel2Curve(self, x ):
# Minimization procedure to fit curve to model
results = minimize(fun=self.fCurve, x0=x)
return results.x
def fCurve(self, x):
# raw data error function
calcCurve = self.getLiborAvg(x, self.datelist)
thisPV = np.multiply(self.cashFlows,calcCurve).mean(axis=1).sum(axis=0)
error = 1e4 * (self.price - thisPV) ** 2
return error
class CouponBond(object):
def __init__(self,
fee,
coupon,
start,
maturity,
freq,
referencedate,
observationdate,
rating,
notional=1,
recovery=0.4):
self.numSim = 10
self.t_step = 1.0 / 365
self.fee = fee
self.coupon = coupon
self.start = start
self.maturity = maturity
self.freq = freq
self.recovery = recovery
self.rating = rating
self.referencedate = referencedate
self.observationdate = observationdate
self.myScheduler = Scheduler()
self.delay = self.myScheduler.extractDelay(freq=freq)
self.referencedate = referencedate
self.getScheduleComplete()
self.fullDateList = self.datelist
self.ntimes = len(self.datelist)
self.pvAvg = 0.0
self.cashFlows = DataFrame()
self.cashFlowsAvg = []
self.yieldIn = 0.0
self.notional = notional
self.errorCurve = []
self.mcSim = MC_Vasicek_Sim()
def getScheduleComplete(self):
self.datelist = self.myScheduler.getSchedule(
start=self.start,
end=self.maturity,
freq=self.freq,
referencedate=self.referencedate)
self.ntimes = len(self.datelist)
fullset = sorted(
set(self.datelist).union([self.referencedate]).union([
self.start
]).union([self.maturity]).union([self.observationdate]))
a = 1
return fullset, self.datelist
def setLibor(self, libor):
self.libor = libor / libor.loc[self.referencedate]
#self.ntimes = np.shape(self.datelist)[0]
self.ntrajectories = np.shape(self.libor)[1]
self.ones = np.ones(shape=[self.ntrajectories])
def getExposure(self, referencedate):
if self.referencedate != referencedate:
self.referencedate = referencedate
self.getScheduleComplete()
deltaT = np.zeros(self.ntrajectories)
if self.ntimes == 0:
pdzeros = pd.DataFrame(data=np.zeros([1, self.ntrajectories]),
index=[referencedate])
self.pv = pdzeros
self.pvAvg = 0.0
self.cashFlows = pdzeros
self.cashFlowsAvg = 0.0
return self.pv
for i in range(1, self.ntimes):
deltaTrow = ((self.datelist[i] - self.datelist[i - 1]).days /
365) * self.ones
deltaT = np.vstack((deltaT, deltaTrow))
self.cashFlows = self.coupon * deltaT
principal = self.ones
if self.ntimes > 1:
self.cashFlows[-1:] += principal
else:
self.cashFlows = self.cashFlows + principal
if (self.datelist[0] <= self.start):
self.cashFlows[0] = -self.fee * self.ones
if self.ntimes > 1:
self.cashFlowsAvg = self.cashFlows.mean(axis=1) * self.notional
else:
self.cashFlowsAvg = self.cashFlows.mean() * self.notional
pv = self.cashFlows * self.libor.loc[self.datelist]
self.pv = pv.sum(axis=0) * self.notional
self.pvAvg = np.average(self.pv) * self.notional
return self.pv
def getPV(self, referencedate):
self.getExposure(referencedate=referencedate)
return self.pv / self.libor.loc[self.referencedate]
def getFullExposure(self):
fullExposure = pd.DataFrame(data=np.zeros(len(self.fullDateList)),
index=self.fullDateList)
for days in self.fullDateList:
fullExposure.loc[days] = self.getExposure(days)
self.fullExposure = fullExposure
def setCorpData(self, corpData):
self.corpData = corpData.loc[self.rating, :, "1 MO"]
def setxQ(self, xQ):
res = optimize.fmin(func=self.mcSim.errorFunction,
x0=np.array([xQ[0], xQ[1]]),
args=(self.corpData.values[:-1],
self.corpData.values[1:], self.t_step))
xQSigma = np.std(self.corpData.values[:-1] - self.corpData.values[1:])
self.xQ = np.append(res, np.array([xQSigma, xQ[3]]))
self.mcSim.setVasicek(minDay=self.start,
maxDay=self.maturity,
x=self.xQ,
simNumber=self.numSim,
t_step=self.t_step)
def setQCurve(self):
self.qCurve = self.mcSim.getLibor().loc[:, 0]
return
def getCVA(self):
self.CVA = (1 - self.recovery) * self.fullExposure.loc[
self.fullDateList, 0].values * self.qCurve.loc[
self.fullDateList] * self.libor.loc[self.fullDateList, 0]
def getLiborAvg(self, yieldIn, datelist):
self.yieldIn = yieldIn
time0 = datelist[0]
# this function is used to calculate exponential single parameter (r or lambda) Survival or Libor Functions
Z = np.exp(-self.yieldIn *
pd.DataFrame(np.tile([(x - time0).days / 365.0
for x in self.datelist],
reps=[self.ntrajectories, 1]).T,
index=self.datelist))
return Z
def getYield(self, price):
# Fit model to curve data
yield0 = 0.05 * self.ones
self.price = price
self.yieldIn = self.fitModel2Curve(x=yield0)
return self.yieldIn
def fitModel2Curve(self, x):
# Minimization procedure to fit curve to model
results = optimize.minimize(fun=self.fCurve, x0=x)
a = 1
return results.x
def fCurve(self, x):
# raw data error function
calcCurve = self.getLiborAvg(x, self.datelist)
thisPV = np.multiply(self.cashFlows,
calcCurve).mean(axis=1).sum(axis=0)
error = 1e4 * (self.price - thisPV)**2
self.errorCurve = error
return error
class CorporateRates(object):
def __init__(self):
self.OIS = []
self.filename = WORKING_DIR + '/CorpData.dat'
self.corporates = []
self.ratings = {
'AAA': "BAMLC0A1CAAA",
'AA': "BAMLC0A2CAA",
'A': "BAMLC0A3CA",
'BBB': "BAMLC0A4CBBB",
'BB': "BAMLH0A1HYBB",
'B': "BAMLH0A2HYB",
'CCC': "BAMLH0A3HYC"
}
self.corpSpreads = {}
self.corporates = pd.DataFrame()
self.Qcorporates = pd.DataFrame() # survival function for corporates
self.tenors = []
self.unPickleMe(file=self.filename)
self.myScheduler = Scheduler()
#self.myVasicek = MC_Vasicek_Sim()
self.R = 0.4
def getCorporatesFred(self, trim_start, trim_end):
self.corpSpreads = {}
self.corpSpreads = {}
fred = Fred(api_key=FRED_API_KEY)
curr_trim_end = trim_start
if (self.corporates.size != 0):
self.trim_start = self.corporates['OIS'].index.min().date()
curr_trim_end = self.corporates['OIS'].index.max().date()
self.trim_start = trim_start
self.trim_end = trim_end
self.OIS = OIS(trim_start=trim_start, trim_end=trim_end)
self.datesAll = self.OIS.datesAll
#print(self.datesAll)
self.datesAll.columns = [x.upper() for x in self.datesAll.columns]
#print(self.datesAll)
self.datesAll.index = self.datesAll.DATE
self.OISData = self.OIS.getOIS()
#print(self.OISData)
for i in np.arange(len(self.OISData.columns)):
freq = self.OISData.columns[i]
self.tenors.append(self.myScheduler.extractDelay(freq=freq))
#print(self.tenors)
for rating in self.ratings.keys():
index = self.ratings[rating]
try:
corpSpreads = 1e-2 * (fred.get_series(
index,
observation_start=trim_start,
observation_end=trim_end).to_frame())
corpSpreads.index = [x.date() for x in corpSpreads.index[:]]
corpSpreads = pd.merge(left=self.datesAll,
right=corpSpreads,
left_index=True,
right_index=True,
how="left")
corpSpreads = corpSpreads.fillna(method='ffill').fillna(
method='bfill')
corpSpreads = corpSpreads.drop("DATE", axis=1)
self.corpSpreads[rating] = corpSpreads.T.fillna(
method='ffill').fillna(method='bfill').T
except Exception as e:
print(e)
print(index, " not found")
self.corpSpreads = pd.Panel.from_dict(self.corpSpreads)
#print(self.corpSpreads)
self.corporates = {}
self.OISData.drop('DATE', axis=1, inplace=True)
ntenors = np.shape(self.OISData)[1]
for rating in self.ratings:
try:
tiledCorps = np.tile(self.corpSpreads[rating][0],
ntenors).reshape(np.shape(self.OISData))
self.corporates[rating] = pd.DataFrame(
data=(tiledCorps + self.OISData.values),
index=self.OISData.index,
columns=self.OISData.columns)
except:
print(rating)
print("Error in addition of Corp Spreads")
self.corporates['OIS'] = self.OISData
self.corporates = pd.Panel(self.corporates)
return self.corporates
def convertCols(self, list):
out = []
for i in range(0, len(list)):
out.append(list[i].split(' ', 1)[0] + list[i].split(' ', 1)[1][0])
return out
def getCorporateData(self, rating, datelist=None):
# This method gets a curve for a given date or date list for a given rating (normally this will be just a date).
# It returns a dict of curves read directly from the corporate rates created by getCorporatesFred.
# Derive delays from self.corporates[rating].columns
#print("GEt corporate data ")
if datelist is None:
return
outCurve = {}
datelist.sort()
#need an iteratble object
cols = self.convertCols(list(self.corporates[rating].columns))
myDelays = []
myDelays.append(relativedelta.relativedelta(days=0))
#just putting an iterable object together
for i in range(0, len(cols)):
myDelays.append(self.myScheduler.extractDelay(freq=cols[i]))
myCurve = self.corporates[rating]
cols = ['0D'] + cols
#print("My curve")
#print(myCurve)
#print(myDelays)
#grabbing only the info with the rating I want and making a datelist to calulate time differences
dates = []
for day in range(0, len(datelist)):
#print(day)
dates.append([(myDelays[x] + datelist[day])
for x in range(0, len(myDelays))])
#print(dates)
#my array of interest rates
r = np.zeros((len(datelist), len(dates[0])))
#print(r)
nrows = len(dates)
#multiplying the rate by the delta t in days and saving it to a spot in the array
for j in range(0, len(datelist)):
#print(datelist[j])
day_tenors = myCurve.loc[datelist[j]]
for i in range(1, len(day_tenors) + 1):
r[j, i] = r[j, i - 1] + day_tenors[i - 1] * (
(dates[j][i] - datelist[j]).days / 365)
# Create curves
# ..............
# ..............
# add curve to outcurve dict
#integrating and taking e^-
intR = r.cumsum(axis=1)
outCurve = np.exp(-intR)
out = pd.DataFrame(outCurve)
out.columns = cols
return out
def getSpreads(self, rating, datelist=None):
if datelist is None:
return
outCurve = {}
#need an iteratble object
#just putting an iterable object together
myCurve = self.corpSpreads[rating]
#grabbing only the info with the rating I want and making a datelist to calulate time differences
#my array of interest rates
r = np.zeros(len(datelist))
#multiplying the rate by the delta t in days and saving it to a spot in the array
for j in range(1, len(datelist)):
r[j] = r[j - 1] + myCurve['VALUE'].loc[j - 1] * (
datelist[j] - datelist[j - 1]).days / 365
# Create curves
# ..............
# ..............
# add curve to outcurve dict
#integrating and taking e^-
intR = r.cumsum(axis=0)
outCurve = np.exp(-intR)
out = pd.DataFrame(outCurve, index=datelist)
return out
def getCorporateQData(self, rating, datelist, R=0.4):
#print("Get gorporate Q data")
self.R = R
if datelist is None:
return
trim_start = datelist[0]
trim_end = datelist[-1]
# Create Q curves using q-tilde equation
outCurve = (
(1 - (1 / (1 - R)) *
(1 - (self.getCorporateData(rating=rating, datelist=datelist) /
self.getCorporateData(rating='OIS', datelist=datelist)))
).values).tolist()
out = pd.DataFrame(outCurve, index=datelist)
out[out < 0] = 0
cols = self.convertCols(list(self.corporates[rating].columns))
cols = ['0D'] + cols
## Get OIS ###
#getOIS = OIS(trim_start = trim_start,trim_end=trim_end)
#print(getOIS.getOIS(datelist = datelist))
#outCurve = ((1 - (1 / (1 - R)) * (1 - (self.getCorporatesFred(trim_start) / self.getCorporateData(rating='OIS', datelist=datelist)))).values).tolist()
out.columns = cols
return out
def pickleMe(self):
data = [self.corporates, self.corpSpreads]
with open(self.filename, "wb") as f:
pickle.dump(len(data), f)
for value in data:
pickle.dump(value, f)
def unPickleMe(self, file):
data = []
if (os.path.exists(file)):
with open(file, "rb") as f:
for _ in range(pickle.load(f)):
data.append(pickle.load(f))
self.corporates = data[0]
self.corpSpreads = data[1]
def saveMeExcel(self, whichdata, fileName):
try:
df = pd.DataFrame(whichdata)
except:
df = whichdata
df.to_excel(fileName)
class PortfolioLossCalculation(object):
def __init__(self, K1, K2, Fs, Rs, betas, start_dates, end_dates, freqs,
coupons, referenceDates, ratings, bootstrap):
self.bootstrap = bootstrap
self.K1 = K1
self.K2 = K2
self.Fs = Fs
self.Rs = Rs
self.betas = betas
self.referenceDates = referenceDates
self.freqs = freqs
self.coupons = coupons
self.start_dates = start_dates
self.end_dates = end_dates
self.ratings = ratings
self.R = Rs[0]
self.beta = betas[0]
self.referenceDate = referenceDates[0]
self.freq = freqs[0]
self.coupon = coupons[0]
self.start_date = start_dates[0]
self.end_date = end_dates[0]
self.rating = ratings[0]
self.maturity = end_dates[0]
self.myScheduler = Scheduler()
def setParameters(self, R, rating, coupon, beta, start_date, referenceDate,
end_date, freq):
self.R = R
self.beta = beta
self.referenceDate = referenceDate
self.freq = freq
self.coupon = coupon
self.start_date = start_date
self.end_date = end_date
self.rating = rating
self.maturity = end_date
def getQ(self, start_date, referenceDate, end_date, freq, coupon, rating,
R):
## Use CorporateDaily to get Q for referencedates ##
# print("GET Q")
# print(self.portfolioScheduleOfCF)
if self.bootstrap:
print("Q bootstrap")
CDSClass = CDS(start_date=start_date,
end_date=end_date,
freq=freq,
coupon=coupon,
referenceDate=referenceDate,
rating=rating,
R=R)
myLad = BootstrapperCDSLadder(start=self.start_date,
freq=[freq],
CDSList=[CDSClass],
R=CDSClass.R).getXList(x0Vas)[freq]
self.Q1M = MC_Vasicek_Sim(
x=myLad,
t_step=1 / 365,
datelist=[CDSClass.referenceDate, CDSClass.end_date],
simNumber=simNumber).getLibor()[0]
print(self.Q1M)
else:
myQ = CorporateRates()
myQ.getCorporatesFred(trim_start=referenceDate, trim_end=end_date)
## Return the calculated Q(t,t_i) for bonds ranging over maturities for a given rating
daterange = pd.date_range(start=referenceDate, end=end_date).date
myQ = myQ.getCorporateQData(rating=rating, datelist=daterange, R=R)
Q1M = myQ[freq]
print(Q1M)
return (Q1M)
def Q_lhp(self, t, K1, K2, R, beta, Q):
"""Calculates the Tranche survival curve for the LHP model.
Args:
T (datetime.date): Should be given in "days" (no hours, minutes, etc.)
K1 (float): The starting value of the tranche. Its value should be
between 0 & 1.
K2 (float): The final value of the tranche.
beta (float): Correlation perameter. Its value should be between 0 and 1.
R (float): Recovery rate. Usually between 0 and 1.
Q (callable function): A function Q that takes in a dateime.date and
outputs a float from 0 to 1. It is assumed that Q(0) = 1 and Q is
decreasing. Represents survival curve of each credit.
"""
if Q(t) == 1:
return 1 # prevents infinity
def emin(K):
# Calculates E[min(L(T), K)] in LHP model
C = norm.ppf(1 - Q(t))
A = (1 / beta) * (C - sqrt(1 - beta * beta) * norm.ppf(K /
(1 - R)))
return (1 - R) * mvn.mvndst(
upper=[C, -1 * A],
lower=[0, 0],
infin=[0, 0], # set lower bounds = -infty
correl=-1 * beta)[1] + K * norm.cdf(A)
return 1 - (emin(K2) - emin(K1)) / (K2 - K1)
def Q_gauss(self, t, K1, K2, Fs, Rs, betas, Qs):
""" Calculate the tranche survival probability in the Gaussian heterogenous model.
Arguments:
t (float): Time. Positive.
K1 (float): Starting tranche value. Between 0 and 1.
K2 (float): Ending tranche value. Between 0 and 1.
Fs (list): List of fractional face values for each credit. Each entry must be
between 0 and 1.
Rs (list): List of recovery rates for each credit. Each entry must be between
0 and 1.
betas (list): Correlation perameters for each credit. Each entry must be between
0 and 1.
Qs (list): Survival curves for each credit. Each entry must be a callable function
that takes in a datetime.date argument and returns a number from 0 to 1.
"""
Cs = [norm.ppf(1 - q(t)) for q in Qs]
N = len(Fs)
def f(K, z):
ps = [
norm.cdf((C - beta * z) / sqrt(1 - beta**2))
for C, beta in zip(Cs, betas)
]
mu = 1 / N * sum([p * F * (1 - R) for p, F, R in zip(ps, Fs, Rs)])
sigma_squared = 1 / N / N * sum([
p * (1 - p) * F**2 * (1 - R)**2 for p, F, R in zip(ps, Fs, Rs)
])
sigma = sqrt(sigma_squared)
return -1 * sigma * norm.pdf(
(mu - K) / sigma) - (mu - K) * norm.cdf((mu - K) / sigma)
emin = lambda K: quad(lambda z: norm.pdf(z) * f(K, z), -10, 10)[0]
return 1 - (emin(K2) - emin(K1)) / (K2 - K1)
def Q_adjbinom(self, t, K1, K2, Fs, Rs, betas, Qs):
""" Calculates the tranche survival probability under the adjusted
binomial model.
Arguments:
t (datetime.date): Time.
K1 (float): Starting tranche value (0 to 1).
K2 (float): Final tranche value (0 to 1).
Fs (list): List of fractional face values (floats) for each credit.
Rs (list): List of recovery rates (floats) for each credit.
betas (list): List of correlation perameters
Qs (list): List of survival probabilities. These are callable functions that
takes in a single datetime.date argument and returns a float.
Returns:
float: The value of the tranche survival curve.
"""
if Qs[0](t) == 1:
return 1.0 # initial value -- avoids weird nan return
N = len(Fs)
Cs = [norm.ppf(1 - Q(t)) for Q in Qs]
L = sum([(1 - R) * F for R, F in zip(Rs, Fs)]) / N
def choose(n, k): # Calculates binomial coeffecient: n choose k.
if k == 0 or k == n:
return 1
return choose(n - 1, k - 1) + choose(n - 1, k)
def g(k, z):
ps = [
norm.cdf((C - beta * z) / sqrt(1 - beta * beta))
for C, beta in zip(Cs, betas)
]
p_avg = sum([(1 - R) * F / L * p
for R, F, p in zip(Rs, Fs, ps)]) / N
f = lambda k: choose(N, k) * p_avg**k * (1 - p_avg)**(N - k)
vA = p_avg * (1 - p_avg) / N
vE = 1 / N / N * sum([((1 - R) * F / L)**2 * p * (1 - p)
for R, F, p in zip(Rs, Fs, ps)])
m = p_avg * N
l = int(m)
u = l + 1
o = (u - m)**2 + ((l - m)**2 - (u - m)**2) * (u - m)
alpha = (vE * N + o) / (vA * N + o)
if k == l:
return f(l) + (1 - alpha) * (u - m)
if k == u:
return f(u) - (1 - alpha) * (l - m)
return alpha * f(k)
I = lambda k: quad(lambda z: norm.pdf(z) * g(k, z), -10, 10)[0]
emin = lambda K: sum([I(k) * min(L * k, K) for k in range(0, N + 1)])
return 1 - (emin(K2) - emin(K1)) / (K2 - K1)
def gcd(self, a, b):
if a * b == 0:
return max(a, b)
if a < b:
return self.gcd(a, b % a)
return self.gcd(a % b, b)
def Q_exact(self, t, K1, K2, Fs, Rs, betas, Qs):
Cs = [norm.ppf(1 - Q(t)) for Q in Qs]
N = len(Fs)
g = round(3600 * Fs[0] * (1 - Rs[0]))
for j in range(1, N):
g = self.gcd(g, round(3600 * Fs[j] * (1 - Rs[j])))
g = g / 3600
ns = [round(F * (1 - R) / g) for F, R in zip(Fs, Rs)]
def f(j, k, z):
if (j, k) == (0, 0):
return 1.0
if j == 0:
return 0.0
ps = [
norm.cdf((C - beta * z) / sqrt(1 - beta**2))
for C, beta in zip(Cs, betas)
]
if k < ns[j - 1]:
return f(j - 1, k, z) * (1 - ps[j - 1])
return f(j - 1, k, z) * (1 - ps[j - 1]) + f(
j - 1, k - ns[j - 1], z) * ps[j - 1]
I = [
quad(lambda z: norm.pdf(z) * f(N, k, z), -12, 12)[0]
for k in range(sum(ns))
]
emin = lambda K: sum([I[k] * min(k * g, K) for k in range(sum(ns))])
return 1 - (emin(K2) - emin(K1)) / (K2 - K1)
def getZ_Vasicek(self):
### Get Z(t,t_i) for t_i in datelist #####
## Simulate Vasicek model with paramters given in workspace.parameters
# xR = [5.0, 0.05, 0.01, 0.05]
# kappa = x[0]
# theta = x[1]
# sigma = x[2]
# r0 = x[3]
vasicekMC = MC_Vasicek_Sim(
datelist=[self.referenceDate, self.end_date],
x=xR,
simNumber=10,
t_step=t_step)
self.myZ = vasicekMC.getLibor()
self.myZ = self.myZ.loc[:, 0]
def getScheduleComplete(self):
datelist = self.myScheduler.getSchedule(
start=self.start_date,
end=self.maturity,
freq=self.freq,
referencedate=self.referenceDate)
ntimes = len(datelist)
fullset = list(
sorted(
list(
set(datelist).union([self.referenceDate]).union([
self.start_date
]).union([self.maturity]).union([self.referenceDate]))))
return fullset, datelist
def getPremiumLegZ(self, myQ):
Q1M = myQ
# Q1M = self.myQ["QTranche"]
fulllist, datelist = self.getScheduleComplete()
portfolioScheduleOfCF = fulllist
timed = portfolioScheduleOfCF[portfolioScheduleOfCF.
index(self.referenceDate):]
Q1M = Q1M.loc[timed]
zbarPremLeg = self.myZ / self.myZ.loc[self.referenceDate]
zbarPremLeg = zbarPremLeg.loc[timed]
## Calculate Q(t_i) + Q(t_(i-1))
Qplus = []
out = 0
for i in range(1, len(Q1M)):
out = out + (Q1M[(i - 1)] + Q1M[i]) * float(
(timed[i] - timed[i - 1]).days / 365) * zbarPremLeg[i]
zbarPremLeg = pd.DataFrame(zbarPremLeg, index=timed)
# print("Premium LEg")
PVpremiumLeg = out * (1 / 2)
# print(PVpremiumLeg)
## Get coupon bond ###
print(PVpremiumLeg)
return PVpremiumLeg
# //////////////// Get Protection leg Z(t_i)( Q(t_(i-1)) - Q(t_i) )
def getProtectionLeg(self, myQ):
print("Protection Leg ")
Q1M = myQ
#Qminus = np.gradient(Q1M)
zbarProtectionLeg = self.myZ
out = 0
for i in range(1, zbarProtectionLeg.shape[0]):
#out = -Qminus[i] * zbarProtectionLeg.iloc[i]*float(1/365)
out = out - (Q1M.iloc[i] -
Q1M.iloc[i - 1]) * zbarProtectionLeg.iloc[i]
## Calculate the PV of the premium leg using the bond class
print(out)
return out
def CDOPortfolio(self):
self.setParameters(R=self.Rs[0],
rating=self.ratings[0],
coupon=self.coupons[0],
beta=self.betas[0],
start_date=start_dates[0],
referenceDate=referenceDates[0],
end_date=end_dates[0],
freq=self.freqs[0])
## price CDOs using LHP
Q_now1 = self.getQ(start_date=self.start_dates[0],
referenceDate=self.referenceDates[0],
end_date=self.end_dates[0],
freq=self.freqs[0],
coupon=self.coupons[0],
rating=self.ratings[0],
R=self.Rs[0])
## Estimate default probabilites from Qtranche list ###
## Assume that lambda is constant over a small period of time
def getApproxDefaultProbs(Qvals, freq, tvalues):
t_start = tvalues[0]
delay = self.myScheduler.extractDelay(freq)
delay_days = ((t_start + delay) - t_start).days
## Estimate constant lambda
lam = -(1 / delay_days) * np.log(Qvals[t_start])
Qvals = [
((Qvals[t_start] * exp(-lam * (t - t_start).days)) / Qvals[t])
for t in tvalues
]
return (Qvals)
########################################################
print(Q_now1)
### Create Q dataframe
tvalues = Q_now1.index.tolist()
Cs = pd.Series(Q_now1, index=tvalues)
for cds_num in range(1, len(Fs)):
Q_add = self.getQ(start_date=self.start_dates[cds_num],
referenceDate=self.referenceDates[cds_num],
end_date=self.end_dates[cds_num],
freq=self.freqs[cds_num],
coupon=self.coupons[cds_num],
rating=self.ratings[cds_num],
R=self.Rs[cds_num])
Q_add = pd.Series(Q_add, index=tvalues)
Cs = pd.concat([Cs, Q_add], axis=1)
def expdecay(n):
return lambda t: Cs.ix[t, n]
##
#Qs = [expdecay(0),expdecay(1)]
Qs = [expdecay(n) for n in range(0, Cs.shape[1])]
self.getZ_Vasicek()
###### LHP Method #####################################################################
Rs_mean = np.mean(self.Rs)
betas_mean = np.mean(betas)
lhpcurve = [
self.Q_lhp(t, self.K1, self.K2, R=Rs[0], beta=betas[0], Q=Qs[0])
for t in tvalues
]
lhpcurve = pd.Series(lhpcurve, index=tvalues)
lhpcurve = getApproxDefaultProbs(lhpcurve,
freq=self.freq,
tvalues=tvalues)
lhpcurve = pd.Series(lhpcurve, index=tvalues)
ProtectionLeg = self.getProtectionLeg(myQ=lhpcurve)
PremiumLeg = self.getPremiumLegZ(myQ=lhpcurve)
spreads = ProtectionLeg / PremiumLeg
print("The spread for LHP is: ", 10000 * spreads, ".")
########################################################################################
###### Gaussian Method #####################################################################
print('Gaussian progression: ', end="")
gaussiancurve = [
self.Q_gauss(t,
self.K1,
self.K2,
Fs=self.Fs,
Rs=self.Rs,
betas=self.betas,
Qs=Qs) for t in tvalues
]
gaussiancurve = pd.Series(gaussiancurve, index=tvalues)
gaussiancurve = getApproxDefaultProbs(gaussiancurve,
freq=self.freq,
tvalues=tvalues)
gaussiancurve = pd.Series(gaussiancurve, index=tvalues)
ProtectionLeg = self.getProtectionLeg(myQ=gaussiancurve)
PremiumLeg = self.getPremiumLegZ(myQ=gaussiancurve)
spreads = ProtectionLeg / PremiumLeg
print("The spread for Gaussian is: ", 10000 * spreads, ".")
########################################################################################
###### Adjusted Binomial Method #####################################################################
adjustedbinomialcurve = [
self.Q_adjbinom(t,
self.K1,
self.K2,
Fs=self.Fs,
Rs=self.Rs,
betas=self.betas,
Qs=Qs) for t in tvalues
]
adjustedbinomialcurve = pd.Series(adjustedbinomialcurve, index=tvalues)
adjustedbinomialcurve = getApproxDefaultProbs(adjustedbinomialcurve,
freq=self.freq,
tvalues=tvalues)
adjustedbinomialcurve = pd.Series(adjustedbinomialcurve, index=tvalues)
#adjustedbinomialcurve = adjustedbinomialcurve.to_frame(self.freqs[0])
ProtectionLeg = self.getProtectionLeg(myQ=adjustedbinomialcurve)
PremiumLeg = self.getPremiumLegZ(myQ=adjustedbinomialcurve)
spreads = ProtectionLeg / PremiumLeg
print("The spread for Ajusted Binomial is: ", 10000 * spreads, ".")
########################################################################################
###### Exact Method #####################################################################
exactcurve = [
self.Q_exact(t,
self.K1,
self.K2,
Fs=self.Fs,
Rs=self.Rs,
betas=self.betas,
Qs=Qs) for t in tvalues
]
exactcurve = pd.Series(exactcurve, index=tvalues)
exactcurve = getApproxDefaultProbs(exactcurve,
freq=self.freq,
tvalues=tvalues)
exactcurve = pd.Series(exactcurve, index=tvalues)
#exactcurve = exactcurve.to_frame(self.freqs[0])
ProtectionLeg = self.getProtectionLeg(myQ=exactcurve)
PremiumLeg = self.getPremiumLegZ(myQ=exactcurve)
spreads = ProtectionLeg / PremiumLeg
print("The spread for Exact is: ", 10000 * spreads, ".")
myScheduler = Scheduler()
ReferenceDateList = myScheduler.getSchedule(start=referenceDate,
end=trim_end,
freq="1M",
referencedate=referenceDate)
# Create Simulator
xOIS = [3.0, 0.07536509, -0.208477, 0.07536509]
myVasicek = MC_Vasicek_Sim(datelist=[trim_start, trim_end],
x=xOIS,
simNumber=simNumber,
t_step=1 / 365.0)
#myVasicek.setVasicek(x=xOIS,minDay=trim_start,maxDay=trim_end,simNumber=simNumber,t_step=1/365.0)
myVasicek.getLibor()
# Create Coupon Bond with several startDates.
SixMonthDelay = myScheduler.extractDelay("6M")
TwoYearsDelay = myScheduler.extractDelay("2Y")
startDates = [
referenceDate + nprnd.randint(0, 3) * SixMonthDelay for r in range(10)
]
# For debugging uncomment this to choose a single date for the forward bond
# print(startDates)
startDates = [
date(2005, 3, 10) + SixMonthDelay,
date(2005, 3, 10) + TwoYearsDelay
]
maturities = [(x + TwoYearsDelay) for x in startDates]
myPortfolio = {}
coupon = 0.07536509
class CDS(object):
def __init__(self,
start_date,
end_date,
freq,
coupon,
referenceDate,
rating,
R=.4):
### Parameters passed to external functions
self.start_date = start_date
self.end_date = end_date
self.freq = freq
self.R = R
self.notional = 1
self.fee = fee
self.rating = rating
self.coupon = coupon
self.referenceDate = referenceDate
self.observationDate = referenceDate
## Get datelist ##
self.myScheduler = Scheduler()
# ReferenceDateList = self.myScheduler.getSchedule(start=referenceDate,end=end_date,freq=freq, referencedate=referenceDate)
## Delay start and maturity
delay = self.myScheduler.extractDelay(self.freq)
cashFlowDays = self.myScheduler.extractDelay("3M")
## Delay maturity and start date
# self.start_date = self.start_date +SixMonthDelay
# self.maturity =self.start_date + delay
self.maturity = self.start_date + delay
fulllist, datelist = self.getScheduleComplete()
self.datelist = datelist
self.portfolioScheduleOfCF = fulllist
self.myZ = None
self.myQ = None
# ////////////////////////////////////////////////////////////////////////////////////////////#
# ////////////////////////////////////////////////////////////////////////////////////////////#
# ////////////////////////////// SET FUNCTIONS ///////////////////////////////////////////////#
# ////////////////////////////////////////////////////////////////////////////////////////////#
# ////////////////////////////////////////////////////////////////////////////////////////////#
def setZ(self, Zin):
## Zin needs to be a pandas dataframe
self.myZ = Zin
def setQ(self, Qin):
self.myQ = Qin
# ////////////////////////////////////////////////////////////////////////////////////////////#
# ////////////////////////////////////////////////////////////////////////////////////////////#
# ////////////////////////////// GET FUNCTIONS ///////////////////////////////////////////////#
# ////////////////////////////////////////////////////////////////////////////////////////////#
# ////////////////////////////////////////////////////////////////////////////////////////////#
# //////////////// Get Vasicek simulated Z(t,t_j)
def getZ_Vasicek(self):
### Get Z(t,t_i) for t_i in datelist #####
## Simulate Vasicek model with paramters given in workspace.parameters
# xR = [5.0, 0.05, 0.01, 0.05]
# kappa = x[0]
# theta = x[1]
# sigma = x[2]
# r0 = x[3]
vasicekMC = MC_Vasicek_Sim(
datelist=[self.referenceDate, self.maturity],
x=xR,
simNumber=100,
t_step=t_step)
self.myZ = vasicekMC.getLibor()
self.myLibor = self.myZ
self.myZ = self.myZ.loc[:, 0]
## get Libor Z for reference dates ##
# self.myZ = vasicekMC.getSmallLibor(datelist= self.portfolioScheduleOfCF).loc[:,0]
# //////////////// Get Corporates simulated Q(t,t_j)
def getQ_Corporate(self):
## Use CorporateDaily to get Q for referencedates ##
# print("GET Q")
# print(self.portfolioScheduleOfCF)
myQ = CorporateRates()
myQ.getCorporatesFred(trim_start=self.referenceDate,
trim_end=self.end_date)
## Return the calculated Q(t,t_i) for bonds ranging over maturities for a given rating
daterange = pd.date_range(start=self.referenceDate,
end=self.maturity).date
self.myQ = myQ.getCorporateQData(rating=self.rating,
datelist=daterange,
R=self.R)
return (self.myQ)
# //////////////// Get Premium leg Z(t_i)( Q(t_(i-1)) + Q(t_i) )
def getPremiumLegZ(self):
## Check if Z and Q exists
if self.myZ is None:
self.getZ_Vasicek()
if self.myQ is None:
self.getQ_Corporate()
## Choose 1month Q
'''
Q1M = self.myQ[self.freq]
# Q1M = self.myQ["QTranche"]
timed = self.portfolioScheduleOfCF[self.portfolioScheduleOfCF.index(self.referenceDate):]
Q1M = Q1M.loc[timed]
Q1M = Q1M.cumprod(axis=0)
zbarPremLeg = self.myZ / self.myZ.loc[self.referenceDate]
zbarPremLeg = zbarPremLeg.loc[timed]
## Calculate Q(t_i) + Q(t_(i-1))
out = 0
for i in range(1, len(Q1M)):
out = out + (Q1M[(i - 1)] + Q1M[i]) * float((timed[i] - timed[i - 1]).days / 365) * zbarPremLeg[i]
## Calculate the PV of the premium leg using the bond class
# zbarPremLeg = zbarPremLeg.cumsum(axis=0)
zbarPremLeg = pd.DataFrame(zbarPremLeg, index=timed)
# print("Premium LEg")
PVpremiumLeg = out * (1 / 2)
# print(PVpremiumLeg)
## Get coupon bond ###
return PVpremiumLeg
'''
Q1M = self.myQ[self.freq]
# Q1M = self.myQ["QTranche"]
timed = self.portfolioScheduleOfCF[self.portfolioScheduleOfCF.
index(self.referenceDate):]
Q1M = Q1M.loc[timed]
Q1M = Q1M.cumprod()
zbarPremLeg = self.myZ / self.myZ.loc[self.referenceDate]
zbarPremLeg = zbarPremLeg.loc[timed]
## Calculate Q(t_i) + Q(t_(i-1))
Qplus = []
out = 0
for i in range(1, len(Q1M)):
out = out + (Q1M[(i - 1)] + Q1M[i]) * float(
(timed[i] - timed[i - 1]).days / 365) * zbarPremLeg[i]
## Calculate the PV of the premium leg using the bond class
# zbarPremLeg = zbarPremLeg.cumsum(axis=0)
zbarPremLeg = pd.DataFrame(zbarPremLeg, index=timed)
# print("Premium LEg")
PVpremiumLeg = out * (1 / 2)
# print(PVpremiumLeg)
## Get coupon bond ###
return PVpremiumLeg
# //////////////// Get Protection leg Z(t_i)( Q(t_(i-1)) - Q(t_i) )
def getProtectionLeg(self):
if self.myZ is None:
self.getZ_Vasicek()
if self.myQ is None:
self.getQ_Corporate()
# Q1M = self.myQ["QTranche"]
## Calculate Q(t_i) + Q(t_(i-1))
# Qminus = np.gradient(np.array(Q1M))
# print(Qminus)
# QArray = np.array(Q1M)
# QArray = np.insert(QArray, obj = 0, values = 1)
# print(QArray)
# Q1M = self.myQ["QTranche"]
'''
Q1M = self.myQ[self.freq]
Q1M = Q1M.cumprod()
timed = Q1M.index.tolist()
timed = self.portfolioScheduleOfCF[self.portfolioScheduleOfCF.index(self.referenceDate):]
Q1M = Q1M.loc[timed]
zbarPremLeg = self.myZ / self.myZ.loc[self.referenceDate]
zbarPremLeg = zbarPremLeg.loc[timed]
## Calculate Q(t_i) + Q(t_(i-1))
Qplus = []
out = 0
for i in range(1, len(Q1M)):
out = out + (Q1M[(i-1)] - Q1M[(i)]) * float((timed[i] - timed[i - 1]).days / 365) * zbarPremLeg[i]
return(out)
## Calculate Z Bar ##
Qminus = np.gradient(Q1M)
zbarProtectionLeg = self.myZ / self.myZ.loc[self.referenceDate]
for i in range(1,zbarProtectionLeg.shape[0]):
zbarProtectionLeg.iloc[i] = -Qminus[i] * zbarProtectionLeg.iloc[i] * (1/365)
## Calculate the PV of the premium leg using the bond class
zbarProtectionLeg = zbarProtectionLeg.cumsum(axis=0)
zbarProtectionLeg = pd.DataFrame(zbarProtectionLeg, index=Q1M.index)
PVprotectionLeg = (1 - self.R) * zbarProtectionLeg
## Get coupon bond ###
return PVprotectionLeg.loc[self.maturity]
'''
Q1M = self.myQ[self.freq]
Q1M = Q1M.cumprod()
Qminus = np.gradient(Q1M)
zbarProtectionLeg = self.myZ / self.myZ.loc[self.referenceDate]
for i in range(zbarProtectionLeg.shape[0]):
zbarProtectionLeg.iloc[
i] = -Qminus[i] * zbarProtectionLeg.iloc[i] * (1 / 365)
## Calculate the PV of the premium leg using the bond class
zbarProtectionLeg = zbarProtectionLeg.cumsum(axis=0)
zbarProtectionLeg = pd.DataFrame(zbarProtectionLeg, index=Q1M.index)
PVprotectionLeg = (1 - self.R) * zbarProtectionLeg
## Get coupon bond ###
return PVprotectionLeg.loc[self.maturity]
# /////////////////////// Functions to get the exposure sum [delta Z(t,t_j)( Q(t,t_(j-1)) +/- Q(t,t_j) )
# ////// These are copied from Coupon bond
def getScheduleComplete(self):
self.datelist = self.myScheduler.getSchedule(
start=self.start_date,
end=self.maturity,
freq='3M',
referencedate=self.referenceDate)
self.ntimes = len(self.datelist)
fullset = list(
sorted(
list(
set(self.datelist).union([self.referenceDate]).union([
self.start_date
]).union([self.maturity]).union([self.observationDate]))))
return fullset, self.datelist
# /////// Get exposure
def getExposure(self, referencedate, libor):
self.ntrajectories = np.shape(libor)[1]
self.ones = np.ones(shape=[self.ntrajectories])
deltaT = np.zeros(self.ntrajectories)
if self.ntimes == 0:
pdzeros = pd.DataFrame(data=np.zeros([1, self.ntrajectories]),
index=[referencedate])
self.pv = pdzeros
self.pvAvg = 0.0
self.cashFlows = pdzeros
self.cashFlowsAvg = 0.0
return self.pv
for i in range(1, self.ntimes):
deltaTrow = ((self.datelist[i] - self.datelist[i - 1]).days /
365) * self.ones
deltaT = np.vstack((deltaT, deltaTrow))
self.cashFlows = self.coupon * deltaT
principal = self.ones
if self.ntimes > 1:
self.cashFlows[-1:] += principal
else:
self.cashFlows = self.cashFlows + principal
if (self.datelist[0] <= self.start_date):
self.cashFlows[0] = -self.fee * self.ones
if self.ntimes > 1:
self.cashFlowsAvg = self.cashFlows.mean(axis=1) * self.notional
else:
self.cashFlowsAvg = self.cashFlows.mean() * self.notional
pv = self.cashFlows * libor.loc[self.datelist]
self.pv = pv.sum(axis=0) * self.notional
self.pvAvg = np.average(self.pv) * self.notional
return self.pv
# ///// Get the calculated Market to Market based on spread
def getValue(self, spread=1, R=0, buyer=True):
## Assume V(t) = S/2 sum Z(t_i) (Q(t_i) + Q(t_{i-1})) - (1-R)sum Z(t_i) (Q(t_{i-1}) - Q(t_{i}))
## Premium leg = S/2 sum Z(t_i) (Q(t_i) + Q(t_{i-1}))
## Protection leg =sum Z(t_i) (Q(t_i) + Q(t_{i-1}))
premiumLeg = self.getPremiumLegZ()
protectionLeg = self.getProtectionLeg()
mtm = (spread / 2) * premiumLeg - (1 - R) * protectionLeg
if buyer is True:
return mtm
else:
return -mtm
def getSpread(self):
out = self.getProtectionLeg() / self.getPremiumLegZ()
return out.values[0]
def changeGuessForSpread(self, x):
'''
inputs a x list of guesses for the Vasicek simulator than changes the myQ
'''
vasicekMC = MC_Vasicek_Sim(
datelist=[self.referenceDate, self.maturity],
x=x,
simNumber=20,
t_step=t_step)
self.myQ = vasicekMC.getLibor()[0]
self.myQ = pd.DataFrame(self.myQ, index=self.myQ.index)
self.myQ.columns = [self.freq]
spread = self.bootProtec() / self.bootPrem()
return spread.values[0]
def bootProtec(self):
Q1M = self.myQ[self.freq]
Q1M = Q1M.cumprod()
Qminus = np.gradient(Q1M)
zbarProtectionLeg = self.myZ / self.myZ.loc[self.referenceDate]
for i in range(zbarProtectionLeg.shape[0]):
zbarProtectionLeg.iloc[
i] = -Qminus[i] * zbarProtectionLeg.iloc[i] * (1 / 365)
## Calculate the PV of the premium leg using the bond class
zbarProtectionLeg = zbarProtectionLeg.cumsum(axis=0)
zbarProtectionLeg = pd.DataFrame(zbarProtectionLeg, index=Q1M.index)
PVprotectionLeg = (1 - self.R) * zbarProtectionLeg
## Get coupon bond ###
return PVprotectionLeg.loc[self.maturity]
def bootPrem(self):
Q1M = self.myQ[self.freq]
# Q1M = self.myQ["QTranche"]
timed = self.portfolioScheduleOfCF[self.portfolioScheduleOfCF.
index(self.referenceDate):]
Q1M = Q1M.loc[timed]
Q1M = Q1M.cumprod()
zbarPremLeg = self.myZ / self.myZ.loc[self.referenceDate]
zbarPremLeg = zbarPremLeg.loc[timed]
## Calculate Q(t_i) + Q(t_(i-1))
Qplus = []
out = 0
for i in range(1, len(Q1M)):
out = out + (Q1M[(i - 1)] + Q1M[i]) * float(
(timed[i] - timed[i - 1]).days / 365) * zbarPremLeg[i]
## Calculate the PV of the premium leg using the bond class
# zbarPremLeg = zbarPremLeg.cumsum(axis=0)
zbarPremLeg = pd.DataFrame(zbarPremLeg, index=timed)
# print("Premium LEg")
PVpremiumLeg = out * (1 / 2)
# print(PVpremiumLeg)
## Get coupon bond ###
return PVpremiumLeg
from Scheduler.Scheduler import Scheduler
import matplotlib.pyplot as plt
import pandas as pd
from datetime import date
# dr(t) = k(θ − r(t))dt + σdW(t)
# self.kappa = x[0]
# self.theta = x[1]
# self.sigma = x[2]
# self.r0 = x[3]
myScheduler = Scheduler()
observationdate = minDay = date(2005, 1, 10) # Observation Date
maxDay = date(2010, 1, 10) # Last Date of the Portfolio
start = date(2005, 3, 30)
maturity = start + myScheduler.extractDelay("1Y")
referenceDate = date(2005, 5, 3) # 6 months after trim_start
simNumber = 5
R = 0.4
inArrears = True
freq = '3M'
t_step = 1.0 / 365
simNumber = 5
coupon = 0.08
fee = 1.0
xR = [3.0, 0.05, 0.04, 0.03]
myBond = CouponBond(fee=fee,
start=start,
maturity=maturity,
class CorporateRates(object):
def __init__(self):
self.OIS = []
self.filename = WORKING_DIR + '/CorpData.dat'
self.corporates = []
self.ratings = ['AAA', 'AA', 'A', 'BBB', 'BB', 'B', 'CCC']
self.corpSpreads = {}
self.corporates = pd.DataFrame()
self.tenors = []
self.unPickleMe(file=self.filename)
self.myScheduler=Scheduler()
def getCorporates(self, trim_start, trim_end):
curr_trim_end=trim_start
if(self.corporates.size!=0):
self.trim_start = self.corporates['OIS'].index.min().date()
curr_trim_end = self.corporates['OIS'].index.max().date()
if trim_end<=curr_trim_end:
self.trim_end = curr_trim_end
return self.corporates
self.trim_start = trim_start
self.trim_end = trim_end
self.OIS = OIS(trim_start=trim_start, trim_end=trim_end)
self.datesAll = self.OIS.datesAll
self.datesAll.columns= [x.upper() for x in self.datesAll.columns]
self.OISData = self.OIS.getOIS()
for i in np.arange(len(self.OISData.columns)):
freq = self.OISData.columns[i]
self.tenors.append(self.myScheduler.extractDelay(freq=freq))
for rating in self.ratings:
index = 'ML/' + rating + 'TRI'
try:
corpSpreads = 1e-4 * (
quandl.get(index, authtoken="Lqsxas8ieaKqpztgYHxk", trim_start=trim_start, trim_end=trim_end))
corpSpreads.reset_index(level=0, inplace=True)
corpSpreads = pd.merge(left=self.datesAll, right=corpSpreads, how='left')
corpSpreads = corpSpreads.fillna(method='ffill').fillna(method='bfill')
self.corpSpreads[rating] = corpSpreads.T.fillna(method='ffill').fillna(method='bfill').T
except:
print(index, " not found")
self.corpSpreads = pd.Panel.from_dict(self.corpSpreads)
self.corporates = {}
self.OISData.drop('DATE', axis=1, inplace=True)
ntenors = np.shape(self.OISData)[1]
for rating in self.ratings:
try:
tiledCorps = np.tile(self.corpSpreads[rating]['VALUE'], ntenors).reshape(np.shape(self.OISData))
self.corporates[rating] = pd.DataFrame(data=(tiledCorps + self.OISData.values),
index=self.OISData.index, columns=self.OISData.columns)
except:
print("Error in addition of Corp Spreads")
self.corporates['OIS'] = self.OISData
self.corporates = pd.Panel(self.corporates)
return self.corporates
def getOISData(self, datelist=[]):
if (len(datelist) != 0):
return self.corporates["OIS"].loc[datelist]
else:
return self.self.corporates["OIS"]
def getCorporateData(self, rating, datelist=[]):
if (len(datelist) != 0):
return self.corporates[rating].loc[datelist]
else:
return self.corporates[rating]
def pickleMe(self):
data = [self.corporates, self.corpSpreads]
with open(self.filename, "wb") as f:
pickle.dump(len(data), f)
for value in data:
pickle.dump(value, f)
def unPickleMe(self, file):
data = []
if (os.path.exists(file)):
with open(file, "rb") as f:
for _ in range(pickle.load(f)):
data.append(pickle.load(f))
self.corporates = data[0]
self.corpSpreads = data[1]
def saveMeExcel(self, whichdata, fileName):
try:
df = pd.DataFrame(whichdata)
except:
df = whichdata
df.to_excel(fileName)
class CorporateRates(object):
def __init__(self):
self.OIS = []
self.filename = WORKING_DIR + '/CorpData.dat'
self.corporates = []
self.ratings = {
'AAA': "BAMLC0A1CAAA",
'AA': "BAMLC0A2CAA",
'A': "BAMLC0A3CA",
'BBB': "BAMLC0A4CBBB",
'BB': "BAMLH0A1HYBB",
'B': "BAMLH0A2HYB",
'CCC': "BAMLH0A3HYC"
}
self.corpSpreads = {}
self.corporates = pd.DataFrame()
self.Qcorporates = pd.DataFrame() # survival function for corporates
self.tenors = []
self.unPickleMe(file=self.filename)
self.myScheduler = Scheduler()
self.myVasicek = MC_Vasicek_Sim()
self.R = 0.4
def getCorporatesFred(self, trim_start, trim_end):
fred = Fred(api_key=FRED_API_KEY)
curr_trim_end = trim_start
if (self.corporates.size != 0):
self.trim_start = self.corporates['OIS'].index.min().date()
curr_trim_end = self.corporates['OIS'].index.max().date()
if trim_end <= curr_trim_end:
self.trim_end = curr_trim_end
return self.corporates
self.trim_start = trim_start
self.trim_end = trim_end
self.OIS = OIS(trim_start=trim_start, trim_end=trim_end)
self.datesAll = self.OIS.datesAll
self.datesAll.columns = [x.upper() for x in self.datesAll.columns]
self.datesAll.index = self.datesAll.DATE
self.OISData = self.OIS.getOIS()
for i in np.arange(len(self.OISData.columns)):
freq = self.OISData.columns[i]
self.tenors.append(self.myScheduler.extractDelay(freq=freq))
for rating in self.ratings.keys():
index = self.ratings[rating]
try:
corpSpreads = 1e-2 * (fred.get_series(
index,
observation_start=trim_start,
observation_end=trim_end).to_frame())
corpSpreads.index = [x.date() for x in corpSpreads.index[:]]
corpSpreads = pd.merge(left=self.datesAll,
right=corpSpreads,
left_index=True,
right_index=True,
how="left")
corpSpreads = corpSpreads.fillna(method='ffill').fillna(
method='bfill')
corpSpreads = corpSpreads.drop("DATE", axis=1)
self.corpSpreads[rating] = corpSpreads.T.fillna(
method='ffill').fillna(method='bfill').T
except Exception as e:
print(e)
print(index, " not found")
self.corpSpreads = pd.Panel.from_dict(self.corpSpreads)
self.corporates = {}
self.OISData.drop('DATE', axis=1, inplace=True)
ntenors = np.shape(self.OISData)[1]
for rating in self.ratings:
try:
tiledCorps = np.tile(self.corpSpreads[rating][0],
ntenors).reshape(np.shape(self.OISData))
self.corporates[rating] = pd.DataFrame(
data=(tiledCorps + self.OISData.values),
index=self.OISData.index,
columns=self.OISData.columns)
except:
print("Error in addition of Corp Spreads")
self.corporates['OIS'] = self.OISData
self.corporates = pd.Panel(self.corporates)
return self.corporates
def getCorporateData(self, rating, datelist=None):
# This method gets a curve for a given date or date list for a given rating (normally this will be just a date).
# It returns a dict of curves read directly from the corporate rates created by getCorporatesFred.
# Derive delays from self.corporates[rating].columns
myDelays = self.myScheduler.extractDelay(
freq=list(self.corporates[rating].columns))
if datelist is None:
return
outCurve = {}
for day in datelist:
# add curve to outcurve dict
myCurve = self.corporates.loc[rating, datelist]
outCurve[day] = myCurve
return outCurve
def getCorporateQData(self, rating, datelist=None, R=0.4):
self.R = R
self.OIS = OIS()
# there is a difference in labels between Fred and Quandl...
colLabel = [
"1M", "3M", "6M", "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", "20Y", "30Y"
]
outCurve = {}
if datelist is None:
return
myCurve = np.ones(11)
tempCurve = np.ones(11)
outCurve[datelist[0]] = myCurve
z = self.OIS.getOIS(datelist)
spread = self.getCorporateData(rating, datelist)
delta = (datelist[1] - datelist[0]).days / 365
interimSum = np.zeros(11)
for day in datelist[1:]:
# Create Q curves using q-tilde equation
endDate = datelist.slice_indexer(start=day).start
for day2 in datelist[endDate - 1:endDate]:
if day2 == datelist[0]:
qPrev = np.ones(11)
else:
qPrev = outCurve[day2 - 1]
qCurr = outCurve[day2]
spread = self.getCorporateData(
rating, pd.date_range(start=day, end=day))
interimSum = interimSum + z.loc[day2].values[1:] * (
outCurve[day2] * delta * spread[day].values - (1 - R) *
(qPrev - qCurr))
myCurve = (z.loc[day].values[1:] * (1 - R) * outCurve[day - 1] -
interimSum) / (z.loc[day].values[1:] *
(delta * spread[day] + 1 - R))
tempCurve = np.vstack((tempCurve, myCurve.values.ravel()))
outCurve[day] = myCurve.values.ravel()
a = 1
tempCurve = pd.DataFrame(data=tempCurve,
columns=colLabel,
index=datelist.values)
return tempCurve
def pickleMe(self):
data = [self.corporates, self.corpSpreads]
with open(self.filename, "wb") as f:
pickle.dump(len(data), f)
for value in data:
pickle.dump(value, f)
def unPickleMe(self, file):
data = []
if (os.path.exists(file)):
with open(file, "rb") as f:
for _ in range(pickle.load(f)):
data.append(pickle.load(f))
self.corporates = data[0]
self.corpSpreads = data[1]
def saveMeExcel(self, whichdata, fileName):
try:
df = pd.DataFrame(whichdata)
except:
df = whichdata
df.to_excel(fileName)
