def __init__(self):
self.dates = []
self.OIS = OIS()
self.corporates = []
self.ratings = ['AAA', 'AA']
self.corpSpreads = {}
self.corporates = pd.DataFrame()
self.corporates_full = {}
def getCorporates(self, trim_start, trim_end, WORKING_DIR='.'):
self.OIS = OIS(trim_start=trim_start,
trim_end=trim_end,
WORKING_DIR=WORKING_DIR)
self.datesAll = self.OIS.datesAll
self.OISData = self.OIS.getOIS()
self.WORKING_DIR = WORKING_DIR
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)
tiledCorps = tiledCorps.reshape(np.shape(self.OISData),
order="F")
self.corporates[rating] = pd.DataFrame(
data=(tiledCorps + self.OISData.values))
self.corporates[rating].drop(
self.corporates[rating].columns[[0]], axis=1, inplace=True)
except:
print("Error in addition of Corp Spreads")
self.corporates = pd.Panel(self.corporates)
return self.corporates
def lsqQ(para):
ois = OIS(trim_start = startdate, trim_end = enddate)
Affine = VasicekAffine(x, para, datelist)
myCDS = CDS(ois.interpolationois(), datelist, Affine.calQ(), recovery)
diff = np.subtract(brz_cds,myCDS.MTM())
return diff
def lsqR(para):
ois = OIS(trim_start = startdate, trim_end = enddate)
Affine = VasicekAffine(x, para, datelist)
diff = np.subtract(ois.interpolationois(),Affine.calR())
return diff
def lsqQ(para):
ois = OIS(trim_start = startdate, trim_end = enddate)
Affine = VasicekAffine(x, para, datelist)
myCDS = CDS(ois.interpolationois(), datelist, Affine.calQ(), recovery)
diff = np.subtract(brz_cds,myCDS.MTM())
def lsqR(para):
ois = OIS(trim_start = startdate, trim_end = enddate)
Affine = VasicekAffine(x, para, datelist)
diff = np.subtract(ois.interpolationois(),Affine.calR())
return diff
class CorporateRates(object):
def __init__(self):
self.dates = []
self.OIS = OIS()
self.corporates = []
self.ratings = ['AAA', 'AA']
self.corpSpreads = {}
self.corporates = pd.DataFrame()
self.corporates_full = {}
def getCorporates(self, trim_start, trim_end, WORKING_DIR='.'):
self.OIS = OIS(trim_start=trim_start,
trim_end=trim_end,
WORKING_DIR=WORKING_DIR)
self.datesAll = self.OIS.datesAll
self.OISData = self.OIS.getOIS()
self.WORKING_DIR = WORKING_DIR
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)
tiledCorps = tiledCorps.reshape(np.shape(self.OISData),
order="F")
self.corporates[rating] = pd.DataFrame(
data=(tiledCorps + self.OISData.values))
self.corporates[rating].drop(
self.corporates[rating].columns[[0]], axis=1, inplace=True)
except:
print("Error in addition of Corp Spreads")
self.corporates = pd.Panel(self.corporates)
return self.corporates
def interpolation_corporates(self):
start = datetime.datetime(2005, 1, 10)
end = datetime.datetime(2010, 1, 10)
try:
row_days = (end - start).days + 1
except:
print("error: may be end day is earlier than start day")
ndate = [29, 89, 179, 364, 729, 1094, 1824, 2554, 3649, 7329, 10949]
for rating in self.ratings:
temp_dict = {}
start = datetime.datetime(2005, 1, 10)
for i in range(0, row_days):
vdate = pd.DataFrame(
self.corporates[rating].iloc[i, 0:11]).values.tolist()
vdate = reduce(lambda x, y: x + y, vdate)
end = start + relativedelta(years=30)
ndays = (end - start).days
d_series = np.arange(0, ndays, 1)
temp_dict[start] = np.interp(d_series, ndate, vdate)
start = start + datetime.timedelta(days=1)
self.corporates_full[rating] = temp_dict
h = self.corporates_full['AAA']
end_temp = datetime.datetime(2008, 1, 10)
g = h[end_temp][7]
# self.corporates_full=pd.Panel.from_dict(self.corporates_full)
return self.corporates_full
def pickleMe(self, file):
pickle.dump(self.Data, open(file, "wb"))
def unPickleMe(self, file):
if (os.path.exists(file)):
self.Data = pickle.load(open(file, "rb"))
def saveMeExcel(self, whichdata, fileName, dir=None):
df = pd.DataFrame(whichdata)
if (dir == None):
fName = os.path.join(self.WORKING_DIR, fileName + '.xlsx')
else:
fName = os.path.join(dir, fileName + '.xlsx')
df.to_excel(fName, sheet_name=fileName, index=False)
# a=range(1,5)
# for i in a:
# print(i)
# print(a)
start = datetime.datetime(2005, 1, 10)
end = datetime.datetime(2010, 1, 10)
test_current_day = datetime.datetime(2009, 3, 5)
test_end_day = datetime.datetime(2037, 9, 10)
start_temp = start
end_temp = start_temp + relativedelta(years=30)
trim_start = "2005-01-10"
trim_end = "2010-01-10"
#a=Quandl.get("USTREASURY/YIELD", authtoken="Lqsxas8ieaKqpztgYHxk", trim_start=trim_start, trim_end=trim_end)
#print(a)
testois = OIS()
print('discounted factor', testois.interpolationois())
a = testois.get_OIS_daily(test_current_day, test_end_day)
#print(a)
#test single:24+i to 28+i
# a=testois.interpolationois()
# ndays=(end_temp-start_temp).days
# d_series=np.arange(0,ndays,1)
# plt.plot(d_series,a)
# plt.show()
# a=(end-start).days
# t=[]
# t=np.arange(0,a,1)
# ndate=[29,89,179,364,729,1094,1824,2554,3649,7329,10949]