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import numpy as np
from ctypes import *
from quantlib.time.schedule import Schedule, CDS
from quantlib.time.api import Actual360, Period, UnitedStates, Following, today
from quantlib.util.converter import qldate_to_pydate, pydate_to_qldate
import pandas as pd
libloss = np.ctypeslib.load_library("lossdistrib", "/home/share/CorpCDOs/code/R")
libloss.fitprob.restype = None
libloss.fitprob.argtypes = [np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
POINTER(c_int),
POINTER(c_double),
POINTER(c_double),
np.ctypeslib.ndpointer('double', ndim=1, flags='F,writeable')]
libloss.stochasticrecov.restype = None
libloss.stochasticrecov.argtypes = [POINTER(c_double),
POINTER(c_double),
np.ctypeslib.ndpointer('double', ndim=2, flags='F'),
np.ctypeslib.ndpointer('double', ndim=2, flags='F'),
POINTER(c_int),
POINTER(c_double),
POINTER(c_double),
POINTER(c_double),
np.ctypeslib.ndpointer('double', ndim=1, flags='F,writeable')]
libloss.BClossdist.restype = None
libloss.BClossdist.argtypes = [np.ctypeslib.ndpointer('double', ndim=2, flags='F'),# defaultprob
POINTER(c_int),# nrow(defaultprob)
POINTER(c_int),# ncol(defaultprob)
np.ctypeslib.ndpointer('double', ndim=1, flags='F'),# issuerweights
np.ctypeslib.ndpointer('double', ndim=1, flags='F'),# recovery
np.ctypeslib.ndpointer('double', ndim=1, flags='F'),# Z
np.ctypeslib.ndpointer('double', ndim=1, flags='F'),# w
POINTER(c_int), # len(Z) = len(w)
np.ctypeslib.ndpointer('double', ndim=1, flags='F'), # rho
POINTER(c_int), # Ngrid
POINTER(c_int), #defaultflag
np.ctypeslib.ndpointer('double', ndim=2, flags='F,writeable'),# output L
np.ctypeslib.ndpointer('double', ndim=2, flags='F,writeable')# output R
]
libgq = np.ctypeslib.load_library("GHquad", ".")
libgq.GHquad.restype = None
libgq.GHquad.argtypes = [c_int, np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
np.ctypeslib.ndpointer('double', ndim=1, flags='F')]
def GHquad(n):
Z = np.zeros(n, dtype='double')
w = np.zeros(n, dtype='double')
libgq.GHquad(n, Z, w)
return Z, w
def stochasticrecov(R, Rtilde, Z, w, rho, porig, pmod):
q = np.zeros_like(Z)
libloss.stochasticrecov(byref(c_double(R)), byref(c_double(Rtilde)), Z, w, byref(c_int(Z.size)),
byref(c_double(rho)), byref(c_double(porig)), byref(c_double(pmod)), q)
return q
def fitprob(Z, w, rho, p0):
result = np.empty_like(Z)
libloss.fitprob(Z, w, byref(c_int(Z.size)), byref(c_double(rho)), byref(c_double(p0)), result)
return result
def BClossdist(defaultprob, issuerweights, recov, rho, Z, w, Ngrid = 101, defaultflag = False):
L = np.zeros((Ngrid, defaultprob.shape[1]), order='F')
R = np.zeros_like(L)
rho = np.repeat(rho, issuerweights.size)
libloss.BClossdist(defaultprob, byref(c_int(defaultprob.shape[0])), byref(c_int(defaultprob.shape[1])),
issuerweights, recov, Z, w, byref(c_int(Z.size)), rho,
byref(c_int(Ngrid)), byref(c_int(defaultflag)), L, R)
return L, R
def adjust_attachments(K, losstodate, factor):
"""
computes the attachments adjusted for losses
on current notional
"""
return np.minimum(np.maximum((K-losstodate)/factor, 0), 1)
def trancheloss(L, K1, K2):
return np.maximum(L - K1, 0) - np.maximum(L - K2, 0)
def trancherecov(R, K1, K2):
return np.maximum(R - 1 + K2, 0) - np.maximum(R - 1 + K1, 0)
def tranche_cl(L, R, cs, K1, K2, scaled = False):
if(K1 == K2):
return 0
else:
support = np.linspace(0, 1, L.shape[0])
size = K2 - K1 - np.dot(trancheloss(support, K1, K2), L) - \
np.dot(trancherecov(support, K1, K2), R)
sizeadj = 0.5 * (size + np.hstack((K2-K1, size[:-1])))
if scaled:
return 1/(K2-K1) * np.dot(sizeadj * cs["coupons"], cs["df"])
else:
return np.dot(sizeadj * cs["coupons"], cs["df"])
def tranche_pl(L, cs, K1, K2, scaled=False):
if(K1 == K2):
return 0
else:
support = np.linspace(0, 1, L.shape[0])
cf = K2 - K1 - np.dot(trancheloss(support, K1, K2), L)
cf = np.hstack((K2-K1, cf))
if scaled:
return 1/(K2-K1) * np.dot(np.diff(cf), cs["df"])
else:
return np.dot(np.diff(cf), cs["df"])
def tranche_pv(L, R, cs, K1, K2):
return tranche_pl(L, cs, K1, K2) + tranche_cl(L, R, cs, K2, K2)
def creditSchedule(tradedate, tenor, coupon, yc, enddate = None):
tradedate = pydate_to_qldate(tradedate)
start = tradedate - Period('4Mo')
enddate = pydate_to_qldate(enddate)
if enddate is None:
enddate = tradedate + Period(tenor)
cal = UnitedStates()
DC = Actual360()
sched = Schedule(start, enddate, Period('3Mo'), cal, date_generation_rule=CDS)
prevpaydate = sched.previous_date(tradedate)
sched = [d for d in sched if d>=prevpaydate]
df = [yc.discount(d) for d in sched if d > tradedate]
dates = pd.to_datetime([str(d) for d in sched if d > tradedate], "%m/%d/%Y")
coupons = np.diff([DC.year_fraction(prevpaydate, d) * coupon for d in sched])
return pd.DataFrame({"df":df, "coupons":coupons}, dates)
def cdsAccrued(tradedate, coupon):
tradedate = pydate_to_qldate(tradedate)
start = tradedate - Period('3Mo')
end = tradedate + Period('3Mo')
start_protection = tradedate + 1
DC = Actual360()
cal = UnitedStates()
sched = Schedule(start, end, Period('3Mo'), cal, date_generation_rule=CDS)
prevpaydate = sched.previous_date(tradedate)
return DC.year_fraction(prevpaydate, start_protection) * coupon
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