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from .basket_index import BasketIndex
from .db import _engine
from .tranche_functions import credit_schedule, adjust_attachments, cds_accrued
from index_data import get_singlenames_curves, get_tranche_quotes
from pyisda.cdsone import upfront_charge
from pandas.tseries.offsets import BDay
import pandas as pd
import numpy as np
class TrancheBasket(BasketIndex):
def __init__(self, index_type: str, series: int, tenor: str, *,
trade_date: pd.Timestamp=pd.Timestamp.today().normalize()):
print(trade_date)
super().__init__(index_type, series, [tenor], trade_date=trade_date)
self.tranche_quotes = get_tranche_quotes(index_type, series, tenor, trade_date.date())
index_desc = self.index_desc.reset_index('maturity').set_index('tenor')
self.maturity = index_desc.loc[tenor].maturity
self.start_date, self.cs = credit_schedule(trade_date, tenor[:-1], 1, self.yc)
self.K_orig = np.hstack([0, self.tranche_quotes.detach])
self.K = adjust_attachments(self.K_orig, self.cumloss, self.factor)
if index_type == "HY":
self.tranche_quotes['quotes'] = 1 - self.tranche_quotes.trancheupfrontmid / 100
else:
self.tranche_quotes['quotes'] = self.tranche_quotes.trancheupfrontmid / 100
self.tranche_quotes['running'] = self.tranche_quotes.trancherunningmid * 1e-4
if index_type == "XO":
coupon = 500 * 1e-4
self.tranche_quotes.quotes.iat[3] = self._snacpv(self.tranche_quotes.running.iat[3],
coupon,
0.4)
self.tranche_quotes.running = coupon
if index_type == "EU":
if series >= 21:
coupon = 100 * 1e-4
for i in [2, 3]:
self.tranche_quotes.quotes.iat[i] = self._snacpv(
self.tranche_quotes.running.iat[i],
coupon,
0. if i == 2 else 0.4)
self.tranche_quotes.running.iat[i] = coupon
elif series == 9:
for i in [3, 4, 5]:
coupon = 25 * 1e-4 if i == 5 else 100 * 1e-4
recov = 0.4 if i == 5 else 0
self.tranche_quotes.quotes.iat[i] = self._snacpv(
self.tranche_quotes.running.iat[i],
coupon,
recov)
self.tranche_quotes.running.iat[i] = coupon
accrued = cds_accrued(self.trade_date, self.tranche_quotes.running)
self.tranche_quotes.quotes -= accrued
self._Ngh = 250
self._Ngrid = 201
self._Z, self._w = GHquad(self._Ngh)
def _get_quotes(self):
refprice = self.tranche_quotes[0]['indexrefprice']
refspread = self.tranche_quotes[0]['indexrefspread']
if refprice is not None:
return {self.maturity: 1 - refprice / 100}
if refspread is not None:
return {self.maturity:
_scnacpv(refspread * 1e-4, self.coupon(maturity), self.recovery)}
raise ValueError("ref is missing")
def _snacpv(self, spread, coupon, recov):
return upfront_charge(self.trade_date, self.value_date, self.start_date,
self.step_in_date, self.start_date, self.maturity,
coupon, self.yc, spread, recov)
@property
def default_prob(self):
return 1 - super().survival_matrix(self.cs.index.values.astype('M8[D]').view('int') + 134774)
def tranche_legs(self, K, rho, complement=False):
if ((K == 0 and not complement) or (K == 1 and complement)):
return {'cl': 0., 'pl': 0.}
elif ((K == 1 and not complement) or (K == 0 and complement)):
return BCindexpv(index)
else:
L, R = BCloss_recov_dist(self.default_prob,
self.weights,
self.recovery,
rho,
self._Z, self._w, self._Ngrid)
if complement:
return {'cl': tranche_cl(L, R, self.cs, K, 1),
'pl': tranche_pl(L, self.cs, K, 1)}
else:
return {'cl': tranche_cl(L, R, self.cs, 0, K),
'pl': tranche_pl(L, self.cs, 0, K)}
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