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from index_data import (get_index_quotes, get_singlenames_curves,
get_tranche_quotes)
from .db import _engine
from dateutil.relativedelta import relativedelta
from pyisda.credit_index import CreditIndex
from typing import List
import numpy as np
import pandas as pd
import datetime
from scipy.optimize import brentq
from pandas.tseries.offsets import BDay
from pyisda.cdsone import upfront_charge
class BasketIndex(CreditIndex):
index_type: str
series: int
recovery: float
step_in_date: pd.Timestamp
value_date: pd.Timestamp
tweaks: List[float]
def __init__(self, index_type: str, series: int, tenors: List[str], *args,
trade_date: pd.Timestamp=pd.Timestamp.today().normalize() - BDay()):
self.index_type = index_type
self.series = series
if index_type == 'IG' or index_type == 'EU':
self.recovery = 0.4
else:
self.recovery = 0.3
self.index_desc = pd.read_sql_query("SELECT tenor, maturity, coupon * 1e-4 AS coupon, " \
"issue_date "\
"FROM index_maturity " \
"WHERE index=%s AND series=%s",
_engine,
index_col='tenor',
params=(index_type, series),
parse_dates=['maturity', 'issue_date'])
self._index_version = pd.read_sql_query("SELECT lastdate, indexfactor, cumulativeloss " \
"FROM index_version " \
"WHERE index=%s AND series = %s" \
"ORDER BY lastdate",
_engine,
index_col='lastdate',
params=(index_type, series),
parse_dates=['lastdate'])
self.issue_date = self.index_desc.issue_date[0]
maturities = self.index_desc.maturity.sort_values().dt.to_pydatetime()
self.index_desc = self.index_desc.reset_index().set_index('maturity')
curves, args = get_singlenames_curves(index_type, series, trade_date)
_, jp_yc, _, step_in_date, value_date, _ = args
self.yc = jp_yc
self.step_in_date = step_in_date
self.value_date = value_date
self._trade_date = trade_date
self.tweaks = []
super().__init__(self.issue_date, maturities, curves)
def _get_quotes(self):
pass
@property
def trade_date(self):
return self._trade_date
@trade_date.setter
def trade_date(self, d: pd.Timestamp):
curves, args = get_singlenames_curves(self.index_type, self.series, d)
_, jp_yc, _, step_in_date, value_date, _ = args
self.yc = jp_yc
self.step_in_date = step_in_date
self.value_date = value_date
self._trade_date = d
self.curves = curves
def pv(self, maturity: pd.Timestamp, epsilon=0.):
return super().pv(self.step_in_date, self.value_date, maturity, self.yc,
self.recovery, self.coupon(maturity), epsilon)
def duration(self, maturity):
return super().duration(self.step_in_date, self.value_date, maturity, self.yc)
def theta(self, maturity):
if self.step_in_date.date() > maturity - relativedelta(years=1):
return np.NaN
else:
index_quote = self.index_quotes.loc[self.trade_date, maturity]
return super().theta(self.step_in_date, self.value_date, maturity,
self.yc, self.recovery, self.coupon(maturity), index_quote)
def coupon(self, maturity):
return self.index_desc.loc[maturity, 'coupon']
def tweak(self):
""" tweak the singlename curves to match index quotes"""
quotes = self._get_quotes()
self.tweaks = []
for m, index_quote in quotes.items():
lo, hi = -0.3, 0.3
while lo > -1:
try:
eps = brentq(lambda epsilon: self.pv(m, epsilon) -
index_quote, lo, hi)
except ValueError:
lo *= 1.1
hi *= 1.1
else:
break
else:
print("couldn't calibrate for date: {} and maturity: {}".
format(self.trade_date.date(), m.date()))
self.tweaks.append(np.NaN)
continue
self.tweaks.append(eps)
self.tweak_portfolio(eps, m)
class MarkitBasketIndex(BasketIndex):
def __init__(self, index_type: str, series: int, tenors: List[str], *args,
trade_date: pd.Timestamp=pd.Timestamp.today().normalize() - BDay()):
super().__init__(index_type, series, tenors, *args, trade_date)
self.index_quotes = (get_index_quotes(index_type, series,
tenors, years=None)['closeprice'].
unstack().
groupby(level='date', as_index=False).nth(0).
reset_index(['index', 'series', 'version'], drop=True))
self.index_quotes.columns = self.index_desc.loc[self.index_quotes.columns, "maturity"]
self.index_quotes = 1 - self.index_quotes / 100
def _get_quotes(self):
return self.index_quotes.loc[self.trade_date]
class TrancheBasket(BasketIndex):
def __init__(self, index_type: str, series: int, tenor: str, *args,
trade_date: pd.Timestamp=pd.Timestamp.today().normalize() - BDay()):
super().__init__(index_type, series, [tenor], *args, 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 = [0] + [q['detach'] for q in self.tranche_quotes]
#self.K = adjust_attachments(self.K_orig, self
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:
upfront_charge(self.trade_date, self.value_date, self.start_date,
self.step_in_date, self.start_date, self.maturity,
self.coupon(self.maturity), self.yc,
refspread * 1e-4, self.recovery)}
raise ValueError("ref is missing")
@property
def survival_matrix(self):
return super().survival_matrix(self.cs.index.values.astype('M8[D]').view('int') + 134774)
if __name__ == "__main__":
ig28 = BasketIndex("IG", 28, ["3yr", "5yr", "7yr", "10yr"])
from quantlib.time.api import Schedule, Rule, Date, Period, WeekendsOnly
from quantlib.settings import Settings
settings = Settings()
cds_schedule = Schedule.from_rule(settings.evaluation_date, Date.from_datetime(ig28.maturities[-1]),
Period('3M'), WeekendsOnly(), date_generation_rule=Rule.CDS2015)
sp = ig28.survival_matrix(cds_schedule.to_npdates().view('int') + 134774)
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