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from .index_data import get_index_quotes, get_singlenames_curves
from .db import _engine
from .utils import tenor_t
from functools import partial
from pandas.util import hash_pandas_object
from pyisda.credit_index import CreditIndex
from typing import List
from yieldcurve import get_curve
import numpy as np
import pandas as pd
from math import exp
from scipy.optimize import brentq
from pandas.tseries.offsets import Day, BDay
from pyisda.cdsone import upfront_charge
def make_index(t, d, args):
instance = t.__new__(t)
CreditIndex.__init__(instance, *args)
instance.__dict__.update(d)
return instance
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], *,
value_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'])
if self.index_desc.empty:
raise ValueError(f"Index {index_type} {series} doesn't exist")
r = _engine.execute("SELECT lastdate, indexfactor/100 AS factor, cumulativeloss/100, version " \
"FROM index_version " \
"WHERE index = %s AND series = %s" \
"ORDER BY lastdate", (index_type, series))
self._version = tuple(tuple(t) for t in r)
self.issue_date = self.index_desc.issue_date[0]
self.index_desc = self.index_desc.loc[tenors]
self.index_desc = self.index_desc.sort_values('maturity')
self.tenors = {t: m.date() for t, m in self.index_desc.maturity.items()}
maturities = self.index_desc.maturity.dt.to_pydatetime()
self.index_desc = self.index_desc.reset_index().set_index('maturity')
self.index_desc.tenor = self.index_desc.tenor.astype(tenor_t)
curves = get_singlenames_curves(index_type, series, value_date)
self.currency = "EUR" if index_type in ["XO", "EU"] else "USD"
self.yc = get_curve(value_date, self.currency)
self.step_in_date = value_date + Day()
self.cash_settle_date = value_date + 3 * BDay()
self.tweaks = []
super().__init__(self.issue_date, maturities, curves, value_date=value_date)
def __reduce__(self):
_, args = CreditIndex.__reduce__(self)
d = vars(self)
return partial(make_index, self.__class__), (d, args)
def __hash__(self):
def aux(v):
if isinstance(v, pd.DataFrame):
return hash_pandas_object(v).sum()
elif isinstance(v, list):
return hash(tuple(v))
elif type(v) is np.ndarray:
return hash(v.tobytes())
else:
return hash(v)
ignore = set(['_Z', '_w', '_skew'])
for k, v in vars(self).items():
if k not in ignore:
print(k, aux(v))
return hash((CreditIndex.__hash__(self),
hash(frozenset([(k, aux(v)) for k, v in vars(self).items() \
if k not in ignore]))))
def _query_version(self, i):
for lastdate, *data in self._version:
if lastdate >= self.value_date:
return data[i]
@property
def factor(self):
return self._query_version(0)
@property
def cumloss(self):
return self._query_version(1)
@property
def version(self):
return self._query_version(2)
def _get_quotes(self):
pass
value_date = property(CreditIndex.value_date.__get__)
@value_date.setter
def value_date(self, d: pd.Timestamp):
self.curves = get_singlenames_curves(self.index_type, self.series, d)
self.yc = get_curve(d, self.currency)
self.step_in_date = d + Day()
self.cash_settle_date = d + 3 * BDay()
CreditIndex.value_date.__set__(self, d)
@property
def recovery_rates(self):
return np.array([c.recovery_rates[0] for c in self.curves])
def pv(self, maturity=None, epsilon=0., coupon=None):
if maturity is None:
r = []
for m in self.maturities:
coupon = self.index_desc.coupon[m]
r.append(super().pv(self.step_in_date, self.cash_settle_date,
m, self.yc, coupon, epsilon))
return pd.Series(r, index=self.index_desc.tenor, name='pv')
else:
if coupon is None:
try:
coupon = self.index_desc.coupon[maturity]
except KeyError:
raise ValueError("Non standard maturity: coupon must be provided")
return super().pv(self.step_in_date, self.cash_settle_date, maturity,
self.yc, coupon, epsilon)
def coupon_leg(self, maturity=None):
return self.index_desc.coupon.values * self.duration()
def protection_leg(self, maturity=None):
return self.pv() + self.coupon_leg()
def spread(self, maturity=None, coupon=None):
if maturity is None:
return (self.index_desc.coupon.values + self.pv() /
self.duration()) * 1e4
else:
return (coupon + self.pv(maturity, coupon=coupon) /
self.duration(maturity)) * 1e4
def duration(self, maturity=None):
if maturity is None:
r = []
for m in self.maturities:
r.append(super().duration(self.step_in_date, self.cash_settle_date,
m, self.yc))
return pd.Series(r, index=self.index_desc.tenor, name='duration')
else:
return super().duration(self.step_in_date, self.cash_settle_date,
maturity, self.yc)
def theta(self, maturity=None, coupon=None, theta_date=None):
""" index thetas
if maturity is None, returns a series of theta for all tenors.
Otherwise computes the theta for that specific maturity (which needs
not be an existing tenor)
if theta_date is provided, computes the theta to that specific date
instead of one-year theta"""
if hasattr(self, "index_quotes"):
index_quotes = self._get_quotes()
else:
index_quotes = {}
if maturity is None:
r = []
for m in self.maturities:
coupon = self.index_desc.coupon[m]
index_quote = index_quotes.get(m, np.nan)
r.append(super().theta(self.step_in_date, self.cash_settle_date, m,
self.yc, coupon, index_quote, theta_date))
return pd.Series(r, index=self.index_desc.tenor, name='theta')
else:
if coupon is None:
try:
coupon = self.index_desc.coupon[maturity]
except KeyError:
raise ValueError("Non standard maturity: coupon must be provided")
return super().theta(self.step_in_date, self.cash_settle_date, maturity,
self.yc, coupon, np.nan, theta_date)
def coupon(self, maturity=None):
if maturity is None:
return self.index_desc.set_index('tenor').coupon
else:
return self.index_desc.coupon[maturity]
def tweak(self, *args):
""" tweak the singlename curves to match index quotes"""
quotes = self._get_quotes(*args)
self.tweaks = []
for m in self.maturities:
if m not in quotes:
self.tweaks.append(np.nan)
continue
else:
index_quote = quotes[m]
lo, hi = -0.3, 0.3
hi_tilde = exp(hi) - 1
while hi_tilde < 5:
# map range to (-1, +inf)
lo_tilde = exp(lo) - 1
hi_tilde = exp(hi) - 1
try:
eps = brentq(lambda epsilon: self.pv(m, epsilon) -
index_quote, lo_tilde, hi_tilde)
except ValueError:
lo *= 1.1
hi *= 1.1
else:
break
else:
print(f"couldn't calibrate for date: {self.value_date} and maturity: {m}")
self.tweaks.append(np.NaN)
continue
self.tweaks.append(eps)
self.tweak_portfolio(eps, m)
def _snacpv(self, spread, coupon, recov):
return upfront_charge(self.value_date, self.cash_settle_date, self.start_date,
self.step_in_date, self.start_date, self.maturity,
coupon, self.yc, spread, recov)
class MarkitBasketIndex(BasketIndex):
def __init__(self, index_type: str, series: int, tenors: List[str], *,
value_date: pd.Timestamp=pd.Timestamp.today().normalize() - BDay()):
super().__init__(index_type, series, tenors, value_date=value_date)
self.index_quotes = (get_index_quotes(index_type, series,
tenors, years=None,
remove_holidays=False)[['close_price', 'id']].
groupby(level=['date', 'tenor'], as_index=True).
nth(0))
self.index_quotes.close_price = 1 - self.index_quotes.close_price / 100
def _get_quotes(self):
quotes = self.index_quotes.loc[self.value_date, "close_price"]
return {self.tenors[t]: q
for t, q in quotes.items()}
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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