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from .index_data import get_index_quotes, get_singlenames_curves
from .db import _engine
from dateutil.relativedelta import relativedelta
from functools import partial
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
import datetime
from scipy.optimize import brentq
from pandas.tseries.offsets import Day, BDay
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], *,
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'])
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]
maturities = self.index_desc.maturity[tenors].sort_values().dt.to_pydatetime()
self.index_desc = self.index_desc.reset_index().set_index('maturity')
curves = get_singlenames_curves(index_type, series, trade_date)
self.currency = "EUR" if index_type in ["XO", "EU"] else "USD"
self.yc = get_curve(trade_date, self.currency)
self.step_in_date = trade_date + Day()
self.value_date = trade_date + 3 * BDay()
self.tweaks = []
super().__init__(self.issue_date, maturities, curves, trade_date=trade_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)
elif isinstance(v, list):
return hash(tuple(v))
else:
return hash(v)
hash(CreditIndex.__hash__(self),
hash(frozenset([(k, aux(v)) for k, v in dirs(self)])))
def _query_version(self, i):
for lastdate, *data in self._version:
if lastdate >= self.trade_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
trade_date = property(CreditIndex.trade_date.__get__)
@trade_date.setter
def trade_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.value_date = d + 3 * BDay()
CreditIndex.trade_date.__set__(self, d)
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 coupon_leg(self, maturity):
return self.coupon(maturity) * self.duration(maturity)
def protection_leg(self, maturity):
return self.pv(maturity) + self.coupon_leg(maturity)
def spread(self, maturity):
return (self.coupon(maturity) + self.pv(maturity) / self.duration(maturity)) * 1e4
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, *args):
""" tweak the singlename curves to match index quotes"""
quotes = self._get_quotes(*args)
self.tweaks = []
for m, index_quote in quotes.items():
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("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], *,
trade_date: pd.Timestamp=pd.Timestamp.today().normalize() - BDay()):
super().__init__(index_type, series, tenors, trade_date=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.reset_index().
set_index('tenor').
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]
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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