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import array
import datetime
import math
import pandas as pd
from pyisda.legs import ContingentLeg, FeeLeg
from quantlib.settings import Settings
from quantlib.time.api import (
Date, Schedule, WeekendsOnly, CDS, Following,
Unadjusted, Period, pydate_from_qldate )
from termcolor import colored
from pandas.tseries.offsets import BDay
from dates import prev_immdate
from db import dbconn
from psycopg2 import DataError
from pyisda.curve import SpreadCurve
from yieldcurve import YC, ql_to_jp, roll_yc, rate_helpers
from quantlib.time.api import Actual365Fixed
serenitasdb = dbconn('serenitasdb')
class Index():
""" minimal class to represent a credit index """
def __init__(self, start_date, end_date, recovery, fixed_rate,
notional = 10e6):
"""
start_date : :class:`datetime.date`
index start_date (Could be issue date, or last imm date)
end_date : :class:`datetime.date`
index last date
recovery :
recovery rate (between 0 and 1)
fixed_rate :
fixed coupon (in bps)
"""
self.fixed_rate = fixed_rate
self.notional = notional
self._sched = Schedule(Date.from_datetime(start_date),
Date.from_datetime(end_date),
Period("3M"),
WeekendsOnly(),
Following,
Unadjusted,
CDS)
self._start_date = start_date
self._end_date = end_date
self.recovery = recovery
self._fee_leg = FeeLeg(self._start_date, end_date, True, 1, 1)
self._default_leg = ContingentLeg(self._start_date, end_date, 1)
self._trade_date = None
self._yc = None
self._risky_annuity = None
self._spread = None
self.name = None
@property
def start_date(self):
return self._start_date
@property
def end_date(self):
return self._end_date
@start_date.setter
def start_date(self, d):
self._fee_leg = FeeLeg(d, self.end_date, True, 1, 1)
self._default_leg = ContingentLeg(d, self.end_date, 1)
self._start_date = d
self._sched = Schedule(Date.from_datetime(d),
Date.from_datetime(self.end_date),
Period("3M"),
WeekendsOnly(),
Following,
Unadjusted,
CDS)
@end_date.setter
def end_date(self, d):
self._fee_leg = FeeLeg(self.start_date, d, True, 1, 1)
self._default_leg = ContingentLeg(self.start_date, d, 1)
self._end_date = d
self._sched = Schedule(self.start_date,
d,
Period("3M"),
WeekendsOnly(),
Following,
Unadjusted,
CDS)
def survival_probability(self, d):
if d > self.trade_date:
return 1
else:
return math.exp( - self.flat_hazard * (d - self.trade_date).days/365)
def forward_pv(self, exercise_date):
"""This is default adjusted forward price at time exercise_date"""
step_in_date = exercise_date + datetime.timedelta(days=1)
a = self._fee_leg.pv(self.trade_date, step_in_date, self._value_date,
self._yc, self._sc, False)
Delta = self._fee_leg.accrued(step_in_date)
value_date = (pd.Timestamp(exercise_date) + 3* BDay()).date()
df = self._yc.discount_factor(value_date)
q = self.survival_probability(exercise_date)
clean_forward_annuity = a - Delta * df * q
dl_pv = self._default_leg.pv(
self.trade_date, step_in_date, self._value_date,
self._yc, self._sc, self.recovery)
forward_price = self.notional * (dl_pv - clean_forward_annuity * self.fixed_rate*1e-4)
fep = self.notional * (1 - self.recovery) * (1 - q)
return forward_price * self._yc.discount_factor(self._value_date) / df + fep
@property
def spread(self):
return self._spread * 1e4
def _update(self):
self._sc = SpreadCurve(self.trade_date, self._yc, self.start_date,
self._step_in_date, self._value_date,
[self.end_date], array.array('d', [self._spread]),
self.recovery)
self._risky_annuity = self._fee_leg.pv(self.trade_date, self._step_in_date,
self._value_date, self._yc,
self._sc, False)
self._dl_pv = self._default_leg.pv(
self.trade_date, self._step_in_date, self._value_date,
self._yc, self._sc, self.recovery)
self._pv = self._dl_pv - self._risky_annuity * self.fixed_rate * 1e-4
self._clean_pv = self._pv + self._accrued * self.fixed_rate * 1e-4
self._price = 100 * (1 - self._clean_pv)
@spread.setter
def spread(self, s: float):
""" s: spread in bps """
self._spread = s * 1e-4
self._update()
@property
def flat_hazard(self):
sc_data = self._sc.inspect()['data']
## conversion to continuous compounding
return math.log(1 + sc_data[0][1])
@property
def pv(self):
return self.notional * self._pv
@property
def accrued(self):
return - self.notional * self._accrued * self.fixed_rate * 1e-4
@property
def days_accrued(self):
return int(self._accrued * 360)
@property
def clean_pv(self):
return self.notional * self._clean_pv
@property
def price(self):
return self._price
@price.setter
def price(self, val):
pass
@property
def DV01(self):
old_pv = self.pv
self.spread += 1
dv01 = self.pv - old_pv
self.spread -= 1
return dv01
@property
def IRDV01(self):
old_pv = self.pv
old_yc = self._yc
for rh in self._helpers:
rh.quote += 1e-4
self._yc = ql_to_jp(self._ql_yc)
self._update() ## to force recomputation
new_pv = self.pv
for r in self._helpers:
r.quote -= 1e-4
self._yc = old_yc
self._update()
return new_pv - old_pv
@property
def rec_risk(self):
old_pv = self.pv
old_recovery = self.recovery
self.recovery = old_recovery - 0.01
self._update()
pv_minus = self.pv
self.recovery = old_recovery + 0.01
self._update()
pv_plus = self.pv
self.recovery = old_recovery
self._update()
return (pv_plus - pv_minus)/2
@property
def jump_to_default(self):
return self.notional * (1 - self.recovery) - self.clean_pv
@property
def risky_annuity(self):
return self._risky_annuity - self._accrued
@property
def trade_date(self):
if self._trade_date is None:
raise AttributeError('Please set trade_date first')
else:
return self._trade_date
@trade_date.setter
def trade_date(self, d):
settings = Settings()
settings.evaluation_date = Date.from_datetime(d)
self.start_date = pydate_from_qldate(
self._sched.previous_date(settings.evaluation_date))
self._helpers = rate_helpers(self.currency)
self._ql_yc = YC(self._helpers)
self._yc = ql_to_jp(self._ql_yc)
self._trade_date = d
self._step_in_date = self.trade_date + datetime.timedelta(days=1)
self._accrued = self._fee_leg.accrued(self._step_in_date)
self._value_date = (pd.Timestamp(self._trade_date) + 3* BDay()).date()
if self._spread is not None:
self._update()
@classmethod
def from_name(cls, index, series, tenor, trade_date = datetime.date.today(),
notional = 10e6):
try:
with serenitasdb.cursor() as c:
c.execute("SELECT maturity, coupon FROM index_maturity " \
"WHERE index=%s AND series=%s AND tenor = %s",
(index.upper(), series, tenor))
maturity, coupon = next(c)
except DataError as e:
raise
else:
recovery = 0.4 if index.lower() == "ig" else 0.3
instance = cls(trade_date, maturity, recovery, coupon)
instance.name = "MARKIT CDX.NA.{}.{} {:%m/%y} ".format(
index.upper(),
series,
maturity)
if index.upper() in ["IG", "HY"]:
instance.currency = "USD"
else:
instance.currency = "EUR"
instance.notional = notional
instance.trade_date = trade_date
return instance
def __repr__(self):
if self.days_accrued > 1:
accrued_str = "Accrued ({} Days)".format(self.days_accrued)
else:
accrued_str = "Accrued ({} Day)".format(self.days_accrued)
s = ["{:<20}\t{:>15}".format("CDS Index", colored(self.name, attrs = ['bold'])),
"",
"{:<20}\t{:>15}".format("Trade Date", ('{:%m/%d/%y}'.
format(self.trade_date))),
"{:<20}\t{:>15.2f}\t\t{:<20}\t{:>10,.2f}".format("Trd Sprd (bp)",
self.spread,
"Coupon (bp)",
self.fixed_rate),
"{:<20}\t{:>15.2f}\t\t{:<20}\t{:>10}".format("1st Accr Start",
self.spread,
"Payment Freq",
"Quarterly"),
"{:<20}\t{:>15}\t\t{:<20}\t{:>10.2f}".format("Maturity Date",
('{:%m/%d/%y}'.
format(self.end_date)),
"Rec Rate",
self.recovery),
"{:<20}\t{:>15}\t\t{:<20}\t{:>10}".format("Bus Day Adj",
"Following",
"Day Count",
"ACT/360"),
"",
colored("Calculator", attrs = ['bold']),
"{:<20}\t{:>15}".format("Valuation Date", ('{:%m/%d/%y}'.
format(self.trade_date))),
"{:<20}\t{:>15}".format("Cash Settled On", ('{:%m/%d/%y}'.
format(self._value_date))),
"",
"{:<20}\t{:>15.8f}\t\t{:<20}\t{:>10,.2f}".format("Price",
self.price,
"Spread DV01",
self.DV01),
"{:<20}\t{:>15,.0f}\t\t{:<20}\t{:>10,.2f}".format("Principal",
self.clean_pv,
"IR DV01",
self.IRDV01),
"{:<20}\t{:>15,.0f}\t\t{:<20}\t{:>10,.2f}".format(accrued_str,
self.accrued,
"Rec Risk (1%)",
self.rec_risk),
"{:<20}\t{:>15,.0f}\t\t{:<20}\t{:>10,.0f}".format("Cash Amount",
self.pv,
"Def Exposure",
self.jump_to_default)
]
return "\n".join(s)
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