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from pyisda.curve import YieldCurve, BadDay, SpreadCurve, fill_curve
from pyisda.credit_index import CreditIndex
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.logging import enable_logging
import datetime
import math
import numpy as np
import pandas as pd
from yieldcurve import YC, ql_to_jp
from quantlib.settings import Settings
from quantlib.time.api import Date
from db import dbconn, dbengine
from multiprocessing import Pool
from itertools import zip_longest, chain
from index_data import get_index_quotes
from pandas.tseries.offsets import BDay
from scipy.optimize import brentq
from pyisda.logging import enable_logging
from analytics.utils import roll_date, previous_twentieth
def get_singlenames_quotes(indexname, date):
conn = dbconn('serenitasdb')
with conn.cursor() as c:
c.execute("SELECT * FROM curve_quotes(%s, %s)", vars=(indexname, date))
return [r for r in c]
def build_curve(r, today_date, yc, start_date, step_in_date, value_date, end_dates):
spread_curve = 1e-4 * np.array(r['spread_curve'][1:], dtype='float')
upfront_curve = 1e-2 * np.array(r['upfront_curve'][1:], dtype='float')
recovery_curve = np.array(r['recovery_curve'][1:], dtype='float')
try:
sc = SpreadCurve(today_date, yc, start_date, step_in_date, value_date,
end_dates, spread_curve, upfront_curve, recovery_curve,
ticker=r['cds_ticker'])
if np.any(np.isnan(upfront_curve)):
sc = fill_curve(sc, end_dates)
except ValueError as e:
print(e)
return None
return sc
def build_curves_dist(quotes, args, workers=4):
## about twice as fast as the non distributed version
## non thread safe for some reason so need ProcessPool
with Pool(workers) as pool:
r = pool.starmap(build_curve, [(q, *args) for q in quotes], 30)
return r
def build_curves(quotes, args):
return [build_curve(q, *args) for q in quotes if q is not None]
def get_singlenames_curves(index_type, series, trade_date):
end_dates = roll_date(trade_date, [1, 2, 3, 4, 5, 7, 10], nd_array=True)
sn_quotes = get_singlenames_quotes("{}{}".format(index_type.lower(), series),
trade_date.date())
Settings().evaluation_date = Date.from_datetime(trade_date)
yc = YC()
jp_yc = ql_to_jp(yc)
start_date = previous_twentieth(trade_date)
step_in_date = trade_date + datetime.timedelta(days=1)
value_date = pd.Timestamp(trade_date) + 3* BDay()
args = (trade_date, jp_yc, start_date, step_in_date, value_date, end_dates)
curves = build_curves_dist(sn_quotes, args)
return curves, args
def all_curves_pv(curves, today_date, jp_yc, start_date, step_in_date, value_date, maturities):
r = {}
for d in maturities:
tenor = {}
coupon_leg = FeeLeg(start_date, d, True, 1., 1.)
default_leg = ContingentLeg(start_date, d, True)
accrued = coupon_leg.accrued(step_in_date)
tickers = []
data = []
for sc in curves:
coupon_leg_pv = coupon_leg.pv(today_date, step_in_date, value_date, jp_yc, sc, False)
default_leg_pv = default_leg.pv(today_date, step_in_date, value_date,
jp_yc, sc, 0.4)
tickers.append(sc.ticker)
data.append((coupon_leg_pv-accrued, default_leg_pv))
r[pd.Timestamp(d)] = pd.DataFrame.from_records(data,
index=tickers,
columns=['duration', 'protection_pv'])
return pd.concat(r, axis=1).swaplevel(axis=1).sort_index(axis=1,level=0)
serenitas_engine = dbengine('serenitasdb')
def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr']):
if index_type == 'IG':
recovery = 0.4
else:
recovery = 0.3
index_quotes = (get_index_quotes(index_type, series,
tenors)['closeprice'].
unstack().
reset_index(level='version').
groupby(level='date').nth(0).
set_index('version', append=True))
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", serenitas_engine,
index_col='tenor', params=(index_type, series),
parse_dates=['maturity', 'issue_date'])
index_quotes.columns = index_desc.loc[index_quotes.columns, "maturity"]
index_quotes = 1 - index_quotes / 100
issue_date = index_desc.issue_date[0]
index_desc = index_desc.set_index('maturity')
maturities = index_quotes.columns.sort_values().to_pydatetime()
curves, _ = get_singlenames_curves(index_type, series, issue_date)
index = CreditIndex(issue_date, maturities, curves)
r = {}
for k, s in index_quotes.iterrows():
trade_date, version = k
curves, args = get_singlenames_curves(index_type, series, trade_date)
_, jp_yc, _, step_in_date, value_date, _ = args
index.curves = curves
tweak, duration, theta = [], [], []
s.name = 'index_quote'
quotes = pd.concat([index_desc, s], axis=1)
for m, coupon, index_quote in quotes[['coupon', 'index_quote']].itertuples():
eps = brentq(lambda epsilon: index.pv(step_in_date, value_date,
m, jp_yc, recovery,
coupon, epsilon) -
index_quote, -0.5, 0.3)
#tweak the curves in place
index.tweak_portfolio(eps, m)
duration.append(index.duration(step_in_date, value_date, m, jp_yc))
theta.append(index.theta(step_in_date, value_date,
m, jp_yc, recovery, coupon))
tweak.append(eps)
r[trade_date] = pd.DataFrame({'duration': duration,
'theta': theta,
'tweak': tweak}, index=tenors)
return pd.concat(r)
if __name__=="__main__":
enable_logging()
df = calibrate_portfolio("IG", 25)
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