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from pyisda.curve import YieldCurve, BadDay, SpreadCurve
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 concurrent.futures import ProcessPoolExecutor, as_completed
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 dateutil.relativedelta import relativedelta
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:])
upfront_curve = 1e-2 * np.array(r['upfront_curve'][1:])
recovery_curve = np.array(r['recovery_curve'][1:])
try:
sc = SpreadCurve(today_date, yc, start_date, step_in_date, value_date,
end_dates, spread_curve, upfront_curve, recovery_curve, True)
except ValueError:
pdb.set_trace()
return (r['cds_ticker'], sc)
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx
args = [iter(iterable)] * n
return zip_longest(fillvalue=fillvalue, *args)
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 ProcessPoolExecutor(workers) as e:
fs = [e.submit(build_curves, *(q, args)) for q in grouper(quotes, 30)]
return list(chain.from_iterable([f.result() for f in as_completed(fs)]))
def build_curves(quotes, args):
return [build_curve(q, *args) for q in quotes if q is not None]
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 ticker, 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(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)
def stack_curves(curves):
dates = [d for d, _ in curves[0].inspect()['data']]
hazard_rates = np.empty((len(curves), len(dates)))
for i, sc in enumerate(curves):
hazard_rates[i] = np.array([h for _, h in sc.inspect()['data']])
return hazard_rates, dates
def forward_hazard_rates(sc):
r = []
t = []
t1 = 0
h1 = 0
base_date = sc.base_date
for d, h in sc.inspect()['data']:
h2 = math.log1p(h)
t2 = (d - base_date).days / 365
r.append( (h2 * t2 - h1 * t1) / (t2 - t1) )
t.append(t2)
h1, t1 = h2, t2
return t, r
serenitas_engine = dbengine('serenitasdb')
def calibrate_portfolio(index_type, series):
if index_type == 'IG':
recovery = 0.4
else:
recovery = 0.3
tenors = ['3yr', '5yr', '7yr', '10yr']
index_quotes = get_index_quotes(index_type, series,
tenors)['closeprice'].unstack()
index_desc = pd.read_sql_query("SELECT tenor, maturity, coupon FROM index_maturity " \
"WHERE index=%s AND series=%s", serenitas_engine,
index_col='tenor', params=(index_type, series))
index_quotes.columns = index_desc.loc[index_quotes.columns, "maturity"]
index_quotes = index_quotes.sort_index(1)
end_dates = [datetime.date(2017, 12, 20),
datetime.date(2018, 12, 20),
datetime.date(2019, 12, 20),
datetime.date(2020, 12, 20),
datetime.date(2021, 12, 20),
datetime.date(2023, 12, 20),
datetime.date(2026, 12, 20)]
maturities = index_desc.maturity.tolist()
start_date = datetime.date(2016, 9, 20)
r = {}
for k, s in index_quotes.iterrows():
trade_date = k[0].date()
print(trade_date)
sn_quotes = get_singlenames_quotes("{}{}".format(index_type.lower(), series),
trade_date)
Settings().evaluation_date = Date.from_datetime(trade_date)
yc = YC()
jp_yc = ql_to_jp(yc)
step_in_date = trade_date + datetime.timedelta(days=1)
value_date = (pd.Timestamp(trade_date) + 3* BDay()).date()
args = (trade_date, jp_yc, start_date, step_in_date, value_date, end_dates)
curves = build_curves_dist(sn_quotes, args)
index = CreditIndex(start_date, maturities, curves)
d = {'tweak':[],
'duration':[],
'theta':[]}
for i, m in enumerate(maturities):
index_quote = 1 - s.iat[i]/100
eps = brentq(lambda epsilon: index.pv(step_in_date,
value_date, m, jp_yc, 0.4, 0.01, epsilon) -
index_quote, -0.3, 0.3)
#tweak the curves in place
index.tweak_portfolio(eps, m)
d['duration'].append(index.duration(step_in_date, value_date, m, jp_yc))
d['theta'].append(index_quote - index.theta(step_in_date, value_date,
m - relativedelta(years=1), jp_yc, 0.4, 0.01) +
0.01)
d['tweak'].append(eps)
r[trade_date] = pd.DataFrame(d, index=tenors)
return pd.concat(d)
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