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from analytics.basket_index import MarkitBasketIndex
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.logging import enable_logging
import datetime
import numpy as np
import pandas as pd
from db import dbconn
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)
def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'],
start_date=None):
index = MarkitBasketIndex(index_type, series, tenors)
r = {}
if start_date:
index.index_quotes = index.index_quotes[start_date:]
for trade_date in index.index_quotes.index.get_level_values(0):
index.trade_date = trade_date
index.tweak()
r[trade_date] = pd.DataFrame({'duration': index.duration(),
'theta': index.theta(),
'tweak': index.tweaks}, index=tenors)
return pd.concat(r)
if __name__=="__main__":
enable_logging()
import argparse
import sys
parser = argparse.ArgumentParser()
parser.add_argument('index', help="index type (IG, HY, EU or XO)")
parser.add_argument('series', help="series", type=int)
parser.add_argument('--latest', required=False, action="store_true")
args = parser.parse_args()
index, series = args.index, args.series
conn = dbconn('serenitasdb')
if args.latest:
with conn.cursor() as c:
c.execute("SELECT min(date) FROM index_quotes WHERE index=%s AND series=%s "
"AND duration2 is NULL", (index, series))
start_date, = c.fetchone()
if start_date is None:
sys.exit(0)
else:
start_date = None
df = calibrate_portfolio(index, series, ['3yr', '5yr', '7yr', '10yr'],
start_date)
with conn.cursor() as c:
for k, s in df.iterrows():
c.execute("UPDATE index_quotes SET duration2=%s, theta2=%s "\
"WHERE date=%s AND tenor=%s AND index=%s AND series=%s",
(s.duration, s.theta, k[0], k[1], index, series))
conn.commit()
conn.close()
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