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from analytics.basket_index import MarkitBasketIndex
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.logging import enable_logging
import pandas as pd
from utils.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):
try:
index = MarkitBasketIndex(index_type, series, tenors)
except ValueError:
return
if start_date:
index.index_quotes = index.index_quotes[start_date:]
for value_date, v in index.index_quotes.groupby('date')['id']:
index.value_date = value_date
index.tweak()
df = pd.concat([index.theta(),
index.duration(),
pd.Series(index.tweaks, index=tenors, name='tweak')], axis=1)
for (_, t), id in v.items():
yield (id, df.loc[t])
if __name__ == "__main__":
enable_logging()
import argparse
import logging
import os
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 max(date) FROM index_quotes_pre "
"RIGHT JOIN index_risk2 USING (id) "
"WHERE index=%s AND series=%s "
"AND tenor in ('3yr', '5yr', '7yr', '10yr')",
(index, series))
start_date, = c.fetchone()
else:
start_date = None
fh = logging.FileHandler(filename=os.path.join(os.getenv("LOG_DIR"), "index_curves.log"))
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
loggers = [logging.getLogger("analytics"), logging.getLogger("index_curves")]
for logger in loggers:
logger.setLevel(logging.INFO)
logger.addHandler(fh)
loggers[1].info(f"filling {index} {series}")
g = calibrate_portfolio(index, series, ['3yr', '5yr', '7yr', '10yr'],
start_date)
with conn.cursor() as c:
for id, t in g:
c.execute("INSERT INTO index_risk2 VALUES(%s, %s, %s, %s) ON CONFLICT (id) "
"DO UPDATE SET theta=%s, duration=%s, tweak=%s",
(id,) + tuple(t) + tuple(t))
conn.commit()
conn.close()
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