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import datetime
import numpy as np
import pandas as pd
from analytics.utils import tenor_t
from pandas.tseries.offsets import BDay
from dateutil.relativedelta import relativedelta
from yieldcurve import get_curve
from utils.db import serenitas_engine
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.curve import SpreadCurve
from pyisda.date import previous_twentieth
tenors = {"IG": ("3yr", "5yr", "7yr", "10yr"),
"HY": ("3yr", "5yr", "7yr"),
"EU": ("3yr", "5yr", "7yr", "10yr"),
"XO": ("3yr", "5yr", "7yr", "10yr")}
sql_str = "INSERT INTO index_risk VALUES(%s, %s, %s)"
def get_legs(index, series, tenors):
fee_legs = {}
contingent_legs = {}
df = pd.read_sql_query("SELECT tenor, maturity, coupon, issue_date "
"FROM index_maturity "
"WHERE index=%s AND series=%s and tenor IN %s "
"ORDER BY maturity",
serenitas_engine,
params=(index, series, tenors),
parse_dates=['maturity', 'issue_date'])
df.coupon *= 1e-4
for tenor, maturity, coupon, issue_date in df.itertuples(index=False):
maturity_short = maturity - relativedelta(years=1)
fee_legs[tenor] = (FeeLeg(issue_date, maturity, True, 1., 1.),
FeeLeg(issue_date, maturity_short,
True, 1., coupon))
contingent_legs[tenor] = (ContingentLeg(issue_date, maturity, True),
ContingentLeg(issue_date, maturity_short, True))
# number of seconds since epoch
# number of days between 1900-1-1 and epoch
df.maturity = (df.maturity.values.view("M8[D]").astype(int) + 134774)
return fee_legs, contingent_legs, df
def index_pv(fl, cl, value_date, step_in_date, cash_settle_date, yc, sc, recovery):
dl_pv = cl.pv(value_date, step_in_date, cash_settle_date, yc, sc, recovery)
cl_pv = fl.pv(value_date, step_in_date, cash_settle_date, yc, sc, True)
return dl_pv - cl_pv
if __name__ == "__main__":
conn = serenitas_engine.raw_connection()
for index in ["IG", "HY", "EU", "XO"]:
if index in ["HY", "XO"]:
recoveries = np.full(len(tenors[index]), 0.3)
else:
recoveries = np.full(len(tenors[index]), 0.4)
for series in range(18, 33):
if index in ["EU", "XO"] and series == 32:
continue
fee_legs, contingent_legs, df = \
get_legs(index, series, tenors[index])
index_quotes = pd.read_sql_query(
"SELECT distinct on (date, tenor) id, date, tenor, version, close_price "
"FROM index_quotes_pre "
"LEFT JOIN index_risk USING (id) "
"WHERE index=%s AND series=%s "
"AND source='MKIT' AND duration is NULL AND tenor IN %s "
"ORDER BY date, tenor, version desc",
serenitas_engine,
params=(index, series, tenors[index]),
parse_dates=['date'],
index_col='id')
if index_quotes.empty:
continue
index_quotes.tenor = index_quotes.tenor.astype(tenor_t)
index_quotes = index_quotes.sort_values('tenor')
index_quotes['close_price'] = 1. - index_quotes['close_price'] / 100
with conn.cursor() as c:
for value_date, data in index_quotes.groupby('date'):
yc = get_curve(value_date, "USD" if index in ["IG", "HY"] else "EUR")
# right_index?? is it a bug?
data = data.merge(df, on='tenor', right_index=True)
step_in_date = value_date + datetime.timedelta(days=1)
cash_settle_date = value_date + 3 * BDay()
start_date = previous_twentieth(value_date)
sc = SpreadCurve(value_date, yc, start_date,
step_in_date, cash_settle_date,
data.maturity.values, data.coupon.values,
data.close_price.values,
recoveries)
for r in data[['coupon', 'tenor', 'close_price']].itertuples():
fl, fl_short = fee_legs[r.tenor]
cl, cl_short = contingent_legs[r.tenor]
duration = fl.pv(value_date, step_in_date,
cash_settle_date, yc, sc,
True)
if cl_short.end_date <= value_date.date():
theta = None
else:
pv = index_pv(fl_short, cl_short,
value_date, step_in_date, cash_settle_date, yc, sc,
recoveries[0])
theta = r.close_price - pv + r.coupon
c.execute("INSERT INTO index_risk VALUES(%s, %s, %s)",
(r.Index, theta, duration))
conn.commit()
conn.close()
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