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from db import dbconn, dbengine
from analytics import TrancheBasket
from pandas.tseries.offsets import BDay
import datetime
import logging
import os
import pandas as pd
from pathlib import Path
from yaml import load
from pickle import load as pload
import argparse
def get_lastdate(conn, index, series, tenor):
sql_str = ("SELECT max(date)::date + 1 AS date FROM risk_numbers "
"WHERE index=%s and series = %s and tenor = %s")
with conn.cursor() as c:
c.execute(sql_str, (index, series, tenor))
date, = c.fetchone()
conn.commit()
return date
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('tranche_calib')
logger.setLevel(logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--update", action="store_true", default=False,
help="Update from the last run date [default %default]")
parser.add_argument("-c", "--config", metavar="config_file",
help="Runs the list of indices provided in CONFIG_FILE")
parser.add_argument("-i", "--index", help="Index name we want to run")
parser.add_argument("--tenor", default="5yr", help="Tenor we want to run [default '5yr']")
parser.add_argument("--until", default=pd.Timestamp.now()-BDay(),
type=lambda s: pd.Timestamp(s))
args = parser.parse_args()
CODE_DIR = Path(os.environ['CODE_DIR'])
start_dates = {# 'hy10': datetime.date(2014, 8, 11),
# 'hy15': datetime.date(2014, 6, 10),
# 'hy17': datetime.date(2013, 1, 1),
# 'hy19': datetime.date(2013, 2, 1),
# 'hy21': datetime.date(2013, 10, 4),
# 'hy23': datetime.date(2014, 10, 16),
# 'hy25': datetime.date(2015, 10, 1),
# 'hy27': datetime.date(2016, 10, 4),
# 'hy29': datetime.date(2017, 10, 3),
# 'hy31': datetime.date(2018, 10, 2),
'ig9': datetime.date(2013, 1, 1),
'ig19': datetime.date(2013, 5, 1),
'ig21': datetime.date(2013, 9, 26),
'ig23': datetime.date(2014, 10, 14),
'ig25': datetime.date(2015, 9, 22),
'ig27': datetime.date(2016, 9, 27),
'ig29': datetime.date(2017, 9, 26),
'ig31': datetime.date(2018, 9, 25),
'xo22': datetime.date(2014, 10, 20),
'xo24': datetime.date(2015, 9, 28),
'xo26': datetime.date(2016, 9, 27),
'xo28': datetime.date(2017, 9, 28),
'eu9': datetime.date(2014, 9, 15),
'eu19': datetime.date(2013, 4, 3),
'eu21': datetime.date(2014, 3, 27),
'eu22': datetime.date(2014, 10, 22),
'eu24': datetime.date(2015, 9, 23),
'eu26': datetime.date(2016, 9, 27),
'eu28': datetime.date(2017, 9, 28),
'eu30': datetime.date(2018, 9, 25),
'xo30': datetime.date(2018, 9, 25)}
serenitas_engine = dbengine('serenitasdb')
serenitas_conn = serenitas_engine.raw_connection()
if args.config is None:
if args.index is None:
raise ValueError("Please provide an index to run")
config = {'runs': [(args.index, args.tenor)]}
else:
with (CODE_DIR / "etc" / args.config).open("r") as fh:
config = load(fh)
for index, tenor in config['runs']:
index, series = index[:2].upper(), int(index[2:])
if args.update:
begin_date = get_lastdate(serenitas_conn, index, series, tenor)
if not args.update or begin_date is None:
try:
begin_date = start_dates[f"{index.lower()}{series}"]
except KeyError:
print(index, series)
continue
dr = pd.bdate_range(begin_date, args.until)
if dr.empty:
continue
logger.info(f"calibrating {index}, {series}, {tenor}")
tranche_index = None
data = {}
for d in dr:
try:
if tranche_index is None:
tranche_index = TrancheBasket(index, series, tenor, value_date=d.date())
else:
tranche_index.value_date = d.date()
except ValueError as e:
logger.warning(e)
continue
logger.debug(d.date())
tranche_index.tweak()
try:
tranche_index.build_skew()
except ValueError as e:
logger.error(e)
continue
df = pd.concat([tranche_index.tranche_deltas(),
tranche_index.tranche_thetas(),
tranche_index.tranche_fwd_deltas(),
tranche_index.tranche_durations(),
tranche_index.tranche_EL(),
tranche_index.tranche_spreads()], axis=1)
df['index_duration'], df['index_expected_loss'], df['index_price'] = \
tranche_index.index_pv()
df['index_expected_loss'] *= -1
df['index_duration'] -= tranche_index.accrued()
df['index_basis'] = tranche_index.tweaks[0]
df['index_theta'] = tranche_index.theta()[tenor]
df['tranche_id'] = tranche_index.tranche_quotes.id.values
df['corr_at_detach'] = tranche_index.rho[1:]
df['corr01'] = tranche_index.tranche_corr01()
del df['fwd_gamma']
data[d] = df
if data:
(pd.concat(data).
to_sql("tranche_risk", serenitas_engine, if_exists='append', index=False))
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