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import pandas as pd
import argparse
from analytics import Index, Swaption
import datetime
from db import dbengine
from functools import partial
from multiprocessing import Pool
from itertools import repeat
serenitas_engine = dbengine('serenitasdb')
def get_data(index, series, date = datetime.date.min):
df = pd.read_sql_query("SELECT * from swaption_ref_quotes JOIN swaption_quotes " \
"USING (quotedate, index, series, expiry) WHERE index=%s and series=%s " \
"and quotedate >=%s ORDER BY quotedate",
serenitas_engine,
params = (index, series, date), parse_dates = ['quotedate', 'expiry'])
df.loc[(df.quote_source == "GS") & (df['index'] =="HY"),
["pay_bid", "pay_offer", "rec_bid", "rec_offer"]] *=100
try:
df.quotedate = df.quotedate.dt.tz_localize('UTC')
except TypeError:
pass
finally:
return df
def get_data_latest():
df = pd.read_sql_query("SELECT swaption_quotes.*, ref FROM swaption_quotes " \
"JOIN swaption_ref_quotes USING (quotedate, index, series, expiry) " \
"LEFT JOIN swaption_calib " \
"USING (quotedate, index, series, expiry, strike) " \
"WHERE swaption_calib.quotedate is NULL",
serenitas_engine,
parse_dates = ['quotedate', 'expiry'])
df.loc[(df.quote_source == "GS") & (df['index'] =="HY"),
["pay_bid", "pay_offer", "rec_bid", "rec_offer"]] *=100
try:
df.quotedate = df.quotedate.dt.tz_localize('UTC')
except TypeError:
pass
finally:
return df
def calib(option, ref, strike, pay_bid, pay_offer, rec_bid, rec_offer):
option.ref = ref
option.strike = strike
r = []
for pv_type in ['pv', 'pv_black']:
for option_type in ['pay', 'rec']:
if option_type == "pay":
mid = (pay_bid + pay_offer) / 2 * 1e-4
option.option_type = 'payer'
else:
mid = (rec_bid + rec_offer) / 2 * 1e-4
option.option_type = 'receiver'
try:
setattr(option, pv_type, mid)
except ValueError as e:
r.append(None)
print(e)
else:
r.append(option.sigma)
return [strike] + r
def calibrate(index_type=None, series=None, date=None, nproc=4, latest=False):
sql_str = ("INSERT INTO swaption_calib VALUES({}) ON CONFLICT DO NOTHING".
format(",".join(["%s"] * 9)))
if latest:
data = get_data_latest()
else:
data = get_data(index_type, series, date)
with Pool(nproc) as pool:
for k, v in data.groupby([data['quotedate'].dt.date, 'index', 'series']):
trade_date, index_type, series = k
index = Index.from_name(index_type, series, "5yr", trade_date)
for expiry, df in v.groupby(['expiry']):
option = Swaption(index, expiry.date(), 100)
mycalib = partial(calib, option)
r = pool.starmap(mycalib, df[['ref', 'strike', 'pay_bid',
'pay_offer', 'rec_bid', 'rec_offer']].
itertuples(index=False, name=None))
to_insert = [[a, index_type, series, expiry] + b for a, b in zip(df.quotedate.tolist(), r)]
serenitas_engine.execute(sql_str, to_insert)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--index', required=False, type=lambda s: s.upper(), dest="index_type")
parser.add_argument('--series', required=False, type=int, default=27)
parser.add_argument('--date', required = False, default=datetime.date.min)
parser.add_argument('--latest', required = False, action="store_true")
parser.add_argument('--nproc', required = False, type=int, default=4)
args = parser.parse_args()
if args.latest:
calibrate(latest=True, nproc=args.nproc)
else:
calibrate(**vars(args))
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