import pandas as pd from analytics import Index, Swaption import datetime from db import dbengine from contextlib import contextmanager from itertools import starmap 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('America/New_York') 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('America/New_York') 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 @contextmanager def MaybePool(nproc): yield Pool(nproc) if nproc > 1 else None 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 MaybePool(nproc) as pool: pstarmap = pool.starmap if pool else starmap 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 = pstarmap(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__": import argparse 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=28) 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))