diff options
| -rw-r--r-- | python/thetas-durations.py | 68 |
1 files changed, 60 insertions, 8 deletions
diff --git a/python/thetas-durations.py b/python/thetas-durations.py index 75f889ff..286a77b5 100644 --- a/python/thetas-durations.py +++ b/python/thetas-durations.py @@ -1,6 +1,13 @@ +import datetime +import numpy as np import pandas as pd -from analytics import Index +from analytics.utils import tenor_t +from pandas.tseries.offsets import BDay +from yieldcurve import get_curve from db import dbengine +from pyisda.legs import FeeLeg, ContingentLeg +from pyisda.curve import SpreadCurve +from pyisda.date import previous_twentieth serenitas_engine = dbengine('serenitasdb') tenors = {"IG": ("3yr", "5yr", "7yr", "10yr"), @@ -10,12 +17,51 @@ tenors = {"IG": ("3yr", "5yr", "7yr", "10yr"), sql_str = "INSERT INTO index_risk VALUES(%s, %s, %s)" +def get_legs(index, series, tenors): + fee_legs = {} + contingent_legs = {} + coupons = [] + end_dates = [] + 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): + fee_legs[tenor] = FeeLeg(issue_date, maturity, True, 1., coupon * 1e-4) + contingent_legs[tenor] = ContingentLeg(issue_date, maturity, True) + coupons.append(coupon * 1e-4) + # number of seconds since epoch + df.maturity = df.maturity.view(int) // int(86400 * 1e9) + # number of days between 1900-1-1 and epoch + df.maturity += 134774 + return fee_legs, contingent_legs, df + +def credit_curve(value_date, quotes, end_dates, coupons, recoveries, yc): + step_in_date = value_date + datetime.timedelta(days=1) + cash_settle_date = pd.Timestamp(value_date) + 3 * BDay() + start_date = previous_twentieth(value_date) + sc = SpreadCurve(value_date, yc, start_date, + step_in_date, cash_settle_date, + end_dates, coupons, quotes, + recoveries) + return sc + 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, 31): if index in ["EU", "XO"] and series == 30: continue - indices = {t: Index.from_name(index, series, t) for t in tenors[index]} + fee_legs, contingent_legs, df = \ + get_legs(index, series, tenors[index]) + index_quotes = pd.read_sql_query( "SELECT id, date, tenor, close_price FROM index_quotes_pre " "LEFT JOIN index_risk USING (id) " @@ -27,13 +73,19 @@ for index in ["IG", "HY", "EU", "XO"]: 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 k, v in index_quotes.groupby('date'): - for id, tenor, close_price in v[['tenor', 'close_price']].itertuples(): - indices[tenor].value_date = k - indices[tenor].price = close_price - duration = indices[tenor].risky_annuity - theta = - indices[tenor].theta / indices[tenor].notional - c.execute(sql_str, (id, theta, duration)) + yc = get_curve(k, "USD" if index in ["IG", "HY"] else "EUR") + v = v.merge(df, on='tenor') + sc = credit_curve(k, v.close_price.values, v.maturity.values, + v.coupon.values, recoveries, yc) + for t in v.tenor.values: + risky_annuity = fee_legs[t].( + k, step_in_date, + cash_settle_date, yc, sc, + False) conn.commit() conn.close() |