diff options
Diffstat (limited to 'python/cds_curve.py')
| -rw-r--r-- | python/cds_curve.py | 102 |
1 files changed, 66 insertions, 36 deletions
diff --git a/python/cds_curve.py b/python/cds_curve.py index d967c50c..ba353269 100644 --- a/python/cds_curve.py +++ b/python/cds_curve.py @@ -2,16 +2,21 @@ from pyisda.curve import YieldCurve, BadDay, SpreadCurve from pyisda.credit_index import CreditIndex from pyisda.legs import FeeLeg, ContingentLeg from pyisda.logging import enable_logging + import datetime import math +import numpy as np import pandas as pd from yieldcurve import YC, ql_to_jp from quantlib.settings import Settings from quantlib.time.api import Date -import numpy as np -from db import dbconn +from db import dbconn, dbengine from concurrent.futures import ProcessPoolExecutor, as_completed from itertools import zip_longest, chain +from index_data import get_index_quotes +from pandas.tseries.offsets import BDay +from scipy.optimize import brentq +from dateutil.relativedelta import relativedelta def get_singlenames_quotes(indexname, date): conn = dbconn('serenitasdb') @@ -23,8 +28,11 @@ def build_curve(r, today_date, yc, start_date, step_in_date, value_date, end_dat spread_curve = 1e-4 * np.array(r['spread_curve'][1:]) upfront_curve = 1e-2 * np.array(r['upfront_curve'][1:]) recovery_curve = np.array(r['recovery_curve'][1:]) - sc = SpreadCurve(today_date, yc, start_date, step_in_date, value_date, - end_dates, spread_curve, upfront_curve, recovery_curve, True) + try: + sc = SpreadCurve(today_date, yc, start_date, step_in_date, value_date, + end_dates, spread_curve, upfront_curve, recovery_curve, True) + except ValueError: + pdb.set_trace() return (r['cds_ticker'], sc) def grouper(iterable, n, fillvalue=None): @@ -43,7 +51,6 @@ def build_curves_dist(quotes, args, workers=4): def build_curves(quotes, args): return [build_curve(q, *args) for q in quotes if q is not None] - def all_curves_pv(curves, today_date, jp_yc, start_date, step_in_date, value_date, maturities): r = {} for d in maturities: @@ -85,36 +92,59 @@ def forward_hazard_rates(sc): h1, t1 = h2, t2 return t, r -Settings().evaluation_date = Date(6, 2, 2017) -yc = YC() -jp_yc = ql_to_jp(yc) -today_date = datetime.date(2017, 2, 6) -step_in_date = datetime.date(2017, 2, 7) -value_date = datetime.date(2017, 2, 9) -start_date = datetime.date(2016, 12, 20) -end_dates = [datetime.date(2017, 12, 20), - datetime.date(2018, 12, 20), - datetime.date(2019, 12, 20), - datetime.date(2020, 12, 20), - datetime.date(2021, 12, 20), - datetime.date(2023, 12, 20), - datetime.date(2026, 12, 20)] - -quotes = get_singlenames_quotes("ig27", today_date) -maturities = [datetime.date(2019, 12, 20), - datetime.date(2021, 12, 20), - datetime.date(2023, 12, 20), - datetime.date(2026, 12, 20)] -args = (today_date, jp_yc, start_date, step_in_date, value_date, maturities) -curves = build_curves_dist(quotes, args) -test = all_curves_pv(curves, *args) -ig27 = CreditIndex(start_date, maturities, curves) -test2 = ig27.pv_vec(step_in_date, value_date, jp_yc, 0.4) +serenitas_engine = dbengine('serenitasdb') -from index_data import get_index_quotes -quotes = get_index_quotes("IG", 27, ['3yr', '5yr', '7yr', '10yr']) +def calibrate_portfolio(index_type, series): + if index_type == 'IG': + recovery = 0.4 + else: + recovery = 0.3 + tenors = ['3yr', '5yr', '7yr', '10yr'] + index_quotes = get_index_quotes(index_type, series, + tenors)['closeprice'].unstack() + index_desc = pd.read_sql_query("SELECT tenor, maturity, coupon FROM index_maturity " \ + "WHERE index=%s AND series=%s", serenitas_engine, + index_col='tenor', params=(index_type, series)) -def calibrate_portfolio(index, step_in_date, value_date, yc, recovery, quotes): - for i, m in index.maturities: - eps = brentq(lambda epsilon: index.pv(step_in_date, value_date, m, yc, recovery, epsilon) - - quote, -0.15, 0.3) + index_quotes.columns = index_desc.loc[index_quotes.columns, "maturity"] + index_quotes = index_quotes.sort_index(1) + end_dates = [datetime.date(2017, 12, 20), + datetime.date(2018, 12, 20), + datetime.date(2019, 12, 20), + datetime.date(2020, 12, 20), + datetime.date(2021, 12, 20), + datetime.date(2023, 12, 20), + datetime.date(2026, 12, 20)] + maturities = index_desc.maturity.tolist() + start_date = datetime.date(2016, 9, 20) + r = {} + for k, s in index_quotes.iterrows(): + trade_date = k[0].date() + print(trade_date) + sn_quotes = get_singlenames_quotes("{}{}".format(index_type.lower(), series), + trade_date) + Settings().evaluation_date = Date.from_datetime(trade_date) + yc = YC() + jp_yc = ql_to_jp(yc) + step_in_date = trade_date + datetime.timedelta(days=1) + value_date = (pd.Timestamp(trade_date) + 3* BDay()).date() + args = (trade_date, jp_yc, start_date, step_in_date, value_date, end_dates) + curves = build_curves_dist(sn_quotes, args) + index = CreditIndex(start_date, maturities, curves) + d = {'tweak':[], + 'duration':[], + 'theta':[]} + for i, m in enumerate(maturities): + index_quote = 1 - s.iat[i]/100 + eps = brentq(lambda epsilon: index.pv(step_in_date, + value_date, m, jp_yc, 0.4, 0.01, epsilon) - + index_quote, -0.3, 0.3) + #tweak the curves in place + index.tweak_portfolio(eps, m) + d['duration'].append(index.duration(step_in_date, value_date, m, jp_yc)) + d['theta'].append(index_quote - index.theta(step_in_date, value_date, + m - relativedelta(years=1), jp_yc, 0.4, 0.01) + + 0.01) + d['tweak'].append(eps) + r[trade_date] = pd.DataFrame(d, index=tenors) + return pd.concat(d) |