diff options
Diffstat (limited to 'python/cds_curve.py')
| -rw-r--r-- | python/cds_curve.py | 56 |
1 files changed, 11 insertions, 45 deletions
diff --git a/python/cds_curve.py b/python/cds_curve.py index 3e44810c..51f017f1 100644 --- a/python/cds_curve.py +++ b/python/cds_curve.py @@ -17,7 +17,6 @@ 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 from pyisda.logging import enable_logging from analytics.utils import roll_date, previous_twentieth @@ -33,19 +32,14 @@ def build_curve(r, today_date, yc, start_date, step_in_date, value_date, end_dat recovery_curve = np.array(r['recovery_curve'][1:], dtype='float') try: sc = SpreadCurve(today_date, yc, start_date, step_in_date, value_date, - end_dates, spread_curve, upfront_curve, recovery_curve, True) + end_dates, spread_curve, upfront_curve, recovery_curve, + ticker=r['cds_ticker']) if np.any(np.isnan(upfront_curve)): sc = fill_curve(sc, end_dates) except ValueError as e: print(e) - return (r['cds_ticker'], None) - return (r['cds_ticker'], sc) - -def grouper(iterable, n, fillvalue=None): - "Collect data into fixed-length chunks or blocks" - # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx - args = [iter(iterable)] * n - return zip_longest(fillvalue=fillvalue, *args) + return None + return sc def build_curves_dist(quotes, args, workers=4): ## about twice as fast as the non distributed version @@ -80,37 +74,17 @@ def all_curves_pv(curves, today_date, jp_yc, start_date, step_in_date, value_dat accrued = coupon_leg.accrued(step_in_date) tickers = [] data = [] - for ticker, sc in curves: + for sc in curves: coupon_leg_pv = coupon_leg.pv(today_date, step_in_date, value_date, jp_yc, sc, False) default_leg_pv = default_leg.pv(today_date, step_in_date, value_date, jp_yc, sc, 0.4) - tickers.append(ticker) + tickers.append(sc.ticker) data.append((coupon_leg_pv-accrued, default_leg_pv)) r[pd.Timestamp(d)] = pd.DataFrame.from_records(data, index=tickers, columns=['duration', 'protection_pv']) return pd.concat(r, axis=1).swaplevel(axis=1).sort_index(axis=1,level=0) -def stack_curves(curves): - dates = [d for d, _ in curves[0].inspect()['data']] - hazard_rates = np.empty((len(curves), len(dates))) - for i, sc in enumerate(curves): - hazard_rates[i] = np.array([h for _, h in sc.inspect()['data']]) - return hazard_rates, dates - -def forward_hazard_rates(sc): - r = [] - t = [] - t1 = 0 - h1 = 0 - base_date = sc.base_date - for d, h2 in sc.inspect()['data']: - t2 = (d - base_date).days / 365 - r.append( (h2 * t2 - h1 * t1) / (t2 - t1) ) - t.append(t2) - h1, t1 = h2, t2 - return t, r - serenitas_engine = dbengine('serenitasdb') def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr']): @@ -140,16 +114,11 @@ def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'] curves, _ = get_singlenames_curves(index_type, series, issue_date) index = CreditIndex(issue_date, maturities, curves) r = {} - old_version = 1 for k, s in index_quotes.iterrows(): trade_date, version = k curves, args = get_singlenames_curves(index_type, series, trade_date) _, jp_yc, _, step_in_date, value_date, _ = args - if version > old_version: - old_version = version - index = CreditIndex(issue_date, maturities, curves) - else: - index.curves = [c for _, c in curves] + index.curves = curves tweak, duration, theta = [], [], [] s.name = 'index_quote' quotes = pd.concat([index_desc, s], axis=1) @@ -157,15 +126,12 @@ def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'] eps = brentq(lambda epsilon: index.pv(step_in_date, value_date, m, jp_yc, recovery, coupon, epsilon) - - index_quote, -0.5, 0.3) + index_quote, -0.5, 0.3) #tweak the curves in place index.tweak_portfolio(eps, m) duration.append(index.duration(step_in_date, value_date, m, jp_yc)) - theta.append(index_quote - - index.theta(step_in_date, value_date, - m - relativedelta(years=1), - jp_yc, recovery, coupon) + - coupon) + theta.append(index.theta(step_in_date, value_date, + m, jp_yc, recovery, coupon)) tweak.append(eps) r[trade_date] = pd.DataFrame({'duration': duration, 'theta': theta, @@ -174,4 +140,4 @@ def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'] if __name__=="__main__": enable_logging() - df = calibrate_portfolio("IG", 27) + df = calibrate_portfolio("IG", 25) |