diff options
Diffstat (limited to 'python/cds_curve.py')
| -rw-r--r-- | python/cds_curve.py | 48 |
1 files changed, 35 insertions, 13 deletions
diff --git a/python/cds_curve.py b/python/cds_curve.py index 96413b10..7b08bb62 100644 --- a/python/cds_curve.py +++ b/python/cds_curve.py @@ -89,7 +89,8 @@ def all_curves_pv(curves, today_date, jp_yc, start_date, step_in_date, value_dat serenitas_engine = dbengine('serenitasdb') -def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr']): +def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'], + start_date=None): if index_type == 'IG': recovery = 0.4 else: @@ -113,28 +114,40 @@ def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'] issue_date = index_desc.issue_date[0] index_desc = index_desc.set_index('maturity') maturities = index_quotes.columns.sort_values().to_pydatetime() - curves, _ = get_singlenames_curves(index_type, series, issue_date) + + curves, _ = get_singlenames_curves(index_type, series, start_date or issue_date) index = CreditIndex(issue_date, maturities, curves) r = {} + lo, hi = -0.5, 0.5 for k, s in index_quotes.iterrows(): trade_date, version = k + if start_date and trade_date < start_date: + continue curves, args = get_singlenames_curves(index_type, series, trade_date) _, jp_yc, _, step_in_date, value_date, _ = args index.curves = curves tweak, duration, theta = [], [], [] s.name = 'index_quote' - quotes = pd.concat([index_desc, s], axis=1) + quotes = pd.concat([index_desc, s], axis=1).dropna() for m, coupon, index_quote in quotes[['coupon', 'index_quote']].itertuples(): - eps = brentq(lambda epsilon: index.pv(step_in_date, value_date, - m, jp_yc, recovery, - coupon, epsilon) - - 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.theta(step_in_date, value_date, + try: + eps = brentq(lambda epsilon: index.pv(step_in_date, value_date, + m, jp_yc, recovery, + coupon, epsilon) - + index_quote, lo, hi) + except ValueError: + print("couldn't calibrate for date: {} and maturity: {}". + format(trade_date,m)) + tweak.append(np.NaN) + duration.append(np.NaN) + theta.append(np.NaN) + else: + #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.theta(step_in_date, value_date, m, jp_yc, recovery, coupon)) - tweak.append(eps) + tweak.append(eps) r[trade_date] = pd.DataFrame({'duration': duration, 'theta': theta, 'tweak': tweak}, index=tenors) @@ -142,4 +155,13 @@ def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'] if __name__=="__main__": enable_logging() - df = calibrate_portfolio("IG", 25) + df = calibrate_portfolio("IG", 22, ['3yr', '5yr', '7yr', '10yr'], + start_date=pd.Timestamp('2014-06-20')) + conn = dbconn('serenitasdb') + with conn.cursor() as c: + for k, s in df.iterrows(): + c.execute("UPDATE index_quotes SET duration2=%s, theta2=%s "\ + "WHERE date=%s AND tenor=%s AND index=%s AND series=%s", + (s.duration, s.theta, k[0], k[1], "IG", 22)) + conn.commit() + conn.close() |