diff options
Diffstat (limited to 'python/analytics')
| -rw-r--r-- | python/analytics/tranche_basket.py | 29 |
1 files changed, 23 insertions, 6 deletions
diff --git a/python/analytics/tranche_basket.py b/python/analytics/tranche_basket.py index 9b63f80e..ee7f25ca 100644 --- a/python/analytics/tranche_basket.py +++ b/python/analytics/tranche_basket.py @@ -251,9 +251,11 @@ class DualCorrTranche(): names=['spread_shock', 'corr_shock'])) def mark(self, **args): - sql_string = ("SELECT corr_at_detach FROM get_tranche_quotes(%s, %s, %s, %s) a " - "LEFT JOIN risk_numbers_new b ON a.id = b.tranche_id " - "WHERE a.detach = %s OR a.attach = %s ORDER BY a.attach") + sql_string = ("SELECT corr_at_detach FROM tranche_risk b " + "LEFT JOIN tranche_quotes a ON a.id = b.tranche_id " + "WHERE a.index=%s AND a.series=%s AND a.tenor=%s " + "AND quotedate::date=%s " + "AND (a.detach = %s OR a.attach = %s) ORDER BY a.attach") with dbconn('serenitasdb') as conn: with conn.cursor() as c: c.execute(sql_string, (self.index_type, self.series, @@ -401,7 +403,7 @@ class TrancheBasket(BasketIndex): if ((K == 0. and not complement) or (K == 1. and complement)): return 0., 0. elif ((K == 1. and not complement) or (K == 0. and complement)): - self.index_pv()[:-1] + return self.index_pv()[:-1] elif np.isnan(rho): raise ValueError("rho needs to be a real number between 0. and 1.") else: @@ -419,7 +421,7 @@ class TrancheBasket(BasketIndex): if complement: return tranche_cl(L, R, cs, K, 1.), tranche_pl(L, cs, K, 1.) else: - return tranche_cl(L, R, self.cs, 0., K), tranche_pl(L, self.cs, 0., K) + return tranche_cl(L, R, cs, 0., K), tranche_pl(L, cs, 0., K) def tranche_pvs(self, protection=False, complement=False, shortened=0): """ computes coupon leg, protection leg and bond price. @@ -516,6 +518,12 @@ class TrancheBasket(BasketIndex): durations.name = 'duration' return durations + def tranche_EL(self, complement=False): + pl = self.tranche_pvs(complement=complement).protection_leg + EL = pd.Series(-pl * np.diff(self.K), index=self._row_names) + EL.name = 'expected_loss' + return EL + def tranche_spreads(self, complement=False): cl, pl, _ = self.tranche_pvs(complement=complement) durations = (cl - self._accrued) / self.tranche_quotes.running.values @@ -569,6 +577,15 @@ class TrancheBasket(BasketIndex): return pd.DataFrame({'delta': deltas, 'gamma': gammas}, index=self._row_names) + def tranche_corr01(self, eps=0.01, complement=False): + bp = self.tranche_pvs(complement=complement).bond_price + rho_saved = self.rho + self.rho = np.power(self.rho, 1-eps) + corr01 = self.tranche_pvs(complement=complement).bond_price - bp + self.rho = rho_saved + return corr01 + + def build_skew(self, skew_type="bottomup"): assert(skew_type == "bottomup" or skew_type == "topdown") dK = np.diff(self.K) @@ -601,7 +618,7 @@ class TrancheBasket(BasketIndex): if r.converged: self.rho[j+1] = x0 else: - print(res.flag) + print(r.flag) break self._skew = CubicSpline(logit(self.K[1:-1]), logit(self.rho[1:-1]), bc_type='natural') |