diff options
Diffstat (limited to 'python/risk/bonds.py')
| -rw-r--r-- | python/risk/bonds.py | 43 |
1 files changed, 34 insertions, 9 deletions
diff --git a/python/risk/bonds.py b/python/risk/bonds.py index b15ed2bf..5b104091 100644 --- a/python/risk/bonds.py +++ b/python/risk/bonds.py @@ -155,6 +155,7 @@ def subprime_risk(pos_date, conn, engine, model_date=None, fund="SERCGMAST"): ), date=pd.Timestamp(pos_date), ) + df_calc = df_calc[(df_calc.usd_market_value != 0)] df_calc.bond_yield += ( np.log(df_calc.pv1 * df_calc.curr_ntl / df_calc.local_market_value) / df_calc.modDur @@ -232,6 +233,7 @@ def get_portfolio(date, conn, asset_class: AssetClass, fund="SERCGMAST"): def crt_risk(date, dawn_conn, crt_engine, fund="SERCGMAST"): analytics.init_ontr(date) + yc = YC(evaluation_date=date) df = get_portfolio(date, dawn_conn, AssetClass.CRT, fund) scen = { datetime.date(2019, 5, 31): "base", @@ -251,7 +253,9 @@ def crt_risk(date, dawn_conn, crt_engine, fund="SERCGMAST"): ) with crt_engine.connect() as c: r = c.execute(sql_string, (scen_type, date - datetime.timedelta(days=15), date)) - (model_date,) = r.fetchone() + model_date = r.fetchone() + if model_date: + model_date = model_date[0] df_model = pd.read_sql_query( "SELECT * from priced_at_market WHERE timestamp = %s", crt_engine, @@ -261,13 +265,22 @@ def crt_risk(date, dawn_conn, crt_engine, fund="SERCGMAST"): if any(~df_model["delta.ir"].isna()): df_model = df_model[~df_model["delta.ir"].isna()] df = df.join(df_model) - df["curr_ntl"] = df.notional * df.factor - df["hy_equiv"] = ( - df.duration_FW - / analytics._ontr["HY"].risky_annuity - * analytics._beta["CRT"] - * df.curr_ntl + df.rename(columns={"duration_FW": "modDur"}, inplace=True) + df = df[(df.notional != 0)] + df = df.assign( + bond_yield=df.modDur.apply( + lambda x: x if np.isnan(x) else float(yc.zero_rate(x)) + ), + curr_ntl=df.notional * df.factor, + hy_equiv=( + df.modDur + / analytics._ontr["HY"].risky_annuity + * analytics._beta["CRT"] + * df.notional + * df.factor + ), ) + df.bond_yield += df.DM / 10000 delta = pd.read_sql_query( "SELECT distinct on (strategy) " "strategy, beta_crt from beta_crt WHERE " @@ -281,6 +294,7 @@ def crt_risk(date, dawn_conn, crt_engine, fund="SERCGMAST"): def clo_risk(date, dawn_conn, et_conn, fund="SERCGMAST"): + yc = YC(evaluation_date=date) df = get_portfolio(date, dawn_conn, AssetClass.CLO, fund) if df.empty: return None @@ -291,6 +305,17 @@ def clo_risk(date, dawn_conn, et_conn, fund="SERCGMAST"): ) model.index = df.index df = df.join(model, lsuffix="mark") - df["curr_ntl"] = df["notional"] * df["factor"] - df["hy_equiv"] = df["curr_ntl"] * df["delta"] + df.rename(columns={"duration": "modDur"}, inplace=True) + df = df.assign( + bond_yield=df.modDur.apply( + lambda x: x if np.isnan(x) else float(yc.zero_rate(x)) + ), + curr_ntl=df.notional * df.factor, + hy_equiv=(df.delta * df.notional * df.factor), + ) + df.bond_yield += ( + np.log(df.curr_ntl / df.local_market_value) / df.modDur + + df.curr_cpn / 100 + - float(yc.zero_rate(0.25)) + ).clip(upper=1.0) return df |