diff options
Diffstat (limited to 'python/risk')
| -rw-r--r-- | python/risk/bonds.py | 51 |
1 files changed, 12 insertions, 39 deletions
diff --git a/python/risk/bonds.py b/python/risk/bonds.py index 7cb14c34..9f02a5fd 100644 --- a/python/risk/bonds.py +++ b/python/risk/bonds.py @@ -225,59 +225,32 @@ def get_portfolio(date, conn, asset_class: AssetClass, fund="SERCGMAST"): return df.set_index("identifier") -def crt_risk(date, dawn_conn, crt_engine, fund="SERCGMAST"): +def crt_risk(date, dawn_conn, fund="SERCGMAST"): Trade.init_ontr(date) yc = YC(evaluation_date=date, curve_type="OIS") - df = get_portfolio(date, dawn_conn, AssetClass.CRT, fund) - scen = { - datetime.date(2019, 5, 31): "base", - datetime.date(2020, 1, 29): "hpi3_ir3", - datetime.date(2020, 3, 18): "hpi4_ir3", - datetime.date(2020, 10, 20): "hpi5_ir3", - datetime.date(3000, 1, 1): "econ6_ir3", - } - scen_type = None - for d, s in scen.items(): - if scen_type is None and date < d: - scen_type = s - sql_string = ( - "SELECT a.*, be.* FROM crt.priced_at_market a " - "JOIN (SELECT cusip, MAX(timestamp) timestamp " - "FROM crt.priced_at_market where timestamp between %s " - "AND date_add(%s, INTERVAL 1 DAY) " - "and model_des = %s GROUP BY cusip) b " - "using (cusip, timestamp) " - "LEFT JOIN map_cusip using (cusip) " - "LEFT JOIN bond_types USING (bond) " - "LEFT JOIN (select bond_type, value from crt.beta_estimates " - "where date = (SELECT MAX(date) FROM crt.beta_estimates where date <= %s)) be " - "using (bond_type)" - ) - df_model = pd.read_sql_query( - sql_string, - crt_engine, - "cusip", - params=(date - datetime.timedelta(days=15), date, scen_type, date), + df = pd.read_sql_query( + "SELECT * FROM list_crt_data(%s, %s)", + dawn_conn, + "identifier", + params=(date, fund), ) - if any(~df_model["delta.ir"].isna()): - df_model = df_model[~df_model["delta.ir"].isna()] - df = df.join(df_model) - df.rename(columns={"duration_FW": "modDur"}, inplace=True) + if any(~df["delta_ir"].isna()): + df = df[~df["delta_ir"].isna()] df = df[(df.notional != 0)] df = df.assign( - swap_rate=df.modDur.apply( + swap_rate=df.duration.apply( lambda x: x if np.isnan(x) else float(yc.zero_rate(x)) ), curr_ntl=df.notional * df.factor, hy_equiv=( - df.modDur + df.duration / Trade._ontr["HY"].risky_annuity - * df.value + * df.delta * df.notional * df.factor ), ) - df["bond_yield"] = df.swap_rate + df.DM / 10000 + df["bond_yield"] = df.swap_rate + df.dm / 10000 return df |