diff options
Diffstat (limited to 'python/risk/bonds.py')
| -rw-r--r-- | python/risk/bonds.py | 41 |
1 files changed, 30 insertions, 11 deletions
diff --git a/python/risk/bonds.py b/python/risk/bonds.py index ff0823c2..23626c3b 100644 --- a/python/risk/bonds.py +++ b/python/risk/bonds.py @@ -1,5 +1,6 @@ import pandas as pd import numpy as np +import analytics from enum import Enum, auto from utils.db import dbengine @@ -14,35 +15,43 @@ class AssetClass(Enum): CRT = auto() -def get_df(date, engine): +def get_df(date, engine, zero_factor=False): + if zero_factor: + normalization = "unnormalized" + table1 = "priced_orig_ntl" + table2 = "priced_percentiles_orig_ntl" + else: + normalization = "current_notional" + table1 = "priced" + table2 = "priced_percentiles" df_prices = pd.read_sql_query( "SELECT cusip, model_version, pv, modDur, delta_yield, " "wal, pv_io, pv_po, pv_RnW, delta_ir_io, delta_ir_po, " "delta_hpi, delta_RnW, delta_mult, delta_ir, pv_FB " - "FROM priced " + f"FROM {table1} " "JOIN model_versions USING (model_id_sub) " "JOIN model_versions_nonagency USING (model_id_sub) " "JOIN simulations_nonagency USING (simulation_id) " "WHERE timestamp BETWEEN %s AND date_add(%s, INTERVAL 1 DAY) " "AND description='normal' " - "AND normalization='current_notional'", + "AND normalization=%s", engine, ["cusip", "model_version"], - params=(date, date), + params=(date, date, normalization), ) df_percentiles = pd.read_sql_query( "SELECT cusip, PV, percentile " - "FROM priced_percentiles " + f"FROM {table2} " "JOIN model_versions USING (model_id_sub) " "JOIN model_versions_nonagency USING (model_id_sub) " "JOIN simulations_nonagency USING (simulation_id) " "WHERE timestamp BETWEEN %s AND date_add(%s, INTERVAL 1 DAY) " "AND model_version=3 " "AND percentile IN (5, 25, 50, 75, 95) " - "AND normalization='current_notional'", + "AND normalization=%s", engine, ["cusip", "percentile"], - params=(date, date), + params=(date, date, normalization), ) df_prices = df_prices.unstack("model_version") df_percentiles = df_percentiles.unstack("percentile") @@ -50,7 +59,9 @@ def get_df(date, engine): def subprime_risk(date, conn, engine): - df = get_df(date, engine) + df = get_df(date, engine, False) + df_zero = get_df(date, engine, True) + df.loc[df_zero.index] = df_zero df_pos = get_portfolio(date, conn, AssetClass.Subprime) df_pv = df.xs("pv", axis=1, level=0) df_pv.columns = ["pv1", "pv2", "pv3"] @@ -92,18 +103,26 @@ def subprime_risk(date, conn, engine): df_calc = df_pos.join(df_risk) yc = YC(evaluation_date=date) df_calc = df_calc.assign( - b_yield=df_calc.modDur.apply( + bond_yield=df_calc.modDur.apply( lambda x: x if np.isnan(x) else float(yc.zero_rate(x)) ), delta_ir=df_calc.delta_ir_io + df_calc.delta_ir_po, curr_ntl=df_calc.notional * df_calc.factor, + # assume beta and ontr is initialized from analytics + hy_equiv=( + df_calc.delta_yield + / analytics._ontr.DV01 + * analytics._beta["SUBPRIME"] + * 1e5 + * df_calc.local_market_value + / df_calc.pv3 + ), ) - df_calc.b_yield += ( + df_calc.bond_yield += ( np.log(df_calc.pv1 * df_calc.curr_ntl / df_calc.local_market_value) / df_calc.modDur ).clip_upper(1.0) # delta scaled by ratio of market_value to model value - df_calc.delta_yield *= df_calc.local_market_value / df_calc.pv3 df_calc.delta_ir *= df_calc.local_market_value / df_calc.pv3 return df_calc |