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import pandas as pd
import numpy as np
from enum import Enum, auto
from utils.db import dbengine
from yieldcurve import YC
from quantlib.termstructures.yield_term_structure import YieldTermStructure
class AssetClass(Enum):
Subprime = auto()
CLO = auto()
CSO = auto()
CRT = auto()
def latest_sim(date, engine):
sql_string = (
"SELECT model_id_sub FROM model_versions "
"JOIN model_versions_nonagency USING (model_id_sub) "
"JOIN simulations_nonagency USING (simulation_id) "
"WHERE (date(start_time) <= %s) AND (description = 'normal') "
"ORDER BY start_time DESC"
)
conn = engine.raw_connection()
c = conn.cursor()
c.execute(sql_string, (date,))
model_id_sub, = next(c)
c.close()
return model_id_sub
def get_df(date, engine):
model_id_sub = latest_sim(date, engine)
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 WHERE "
"timestamp BETWEEN %s AND date_add(%s, INTERVAL 1 DAY) "
"AND model_id_sub=%s "
"AND normalization='current_notional'",
engine,
["cusip", "model_version"],
params=(date, date, model_id_sub),
)
df_percentiles = pd.read_sql_query(
"SELECT cusip, PV, percentile "
"FROM priced_percentiles WHERE "
"timestamp BETWEEN %s AND date_add(%s, INTERVAL 1 DAY) "
"AND model_version=3 "
"AND model_id_sub=%s "
"AND percentile IN (5, 25, 50, 75, 95) "
"AND normalization='current_notional'",
engine,
["cusip", "percentile"],
params=(date, date, model_id_sub),
)
df_prices = df_prices.unstack("model_version")
df_percentiles = df_percentiles.unstack("percentile")
return df_prices.join(df_percentiles, how="left")
def subprime_risk(date, conn, engine):
df = get_df(date, engine)
df_pos = get_portfolio(date, conn, AssetClass.Subprime)
df_pv = df.xs("pv", axis=1, level=0)
df_pv.columns = ["pv1", "pv2", "pv3"]
df_pv_perct = df.xs("PV", axis=1, level=0)
df_pv_perct.columns = ["pv5", "pv25", "pv50", "pv75", "pv95"]
df_modDur = df[("modDur", 1)]
df_modDur.name = "modDur"
df_v1 = df.xs(1, axis=1, level="model_version")[
["pv_RnW", "delta_mult", "delta_hpi", "delta_ir"]
]
df_v1.columns = ["v1pv_RnW", "v1_lsdel", "v1_hpidel", "v1_irdel"]
df_pv_FB = df[("pv_FB", 3)]
df_pv_FB.name = "pv_FB"
df_risk = pd.concat(
[
df_pv,
df_modDur,
df_pv_perct,
df.xs(3, axis=1, level="model_version")[
[
"delta_yield",
"wal",
"pv_io",
"pv_po",
"pv_RnW",
"delta_ir_io",
"delta_ir_po",
"delta_hpi",
"delta_RnW",
"delta_mult",
]
],
df_v1,
df_pv_FB,
],
axis=1,
)
df_calc = df_pos.join(df_risk)
yc = YC(evaluation_date=date)
df_calc = df_calc.assign(
b_yield=df_calc.modDur.apply(lambda x: 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,
)
df_calc.b_yield += (
(df_calc.pv1 * df_calc.curr_ntl / df_calc.local_market_value).log()
/ 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
def get_portfolio(date, conn, asset_class: AssetClass, fund="SERCGMAST"):
df = pd.read_sql_query(
"SELECT * FROM risk_positions(%s, %s, %s)",
conn,
params=(date, asset_class.name, fund),
)
if asset_class is AssetClass.CLO:
with conn.cursor() as c:
c.execute(
"SELECT cusip, identifier FROM securities " "WHERE asset_class = 'CLO'"
)
cusip_map = {identifier: cusip for cusip, identifier in c.fetchall()}
df["cusip"] = df["identifier"].replace(cusip_map)
else: # only CLOs used ISIN for now
df["cusip"] = df.identifier.str.slice(0, 9)
return df.set_index("cusip")
def crt_risk(date, dawn_conn, engine):
df = get_portfolio(date, dawn_conn, AssetClass.CRT)
df_model = pd.read_sql_query(
"SELECT * from priced_at_market WHERE "
"timestamp BETWEEN %s AND date_add(%s, INTERVAL 1 DAY) "
"AND model_des = 'hpi3_ir3'",
engine,
"cusip",
params=(date, date),
)
df = df.join(df_model)
df["curr_ntl"] = df["notional"] * df["factor"]
df["delta_yield"] = df["curr_ntl"] * df["duration_FW"]
return df
def clo_risk(date, dawn_conn, et_conn):
df = get_portfolio(date, dawn_conn, AssetClass.CLO)
placeholders = ",".join(["%s"] * df.shape[0])
sql_string = f"SELECT * FROM historical_cusip_risk(%s, {placeholders})"
model = pd.read_sql_query(
sql_string, et_conn, parse_dates=["pricingdate"], params=(date, *df.index)
)
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"]
return df
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