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import pandas as pd
import numpy as np
from risk.portfolio import build_portfolio, generate_vol_surface
from serenitas.analytics.scenarios import run_portfolio_scenarios
from serenitas.analytics.base import Trade
from serenitas.analytics.index_data import load_all_curves
def gen_shocks(portf, shock_date, fund):
Trade.init_ontr(shock_date)
ontr_spread = Trade._ontr["HY"].spread
spread_shock = np.array([-25.0, 1.0, +25.0, 100.0, 200.0, 500, 1000])
spread_shock /= ontr_spread
# Add in 2020 HY Wides, 2021 HY Tights, 2022 HY Wides scenarios
historic_spreads = np.array([872, 269, 626])
spread_shock = np.append(spread_shock, historic_spreads / ontr_spread - 1.0)
vol_surface = generate_vol_surface(
portf, lookback=10, source_list=["BAML", "MS", "JPM"]
)
scens = run_portfolio_scenarios(
portf,
date_range=[pd.Timestamp(shock_date)],
params=["pnl", "hy_equiv"],
spread_shock=spread_shock,
vol_shock=[0.0],
corr_shock=[0.0],
vol_surface=vol_surface,
)
strategies = {
s: "options"
for s in ["HYOPTDEL", "HYPAYER", "HYREC", "IGOPTDEL", "IGPAYER", "IGREC"]
} | {
s: "tranches"
for s in [
"HYSNR",
"HYMEZ",
"HYINX",
"HYEQY",
"IGSNR",
"IGMEZ",
"IGINX",
"IGEQY",
"EUSNR",
"EUMEZ",
"EUINX",
"EUEQY",
"XOSNR",
"XOMEZ",
"XOINX",
"XOEQY",
"BSPK",
]
}
if fund == "BRINKER":
scens = scens.xs(0, level="corr_shock")
else:
scens = scens.xs((0.0, 0.0), level=["vol_shock", "corr_shock"])
scens.columns.names = ["strategy", "trade_id", "scen_type"]
results = scens.stack(level="scen_type").reorder_levels([2, 0, 1]).sort_index()
results = results.groupby(["strategy"], axis=1).sum()
results = results.groupby(lambda s: strategies.get(s, s), axis=1).sum()
# map shocks back to absolute spread diff
results.index = results.index.set_levels(
results.index.levels[2] * ontr_spread, level="spread_shock"
)
results["total"] = results.sum(axis=1)
results = results.stack().reset_index()
results.scen_type = results.scen_type.str.upper()
results.insert(0, "date", results.pop("date"))
return results
def save_shocks(conn, date, df, fund):
with conn.cursor() as c:
c.execute(
"DELETE FROM shocks WHERE fund=%s AND date=%s",
(
fund,
args.date,
),
)
conn.commit()
c.executemany(
"INSERT INTO shocks VALUES (%s, %s, %s, %s, %s, %s)",
[(*t, fund) for t in df.itertuples(index=False)],
)
conn.commit()
def get_survival_curves(conn, date):
surv_curves = load_all_curves(conn, date)
fun = lambda sc: (np.array([h for d, h in sc]) * (1 - sc.recovery_rates))[[1, 3, 5]]
surv_curves[["1yr", "3yr", "5yr"]] = np.stack(
[fun(sc) for sc in surv_curves["curve"].values], axis=0
)
return surv_curves.groupby(level=0).first()[
["name", "company_id", "1yr", "3yr", "5yr"]
]
def gen_jtd(portf, survival_curves):
jtd = portf.jtd_single_names()
jtd = survival_curves.join(jtd.iloc[:, 0], how="right")
jtd.columns = [
"name",
"company_id",
"one_year_spread",
"three_year_spread",
"five_year_spread",
"jtd",
]
return jtd.groupby(["company_id", "name"], as_index=False).sum()
def save_jtd(conn, date, df, fund):
with conn.cursor() as c:
c.execute(
"DELETE FROM jtd_risks WHERE fund=%s AND date=%s",
(
fund,
date,
),
)
conn.commit()
c.executemany(
'INSERT INTO jtd_risks(date, fund, company_id, name, "1yr_spread", "3yr_spread", "5yr_spread", jtd)'
"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)",
[(date, fund, *t) for t in df.itertuples(index=False)],
)
conn.commit()
if __name__ == "__main__":
import datetime
import argparse
import warnings
from serenitas.analytics.dates import prev_business_day
from serenitas.utils.db2 import dbconn
from serenitas.analytics.config import C
parser = argparse.ArgumentParser(
description="Shock data/ calculate JTD and insert into DB"
)
parser.add_argument(
"date",
nargs="?",
type=datetime.date.fromisoformat,
default=prev_business_day(datetime.date.today()),
)
parser.add_argument("-n", "--no-upload", action="store_true", help="do not upload")
args = parser.parse_args()
C.local = False
C.dual_corr_tranche_cache_size = 2**14
survival_curves = get_survival_curves(Trade._conn, args.date)
conn = dbconn("dawndb")
for fund in (
"SERCGMAST",
"BOWDST",
"ISOSEL",
"BRINKER",
):
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="pandas only supports SQLAlchemy connectable"
)
warnings.filterwarnings("ignore", message="skipped 1 empty curves")
portf, syn_portf = build_portfolio(args.date, args.date, fund)
shocks = gen_shocks(portf, args.date, fund)
save_shocks(conn, args.date, shocks, fund)
print(f"{args.date}: {fund} Shocks Done")
jtd = gen_jtd(syn_portf, survival_curves)
save_jtd(conn, args.date, jtd, fund)
print(f"{args.date}: {fund} JTD Done")
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