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import datetime
import pandas as pd
import numpy as np
import argparse
from risk.portfolio import build_portfolio, generate_vol_surface
import serenitas.analytics as ana
from serenitas.analytics.scenarios import run_portfolio_scenarios
from serenitas.analytics.base import Trade
from serenitas.utils.db2 import dbconn
from serenitas.utils.db import dawn_engine
from serenitas.analytics.dates import prev_business_day
def parse_args():
"""Parses command line arguments"""
parser = argparse.ArgumentParser(description="Shock data 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")
return parser.parse_args()
def vol_surface(portf, try_days_back):
for source in ("BAML", "GS", "MS", "JPM"):
try:
vol_surface = generate_vol_surface(portf, 10, source)
except IndexError:
pass
else:
return vol_surface
def gen_spreads(shock_date, fund):
Trade.init_ontr(shock_date)
ana._local = False
spread_shock = np.array([-25.0, 1.0, +25.0, 100.0, 200.0, 500, 1000])
spread_shock /= Trade._ontr["HY"].spread
# Add in 2020 HY Wides, 2021 HY Tights, 2022 HY Wides scenarios
spread_shock = np.append(
spread_shock,
[
(872 / Trade._ontr["HY"].spread - 1),
(269 / Trade._ontr["HY"].spread - 1),
(626 / Trade._ontr["HY"].spread - 1),
],
)
portf, _ = build_portfolio(shock_date, shock_date, fund)
vol_surface = generate_vol_surface(portf, 10)
portf.reset_pv()
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 = {}
strategies["options"] = [
"HYOPTDEL",
"HYPAYER",
"HYREC",
"IGOPTDEL",
"IGPAYER",
"IGREC",
]
strategies["tranches"] = [
"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 = {}
for i, g in scens.groupby(level="scen_type", axis=1):
temp = g.groupby(level="strategy", axis=1).sum()
for key, item in strategies.items():
exist_columns = list(set(temp.columns).intersection(item))
temp[key] = temp[exist_columns].sum(axis=1)
temp.drop(exist_columns, axis=1, inplace=True)
temp["total"] = temp.sum(axis=1)
results[i] = temp
results = pd.concat(results)
results.index.set_levels(
results.index.levels[results.index.names.index("spread_shock")]
* Trade._ontr["HY"].spread,
level="spread_shock",
inplace=True,
)
return results
def process_dataframe(raw_df):
"""Clean and transform the input dataframe to insert into database."""
transformed_df = raw_df.reset_index()
transformed_df = transformed_df.rename(columns={"level_0": "unit"})
strategy_columns = transformed_df.columns[3:]
transformed_df = pd.melt(
transformed_df,
id_vars=["unit", "date", "spread_shock"],
value_vars=strategy_columns,
)
transformed_df = transformed_df.rename(
columns={"variable": "strategy", "value": "value", "unit": "risk_type"}
)
transformed_df.risk_type = transformed_df.risk_type.str.upper()
return transformed_df
if __name__ == "__main__":
args = parse_args()
conn = dbconn("dawndb")
for fund in ("SERCGMAST", "BOWDST", "ISOSEL", "BRINKER"):
results = gen_spreads(args.date, fund)
with conn.cursor() as c:
c.execute(
"DELETE FROM shocks WHERE fund=%s AND date=%s",
(
fund,
args.date,
),
)
conn.commit()
df = process_dataframe(results)
df["fund"] = fund
df.to_sql("shocks", dawn_engine, if_exists="append", index=False)
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