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import analytics
import pandas as pd
from analytics import CreditIndex, Portfolio, BlackSwaptionVolSurface
from copy import deepcopy
from risk.tranches import get_tranche_portfolio
from risk.swaptions import get_swaption_portfolio
from risk.bonds import subprime_risk, clo_risk, crt_risk
from utils.db import dbconn, dbengine, serenitas_engine, dawn_engine
from pandas.tseries.offsets import BDay
def build_portf(position_date, spread_date=None):
"""
Output two portfolios:
1) All synthetic + curve with just delta-proxy + dummy index as cash bonds proxy (portf)
2) All synthetic (portf_syn)
"""
analytics._local = False
if spread_date is None:
spread_date = position_date
analytics.init_ontr(spread_date)
conn = dawn_engine.raw_connection()
conn.autocommit = True
on_the_run_index = analytics._ontr["HY"]
on_the_run_index.value_date = position_date
portf = get_tranche_portfolio(position_date, conn, False, "SERCGMAST")
s_portf = get_swaption_portfolio(position_date, conn)
if bool(s_portf):
for t, id in zip(s_portf.trades, s_portf.trade_ids):
portf.add_trade(t, id)
portf_syn = deepcopy(portf)
df = pd.read_sql_query(
"SELECT * from list_cds_positions_by_strat(%s)",
dawn_engine,
params=(position_date,),
)
if not (df.empty):
for t in df.itertuples(index=False):
portf_syn.add_trade(
CreditIndex(
redcode=t.security_id, maturity=t.maturity, notional=t.notional
),
(t.folder, t.security_desc),
)
df_no_curve = df[~df.folder.str.contains("CURVE")]
for t in df_no_curve.itertuples(index=False):
portf.add_trade(
CreditIndex(
redcode=t.security_id, maturity=t.maturity, notional=t.notional
),
(t.folder, t.security_desc),
)
# separately add in curve delta
df_curve = df[df["folder"].str.contains("CURVE")]
curve_portf = Portfolio(
[
CreditIndex(
redcode=t.security_id, maturity=t.maturity, notional=t.notional
)
for t in df_curve.itertuples(index=False)
]
)
curve_portf.value_date = spread_date
curve_portf.mark()
hyontr = deepcopy(on_the_run_index)
hyontr.notional = curve_portf.hy_equiv
portf.add_trade(hyontr, ("curve_trades", ""))
# get bond risks:
sql_string = (
"SELECT distinct timestamp::date FROM priced where normalization = 'current_notional' and model_version = 1 "
"and date(timestamp) <= %s and date(timestamp) >= %s order by timestamp desc"
)
with dbconn("etdb") as etconn, dbconn("dawndb") as dawnconn:
timestamps = pd.read_sql_query(
sql_string,
dawn_engine,
parse_dates=["timestamp"],
params=[
position_date,
position_date - pd.tseries.offsets.DateOffset(15, "D"),
],
)
rmbs_pos = subprime_risk(
position_date,
dawnconn,
dbengine("rmbs_model"),
timestamps.iloc[0][0].date(),
)
clo_pos = clo_risk(position_date, dawnconn, etconn)
crt_pos = crt_risk(
position_date, dawnconn, dbengine("crt"), model_version="hpi5_ir3_btm"
)
# CRT model version changes with time, need to check
rmbs_notional = 0
for pos in [rmbs_pos, crt_pos]:
rmbs_notional += pos["hy_equiv"].sum() if pos is not None else 0
hyontr_rmbs = deepcopy(on_the_run_index)
hyontr_rmbs.notional = -rmbs_notional
portf.add_trade(hyontr_rmbs, ("rmbs_bonds", ""))
if isinstance(clo_pos, pd.DataFrame):
hyontr_clos = deepcopy(on_the_run_index)
hyontr_clos.notional = -clo_pos["hy_equiv"].sum()
portf.add_trade(hyontr_clos, ("clo_bonds", ""))
for p in [portf, portf_syn]:
p.value_date = spread_date
p.mark(interp_method="bivariate_linear")
p.reset_pv()
return portf, portf_syn
def generate_vol_surface(portf, try_days_back=5):
vol_surface = {}
for trade in portf.swaptions:
try:
vs = BlackSwaptionVolSurface(
trade.index.index_type,
trade.index.series,
value_date=portf.value_date,
interp_method="bivariate_linear",
)
except:
vs = BlackSwaptionVolSurface(
trade.index.index_type,
trade.index.series,
value_date=portf.value_date - BDay(try_days_back),
interp_method="bivariate_linear",
)
vol_surface[
(trade.index.index_type, trade.index.series, trade.option_type)
] = vs[vs.list(source="MS", option_type=trade.option_type)[-1]]
return vol_surface
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