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import datetime
import pandas as pd
from analytics.utils import get_fx
from dates import bus_day
from psycopg2.errors import SyntaxError
from psycopg2.extensions import connection
from risk.swaptions import get_swaption_portfolio
from risk.indices import get_index_portfolio
from pandas.tseries.offsets import BDay
from pyisda.date import previous_twentieth
from typing import Tuple, Union
def get_index_pv(
start_date: datetime.date,
end_date: datetime.date,
conn: connection,
strategies: Union[Tuple[str], None] = None,
):
dr = pd.bdate_range(start_date, end_date, freq=bus_day)
pvs = []
daily = []
dates = []
for d in dr:
prev_day = (d - bus_day).date()
if previous_twentieth(d, roll=True) == d.date():
accrued = 0.0
for t in portf.trades:
_, amount = t._fee_leg.cashflows[0]
amount *= get_fx(d, t.currency)
accrued -= amount * t.notional * t.factor * t.fixed_rate * 1e-4
else:
accrued = 0.0
portf = get_index_portfolio(prev_day, conn, strategies)
nav = 0.0
with conn.cursor() as c:
try:
c.execute(
"SELECT upfront, currency FROM cds WHERE trade_date=%s "
"AND folder in %s",
(prev_day, strategies),
)
except SyntaxError as e:
conn.reset()
raise e
for (fee, curr) in c:
nav += fee * get_fx(d, curr)
daily.append(nav + accrued)
pvs.append(portf.pv)
dates.append(prev_day)
df = pd.DataFrame({"pv": pvs, "daily": daily}, index=pd.to_datetime(dates))
return df
def get_swaption_pv(
start_date: datetime.date, end_date: datetime.date, conn: connection, **kwargs
):
dr = pd.bdate_range(start_date, end_date, freq=bus_day)
pv = []
daily = []
dates = []
for d in dr:
prev_day = (d - bus_day).date()
portf = get_swaption_portfolio(prev_day, conn, **kwargs)
nav = 0.0
# add terminations
with conn.cursor() as c:
c.execute(
"SELECT termination_fee "
"FROM swaptions WHERE termination_date=%s AND folder != 'STEEP'",
(prev_day,),
)
for (fee,) in c:
nav += fee
# add new trades
with conn.cursor() as c:
c.execute(
"SELECT notional * price/100 * (CASE WHEN buysell THEN -1. ELSE 1. END) "
"FROM swaptions WHERE trade_date=%s AND folder != 'STEEP'",
(prev_day,),
)
for (fee,) in c:
nav += fee
dates.append(prev_day)
pv.append(portf.pv)
daily.append(nav)
df = pd.DataFrame({"pv": pv, "daily": daily}, index=pd.to_datetime(dates))
return df
if __name__ == "__main__":
import argparse
from utils.db import dbconn
dawndb = dbconn("dawndb")
parser = argparse.ArgumentParser()
parser.add_argument("start_date", type=datetime.datetime.fromisoformat)
parser.add_argument("end_date", type=datetime.datetime.fromisoformat)
parser.add_argument(
"-e",
"--external",
action="store_true",
default=False,
dest="use_external",
help="use brokers' marks",
)
parser.add_argument(
"-s",
"--source",
action="append",
default=["GS"],
dest="source_list",
help="quote source",
)
args = parser.parse_args()
df_index = get_index_pv(
args.start_date, args.end_date, dawndb, ("IGOPTDEL", "HYOPTDEL")
)
df_swaption = get_swaption_pv(conn=dawndb, **vars(args))
pnl_index = df_index.pv.diff() + df_index.daily
pnl_swaption = df_swaption.pv.diff() + df_swaption.daily
pnl = pd.concat([pnl_index, pnl_swaption], keys=["index", "swaption"], axis=1)
print(
pd.concat(
[pnl.sum(axis=1), pnl.sum(axis=1).cumsum()],
axis=1,
keys=["daily", "cumulative"],
)
)
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