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from serenitas.analytics.basket_index import MarkitBasketIndex
from serenitas.analytics.exceptions import MissingDataError
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.logging import enable_logging
import logging
import pandas as pd
from serenitas.utils import SerenitasFileHandler
logger = logging.getLogger(__name__)
def all_curves_pv(
curves, today_date, jp_yc, start_date, step_in_date, value_date, maturities
):
r = {}
for d in maturities:
coupon_leg = FeeLeg(start_date, d, True, 1.0, 1.0)
default_leg = ContingentLeg(start_date, d, True)
accrued = coupon_leg.accrued(step_in_date)
tickers = []
data = []
for sc in curves:
coupon_leg_pv = coupon_leg.pv(
today_date, step_in_date, value_date, jp_yc, sc, False
)
default_leg_pv = default_leg.pv(
today_date, step_in_date, value_date, jp_yc, sc, 0.4
)
tickers.append(sc.ticker)
data.append((coupon_leg_pv - accrued, default_leg_pv))
r[pd.Timestamp(d)] = pd.DataFrame.from_records(
data, index=tickers, columns=["duration", "protection_pv"]
)
return pd.concat(r, axis=1).swaplevel(axis=1).sort_index(axis=1, level=0)
def calibrate_portfolio(
index_type, series, tenors=["3yr", "5yr", "7yr", "10yr"], start_date=None
):
try:
index = MarkitBasketIndex(index_type, series, tenors)
except (ValueError, TypeError, AttributeError) as e:
logging.error(f"error with {index_type} {series}")
print(str(e))
return
if start_date:
index.index_quotes = index.index_quotes[start_date:]
for value_date, v in index.index_quotes.groupby("date")["id"]:
try:
index.value_date = value_date
except MissingDataError as e:
print(e)
continue
index.tweak()
df = pd.concat(
[
index.theta(),
index.duration(),
pd.Series(index.tweaks, index=tenors, name="tweak"),
index.dispersion(),
index.dispersion(use_gini=True, use_log=False, exp_loss=True),
],
axis=1,
)
for (_, version, t), id in v.items():
if version == index.version:
yield (id, df.loc[t])
if __name__ == "__main__":
enable_logging()
import argparse
import logging
parser = argparse.ArgumentParser()
parser.add_argument("index", help="index type (IG, HY, EU, XO or HYBB)")
parser.add_argument("series", help="series", type=int)
parser.add_argument(
"--latest",
required=False,
action="store_true",
help="fills missing data from the end",
)
args = parser.parse_args()
index, series = args.index, args.series
from serenitas.utils.db2 import NaNtoNone
from serenitas.utils.pool import serenitas_pool
if args.latest:
with serenitas_pool.connection() as conn:
with conn.cursor() as c:
c.execute(
"SELECT max(date) FROM index_quotes_pre "
"RIGHT JOIN index_risk2 USING (id) "
"WHERE index=%s AND series=%s "
"AND tenor in ('3yr', '5yr', '7yr', '10yr')",
(index, series),
)
(start_date,) = c.fetchone()
start_date = pd.Timestamp(start_date)
conn.commit()
else:
start_date = None
fh = SerenitasFileHandler("index_curves.log")
loggers = [logging.getLogger("analytics"), logging.getLogger("index_curves")]
for logger in loggers:
logger.setLevel(logging.INFO)
logger.addHandler(fh)
loggers[1].info(f"filling {index} {series}")
if index == "HYBB":
tenors = ["5yr"]
else:
tenors = ["3yr", "5yr", "7yr", "10yr"]
g = calibrate_portfolio(index, series, tenors, start_date)
update_str = ",".join(
[
f"{c}=EXCLUDED.{c}"
for c in ("theta", "duration", "tweak", "dispersion", "gini")
]
)
with serenitas_pool.connection() as conn:
with conn.cursor() as c:
for id, t in g:
c.execute(
"INSERT INTO index_risk2 VALUES(%s, %s, %s, %s, %s, %s) ON CONFLICT (id) "
f"DO UPDATE SET {update_str}",
tuple(map(NaNtoNone, (id,) + tuple(t))),
)
conn.commit()
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