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import datetime
import pandas as pd
import numpy as np
from dates import bond_cal
from . import serenitas_engine, serenitas_pool
from .utils import tenor_t
def get_tranche_quotes(
index=None,
series=None,
tenor=None,
from_date=None,
end_date=None,
years=3,
remove_holidays=True,
):
args = locals().copy()
del args["remove_holidays"]
if args["end_date"] is None:
args["end_date"] = datetime.date.today()
if args["years"] is not None:
args["from_date"] = (args["end_date"] - pd.DateOffset(years=years)).date()
del args["years"]
def make_str(key, val):
col_key = key
if isinstance(val, list) or isinstance(val, tuple):
op = "IN"
return "{} IN %({})s".format(key, key)
elif key == "from_date":
col_key = "date"
op = ">="
elif key == "end_date":
col_key = "date"
op = "<="
else:
op = "="
return "{} {} %({})s".format("d." + col_key, op, key)
where_clause = " AND ".join(
make_str(k, v) for k, v in args.items() if v is not None
)
sql_str = (
"SELECT * from "
"(SELECT quotedate as date, b.index, b.series, a.tenor, b.version, "
"a.attach, a.detach, (1-upfront_mid) as close_price, a.index_price, indexfactor/100 as indexfactor, "
"cumulativeloss, c.delta, a.tranche_spread "
"from markit_tranche_quotes a "
"left join index_version b using (basketid)"
"inner join risk_numbers c on a.quotedate=date(c.date) "
"and b.index=c.index and b.series=c.series and "
"a.tenor=c.tenor and a.attach=c.attach) d "
)
if where_clause:
sql_str = " WHERE ".join([sql_str, where_clause])
def make_params(args):
return {
k: tuple(v) if isinstance(v, list) else v
for k, v in args.items()
if v is not None
}
df = pd.read_sql_query(
sql_str,
serenitas_engine,
parse_dates={"date"},
index_col=["date", "index", "series", "version"],
params=make_params(args),
)
df.tenor = df.tenor.astype(tenor_t)
df = df.set_index("tenor", append=True)
df.sort_index(inplace=True)
df = df.assign(
attach_adj=lambda x: np.maximum(
(x.attach - x.cumulativeloss) / (x.indexfactor * 100), 0
),
detach_adj=lambda x: np.minimum(
(x.detach - x.cumulativeloss) / (x.indexfactor * 100), 1
),
orig_thickness=lambda x: (x.detach - x.attach) / 100,
adj_thickness=lambda x: x.detach_adj - x.attach_adj,
tranche_factor=lambda x: x.adj_thickness * x.indexfactor / x.orig_thickness,
)
df.set_index("attach", append=True, inplace=True)
# get rid of US holidays
if remove_holidays:
dates = df.index.levels[0]
if index in ["IG", "HY"]:
holidays = bond_cal().holidays(start=dates[0], end=dates[-1])
df = df.loc(axis=0)[dates.difference(holidays), :, :]
return df
def tranche_returns(
df=None, index=None, series=None, tenor=None, from_date=None, end_date=None, years=3
):
"""computes spreads and price returns
Parameters
----------
df : pandas.DataFrame
index : str or List[str], optional
index type, one of 'IG', 'HY', 'EU', 'XO'
series : int or List[int], optional
tenor : str or List[str], optional
tenor in years e.g: '3yr', '5yr'
date : datetime.date, optional
starting date
years : int, optional
limits many years do we go back starting from today.
"""
if df is None:
df = get_tranche_quotes(index, series, tenor, from_date, end_date, years)
df = df.groupby(level=["date", "index", "series", "tenor", "attach"]).nth(0)
coupon_data = pd.read_sql_query(
"SELECT index, series, tenor, coupon * 1e-4 AS coupon "
" FROM index_maturity WHERE coupon is NOT NULL",
serenitas_engine,
index_col=["index", "series", "tenor"],
)
df = df.join(coupon_data)
df["date_1"] = df.index.get_level_values(level="date")
# skip missing dates
returns = []
for i, g in df.groupby(level=["index", "series", "tenor", "attach"]):
g = g.dropna()
day_frac = g["date_1"].transform(
lambda s: s.diff().astype("timedelta64[D]") / 360
)
index_loss = g.cumulativeloss - g.cumulativeloss.shift(1)
tranche_loss = (
(
g.adj_thickness.shift(1) * g.indexfactor.shift(1)
- g.adj_thickness * g.indexfactor
)
/ g.orig_thickness
if g.detach[0] != 100
else 0
)
tranche_return = g.close_price - (
1
- ((1 - g.close_price.shift(1)) * g.tranche_factor.shift(1) - tranche_loss)
/ g.tranche_factor
)
index_return = g.index_price - (
1
- ((1 - g.index_price.shift(1)) * g.indexfactor.shift(1) - index_loss / 100)
/ g.indexfactor
)
tranche_return += day_frac * g.tranche_spread / 10000
index_return += day_frac * g.coupon
delhedged_return = (
tranche_return
- g.delta.shift(1) * index_return * g.indexfactor / g.tranche_factor
)
returns.append(
pd.concat(
[index_return, tranche_return, delhedged_return],
axis=1,
keys=["index_return", "tranche_return", "delhedged_return"],
)
)
df = df.merge(pd.concat(returns), left_index=True, right_index=True, how="left")
df = df.drop(["date_1", "tranche_spread", "detach", "coupon"], axis=1)
return df
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