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import datetime
import numpy as np
import pandas as pd
from .exceptions import MissingDataError
from scipy.special import h_roots
from dateutil.relativedelta import relativedelta, WE
from functools import partial, wraps
from pyisda.date import pydate_to_TDate
from pandas.api.types import CategoricalDtype
from pandas.tseries.offsets import CustomBusinessDay
from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory, GoodFriday
fed_cal = get_calendar("USFederalHolidayCalendar")
bond_cal = HolidayCalendarFactory("BondCalendar", fed_cal, GoodFriday)
bus_day = CustomBusinessDay(calendar=bond_cal())
from quantlib.time.date import nth_weekday, Wednesday, Date
tenor_t = CategoricalDtype(
[
"1m",
"3m",
"6m",
"1yr",
"2yr",
"3yr",
"4yr",
"5yr",
"7yr",
"10yr",
"15yr",
"20yr",
"25yr",
"30yr",
],
ordered=True,
)
def GHquad(n):
"""Gauss-Hermite quadrature weights"""
Z, w = h_roots(n)
return Z * np.sqrt(2), w / np.sqrt(np.pi)
def next_twentieth(d):
r = d + relativedelta(day=20)
if r < d:
r += relativedelta(months=1)
mod = r.month % 3
if mod != 0:
r += relativedelta(months=3 - mod)
return r
def third_wednesday(d):
if isinstance(d, datetime.date):
return d + relativedelta(day=1, weekday=WE(3))
elif isinstance(d, Date):
return nth_weekday(3, Wednesday, d.month, d.year)
def next_third_wed(d):
y = third_wednesday(d)
if y < d:
return third_wednesday(d + relativedelta(months=1))
else:
return y
def roll_date(d, tenor, nd_array=False):
""" roll date d to the next CDS maturity"""
cutoff = pd.Timestamp("2015-09-20")
def kwargs(t):
if abs(t) == 0.5:
return {"months": int(12 * t)}
else:
return {"years": int(t)}
if not isinstance(d, pd.Timestamp):
cutoff = cutoff.date()
if d <= cutoff:
if isinstance(tenor, (int, float)):
d_rolled = d + relativedelta(**kwargs(tenor), days=1)
return next_twentieth(d_rolled)
elif hasattr(tenor, "__iter__"):
v = [next_twentieth(d + relativedelta(**kwargs(t), days=1)) for t in tenor]
if nd_array:
return np.array([pydate_to_TDate(d) for d in v])
else:
return v
else:
raise TypeError("tenor is not a number nor an iterable")
else: # semi-annual rolling starting 2015-12-20
if isinstance(tenor, (int, float)):
d_rolled = d + relativedelta(**kwargs(tenor))
elif hasattr(tenor, "__iter__"):
d_rolled = d + relativedelta(years=1)
else:
raise TypeError("tenor is not a number nor an iterable")
if (d >= d + relativedelta(month=9, day=20)) or (
d < d + relativedelta(month=3, day=20)
):
d_rolled += relativedelta(month=12, day=20)
if d.month <= 3:
d_rolled -= relativedelta(years=1)
else:
d_rolled += relativedelta(month=6, day=20)
if isinstance(tenor, (int, float)):
return d_rolled
else:
v = [d_rolled + relativedelta(**kwargs(t - 1)) for t in tenor]
if nd_array:
return np.array([pydate_to_TDate(d) for d in v])
else:
return v
def build_table(rows, format_strings, row_format):
def apply_format(row, format_string):
for r, f in zip(row, format_string):
if f is None:
yield r
else:
if callable(f):
yield f(r)
elif isinstance(f, str):
if isinstance(r, tuple):
yield f.format(*r)
else:
yield f.format(r)
return [
row_format.format(*apply_format(row, format_string))
for row, format_string in zip(rows, format_strings)
]
def memoize(f=None, *, hasher=lambda args: (hash(args),)):
if f is None:
return partial(memoize, hasher=hasher)
@wraps(f)
def cached_f(*args, **kwargs):
self = args[0]
key = (f.__name__, *hasher(args))
if key in self._cache:
return self._cache[key]
else:
v = f(*args, **kwargs)
self._cache[key] = v
return v
return cached_f
def to_TDate(arr: np.ndarray):
""" convert an array of numpy datetime to TDate"""
return arr.view("int") + 134774
def get_external_nav(engine, trade_id, value_date=None, trade_type="swaption"):
query = (
"SELECT date, "
"nav, "
"(case when date < settle_date "
"then price * notional/100 * (2 * buysell::integer - 1) "
"else 0."
"end) as upfront FROM external_marks_deriv "
f"LEFT JOIN {trade_type} "
"ON cpty_id = identifier WHERE id=%s "
)
if value_date:
query += "AND date=%s"
r = engine.execute(query, (trade_id, value_date))
try:
date, nav, upfront = next(r)
except StopIteration:
raise MissingDataError(
f"No quote available for {trade_type} {trade_id} on {value_date}"
)
return nav + upfront
else:
query += "ORDER BY DATE"
return pd.read_sql_query(
query, engine, params=(trade_id,), parse_dates=["date"], index_col=["date"]
)
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