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from analytics.cms_spread import (
quantlib_model, globeop_model, build_spread_index, VolatilityType)
from yieldcurve import YC
import pandas as pd
from quantlib.time.api import Date
swap_index_30y2y, yc = build_spread_index(30, 2)
cap = 0.00758
corr = 0.8
r = []
maturity = pd.Timestamp("2020-01-19")
today = pd.Timestamp.today()
for d in pd.bdate_range("2018-01-19", today, closed="left", normalize=True):
d = pd.Timestamp("2018-01-19")
yc.link_to(YC(evaluation_date=d.date()))
yc.extrapolation = True
if d == pd.Timestamp("2018-02-16"):
continue
capped_floored_cms_spread_coupon_ln = \
quantlib_model(d, swap_index_30y2y, yc, cap, corr, maturity)
rate1 = capped_floored_cms_spread_coupon_ln.rate
cms_spread_coupon_n = quantlib_model(d, swap_index_30y2y, yc, cap, corr, maturity,
VolatilityType.Normal)
rate2 = cms_spread_coupon_n.rate
rate3 = globeop_model(d, swap_index_30y2y, yc, cap, corr - 0.075, maturity)
# df = pd.DataFrame(r, columns=['date', 'QL_ln', 'QL_n', 'Globeop']).set_index('date')
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