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from analytics import ATMstrike
from joblib import delayed, Parallel
import pandas as pd
from copy import deepcopy
def run_swaption_scenarios(swaption, date_range, spread_shock, vol_shock,
vol_surface, params=["pv"]):
"""computes the pv of a swaption for a range of scenarios
Parameters
----------
swaption_copy : Swaption
date_range : `pandas.Datetime.Index`
spread_shock : `np.array`
vol_shock : `np.array`
vol_surface
params : list of strings
list of attributes to call on the swaption object.
"""
r = []
swaption = deepcopy(swaption)
spreads = swaption.ref * (1 + spread_shock)
for date in date_range:
swaption.index.trade_date = date.date()
T = swaption.T
for s in spreads:
swaption.ref = s
curr_vol = float(vol_surface(T, swaption.moneyness))
for vs in vol_shock:
swaption.sigma = curr_vol * (1 + vs)
r.append([date, s, vs] + [getattr(swaption, p) for p in params])
df = pd.DataFrame.from_records(r, columns=['date', 'spread_shock', 'vol_shock'] + params)
return df.set_index('date')
def run_index_scenarios(index, date_range, spread_shock):
r = []
index = deepcopy(index)
starting_pv = index.clean_pv
starting_date = index.trade_date
spreads = index.spread * (1 + spread_shock)
for date in date_range:
index.trade_date = date.date()
for s in spread:
index.spread = s
scen_pv = index.clean_pv +
index.notional * (date.date()-starting_date).days /360 * index.fixed_rate * 1e-4 - starting_pv
r.append([date, s, scen_pv])
df = pd.DataFrame.from_records(r, columns=['date', 'spread', 'pnl'])
return df.set_index('date')
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