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from analytics import ATMstrike
from joblib import delayed, Parallel
import pandas as pd
from copy import deepcopy
import numpy as np
from contextlib import contextmanager
from itertools import chain
from functools import partial
from multiprocessing import Pool
from .index_data import _get_singlenames_curves
from .curve_trades import curve_shape
from scipy.interpolate import RectBivariateSpline
def run_swaption_scenarios(swaption, date_range, spread_shock, vol_shock,
vol_surface, params=["pv"], vol_time_roll=True):
"""computes the pv of a swaption for a range of scenarios
Parameters
----------
swaption : 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.
"""
swaption = deepcopy(swaption)
spreads = swaption.ref * (1 + spread_shock)
T = swaption.T
r = []
for date in date_range:
swaption.index.value_date = date.date()
if vol_time_roll: T = swaption.T
for s in spreads:
swaption.ref = s
curr_vol = max(0, 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', 'vol_shock'] + params)
return df.set_index('date')
def run_index_scenarios(index, date_range, spread_shock, params=['pnl']):
index = deepcopy(index)
spreads = index.spread * (1 + spread_shock)
r = []
for date in date_range:
index.value_date = date.date()
for s in spreads:
index.spread = s
r.append([date, s] + [getattr(index, p) for p in params])
df = pd.DataFrame.from_records(r, columns=['date', 'spread'] + params)
return df.set_index('date')
def _aux(portf, curr_vols, params, vs):
for swaption, curr_vol in zip(portf.swaptions, curr_vols):
swaption.sigma = curr_vol * (1 + vs)
return [vs] + [getattr(portf, p) for p in params]
@contextmanager
def MaybePool(nproc):
yield Pool(nproc) if nproc > 0 else None
def run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock,
vol_surface, params=["pnl"], nproc=-1, vol_time_roll=True):
"""computes the pnl of a portfolio for a range of scenarios
Parameters
----------
swaption : Swaption
date_range : `pandas.Datetime.Index`
spread_shock : `np.array`
vol_shock : `np.array`
vol_surface : VolSurface
params : list of strings
list of attributes to call on the Portfolio object.
nproc : int
if nproc > 0 run with nproc processes.
"""
portf = deepcopy(portf)
spreads = np.hstack([index.spread * (1 + spread_shock) for index in portf.indices])
t = [swaption.T for swaption in portf.swaptions]
r = []
with MaybePool(nproc) as pool:
pmap = pool.map if pool else map
for date in date_range:
portf.value_date = date.date()
if vol_time_roll:
t = [swaption.T for swaption in portf.swaptions]
for s in spreads:
portf.spread = s
mon = [swaption.moneyness for swaption in portf.swaptions]
curr_vols = np.maximum(vol_surface.ev(t, mon), 0)
temp = pmap(partial(_aux, portf, curr_vols, params), vol_shock)
r.append([[date, s] + rec for rec in temp])
df = pd.DataFrame.from_records(chain(*r), columns=['date', 'spread', 'vol_shock'] + params)
return df.set_index('date')
def run_tranche_scenarios(tranche, spread_range, date_range, corr_map=False):
"""computes the pnl of a tranche for a range of spread scenarios
Parameters
----------
tranche : TrancheBasket
spread_range : `np.array`, spread range to run (different from swaption)
corr_map: static correlation or mapped correlation
"""
_get_singlenames_curves.cache_clear()
if np.isnan(tranche.rho[1]):
tranche.build_skew()
temp_tranche = deepcopy(tranche)
orig_tranche_pvs = tranche.tranche_pvs().bond_price
results = []
index_pv = np.empty_like(spread_range)
tranche_pv = np.empty((len(spread_range), tranche.K.size - 1))
tranche_delta = np.empty((len(spread_range), tranche.K.size - 1))
for d in date_range:
try:
temp_tranche.value_date = d.date()
except ValueError: # we shocked in the future probably
pass
for i, spread in enumerate(spread_range):
print(spread)
temp_tranche.tweak(spread)
if corr_map:
temp_tranche.rho = tranche.map_skew(temp_tranche, 'TLP')
index_pv[i] = temp_tranche._snacpv(spread * 1e-4,
temp_tranche.coupon(temp_tranche.maturity),
temp_tranche.recovery)
tranche_pv[i] = temp_tranche.tranche_pvs().bond_price
tranche_delta[i] = temp_tranche.tranche_deltas()['delta']
columns = pd.MultiIndex.from_product([['pv', 'delta'], tranche._row_names])
df = pd.DataFrame(np.hstack([tranche_pv, tranche_delta]), columns=columns,
index=spread_range)
carry = pd.Series((d.date() - tranche.value_date).days / 360 * \
tranche.tranche_quotes.running.values,
index=tranche._row_names)
df = df.join(
pd.concat({'pnl': df['pv'] - orig_tranche_pvs + carry,
'index_price_snac_pv': pd.Series(index_pv, index=spread_range,
name='pv')},
axis=1))
results.append(df)
results = pd.concat(results, keys=date_range)
results.index.names = ['date', 'spread_range']
return results
def run_tranche_scenarios_rolldown(tranche, spread_range, date_range, corr_map=False):
"""computes the pnl of a tranche for a range of spread scenarios
curve roll down from the back, and valuations interpolated in the dates in between
Parameters
----------
tranche : TrancheBasket
spread_range : `np.array`, spread range to run (different from swaption)
corr_map: static correlation or mapped correlation
"""
if np.isnan(tranche.rho[2]):
tranche.build_skew()
temp_tranche = deepcopy(tranche)
orig_tranche_pvs = tranche.tranche_pvs().bond_price
#create blanks
tranche_pv, tranche_delta = [], []
tranche_pv_f, tranche_delta_f = [], []
index_pv = np.empty(smaller_spread_range.shape[0], days.shape[0])
#do less scenarios, takes less time since the convexity is not as strong as swaptions
days = np.diff((tranche.cs.index - date_range[0]).days.values)
num_shortened = np.sum(tranche.cs.index < date_range[-1])
shorten_by = np.arange(0, max(1, num_shortened)+1, 1)
days = np.append(0, np.cumsum(np.flip(days,0))[:len(shorten_by)-1])
smaller_spread_range = np.linspace(spread_range[0], spread_range[-1], 10)
for i, spread in enumerate(smaller_spread_range):
for shortened in shorten_by:
if shortened > 0:
temp_tranche.cs = tranche.cs.iloc[:-shortened]
else:
temp_tranche.cs = tranche.cs
temp_tranche.tweak(spread)
if corr_map:
temp_tranche.rho = tranche.map_skew(temp_tranche, 'TLP')
index_pv[i] = temp_tranche.index_pv().bond_price
tranche_pv.append(temp_tranche.tranche_pvs().bond_price)
tranche_delta.append(temp_tranche.tranche_deltas()['delta'])
tranche_pv = np.array(tranche_pv).transpose()
tranche_delta = np.array(tranche_delta).transpose()
index_pv_f = RectBivariateSpline(days, smaller_spread_range, index_pv, kx=1, ky=1)
for pv, delta in zip(tranche_pv, tranche_delta):
pv = np.reshape(pv, (smaller_spread_range.shape[0], days.shape[0])).transpose()
delta = np.reshape(delta, (smaller_spread_range.shape[0], days.shape[0])).transpose()
tranche_pv_f.append(RectBivariateSpline(days, smaller_spread_range, pv, kx=1, ky=1))
tranche_delta_f.append(RectBivariateSpline(days, smaller_spread_range, delta, kx=1, ky=1))
#Reset the blanks
date_range_days = (date_range - date_range[0]).days.values
tranche_pv = np.empty((tranche.K.size - 1, len(date_range_days), len(spread_range)))
tranche_delta = np.empty((tranche.K.size - 1, len(date_range_days), len(spread_range)))
index_pv = index_pv_f(date_range_days, spread_range)
for i in range(len(tranche_pv_f)):
tranche_pv[i] = tranche_pv_f[i](date_range_days, spread_range)
tranche_delta[i] = tranche_delta_f[i](date_range_days, spread_range)
index_pv = index_pv.reshape(1,len(date_range_days) * len(spread_range)).T
tranche_pv = tranche_pv.reshape(len(tranche._row_names),len(date_range_days) * len(spread_range)).T
tranche_delta = tranche_delta.reshape(len(tranche._row_names),len(date_range_days) * len(spread_range)).T
days_diff = np.tile(((date_range - date_range[0]).days/360).values, len(tranche._row_names))
carry = pd.DataFrame(days_diff.reshape(len(tranche._row_names),len(date_range)).T,
index=date_range,
columns=pd.MultiIndex.from_product([['carry'], tranche._row_names]))
carry.index.name = 'date'
df = pd.concat({'index_pv': pd.DataFrame(index_pv,
index=pd.MultiIndex.from_product([date_range, spread_range]),
columns=['index_pv']),
'pv': pd.DataFrame(tranche_pv,
index=pd.MultiIndex.from_product([date_range, spread_range]),
columns=tranche._row_names),
'delta': pd.DataFrame(tranche_delta,
index=pd.MultiIndex.from_product([date_range, spread_range]),
columns=tranche._row_names)},
axis=1)
df.index.names = ['date', 'spread_range']
df = df.join(carry)
df = df.join(pd.concat({'pnl': df['pv'].sub(orig_tranche_pvs)}, axis=1))
return df
def run_curve_scenarios(portf, spread_range, date_range, curve_per):
"""computes the pnl of a portfolio of indices for a range of spread/curve scenarios
Parameters
----------
portf : Portfolio
spread_range : `np.array`
date_range : `pandas.Datetime.Index`
"""
portf.reset_pv()
portf = deepcopy(portf)
index = portf.indices[0].index_type
r = []
for p in curve_per:
new_curve = curve_shape(date_range[0], index, p, 100)
for date in date_range:
portf.value_date = date.date()
for s in spread_range:
for ind in portf.indices:
ind.spread = new_curve((pd.to_datetime(ind.end_date) - date).days/365) * s/100
r.append([[date, s, p] + [portf.pnl]])
df = pd.DataFrame.from_records(chain(*r), columns=['date', 'spread', 'curve_per', 'pnl'])
return df.set_index('date')
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