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from analytics import (Portfolio, BlackSwaption, Index,
VolatilitySurface, Swaption)
from analytics.scenarios import run_portfolio_scenarios
import pandas as pd
from pandas.tseries.offsets import BDay
import numpy as np
import datetime
# option_delta = Index.from_tradeid(874)
# option1 = BlackSwaption.from_tradeid(7, option_delta)
# option2 = BlackSwaption.from_tradeid(8, option_delta)
# portf = Portfolio([option1, option2, option_delta])
# date_range = pd.bdate_range(option_delta.trade_date,
# pd.Timestamp('2017-05-01'), freq = 'B')
# pnl = []
# for date in date_range:
# portf.trade_date = date.date()
# portf.mark(source_list=["BAML", "GS"], model="black")
# pnl.append(portf.pnl)
# df = pd.DataFrame({'pnl': pnl}, index=date_range)
option_delta = Index.from_tradeid(870)
option1 = BlackSwaption.from_tradeid(5, option_delta)
option2 = BlackSwaption.from_tradeid(6, option_delta)
portf = Portfolio([option1, option2, option_delta])
date_range = pd.bdate_range(option_delta.trade_date,
pd.Timestamp('2017-04-19'), freq = 'W')
vol_shock = np.arange(-0.15, 0.3, 0.01)
spread_shock = np.arange(-0.2, 0.3, 0.01)
vs = VolatilitySurface("IG", 27, trade_date=option_delta.trade_date)
vol_surface = vs[vs.list(model="black")[-1]]
df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock, vol_surface,
['pv', 'delta'])
# pnl = []
# for date in date_range:
# portf.trade_date = date.date()
# try:
# portf.mark(source_list=["BAML", "GS"], model="black")
# except ValueError:
# pnl.append(None)
# continue
# else:
# pnl.append(portf.pnl)
# df = pd.DataFrame({'pnl': pnl}, index=date_range)
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