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import sys
#don't do this at home
sys.path.append("..")
from analytics import (Swaption, BlackSwaption, BlackSwaptionVolSurface,
Index, ProbSurface, Portfolio)
from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios
from pandas.tseries.offsets import BDay
import datetime
import numpy as np
import pandas as pd
from scipy.interpolate import SmoothBivariateSpline
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from graphics import plot_time_color_map, plot_color_map
from db import dbengine
engine = dbengine('serenitasdb')
def plot_df(df, spread_shock, vol_shock, attr="pnl"):
val_date = df.index[0].date()
fig = plt.figure()
ax = fig.gca(projection='3d')
## use smoothing spline on a finer grid
series = df[attr]
f = SmoothBivariateSpline(df.vol_shock.values, df.spread_shock.values, series.values)
xx, yy = np.meshgrid(vol_shock, spread_shock)
surf = ax.plot_surface(xx, yy, f(vol_shock, spread_shock).T, cmap=cm.viridis)
ax.set_xlabel("Volatility shock")
ax.set_ylabel("Spread")
ax.set_zlabel("PnL")
ax.set_title('{} of Trade on {}'.format(attr.title(), val_date))
def plot_trade_scenarios(portf, shock_min=-.15, shock_max=.2, period=-1, vol_time_roll=True):
portf.reset_pv()
earliest_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date
date_range = pd.bdate_range(portf.indices[0].trade_date,
earliest_date - BDay(), freq='3B')
vol_shock = np.arange(-0.15, 0.3, 0.01)
spread_shock = np.arange(shock_min, shock_max, 0.01)
index = portf.indices[0].name.split()[1]
series = portf.indices[0].name.split()[3][1:]
vs = BlackSwaptionVolSurface(index, series, trade_date=portf.indices[0].trade_date)
vol_surface = vs[vs.list(option_type='payer')[-1]]
df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock, vol_surface,
params=["pnl","delta"])
hy_plot_range = 100 + (500 - portf.indices[0].spread * (1 + spread_shock)) * \
abs(portf.indices[0].DV01) / portf.indices[0].notional * 100
shock = hy_plot_range if index == 'HY' else portf.indices[0].spread * (1 + spread_shock)
plot_time_color_map(df[round(df.vol_shock,2)==0], shock, 'pnl', index=index)
plot_time_color_map(df[round(df.vol_shock,2)==.2], shock, 'pnl', index=index)
plot_color_map(df.loc[date_range[period]], shock, vol_shock, 'pnl', index=index)
return df
def exercise_probability():
engine = dbengine('serenitasdb')
#Ad hoc
option_delta = Index.from_name('HY', 29, '5yr')
option_delta.price = 107.875
option1 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 107, option_type="payer")
option2 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 105, option_type="payer")
option1.sigma = .280
option2.sigma = .371
option1.notional = 20_000_000
option2.notional = 40_000_000
option1.direction = 'Long'
option2.direction = 'Short'
option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta
option_delta.direction = 'Seller' if option_delta.notional > 0 else 'Buyer'
option_delta.notional = abs(option_delta.notional)
portf = Portfolio([option1, option2, option_delta])
portf.reset_pv()
earliest_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date
date_range = pd.bdate_range(portf.indices[0].trade_date, earliest_date - BDay(), freq='5B')
vol_shock = np.arange(-0.15, 0.3, 0.01)
spread_shock = np.arange(-0.15, 0.35, 0.01)
index = portf.indices[0].name.split()[1]
series = portf.indices[0].name.split()[3][1:]
vs = ProbSurface(index, series, trade_date=portf.indices[0].trade_date)
vs.plot(vs.list()[-1])
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