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import sys
#don't do this at home
sys.path.append("..")
from analytics import BlackSwaption, Swaption, Index, VolatilitySurface
from analytics.scenarios import run_swaption_scenarios
from pandas.tseries.offsets import BDay
import datetime
import numpy as np
import pandas as pd
from scipy.interpolate import SmoothBivariateSpline
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from copy import deepcopy
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):
'''
Function to offset the "center" of a colormap. Useful for
data with a negative min and positive max and you want the
middle of the colormap's dynamic range to be at zero
Input
-----
cmap : The matplotlib colormap to be altered
start : Offset from lowest point in the colormap's range.
Defaults to 0.0 (no lower ofset). Should be between
0.0 and `midpoint`.
midpoint : The new center of the colormap. Defaults to
0.5 (no shift). Should be between 0.0 and 1.0. In
general, this should be 1 - vmax/(vmax + abs(vmin))
For example if your data range from -15.0 to +5.0 and
you want the center of the colormap at 0.0, `midpoint`
should be set to 1 - 5/(5 + 15)) or 0.75
stop : Offset from highets point in the colormap's range.
Defaults to 1.0 (no upper ofset). Should be between
`midpoint` and 1.0.
'''
cdict = {
'red': [],
'green': [],
'blue': [],
'alpha': []
}
# regular index to compute the colors
reg_index = np.linspace(start, stop, 257)
# shifted index to match the data
shift_index = np.hstack([
np.linspace(0.0, midpoint, 128, endpoint=False),
np.linspace(midpoint, 1.0, 129, endpoint=True)
])
for ri, si in zip(reg_index, shift_index):
r, g, b, a = cmap(ri)
cdict['red'].append((si, r, r))
cdict['green'].append((si, g, g))
cdict['blue'].append((si, b, b))
cdict['alpha'].append((si, a, a))
newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)
plt.register_cmap(cmap=newcmap)
return newcmap
def plot_df(df, spread_shock, vol_shock):
fig = plt.figure()
ax = fig.gca(projection='3d')
## use smoothing spline on a finer grid
f = SmoothBivariateSpline(df.vol_shock.values, df.spread_shock.values,
df.pv.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("PV")
def plot_color_map(df, spread_shock, vol_shock, attr="pv", path="."):
val_date = df.index[0].date()
#rows are spread, columns are volatility surface shift
fig, ax = plt.subplots()
series = df[attr]
#Different ways to do a colormap: imshow and pcolormesh. using imshow here
midpoint = 1 - series.max() / (series.max() + abs(series.min()))
shifted_cmap = shiftedColorMap(cm.RdYlGn, midpoint=midpoint, name='shifted')
chart = ax.imshow(series.values.reshape(spread_shock.size, vol_shock.size).T,
extent=(spread_shock.min(), spread_shock.max(),
vol_shock.min(), vol_shock.max()),
aspect='auto', interpolation='bilinear', cmap=shifted_cmap)
ax.set_xlabel('Spread')
ax.set_ylabel('Volatility shock')
ax.set_title('{} of Trade on {}'.format(attr.title(), val_date))
fig.colorbar(chart, shrink=.8)
fig.savefig(os.path.join(path, "payer_swap_{}.png".format(val_date)))
def calc_delta_pnl(index, date_range, spread_shock):
r = []
index_obj = deepcopy(index)
startingpv = -index_obj.clean_pv
spread_start = index_obj.spread
for date in date_range:
index_obj.trade_date = date.date()
for ss in spread_shock:
index_obj.spread = spread_start * (1 + ss)
index_obj._update()
scen_pv = -index_obj.clean_pv + index_obj.notional * (date.date()-trade_date).days/360* index_obj.fixed_rate/10000 - startingpv
r.append([date, index_obj.spread, scen_pv])
df = pd.DataFrame.from_records(r, columns=['date', 'spread_shock', 'pv'])
return df.set_index('date')
trade_date = datetime.date(2017, 2, 23)
ig27 = Index.from_name("IG", 27, '5yr', trade_date=trade_date)
ig27.ref = 62
ig27.notional = 13000000
payer1 = BlackSwaption(ig27, datetime.date(2017, 4, 19), 65)
payer2 = BlackSwaption(ig27, datetime.date(2017, 5, 17), 72)
payer1.notional = 100e6
payer2.notional = 100e6
date_range = pd.bdate_range(trade_date, pd.Timestamp('2017-04-19') - BDay(), freq = '5B')
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=trade_date)
vol_surface = vs[vs.list()[-1]]
df1 = run_swaption_scenarios(payer1, date_range, spread_shock, vol_shock, vol_surface)
df2 = run_swaption_scenarios(payer2, date_range, spread_shock, vol_shock, vol_surface)
df3 = calc_delta_pnl(ig27, date_range, spread_shock)
#plot it
week = -1
df = df1
df = df.assign(pv=df1.pv-df2.pv)
spread_plot_range = ig27.ref * (1 + np.arange(-0.2, 0.3, 0.001))
plot_df(df.loc[date_range[week]], spread_plot_range, np.arange(-0.15, 0.3, 0.001))
plot_color_map(df.loc[date_range[week]], ig27.ref *(1 + spread_shock),
vol_shock)
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