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import sys
#don't do this at home
sys.path.append("..")
from analytics import Swaption, BlackSwaption, Index, VolatilitySurface
from analytics.scenarios import run_swaption_scenarios, run_index_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
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, attr="pv"):
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_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()
#We are plotting an image, so we have to sort from high to low on the Y axis
df.sort_values(by=['spread_shock','vol_shock'], ascending = [True,False], inplace = True)
series = df[attr]
#import pdb; pdb.set_trace()
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_", attr, "_{}.png".format(val_date)))
def plot_time_color_map(df, spread_shock, attr="pv", path=".", color_map = cm.RdYlGn):
val_date = df.index[0].date()
df = df.reset_index()
df['days'] = (df['date'] - val_date).dt.days
df.sort_values(by=['date','spread_shock'], ascending = [True,False], inplace = True)
date_range = df.days.unique()
fig, ax = plt.subplots()
series = df[attr]
midpoint = 1 - series.max() / (series.max() + abs(series.min()))
shifted_cmap = shiftedColorMap(color_map, midpoint=midpoint, name='shifted')
chart = ax.imshow(series.values.reshape(date_range.size, spread_shock.size).T,
extent=(date_range.min(), date_range.max(),
spread_shock.min(), spread_shock.max()),
aspect='auto', interpolation='bilinear', cmap=shifted_cmap)
ax.set_xlabel('Days')
ax.set_ylabel('Spread')
ax.set_title('{} of Trade'.format(attr.title()))
fig.colorbar(chart, shrink=.8)
#fig.savefig(os.path.join(path, "payer_swap_", attr, "_{}.png".format(val_date)))
def april_may_2017_trade():
option_delta = Index.from_tradeid(870)
ref = option_delta.spread
payer1 = BlackSwaption(option_delta, datetime.date(2017, 4, 19), 65)
payer2 = BlackSwaption(option_delta, datetime.date(2017, 5, 17), 72.5)
payer1.sigma = .348
payer2.sigma = .466
payer1.notional = 100_000_000
payer2.notional = 100_000_000
cost = 5000
date_range = pd.bdate_range(option_delta.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=option_delta.trade_date)
vol_surface = vs[vs.list()[-1]]
# #
df1 = run_swaption_scenarios(payer1, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta'])
df2 = run_swaption_scenarios(payer2, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta'])
df3 = run_index_scenarios(option_delta, date_range, spread_shock)
# #plot it
week = -1
df = df1.reset_index()
df3 = df3.reset_index()
df = df.merge(df3, on=['date','spread_shock'])
df = df.set_index('date')
df = df.assign(pv=df1.pv-df2.pv+df.pnl-cost)
df = df.assign(delta=df1.delta*payer1.notional-df2.delta*payer2.notional+option_delta.notional)
spread_plot_range = ref * (1 + np.arange(-0.2, 0.3, 0.001))
vol_shock_range = np.arange(-0.15, 0.3, 0.001)
plot_df(df.loc[date_range[week]], spread_plot_range, vol_shock_range)
plot_color_map(df.loc[date_range[week]], ref * (1 + spread_shock), vol_shock)
plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'delta')
plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'pv')
option_delta = Index.from_name('ig', 28, '5yr')
option_delta.spread = 68
payer1 = BlackSwaption(option_delta, datetime.date(2017, 6, 21), 65)
payer2 = BlackSwaption(option_delta, datetime.date(2017, 5, 17), 75)
payer3 = BlackSwaption(option_delta, datetime.date(2017, 7, 19), 75)
payer1.sigma = .388
payer2.sigma = .503
payer3.sigma = .43
payer1.notional = 150_000_000
payer2.notional = -100_000_000
payer3.notional = 100_000_000 *0
vol_time_roll = False
no_delta = False
ref = option_delta.spread
if payer2.notional == 0:
option_delta.notional = payer1.notional * payer1.delta
elif payer3.notional == 0:
option_delta.notional = payer1.notional * payer1.delta + payer2.notional * payer2.delta
else:
option_delta.notional = payer1.notional * payer1.delta + payer2.notional * payer2.delta + payer3.notional * payer3.delta
#option_delta.notional = -100_000_000
if option_delta.notional > 0: option_delta.direction = 'Seller'
if no_delta: option_delta.notional = 0.01
option_delta._original_clean_pv = option_delta._clean_pv
option_delta._original_trade_date = option_delta.trade_date
cost = payer1.pv + payer2.pv + payer3.pv
date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-05-16') - BDay(), freq = '3B')
vol_shock = np.arange(-0.15, 0.3, 0.01)
spread_shock = np.arange(-0.2, 0.3, 0.01)
vs = VolatilitySurface("IG", 28, trade_date=option_delta.trade_date)
vol_select = max([t for t in vs.list() if t[1] == 'BAML' and t[2] == 'payer' and t[3] == 'black'])
vol_surface = vs[vol_select]
# #
df1 = run_swaption_scenarios(payer1, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta'], vol_time_roll)
if payer2.notional != 0: df2 = run_swaption_scenarios(payer2, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta'], vol_time_roll)
if payer3.notional != 0: df3 = run_swaption_scenarios(payer3, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta'], vol_time_roll)
dfdelta = run_index_scenarios(option_delta, date_range, spread_shock)
# #plot it
week = -4
df = df1.reset_index()
dfdelta = dfdelta.reset_index()
df = df.merge(dfdelta, on=['date','spread_shock'])
df = df.set_index('date')
if payer2.notional == 0:
df = df.assign(pv=df1.pv+df.pnl-cost)
df = df.assign(delta=df1.delta*payer1.notional+option_delta.notional)
elif payer3.notional == 0:
df = df.assign(pv=df1.pv+df2.pv+df.pnl-cost)
df = df.assign(delta=df1.delta*payer1.notional+df2.delta*payer2.notional+option_delta.notional)
else:
df = df.assign(pv=df1.pv+df2.pv+df3.pv+df.pnl-cost)
df = df.assign(delta=df1.delta*payer1.notional+df2.delta*payer2.notional+df3.delta*payer3.notional+option_delta.notional)
spread_plot_range = ref * (1 + np.arange(-0.2, 0.3, 0.001))
vol_shock_range = np.arange(-0.15, 0.3, 0.001)
#plot_df(df.loc[date_range[week]], spread_plot_range, vol_shock_range)
#plot_color_map(df.loc[date_range[week]], ref * (1 + spread_shock), vol_shock)
plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'pv')
plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'delta', color_map = cm.coolwarm)
#down 10% vol
#plot_time_color_map(df[round(df.vol_shock,2)==-.1], ref * (1 + spread_shock), 'pv')
#plot_time_color_map(df[round(df.vol_shock,2)==-.1], ref * (1 + spread_shock), 'delta')
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