1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
|
import sys
#don't do this at home
sys.path.append("..")
from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, 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
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 graphics import plot_time_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_color_map(df, spread_shock, vol_shock, attr="pnl", path=".", index ='IG'):
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
ascending = [False,False] if index == 'HY' else [True,False]
df.sort_values(by=['spread','vol_shock'], ascending = ascending, inplace = True)
series = df[attr]
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('Price') if index == 'HY' else 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, "vol_spread_color_map"+ attr+ "_{}.png".format(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
#earliest_date = max(portf.swaptions,key=attrgetter('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 = VolatilitySurface(index, series, trade_date=portf.indices[0].trade_date)
vol_select = vs.list(option_type='payer', model='black')[-1]
vol_surface = vs[vol_select]
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_time_color_map(df[round(df.vol_shock,2)==0], shock, 'delta', color_map = cm.coolwarm_r, index=index)
plot_color_map(df.loc[date_range[period]], shock, vol_shock, 'pnl', index=index)
#plot_df(df.loc[date_range[period]], shock, vol_shock)
return df
def exercise_probability():
from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, Portfolio, ProbSurface, QuoteSurface, VolSurface
from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios
import datetime
from operator import attrgetter
import exploration.swaption_calendar_spread as spread
import sys
#don't do this at home
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 operator import attrgetter
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import re
from db import dbengine
engine = dbengine('serenitasdb')
#import swaption_calendar_spread as spread
#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=attrgetter('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 = QuoteSurface(index, series, trade_date=portf.indices[0].trade_date)
vs = VolatilitySurface(index, series, trade_date=portf.indices[0].trade_date)
vol_select = vs.list(option_type='payer', model='black')[-1]
vol_surface = vs[vol_select]
prob = vs.prob_surf(vol_select)
vs.prob_plot(vol_select)
|
