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
|
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LinearSegmentedColormap
from scipy.interpolate import griddata, SmoothBivariateSpline
from scipy.stats import norm
from scipy.optimize import curve_fit
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 = LinearSegmentedColormap(name, cdict)
plt.register_cmap(cmap=newcmap)
return newcmap
def plot_color_map(series, sort_order=[True, True], color_map=cm.RdYlGn, centered=True):
"""
2D heat map - if x-axis is time translate to days instead
Parameters
-----
series: Series with multilevel index (x: first level, y: second level)
sort_order: sorting in the x,y axis
color_map: default Red-Yellow-Green
centered: center yellow as 0 of the series.
"""
x = series.index.get_level_values(0)
y = series.index.get_level_values(1)
if x.dtype == "<M8[ns]":
x = (x - x[0]).days
x.name = "Days"
series.sort_index(ascending=sort_order, inplace=True)
fig, ax = plt.subplots()
midpoint = (
1 - series.max() / (series.max() + abs(series.min()))
if centered is True
else 0.5
)
color_map = shiftedColorMap(color_map, midpoint=midpoint)
chart = ax.imshow(
series.values.reshape(x.unique().size, y.unique().size).T,
extent=(x.min(), x.max(), y.min(), y.max()),
aspect="auto",
interpolation="bilinear",
cmap=color_map,
)
ax.set_xlabel(x.name)
ax.set_ylabel(y.name)
ax.set_title("{} of Trade".format(series.name))
fig.colorbar(chart, shrink=0.8)
def plot_prob_map(df, attr="pnl", path=".", color_map=cm.RdYlGn, index="IG"):
val_date = df.index[0].date()
df = df.reset_index()
df["days"] = (df["date"] - val_date).dt.days
series = df[attr]
days_defined = np.linspace(df.days.min(), df.days.max(), 1000)
prob_defined = np.linspace(0.001, 0.999, 1000)
midpoint = 1 - series.max() / (series.max() + abs(series.min()))
shifted_cmap = shiftedColorMap(color_map, midpoint=midpoint, name="shifted")
resampled = griddata(
(df.days, df.prob),
series,
(days_defined[None, :], prob_defined[:, None]),
method="linear",
)
# plot
fig, ax = plt.subplots()
chart = ax.imshow(
resampled.reshape(days_defined.size, prob_defined.size),
extent=(df.days.min(), df.days.max(), 0, 1),
aspect="auto",
interpolation="bilinear",
cmap=shifted_cmap,
)
ax.set_xlabel("Days")
ax.set_ylabel("Probability")
ax.set_title("{} of Trade".format(attr.title()))
fig.colorbar(chart, shrink=0.8)
def plot_swaption_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))
|
