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
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
|
from .index import Index, _key_from_index
from .option import BlackSwaption, VolatilitySurface
from db import dbengine
from warnings import warn
import pandas as pd
import numpy as np
serenitasdb = dbengine('serenitasdb')
def portf_repr(method):
def f(*args):
obj = args[0]
thousands = lambda x: "{:,.2f}".format(x)
percent = lambda x: "N/A" if np.isnan(x) else f"{100*x:.2f}%"
header = "Portfolio {}\n\n".format(obj.trade_date)
kwargs = {'formatters': {'Notional': thousands,
'PV': thousands,
'Delta': percent,
'Gamma': percent,
'Theta': thousands,
'Vega': thousands,
'Vol': percent,
'Ref': thousands},
'index': False}
if method == 'string':
kwargs['line_width'] = 100
s = getattr(obj._todf(), 'to_' + method)(**kwargs)
return header + s
return f
class Portfolio:
def __init__(self, trades):
self.trades = trades
self.indices = [t for t in trades if isinstance(t, Index)]
self.swaptions = [t for t in trades if isinstance(t, BlackSwaption)]
self._keys = []
trade_dates = set()
for index in self.indices:
self._keys.append(_key_from_index(index))
#pick the first available trade_date
trade_dates.add(index.trade_date)
for swaption in self.swaptions:
trade_dates.add(swaption.index.trade_date)
self._trade_date = trade_dates.pop()
if len(trade_dates) >= 1:
warn("not all instruments have the same trade date, picking {}".
format(self._trade_date))
self._vs = {}
@property
def pnl(self):
return sum(t.pnl for t in self.trades)
@property
def pnl_list(self):
return [t.pnl for t in self.trades]
@property
def pv(self):
return sum(t.pv for t in self.trades)
@property
def pv_list(self):
return [t.pv for t in self.trades]
def reset_pv(self):
for t in self.trades:
t.reset_pv()
@property
def trade_date(self):
return self._trade_date
@trade_date.setter
def trade_date(self, d):
#we try to keep everybody in sync
for index in self.indices:
index.trade_date = d
if len(self.indices) == 0:
for swaption in self.swaptions:
self.swaption.trade_date = d
self._trade_date = d
def mark(self, source_list=[], option_type=None, model="black", surface_id=None):
#add None so that we always try everything
source_list = source_list + [None]
for index, (index_type, series, tenor) in zip(self.indices, self._keys):
index.mark()
if tenor != '5yr':
continue
k = (index.trade_date, index_type, series, tenor)
if self.swaptions:
if k not in self._vs:
vs = VolatilitySurface(index_type, series, tenor, index.trade_date)
if surface_id is None:
for source in source_list:
if len(vs.list(source, option_type, model)) >=1:
break
else:
raise ValueError("No market data available for this day")
self._vs[k] = vs[vs.list(source, option_type, model)[-1]]
else:
self._vs[k] = vs[surface_id]
for swaption in self.swaptions:
vol_surface = self._vs[(swaption.index.trade_date, ) + \
_key_from_index(swaption.index)]
swaption.sigma = float(self._vs[(swaption.index.trade_date, ) \
+ _key_from_index(swaption.index)].
ev(swaption.T, swaption.moneyness))
@property
def ref(self):
if len(self.indices) == 1:
return self.indices[0].ref
else:
return [index.ref for index in self.indices]
@ref.setter
def ref(self, val):
if len(self.indices) == 1:
self.indices[0].ref = val
elif len(self.indices) == 0:
##no index, so set the individual refs
for t in trades:
t.index.ref = val
elif len(self.indices) == len(val):
for index, val in zip(self.indices, val):
index.ref = val
else:
raise ValueError("The number of refs doesn't match the number of indices")
@property
def delta(self):
"""returns the equivalent protection notional
makes sense only where there is a single index."""
return sum([getattr(t, 'delta', -t._direction) * t.notional for t in self.trades])
@property
def gamma(self):
return sum([getattr(t, 'gamma', 0) * t.notional for t in self.trades])
@property
def dv01(self):
return sum(t.dv01 for t in self.trades)
@property
def theta(self):
return sum(t.theta for t in self.trades)
def _todf(self):
headers = ["Product", "Index", "Notional", "Ref", "Direction", "Expiry",
"Vol", "PV", "Delta", "Gamma", "Theta", "Vega"]
rec = []
for t in self.trades:
if isinstance(t, Index):
index_type, series, tenor = _key_from_index(t)
r = ("Index", f"{index_type}{series} {tenor}",
t.notional, t.ref, t.direction, "N/A", None, t.pv, 1., 0., t.theta, 0.)
elif isinstance(t, BlackSwaption):
index_type, series, tenor = _key_from_index(t.index)
r = ("Swaption", f"{index_type}{series} {tenor}",
t.notional, t.ref, t.direction, t.forward_date, t.sigma, t.pv,
t.delta, t.gamma, t.theta, t.vega)
else:
raise TypeError
rec.append(r)
return pd.DataFrame.from_records(rec, columns=headers)
__repr__ = portf_repr('string')
_repr_html_ = portf_repr('html')
|
