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
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
|
from .index import CreditIndex
from .option import BlackSwaption
from .tranche_basket import DualCorrTranche
from warnings import warn
import pandas as pd
import numpy as np
def portf_repr(method):
def f(*args):
obj = args[0]
thousands = "{:,.2f}".format
def percent(x):
if np.isnan(x):
return "N/A"
else:
return f"{100*x:.2f}%"
header = f"Portfolio {obj.value_date}\n\n"
kwargs = {'formatters': {'Notional': thousands,
'PV': thousands,
'Delta': percent,
'Gamma': percent,
'Theta': thousands,
'Vega': thousands,
'Vol': percent,
'Ref': thousands,
'Attach Rho': percent,
'Detach Rho': percent},
'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, trade_ids=None):
self.trades = trades
self.trade_ids = trade_ids
value_dates = set(t.value_date for t in self.trades)
self._value_date = value_dates.pop()
if len(value_dates) >= 1:
warn(f"not all instruments have the same trade date, picking {self._value_date}")
def add_trade(self, trades, trade_ids):
self.trades.append(trades)
self.trade_ids.append(trade_ids)
def __iter__(self):
for t in self.trades:
yield t
@property
def indices(self):
return [t for t in self.trades if isinstance(t, CreditIndex)]
@property
def swaptions(self):
return [t for t in self.trades if isinstance(t, BlackSwaption)]
@property
def tranches(self):
return [t for t in self.trades if isinstance(t, DualCorrTranche)]
def items(self):
for trade_id, trade in zip(self.trade_ids, self.trades):
yield (trade_id, trade)
@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 value_date(self):
return self._value_date
@value_date.setter
def value_date(self, d):
for t in self.trades:
t.value_date = d
self._value_date = d
def mark(self, **kwargs):
for t in self.trades:
t.mark(**kwargs)
def shock(self, params=["pnl"], **kwargs):
return {trade_id: trade.shock(params, **kwargs) for trade_id, trade in self.items()}
@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 self.swaptions:
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 spread(self):
if len(self.indices) == 1:
return self.indices[0].spread
else:
return [index.spread for index in self.indices]
@spread.setter
def spread(self, val):
if len(self.indices) == 1:
self.indices[0].spread = val
elif len(self.indices) == 0:
# no index, so set the individual refs
for t in self.swaptions:
t.index.spread = val
elif len(self.indices) == len(val):
for index, val in zip(self.indices, val):
index.spread = val
else:
raise ValueError("The number of spreads 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", "Strike", "Direction",
"Type", "Expiry", "Vol", "PV", "Delta", "Gamma", "Theta",
"Vega", "attach", "detach", "Attach Rho", "Detach Rho"]
rec = []
for t in self.trades:
if isinstance(t, CreditIndex):
name = f"{t.index_type}{t.series} {t.tenor}"
r = ("Index", name, t.notional, t.ref, "N/A",
t.direction, "N/A", "N/A", None, t.pv,
1., 0., t.theta, 0.,
None, None, None, None)
elif isinstance(t, BlackSwaption):
name = f"{t.index.index_type}{t.index.series} {t.index.tenor}"
r = ("Swaption", name, t.notional, t.ref, t.strike,
t.direction, t.option_type, t.forward_date, t.sigma, t.pv,
t.delta, t.gamma, t.theta, t.vega,
None, None, None, None)
elif isinstance(t, DualCorrTranche):
name = f"{t.index_type}{t.series} {t.tenor}"
r = ("Tranche", name, t.notional, None, None,
t.direction, None, None, None, t.upfront,
t.delta, t.gamma, None, None,
t.attach, t.detach, t.rho[0], t.rho[1])
else:
raise TypeError
rec.append(r)
return pd.DataFrame.from_records(rec, columns=headers, index=self.trade_ids)
__repr__ = portf_repr('string')
_repr_html_ = portf_repr('html')
|
