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from .index import CreditIndex
from .option import BlackSwaption
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},
'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
self.indices = [t for t in trades if isinstance(t, CreditIndex)]
self.swaptions = [t for t in trades if isinstance(t, BlackSwaption)]
value_dates = set(t.value_date for t in self.trades)
self._keys = set([(index.index_type, index.series, index.tenor) for index in self.indices])
for swaption in self.swaptions:
self._keys.add((swaption.index.index_type, swaption.index.series, swaption.index.tenor))
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 __iter__(self):
for t in self.trades:
yield t
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"]
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.)
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)
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')
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