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from .index import CreditIndex
from .option import BlackSwaption
from .tranche_basket import DualCorrTranche
import pandas as pd
import numpy as np
import logging
logger = logging.getLogger(__name__)
def portf_repr(method):
def f(*args):
portf = args[0]
thousands = "{:,.2f}".format
def percent(x):
if np.isnan(x):
return "N/A"
else:
return f"{100*x:.2f}%"
header = f"Portfolio {portf.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,
"HY Equiv": thousands,
"Strike": lambda x: "N/A" if np.isnan(x) else str(x),
"Type": lambda x: "N/A" if x is None else x,
"Corr01": thousands,
},
"index": True,
}
if method == "string":
kwargs["line_width"] = 100
s = getattr(portf._todf().dropna(axis=1, how="all"), "to_" + method)(**kwargs)
return header + s
return f
class Portfolio:
def __init__(self, trades, trade_ids=None):
self.trades = trades
if trade_ids is not None:
self.trade_ids = trade_ids
else:
self.trade_ids = (None,) * len(trades)
if trades:
value_dates = set(t.value_date for t in self.trades)
self._value_date = value_dates.pop()
if len(value_dates) >= 1:
logger.warn(
f"not all instruments have the same trade date, picking {self._value_date}"
)
def __bool__(self):
return bool(self.trades)
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
def __getitem__(self, trade_id):
for tid, trade in zip(self.trade_ids, self.trades):
if tid == trade_id:
break
else:
raise ValueError(f"{trade_id} not found")
return trade
def __bool__(self):
return self.trades != []
@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 tid, t in self.items():
try:
t.mark(**kwargs)
logger.debug(f"marking {tid} to {t.pv}")
except Exception as e:
raise
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)
@property
def hy_equiv(self):
return sum(t.hy_equiv for t in self.trades)
@property
def corr01(self):
return sum(t.corr01 for t in self.trades)
@property
def vega(self):
return sum(t.vega for t in self.trades)
def _todf(self):
headers = [
"Product",
"Index",
"Notional",
"Ref",
"Strike",
"Direction",
"Type",
"Expiry",
"Vol",
"PV",
"Delta",
"Gamma",
"Theta",
"Corr01",
"IRDV01",
"Vega",
"attach",
"detach",
"Attach Rho",
"Detach Rho",
"HY Equiv",
]
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,
None,
t.direction,
getattr(t, "option_type", None),
getattr(t, "forward_date", None),
None,
t.pv,
1.0,
None,
t.theta,
getattr(t, "corr01", None),
getattr(t, "IRDV01", None),
getattr(t, "vega", None),
None,
None,
None,
None,
t.hy_equiv,
)
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,
getattr(t, "corr01", None),
getattr(t, "IRDV01", None),
t.vega,
None,
None,
None,
None,
t.hy_equiv,
)
elif isinstance(t, DualCorrTranche):
name = f"{t.index_type}{t.series} {t.tenor}"
try:
theta = t.theta()
except ValueError:
theta = t.pv / t.notional / t.duration + t.tranche_running * 1e-4
r = (
"Tranche",
name,
t.notional,
None,
None,
t.direction,
None,
None,
None,
t.pv,
t.delta,
t.gamma,
theta,
getattr(t, "corr01", None),
getattr(t, "IRDV01", None),
None,
t.attach,
t.detach,
t.rho[0],
t.rho[1],
t.hy_equiv,
)
else:
raise TypeError
rec.append(r)
if isinstance(self.trade_ids[0], tuple):
index = [tid[1] for tid in self.trade_ids]
else:
index = self.trade_ids
df = pd.DataFrame.from_records(rec, columns=headers, index=index)
df.index.name = "ids"
return df
__repr__ = portf_repr("string")
_repr_html_ = portf_repr("html")
|
