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from .index import 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)]
trade_dates = set(index.trade_date for index in self.indices)
self._keys = [(index.index_type, index.series, index.tenor) for index in self.indices]
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:
ind = swaption.index
vol_surface = self._vs[(ind.trade_date, ind.index_type, ind.series, ind.tenor)]
swaption.sigma = float(vol_surface.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 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 trades:
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", "Expiry",
"Vol", "PV", "Delta", "Gamma", "Theta", "Vega"]
rec = []
for t in self.trades:
if isinstance(t, Index):
name = f"{t.index_type}{t.series} {t.tenor}"
r = ("Index", name,
t.notional, t.ref, "N/A", t.direction, "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.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')
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