def curve(self, **kw):
"""
Returns an AccountCurve for this Portfolio.
"""
kw2={'portfolio_weights': self.valid_weights()}
kw2.update(kw)
return accountCurve(list(self.valid_instruments().values()), **kw2)
def curve(self, **kw):
"""
Returns an AccountCurve for this Portfolio.
"""
kw2 = {'portfolio_weights': self.valid_weights()}
kw2.update(kw)
return accountCurve(list(self.valid_instruments().values()), **kw2)
def forecast_returns(self, **kw):
"""
Estimated returns for individual trading rules
"""
f = self.forecasts(**kw)
positions = self.position(forecasts=f).dropna()
curves = positions.apply(lambda x: accountCurve([self], positions=x,
panama_prices = self.panama_prices()))
return curves.apply(lambda x: x.returns()[self.name]).transpose()
def bootstrap(self, **kw):
"""
Optimize rule weights using bootstrapping
"""
import trading.bootstrap
print("Bootstrap", self.name, "starting")
result = trading.bootstrap.bootstrap(self, **kw)
self.bootstrapped_weights = result.mean()
print(accountCurve([self], positions=self.position(forecasts=weight_forecast(
self.forecasts(), result.mean())), panama_prices=self.panama_prices()))
return result.mean()
def function(w, instrument, sample):
"""This is the function that is minimized iteratively using scipy.optimize.minimize to find the best weights (w)"""
wf = weight_forecast(sample, w)
# We introduce a capital term, as certain currencies like HKD are very 'numerate', which means we need millions of HKD to get a
# significant position
position = instrument.position(forecasts = wf, nofx=True, capital=10E7).rename(instrument.name).to_frame().dropna()
# position = instrument.position(forecasts = wf, nofx=True).rename(instrument.name).to_frame().dropna()
l = accountCurve([instrument], positions = position, panama_prices=instrument.panama_prices().dropna(), nofx=True)
s = l.sortino()
try:
assert np.isnan(s) == False
except:
print(sample, position)
raise
return -s
def function(w, instrument, sample):
"""This is the function that is minimized iteratively using scipy.optimize.minimize to find the best weights (w)"""
wf = weight_forecast(sample, w)
# We introduce a capital term, as certain currencies like HKD are very 'numerate', which means we need millions of HKD to get a
# significant position
position = instrument.position(
forecasts=wf, nofx=True,
capital=10E7).rename(instrument.name).to_frame().dropna()
# position = instrument.position(forecasts = wf, nofx=True).rename(instrument.name).to_frame().dropna()
l = accountCurve([instrument],
positions=position,
panama_prices=instrument.panama_prices().dropna(),
nofx=True)
s = l.sortino()
try:
assert np.isnan(s) == False
except:
print(sample, position)
raise
return -s
def curve(self, **kw):
return accountCurve([self], **kw)