def savefig(self):
returns = self.port.returns
model = 'Classic'
# 포트폴리오 수익률과 비중 저장
with open(
'returns-{}_{}.pkl'.format(self.risk_profile,
self.current_date), 'wb') as f:
pl.dump(returns, f)
with open('w-{}_{}.pkl'.format(self.risk_profile, self.current_date),
'wb') as f:
pl.dump(self.w, f)
# Efficient fronter 저장
points = 50 # Number of points of the frontier
frontier = self.port.efficient_frontier(model=model,
rm=self.rm,
points=points,
rf=self.rf,
hist=True)
label = 'Max-return Portfolio' if self.risk_profile == 4 else 'Min-risk Portfolio'
mu = self.port.mu # Expected returns
cov = self.port.cov # Covariance matrix
fig1, ax1 = plt.subplots()
ax1 = plf.plot_frontier(w_frontier=frontier,
mu=mu,
cov=cov,
returns=returns,
rm=self.rm,
rf=self.rf,
alpha=0.05,
cmap='viridis',
w=self.w,
label=label,
marker='*',
s=16,
c='r',
height=6,
width=10,
ax=ax1)
fig1.savefig('ef-{}_{}.png'.format(self.risk_profile,
self.current_date))
fig2, ax2 = plt.subplots(nrows=1, ncols=1)
# Plotting efficient frontier composition
ax2 = plf.plot_frontier_area(w_frontier=frontier,
cmap="tab20",
height=6,
width=10,
ax=None)
fig2.savefig('ef_area-{}_{}.png'.format(self.risk_profile,
self.current_date))
rf=rf,
alpha=0.01,
cmap='viridis',
w=w,
label=label,
marker='*',
s=16,
c='r',
height=6,
width=10,
ax=None)
# Plotting efficient frontier composition
ax = plf.plot_frontier_area(w_frontier=frontier,
cmap="tab20",
height=6,
width=10,
ax=None)
rm = 'CVaR' # Risk measure
w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)
print(w.T)
ax = plf.plot_pie(w=w,
title='Sharpe Mean CVaR',
others=0.05,
nrow=25,
cmap="tab20",
height=6,
width=10,
ax=None)
points = 50 # Number of points of the frontier