def cla_min_vol_weights(rets_bl, covar_bl, config):
cla = CLA(rets_bl, covar_bl, weight_bounds= \
(config['min_position_size'] ,config['max_position_size']))
cla.min_volatility()
weights = cla.clean_weights()
weights = pd.DataFrame.from_dict(weights, orient='index')
weights.columns = ['CLA Min Vol']
return weights, cla
def omptimizePortCLA(port,
weights,
start,
plot=False,
short=False,
printBasicStats=True,
how='Sharpe'):
#Getting Data
df = bpf.getData(port, start)
#Plotting the portfolio
if plot:
bpf.plotPort(df, port)
if printBasicStats:
bpf.basicStats(df, weights)
#Optimization for Sharpe using Efficient Frontier
if short:
bounds = (-1, 1)
else:
bounds = (0, 1)
mu = df.pct_change().mean() * 252
S = risk_models.sample_cov(df)
if how == 'Sharpe':
# Maximized on Sharpe Ratio
cla = CLA(
mu, S
) #Here the weight bounds are being used to allow short positions as well
weights = cla.max_sharpe()
cleaned_weights = dict(cla.clean_weights())
print("Weights of an optimal portfolio maximised on Sharpe Ratio:")
print(cleaned_weights)
cla.portfolio_performance(verbose=True)
bpf.getDiscreteAllocations(df, weights)
plot_ef(cla)
plotting.plot_weights(weights)
elif how == "Vol":
# Minimized on Volatility
cla = CLA(mu, S)
cla.min_volatility()
w = dict(cla.clean_weights())
print("Weights of an optimal portfolio minimized on Volatilty (Risk):")
print(w)
cla.portfolio_performance(verbose=True)
bpf.getDiscreteAllocations(df, w)
plot_ef(cla)
plotting.plot_weights(w)
# In[265]:
type(e_cov_during)
# In[266]:
returns_during = np.array(df6.mean())
# In[267]:
efficient_portfolio_during = CLA(returns_during, e_cov_during)
# In[268]:
print(efficient_portfolio_during.min_volatility())
# In[269]:
# Compute the efficient frontier
(ret, vol, weights) = efficient_portfolio_during.efficient_frontier()
# In[278]:
plt.figure(figsize=(20, 12))
plt.xlabel('Standard Deviation/Volatiltiy/Risk')
plt.ylabel('Return for period 2007-2008')
plt.title('Efficient Frontier during crisis', size=20)
plt.plot(vol, ret, c='r')
# In[271]:
#we also create a barchart
#create barchart
barchart = pd.Series(
tickers['Weight']).sort_values(ascending=True).plot.barh(figsize=(10, 6))
plt.show(barchart)
#covariance heatmap
#to get a grasp how our chosen portfolio correlates with each other asset in the portfolio we state a cov heatmap
plotting.plot_covariance(cov_matrix_tickers, plot_correlation=True)
##create the Efficient frontier line and visualize it
#in order to visualize the efficient frontier from the optimized portfolio, we need to initiate a new built-in method
#we use the CLA method, because it is more robust than the default option
cla = CLA(mu_target_tickers, cov_matrix_tickers)
if risk_tolerance == 'Low':
cla.min_volatility()
else:
cla.max_sharpe()
#plot the efficient frontier line
ax_portfolio = plotting.plot_efficient_frontier(cla,
ef_param='risk',
ef_param_range=np.linspace(
0.2, 0.6, 100),
points=1000)
#initialize Discrete Allocation to get a full picture what you could buy with a given amount
#the Discrete Allocation gives you the discrete amount of shares you have to allocate given your available funds
from pypfopt import DiscreteAllocation
from datetime import datetime
from datetime import timedelta