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)
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