def effrontier(mu, S, sht, nameplot):
cla = CLA(mu, S)
fig = plt.figure(figsize=(8, 8))
cla.max_sharpe()
ax = cla.plot_efficient_frontier(showfig=False)
fig = ax.get_figure()
sht.pictures.add(fig, name=nameplot, update=True)
def cla_max_sharpe_weights(rets_bl, covar_bl, config):
cla = CLA(rets_bl, covar_bl, weight_bounds= \
(config['min_position_size'] ,config['max_position_size']))
cla.max_sharpe()
weights = cla.clean_weights()
weights = pd.DataFrame.from_dict(weights, orient='index')
weights.columns = ['CLA Max Sharpe']
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)
'GOOG': 0.049560084289929286,
'JPM': 0.017675709092515708,
'MA': 0.03812737349732021,
'PFE': 0.07786528342813454,
'RRC': 0.03161528695094597,
'SBUX': 0.039844436656239136,
'SHLD': 0.027113184241298865,
'T': 0.11138956508836476,
'UAA': 0.02711590957075009,
'WMT': 0.10569551148587905,
'XOM': 0.11175337115721229}
"""
# Crticial Line Algorithm
cla = CLA(mu, S)
print(cla.max_sharpe())
cla.portfolio_performance(verbose=True)
plotting.plot_efficient_frontier(cla) # to plot
"""
{'GOOG': 0.020889868669945022,
'AAPL': 0.08867994115132602,
'FB': 0.19417572932251745,
'BABA': 0.10492386821217001,
'AMZN': 0.0644908140418782,
'GE': 0.0,
'AMD': 0.0,
'WMT': 0.0034898157701416382,
'BAC': 0.0,
'GM': 0.0,
'T': 2.4138966206946562e-19,
'UAA': 0.0,
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
#create list out of sliced tickers dataframe