for x in epochs.keys():
sub_price = assets.loc[epochs[x]['start']:epochs[x]['end']]
e_return[x] = mean_historical_return(assets, frequency=252)
e_cov[x] = CovarianceShrinkage(sub_price).ledoit_wolf()
# Display the efficient covariance matrices for all epochs
print("Efficient Covariance Matrices\n", e_cov)
# Initialize the Crtical Line Algorithm object
efficient_portfolio_during = CLA(e_return["during"], e_cov["during"])
# Find the minimum volatility portfolio weights and display them
print(efficient_portfolio_during.min_volatility())
# Compute the efficient frontier
(ret, vol, weights) = efficient_portfolio_during.efficient_frontier()
# Add the frontier to the plot showing the 'before' and 'after' frontiers
plt.scatter(vol, ret, s=4, c='g', marker='.', label='During')
plt.legend()
plt.show()
# plotting using PyPortfolioOpt
pplot.plot_covariance(cs, plot_correlation=False, show_tickers=True)
pplot.plot_efficient_frontier(efficient_portfolio_during,
points=100,
show_assets=True)
pplot.plot_weights(cw)
# Dealing with many negligible weights
# efficient portfolio allocation
def _get_efficient_weights(self):
cla = CLA(self.mu, self.S)
cla.max_sharpe()
(mu, sigma, weights) = cla.efficient_frontier()
return mu, sigma, weights
def optimal_portfolio(mu, S, objective='max_sharpe', get_entire_frontier=True, **kwargs):
"""Solve for optimal portfolio. Wrapper for pypfopt functions
Arguments:
mu (pd.Series) - Expected annual returns
S (pd.DataFrame/np.ndarray) - Expected annual volatility
objective (string, optional) - Optimise for either 'max_sharpe', or 'min_volatility', defaults to 'max_sharpe'
get_entire_frontier (boolean, optional) - Also get the entire efficient frontier, defaults to True
"""
# if need to efficiently compute the entire efficient frontier for plotting, use CLA
# else use standard EfficientFrontier optimiser.
# (Note that optimum weights might be slightly different depending on whether CLA or EfficientFrontier was used)
Optimiser = CLA if get_entire_frontier else EfficientFrontier
op = Optimiser(mu, S)
# risk_aversion = kwargs.get("risk_aversion", 1) # only for max quadratic utility
if (objective is None):
# Get weights for both max_sharpe and min_volatility
opt_weights = []
op.max_sharpe()
opt_weights.append(op.clean_weights())
op.min_volatility()
opt_weights.append(op.clean_weights())
# ef = EfficientFrontier(mu, S)
# ef.max_quadratic_utility(risk_aversion)
# opt_weights.append(ef.clean_weights())
else:
if (objective == 'max_sharpe'):
op.max_sharpe()
elif ('min_vol' in objective):
op.min_volatility()
elif (objective == 'efficient_risk'):
target_volatility = kwargs.get("target_volatility", None)
if target_volatility is None:
print("Error: You have to specify the target_volatility!")
return None, None, None, None
else:
try:
op.efficient_risk(target_volatility)
except ValueError:
# could not solve based on target_volatility, we try lookup table instead
cla = CLA(mu, S)
cla.max_sharpe()
ef_returns, ef_risks, ef_weights = cla.efficient_frontier(points=300)
lookup_v_w = dict(zip(ef_risks, ef_weights))
lookup_v_w = OrderedDict(sorted(lookup_v_w.items()))
w = lookup_v_w[min(lookup_v_w.keys(), key=lambda key: abs(key-target_volatility))]
w = [i[0] for i in w] # flatten
return w, None, None
elif (objective == 'efficient_return'):
target_return = kwargs.get("target_return", None)
if target_return is None:
print("Error: You have to specify the target_return!")
return None, None, None, None
else:
op.efficient_return(target_return)
# elif (objective == 'max_quadratic_utility'):
# op.max_quadratic_utility(risk_aversion)
# # print("Using MAX_QUADRATIC UTILITY")
opt_weights = op.clean_weights()
if get_entire_frontier:
opt_returns, opt_risks, _ = op.efficient_frontier(points=200)
return opt_weights, opt_returns, opt_risks
else:
return opt_weights, None, None
