def _get_gmv_monthly(self) -> Tuple[float, float]:
"""
Returns the risk and return (mean, monthly) of the Global Minimum Volatility portfolio
"""
return (
Rebalance.rebalanced_portfolio_return_ts(
self.gmv_monthly_weights, self.ror, period=self.reb_period
).std(),
Rebalance.rebalanced_portfolio_return_ts(
self.gmv_monthly_weights, self.ror, period=self.reb_period
).mean(),
)
def objective_function(w):
# annual risk
ts = Rebalance.rebalanced_portfolio_return_ts(w, self.ror, period=self.reb_period)
risk_monthly = ts.std()
mean_return = ts.mean()
result = - Float.annualize_risk(risk_monthly, mean_return)
return result
def gmv_annual_values(self) -> Tuple[float, float]:
"""
Returns the annual risk (std) and CAGR of the Global Minimum Volatility portfolio.
"""
returns = Rebalance.rebalanced_portfolio_return_ts(self.gmv_annual_weights, self.ror, period=self.reb_period)
return (
Float.annualize_risk(returns.std(), returns.mean()),
(returns + 1.0).prod() ** (_MONTHS_PER_YEAR / returns.shape[0]) - 1.0,
)
def _get_cagr(self, weights):
ts = Rebalance.rebalanced_portfolio_return_ts(weights, self.ror, period=self.reb_period)
acc_return = (ts + 1.).prod() - 1.
return (1. + acc_return) ** (_MONTHS_PER_YEAR / ts.shape[0]) - 1.
def objective_function(w):
# Accumulated return for rebalanced portfolio time series
objective_function.returns = Rebalance.rebalanced_portfolio_return_ts(w, ror, period=period)
accumulated_return = (objective_function.returns + 1.).prod() - 1.
return - accumulated_return
def objective_function(w):
ts = Rebalance.rebalanced_portfolio_return_ts(w, ror, period=period)
mean_return = ts.mean()
risk = ts.std()
return Float.annualize_risk(risk=risk, mean_return=mean_return)
def objective_function(w):
risk = Rebalance.rebalanced_portfolio_return_ts(w, ror, period=period).std()
return risk