def gmv_monthly(self) -> Tuple[float, float]:
"""
Returns the monthly risk and return of the Global Minimum Volatility portfolio
"""
return (
Frame.get_portfolio_risk(self.gmv_weights, self.ror),
Frame.get_portfolio_mean_return(self.gmv_weights, self.ror),
)
def optimize_return(self, option: str = "max") -> dict:
"""
Finds global max or min for the rate of return.
Returns monthly values for the risk, mean return and the weights.
'max' - search for global maximum
'min' - search for global minimum
"""
n = self.ror.shape[1] # Number of assets
init_guess = np.repeat(1 / n, n)
# Set the objective function
if option == "max":
def objective_function(w, ror):
month_return_value = Frame.get_portfolio_mean_return(w, ror)
return -month_return_value
elif option == "min":
def objective_function(w, ror):
month_return_value = Frame.get_portfolio_mean_return(w, ror)
return month_return_value
else:
raise ValueError('option should be "max" or "min"')
# construct the constraints
weights_sum_to_1 = {"type": "eq", "fun": lambda weights: np.sum(weights) - 1}
weights = minimize(
objective_function,
init_guess,
args=(self.ror,),
method="SLSQP",
constraints=(weights_sum_to_1,),
bounds=self.bounds,
options={
"disp": False,
"ftol": 1e-08,
}, # 1e-06 is not enough to optimize monthly returns
)
if weights.success:
portfolio_risk = Frame.get_portfolio_risk(weights.x, self.ror)
if option.lower() == "max":
optimized_return = -weights.fun
else:
optimized_return = weights.fun
point = {
"Weights": weights.x,
"Mean_return_monthly": optimized_return,
"Risk_monthly": portfolio_risk,
}
return point
else:
raise Exception("No solutions where found")
def get_monte_carlo(self, n: int = 100, kind: str = "mean") -> pd.DataFrame:
"""
Generate N random risk / cagr point for portfolios.
Risk and cagr are calculated for a set of random weights.
"""
weights_series = Float.get_random_weights(n, self.ror.shape[1])
# Portfolio risk and return for each set of weights
random_portfolios = pd.DataFrame(dtype=float)
for weights in weights_series:
risk_monthly = Frame.get_portfolio_risk(weights, self.ror)
mean_return_monthly = Frame.get_portfolio_mean_return(weights, self.ror)
risk = Float.annualize_risk(risk_monthly, mean_return_monthly)
mean_return = Float.annualize_return(mean_return_monthly)
if kind.lower() == "cagr":
cagr = Float.approx_return_risk_adjusted(mean_return, risk)
row = dict(Risk=risk, CAGR=cagr)
elif kind.lower() == "mean":
row = dict(Risk=risk, Return=mean_return)
else:
raise ValueError('kind should be "mean" or "cagr"')
random_portfolios = random_portfolios.append(row, ignore_index=True)
return random_portfolios
def objective_function(w):
return Frame.get_portfolio_risk(w, ror)