def gmv_annualized(self) -> Tuple[float, float]: """ Returns the annualized risk and return of the Global Minimum Volatility portfolio """ return ( Float.annualize_risk(self.gmv_monthly[0], self.gmv_monthly[1]), Float.annualize_return(self.gmv_monthly[1]), )
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 minimize_risk( self, target_return: float, monthly_return: bool = False, tolerance: float = 1e-08, ) -> Dict[str, float]: """ Finds minimal risk given the target return. Returns a "point" with monthly values: - weights - mean return - CAGR - risk (std) Target return is a monthly or annual value: monthly_return = False / True tolerance - sets the accuracy for the solver """ if not monthly_return: target_return = Float.get_monthly_return_from_annual(target_return) ror = self.ror n = ror.shape[1] # number of assets init_guess = np.repeat(1 / n, n) # initial weights def objective_function(w): return Frame.get_portfolio_risk(w, ror) # construct the constraints weights_sum_to_1 = {"type": "eq", "fun": lambda weights: np.sum(weights) - 1} return_is_target = { "type": "eq", "fun": lambda weights: target_return - Frame.get_portfolio_mean_return(weights, ror), } weights = minimize( objective_function, init_guess, method="SLSQP", constraints=(weights_sum_to_1, return_is_target), bounds=self.bounds, options={"disp": False, "ftol": tolerance}, ) if weights.success: # Calculate point of EF given optimal weights risk = weights.fun # Annualize risk and return a_r = Float.annualize_return(target_return) a_risk = Float.annualize_risk(risk=risk, mean_return=target_return) # # Risk adjusted return approximation # r_gmean = Float.approx_return_risk_adjusted(mean_return=a_r, std=a_risk) # CAGR calculation portfolio_return_ts = Frame.get_portfolio_return_ts(weights.x, ror) cagr = Frame.get_cagr(portfolio_return_ts) if not self.labels_are_tickers: asset_labels = list(self.names.values()) else: asset_labels = self.symbols point = {x: y for x, y in zip(asset_labels, weights.x)} point["Mean return"] = a_r point["CAGR"] = cagr # point['CAGR (approx)'] = r_gmean point["Risk"] = a_risk else: raise Exception("No solutions were found") return point