docs: def efficient_risk(self, target_risk, risk_free_rate=0.02, market_neutral=False):
          """
          Calculate the Sharpe-maximising portfolio for a given volatility (i.e max return
          for a target risk).
  
          :param target_risk: the desired volatility of the resulting portfolio.
          :type target_risk: float
          :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02
          :type risk_free_rate: float, optional
          :param market_neutral: whether the portfolio should be market neutral (weights sum to zero),
                                 defaults to False. Requires negative lower weight bound.
          :param market_neutral: bool, optional
          :raises ValueError: if ``target_risk`` is not a positive float
          :raises ValueError: if ``risk_free_rate`` is non-numeric
          :return: asset weights for the efficient risk portfolio
          :rtype: dict
          """
          if not isinstance(target_risk, float) or target_risk < 0:
              raise ValueError("target_risk should be a positive float")
          if not isinstance(risk_free_rate, (int, float)):
              raise ValueError("risk_free_rate should be numeric")
  
          args = (self.expected_returns, self.cov_matrix, self.gamma, risk_free_rate)
          target_constraint = {
              "type": "ineq",
              "fun": lambda w: target_risk
              - np.sqrt(objective_functions.volatility(w, self.cov_matrix)),
          }
          # The equality constraint is either "weights sum to 1" (default), or
          # "weights sum to 0" (market neutral).
          if market_neutral:
              if self.bounds[0][0] is not None and self.bounds[0][0] >= 0:
                  warnings.warn(
                      "Market neutrality requires shorting - bounds have been amended",
                      RuntimeWarning,
                  )
                  self.bounds = self._make_valid_bounds((-1, 1))
              constraints = [
                  {"type": "eq", "fun": lambda x: np.sum(x)},
                  target_constraint,
              ]
          else:
              constraints = self.constraints + [target_constraint]
  
          result = sco.minimize(
              objective_functions.negative_sharpe,
              x0=self.initial_guess,
              args=args,
              method="SLSQP",
              bounds=self.bounds,
              constraints=constraints,
          )
          self.weights = result["x"]
          return dict(zip(self.tickers, self.weights))