2bda7e5dd9e0b232caca4a71b08bc7ccf8661a7d,pypfopt/efficient_frontier.py,EfficientFrontier,max_sharpe,#EfficientFrontier#Any#,155

Before Change

        ]
        //  Rebuild original constraints with scaling factor
        for raw_constr in self._additional_constraints_raw:
            self._constraints.append(raw_constr(self._w / k))
        // Sharpe ratio is invariant w.r.t scaled weights, so we must
        // replace infinities and negative infinities
        // new_lower_bound = np.nan_to_num(self._lower_bounds, neginf=-1)
        // new_upper_bound = np.nan_to_num(self._upper_bounds, posinf=1)

After Change

        new_constraints = []
        // Must rebuild the constraints
        for constr in self._constraints:
            if isinstance(constr, cp.constraints.nonpos.Inequality):
                // Either the first or second item is the expression
                if isinstance(
                    constr.args[0], cp.expressions.constants.constant.Constant
                ):
                    new_constraints.append(constr.args[1] >= constr.args[0] * k)
                else:
                    new_constraints.append(constr.args[0] <= constr.args[1] * k)
            elif isinstance(constr, cp.constraints.zero.Equality):
                new_constraints.append(constr.args[0] == constr.args[1] * k)
            else:
                raise TypeError(
                    "Please check that your constraints are in a suitable format"
                )

        // Transformed max_sharpe convex problem:
        self._constraints = [
            (self.expected_returns - risk_free_rate).T * self._w == 1,
            cp.sum(self._w) == k,
            k >= 0,

Instances