We are being frequently asked about the Sharpe ratio, its formulation in the conic framework, and implementation in Fusion. This involves a class of problems with an objective of the type $$\mathrm{maximize}_x\quad \frac{r^Tx-r_f}{\|Fx\|_2}$$ i.e. an affine function over a 2-norm, where $r^Tx-r_f>0$, $x\in\mathbb{R}^n$. In practical portfolio optimization $r$ would be the vector of expected returns, $r_f$ is the risk-free return rate, $x$ is the vector of asset allocations and $\|Fx\|_2 = \sqrt{x^T\Sigma x}$ is the risk associated with the covariance matrix $\Sigma$ (formulating the risk term as a 2-norm is standard and we don't go into details, see here or here). Typically there will be various constraints on $x$, for example $$\mathbb{1}^Tx=1,\ x\geq 0$$ would correspond to a fully invested portfolio with no short-selling.
Let us explain step by step how one can derive a conic formulation of (1) suitable for a solver like MOSEK. We present it in detail so that the reader can apply it almost verbatim to more complicated models of this kind. First, note that if we could fix $$r^Tx-r_f=\mathrm{const}$$ then the objective would be equivalent to minimizing $\|Fx\|_2$, that is a standard second-order cone problem. However, we don't know in advance what "const" should be. (In fact solving the problem for all values of "const" corresponds to computing the efficient frontier.) Therefore, for reasons which will become clear in a moment, we denote the "const" value by $1/z$, where $z\geq0$ is a new scalar variable, i.e. $$r^Tx-r_f=1/z$$ that is $$zr^Tx-r_fz=1.$$ Denoting $y=zx$ ($y$ is now a new vector variable) the last equation becomes $$r^Ty-r_fz=1.$$ Now $x=y/z$ and the objective function becomes $$\frac{r^Tx-r_f}{\|Fx\|_2} = \frac{1/z}{\|F\frac{y}{z}\|_2} = \frac{1}{\|Fy\|_2}$$ hence we can write the original problem as $$\begin{array}{rl}\mathrm{minimize}&\|Fy\|_2\\ \mathrm{s.t.} & r^Ty-r_fz=1,\\ & z\geq 0.\end{array}$$ Note that the new problem involves variables $y$ and $z$, but $x$ has been eliminated. Any additional constraints must also be reformulated by substituting $x=y/z$, for example $\mathbb{1}^Tx=1,\ x\geq 0$ becomes $$\mathbb{1}^Ty=z,\ y\geq 0$$ and in fact any other linear constraint $Ax=b$ becomes $$Ay=bz.$$ A solution $(y,z)$ to the reformulation gives a solution $x=y/z$ to the original problem (assuming that the problem has a feasible point with $r^Tx-r_f>0$, so that the reformulation has a solution with $z>0$).
Certain other types of constraints can also be carried through the reformulation. For example a cardinality constraint on $x$ (at most $k$ entries in $x$ are nonzero) can be imposed on $y$ using the same mixed-integer model. Another quadratic bound of the form $\|Hx\|_2\leq 1$ will become $\|Hy\|_2\leq z$ using $x=y/z$.
A sample Fusion implementation can be found here:
