Using fminsearch for solving a problem with constraints

I have an optimization problem that is suitable for fmincon but fmincon fails to converge. I have to increase the tolerances to absurd levels (22%) to make it work.

I am trying to find an optimum portfolio from a set of 8 assets. The program will determine optimal percentage weight (W) of each asset in the portfolio.

I would like to try fminsearch and therefore have to come up with ways to make sure that:

the weights (variable) don't go negative

and that the weights of all assets add up to 1.

Is there a trick to put either or both of these constraints somewhere (either in the functions or in the optimization tool)?

I have put these constraints in the function and the function by itself works but fminsearch does not work on this function. It stops at zero iteration and gives the following error mesasge:

Output argument "RARV" (and maybe others) not assigned during call to "C:\Users\ ....

The code I am using is the following:

function RARV=RARvariance2(A,B,C,D,E,F,G,H,W)

T=length(A); %Time period, the same for all assets

SW=sum(W);

    if abs(SW-1)<.00000001
        if W(1,:)>0

    DD=[A-mean(A), B-mean(B), C-mean(C), D-mean(D), E-mean(E), F-mean(F), G-mean(G), H-mean(H)]; %Temporal, needed for covariances
    Cov=DD'*DD/T;

    V=W(1,:)*Cov*W(1,:)';

    %Portfolio expected returns
    ER=[mean(A), mean(B), mean(C), mean(D), mean(E), mean(F), mean(G), mean(H)]*W(1,:)';

    %(Negative) risk adjusted return
    RARV=-(ER)/sqrt(V); %Change V to sqrt(V) for semi-standard deviation
    else
        end    

   end

Please help. Thanks.