Turn For Loop into Vectorization for Genetic Algorithm

I am using a genetic algorithm with the objective function below. Its actually pretty slow even with the Parallelism Enabled and a few constraints. I believe vectorization would help in replacing the nested for loops, but I am not confident with the indexing here in combination with the ga, and I have had many attempts at trying to vectorize. Any pointers on this? Thanks!

function [f] = CalcBestStratPart6ModforPosition(x,Delta,Vega,Gamma,Theta,Rho,Da,IV,DVIVPriceTime,NumberOpts,CurrentPriceRange,SizeCurrentP,SizeCurrentT,IVPriceTime,PercentageRange)
%
%Simulate Time Changes
for k=1:SizeCurrentT;
%   
%Simulate Price Changes
 for i=1:SizeCurrentP; 
%
ThetaAllPrices=-(Theta(i,k,x(1)).*x(3))+(Theta(i,k,x(2)).*x(3))+-(Theta(i,k,x(4)).*x(6))+(Theta(i,k,x(5)).*x(6))+(Theta(i,k,x(7)).*x(9))+(Theta(i,k,x(8)).*x(9))+(Theta(i,k,x(10)).*x(12))+(Theta(i,k,x(11)).*x(12))+-(Theta(i,k,x(13)).*x(15))+(Theta(i,k,x(14)).*x(15))+-(Theta(i,k,x(16)).*x(18))+(Theta(i,k,x(17)).*x(18))+-(Theta(i,k,x(19)).*x(21))+(Theta(i,k,x(20)).*x(21))+-(Theta(i,k,x(22)).*x(24))+(Theta(i,k,x(23)).*x(24));      
DeltaAllPrices=-(Delta(i,k,x(1)).*x(3))+(Delta(i,k,x(2)).*x(3))+-(Delta(i,k,x(4)).*x(6))+(Delta(i,k,x(5)).*x(6))+(Delta(i,k,x(7)).*x(9))+(Delta(i,k,x(8)).*x(9))+(Delta(i,k,x(10)).*x(12))+(Delta(i,k,x(11)).*x(12))+-(Delta(i,k,x(13)).*x(15))+(Delta(i,k,x(14)).*x(15))+-(Delta(i,k,x(16)).*x(18))+(Delta(i,k,x(17)).*x(18))+-(Delta(i,k,x(19)).*x(21))+(Delta(i,k,x(20)).*x(21))+-(Delta(i,k,x(22)).*x(24))+(Delta(i,k,x(23)).*x(24));
VegaAllPrices=-(Vega(i,k,x(1)).*x(3))+(Vega(i,k,x(2)).*x(3))+-(Vega(i,k,x(4)).*x(6))+(Vega(i,k,x(5)).*x(6))+(Vega(i,k,x(7)).*x(9))+(Vega(i,k,x(8)).*x(9))+(Vega(i,k,x(10)).*x(12))+(Vega(i,k,x(11)).*x(12))+-(Vega(i,k,x(13)).*x(15))+(Vega(i,k,x(14)).*x(15))+-(Vega(i,k,x(16)).*x(18))+(Vega(i,k,x(17)).*x(18))+-(Vega(i,k,x(19)).*x(21))+(Vega(i,k,x(20)).*x(21))+-(Vega(i,k,x(22)).*x(24))+(Vega(i,k,x(23)).*x(24));
GammaAllPrices=-(Gamma(i,k,x(1)).*x(3))+(Gamma(i,k,x(2)).*x(3))+-(Gamma(i,k,x(4)).*x(6))+(Gamma(i,k,x(5)).*x(6))+(Gamma(i,k,x(7)).*x(9))+(Gamma(i,k,x(8)).*x(9))+(Gamma(i,k,x(10)).*x(12))+(Gamma(i,k,x(11)).*x(12))+-(Gamma(i,k,x(13)).*x(15))+(Gamma(i,k,x(14)).*x(15))+-(Gamma(i,k,x(16)).*x(18))+(Gamma(i,k,x(17)).*x(18))+-(Gamma(i,k,x(19)).*x(21))+(Gamma(i,k,x(20)).*x(21))+-(Gamma(i,k,x(22)).*x(24))+(Gamma(i,k,x(23)).*x(24));
%   
    ECIV = DVIVPriceTime(i,k).*((Da.* (5./7)).^0.5);
% 
    EPC = CurrentPriceRange(i).*(exp(IVPriceTime(i,k).*((Da./365).^0.5))-1);
%  
    DeltaEffect(i,k)=(-abs(DeltaAllPrices).*EPC).*PercentageRange(i);   %Multiply by some factor - probability weighted
%      
    GammaEffect(i,k) = (GammaAllPrices.*(EPC.^ 2)./2).*PercentageRange(i);
%
    VegaEffect(i,k) = (-abs(VegaAllPrices).* ECIV).*PercentageRange(i);
%
    ThetaEffect(i,k) = (ThetaAllPrices.*Da).*PercentageRange(i);
%
%      DeltaEffect=-abs(DeltaAllPrices).*EPC;
%     
%     GammaEffect = GammaAllPrices.*(EPC.^ 2)./2;
% 
%     VegaEffect = -abs(VegaAllPrices).* ECIV;
% 
%     ThetaEffect = ThetaAllPrices.*Da;
%     
%  f=-(DeltaEffect+GammaEffect+VegaEffect+ThetaEffect);
 end
end
%  f=-(sum(DeltaEffect(:))+sum(GammaEffect(:))+sum(VegaEffect(:))+sum(ThetaEffect(:)));  
%  
end