% Monthly Data  
% Construct the coming SCI monthly returns based on previous SCI monthly returns
clc  
clear  
[NUMERIC,TXT,RAW]=xlsread('\path\IDX_Idxtrdmth.xlsx','Shanghai Composite Index');
[a b]=size(NUMERIC);
for m=1:72  
    NUMERIC1=NUMERIC(:,7);  
    NUMERIC2(m,:)=NUMERIC1(randint(1,1,[1 a]));  
    % We randomly choose a previous SCI monthly return between -1 and 1 to be the coming SCI monthly return.  
    if NUMERIC1(randint(1,1,[1 a]))<1 & NUMERIC1(randint(1,1,[1 a]))>-1  
       NUMERIC2(m,:)=NUMERIC1(randint(1,1,[1 a]));  
    end  
    NUMERIC3=[NUMERIC1; NUMERIC2];  
end  
% Find the data which satisfies the two requirements of 'dual reversal' timing strategy model.
for n=185:a  
    if mean(NUMERIC(n-2:n,7))<0 & NUMERIC(n,9)<nanmean(NUMERIC(:,9))  
        for k=1:24  
            ALLRET(n,k)=sum(NUMERIC3(n+1:n+3*k,:));  
        end  
    end  
end  
[c,d]=size(ALLRET);
% Calculate all the discription statistics.
for j=1:1:d  
    Neg(:,j)=length(find(ALLRET(:,j)<0));  
    Pos(:,j)=length(find(ALLRET(:,j)>0));  
    Zero(:,j)=c-Neg(:,j)-Pos(:,j);  
    Max(:,j)=max(ALLRET(:,j));  
    Min(:,j)=min(ALLRET(:,j));  
    Sum(:,j)=sum(ALLRET(:,j));  
    Mean(:,j)=sum(ALLRET(:,j))/(Pos(:,j)+Neg(:,j));  
    Std(:,j)=std(ALLRET(:,j));  
    Sharpe(:,j)=Mean(:,j)/Std(:,j);  
    Success(:,j)=Pos(:,j)/(Pos(:,j)+Neg(:,j));  
end  
% Calculate the average SCI monthly return and the monthly maximum loss. 
for l=1:24  
Monthly(:,l)=Mean(:,l)/(3*l);  
Monthloss(:,l)=Min(:,l)/(3*l);  
end  
% Collect all the results into the table named 'Results'.
Results=[ALLRET;Neg;Pos;Zero;Max;Min;Monthloss;Sum;Mean;Monthly;Std;Sharpe;Success];

% Daily Data (based on 5-day time window)
% Construct the coming SCI daily returns based on previous SCI daily returns.
% Determine the lagged periods of both previous SCI daily return and Baidu Index based on R-square.
clc  
clear  
[NUMERIC,TXT,RAW]=xlsread('\path\IDX_Idxtrdmth.xlsx','Daily Data');
x1=[];x2=[];Y=[];  
[a b]=size(NUMERIC);
for i=1:12  
    for j=1:12  
        k=max(i,j);  
for n=1:a-k  
    x1(n,:)=mean(NUMERIC(k-i+1:k+n-1,1));  
    x2(n,:)=log(mean(NUMERIC(k-j+1:k+n-1,2)))/100;  
Y(n,:)=NUMERIC(k+n,2);  
end  
X=[x1 x2];  
stats = regstats(Y,X);  
beta=stats.beta';  
t=stats.tstat.t';  
R2=stats.rsquare;  
R20(:,j)=stats.rsquare;  
Results0(j,:)=[beta NaN t NaN R2];  
    end  
Results1(i,:)=R20;% R-square  
Results(13*i-12:13*i-1,:)=Results0; %beta, t-statistic, R-square  
end

% Construct 5-day time window 'dual momentum' timing strategy model.
% The code of 10-day time window 'dual momentum' timing strategy model are the same. Here we just present 5-day model.
clc  
clear  
% Find the data which satisfies the two requirements of 'dual momentum' timing strategy model.
[NUMERIC,TXT,RAW]=xlsread('\path\IDX_Idxtrdmth.xlsx','Daily Data');
[a b]=size(NUMERIC);
for n=10:a-10  
    if mean(NUMERIC(n-4:n,1))>0 & NUMERIC(n,3)>nanmean(NUMERIC(:,3))  
        for k=1:10  
            ALLRET(n+1,k)=sum(NUMERIC(n+1:n+k,1));  
        end  
    end  
end  
[c,d]=size(ALLRET);
% Calculate all the discription statistics.
for j=1:1:d  
    Neg(:,j)=length(find(ALLRET(:,j)<0));  
    Pos(:,j)=length(find(ALLRET(:,j)>0));  
    Zero(:,j)=c-Neg(:,j)-Pos(:,j);  
    Max(:,j)=max(ALLRET(:,j));  
    Min(:,j)=min(ALLRET(:,j));  
    Sum(:,j)=sum(ALLRET(:,j));  
    Mean(:,j)=sum(ALLRET(:,j))/(Pos(:,j)+Neg(:,j));  
    Std(:,j)=std(ALLRET(:,j));  
    Sharpe(:,j)=Mean(:,j)/Std(:,j);  
    Success(:,j)=Pos(:,j)/(Pos(:,j)+Neg(:,j));  
    for i=1:c  
        Asum(i,j)=sum(ALLRET(1:i,j)); % Calculate accumulative SCI daily returns. (5-day rolling)  
    end  
end  
for i=1:10  
    Max_Recall(1,i)=min(Min(:,1:i)); % Calculate maximum retracement.  
end  
for l=1:10  
Dayly(:,l)=Mean(:,l)/(l);  
Dayloss(:,l)=Min(:,l)/(l);  
end  
% Collect all the results into the table named 'Results'.
Results=[ALLRET;Neg;Pos;Zero;Max;Min;Dayloss;Sum;Mean;Dayly;Std;Sharpe;Success;Max_Recall];



> Blockquote