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example3_6.m
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%
% written by:
% Ernest Chan
%
% Author of “Quantitative Trading:
% How to Start Your Own Algorithmic Trading Business”
%
% www.epchan.com
clear; % make sure previously defined variables are erased.
[num, txt]=xlsread('GLD'); % read a spreadsheet named "GLD.xls" into MATLAB.
tday1=txt(2:end, 1); % the first column (starting from the second row) is the trading days in format mm/dd/yyyy.
tday1=datestr(datenum(tday1, 'mm/dd/yyyy'), 'yyyymmdd'); % convert the format into yyyymmdd.
tday1=str2double(cellstr(tday1)); % convert the date strings first into cell arrays and then into numeric format.
adjcls1=num(:, end); % the last column contains the adjusted close prices.
[num, txt]=xlsread('GDX'); % read a spreadsheet named "GDX.xls" into MATLAB.
tday2=txt(2:end, 1); % the first column (starting from the second row) is the trading days in format mm/dd/yyyy.
tday2=datestr(datenum(tday2, 'mm/dd/yyyy'), 'yyyymmdd'); % convert the format into yyyymmdd.
tday2=str2double(cellstr(tday2)); % convert the date strings first into cell arrays and then into numeric format.
adjcls2=num(:, end); % the last column contains the adjusted close prices.
[tday, idx1, idx2]=intersect(tday1, tday2); % find the intersection of the two data sets, and sort them in ascending order
cl1=adjcls1(idx1);
cl2=adjcls2(idx2);
trainset=1:252; % define indices for training set
testset=trainset(end)+1:length(tday); % define indices for test set
% determines the hedge ratio on the trainset
results=ols(cl1(trainset), cl2(trainset)); % use regression function
hedgeRatio=results.beta;
spread=cl1-hedgeRatio*cl2; % spread = GLD - hedgeRatio*GDX
plot(spread(trainset));
figure;
plot(spread(testset));
figure;
spreadMean=mean(spread(trainset)); % mean of spread on trainset
spreadStd=std(spread(trainset)); % standard deviation of spread on trainset
zscore=(spread - spreadMean)./spreadStd; % z-score of spread
longs=zscore<=-2; % buy spread when its value drops below 2 standard deviations.
shorts=zscore>=2; % short spread when its value rises above 2 standard deviations.
exits=abs(zscore)<=1; % exit any spread position when its value is within 1 standard deviation of its mean.
positions=NaN(length(tday), 2); % initialize positions array
positions(shorts, :)=repmat([-1 1], [length(find(shorts)) 1]); % long entries
positions(longs, :)=repmat([1 -1], [length(find(longs)) 1]); % short entries
positions(exits, :)=zeros(length(find(exits)), 2); % exit positions
positions=fillMissingData(positions); % ensure existing positions are carried forward unless there is an exit signal
cl=[cl1 cl2]; % combine the 2 price series
dailyret=(cl - lag1(cl))./lag1(cl);
pnl=sum(lag1(positions).*dailyret, 2);
sharpeTrainset=sqrt(252)*mean(pnl(trainset(2:end)))./std(pnl(trainset(2:end))) % the Sharpe ratio on the training set should be about 2.3
sharpeTestset=sqrt(252)*mean(pnl(testset))./std(pnl(testset)) % the Sharpe ratio on the test set should be about 1.5
plot(cumsum(pnl(testset)));
save example3_6_positions positions; % save positions file for checking look-ahead bias.