% Read a table of daily adjusted close prices for 2006 DJIA stocks.
T = readtable('dowPortfolio.xlsx');

% For readability, use only 15 of the 30 DJI component stocks.
assetSymbols = ["AA","CAT","DIS","GM","HPQ","JNJ","MCD","MMM","MO","MRK","MSFT","PFE","PG","T","XOM"];

% Prune the table to hold only the dates and selected stocks.
timeColumn = "Dates";
T = T(:,[timeColumn assetSymbols]);

% Convert to the table to a timetable.
pricesTT = table2timetable(T,'RowTimes','Dates');

% View the structure of the prices timetable.
head(pricesTT)

% View the size of the asset price data set.
numSample = size(pricesTT.Variables, 1);
numAssets = size(pricesTT.Variables, 2);
table(numSample, numAssets)

warmupPeriod = 40;

% No current weights (100% cash position).
current_weights = zeros(1,numAssets);

% Warm-up partition of data set timetable.
warmupTT = pricesTT(1:warmupPeriod,:);

% Compute the initial portfolio weights for each strategy.
equalWeight_initial     = equalWeightFcn(current_weights,warmupTT);
maxSharpeRatio_initial  = maxSharpeRatioFcn(current_weights,warmupTT);
inverseVariance_initial = inverseVarianceFcn(current_weights,warmupTT);
markowitz_initial       = markowitzFcn(current_weights,warmupTT);
robustOptim_initial     = robustOptimFcn(current_weights,warmupTT);

strategyNames = {'Equal Weighted', 'Max Sharpe Ratio', 'Inverse Variance', 'Markowitz Optimization','Robust Optimization'};
assetSymbols = pricesTT.Properties.VariableNames;
initialWeights = [equalWeight_initial(:), maxSharpeRatio_initial(:), inverseVariance_initial(:), markowitz_initial(:), robustOptim_initial(:)];
heatmap(strategyNames, assetSymbols, initialWeights, 'title','Initial Asset Allocations','Colormap', parula);

% Rebalance approximately every 1 month (252 / 12 = 21).
rebalFreq = 21;

% Set the rolling lookback window to be at least 40 days and at most 126
% days (about 6 months).
lookback  = [40 126];

% Use a fixed transaction cost (buy and sell costs are both 0.5% of amount
% traded).
transactionsFixed = 0.005;

% Customize the transaction costs using a function. See the
% variableTransactionCosts function below for an example.
transactionsVariable = @variableTransactionCosts;

% The first two strategies use fixed transaction costs. The equal-weighted
% strategy does not require a lookback window of trailing data, as its
% allocation is fixed.
strat1 = backtestStrategy('Equal Weighted', @equalWeightFcn,...
    'RebalanceFrequency', rebalFreq,...
    'LookbackWindow', 0,...
    'TransactionCosts', transactionsFixed,...
    'InitialWeights', equalWeight_initial);

strat2 = backtestStrategy('Max Sharpe Ratio', @maxSharpeRatioFcn,...
    'RebalanceFrequency', rebalFreq,...
    'LookbackWindow', lookback,...
    'TransactionCosts', transactionsFixed,...
    'InitialWeights', maxSharpeRatio_initial);

% Use variable transaction costs for the remaining strategies.
strat3 = backtestStrategy('Inverse Variance', @inverseVarianceFcn,...
    'RebalanceFrequency', rebalFreq,...
    'LookbackWindow', lookback,...
    'TransactionCosts', @variableTransactionCosts,...
    'InitialWeights', inverseVariance_initial);
strat4 = backtestStrategy('Markowitz Optimization', @markowitzFcn,...
    'RebalanceFrequency', rebalFreq,...
    'LookbackWindow', lookback,...
    'TransactionCosts', transactionsFixed,...
    'InitialWeights', markowitz_initial);
strat5 = backtestStrategy('Robust Optimization', @robustOptimFcn,...
    'RebalanceFrequency', rebalFreq,...
    'LookbackWindow', lookback,...
    'TransactionCosts', transactionsFixed,...
    'InitialWeights', robustOptim_initial);

% Aggregate the strategy objects into an array.
strategies = [strat1, strat2, strat3, strat4, strat5];

% Risk-free rate is 1% annualized
annualRiskFreeRate = 0.01;

% Create the backtesting engine object
backtester = backtestEngine(strategies, 'RiskFreeRate', annualRiskFreeRate)

backtester = runBacktest(backtester, pricesTT, 'Start', warmupPeriod)

summaryByStrategies = summary(backtester)

equityCurve(backtester)

% Transpose the summary table to plot the metrics.
summaryByMetrics = rows2vars(summaryByStrategies);
summaryByMetrics.Properties.VariableNames{1} = 'Strategy'

% Compare the strategy turnover.
names = [backtester.Strategies.Name];
nameLabels = strrep(names,'_',' ');
bar(summaryByMetrics.AverageTurnover)
title('Average Turnover')
ylabel('Daily Turnover (%)')
set(gca,'xticklabel',nameLabels)

strategyName = 'Max_Sharpe_Ratio';
assetAreaPlot (backter, strategyName)

function new_weights = equalWeightFcn(current_weights, pricesTT)
% Equal-weighted portfolio allocation

nAssets = size(pricesTT, 2);
new_weights = ones(1,nAssets);
new_weights = new_weights / sum(new_weights);

end

function new_weights = maxSharpeRatioFcn(current_weights, pricesTT)
% Mean-variance portfolio allocation

nAssets = size(pricesTT, 2);
assetReturns = tick2ret(pricesTT);
% Max 25% into a single asset (including cash)
p = Portfolio('NumAssets',nAssets,...
    'LowerBound',0,'UpperBound',0.1,...
    'LowerBudget',1,'UpperBudget',1);
p = estimateAssetMoments(p, assetReturns{:,:});
new_weights = estimateMaxSharpeRatio(p);

end

function new_weights = inverseVarianceFcn(current_weights, pricesTT) 
% Inverse-variance portfolio allocation

assetReturns = tick2ret(pricesTT);
assetCov = cov(assetReturns{:,:});
new_weights = 1 ./ diag(assetCov);
new_weights = new_weights / sum(new_weights);

end

function new_weights = robustOptimFcn(current_weights, pricesTT) 
% Robust portfolio allocation

nAssets = size(pricesTT, 2);
assetReturns = tick2ret(pricesTT);

Q = cov(table2array(assetReturns));
SIGMAx = diag(diag(Q));

% Robust aversion coefficient
k = 1.1;

% Robust aversion coefficient
lambda = 0.05;

rPortfolio = mean(table2array(assetReturns))';

% Create the optimization problem
pRobust = optimproblem('Description','Robust Portfolio');

% Define the variables
% xRobust - x  allocation vector
xRobust = optimvar('x',nAssets,1,'Type','continuous','LowerBound',0.0,'UpperBound',0.1);
zRobust = optimvar('z','LowerBound',0);

% Define the budget constraint
pRobust.Constraints.budget = sum(xRobust) == 1;

% Define the robust constraint
pRobust.Constraints.robust = xRobust'*SIGMAx*xRobust - zRobust*zRobust <=0;
pRobust.Objective = -rPortfolio'*xRobust + k*zRobust + lambda*xRobust'*Q*xRobust;
x0.x = zeros(nAssets,1);
x0.z = 0;
opt = optimoptions('fmincon','Display','off');
[solRobust,~,~] = solve(pRobust,x0,'Options',opt);
new_weights = solRobust.x;

end

function new_weights = markowitzFcn(current_weights, pricesTT) 
% Robust portfolio allocation

nAssets = size(pricesTT, 2);
assetReturns = tick2ret(pricesTT);

Q = cov(table2array(assetReturns));

% Risk aversion coefficient
lambda = 0.05;

rPortfolio = mean(table2array(assetReturns))';

% Create the optimization problem
pMrkwtz = optimproblem('Description','Markowitz Mean Variance Portfolio ');

% Define the variables
% xRobust - x  allocation vector
xMrkwtz = optimvar('x',nAssets,1,'Type','continuous','LowerBound',0.0,'UpperBound',0.1);

% Define the budget constraint
pMrkwtz.Constraints.budget = sum(xMrkwtz) == 1;

% Define the Markowitz objective
pMrkwtz.Objective = -rPortfolio'*xMrkwtz + lambda*xMrkwtz'*Q*xMrkwtz;
x0.x = zeros(nAssets,1);

opt = optimoptions('quadprog','Display','off');
[solMrkwtz,~,~] = solve(pMrkwtz,x0,'Options',opt);
new_weights = solMrkwtz.x;

end

function [buy, sell] = variableTransactionCosts(deltaPositions)
% Variable transaction cost function
%
% This function is an example of how to compute variable transaction costs.
%
% Compute scaled transaction costs based on the change in market value of
% each asset after a rebalance.  Costs are computed at the following rates:
%
% Buys:
%   $0-$10,000 : 0.5%
%   $10,000+   : 0.35%
% Sells:
%   $0-$1,000  : 0.75%
%   $1,000+    : 0.5%

buy  = zeros(1,numel(deltaPositions));
sell = zeros(1,numel(deltaPositions));

% Buys
idx = 0 < deltaPositions & deltaPositions < 1e4;
buy(idx) = 0.005 * deltaPositions(idx); % 50 basis points
idx = 1e4 <= deltaPositions;
buy(idx) = 0.0035 * deltaPositions(idx); % 35 basis ponits
buy = sum(buy);

% Sells
idx = -1e3 < deltaPositions & deltaPositions < 0;
sell(idx) = 0.0075 * -deltaPositions(idx); % 75 basis points
idx = deltaPositions <= -1e3;
sell(idx) = 0.005 * -deltaPositions(idx); % 50 basis points
sell = sum(sell);

end

function assetAreaPlot(backtester,strategyName)
% Plot the asset allocation as an area plot.

t = backtester.Positions.(strategyName).Time;
positions = backtester.Positions.(strategyName).Variables;
h = area(t,positions);
title(sprintf('%s Positions',strrep(strategyName,'_',' ')));
xlabel('Date');
ylabel('Asset Positions');
datetick('x','mm/dd','keepticks');
xlim([t(1) t(end)])
oldylim = ylim;
ylim([0 oldylim(2)]);
cm = parula(numel(h));
for i = 1:numel(h)
    set(h(i),'FaceColor',cm(i,:));
end
legend(backtester.Positions.(strategyName).Properties.VariableNames)

end