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From $ROOTSYS/tutorials/quadp/portfolio.C

#include "Riostream.h"
#include "TCanvas.h"
#include "TFile.h"
#include "TMath.h"
#include "TTree.h"
#include "TArrayF.h"
#include "TH1.h"
#include "TF1.h"
#include "TLegend.h"
#include "TSystem.h"

#include "TMatrixD.h"
#include "TMatrixDSym.h"
#include "TVectorD.h"
#include "TQpProbDens.h"
#include "TGondzioSolver.h"

//+ This macro shows in detail the use of the quadratic programming package quadp .
// Running this macro :
//     .x portfolio.C+
// or  gSystem->Load("libQuadp"); .L portFolio.C+; portfolio()
// Let's first review what we exactly mean by "quadratic programming" :
// We want to minimize the following objective function :
//   c^T x + ( 1/2 ) x^T Q x    wrt. the vector x
//   c is a vector and Q a symmetric positive definite matrix
// You might wonder what is so special about this objective which is quadratic in
// the unknowns, that can not be done by Minuit/Fumili . Well, we have in addition
// the following boundary conditions on x:
//          A x =  b
//  clo <=  C x <= cup
//  xlo <=    x <= xup  ,  where A and C are arbitray matrices and the rest are vectors
// Not all these constraints have to be defined . Our example will only use xlo, A and b
// Still, this could be handled by a general non-linear minimizer like Minuit by introducing
// so-called "slack" variables . However, quadp is tailored to objective functions not more
// complex than being quadratic . This allows usage of solving techniques which are even
// stable for problems involving for instance 500 variables, 100 inequality conditions
// and 50 equality conditions .
// Enough said about quadratic programming, let's return to our example .
// Suppose, after a long day of doing physics, you have a look at your investments and
// realize that an early retirement is not possible, given the returns of your stocks .
// So what now ? ROOT to the rescue ......
// In 1990 Harry Markowitz was awarded the Noble prize for economics : " his work provided new tools
// for weighing the risks and rewards of different investments and for valuing corporate stocks and bonds" .
// In plain English, he developed the tools to balance greed and fear, we want the maximum return 
// with the minimum amount of risk. Our stock portfolio should be at the "Efficient Frontier",
// see .
// To quantify better the risk we are willing to take, we define a utility function U(x) . It describes
// as a function of our total assets x, our "satisfaction" . A common choice is 1-exp(-k*x) (the reason for
// the exponent will be clear later)  . The parameter k is the risk-aversion factor . For small values of k
// the satisfaction is small for small values of x; by increasing x the satisfaction can still be increased
// significantly . For large values of k, U(x) increases rapidly to 1, there is no increase in satisfaction
// for additional dollars earned .
// In summary : small k ==> risk-loving investor
//              large k ==> risk-averse investor
// Suppose we have for nrStocks the historical daily returns r = closing_price(n) - closing_price(n-1) .
// Define a vector x of length of nrStocks, which contains the fraction of our money invested in
// each stock . We can calculate the average daily return z of our portfolio and its variance using
// the portfolio covariance Covar :
//  z = r^T x   and var = x^T Covar x
// Assuming that the daily returns have a Normal distribution, N(x), so will z with mean r^T x
// and variance x^T Covar x
// The expected value of the utility function is : E(u(x)) = Int (1-exp(-k*x) N(x) dx
//                                                         = 1-exp(-k (r^T x - 0.5 k x^T Covar x) )
// Its value is maximized by maximizing  r^T x -0.5 k x^T Covar x
// under the condition sum (x_i) = 1, meaning we want all our money invested and
// x_i >= 0 , we can not "short" a stock
// For 10 stocks we got the historical daily data for Sep-2000 to Jun-2004:
// GE   : General Electric Co
// SUNW : Sun Microsystems Inc
// QCOM : Qualcomm Inc
// BRCM : Broadcom Corp
// TYC  : Tyco International Ltd
// IBM  : International Business Machines Corp
// AMAT : Applied Materials Inc
// C    : Citigroup Inc
// PFE  : Pfizer Inc
// HD   : Home Depot Inc
// We calculate the optimal portfolio for 2.0 and 10.0 .
// Food for thought :
// - We assumed that the stock returns have a Normal distribution . Check this assumption by 
//   histogramming the stock returns !
// - We used for the expected return in the objective function, the flat average over a time
//   period . Investment firms will put significant resources in improving the return predicton .
// - If you want to trade significant number of shares, several other considerations have
//   to be taken into account :
//    +  If you are going to buy, you will drive the price up (so-called "slippage") .
//       This can be taken into account by adding terms to the objective
//       (Google for "slippage optimization")
//    +  FTC regulations might have to be added to the inequality constraints 
// - Investment firms do not want to be exposed to the "market" as defined by a broad
//   index like the S&P and "hedge" this exposure away . A perfect hedge this can be added
//    as an equality constrain, otherwise add an inequality constrain .
//Author: Eddy Offermann

const Int_t nrStocks = 10;
static const Char_t *stocks[] =

class TStockDaily  {
  Int_t fDate;
  Int_t fOpen;     // 100*open_price
  Int_t fHigh;     // 100*high_price
  Int_t fLow;      // 100*low_price
  Int_t fClose;    // 100*close_price
  Int_t fVol;
  Int_t fCloseAdj; // 100*close_price adjusted for splits and dividend

  TStockDaily() {
     fDate = fVol = fOpen = fHigh = fLow = fClose = fCloseAdj = 0; 
  virtual ~TStockDaily() {}


Double_t RiskProfile(Double_t *x, Double_t *par) {
  Double_t riskFactor = par[0];
  return 1-TMath::Exp(-riskFactor*x[0]);

TArrayF &StockReturn(TFile *f,const TString &name,Int_t sDay,Int_t eDay)
  TTree *tDaily = (TTree*)f->Get(name);
  TStockDaily *data = 0;
  TBranch *b_closeAdj = tDaily->GetBranch("fCloseAdj");
  TBranch *b_date     = tDaily->GetBranch("fDate");
  //read only the "adjusted close" branch for all entries
  const Int_t nrEntries = (Int_t)tDaily->GetEntries();
  TArrayF closeAdj(nrEntries);
  for (Int_t i = 0; i < nrEntries; i++) {
    if (data->fDate >= sDay && data->fDate <= eDay)
#ifdef __CINT__
      closeAdj.AddAt(data->fCloseAdj/100. , i );
      closeAdj[i] = data->fCloseAdj/100.;

  TArrayF *r = new TArrayF(nrEntries-1);
  for (Int_t i = 1; i < nrEntries; i++)
//    (*r)[i-1] = closeAdj[i]-closeAdj[i-1];
#ifdef __CINT__
    (*r)[i-1] = closeAdj[i]/closeAdj[i-1];

  return *r;

#ifndef __MAKECINT__
TVectorD OptimalInvest(Double_t riskFactor,TVectorD r,TMatrixDSym Covar)
// what the quadratic programming package will do:
//  minimize    c^T x + ( 1/2 ) x^T Q x       
//  subject to                A x  = b  
//                    clo <=  C x <= cup
//                    xlo <=    x <= xup
// what we want :
//  maximize    c^T x - k ( 1/2 ) x^T Q x
//  subject to        sum_x x_i = 1
//                    0 <= x_i

  // We have nrStocks weights to determine,
  // 1 equality- and 0 inequality- equations (the simple square boundary
  // condition (xlo <= x <= xup) does not count)

  const Int_t nrVar     = nrStocks;
  const Int_t nrEqual   = 1;
  const Int_t nrInEqual = 0;

  // flip the sign of the objective function because we want to maximize
  TVectorD    c = -1.*r;
  TMatrixDSym Q = riskFactor*Covar;

  // equality equation
  TMatrixD A(nrEqual,nrVar); A = 1;
  TVectorD b(nrEqual); b = 1;

  // inequality equation
  // - although not applicable in the current situatio since nrInEqual = 0, one
  //   has to specify not only clo and cup but also an index vector iclo and icup,
  //   whose values are either 0 or 1 . If iclo[j] = 1, the lower boundary condition 
  //   is active on x[j], etc. ...

  TMatrixD C   (nrInEqual,nrVar);
  TVectorD clo (nrInEqual);
  TVectorD cup (nrInEqual);
  TVectorD iclo(nrInEqual);
  TVectorD icup(nrInEqual);

  // simple square boundary condition : 0 <= x_i, so only xlo is relevant .
  // Like for clo and cup above, we have to define an index vector ixlo and ixup .
  // Since each variable has the lower boundary, we can set the whole vector
  // ixlo = 1

  TVectorD xlo (nrVar); xlo  = 0;
  TVectorD xup (nrVar); xup  = 0;
  TVectorD ixlo(nrVar); ixlo = 1;
  TVectorD ixup(nrVar); ixup = 0;

  // setup the quadratic programming problem . Since a small number of variables are
  // involved and "Q" has everywhere entries, we chose the dense version "TQpProbDens" .
  // In case of a sparse formulation, simply replace all "Dens" by "Sparse" below and
  // use TMatrixDSparse instead of TMatrixDSym and TMatrixD

  TQpProbDens *qp = new TQpProbDens(nrVar,nrEqual,nrInEqual);

  // stuff all the matrices/vectors defined above in the proper places

  TQpDataDens *prob = (TQpDataDens *)qp->MakeData(c,Q,xlo,ixlo,xup,ixup,A,b,C,clo,iclo,cup,icup);

  // setup the nrStock variables, vars->fX will contain the final solution

  TQpVar      *vars  = qp->MakeVariables(prob);
  TQpResidual *resid = qp->MakeResiduals(prob);

  // Now we have to choose the method of solving, either TGondzioSolver or TMehrotraSolver
  // The Gondzio method is more sophisticated and therefore numerically more involved
  // If one want the Mehrotra method, simply replace "Gondzio" by "Mehrotra" .

  TGondzioSolver *s = new TGondzioSolver(qp,prob);
  const Int_t status = s->Solve(prob,vars,resid);

  const TVectorD weight = vars->fX;

  delete qp; delete prob; delete vars; delete resid; delete s;
  if (status != 0) {
    cout << "Could not solve this problem." <<endl;
    return TVectorD(nrStocks);

  return weight;

void portfolio()
  const Int_t sDay = 20000809;
  const Int_t eDay = 20040602;

  const char *fname = "stock.root";
  TFile *f = 0;
  if (!gSystem->AccessPathName(fname)) {
     f = TFile::Open(fname);
  } else {
     printf("accessing %s file from\n",fname);
     f = TFile::Open(Form("",fname));
  if (!f) return;

  TArrayF *data = new TArrayF[nrStocks];
  for (Int_t i = 0; i < nrStocks; i++) {
    const TString symbol = stocks[i];
    data[i] = StockReturn(f,symbol,sDay,eDay);

  const Int_t nrData = data[0].GetSize();

  TVectorD r(nrStocks);
  for (Int_t i = 0; i < nrStocks; i++)
    r[i] = data[i].GetSum()/nrData;

  TMatrixDSym Covar(nrStocks);
  for (Int_t i = 0; i < nrStocks; i++) {
    for (Int_t j = 0; j <= i; j++) {
      Double_t sum = 0.;
      for (Int_t k = 0; k < nrData; k++)
        sum += (data[i][k]-r[i])*(data[j][k]-r[j]);
      Covar(i,j) = Covar(j,i) = sum/nrData;

  const TVectorD weight1 = OptimalInvest(2.0,r,Covar);
  const TVectorD weight2 = OptimalInvest(10.,r,Covar);

  cout << "stock     daily  daily   w1     w2" <<endl;
  cout << "symb      return sdv              " <<endl;
  for (Int_t i = 0; i < nrStocks; i++)
    printf("%s\t: %.3f  %.3f  %.3f  %.3f\n",stocks[i],r[i],TMath::Sqrt(Covar[i][i]),weight1[i],weight2[i]);

  TCanvas *c1 = new TCanvas("c1","Portfolio Optimizations",10,10,800,900);

  // utility function / risk profile


  TF1 *f1 = new TF1("f1",RiskProfile,0,2.5,1);
  TF1 *f2 = new TF1("f2",RiskProfile,0,2.5,1);

  TLegend *legend1 = new TLegend(0.50,0.65,0.70,0.82);

  // vertical bar chart of portfolio distribution

  TH1F *h1 = new TH1F("h1","Portfolio Distribution",nrStocks,0,0);
  TH1F *h2 = new TH1F("h2","Portfolio Distribution",nrStocks,0,0);
  for (Int_t i = 0; i < nrStocks; i++) {


  TLegend *legend2 = new TLegend(0.50,0.65,0.70,0.82);
  legend2->AddEntry(h1,"high risk","f");
  legend2->AddEntry(h2,"low  risk","f");