Re: [ojAlgo-user] Markowitz Model Problem

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<p><font size="2"><b>Hi，i am using v25 and here's my code:</b></font></p>

<p><strong><font size="2">/********************************************************************************/</font></strong></p>

<p>import java.math.BigDecimal;
<br />import java.util.List;</p>

<p>import org.ojalgo.finance.portfolio.MarketEquilibrium;
<br />import org.ojalgo.finance.portfolio.MarkowitzModel;
<br />import org.ojalgo.matrix.*;
<br />import org.ojalgo.matrix.store.BigDenseStore;
<br />import org.ojalgo.matrix.store.MatrixStore;</p>

<p>
<br />public class TestMarkowitz {
<br />&nbsp;
<br />&nbsp;public double getCovarance(double[] value1, double[] value2){
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;int n = value1.length;&nbsp;
<br />&nbsp;&nbsp;double averOne = 0;
<br />&nbsp;&nbsp;double averTwo = 0;
<br />&nbsp;&nbsp;for(int i=0;i&lt;n;i++)
<br />&nbsp;&nbsp;{
<br />&nbsp;&nbsp;&nbsp;averOne+=value1[i];
<br />&nbsp;&nbsp;&nbsp;averTwo+=value2[i];
<br />&nbsp;&nbsp;}
<br />&nbsp;&nbsp;averOne /= n;
<br />&nbsp;&nbsp;averTwo /= n;
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;double sum = 0;&nbsp;&nbsp;
<br />&nbsp;&nbsp;for(int i=0;i&lt;n;i++)
<br />&nbsp;&nbsp;&nbsp;sum+=(value1[i]-averOne)*(value2[i]-averTwo);</p>

<p>&nbsp;&nbsp;return sum / (n-1);
<br />&nbsp;}
<br />&nbsp;
<br />&nbsp;public BasicMatrix getACovariances(double[][] returns){
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;int row = returns.length;
<br />&nbsp;&nbsp;int col = returns[0].length;
<br />&nbsp;&nbsp;MatrixStore&lt;BigDecimal&gt; a = BigDenseStore.FACTORY.makeZero(row, col);
<br />&nbsp;&nbsp;BasicMatrix covariances = new BigMatrix(a);
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;for(int i=1;i&lt;=row;i++){
<br />&nbsp;&nbsp;&nbsp;for(int j=i;j&lt;=col;j++){&nbsp;&nbsp;&nbsp;&nbsp;
<br />&nbsp;&nbsp;&nbsp;&nbsp;double tmp = this.getCovarance(returns[i-1], returns[j-1]);
<br />&nbsp;&nbsp;&nbsp;&nbsp;covariances = covariances.set(i-1, j-1,tmp);
<br />&nbsp;&nbsp;&nbsp;&nbsp;covariances = covariances.set(j-1, i-1,tmp);
<br />&nbsp;&nbsp;&nbsp;}
<br />&nbsp;&nbsp;}
<br />&nbsp;&nbsp;return covariances;
<br />&nbsp;}
<br />&nbsp;
<br />&nbsp;public BasicMatrix getAnExpectedExcessReturns(double[][] returns){
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;int row = returns.length;
<br />&nbsp;&nbsp;int col = returns[0].length;
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;MatrixStore&lt;BigDecimal&gt; a = BigDenseStore.FACTORY.makeZero(row, 1);
<br />&nbsp;&nbsp;BasicMatrix expectedExcessReturns = new BigMatrix(a);
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;double riskFreeReturn = 0;
<br />&nbsp;&nbsp;for(int i=1;i&lt;=col;i++)
<br />&nbsp;&nbsp;&nbsp;riskFreeReturn+=returns[row-1][i-1];
<br />&nbsp;&nbsp;riskFreeReturn/=row;
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;for(int i=1;i&lt;=row;i++){
<br />&nbsp;&nbsp;&nbsp;
<br />&nbsp;&nbsp;&nbsp;double tmp = 0;
<br />&nbsp;&nbsp;&nbsp;for(int j=1;j&lt;=col;j++)
<br />&nbsp;&nbsp;&nbsp;&nbsp;tmp+=returns[i-1][j-1];
<br />&nbsp;&nbsp;&nbsp;tmp/=col;
<br />&nbsp;&nbsp;&nbsp;
<br />&nbsp;&nbsp;&nbsp;expectedExcessReturns = expectedExcessReturns.set(i-1, 0, tmp-riskFreeReturn);&nbsp;&nbsp;
<br />&nbsp;&nbsp;}
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;return expectedExcessReturns;
<br />&nbsp;}</p>

<p>&nbsp;public static void main(String[] args)
<br />&nbsp;{&nbsp;&nbsp;
<br />&nbsp;&nbsp;int assetNum=7;
<br />&nbsp;&nbsp;double[][] assets_return={
<br />&nbsp;&nbsp;&nbsp;{-1.5905837442343828E-4, -0.03062360801781757, -0.029857534032853142, -0.011811692726036832, -0.017972310602803136, 0.017338003502626997, 0.0},
<br />&nbsp;&nbsp;&nbsp;{-0.02757158006362653, -0.02562704471101405, -0.011751538891997735, -0.024915062287655786, -0.01684088269454123, 0.013585351447135364, 0.0},
<br />&nbsp;&nbsp;&nbsp;{-0.00699300699300693, -0.033802816901408676, -0.04675196850393671, -0.021166752710376546, -0.007911392405063583, 0.03827751196172254, 0.0},
<br />&nbsp;&nbsp;&nbsp;{-0.007626310772164015, 0.0038424591738713027, 0.02488038277511978, 0.025210084033613675, -0.02003642987249557, -0.09758364312267642, 0.0},
<br />&nbsp;&nbsp;&nbsp;{-0.03965053763440893, 0.021693491952414375, 0.01643835616438392, -0.007412398921833087, 0.01765105227427014, -0.010006671114076025, 0.0},
<br />&nbsp;&nbsp;&nbsp;{-0.017821782178217872, 0.005040322580645311, 0.006018054162487363, 9.008107296569024E-4, 0.002999999999999824, -0.01196410767696908, 0.0},
<br />&nbsp;&nbsp;&nbsp;{2.630552127527583E-4, 2.5867028174649627E-4, 2.3866431891514327E-4, 1.9564035993080523E-4, 2.351016690966669E-4, 1.9070675120065465E-4, 0.0}
<br />&nbsp;&nbsp;};
<br />&nbsp;&nbsp;&nbsp;&nbsp;
<br />&nbsp;&nbsp;TestMarkowitz tm = new TestMarkowitz();
<br />&nbsp;&nbsp;BasicMatrix covariances = tm.getACovariances(assets_return);
<br />&nbsp;&nbsp;BasicMatrix expectedExcessReturns = tm.getAnExpectedExcessReturns(assets_return);&nbsp;&nbsp;
<br />&nbsp;&nbsp;BigDecimal riskAversion = new BigDecimal(1.0);
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;MarketEquilibrium marketEquilibrium = new MarketEquilibrium(covariances,riskAversion);
<br />&nbsp;&nbsp;MarkowitzModel markowitzModel = new MarkowitzModel(marketEquilibrium,expectedExcessReturns);
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;for(int i=0;i&lt;assetNum;i++){
<br />&nbsp;&nbsp;&nbsp;markowitzModel.setLowerLimit(i, new BigDecimal(0.0));
<br />&nbsp;&nbsp;&nbsp;markowitzModel.setUpperLimit(i, new BigDecimal(1.0));
<br />&nbsp;&nbsp;}
<br />&nbsp;&nbsp;List&lt;BigDecimal&gt; re = markowitzModel.getWeights();
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;System.out.println(&quot;=======result====================&quot;);
<br />&nbsp;&nbsp;for(int i=0;i&lt;re.size();i++)
<br />&nbsp;&nbsp;&nbsp;System.out.println(re.get(i));
<br />&nbsp;&nbsp;
<br />&nbsp;&nbsp;return;
<br />&nbsp;}
<br />&nbsp;
<br />}
<br /></p>

<p><strong><font size="2">/********************************************************************************/</font></strong></p>

<p><font size="2"><b>and the result turn out as:</b></font></p>

<p>-0.25
<br />-0.25
<br />-0.25
<br />-0.25
<br />0.75
<br />0.75
<br />0.75</p>

<p><strong><font size="2">/****************************************************************************/</font></strong></p>

<p><strong><font size="2">If you find out any problem, just let me know , thanks a lot!</font></strong></p>
</font>
<p><font size="2">On Thu, 15 Jan 2009 09:20:52 +0100, Anders Peterson wrote 
<br />&gt; You have to send code (snippets) that I can execute. 
<br />&gt; 
<br />&gt; Are you using v25 or the latest from cvs? 
<br />&gt; 
<br />&gt; How do you instantiate the ActiveSetSolver? 
<br />&gt; 
<br />&gt; What's the correct solution? 
<br />&gt; 
<br />&gt; You say that if you change the sign of &quot;C&quot; you get an ok result. 
<br />&gt; What &nbsp;does this mean? Did you make a mistake with the sign, or do 
<br />&gt; you &nbsp;suspect there is a bug in ojAlgo? 
<br />&gt; 
<br />&gt; I need better info regarding what you do, and what the problem is! 
<br />&gt; 
<br />&gt; /Anders 
<br />&gt; 
<br />&gt; On 15 jan 2009, at 06.14, Ruan Weichao wrote: 
<br />&gt; 
<br />&gt; &gt; 
<br />&gt; &gt; Thank you for your response. 
<br />&gt; &gt; 
<br />&gt; &gt; Here's my model's detail: 
<br />&gt; &gt; 
<br />&gt; &gt; [AE]={1.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; &nbsp; 1.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; &nbsp; 1.0} 
<br />&gt; &gt; 
<br />&gt; &gt; 
<br />&gt; &gt; [be]={1.0} 
<br />&gt; &gt; 
<br />&gt; &gt; 
<br />&gt; &gt; [Q]={3.048907897157133E-4, 1.6671472561019247E-4, &nbsp; 
<br />&gt; &gt; 4.4500080981934345E-4, -5.389129745055723E-4, &nbsp; 
<br />&gt; &gt; -2.6090705011393183E-4, -1.2633284900760366E-4, &nbsp; 
<br />&gt; &gt; -6.485428846447651E-7}, 
<br />&gt; &gt; {1.6671472561019247E-4, 2.341985572849691E-4, &nbsp; 
<br />&gt; &gt; 2.9113916450678265E-4, -4.5760873539850514E-4, &nbsp; 
<br />&gt; &gt; 1.3078636134987255E-5, -2.354289901013046E-5, -7.578030042426654E-7}, 
<br />&gt; &gt; {4.4500080981934345E-4, 2.9113916450678265E-4, 7.46023915996829E-4, &nbsp; 
<br />&gt; &gt; -0.0010247176498305568, -2.6745504327902895E-4, &nbsp; 
<br />&gt; &gt; -1.6563544154823496E-4, -8.293698990696063E-7}, 
<br />&gt; &gt; {-5.389129745055723E-4, -4.5760873539850514E-4, &nbsp; 
<br />&gt; &gt; -0.0010247176498305568, 0.001754169535149865, &nbsp; 
<br />&gt; &gt; 2.0293065310212377E-4, 2.1401092557826588E-4, 1.0252846778608953E-7}, 
<br />&gt; &gt; {-2.6090705011393183E-4, 1.3078636134987255E-5, &nbsp; 
<br />&gt; &gt; -2.6745504327902895E-4, 2.0293065310212377E-4, &nbsp; 
<br />&gt; &gt; 4.632320892679136E-4, 1.7969731066037214E-4, 2.4953495129362833E-8}, 
<br />&gt; &gt; {-1.2633284900760366E-4, -2.354289901013046E-5, &nbsp; 
<br />&gt; &gt; -1.6563544154823496E-4, 2.1401092557826588E-4, &nbsp; 
<br />&gt; &gt; 1.7969731066037214E-4, 8..346410612364995E-5, -7.02099350897589E-8}, 
<br />&gt; &gt; {-6.485428846447651E-7, -7.578030042426654E-7, &nbsp; 
<br />&gt; &gt; -8.293698990696063E-7, 1.0252846778608953E-7, &nbsp; 
<br />&gt; &gt; 2.4953495129362833E-8, -7.02099350897589E-8, 8.367244992498656E-9}} 
<br />&gt; &gt; 
<br />&gt; &gt; [c]={{0.010638291263564232}, 
<br />&gt; &gt; {0.013500370827906071}, 
<br />&gt; &gt; {0.011390037735101773}, 
<br />&gt; &gt; {0.010385042339767682}, 
<br />&gt; &gt; {3.812208389845893E-4}, 
<br />&gt; &gt; {0.002315505853720011}, 
<br />&gt; &gt; {0.0}} 
<br />&gt; &gt; 
<br />&gt; &gt; 
<br />&gt; &gt; [AI]={{1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 1.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 1.0}, 
<br />&gt; &gt; {-1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; -1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; -1.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;-1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; -1.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; -1.0, &nbsp; &nbsp; &nbsp; 0.0}, 
<br />&gt; &gt; {0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp;0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; 0.0, &nbsp; &nbsp; &nbsp; -1.0}} 
<br />&gt; &gt; 
<br />&gt; &gt; [bi]={{1.0}, 
<br />&gt; &gt; {1.0}, 
<br />&gt; &gt; {1.0}, 
<br />&gt; &gt; {1.0}, 
<br />&gt; &gt; {1.0}, 
<br />&gt; &gt; {1.0}, 
<br />&gt; &gt; {1.0}, 
<br />&gt; &gt; {0.0}, 
<br />&gt; &gt; {0.0}, 
<br />&gt; &gt; {0.0}, 
<br />&gt; &gt; {0.0}, 
<br />&gt; &gt; {0.0}, 
<br />&gt; &gt; {0.0}, 
<br />&gt; &gt; {0.0}} 
<br />&gt; &gt; 
<br />&gt; &gt; And here's the result: 
<br />&gt; &gt; 
<br />&gt; &gt; -0.25 
<br />&gt; &gt; -0.25 
<br />&gt; &gt; -0.25 
<br />&gt; &gt; -0.25 
<br />&gt; &gt; 0.75 
<br />&gt; &gt; 0.75 
<br />&gt; &gt; 0.75 
<br />&gt; &gt; 
<br />&gt; &gt; And i found that if i change the nagetive ExceptedReturn to &nbsp; 
<br />&gt; &gt; positive, the result will be fine. 
<br />&gt; &gt; 
<br />&gt; &gt; 
<br />&gt; &gt; On Wed, 14 Jan 2009 09:27:53 +0100, Anders Peterson wrote 
<br />&gt; &gt; &gt; How small are the negative values? 
<br />&gt; &gt; &gt; 
<br />&gt; &gt; &gt; Give me a unit test (something that I can easily integrate with the 
<br />&gt; &gt; &gt; existing junit tests) that shows what you do, and where/how you 
<br />&gt; &gt; &gt; think &nbsp;it goes wrong. 
<br />&gt; &gt; &gt; 
<br />&gt; &gt; &gt; /Anders 
<br />&gt; &gt; &gt; 
<br />&gt; &gt; &gt; On 14 jan 2009, at 03.58, Ruan Weichao wrote: 
<br />&gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; Hi, 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; I use the &nbsp;Markowitz model to calculate the optimize some assets' 
<br />&gt; &gt; &gt; &gt; weights, 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; and set all the assets' lowerlimit with Zero and set all the 
<br />&gt; &gt; &gt; &gt; upperlimits with 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; One, but it turns out with some negative value which are little 
<br />&gt; &gt; &gt; &gt; than Zero, it 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; seems that the lowerlimit does not work. 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; So I want to ask what should I do it to confirm the lowerlimit? 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; -- 
<br />&gt; &gt; &gt; &gt; Best regards 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; weichao 
<br />&gt; &gt; &gt; &gt; 
<br />&gt; &gt; &gt; &gt; &nbsp; 
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Re: [ojAlgo-user] Markowitz Model Problem

Mathematics, linear algebra and optimisation

Re: [ojAlgo-user] Markowitz Model Problem