Re: [ojAlgo-user] Markowitz optimisation
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Re: [ojAlgo-user] Markowitz optimisation
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From: Anders P. <an...@op...> - 2008-01-19 01:22:51
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Works now. /Anders
############################################
0.050 <= : 0.350 (-10.003) <= 0.350
0.050 <= : 0.050 (-9.990) <= 0.350
0.050 <= : 0.050 (-9.988) <= 0.350
0.050 <= : 0.050 (-9.988) <= 0.350
0.050 <= : 0.250 (-9.997) <= 0.350
0.050 <= : 0.050 (-9.991) <= 0.350
0.050 <= : 0.050 (-9.995) <= 0.350
0.050 <= : 0.050 (-9.995) <= 0.350
0.050 <= : 0.050 (-9.984) <= 0.350
0.050 <= : 0.050 (-9.988) <= 0.350
Variance: 0.001 (4.270)
1.000 <= Balance: 1.000 <= 1.000
############################################
On 18 jan 2008, at 17.31, Arthur McGibbon wrote:
> Hi Anders,
>
> Here's the test case for zero weightings at default risk aversion
> factor.
> Changing the RAF to "1" works although I'm wondering whether a
> better solution would be with the 0.35% and 0.25% weights swapped
> round. All other assets get a 0.05% weight.
>
> regards
> Arthur
>
> public void testEfficientFrontier2()
> {
> // create expected returns matrix
> PrimitiveMatrix expectedReturnsMatrix = new PrimitiveMatrix(new
> double[][] { {10.003264}, {9.989771}, {9.987513}, {9.988449},
> {9.996579}, {9.990690}, {9.994904}, {9.994514}, {9.984064},
> {9.987534}});
> // create covariance matrix
> PrimitiveMatrix covarianceMatrix = new PrimitiveMatrix(new double
> [][]
> {{6.483565230120298E-4, -1.3344603795915894E-4, -4.610345510893708E-4,
> -7.334405624030001E-4,
> 1.1551383115707195E-5, -0.00104145662863434, -1.0725896685568462E-4, -
> 1.221384153392056E-4, -4.173413644389791E-4, -2.4861043894946935E-4},
> {-1.3344603795915894E-4, 0.0026045957224784455,
> 0.0012394355327235707, 9.243919166568456E-4, -8.653805945112411E-5,
> 8.100239312410631E-4, 4.215960274481846E-4, 5.243272007211247E-4,
> 0.0013062718630332956, 1.4766450293395405E-4},
> {-4.610345510893708E-4, 0.0012394355327235707,
> 0.002361436913752224,
> 0.0020101714731002238, -1.4236763916609785E-5,
> 0.002120395905829043, 5.399158658928662E-4, 5.048790842067473E-4,
> 0.0014855261720730444, 4.841458106181396E-4},
> {-7.334405624030001E-4, 9.243919166568456E-4,
> 0.0020101714731002238,
> 0.0028542819089926895, -4.311102526746861E-6,
> 0.0028465650900869476, 6.242643883624462E-4, 4.086484048798765E-4,
> 0.001647437646316569, 7.58419663970477E-4},
>
> {1.1551383115707195E-5, -8.653805945112411E-5, -1.4236763916609785E-5,
> -4.311102526746861E-6,
> 1.213366124417227E-4, -9.027529241741836E-5,
> 7.241389994693716E-6, -3.166855950737129E-5, -1.2445276374560802E-5, -
> 5.3976919759028745E-5},
> {-0.00104145662863434, 8.100239312410631E-4,
> 0.002120395905829043, 0.0028465650900869476, -9.027529241741836E-5,
> 0.0064756879298965295, 2.8076277564885113E-4,
> 3.6082073553997553E-4, 0.001945238279500792, 0.0012421132342988626},
> {-1.0725896685568462E-4, 4.215960274481846E-4,
> 5.399158658928662E-4, 6.242643883624462E-4, 7.241389994693716E-6,
> 2.8076277564885113E-4, 0.0010121500024739688, 8.206099676659543E-5,
> 1.6129237403855146E-4, 7.550465994733837E-4},
> {-1.221384153392056E-4, 5.243272007211247E-4,
> 5.048790842067473E-4, 4.086484048798765E-4, -3.166855950737129E-5,
> 3.6082073553997553E-4, 8.206099676659543E-5, 4.504461842318998E-4,
> 4.7980942831718363E-4, -4.763223568683059E-5},
> {-4.173413644389791E-4, 0.0013062718630332956,
> 0.0014855261720730444,
> 0.001647437646316569, -1.2445276374560802E-5, 0.001945238279500792,
> 1.6129237403855146E-4, 4.7980942831718363E-4, 0.002228245076175045,
> 3.2083564921169634E-4},
> {-2.4861043894946935E-4, 1.4766450293395405E-4,
> 4.841458106181396E-4, 7.58419663970477E-4, -5.3976919759028745E-5,
> 0.0012421132342988626, 7.550465994733837E-4, -4.763223568683059E-5,
> 3.2083564921169634E-4, 0.0017093327832123186}});
>
> MarketEquilibrium myMarket = new MarketEquilibrium
> (covarianceMatrix);
>
> // create asset variables - cost and weighting constraints
> Variable[] tmpVariables = new Variable
> [expectedReturnsMatrix.getRowDim()];
> for (int i = 0; i < tmpVariables.length; i++)
> {
> tmpVariables[i] = new Variable("");
> tmpVariables[i].setCost(expectedReturnsMatrix.toBigDecimal(i,
> 0).negate());
> // set the constraints on the asset weights
> // require at least a 2% allocation to each asset
> tmpVariables[i].setLowerLimit(new BigDecimal("0.05"));
> // require no more than 80% allocation to each asset
> tmpVariables[i].setUpperLimit(new BigDecimal("0.35"));
> }
>
> QuadraticModel quadModel = new QuadraticModel(tmpVariables);
>
> Expression tmpExpr = quadModel.addCorrelationExpression
> ("Variance", covarianceMatrix.toBigStore().toArray());
> BigDecimal tmpRiskAversion = myMarket.getRiskAversion();
> tmpExpr.setCost(tmpRiskAversion.multiply(BigMath.HALF));
>
> // set balance expression
> tmpExpr = quadModel.addSimpleWeightExpression("Balance");
> tmpExpr.setLowerLimit(BigMath.ONE);
> tmpExpr.setUpperLimit(BigMath.ONE);
>
> // exception here...
> BasicMatrix bm = BigMatrix.FACTORY.buildColumnVector
> (quadModel.getDefaultSolver().getSolution());
>
> System.out.print(bm.toString());
> }
>
>
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