Re: [ojAlgo-user] Markowitz optimisation
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Re: [ojAlgo-user] Markowitz optimisation
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From: Anders P. <an...@op...> - 2008-01-17 17:22:10
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I assume this is the solution you want:
############################################
0.020 <= : 0.020 (0.007) <= 0.800
0.020 <= : 0.020 (0.004) <= 0.800
0.020 <= : 0.020 (0.004) <= 0.800
0.020 <= : 0.020 (0.006) <= 0.800
0.020 <= : 0.800 (-0.001) <= 0.800
0.020 <= : 0.060 (0.000) <= 0.800
0.020 <= : 0.020 (0.006) <= 0.800
0.020 <= : 0.020 (0.004) <= 0.800
0.020 <= : 0.020 (0.002) <= 0.800
Variance: 0.000 (4.270)
1.000 <= Balance: 1.000 <= 1.000
############################################
There was a problem with the ActiveSetSolver that I've fixed now.
You'll have to wait some days for a new release (or get the source
from CVS).
/Anders
On 17 jan 2008, at 15.58, Arthur McGibbon wrote:
> Hi Anders,
>
> Here's an example that gives an exception. Hopefully this code
> will retain a semblance of formatting on emailing.
>
> public class TestStats extends TestCase
> {
> public void testEfficientFrontier()
> {
> // create expected returns matrix
> PrimitiveMatrix expectedReturnsMatrix = new PrimitiveMatrix(new
> double[][] { { -0.007155942261937039 },
> { -0.003665887902733331 }, { -0.004130184341000032 },
> { -0.005639860515211043 },
> { 0.0007211966666666817 }, { 0.0003258225000000077 },
> { -0.005754291666666666 },
> { -0.004264291666666667 }, { -0.0017500000000000003 } });
> // create covariance matrix
> PrimitiveMatrix covarianceMatrix = new PrimitiveMatrix(new double
> [][] {
> { 0.001561410465201063, 0.00006366128201274021,
> -0.0001323096896759724, 0.0000909074052724909,
> 0.00003172000033558704, 0.00001955483223848944,
> -0.00013771504482647386, -0.00004858457275314645,
> -0.000012954723060403266 },
> { 0.00006366128201274021, 0.00016419786524761803,
> -0.00001566288911558343, -0.00008688646089751923,
> 0.0000027349925543017186, 0.0000012356159598500247,
> -0.000024367796639005863, -0.000017576048221096555,
> -0.0000070052245518771815 },
> { -0.0001323096896759724, -0.00001566288911558343,
> 0.0001430155985985913, 0.00007675339168559199,
> -0.00007600590426518823, 0.000032976538909267937,
> 0.00009520305608240259, 0.00007373075639042642,
> -0.000007477057858706954 },
> { 0.0000909074052724909, -0.00008688646089751923,
> 0.00007675339168559199, 0.000967519991100896,
> -0.0000533460293834595, 0.00008665760416026126,
> 0.00014591175388747613, 0.0001232364989586903,
> 0.00011097998789484925 },
> { 0.00003172000033558704, 0.0000027349925543017186,
> -0.00007600590426518823, -0.0000533460293834595,
> 0.000025267064307337795, -0.00003089584520279407,
> -0.00005593123237578969, -0.000017013960349712132,
> 0.0000013056146551724419 },
> { 0.00001955483223848944, 0.0000012356159598500247,
> 0.000032976538909267937, 0.00008665760416026126,
> -0.00003089584520279407, 0.0001625499447274783,
> 0.00008242949058588471, 0.00010276895784859992,
> 0.0000005898510775862205 },
> { -0.00013771504482647386, -0.000024367796639005863,
> 0.00009520305608240259, 0.00014591175388747613,
> -0.00005593123237578969, 0.00008242949058588471,
> 0.000560956958802083, 0.0002838794236862429,
> 0.00009143821659482758 },
> { -0.00004858457275314645, -0.000017576048221096555,
> 0.00007373075639042642, 0.0001232364989586903,
> -0.000017013960349712132, 0.00010276895784859992,
> 0.0002838794236862429, 0.00021068964250359204,
> 0.00004461044181034483 },
> { -0.000012954723060403266, -0.0000070052245518771815,
> -0.000007477057858706954, 0.00011097998789484925,
> 0.0000013056146551724419, 0.0000005898510775862205,
> 0.00009143821659482758, 0.00004461044181034483,
> 0.00006761920797413792 } });
>
> 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("0.02"));
> // require no more than 80% allocation to each asset
> tmpVariables[i].setUpperLimit(new BigDecimal("0.80"));
> }
>
> 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...
> BigMatrix.FACTORY.buildColumnVector(quadModel.getDefaultSolver
> ().getSolution());
> }
> }
>
>
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