Re: [ojAlgo-user] Markowitz optimisation

[Anders P. <an...@op...>]

I've patched the ActiveSetSolver, again...

Please verify that that you get correct solutions (at least that they  
seem reasonable).

/Anders


On 4 feb 2008, at 12.57, Arthur McGibbon wrote:

> Hi Anders,
>
> I've been using the new version for a while and it has been working  
> well on many different constraints - thanks for all the work you've  
> put in here.
>
> Unfortunately I've got another issue with NullPointerExceptions.   
> I'd run through the code myself but I don't understand the maths  
> behind the constraints - hence my use of your library.
>
> Could you have another look please
>
> regards
> Arthur
>
>
>
>
>
> public void testojAlgo()
> 	{
> 		// create expected returns matrix
> 		PrimitiveMatrix expectedReturnsMatrix = new PrimitiveMatrix(new  
> double[][] { {9.994620}, {10.011389}, {10.004353}, {9.998293},  
> {10.056851}, {9.997920}, {9.999011}, {10.050971}, {9.989124},  
> {9.989912}});
> 		// create covariance matrix
> 		PrimitiveMatrix covarianceMatrix = new PrimitiveMatrix(new double 
> [][] {{0.014531344652473037,	4.444675045533674E-4,	 
> 0.007234717654072837,	-9.455312097865225E-4,	0.0016345464996349748,	 
> 1.5256808879495097E-4,	0.00226325818749439,	 
> 0.003534367267672946,	-4.2669306842991344E-5,	6.902267133060073E-5},
> 			{4.444675045533674E-4,	0.008511422662647488,	 
> 0.0039821105759899845,	 
> 5.543408872612397E-4,	-0.0015797828516888929,	 
> 1.3505400134130176E-4,	-1.5215492836142527E-4,	 
> 9.381119889780555E-4,	-4.5861204247023084E-4,	-2.4226694503921645E-5},
> 			{0.007234717654072837,	0.0039821105759899845,	 
> 0.031037646466036784,	-0.0022701157440735394,	-3.187028053841407E-4,	5 
> .182461519304137E-4,	-3.681340242039795E-4,	0.001526984686166616,	 
> 1.603885118040309E-4,	-1.359858314115312E-4},
> 			{-9.455312097865225E-4,	 
> 5.543408872612397E-4,	-0.0022701157440735394,	0.005637141895898889,	 
> 7.89377521930992E-4,	 
> 5.004781934410127E-4,	-9.79221967172284E-4,	-2.912861228906251E-4,	 
> 7.842012412867984E-4,	0.0010866808807429532},
> 			 
> {0.0016345464996349748,	-0.0015797828516888929,	-3.187028053841407E-4, 
> 	7.89377521930992E-4,	0.03263062480163135,	6.041130577612135E-5,	 
> 6.883489096710362E-4,	0.010830183513887228,	0.0016425608963272292,	 
> 0.002481787652249504},
> 			{1.5256808879495097E-4,	1.3505400134130176E-4,	 
> 5.182461519304137E-4,	5.004781934410127E-4,	6.041130577612135E-5,	 
> 0.001733612375709255,	2.8742157640452992E-5,	-3.654534740999083E-4,	 
> 9.896178753749563E-5,	-1.703972415991329E-5},
> 			 
> {0.00226325818749439,	-1.5215492836142527E-4,	-3.681340242039795E-4,	- 
> 9.79221967172284E-4,	6.883489096710362E-4,	2.8742157640452992E-5,	 
> 0.008167191690212253,	-0.0010075092076978207,	-4.293010139199468E-4,	- 
> 6.615640978331292E-4},
> 			{0.003534367267672946,	9.381119889780555E-4,	 
> 0.001526984686166616,	-2.912861228906251E-4,	 
> 0.010830183513887228,	-3.654534740999083E-4,	-0.0010075092076978207,	0 
> .013796198054188104,	0.0013541164478127973,	-2.2401086720669167E-5},
> 			{-4.2669306842991344E-5,	-4.5861204247023084E-4,	 
> 1.603885118040309E-4,	7.842012412867984E-4,	0.0016425608963272292,	 
> 9.896178753749563E-5,	-4.293010139199468E-4,	0.0013541164478127973,	 
> 0.004743485149287524,	0.0011464293217708277},
> 			 
> {6.902267133060073E-5,	-2.4226694503921645E-5,	-1.359858314115312E-4,	 
> 0.0010866808807429532,	 
> 0.002481787652249504,	-1.703972415991329E-5,	-6.615640978331292E-4,	-2 
> .2401086720669167E-5,	0.0011464293217708277,	0.007398229661528494}});
>
> 		// 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 8% allocation to each asset
> 			tmpVariables[i].setLowerLimit(new BigDecimal("0.08"));
> 			// require no more than 12% allocation to each asset
> 			tmpVariables[i].setUpperLimit(new BigDecimal("0.12"));
> 		}
>
> 		QuadraticModel quadModel = new QuadraticModel(tmpVariables);
>
> 		Expression tmpExpr = quadModel.addCorrelationExpression 
> ("Variance", covarianceMatrix.toBigStore().toArray());
> 		BigDecimal riskAversion = BigMath.PI.multiply(BigMath.E);
> 		tmpExpr.setCost(riskAversion.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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