Re: [ojAlgo-user] Markowitz optimisation

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

There's a fix in CVS.

There seems to always be a way to end up in a pattern repeating loop.  
This time I introduced an element of randomness in the (de)activation  
strategy (gave up on being clever).

/Anders

On 8 feb 2008, at 15.12, Arthur McGibbon wrote:

> Hi Anders,
>
> Sorry - but I've got another example of a NullPointerException.
>
> Thanks for patching all of these - it's taken a while to get  
> another one.  I'd give you all the examples at once but they're  
> dependent on previous optimisation results.
>
> regards
> Arthur
>
> public void testojAlgo6()
> 	{
> 		// create expected returns matrix
> 		PrimitiveMatrix expectedReturnsMatrix = new PrimitiveMatrix(new  
> double[][] { {9.997829}, {10.008909}, {10.010849}, {9.998919},  
> {10.055549}, {9.999127}, {9.999720}, {10.049002}, {9.988769},  
> {9.990095}});
> 		// create covariance matrix
> 		PrimitiveMatrix covarianceMatrix = new PrimitiveMatrix(new double 
> [][] {{0.014661954677318977,	3.459112088561122E-4,	 
> 0.007798752920910871,	0.0020921425081866503,	0.001846944297640248,	 
> 1.0531906931335766E-4,	-2.7515614291198E-4,	0.0034083900074454894,	 
> 1.1859491261103433E-4,	-0.0027421673864628264},
> 				{3.459112088561122E-4,	0.008695862475003915,	 
> 0.004154360841751649,	-2.661685231819661E-4,	-0.0015999007544258263,	3 
> .590680217774603E-4,	-0.00186976624370318,	 
> 0.0010975416828213752,	-5.512038393911129E-4,	-0.0010605923775744853},
> 				{0.007798752920910871,	0.004154360841751649,	 
> 0.032945930970836965,	 
> 0.0037716078815399324,	-2.2919474365382624E-4,	 
> 3.3938035033219876E-4,	-0.0015613122026082874,	 
> 0.0010975697179894332,	2.296422665244149E-4,	-0.001709517941787044},
> 				{0.0020921425081866503,	-2.661685231819661E-4,	 
> 0.0037716078815399324,	0.0057162979859706736,	5.573137056500744E-4,	 
> 4.91132887765294E-4,	-9.94830474250937E-4,	 
> 8.331708084069932E-4,	-6.595917138470072E-4,	-0.0018693519327569541},
> 				 
> {0.001846944297640248,	-0.0015999007544258263,	-2.2919474365382624E-4, 
> 	5.573137056500744E-4,	 
> 0.03230071314144326,	-2.2320789666419312E-4,	-2.2639506820057415E-4,	0 
> .010695663287043154,	0.0014569847730040847,	0.002160537177809949},
> 				{1.0531906931335766E-4,	3.590680217774603E-4,	 
> 3.3938035033219876E-4,	4.91132887765294E-4,	-2.2320789666419312E-4,	 
> 0.0017540170708301957,	5.153195618913916E-5,	 
> 7.339825618468765E-4,	-9.309096233432093E-6,	-1.814362059740286E-4},
> 				 
> {-2.7515614291198E-4,	-0.00186976624370318,	-0.0015613122026082874,	-9 
> .94830474250937E-4,	-2.2639506820057415E-4,	5.153195618913916E-5,	 
> 0.00809348822665732,	-0.0017672866424053742,	3.058672988166145E-4,	 
> 0.001201578905822851},
> 				{0.0034083900074454894,	0.0010975416828213752,	 
> 0.0010975697179894332,	8.331708084069932E-4,	0.010695663287043154,	 
> 7.339825618468765E-4,	-0.0017672866424053742,	0.013913761913235494,	 
> 0.0012785124957521252,	5.298368056593439E-4},
> 				{1.1859491261103433E-4,	-5.512038393911129E-4,	 
> 2.296422665244149E-4,	-6.595917138470072E-4,	 
> 0.0014569847730040847,	-9.309096233432093E-6,	3.058672988166145E-4,	 
> 0.0012785124957521252,	0.004650801896027841,	5.437156659657787E-4},
> 				 
> {-0.0027421673864628264,	-0.0010605923775744853,	-0.001709517941787044 
> ,	-0.0018693519327569541,	 
> 0.002160537177809949,	-1.814362059740286E-4,	0.001201578905822851,	 
> 5.298368056593439E-4,	5.437156659657787E-4,	0.007359495478781133}});
>
> 		// 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].setContribution 
> (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.setContribution(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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