[ojAlgo-user] 答复: Report A Bug Of ojalgo finace library

[yifa w. <fag...@ho...>]

hi,


I try to use ojalgo to do a portfolio optimization. The target is find an efficient frontier for 15 securities or more. I generate random returns for simulation. However, when I run the program, I get the state as "UNEXPLORED". Usually, we get OPTIMAL.  What does this mean? Is it possible to get the optimal weight for more than 50 securities?


Thanks.

Jerry





________________________________
发件人: lvz...@gt... <lvz...@gt...>
发送时间: 2017年5月8日 下午 8:20:21
收件人: oja...@li...
主题: [ojAlgo-user] Report A Bug Of ojalgo finace library


Hi,
      I have a problem when calculate best invest group by using markowitzModel in ojalgo with code below(for test);




          for(int j = 0; j < 100; j ++)
         { Builder<PrimitiveMatrix> tmpBuilder = PrimitiveMatrix.FACTORY.getBuilder(2, 2);
    tmpBuilder.add(0, 0, 0.040000);
    tmpBuilder.add(0, 1, 0.1000);
    tmpBuilder.add(1, 0, 0.1000);
    tmpBuilder.add(1, 1, 0.250000);
        BasicMatrix covariances = tmpBuilder.build();
        tmpBuilder = PrimitiveMatrix.FACTORY.getBuilder(2);
        tmpBuilder.add(0, 0.20000);
        tmpBuilder.add(1, 0.40000);
        BasicMatrix expectedExcessReturns = tmpBuilder.build();

       MarketEquilibrium equilibrium = new MarketEquilibrium(covariances);
        MarkowitzModel markowitzModel = new MarkowitzModel(equilibrium, expectedExcessReturns);
        markowitzModel.setShortingAllowed(false);
       // markowitzModel.optimiser().validate(true);
        markowitzModel.setTargetReturn(new BigDecimal(0.2 + (0.002 * j)));
        for (int i = 0; i < 2; i++) {
            markowitzModel.setLowerLimit(i, new BigDecimal(0.00000));
            markowitzModel.setUpperLimit(i, new BigDecimal(1.00000));
        }
        System.out.println(0.2 + (0.002 * j) + "," + markowitzModel.getReturnVariance() + " , "  +  markowitzModel.getMeanReturn()  + "," + markowitzModel.getWeights());


}


      when TargetReturn equal to 0.206,Variance equal to 0.04 , weights equal to [1.0, 0.0]
      when TargetReturn  equal to  0.208, Variance equal to 0.25,weights equal to [0.0, 1.0]
      Below  Relationship of TargetReturn And Target Variance（Y-Axis TargetReturn,X-AXis standard deviation）



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