Examples with PrimitiveMatrix org.ojalgo.matrix.PrimitiveMatrix used on opensource projects

Example 16 with PrimitiveMatrix

use of org.ojalgo.matrix.PrimitiveMatrix in project ojAlgo-finance by optimatika.

the class TestEquilibrium method testRandomProblemsComparedToEquilibrium.

@Test
public void testRandomProblemsComparedToEquilibrium() {
    final NumberContext tmpWeightsContext = StandardType.PERCENT.newPrecision(5);
    final int tmpDim = 9;
    final Uniform tmpRndmCorrelation = new Uniform(-0.5, 1.0);
    final Uniform tmpRndmVolatility = new Uniform(0.01, 0.10);
    final Uniform tmpRndmRiskAversionExponent = new Uniform(-1.0, 3.0);
    final Uniform tmpRndmWeight = new Uniform(0.0, 1.0);
    final PhysicalStore<Double> tmpCovariances = PrimitiveDenseStore.FACTORY.makeFilled(tmpDim, tmpDim, tmpRndmCorrelation);
    tmpCovariances.fillDiagonal(0, 0, 0.5);
    tmpCovariances.modifyMatching(PrimitiveFunction.ADD, tmpCovariances.transpose());
    for (int ij = 0; ij < tmpDim; ij++) {
        final UnaryFunction<Double> tmpFunc = PrimitiveFunction.MULTIPLY.first(tmpRndmVolatility.doubleValue());
        tmpCovariances.modifyRow(ij, 0, tmpFunc);
        tmpCovariances.modifyColumn(0, ij, tmpFunc);
    }
    final BigDecimal tmpRAF = new BigDecimal(PrimitiveFunction.POW.invoke(10.0, tmpRndmRiskAversionExponent.doubleValue()));
    final MarketEquilibrium tmpEquilibrium = new MarketEquilibrium(PrimitiveMatrix.FACTORY.copy(tmpCovariances), tmpRAF).clean();
    final double[] tmpRawWeights = PrimitiveMatrix.FACTORY.makeFilled(tmpDim, 1, tmpRndmWeight).toRawCopy1D();
    final List<BigDecimal> tmpNormalisedWeights = new SimplePortfolio(tmpRawWeights).normalise().getWeights();
    @SuppressWarnings("unchecked") final PrimitiveMatrix tmpGeneratedWeights = PrimitiveMatrix.FACTORY.columns(tmpNormalisedWeights);
    final BasicMatrix tmpMatchingReturns = tmpEquilibrium.calculateAssetReturns(tmpGeneratedWeights);
    TestUtils.assertEquals(tmpGeneratedWeights, tmpEquilibrium.calculateAssetWeights(tmpMatchingReturns), tmpWeightsContext);
    final FixedWeightsPortfolio tmpFW = new FixedWeightsPortfolio(tmpEquilibrium, tmpGeneratedWeights);
    TestUtils.assertEquals(tmpMatchingReturns, tmpFW.getAssetReturns(), tmpWeightsContext);
    final FixedReturnsPortfolio tmpFR = new FixedReturnsPortfolio(tmpEquilibrium, tmpMatchingReturns);
    TestUtils.assertEquals(tmpGeneratedWeights, tmpFR.getAssetWeights(), tmpWeightsContext);
    final BlackLittermanModel tmpBLM = new BlackLittermanModel(tmpEquilibrium, tmpGeneratedWeights);
    for (int i = 0; i < tmpDim; i++) {
        final List<BigDecimal> tmpViewAssetWeights = new ArrayList<>();
        for (int j = 0; j < tmpDim; j++) {
            if (i == j) {
                tmpViewAssetWeights.add(BigMath.ONE);
            } else {
                tmpViewAssetWeights.add(BigMath.ZERO);
            }
        }
        final int row = i;
        final BigDecimal tmpViewReturn = TypeUtils.toBigDecimal(tmpMatchingReturns.get(row, 0));
        tmpBLM.addViewWithScaledConfidence(tmpViewAssetWeights, tmpViewReturn, BigMath.ONE);
    }
    TestUtils.assertEquals(tmpGeneratedWeights, tmpBLM.getAssetWeights(), tmpWeightsContext);
    final MarkowitzModel tmpMM = new MarkowitzModel(tmpEquilibrium, tmpMatchingReturns);
    final BasicMatrix tmpActual = tmpMM.getAssetWeights();
    TestUtils.assertEquals(tmpGeneratedWeights, tmpActual, tmpWeightsContext);
}

Example 17 with PrimitiveMatrix

use of org.ojalgo.matrix.PrimitiveMatrix in project ojAlgo-finance by optimatika.

the class TestEquilibrium method main.

public static void main(final String[] args) {
    final int assetNum = 16;
    final double[][] om = { { 0.003330616, 0.003524811, 0.00386567, 0.003656347, 0.004494241, 0.004623772, 0.00458625, 0.004365933, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.003524811, 0.004274864, 0.004372518, 0.004135748, 0.005144421, 0.005292691, 0.005249742, 0.004997551, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.00386567, 0.004372518, 0.005114057, 0.004535687, 0.005641369, 0.005803962, 0.005756863, 0.005480312, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.003656347, 0.004135748, 0.004535687, 0.004728464, 0.005511769, 0.005670626, 0.00562461, 0.005354411, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.004494241, 0.005144421, 0.005641369, 0.005511769, 0.007284319, 0.006518612, 0.006359324, 0.00635862, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.004623772, 0.005292691, 0.005803962, 0.005670626, 0.006518612, 0.007542516, 0.006562129, 0.006561403, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.00458625, 0.005249742, 0.005756863, 0.00562461, 0.006359324, 0.006562129, 0.007513433, 0.00640107, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0.004365933, 0.004997551, 0.005480312, 0.005354411, 0.00635862, 0.006561403, 0.00640107, 0.006805889, 0, 0, 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0, 0, 0, 0.000152891, 0.000120753, 7.98783E-05, 0.000116607, 8.11225E-05, 4.31112E-05, 6.11874E-05, 1.49376E-07 }, { 0, 0, 0, 0, 0, 0, 0, 0, 0.000120753, 0.000215819, 0.000108447, 0.000158311, 7.62318E-05, 4.05121E-05, 5.74986E-05, 1.4037E-07 }, { 0, 0, 0, 0, 0, 0, 0, 0, 7.98783E-05, 0.000108447, 0.000251455, 0.000104723, 0.000105337, 5.59796E-05, 7.94514E-05, 1.93963E-07 }, { 0, 0, 0, 0, 0, 0, 0, 0, 0.000116607, 0.000158311, 0.000104723, 0.000285454, 0.000109622, 5.82568E-05, 8.26835E-05, 2.01854E-07 }, { 0, 0, 0, 0, 0, 0, 0, 0, 8.11225E-05, 7.62318E-05, 0.000105337, 0.000109622, 0.000110962, 5.62808E-05, 7.47961E-05, 1.97502E-06 }, { 0, 0, 0, 0, 0, 0, 0, 0, 4.31112E-05, 4.05121E-05, 5.59796E-05, 5.82568E-05, 5.62808E-05, 4.12145E-05, 4.25652E-05, 1.12395E-06 }, { 0, 0, 0, 0, 0, 0, 0, 0, 6.11874E-05, 5.74986E-05, 7.94514E-05, 8.26835E-05, 7.47961E-05, 4.25652E-05, 7.62351E-05, 1.4937E-06 }, { 0, 0, 0, 0, 0, 0, 0, 0, 1.49376E-07, 1.4037E-07, 1.93963E-07, 2.01854E-07, 1.97502E-06, 1.12395E-06, 1.4937E-06, 6.52443E-06 } };
    final TestEquilibrium tm = new TestEquilibrium();
    final BasicMatrix covariances = tm.getACovariances(om);
    System.out.println(covariances);
    final BigDecimal riskAversion = new BigDecimal(1000.0);
    final MarketEquilibrium marketEquilibrium = new MarketEquilibrium(covariances, riskAversion);
    final Builder<PrimitiveMatrix> expectedExcessReturns1 = PrimitiveMatrix.FACTORY.getBuilder(assetNum, 1);
    expectedExcessReturns1.set(0, 0, 0.03360872);
    expectedExcessReturns1.set(1, 0, 0.027322319);
    expectedExcessReturns1.set(2, 0, 0.027668137);
    expectedExcessReturns1.set(3, 0, 0.03080239);
    expectedExcessReturns1.set(4, 0, 0.025067124);
    expectedExcessReturns1.set(5, 0, 0.016578507);
    expectedExcessReturns1.set(6, 0, 0.022622714);
    expectedExcessReturns1.set(7, 0, 0.028957183);
    expectedExcessReturns1.set(8, 0, 0.009939058);
    expectedExcessReturns1.set(9, 0, 0.010014445);
    expectedExcessReturns1.set(10, 0, 0.011565874);
    expectedExcessReturns1.set(11, 0, 0.011609169);
    expectedExcessReturns1.set(12, 0, 0.006286505);
    expectedExcessReturns1.set(13, 0, 0.004240681);
    expectedExcessReturns1.set(14, 0, 0.006162067);
    expectedExcessReturns1.set(15, 0, 0.003081388);
    System.out.println("Return Matrix" + expectedExcessReturns1.build());
    final MarkowitzModel markowitzModel = new MarkowitzModel(marketEquilibrium, expectedExcessReturns1.build());
    // markowitzModel.setTargetReturn(new BigDecimal("0.01051787"));
    markowitzModel.setTargetReturn(new BigDecimal("0.003081388"));
    for (int i = 0; i < assetNum; i++) {
        markowitzModel.setLowerLimit(i, new BigDecimal(0.0));
        markowitzModel.setUpperLimit(i, new BigDecimal(1.0));
    }
    final List<BigDecimal> re = markowitzModel.getWeights();
    System.out.println("=======result====================");
    for (int i = 0; i < re.size(); i++) {
        System.out.println(re.get(i));
    }
    System.out.println("=======result====================");
    System.out.println(markowitzModel.getMeanReturn());
    System.out.println(markowitzModel.getReturnVariance());
    return;
}

Example 18 with PrimitiveMatrix

use of org.ojalgo.matrix.PrimitiveMatrix in project ojAlgo-finance by optimatika.

the class MarketEquilibrium method clean.

/**
 * Equivalent to copying, but additionally the covariance matrix will be cleaned of negative and very
 * small eigenvalues to make it positive definite.
 */
public MarketEquilibrium clean() {
    final PrimitiveMatrix tmpAssetVolatilities = FinanceUtils.toVolatilities(myCovariances, true);
    final PrimitiveMatrix tmpCleanedCorrelations = FinanceUtils.toCorrelations(myCovariances, true);
    final PrimitiveMatrix tmpCovariances = FinanceUtils.toCovariances(tmpAssetVolatilities, tmpCleanedCorrelations);
    return new MarketEquilibrium(myAssetKeys, tmpCovariances, myRiskAversion);
}

Example 19 with PrimitiveMatrix

use of org.ojalgo.matrix.PrimitiveMatrix in project ojAlgo-finance by optimatika.

the class OptimisedPortfolio method handle.

protected final BasicMatrix handle(final Optimisation.Result optimisationResult) {
    final int tmpLength = myVariables.length;
    myOptimisationState = optimisationResult.getState();
    final boolean tmpFeasible = optimisationResult.getState().isFeasible();
    final boolean tmpShortingAllowed = this.isShortingAllowed();
    final Builder<PrimitiveMatrix> tmpMtrxBuilder = MATRIX_FACTORY.getBuilder(tmpLength);
    BigDecimal tmpValue;
    for (int i = 0; i < tmpLength; i++) {
        if (tmpFeasible) {
            tmpValue = tmpShortingAllowed ? optimisationResult.get(i) : optimisationResult.get(i).max(ZERO);
        } else {
            tmpValue = ZERO;
        }
        myVariables[i].setValue(tmpValue);
        tmpMtrxBuilder.set(i, tmpValue);
    }
    return tmpMtrxBuilder.get();
}