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

Example 1 with PrimitiveMatrix

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

the class FinanceUtilsTest method doTestCleaning.

private static void doTestCleaning(final double[][] original) {
    final NumberContext tmpEvalCntx = NumberContext.getGeneral(6, 12);
    final PrimitiveDenseStore tmpOriginal = PrimitiveDenseStore.FACTORY.rows(original);
    final SingularValue<Double> tmpSVD = SingularValue.make(tmpOriginal);
    tmpSVD.decompose(tmpOriginal);
    final double tmpRefCond = tmpSVD.getCondition();
    final int tmpRefRank = tmpSVD.getRank();
    final double tmpRefNorm = tmpSVD.getFrobeniusNorm();
    final PrimitiveMatrix tmpCorrelations = FinanceUtils.toCorrelations(tmpOriginal, true);
    final PrimitiveMatrix tmpVolatilities = FinanceUtils.toVolatilities(tmpOriginal, true);
    final PrimitiveMatrix tmpCovariances = FinanceUtils.toCovariances(tmpVolatilities, tmpCorrelations);
    tmpSVD.decompose(PrimitiveDenseStore.FACTORY.copy(tmpCovariances));
    final double tmpNewCond = tmpSVD.getCondition();
    final int tmpNewRank = tmpSVD.getRank();
    final double tmpNewNorm = tmpSVD.getFrobeniusNorm();
    TestUtils.assertTrue("Improved the condition", tmpNewCond <= tmpRefCond);
    TestUtils.assertTrue("Improved the rank", tmpNewRank >= tmpRefRank);
    TestUtils.assertEquals("Full rank", original.length, tmpNewRank);
    TestUtils.assertEquals("Roughly the same frob norm", tmpRefNorm, tmpNewNorm, tmpEvalCntx);
    if (DEBUG) {
        BasicLogger.debug("Original", tmpOriginal);
        BasicLogger.debug("Cleaned", tmpCovariances);
        BasicLogger.debug("Difference", tmpOriginal.subtract(PrimitiveDenseStore.FACTORY.copy(tmpCovariances)), tmpEvalCntx);
    }
}

Example 2 with PrimitiveMatrix

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

the class PortfolioProblems method testP20160608.

/**
 * <a href="https://github.com/optimatika/ojAlgo/issues/23">GitHub Issue 23</a> The problem was that since
 * the model allows shorting the pure profit maximisation is unbounded (initial LP). The algorithm did not
 * handle the case where "target" could be >= the max possible when shorting not allowed (bounded LP).
 */
@Test
public void testP20160608() {
    final BasicMatrix.Factory<PrimitiveMatrix> matrixFactory = PrimitiveMatrix.FACTORY;
    final PrimitiveMatrix cov = matrixFactory.rows(new double[][] { { 0.01, 0.0018, 0.0011 }, { 0.0018, 0.0109, 0.0026 }, { 0.0011, 0.0026, 0.0199 } });
    final PrimitiveMatrix ret = matrixFactory.columns(new double[] { 0.0427, 0.0015, 0.0285 });
    final MarketEquilibrium marketEquilibrium = new MarketEquilibrium(cov);
    final MarkowitzModel markowitz = new MarkowitzModel(marketEquilibrium, ret);
    markowitz.setShortingAllowed(true);
    markowitz.setTargetReturn(BigDecimal.valueOf(0.0427));
    List<BigDecimal> tmpWeights = markowitz.getWeights();
    TestUtils.assertTrue(markowitz.optimiser().getState().isFeasible());
    final NumberContext tmpTestPrecision = StandardType.PERCENT.newPrecision(4);
    // Solution reachable without shorting, but since it is allowed the optimal solution is different
    // 0.82745
    TestUtils.assertEquals(0.82745, tmpWeights.get(0).doubleValue(), tmpTestPrecision);
    // -0.09075
    TestUtils.assertEquals(-0.09075, tmpWeights.get(1).doubleValue(), tmpTestPrecision);
    // 0.26329
    TestUtils.assertEquals(0.26329, tmpWeights.get(2).doubleValue(), tmpTestPrecision);
    TestUtils.assertEquals(0.0427, markowitz.getMeanReturn(), tmpTestPrecision);
    TestUtils.assertEquals(0.0084, markowitz.getReturnVariance(), tmpTestPrecision);
    // Also verify that it's posible to reach 10% return by shorting
    markowitz.setTargetReturn(BigDecimal.valueOf(0.1));
    TestUtils.assertEquals(0.1, markowitz.getMeanReturn(), tmpTestPrecision);
    TestUtils.assertTrue(markowitz.optimiser().getState().isFeasible());
    // Min risk portfolio, very high risk aversion means minimum risk.
    markowitz.setTargetReturn(null);
    markowitz.setRiskAversion(new BigDecimal(1000000));
    tmpWeights = markowitz.getWeights();
    TestUtils.assertTrue(markowitz.optimiser().getState().isFeasible());
    // 0.4411
    TestUtils.assertEquals(0.4411, tmpWeights.get(0).doubleValue(), tmpTestPrecision);
    // 0.3656
    TestUtils.assertEquals(0.3656, tmpWeights.get(1).doubleValue(), tmpTestPrecision);
    // 0.1933
    TestUtils.assertEquals(0.1933, tmpWeights.get(2).doubleValue(), tmpTestPrecision);
}

Example 3 with PrimitiveMatrix

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

the class FinanceUtils method makeCovarianceMatrix.

/**
 * @param listOfTimeSeries An ordered collection of time series
 * @param mayBeMissingValues Individual series may be missing some values - try to fix this or not
 * @return Annualised covariances
 */
public static <N extends Number> PrimitiveMatrix makeCovarianceMatrix(final List<CalendarDateSeries<N>> listOfTimeSeries, final boolean mayBeMissingValues) {
    final int tmpSize = listOfTimeSeries.size();
    final CoordinationSet<N> tmpUncoordinated = new CoordinationSet<>(listOfTimeSeries);
    final CalendarDateUnit tmpDataResolution = tmpUncoordinated.getResolution();
    if (mayBeMissingValues) {
        tmpUncoordinated.complete();
    }
    final CoordinationSet<N> tmpCoordinated = tmpUncoordinated.prune(tmpDataResolution);
    final Builder<PrimitiveMatrix> tmpMatrixBuilder = PrimitiveMatrix.FACTORY.getBuilder(tmpSize, tmpSize);
    final double tmpToYearFactor = (double) CalendarDateUnit.YEAR.size() / (double) tmpDataResolution.size();
    SampleSet tmpSampleSet;
    final SampleSet[] tmpSampleSets = new SampleSet[tmpSize];
    for (int j = 0; j < tmpSize; j++) {
        final PrimitiveSeries tmpPrimitiveSeries = tmpCoordinated.get(listOfTimeSeries.get(j).getName()).asPrimitive();
        tmpSampleSet = SampleSet.wrap(tmpPrimitiveSeries.quotients().log().toDataSeries());
        tmpMatrixBuilder.set(j, j, tmpToYearFactor * tmpSampleSet.getVariance());
        for (int i = 0; i < j; i++) {
            final double tmpCovariance = tmpToYearFactor * tmpSampleSets[i].getCovariance(tmpSampleSet);
            tmpMatrixBuilder.set(i, j, tmpCovariance);
            tmpMatrixBuilder.set(j, i, tmpCovariance);
        }
        tmpSampleSets[j] = tmpSampleSet;
    }
    return tmpMatrixBuilder.get();
}

Example 4 with PrimitiveMatrix

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

the class ConvexProblems method testP20080208.

/**
 * Another case of looping in the ActiveSetSolver's constraint (de)activation.
 */
@Test
public void testP20080208() {
    // create expected returns matrix
    final PrimitiveMatrix tmpExpectedReturns = PrimitiveMatrix.FACTORY.rows(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
    final PrimitiveMatrix tmpCovariances = PrimitiveMatrix.FACTORY.rows(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
    final Variable[] tmpVariables = new Variable[(int) tmpExpectedReturns.countRows()];
    for (int i = 0; i < tmpVariables.length; i++) {
        tmpVariables[i] = new Variable("VAR" + i);
        final int row = i;
        tmpVariables[i].weight(TypeUtils.toBigDecimal(tmpExpectedReturns.get(row, 0)).negate());
        // set the constraints on the asset weights
        // require at least a 8% allocation to each asset
        tmpVariables[i].lower(new BigDecimal("0.08"));
        // require no more than 12% allocation to each asset
        tmpVariables[i].upper(new BigDecimal("0.12"));
    }
    // exception here...
    final RationalMatrix tmpExpected = RationalMatrix.FACTORY.rows(new double[][] { { 0.07999999999998897 }, { 0.1199999999999636 }, { 0.07999999999999526 }, { 0.08000000000004488 }, { 0.11999999999999084 }, { 0.12000000000018606 }, { 0.11999999999996151 }, { 0.12000000000000167 }, { 0.08000000000001738 }, { 0.08000000000005617 } });
    ConvexProblems.doEarly2008(tmpVariables, tmpCovariances, tmpExpected);
}

Example 5 with PrimitiveMatrix

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

the class ConvexProblems method testP20080124.

/**
 * Another case of looping in the ActiveSetSolver's constraint (de)activation.
 */
@Test
public void testP20080124() {
    // create expected returns matrix
    final PrimitiveMatrix expectedReturnsMatrix = PrimitiveMatrix.FACTORY.rows(new double[][] { { 10.012158 }, { 9.996046 }, { 10.000744 }, { 9.990585 }, { 9.998392 }, { 9.996614 }, { 10.010531 }, { 10.001401 }, { 9.997447 }, { 9.993817 }, { 9.998537 }, { 9.995741 }, { 9.987224 }, { 9.992392 } });
    // create covariance matrix
    final PrimitiveMatrix covarianceMatrix = PrimitiveMatrix.FACTORY.rows(new double[][] { { 0.0013191354374342357, 7.786471466322114E-5, -3.810886655309235E-5, -2.28102405899103E-4, -1.2589115740653127E-4, -1.3247692268411991E-5, 1.422624656557158E-4, -2.7176361887359125E-5, 8.675127894495302E-5, -8.116577287090551E-5, -8.468380774247271E-6, 4.930080166695193E-5, -2.774138231533918E-4, -3.148322898570031E-5 }, { 7.786471466322114E-5, 0.001028250547816086, 8.986425197170406E-4, -1.0341435238579975E-5, 6.472902968147139E-4, 2.9014435841747375E-4, 1.0640414444602855E-4, 5.638694128451113E-4, 6.024515366195699E-4, -1.094867665517237E-4, 6.177221606260711E-6, -5.682215091954099E-5, 2.7178074500896235E-4, 0.0010146062950574643 }, { -3.810886655309235E-5, 8.986425197170406E-4, 0.0012477403456464075, -1.8104847201530489E-4, 9.299199981666304E-4, 3.486383951982303E-4, 1.0246402606579107E-4, 7.009722990366382E-4, 6.545695073447614E-4, -1.1680969171500155E-4, 7.123493385355658E-5, 1.559414390174896E-5, 1.972605480880284E-4, 9.368808845809186E-4 }, { -2.28102405899103E-4, -1.0341435238579975E-5, -1.8104847201530489E-4, 6.250793590180099E-4, -5.4721911720097E-6, 1.3081826023829458E-4, -5.644046856412501E-5, -1.1282043806099452E-5, -6.729835202722053E-5, 1.3929681542737307E-4, 3.698155248637573E-6, 5.0269944317023966E-5, 5.344931460074395E-4, -1.1654882792112444E-4 }, { -1.2589115740653127E-4, 6.472902968147139E-4, 9.299199981666304E-4, -5.4721911720097E-6, 0.001181357476541527, 3.0334522038028824E-4, 2.6983840497611894E-4, 6.983493701701867E-4, 5.68816790613126E-4, -7.899505299987754E-5, 1.05074262063586E-5, 1.137295188785598E-4, 1.9732025136606058E-4, 6.631330613471645E-4 }, { -1.3247692268411991E-5, 2.9014435841747375E-4, 3.486383951982303E-4, 1.3081826023829458E-4, 3.0334522038028824E-4, 3.372068413122505E-4, 1.1067468759384309E-4, 2.6589126866881173E-4, 2.1364931019670806E-4, -4.201239472520589E-5, 2.32769639721745E-5, 5.847559594073046E-6, 1.9925897592339058E-4, 1.9671375386540353E-4 }, { 1.422624656557158E-4, 1.0640414444602855E-4, 1.0246402606579107E-4, -5.644046856412501E-5, 2.6983840497611894E-4, 1.1067468759384309E-4, 0.001484755064835215, 1.2295961703024863E-4, 1.0843198781689372E-4, -2.1292328294313923E-5, -4.152686600769749E-6, 1.163599038579726E-4, -3.14739599261259E-4, 2.4519847977412686E-4 }, { -2.7176361887359125E-5, 5.638694128451113E-4, 7.009722990366382E-4, -1.1282043806099452E-5, 6.983493701701867E-4, 2.6589126866881173E-4, 1.2295961703024863E-4, 5.563328439145604E-4, 4.4816730200338125E-4, -3.4729832814007256E-5, -6.028818604193519E-7, 3.192976987126335E-5, 1.7402262469809026E-4, 5.182632389125651E-4 }, { 8.675127894495302E-5, 6.024515366195699E-4, 6.545695073447614E-4, -6.729835202722053E-5, 5.68816790613126E-4, 2.1364931019670806E-4, 1.0843198781689372E-4, 4.4816730200338125E-4, 6.277134808325468E-4, -4.988229718603287E-5, -5.5018781802344255E-6, -1.3231260300518203E-5, 8.214207901880769E-5, 5.841470978796527E-4 }, { -8.116577287090551E-5, -1.094867665517237E-4, -1.1680969171500155E-4, 1.3929681542737307E-4, -7.899505299987754E-5, -4.201239472520589E-5, -2.1292328294313923E-5, -3.4729832814007256E-5, -4.988229718603287E-5, 3.5152692612068785E-4, -9.358092257358399E-6, 4.962216896551324E-6, 1.291957229930161E-4, -1.5046975508620905E-4 }, { -8.468380774247271E-6, 6.177221606260711E-6, 7.123493385355658E-5, 3.698155248637573E-6, 1.05074262063586E-5, 2.32769639721745E-5, -4.152686600769749E-6, -6.028818604193519E-7, -5.5018781802344255E-6, -9.358092257358399E-6, 4.8495980378967104E-5, 1.1704645004909169E-5, 1.814918597253607E-5, 1.2448218299234062E-5 }, { 4.930080166695193E-5, -5.682215091954099E-5, 1.559414390174896E-5, 5.0269944317023966E-5, 1.137295188785598E-4, 5.847559594073046E-6, 1.163599038579726E-4, 3.192976987126335E-5, -1.3231260300518203E-5, 4.962216896551324E-6, 1.1704645004909169E-5, 1.802684481609152E-4, 1.0475986793792914E-5, -4.113641419540392E-5 }, { -2.774138231533918E-4, 2.7178074500896235E-4, 1.972605480880284E-4, 5.344931460074395E-4, 1.9732025136606058E-4, 1.9925897592339058E-4, -3.14739599261259E-4, 1.7402262469809026E-4, 8.214207901880769E-5, 1.291957229930161E-4, 1.814918597253607E-5, 1.0475986793792914E-5, 7.843917688960864E-4, 1.231995848356005E-4 }, { -3.148322898570031E-5, 0.0010146062950574643, 9.368808845809186E-4, -1.1654882792112444E-4, 6.631330613471645E-4, 1.9671375386540353E-4, 2.4519847977412686E-4, 5.182632389125651E-4, 5.841470978796527E-4, -1.5046975508620905E-4, 1.2448218299234062E-5, -4.113641419540392E-5, 1.231995848356005E-4, 0.0011885193322126312 } });
    // create asset variables - cost and weighting constraints
    final Variable[] tmpVariables = new Variable[(int) expectedReturnsMatrix.countRows()];
    for (int i = 0; i < tmpVariables.length; i++) {
        tmpVariables[i] = new Variable("VAR" + i);
        final int row = i;
        tmpVariables[i].weight(TypeUtils.toBigDecimal(expectedReturnsMatrix.get(row, 0)).negate());
        // set the constraints on the asset weights
        // require at least a 2% allocation to each asset
        tmpVariables[i].lower(new BigDecimal("0.05"));
        // require no more than 80% allocation to each asset
        tmpVariables[i].upper(new BigDecimal("0.35"));
    // tmpVariables[i].setUpperLimit(new BigDecimal("1.00"));
    }
    final RationalMatrix tmpExpected = RationalMatrix.FACTORY.rows(new double[][] { { 0.3166116715239731 }, { 0.050000000001624065 }, { 0.04999999999827016 }, { 0.05000000000034928 }, { 0.049999999999891145 }, { 0.049999999997416125 }, { 0.08338832846287945 }, { 0.05000000000178943 }, { 0.05000000000085164 }, { 0.04999999999937388 }, { 0.050000000012470555 }, { 0.04999999999966884 }, { 0.050000000000484546 }, { 0.049999999995857476 } });
    ConvexProblems.doEarly2008(tmpVariables, covarianceMatrix, tmpExpected);
}