Re: [ojAlgo-user] MarkowitzModel - what is optimal?
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Re: [ojAlgo-user] MarkowitzModel - what is optimal?
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From: Anthony E. <ant...@al...> - 2013-10-26 08:43:19
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Ahhh ..... Wise words. Thanks for looking into this.
I begin the walk of shame .... thanks for your help
-----Original Message-----
From: Anders Peterson
Sent: Friday, October 25, 2013 7:27 PM
To: ojAlgo ojAlgo
Subject: Re: [ojAlgo-user] MarkowitzModel - what is optimal?
If you change
MatrixStore<Double> erms = cvs.builder().build();
into
MatrixStore<Double> erms = ers;
everything works fine!
If you want a BasicMatrix (PrimitiveMatrix) instance, why do you first
create a PrimitiveDenseStore?
..and if you want to create a PrimitiveDenseStore, then why set all values
in a double[] first?
Just call PrimitiveMatrix.getBuilder(int, int) or
PrimitiveMatrix.getBuilder(int), set your values, and the call build();
http://code.google.com/p/ojalgo/wiki/GettingStartedLinearAlgebra
I've modified ojAlgo to throw an IllegalArgumentException if the dimensions
of the covariance matrix and the returns vector don't match.
/Anders
On 25 okt 2013, at 11:38, Anthony Edwards
<ant...@al...> wrote:
> Hi Anders
>
> Test.java attached.
>
> Thanks - really appreciate your help.
>
> Tony
>
> -----Original Message-----
>> From: Anders Peterson
> Sent: Friday, October 25, 2013 8:15 AM
> To: ojAlgo ojAlgo
> Subject: Re: [ojAlgo-user] MarkowitzModel - what is optimal?
>
> Please attach a *.java file that I can use to run the test/example without
> any modifications. /Anders
>
> On 25 okt 2013, at 08:45, Anthony Edwards
> <ant...@al...> wrote:
>
>> Hi Anders
>>
>> Ive setup a simple example and extracted the data and code that I am
>> using
>> (see below). Is there any chance you can take a look to see what I am
>> doing
>> wrong please?
>> Thanks - appreciate your help.
>>
>> Tony
>>
>> -------------- JAVA Code
>>
>> public double [] getWeights(double [] er, double [][] cv, double []
>> minWgt,
>> double [] maxWgt, boolean shortingAllowed, double targetReturn) {
>> int row = er.length;
>> int col = er.length;
>>
>> if (_dbg) System.out.println("AlgoMarkowitz.getWeights() -
>> row="+row);
>>
>> PrimitiveDenseStore cvs =
>> PrimitiveDenseStore.FACTORY.makeZero(row,col);
>> for (int i = 0; i < row; i++) {
>> for (int j = 0; j < col; j++) {
>> cvs.set(i, j, cv[i][j]);
>> if (_dbg) System.out.println("cv["+i+"]["+j+"] -->
>> "+cv[i][j]);
>> }
>> }
>>
>> if (_dbg) {
>> System.out.println("Covar()");
>> for (int r=0; r<row; r++) {
>> StringBuilder sb = new StringBuilder();
>> for (int c=0; c<col; c++) {
>> sb.append(Utils.formatp(cv[r][c]*100.0,2)+"% ");
>> }
>> System.out.println(sb.toString());
>> }
>> }
>> if (_dbg) System.out.println("AlgoMarkowitz.getWeights() - Covar
>> Set");
>>
>> PrimitiveDenseStore ers =
>> PrimitiveDenseStore.FACTORY.makeZero(1,col);
>>
>> System.out.println(ers.countColumns()+" cols, "+ers.countRows()+"
>> rows. ");
>> for (int i = 0; i < row; i++) {
>> if (_dbg) System.out.println("er["+i+"] --> "+er[i]);
>> ers.set(0,i,er[i]);
>> }
>> if (_dbg) System.out.println("AlgoMarkowitz.getWeights() -
>> Expeected
>> Returns set");
>> System.out.println(""+ers.toString());
>>
>> MatrixStore<Double> cvms = cvs.builder().build();
>> MatrixStore<Double> erms = cvs.builder().build();
>>
>> BasicMatrix cvm = PrimitiveMatrix.FACTORY.copy(cvms);
>> BasicMatrix erm = PrimitiveMatrix.FACTORY.copy(erms);
>>
>> MarkowitzModel markowitzModel = new MarkowitzModel(cvm, erm);
>>
>> markowitzModel.setTargetReturn(new BigDecimal(targetReturn));
>>
>> System.out.println("Target Return = "+targetReturn);
>>
>> markowitzModel.setShortingAllowed(shortingAllowed); // False
>>
>> for (int i=0; i<er.length; i++) {
>> markowitzModel.setLowerLimit(i, new BigDecimal(minWgt[i])); //
>> 0.0
>> markowitzModel.setUpperLimit(i, new BigDecimal(maxWgt[i])); //
>> 0.5
>> }
>>
>> double [] weights = new double[er.length];
>> final List<BigDecimal> re = markowitzModel.getWeights();
>>
>> for (int nn = 0; nn < re.size(); nn++) {
>> System.out.println("WGT["+nn+"] -->"+re.get(nn)+" -->
>> "+re.get(nn).doubleValue() );
>> weights[nn] = re.get(nn).doubleValue();
>> }
>>
>> if (_dbg) {
>> _meanReturn = markowitzModel.getMeanReturn();
>> _sharpeRatio = markowitzModel.getSharpeRatio();
>> _returnVariance = markowitzModel.getReturnVariance();
>> _lossProbability = markowitzModel.getLossProbability();
>> _volatility = markowitzModel.getVolatility();
>> _riskAversion = markowitzModel.getRiskAversion().doubleValue();
>> _resultSuccess =
>> markowitzModel.getOptimisationState().isSuccess();
>> _resultFailure =
>> markowitzModel.getOptimisationState().isFailure();
>> _resultOptimal =
>> markowitzModel.getOptimisationState().isOptimal();
>> _resultValid = markowitzModel.getOptimisationState().isValid();
>> _resultDistinct =
>> markowitzModel.getOptimisationState().isDistinct();
>> _resultFeasible =
>> markowitzModel.getOptimisationState().isFeasible();
>> _resultUnexplored =
>> markowitzModel.getOptimisationState().isUnexplored();
>> System.out.println("Model : Mean Return = "+_meanReturn);
>> System.out.println("Model : Sharpe Ratio = "+_sharpeRatio);
>> System.out.println("Model : Return Variance =
>> "+_returnVariance);
>> System.out.println("Model : Loss Probability =
>> "+_lossProbability);
>> System.out.println("Model : Volatility = "+_volatility);
>> System.out.println("Model : Optimal Result = "+_resultOptimal);
>>
>> }
>> return weights;
>> }
>>
>> -------------- Input Data & Debugging Output
>>
>> AlgoMarkowitz.getWeights() - row=5
>> cv[0][0] --> 0.07843955354726917
>> cv[0][1] --> 0.010917730527180272
>> cv[0][2] --> 0.002617703304568447
>> cv[0][3] --> 0.0015367718256236922
>> cv[0][4] --> -0.007527000582038881
>> cv[1][0] --> 0.010917730527180272
>> cv[1][1] --> 0.010564384563013356
>> cv[1][2] --> 0.009059249805530113
>> cv[1][3] --> 0.007582121464911981
>> cv[1][4] --> -7.085933099370378E-4
>> cv[2][0] --> 0.002617703304568447
>> cv[2][1] --> 0.009059249805530113
>> cv[2][2] --> 0.019431716713640173
>> cv[2][3] --> 0.009759425136794966
>> cv[2][4] --> 0.0018625781574573416
>> cv[3][0] --> 0.0015367718256236922
>> cv[3][1] --> 0.007582121464911981
>> cv[3][2] --> 0.009759425136794966
>> cv[3][3] --> 0.008011558847885328
>> cv[3][4] --> -7.456016323607308E-4
>> cv[4][0] --> -0.007527000582038881
>> cv[4][1] --> -7.085933099370378E-4
>> cv[4][2] --> 0.0018625781574573416
>> cv[4][3] --> -7.456016323607308E-4
>> cv[4][4] --> 0.021224468167287004
>> Covar()
>> 7.84% 1.09% 0.26% 0.15% -0.75%
>> 1.09% 1.06% 0.91% 0.76% -0.07%
>> 0.26% 0.91% 1.94% 0.98% 0.19%
>> 0.15% 0.76% 0.98% 0.80% -0.07%
>> -0.75% -0.07% 0.19% -0.07% 2.12%
>> AlgoMarkowitz.getWeights() - Covar Set
>> 5 cols, 1 rows.
>> er[0] --> 0.08468993625647991
>> er[1] --> 0.05571192270463743
>> er[2] --> -0.053384899277547415
>> er[3] --> 0.0363486250229359
>> er[4] --> -0.10226497575724715
>> AlgoMarkowitz.getWeights() - Expeected Returns set
>> org.ojalgo.matrix.store.PrimitiveDenseStore < 1 x 5 >
>> { { 0.08468993625647991,
>> 0.05571192270463743, -0.053384899277547415,
>> 0.0363486250229359, -0.10226497575724715 } }
>> Target Return = 0.05
>> WGT[0] -->0.5814 --> 0.5814
>> WGT[1] -->0.5 --> 0.5
>> WGT[2] -->0.0 --> 0.0
>> WGT[3] -->-0.1848 --> -0.1848
>> WGT[4] -->0.1034 --> 0.1034
>> Model : Mean Return = 0.05000133440241435
>> Model : Sharpe Ratio = 0.2739126173775806
>> Model : Return Variance = 0.03332261792985292
>> Model : Loss Probability = 0.42212046844930723
>> Model : Volatility = 0.18254483813532751
>> Model : Optimal Result = false
>>
>>
>>
>>
>>
>> -------------- HTML Output
>> Run Strategy 'Narrow Test Strategy'
>> Expected Returns
>> 0 AAPL US Equity 0.08469
>> 1 QQQ US Equity 0.05571
>> 2 RWX US Equity -0.05338
>> 3 SPY US Equity 0.03635
>> 4 TLT US Equity -0.10226
>> Efficient Frontier
>> Id Freeze Date Calculation Time TARGET_RETURN MEAN_RETURN
>> SHARPE_RATIO LOSS_PROBABILITY RETURN_VARIANCE VOLATILITY
>> RISK_AVERSION RESULT_SUCCESS RESULT_FAILURE RESULT_OPTIMAL
>> RESULT_DISTINCT RESULT_FEASIBLE RESULT_UNEXPLORED WEIGHT_DELTA
>> CALCULATION_FAILED
>> 1382682291161 2013-09-30 2013-10-25 6:24:51 0.0500 0.0500
>> 0.2739 0.4221 0.0333 0.1825 1.0000 1.0000 0.0000 0.0000
>> 0.0000 0.0000 0.0000 0.0000 0.0000 AAPL US Equity / 0.581
>> QQQ US Equity / 0.500 RWX US Equity / 0.000 SPY US Equity / -0.185
>> TLT US Equity / 0.103
>> (Key point is that the RESULT_OPTIMAL is zero, and clearly the weights
>> are
>> outside the limits, but as this is also an infeasible result that that is
>> fine)
>>
>>
>> -----Original Message-----
>>> From: Anders Peterson
>> Sent: Thursday, October 24, 2013 8:39 PM
>> To: ojAlgo ojAlgo
>> Subject: Re: [ojAlgo-user] MarkowitzModel - what is optimal?
>>
>> Have you read the javadoc for the MarkowitzModel class and its methods? I
>> think it does specify how it optimizes.
>>
>> The results you describe seems "wrong" but without a test case where I
>> can
>> see exactly what you do I cannot comment.
>>
>> If you don't like the MarkowitzModel class you can build your own
>> optimization models directly using ExpressionsBasedModel
>>
>> http://ojalgo.org/generated/org/ojalgo/optimisation/ExpressionsBasedModel.html
>>
>> /Anders
>>
>>
>> On 24 okt 2013, at 11:09, Anthony Edwards
>> <ant...@al...> wrote:
>>
>>> Hi
>>>
>>> I’m still no nearer in understanding from any of the documentation or
>>> javadocs what is optimal. When I specify a target return, I get a set
>>> of
>>> weights and various stats like ShapreRatio, Variance etc. What is the
>>> optimiser maximising/minimising to arrive at this portfolio? Is it
>>> maximising the sharp? or maximising mean return / variance?
>>>
>>> We are trying (with no joy at all) to compare an excel based solver with
>>> ojalgo, but cant get anywhere near the same results for the same inputs
>>> .....
>>>
>>> Below is the covar and expected returns
>>>
>>> Covar()
>>> 7.84% 1.09% 0.26% 0.15% -0.75%
>>> 1.09% 1.06% 0.91% 0.76% -0.07%
>>> 0.26% 0.91% 1.94% 0.98% 0.19%
>>> 0.15% 0.76% 0.98% 0.80% -0.07%
>>> -0.75% -0.07% 0.19% -0.07% 2.12%
>>> er[0] --> 0.08468993625647991
>>> er[1] --> 0.05571192270463743
>>> er[2] --> -0.053384899277547415
>>> er[3] --> 0.0363486250229359
>>> er[4] --> -0.10226497575724715
>>>
>>> The highest mean return solution we get is 4.4% with limits set at 0 to
>>> 0.5 for all 5 assets. Clearly that is wrong. There is obviously a
>>> solution at the mean of 8.4% and 5.5% with 50% in assets 1 and 2 and the
>>> other weights at zero. When I run for a higher target return there is
>>> no
>>> optimal solution (and weights that outside limits then). The covar
>>> looks
>>> ok, the expected returns look ok, all assets are limited weights from 0
>>> to
>>> 0.5 and the only variable I can control is the targetReturn(), so I’m at
>>> a
>>> loss here to know what else I can do.
>>>
>>> Any ideas? Thanks in advance ...
>>>
>>> Tony
>>> ------------------------------------------------------------------------------
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>>
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>
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> <Test.java>------------------------------------------------------------------------------
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Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most
from
the latest Intel processors and coprocessors. See abstracts and register >
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