/**
* @covers EfficientRiskyAssetPortfolio::performanceIncreasingPortfolio
*/
void EfficientRiskyAssetPortfolioTest::testEfficientPortfolio()
{
_createReturnStatistics();
EfficientRiskyAssetPortfolio model(*_mean, *_variance);
{
// more than mvp return
Eigen::VectorXd ep = model.efficientPortfolio(0.1);
CPPUNIT_ASSERT_DOUBLES_EQUAL(28.0 / 97.0, ep.coeff(0), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(41.0 / 97.0, ep.coeff(1), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(28.0 / 97.0, ep.coeff(2), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.1, model.portfolioReturn(ep), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(192.0 / 2425.0, model.portfolioVariance(ep), EPSILON);
}
{
// less than mvp return, i.e. mvp is the efficient portfolio
Eigen::VectorXd ep = model.efficientPortfolio(0.01);
CPPUNIT_ASSERT_DOUBLES_EQUAL(-24.0 / 70.0, ep.coeff(0), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(51.0 / 70.0, ep.coeff(1), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(43.0 / 70.0, ep.coeff(2), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(73.0 / 1400.0, model.portfolioReturn(ep), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(23.0 / 875.0, model.portfolioVariance(ep), EPSILON);
}
_destroyReturnStatistics();
}
/**
* @covers EfficientRiskyAssetPortfolio::performanceIncreasingPortfolio
*/
void EfficientRiskyAssetPortfolioTest::testPerformanceIncreasingPortfolio()
{
_createReturnStatistics();
EfficientRiskyAssetPortfolio model(*_mean, *_variance);
Eigen::VectorXd pip = model.performanceIncreasingPortfolio();
CPPUNIT_ASSERT_DOUBLES_EQUAL(1280.0 / 97.0, pip.coeff(0), 1e-14);
CPPUNIT_ASSERT_DOUBLES_EQUAL(-620.0 / 97.0, pip.coeff(1), 1e-14);
CPPUNIT_ASSERT_DOUBLES_EQUAL(-660.0 / 97.0, pip.coeff(2), 1e-14);
_destroyReturnStatistics();
}
/**
* @covers EfficientPortfolioWithRisklessAssetTest::performanceIncreasingPortfolio
*/
void EfficientPortfolioWithRisklessAssetTest::testPerformanceIncreasingPortfolio()
{
_createReturnStatistics();
EfficientPortfolioWithRisklessAsset model(0.04, *_mean, *_variance);
Eigen::VectorXd pip = model.performanceIncreasingPortfolio();
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, pip.coeff(0), 1e-14);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, pip.coeff(1), 1e-14);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, pip.coeff(2), 1e-14);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, pip.coeff(3), 1e-14);
_destroyReturnStatistics();
}
/**
* @covers EfficientRiskyAssetPortfolio::minimumVariancePortfolio
* @covers EfficientRiskyAssetPortfolio::portfolioReturn
* @covers EfficientRiskyAssetPortfolio::portfolioVariance
*/
void EfficientRiskyAssetPortfolioTest::testMinimumVariancePortfolio()
{
_createReturnStatistics();
EfficientRiskyAssetPortfolio model(*_mean, *_variance);
Eigen::VectorXd mvp = model.minimumVariancePortfolio();
CPPUNIT_ASSERT_DOUBLES_EQUAL(-24.0 / 70.0, mvp.coeff(0), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(51.0 / 70.0, mvp.coeff(1), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(43.0 / 70.0, mvp.coeff(2), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(73.0 / 1400.0, model.portfolioReturn(mvp), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(23.0 / 875.0, model.portfolioVariance(mvp), EPSILON);
_destroyReturnStatistics();
}
/**
* @covers EfficientPortfolioWithRisklessAssetTest::minimumVariancePortfolio
* @covers EfficientPortfolioWithRisklessAssetTest::portfolioReturn
* @covers EfficientPortfolioWithRisklessAssetTest::portfolioVariance
*/
void EfficientPortfolioWithRisklessAssetTest::testMinimumVariancePortfolio()
{
_createReturnStatistics();
EfficientPortfolioWithRisklessAsset model(0.04, *_mean, *_variance);
Eigen::VectorXd mvp = model.minimumVariancePortfolio();
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, mvp.coeff(0), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, mvp.coeff(1), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, mvp.coeff(2), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(1.0, mvp.coeff(3), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.04, model.portfolioReturn(mvp), EPSILON);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0, model.portfolioVariance(mvp), EPSILON);
_destroyReturnStatistics();
}
double test(const lbfgsfloatval_t* parameter,
int featsize,
boost::shared_ptr<Eigen::SparseMatrix<double, Eigen::RowMajor> >& samples,
boost::shared_ptr<Eigen::VectorXi>& labels){
Eigen::Map<Eigen::VectorXd> param((double*)parameter, featsize, 1);
Eigen::VectorXd xw = (*(samples))*param;
double allcnt=0, rightcnt =0 ;
for(int i=0; i < labels->rows(); ++i){
if( xw.coeff(i)*labels->coeff(i) > 0){
rightcnt += 1;
}
allcnt += 1;
}
return rightcnt /allcnt;
}
static lbfgsfloatval_t evaluate(
void *instance,
const lbfgsfloatval_t *x,
lbfgsfloatval_t *g,
const int n,
const lbfgsfloatval_t step
){
double fx = 0;
timeutil t;
optimization_instance_t* optInst = (optimization_instance_t*)instance;
Eigen::Map<Eigen::VectorXd> param((double*)x,
n, 1);
Eigen::Map<Eigen::VectorXd> grad((double*)g,
n, 1);
// price for sparse A*x
t.tic();
Eigen::VectorXd xw = (*(optInst->samples_))*param;
if( (xw.cols() != 1) || (xw.rows() != optInst->samples_->rows())){
std::cerr << "Error, column size must be 1" << std::endl;
std::cerr << "product of Xw is " << xw.rows()
<< " by " << xw.cols() << std::endl;
std::exit(-1);
}
int effective_cnt = 0;
Eigen::ArrayXd xwy(xw.rows());
xwy.setZero();
for(int i=0; i<xw.rows(); ++i){
double z = xw.coeff(i)*(optInst->labels_->coeff(i));
xwy.coeffRef(i) = z;
if( z < 1 ){
fx += (1-z)*(1-z);
++effective_cnt;
}
}
// fx /= optInst->labels_->rows();
// l2 regularization value
for(int i = optInst->prog_params_->l2start;
i <= optInst->prog_params_->l2end;
++i){
fx += 0.5*optInst->prog_params_->l2c * std::pow(param.coeff(i), 2);
}
grad.setZero();
for (int k = 0; k < optInst->samples_->outerSize(); ++k){
double z = xwy.coeff(k);
if(z >= 1){
continue;
}
double factor = -2 * optInst->labels_->coeff(k) * (1-z);
for (Eigen::SparseMatrix<double, Eigen::RowMajor>::InnerIterator it( *(optInst->samples_) ,k);
it; ++it){
int colIdx = it.col();
grad.coeffRef(colIdx) += factor*it.value();
}
}
// grad /= optInst->samples_->rows();
//l2 regularization
for(int i = optInst->prog_params_->l2start;
i <= optInst->prog_params_->l2end;
++i){
grad.coeffRef(i) += optInst->prog_params_->l2c * param.coeff(i);
}
std::cout << "One computation of function value & gradient costs " << t.toc() << " seconds"
<< std::endl;
return fx;
}