public double[] ScaleSeries(double[] rawWeights_, double targetVol_, bool diagOnly_=false, bool useNMath_=true)
{
// catch empty weights coming in
if (Math.Abs(rawWeights_.Select(x => Math.Abs(x)).Sum()) < 0.00000001)
return rawWeights_;
var matrixRawWeights = new double[1, rawWeights_.Length];
for (int i = 0; i < rawWeights_.Length; ++i)
matrixRawWeights[0, i] = rawWeights_[i];
var scalingFactor = 0d;
if (useNMath_)
{
// put weights and covariance into NMath constructs
var weights = new NMathCore.DoubleMatrix(matrixRawWeights);
var cov = new NMathCore.DoubleMatrix((diagOnly_) ? DiagonalData : Data);
// multiply out to get variance
var a = NMathCore.NMathFunctions.Product(weights, cov);
var b = NMathCore.NMathFunctions.Product(a, weights.Transpose());
// extract vol
var vol = Math.Sqrt(b[0, 0]);
if (vol == 0d) return rawWeights_; // must be empty weights coming in
scalingFactor = vol/targetVol_;
}
else
{
var weights = Matrix<double>.Build.Dense(1, rawWeights_.Length, rawWeights_);
var cov = Matrix<double>.Build.Dense(Data.GetLength(0), Data.GetLength(1),
delegate(int i, int j) { return diagOnly_ ? DiagonalData[i, j] : Data[i, j]; });
var wCov = weights * cov;
var b = wCov * weights.Transpose();
var vol = Math.Sqrt(b[0, 0]);
if (vol == 0d) return rawWeights_;
scalingFactor = vol/targetVol_;
}
double[] ret = new double[rawWeights_.Length];
// scale the weights
for (int j = 0; j < ret.Length; ++j)
if (scalingFactor == 0)
ret[j] = 0;
else
ret[j] = rawWeights_[j] / scalingFactor;
return ret;
}
