private void AddRegressionResult(RegressionResult resultToAdd)
{
var eh = AAddRegressionResult;
if (eh != null)
{
eh(resultToAdd);
}
}
public void OnAddRegressionResult(RegressionResult r)
{
if (this.InvokeRequired)
{
this.Invoke((MethodInvoker) delegate {
// Running on the UI thread
OnAddRegressionResult(r);
});
}
else
{
mRegressionResults.Add(r);
resultsDgv.Invalidate();
}
}
private static RegressionResult PerformOls(double[] yVal, string[] xVars, double[][] xVals)
{
var ols = new OrdinaryLeastSquares()
{
// intercept should represent the return if no impact from inputs. (mean daily return)
// forcing to 0 as we assume that all explanation of price move should be due to independenet variables.
// in reality there is likely an unexplainable drift. For example daily bleed (MER, funding etc).
UseIntercept = false
};
MultipleLinearRegression regression = ols.Learn(xVals, yVal);
double[] predicted = regression.Transform(xVals);
RegressionResult r = new RegressionResult();
r.ModelType = "OLS";
r.StandardError = regression.GetStandardError(xVals, yVal);
// r.RSquared = new RSquaredLoss(xVals.Length, yVal, ).Loss(predicted);
r.RSquared = regression.CoefficientOfDetermination(xVals, yVal, false);
r.AdjRSquared = regression.CoefficientOfDetermination(xVals, yVal, true);
r.Betas = SgtStringUtils.DoubleArrayToRoundedDelimitedString(regression.Weights);
r.xVars = string.Join(";", xVars);
// TODO need to validate the below section. add p-values?
double[] coeffStandardErrors = regression.GetStandardErrors(r.StandardError, ols.GetInformationMatrix());
double[] coeffTScores = DoubleDivide(regression.Weights, coeffStandardErrors);
double[] coeffPVals = new double[coeffTScores.Length];
double df = xVals.Length - 1;
for (int i = 0; i < coeffTScores.Length; i++)
{
TTest tTest = new TTest(coeffTScores[i], df, OneSampleHypothesis.ValueIsDifferentFromHypothesis);
coeffPVals[i] = tTest.PValue;
}
r.CoeffStandardErrors = SgtStringUtils.DoubleArrayToRoundedDelimitedString(coeffStandardErrors);
r.CoeffTScores = SgtStringUtils.DoubleArrayToRoundedDelimitedString(coeffTScores);
r.CoeffPVals = SgtStringUtils.DoubleArrayToRoundedDelimitedString(coeffPVals);
// /TODO
r.xVarCount = regression.NumberOfInputs;
r.SamplesCount = yVal.Length;
r.Mean = regression.Intercept;
return(r);
}
private RegressionResult PerformUnhedged(double[] yVal)
{
RegressionResult r = new RegressionResult();
double average = yVal.Average();
r.Mean = average;
double sumOfSquaresOfDifferences = yVal.Select(val => (val - average) * (val - average)).Sum();
double sd = Math.Sqrt(sumOfSquaresOfDifferences / (yVal.Length - 1));
r.ModelType = "UNHEDGED";
r.RSquared = 0;
r.AdjRSquared = 0;
r.StandardError = sd;
r.xVarCount = 0;
r.SamplesCount = yVal.Length;
return(r);
}
