public void FinishAndProcess()
{
try
{
var priceData = new DenseMatrix(symbols.Length, numTicks);
for (int j = 0; j < symbols.Length; j++)
{
SortedList <DateTime, Tick> d = mktData[j].data.Data;
for (int k = 0; k < d.Count; k++)
{
priceData[j, k] = d.Values[k].BidClose;
}
}
for (int i = 0; i < symbols.Length; i++)
{
for (int j = 0; j < symbols.Length; j++)
{
double[] pDatai = priceData.Row(i).ToArray();
double[] pDataj = priceData.Row(j).ToArray();
switch (cType)
{
case CovarianceType.LogReturn:
{
pDatai = priceData.Row(i).ToArray().LogRateOfReturn();
pDataj = priceData.Row(j).ToArray().LogRateOfReturn();
break;
}
case CovarianceType.RawReturn:
{
pDatai = priceData.Row(i).ToArray().RawRateOfReturn();
pDataj = priceData.Row(j).ToArray().RawRateOfReturn();
break;
}
}
correlation[i, j] = StatisticsExtension.Correlation(pDatai, pDataj);
covariance[i, j] = StatisticsExtension.Covariance((DenseVector)priceData.Row(i),
(DenseVector)priceData.Row(j));
}
}
Visualize.GenerateHeatMatrix(symbols, correlation, "C:\\Users\\Ethan\\Work\\QuantSysdata.html");
Console.WriteLine("Finished Generating Correlation Matrix.");
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
public double HandleNextTick(double d)
{
double value = double.NaN;
X.Enqueue(d);
if (X.Count.Equals(X.Capacity))
{
double cov = StatisticsExtension.Covariance(new DenseVector(X.ToArray()), Y);
value = cov / (X.ToArray().Variance() * Y.Variance());
}
indicatorData.Enqueue(value);
return(value);
}
