private bool BuildPopulationMatrix()
{
bool success = true;
int i, nSize;
List <Double> eqMemberX, eqMemberY;
ClassificationKey keyX = (cbProtoAttributeX.SelectedIndex < 0) ? ClassificationKey.NULL : ClassKeyList[cbProtoAttributeX.SelectedIndex];
ClassificationKey keyY = (cbProtoAttributeY.SelectedIndex < 0) ? ClassificationKey.NULL : ClassKeyList[cbProtoAttributeY.SelectedIndex];
nSize = DataSrc.HistoricalData.Constituents.Count;
PopulationToClassify = new double[nSize, 2];
eqMemberX = null;
eqMemberY = null;
if (keyX == keyY || keyX == ClassificationKey.NULL || keyY == ClassificationKey.NULL)
{
success = false;
}
else
{
for (i = 0; i < nSize; i++)
{
eqMemberX = SelectDataComponent(keyX, DataSrc.HistoricalData.Constituents[i]);
eqMemberY = SelectDataComponent(keyY, DataSrc.HistoricalData.Constituents[i]);
if (eqMemberX != null && eqMemberY != null)
{
switch (ClassFnList[cbProtoFunction.SelectedIndex])
{
case PrototypeFunction.AVERAGE:
PopulationToClassify[i, 0] = eqMemberX.Average();
PopulationToClassify[i, 1] = eqMemberY.Average();
break;
case PrototypeFunction.STD_DEV:
PopulationToClassify[i, 0] = Helpers.StdDev(eqMemberX);
PopulationToClassify[i, 1] = Helpers.StdDev(eqMemberY);
break;
case PrototypeFunction.VARIANCE:
default:
break;
}
}
else
{
success = false;
break;
}
}
}
return(success);
}
private List <double> SelectDataComponent(ClassificationKey pKey, Equity pSrc)
{
List <double> return_list;
switch (pKey)
{
case ClassificationKey.CLOSE:
return_list = pSrc.HistoricalPrice;
break;
case ClassificationKey.HIGH:
return_list = pSrc.HistoricalHighs;
break;
case ClassificationKey.LOW:
return_list = pSrc.HistoricalLows;
break;
case ClassificationKey.OPEN:
return_list = pSrc.HistoricalOpens;
break;
case ClassificationKey.PCT_CHANGE:
return_list = pSrc.HistoricalPctChange;
break;
case ClassificationKey.VOLUME:
return_list = Algorithms.IncrementalPercentChange(pSrc.HistoricalVolumes, 0);
break;
default:
return_list = new List <double>();
break;
}
return(return_list);
}
private void BuildMovingChart()
{
int i, n;
List <double> x_seed;
List <double> y_seed;
ClassificationKey keyX = (cbProtoAttributeX.SelectedIndex < 0) ? ClassificationKey.NULL : ClassKeyList[cbProtoAttributeX.SelectedIndex];
ClassificationKey keyY = (cbProtoAttributeY.SelectedIndex < 0) ? ClassificationKey.NULL : ClassKeyList[cbProtoAttributeY.SelectedIndex];
this.daily_chart_x_values = new List <List <double> >();
this.daily_chart_y_values = new List <List <double> >();
n = this.DataSrc.HistoricalData.Constituents.Count;
x_seed = new List <double>(new double[n]);
this.daily_chart_x_values.Add(x_seed);
//If no feature is selected for the y-axis, treat the data as 1 dimensional
// To do this, each X-point for a particular equity will be placed at a unique but constant y-value
if (this.cbProtoAttributeY.SelectedIndex < 0)
{
y_seed = Enumerable.Range(1, n).Select(x => (double)x).ToList();
}
else
{
//Otherwise, start all data points at (0, 0)
y_seed = new List <double>(new double[n]);
}
this.daily_chart_y_values.Add(y_seed);
//Problem: not guaranteed that the date associated with a data-point-index is the same accross the entire array of Constituents. Need to align first.
//Create each subsequent series by adding the selected x attribute at the given index to it's previous value for each equity in the list
for (i = 0; i < this.DataSrc.HistoricalData.Constituents[0].HistoricalPriceDate.Count; i++)
{
switch (keyX)
{
case ClassificationKey.PCT_CHANGE:
x_seed = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalPctChange[i]).ToList();
break;
case ClassificationKey.VOLUME:
x_seed = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalVolumes[i]).ToList();
break;
default:
x_seed = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalPctChange[i]).ToList();
break;
}
//If no feature is selected for the y-axis, treat the data as 1 dimensional
// To do this, each X-point for a particular equity will be placed at a unique but constant y-value
if (this.cbProtoAttributeY.SelectedIndex < 0)
{
y_seed = Enumerable.Range(1, n).Select(x => (double)x).ToList();
this.daily_chart_y_values.Add(y_seed);
}
else
{
//Otherwise increment the data from the center point
switch (keyY)
{
case ClassificationKey.PCT_CHANGE:
y_seed = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalPctChange[i]).ToList();
break;
case ClassificationKey.VOLUME:
y_seed = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalVolumes[i]).ToList();
break;
default:
y_seed = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalPctChange[i]).ToList();
break;
}
this.daily_chart_y_values.Add(this.daily_chart_y_values[i].Zip(y_seed, (x, y) => x + y).ToList());
}
this.daily_chart_x_values.Add(this.daily_chart_x_values[i].Zip(x_seed, (x, y) => x + y).ToList());
}
this.chartSlider.Maximum = this.DataSrc.HistoricalData.Constituents.Select(x => x.HistoricalPriceDate.Count).Max();
BindChartData(this.daily_chart_x_values[0], this.daily_chart_y_values[0], new List <int>(new int[n]));
}
