public override void HandleNextTick(Tick t)
{
double emaVal = EMA.HandleNextTick(t);
LRValues.Enqueue(emaVal);
double[] Y = LRValues.ToArray();
double stdErr = 0;
if (Y.Count() == X.Length)
{
Vector x_data = new Vector(X);
Vector y_data = new Vector(LRValues.ToArray());
var poly = new PolynomialRegression(x_data, y_data, 2);
for (int i = 0; i < Period; i++)
{
double x = (i);
double y = poly.Fit(x);
stdErr += Math.Pow(LRValues.ToArray()[i] - y, 2);
}
stdErr = Math.Sqrt(stdErr);
}
HighData.Enqueue(EMA[0] + STDEV * stdErr);
MiddleData.Enqueue(EMA[0]);
LowData.Enqueue(EMA[0] - STDEV * stdErr);
}
public override void HandleNextTick(Tick t)
{
MiddleData.Enqueue(MIDDLE.HandleNextTick(t));
var stdev = MIDDLE.ToArray().StandardDeviation();
HighData.Enqueue(MIDDLE[0] + stdev * 2);
LowData.Enqueue(MIDDLE[0] - stdev * 2);
}
public override void HandleNextTick(Tick t)
{
double emaVal = EMA.HandleNextTick(t);
LRValues.Enqueue(emaVal);
double[] Y = LRValues.ToArray();
LinearRegression l = new LinearRegression();
l.Model(Y);
HighData.Enqueue(EMA[0] + STDEV * l.STDERR);
MiddleData.Enqueue(EMA[0]);
LowData.Enqueue(EMA[0] - STDEV * l.STDERR);
}
IEnumerable <float> CalcPatternsCore(string methodName)
{
using (Py.GIL()) {
dynamic np = Py.Import("numpy");
dynamic ta = Py.Import("talib._ta_lib");
dynamic npopen = np.array(OpenData.Select(x => (float)x).ToList());
dynamic npclose = np.array(CloseData.Select(x => (float)x).ToList());
dynamic nphigh = np.array(HighData.Select(x => (float)x).ToList());
dynamic nplow = np.array(LowData.Select(x => (float)x).ToList());
IEnumerable <float> result = ta.CDL3OUTSIDE(npopen, nphigh, nplow, npclose).AsManagedObject(typeof(float[]));
var val = result.FirstOrDefault(x => x != 0);
return(result);
}
}
public double HI(int index)
{
return((HighData.Count > index) ? HighData.ToArray()[HighData.Count - index - 1] : double.NaN);
}
