public KdjStochastics(int kWindowSize, int kDecay, int jCoeff)
: base(0)
{
if (kWindowSize <= 0 || kDecay <= 0 || jCoeff <= 0)
{
throw new ArgumentOutOfRangeException();
}
_kDecay = kDecay;
_jCoeff = jCoeff;
_highest = new Highest(kWindowSize);
_lowest = new Lowest(kWindowSize);
Values = new double[3];
}
public StopAndReverse(int windowSize, double accelerateFactor, double accelerateFactorStep, double maxAccelerateFactor)
: base(windowSize)
{
if (windowSize <= 1 ||
accelerateFactor <= 0.0 ||
accelerateFactorStep <= 0.0 ||
maxAccelerateFactor <= accelerateFactor)
{
throw new ArgumentException();
}
_highestMetric = new Highest(windowSize);
_lowestMetric = new Lowest(windowSize);
_initialAccelerateFactor = accelerateFactor;
_accelerateFactorStep = accelerateFactorStep;
_maxAccelerateFactor = maxAccelerateFactor;
_accelerateFactor = _initialAccelerateFactor;
}
public FractalAdaptiveMovingAverageExtend(int period, int fastMovingAverage, int slowMovingAverage)
: base(0)
{
if (period <= 0)
{
throw new ArgumentException("period must be greater than 0");
}
if (period % 2 != 0)
{
throw new ArgumentException("period must be even number");
}
if (slowMovingAverage <= 0 || fastMovingAverage <= 0)
{
throw new ArgumentException("slow moving average or fast moving average must be greather than 0");
}
if (fastMovingAverage >= slowMovingAverage)
{
throw new ArgumentException("fast moving average must be smaller than slow moving average");
}
_sc = slowMovingAverage;
_fc = fastMovingAverage;
_h = Math.Max(_period - 1, Even((_sc - _fc) / 2) + _fc);
_period = period;
_halfPeriod = period / 2;
_minAlpha = 2.0 / (_sc + 1);
_maxAlpha = 1.0;
_w = Math.Log(_minAlpha);
_initialFrama = new MovingAverage(_h);
_firstHalfHighest = new Highest(_halfPeriod);
_firstHalfLowest = new Lowest(_halfPeriod);
_secondHalfHighest = new Highest(_halfPeriod);
_secondHalfLowest = new Lowest(_halfPeriod);
}
public WilliamOverboughtOversoldIndex(int windowSize)
: base(0)
{
_highest = new Highest(windowSize);
_lowest = new Lowest(windowSize);
}
