private void BindCore(int startIndex)
{
int period = this.Period;
SizedQueue currentItems = new SizedQueue(period);
int currentIndex = 0;
foreach (var item in this.itemsSource)
{
object val = this.ValueBinding.GetValue(item);
currentItems.EnqueueItem((double)val);
if (currentIndex >= startIndex)
{
double currentAverage = CalculateCurrentValue(currentItems);
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentAverage;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentAverage;
this.Owner.Model.DataPointsInternal.Add(point);
}
}
currentIndex++;
}
}
protected override void BindCore()
{
int period = this.Period;
SizedQueue currentItems = new SizedQueue(period);
int currentIndex = 0;
object previousItem = null;
double value;
foreach (var item in this.itemsSource)
{
value = TrueRangeIndicatorDataSource.CalculateValue(this.HighBinding, this.LowBinding, this.CloseBinding, previousItem, item);
currentItems.EnqueueItem(value);
double currentValue = MovingAverageIndicatorDataSource.CalculateCurrentValue(currentItems);
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentValue;
this.Owner.Model.DataPointsInternal.Add(point);
}
currentIndex++;
previousItem = item;
}
}
protected double CalculateMacdValue(SizedQueue longPeriodItems, SizedQueue shortPeriodItems, int currentIndex, object item)
{
double value = (double)this.ValueBinding.GetValue(item);
longPeriodItems.EnqueueItem(value);
shortPeriodItems.EnqueueItem(value);
if (currentIndex < this.LongPeriod)
{
this.currentLongEMA = MovingAverageIndicatorDataSource.CalculateCurrentValue(longPeriodItems);
}
else
{
this.currentLongEMA = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, this.LongPeriod, value, this.currentLongEMA);
}
if (currentIndex < this.ShortPeriod)
{
this.currentShortEMA = MovingAverageIndicatorDataSource.CalculateCurrentValue(shortPeriodItems);
}
else
{
this.currentShortEMA = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, this.ShortPeriod, value, this.currentShortEMA);
}
return(this.currentShortEMA - this.currentLongEMA);
}
protected virtual void GenerateDataPoints(SizedQueue longPeriodItems, SizedQueue shortPeriodItems, SizedQueue signalPeriodItems)
{
ChartSeriesModel model = this.Owner.Model;
int currentIndex = 0;
CategoricalDataPoint point;
double signalEMA = 0d;
double macd = 0d;
foreach (var item in this.itemsSource)
{
macd = this.CalculateMacdValue(longPeriodItems, shortPeriodItems, currentIndex, item);
signalPeriodItems.EnqueueItem(macd);
signalEMA = CalculateSignal(this.signalPeriod, signalPeriodItems, currentIndex, signalEMA, macd);
if (model.DataPointsInternal.Count > currentIndex)
{
point = model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = macd - signalEMA;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = macd - signalEMA;
model.DataPointsInternal.Add(point);
}
currentIndex++;
}
}
protected override void BindCore()
{
int currentIndex = 0;
StochasticSlowIndicator owner = this.Owner as StochasticSlowIndicator;
ChartSeriesModel model = this.Owner.Model;
ChartSeriesModel signalModel = owner.SignalModel;
int currentMainPeriod = this.MainPeriod;
int currentSlowingPeriod = this.SlowingPeriod;
int currentSignalPeriod = this.SignalPeriod;
SizedQueue highValues = new SizedQueue(currentMainPeriod);
SizedQueue lowValues = new SizedQueue(currentMainPeriod);
SizedQueue fastStochValues = new SizedQueue(currentSlowingPeriod);
SizedQueue slowStochValues = new SizedQueue(currentSignalPeriod);
foreach (var item in this.itemsSource)
{
double high = (double)this.HighBinding.GetValue(item);
double low = (double)this.LowBinding.GetValue(item);
double close = (double)this.CloseBinding.GetValue(item);
double fastStochValue = CalculateMainValue(highValues, lowValues, high, low, close);
fastStochValues.EnqueueItem(fastStochValue);
double slowStochValue = MovingAverageIndicatorDataSource.CalculateCurrentValue(fastStochValues);
slowStochValues.EnqueueItem(slowStochValue);
double slowSignalValue = MovingAverageIndicatorDataSource.CalculateCurrentValue(slowStochValues);
CategoricalDataPoint point, point2;
if (model.DataPointsInternal.Count > currentIndex)
{
point = model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = slowStochValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = slowStochValue;
model.DataPointsInternal.Add(point);
}
if (signalModel.DataPointsInternal.Count > currentIndex)
{
point2 = signalModel.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point2.Value = slowSignalValue;
}
else
{
point2 = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point2.Value = slowSignalValue;
signalModel.DataPointsInternal.Add(point2);
}
currentIndex++;
}
}
private static double CalculateMainValue(SizedQueue highValues, SizedQueue lowValues, double high, double low, double close)
{
highValues.EnqueueItem(high);
lowValues.EnqueueItem(low);
double max = highValues.Max();
double min = lowValues.Min();
double mainValue = (close - min) / (max - min) * 100;
return(mainValue);
}
protected override void BindCore()
{
SizedQueue typicalPrices = new SizedQueue(this.Period);
int currentIndex = 0;
foreach (var item in this.itemsSource)
{
double high = (double)this.HighBinding.GetValue(item);
double low = (double)this.LowBinding.GetValue(item);
double close = (double)this.CloseBinding.GetValue(item);
double typicalPrice = (high + low + close) / 3d;
typicalPrices.EnqueueItem(typicalPrice);
double currentValue = 0d;
if (currentIndex > 0)
{
double typicalPriceMA = MovingAverageIndicatorDataSource.CalculateCurrentValue(typicalPrices);
double meanDeviation = 0;
foreach (var price in typicalPrices)
{
meanDeviation += Math.Abs(typicalPriceMA - price);
}
meanDeviation /= typicalPrices.Count;
currentValue = (typicalPrice - typicalPriceMA) / 0.015 / meanDeviation;
}
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentValue;
this.Owner.Model.DataPointsInternal.Add(point);
}
currentIndex++;
}
}
protected override void BindCore()
{
int period = this.Period;
SizedQueue currentItems = new SizedQueue(period);
double currentAverage = 0;
double prevEMA = 0;
int currentIndex = 0;
double multiplier = this.IsModified ? 1d / period : 2d / (1 + period);
foreach (var item in this.itemsSource)
{
double value = (double)this.ValueBinding.GetValue(item);
//// The first values are calculated as SMA
if (currentIndex < period)
{
currentItems.EnqueueItem(value);
currentAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(currentItems);
}
else
{
currentAverage = CalculateCurrentValue(multiplier, value, prevEMA);
}
prevEMA = currentAverage;
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentAverage;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentAverage;
this.Owner.Model.DataPointsInternal.Add(point);
}
currentIndex++;
}
}
protected override void BindCore()
{
BollingerBandsIndicator indicator = this.Owner as BollingerBandsIndicator;
int period = this.Period;
double deviations = this.StandardDeviations;
SizedQueue currentItems = new SizedQueue(period);
int currentIndex = 0;
double stdDeviation = 0;
foreach (var item in this.itemsSource)
{
object val = this.ValueBinding.GetValue(item);
currentItems.EnqueueItem((double)val);
double currentAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(currentItems);
// TODO: Nested loop, possible performance degrade for large data
stdDeviation = CalculateStandardDeviation(currentItems, currentAverage);
CategoricalDataPoint point, secondPoint;
if (indicator.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentAverage + (deviations * stdDeviation);
secondPoint = indicator.lowerBandModel.DataPointsInternal[currentIndex] as CategoricalDataPoint;
secondPoint.Value = currentAverage - (deviations * stdDeviation);
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentAverage + (deviations * stdDeviation);
indicator.model.DataPointsInternal.Add(point);
secondPoint = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
secondPoint.Value = currentAverage - (deviations * stdDeviation);
indicator.lowerBandModel.DataPointsInternal.Add(secondPoint);
}
currentIndex++;
}
}
protected override void BindCore()
{
int period = this.Period;
double currentAverage = 0;
double prevKAMA = 0;
int currentIndex = 0;
SizedQueue currentItems = new SizedQueue(period + 1);
foreach (var item in this.itemsSource)
{
double value = (double)this.ValueBinding.GetValue(item);
currentItems.EnqueueItem(value);
//// The raw value is used for the first elements
if (currentIndex < period)
{
currentAverage = value;
prevKAMA = value;
}
else
{
currentAverage = CalculateCurrentValue(currentItems, this.slowPeriod, this.fastPeriod, prevKAMA);
prevKAMA = currentAverage;
}
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentAverage;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentAverage;
this.Owner.Model.DataPointsInternal.Add(point);
}
currentIndex++;
}
}
private void BindCore(int startIndex)
{
int shortPeriod = this.ShortPeriod;
int longPeriod = this.LongPeriod;
SizedQueue currentItemsShort = new SizedQueue(shortPeriod);
SizedQueue currentItemsLong = new SizedQueue(longPeriod);
int currentIndex = 0;
foreach (var item in this.itemsSource)
{
double value = (double)this.ValueBinding.GetValue(item);
currentItemsShort.EnqueueItem(value);
currentItemsLong.EnqueueItem(value);
if (currentIndex >= startIndex)
{
double shortAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(currentItemsShort);
double longAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(currentItemsLong);
double currentValue = (shortAverage - longAverage) / longAverage * 100;
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentValue;
this.Owner.Model.DataPointsInternal.Add(point);
}
}
currentIndex++;
}
}
protected override void BindCore()
{
int period = this.Period;
SizedQueue currentItems = new SizedQueue(period);
SizedQueue emaOneItems = new SizedQueue(period);
SizedQueue emaTwoItems = new SizedQueue(period);
int currentIndex = 0;
double emaOne, emaTwo, emaThree;
double lastEmaOne = 0;
double lastEmaTwo = 0;
double lastEmaThree = 0;
double currentValue;
foreach (var item in this.itemsSource)
{
double value = (double)this.ValueBinding.GetValue(item);
currentItems.EnqueueItem(value);
if (currentIndex < period)
{
emaOne = MovingAverageIndicatorDataSource.CalculateCurrentValue(currentItems);
emaOneItems.EnqueueItem(emaOne);
emaTwo = MovingAverageIndicatorDataSource.CalculateCurrentValue(emaOneItems);
emaTwoItems.EnqueueItem(emaTwo);
emaThree = MovingAverageIndicatorDataSource.CalculateCurrentValue(emaTwoItems);
}
else
{
emaOne = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, period, value, lastEmaOne);
emaOneItems.EnqueueItem(emaOne);
emaTwo = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, period, emaOne, lastEmaTwo);
emaTwoItems.EnqueueItem(emaTwo);
emaThree = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, period, emaTwo, lastEmaThree);
}
if (currentIndex == 0)
{
currentValue = 0;
}
else
{
currentValue = 100 * (emaThree - lastEmaThree) / emaThree;
}
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentValue;
this.Owner.Model.DataPointsInternal.Add(point);
}
lastEmaOne = emaOne;
lastEmaTwo = emaTwo;
lastEmaThree = emaThree;
currentIndex++;
}
}
protected override void BindCore()
{
int period = this.Period;
int period2 = this.Period2;
int period3 = this.Period3;
SizedQueue items = new SizedQueue(period);
SizedQueue items2 = new SizedQueue(period2);
SizedQueue items3 = new SizedQueue(period3);
SizedQueue trueRangeItems = new SizedQueue(period);
SizedQueue trueRangeItems2 = new SizedQueue(period2);
SizedQueue trueRangeItems3 = new SizedQueue(period3);
int currentIndex = 0;
double trueHigh, trueLow, trueRange;
double high, low, close, range;
double average1, average2, average3;
double trueRangeAverage1, trueRangeAverage2, trueRangeAverage3;
double previousClose = 0;
foreach (var item in this.itemsSource)
{
high = (double)this.HighBinding.GetValue(item);
low = (double)this.LowBinding.GetValue(item);
close = (double)this.CloseBinding.GetValue(item);
if (currentIndex == 0)
{
previousClose = close;
}
trueHigh = Math.Max(high, previousClose);
trueLow = Math.Min(low, previousClose);
trueRange = trueHigh - trueLow;
range = close - trueLow;
items.EnqueueItem(range);
items2.EnqueueItem(range);
items3.EnqueueItem(range);
trueRangeItems.EnqueueItem(trueRange);
trueRangeItems2.EnqueueItem(trueRange);
trueRangeItems3.EnqueueItem(trueRange);
average1 = MovingAverageIndicatorDataSource.CalculateCurrentValue(items);
average2 = MovingAverageIndicatorDataSource.CalculateCurrentValue(items2);
average3 = MovingAverageIndicatorDataSource.CalculateCurrentValue(items3);
trueRangeAverage1 = MovingAverageIndicatorDataSource.CalculateCurrentValue(trueRangeItems);
trueRangeAverage2 = MovingAverageIndicatorDataSource.CalculateCurrentValue(trueRangeItems2);
trueRangeAverage3 = MovingAverageIndicatorDataSource.CalculateCurrentValue(trueRangeItems3);
double value = 100 * ((4 * average1 / trueRangeAverage1) + (2 * average2 / trueRangeAverage2) + (average3 / trueRangeAverage3)) / 7d;
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = value;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = value;
this.Owner.Model.DataPointsInternal.Add(point);
}
previousClose = close;
currentIndex++;
}
}
protected override void BindCore()
{
int period = this.Period;
SizedQueue losses = new SizedQueue(period);
SizedQueue gains = new SizedQueue(period);
double prevValue = 0;
double currentValue = 0;
double lossesAverage = 0;
double gainsAverage = 0;
int currentIndex = 0;
foreach (var item in this.itemsSource)
{
double value = (double)this.ValueBinding.GetValue(item);
double difference = 0d;
if (currentIndex > 0)
{
difference = Math.Abs(value - prevValue);
}
double gain = 0;
double loss = 0;
if (value > prevValue)
{
gain = difference;
loss = 0d;
}
else
{
gain = 0d;
loss = difference;
}
gains.EnqueueItem(gain);
losses.EnqueueItem(loss);
if (currentIndex < period)
{
lossesAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(losses);
gainsAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(gains);
}
else
{
gainsAverage = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, period, gain, gainsAverage);
lossesAverage = ExponentialMovingAverageIndicatorDataSource.CalculateCurrentValue(false, period, loss, lossesAverage);
currentValue = 100 - (100 / (1 + (gainsAverage / lossesAverage)));
}
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentValue;
this.Owner.Model.DataPointsInternal.Add(point);
}
prevValue = value;
currentIndex++;
}
}
protected override void BindCore()
{
int currentIndex = 0;
int period = this.Period;
int momentumShift = this.MomentumPeriod;
SizedQueue values = new SizedQueue(momentumShift);
SizedQueue upMomentumValues = new SizedQueue(period);
SizedQueue downMomentumValues = new SizedQueue(period);
double oldValue, value, currentIndicatorValue;
double upMomentumAverage, downMomentumAverage;
double up, down;
foreach (var item in this.itemsSource)
{
value = (double)this.ValueBinding.GetValue(item);
if (currentIndex == 0)
{
oldValue = value;
}
else if (currentIndex < momentumShift)
{
oldValue = values.Peek();
}
else
{
oldValue = values.DequeueItem();
}
if (oldValue > value)
{
up = 0;
down = oldValue - value;
}
else
{
up = value - oldValue;
down = 0;
}
upMomentumValues.EnqueueItem(up);
downMomentumValues.EnqueueItem(down);
upMomentumAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(upMomentumValues);
downMomentumAverage = MovingAverageIndicatorDataSource.CalculateCurrentValue(downMomentumValues);
if (Math.Round(upMomentumAverage + downMomentumAverage, 6) == 0)
{
currentIndicatorValue = 100;
}
else
{
currentIndicatorValue = 100 * upMomentumAverage / (upMomentumAverage + downMomentumAverage);
}
values.EnqueueItem(value);
CategoricalDataPoint point;
if (this.Owner.Model.DataPointsInternal.Count > currentIndex)
{
point = this.Owner.Model.DataPointsInternal[currentIndex] as CategoricalDataPoint;
point.Value = currentIndicatorValue;
}
else
{
point = this.GenerateDataPoint(item, -1) as CategoricalDataPoint;
point.Value = currentIndicatorValue;
this.Owner.Model.DataPointsInternal.Add(point);
}
currentIndex++;
}
}