/// <summary>
/// Creates a new Stochastics Indicator from the specified periods.
/// </summary>
/// <param name="name">The name of this indicator.</param>
/// <param name="period">The period given to calculate the Fast %K</param>
/// <param name="kPeriod">The K period given to calculated the Slow %K</param>
/// <param name="dPeriod">The D period given to calculated the Slow %D</param>
public Stochastic(string name, int period, int kPeriod, int dPeriod)
: base(name)
{
_maximum = new Maximum(name + "_Max", period);
_minimum = new Minimum(name + "_Min", period);
_sumFastK = new Sum(name + "_SumFastK", kPeriod);
_sumSlowK = new Sum(name + "_SumD", dPeriod);
FastStoch = new FunctionalIndicator(name + "_FastStoch",
(time, input) => ComputeFastStoch(period, time, input),
fastStoch => _maximum.IsReady,
() => { }
);
StochK = new FunctionalIndicator(name + "_StochK",
(time, input) => ComputeStochK(period, kPeriod, time, input),
stochK => _maximum.IsReady,
() => { }
);
StochD = new FunctionalIndicator(
name + "_StochD",
(time, input) => ComputeStochD(period, kPeriod, dPeriod),
stochD => _maximum.IsReady,
() => { }
);
WarmUpPeriod = period;
}
/// <summary>
/// Creates a new IchimokuKinkoHyo indicator from the specific periods
/// </summary>
/// <param name="name">The name of this indicator</param>
/// <param name="tenkanPeriod">The Tenkan-sen period</param>
/// <param name="kijunPeriod">The Kijun-sen period</param>
/// <param name="senkouAPeriod">The Senkou A Span period</param>
/// <param name="senkouBPeriod">The Senkou B Span period</param>
/// <param name="senkouADelayPeriod">The Senkou A Span delay</param>
/// <param name="senkouBDelayPeriod">The Senkou B Span delay</param>
public IchimokuKinkoHyo(string name, int tenkanPeriod = 9, int kijunPeriod = 26, int senkouAPeriod = 26, int senkouBPeriod = 52, int senkouADelayPeriod = 26, int senkouBDelayPeriod = 26)
: base(name)
{
WarmUpPeriod = Math.Max(tenkanPeriod + senkouADelayPeriod, kijunPeriod + senkouADelayPeriod);
WarmUpPeriod = Math.Max(WarmUpPeriod, senkouBPeriod + senkouBDelayPeriod);
TenkanMaximum = new Maximum(name + "_TenkanMax", tenkanPeriod);
TenkanMinimum = new Minimum(name + "_TenkanMin", tenkanPeriod);
KijunMaximum = new Maximum(name + "_KijunMax", kijunPeriod);
KijunMinimum = new Minimum(name + "_KijunMin", kijunPeriod);
SenkouBMaximum = new Maximum(name + "_SenkouBMaximum", senkouBPeriod);
SenkouBMinimum = new Minimum(name + "_SenkouBMinimum", senkouBPeriod);
DelayedTenkanSenkouA = new Delay(name + "DelayedTenkan", senkouADelayPeriod);
DelayedKijunSenkouA = new Delay(name + "DelayedKijun", senkouADelayPeriod);
DelayedMaximumSenkouB = new Delay(name + "DelayedMax", senkouBDelayPeriod);
DelayedMinimumSenkouB = new Delay(name + "DelayedMin", senkouBDelayPeriod);
Chikou = new Delay(name + "_Chikou", senkouADelayPeriod);
SenkouA = new FunctionalIndicator(
name + "_SenkouA",
(time, input) => SenkouA.IsReady ? (DelayedTenkanSenkouA + DelayedKijunSenkouA) / 2 : Constants.Zero,
senkouA => DelayedTenkanSenkouA.IsReady && DelayedKijunSenkouA.IsReady,
() => {
Tenkan.Reset();
Kijun.Reset();
});
SenkouB = new FunctionalIndicator(
name + "_SenkouB",
(time, input) => SenkouB.IsReady ? (DelayedMaximumSenkouB + DelayedMinimumSenkouB) / 2 : Constants.Zero,
senkouA => DelayedMaximumSenkouB.IsReady && DelayedMinimumSenkouB.IsReady,
() => {
Tenkan.Reset();
Kijun.Reset();
});
Tenkan = new FunctionalIndicator(
name + "_Tenkan",
(time, input) => Tenkan.IsReady ? (TenkanMaximum + TenkanMinimum) / 2 : Constants.Zero,
tenkan => TenkanMaximum.IsReady && TenkanMinimum.IsReady,
() => {
TenkanMaximum.Reset();
TenkanMinimum.Reset();
});
Kijun = new FunctionalIndicator(
name + "_Kijun",
(time, input) => Kijun.IsReady ? (KijunMaximum + KijunMinimum) / 2 : Constants.Zero,
kijun => KijunMaximum.IsReady && KijunMinimum.IsReady,
() => {
KijunMaximum.Reset();
KijunMinimum.Reset();
});
}
/// <summary>
/// Creates a new AverageTrueRange indicator using the specified period and moving average type
/// </summary>
/// <param name="name">The name of this indicator</param>
/// <param name="period">The smoothing period used to smooth the true range values</param>
/// <param name="movingAverageType">The type of smoothing used to smooth the true range values</param>
public AverageTrueRange(string name, int period, MovingAverageType movingAverageType = MovingAverageType.Wilders)
: base(name)
{
WarmUpPeriod = period;
_smoother = movingAverageType.AsIndicator($"{name}_{movingAverageType}", period);
TrueRange = new FunctionalIndicator(name + "_TrueRange", (time, currentBar) => {
// in our Forward function we'll just call the ComputeTrueRange
var nextValue = ComputeTrueRange(_previous, currentBar);
_previous = currentBar;
return(nextValue);
} // in our IsReady function we just need at least one sample
, trueRangeIndicator => trueRangeIndicator.Samples >= 1
);
}
/// <summary>
/// Creates a new AroonOscillator from the specified up/down periods.
/// </summary>
/// <param name="name">The name of this indicator</param>
/// <param name="upPeriod">The lookback period to determine the highest high for the AroonDown</param>
/// <param name="downPeriod">The lookback period to determine the lowest low for the AroonUp</param>
public AroonOscillator(string name, int upPeriod, int downPeriod)
: base(name)
{
var max = new Maximum(name + "_Max", upPeriod + 1);
AroonUp = new FunctionalIndicator(name + "_AroonUp",
(time, input) => ComputeAroonUp(upPeriod, max, time, input),
aroonUp => max.IsReady,
() => max.Reset()
);
var min = new Minimum(name + "_Min", downPeriod + 1);
AroonDown = new FunctionalIndicator(name + "_AroonDown",
(time, input) => ComputeAroonDown(downPeriod, min, time, input),
aroonDown => min.IsReady,
() => min.Reset()
);
WarmUpPeriod = 1 + Math.Max(upPeriod, downPeriod);
}
/// <summary>
/// Initializes a new instance of the KeltnerChannels class
/// </summary>
/// <param name="name">The name of this indicator</param>
/// <param name="period">The period of the average true range and moving average (middle band)</param>
/// <param name="k">The number of multiples specifying the distance between the middle band and upper or lower bands</param>
/// <param name="movingAverageType">The type of moving average to be used</param>
public KeltnerChannels(string name, int period, double k, MovingAverageType movingAverageType = MovingAverageType.Simple)
: base(name)
{
_k = k;
WarmUpPeriod = period;
//Initialise ATR and SMA
AverageTrueRange = new AverageTrueRange(name + "_AverageTrueRange", period, MovingAverageType.Simple);
MiddleBand = movingAverageType.AsIndicator(name + "_MiddleBand", period);
//Compute Lower Band
LowerBand = new FunctionalIndicator(name + "_LowerBand",
(time, input) => MiddleBand.IsReady ? MiddleBand - AverageTrueRange * _k : Constants.Zero,
lowerBand => MiddleBand.IsReady,
() => MiddleBand.Reset()
);
//Compute Upper Band
UpperBand = new FunctionalIndicator(name + "_UpperBand",
(time, input) => MiddleBand.IsReady ? MiddleBand + AverageTrueRange * _k : Constants.Zero,
upperBand => MiddleBand.IsReady,
() => MiddleBand.Reset()
);
}
/// <summary>
/// Initializes a new instance of the <see cref="AverageDirectionalIndex"/> class.
/// </summary>
/// <param name="name">The name.</param>
/// <param name="period">The period.</param>
public AverageDirectionalIndex(string name, int period)
: base(name)
{
_period = period;
_trueRange = new FunctionalIndicator(name + "_TrueRange",
ComputeTrueRange,
isReady => _previousInput != null
);
_directionalMovementPlus = new FunctionalIndicator(name + "_PositiveDirectionalMovement",
ComputePositiveDirectionalMovement,
isReady => _previousInput != null
);
_directionalMovementMinus = new FunctionalIndicator(name + "_NegativeDirectionalMovement",
ComputeNegativeDirectionalMovement,
isReady => _previousInput != null
);
PositiveDirectionalIndex = new FunctionalIndicator(name + "_PositiveDirectionalIndex",
(time, input) => {
// Computes the Plus Directional Indicator(+DI period).
if (_smoothedTrueRange != 0 && _smoothedDirectionalMovementPlus.IsReady)
{
return((DoubleArray)100d * _smoothedDirectionalMovementPlus / _smoothedTrueRange);
}
return(Constants.Zero);
},
positiveDirectionalIndex => _smoothedDirectionalMovementPlus.IsReady,
() => {
_directionalMovementPlus.Reset();
_trueRange.Reset();
}
);
NegativeDirectionalIndex = new FunctionalIndicator(name + "_NegativeDirectionalIndex",
(time, input) => {
// Computes the Minus Directional Indicator(-DI period).
if (_smoothedTrueRange != 0 && _smoothedDirectionalMovementMinus.IsReady)
{
return(100d * _smoothedDirectionalMovementMinus / _smoothedTrueRange);
}
return(Constants.Zero);
},
negativeDirectionalIndex => _smoothedDirectionalMovementMinus.IsReady,
() => {
_directionalMovementMinus.Reset();
_trueRange.Reset();
}
);
_smoothedTrueRange = new FunctionalIndicator(name + "_SmoothedTrueRange",
(time, input) => {
// Computes the Smoothed True Range value.
var value = Samples > _period + 1 ? _smoothedTrueRange / _period : Constants.Zero;
return(_smoothedTrueRange + _trueRange - value);
},
isReady => Samples > period
);
_smoothedDirectionalMovementPlus = new FunctionalIndicator(name + "_SmoothedDirectionalMovementPlus",
(time, input) => {
// Computes the Smoothed Directional Movement Plus value.
var value = Samples > _period + 1 ? _smoothedDirectionalMovementPlus / _period : Constants.Zero;
return(_smoothedDirectionalMovementPlus + _directionalMovementPlus - value);
},
isReady => Samples > period
);
_smoothedDirectionalMovementMinus = new FunctionalIndicator(name + "_SmoothedDirectionalMovementMinus",
(time, input) => {
// Computes the Smoothed Directional Movement Minus value.
var value = Samples > _period + 1 ? _smoothedDirectionalMovementMinus / _period : Constants.Zero;
return(_smoothedDirectionalMovementMinus + _directionalMovementMinus - value);
},
isReady => Samples > period
);
_averageDirectionalIndex = new WilderMovingAverage(period);
}