Supported Indicators
Kaufman Adaptive Moving Average
Introduction
This indicator computes the Kaufman Adaptive Moving Average (KAMA). The Kaufman Adaptive Moving Average is calculated as explained here: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average
To view the implementation of this indicator, see the LEAN GitHub repository.
Using KAMA Indicator
To create an automatic indicators for KaufmanAdaptiveMovingAverage, call the KAMA helper method from the QCAlgorithm class. The KAMA method creates a KaufmanAdaptiveMovingAverage object, hooks it up for automatic updates, and returns it so you can used it in your algorithm. In most cases, you should call the helper method in the Initialize method.
public class KaufmanAdaptiveMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private KaufmanAdaptiveMovingAverage _kama;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_kama = KAMA("SPY", 20, 10, 20);
}
public override void OnData(Slice data)
{
if (_kama.IsReady)
{
Plot("KaufmanAdaptiveMovingAverage", "kama", _kama);
}
}
} class KaufmanAdaptiveMovingAverageAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.kama = self.KAMA("SPY", 20, 10, 20)
def OnData(self, slice: Slice) -> None:
if self.kama.IsReady:
self.Plot("KaufmanAdaptiveMovingAverage", "kama", self.kama.Current.Value)
The following reference table describes the KAMA method:
KAMA()1/2
KaufmanAdaptiveMovingAverage QuantConnect.Algorithm.QCAlgorithm.KAMA (
Symbol symbol,
Int32 period,
*Nullable<Resolution> resolution,
*Func<IBaseData, Decimal> selector
)
Creates a new KaufmanAdaptiveMovingAverage indicator.
KAMA()2/2
KaufmanAdaptiveMovingAverage QuantConnect.Algorithm.QCAlgorithm.KAMA (
Symbol symbol,
Int32 period,
Int32 fastEmaPeriod,
Int32 slowEmaPeriod,
*Nullable<Resolution> resolution,
*Func<IBaseData, Decimal> selector
)
Creates a new KaufmanAdaptiveMovingAverage indicator.
If you don't provide a resolution, it defaults to the security resolution. If you provide a resolution, it must be greater than or equal to the resolution of the security. For instance, if you subscribe to hourly data for a security, you should update its indicator with data that spans 1 hour or longer.
For more information about the selector argument, see Alternative Price Fields.
For more information about plotting indicators, see Plotting Indicators.
You can manually create a KaufmanAdaptiveMovingAverage indicator, so it doesn't automatically update. Manual indicators let you update their values with any data you choose.
Updating your indicator manually enables you to control when the indicator is updated and what data you use to update it. To manually update the indicator, call the Update method with time/number pair, or an IndicatorDataPoint. The indicator will only be ready after you prime it with enough data.
public class KaufmanAdaptiveMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private KaufmanAdaptiveMovingAverage _kama;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_kama = new KaufmanAdaptiveMovingAverage(20, 10, 20);
}
public override void OnData(Slice data)
{
if (data.Bars.TryGeValue(_symbol, out var bar))
{
_kama.Update(bar.EndTime, bar.Close);
}
if (_kama.IsReady)
{
Plot("KaufmanAdaptiveMovingAverage", "kama", _kama);
}
}
} class KaufmanAdaptiveMovingAverageAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.kama = KaufmanAdaptiveMovingAverage(20, 10, 20)
def OnData(self, slice: Slice) -> None:
bar = data.Bars.get(self.symbol)
if bar:
self.kama.Update(bar.EndTime, bar.Close)
if self.kama.IsReady:
self.Plot("KaufmanAdaptiveMovingAverage", "kama", self.kama.Current.Value)
To register a manual indicator for automatic updates with the security data, call the RegisterIndicator method.
public class KaufmanAdaptiveMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private KaufmanAdaptiveMovingAverage _kama;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_kama = new KaufmanAdaptiveMovingAverage(20, 10, 20);
RegisterIndicator(_symbol, _kama, Resolution.Daily);
}
public override void OnData(Slice data)
{
if (_kama.IsReady)
{
Plot("KaufmanAdaptiveMovingAverage", "kama", _kama);
}
}
} class KaufmanAdaptiveMovingAverageAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.kama = KaufmanAdaptiveMovingAverage(20, 10, 20)
self.RegisterIndicator(self.symbol, self.kama, Resolution.Daily)
def OnData(self, slice: Slice) -> None:
if self.kama.IsReady:
self.Plot("KaufmanAdaptiveMovingAverage", "kama", self.kama.Current.Value)
The following reference table describes the KaufmanAdaptiveMovingAverage constructor:
KaufmanAdaptiveMovingAverage()1/2
KaufmanAdaptiveMovingAverage QuantConnect.Indicators.KaufmanAdaptiveMovingAverage (
string name,
int period,
*int fastEmaPeriod,
*int slowEmaPeriod
)
Initializes a new instance of the KaufmanAdaptiveMovingAverag class using the specified name and period.
KaufmanAdaptiveMovingAverage()2/2
KaufmanAdaptiveMovingAverage QuantConnect.Indicators.KaufmanAdaptiveMovingAverage (
int period,
*int fastEmaPeriod,
*int slowEmaPeriod
)
Initializes a new instance of the KaufmanAdaptiveMovingAverag class using the specified period.
Visualization
The following image shows plot values of selected properties of KaufmanAdaptiveMovingAverage using the plotly library.
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