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 indicator for KaufmanAdaptiveMovingAverage, call the KAMAkama helper method from the QCAlgorithm class. The KAMAkama 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 Initializeinitialize method.

public class KaufmanAdaptiveMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private KaufmanAdaptiveMovingAverage _kama;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _kama = KAMA(_symbol, 20, 10, 20);
    }

    public override void OnData(Slice data)
    {

        if (_kama.IsReady)
        {
            // The current value of _kama is represented by itself (_kama)
            // or _kama.Current.Value
            Plot("KaufmanAdaptiveMovingAverage", "kama", _kama);
        }
    }
}
class KaufmanAdaptiveMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._kama = self.kama(self._symbol, 20, 10, 20)

    def on_data(self, slice: Slice) -> None:

        if self._kama.is_ready:
            # The current value of self._kama is represented by self._kama.current.value
            self.plot("KaufmanAdaptiveMovingAverage", "kama", self._kama.current.value)

For more information about this method, see the QCAlgorithm classQCAlgorithm class.

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 Updateupdate method. The indicator will only be ready after you prime it with enough data.

public class KaufmanAdaptiveMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private KaufmanAdaptiveMovingAverage _kaufmanadaptivemovingaverage;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _kaufmanadaptivemovingaverage = new KaufmanAdaptiveMovingAverage(20, 10, 20);
    }

    public override void OnData(Slice data)
    {
        if (data.Bars.TryGetValue(_symbol, out var bar))
            _kaufmanadaptivemovingaverage.Update(bar.EndTime, bar.Close);

        if (_kaufmanadaptivemovingaverage.IsReady)
        {
            // The current value of _kaufmanadaptivemovingaverage is represented by itself (_kaufmanadaptivemovingaverage)
            // or _kaufmanadaptivemovingaverage.Current.Value
            Plot("KaufmanAdaptiveMovingAverage", "kaufmanadaptivemovingaverage", _kaufmanadaptivemovingaverage);
        }
    }
}
class KaufmanAdaptiveMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._kaufmanadaptivemovingaverage = KaufmanAdaptiveMovingAverage(20, 10, 20)

    def on_data(self, slice: Slice) -> None:
        bar = slice.bars.get(self._symbol)
        if bar:
            self._kaufmanadaptivemovingaverage.update(bar.end_time, bar.close)

        if self._kaufmanadaptivemovingaverage.is_ready:
            # The current value of self._kaufmanadaptivemovingaverage is represented by self._kaufmanadaptivemovingaverage.current.value
            self.plot("KaufmanAdaptiveMovingAverage", "kaufmanadaptivemovingaverage", self._kaufmanadaptivemovingaverage.current.value)

For more information about this indicator, see its referencereference.

Visualization

The following plot shows values for some of the KaufmanAdaptiveMovingAverage indicator properties:

KaufmanAdaptiveMovingAverage line plot.

Indicator History

To get the historical data of the KaufmanAdaptiveMovingAverage indicator, call the IndicatorHistoryself.indicator_history method. This method resets your indicator, makes a history request, and updates the indicator with the historical data. Just like with regular history requests, the IndicatorHistoryindicator_history method supports time periods based on a trailing number of bars, a trailing period of time, or a defined period of time. If you don't provide a resolution argument, it defaults to match the resolution of the security subscription. 

public class KaufmanAdaptiveMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private KaufmanAdaptiveMovingAverage _kama;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _kama = KAMA(_symbol, 20, 10, 20);

        var indicatorHistory = IndicatorHistory(_kama, _symbol, 100, Resolution.Minute);
        var timeSpanIndicatorHistory = IndicatorHistory(_kama, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
        var timePeriodIndicatorHistory = IndicatorHistory(_kama, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
    }
}
class KaufmanAdaptiveMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._kama = self.kama(self._symbol, 20, 10, 20)

        indicator_history = self.indicator_history(self._kama, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._kama, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._kama, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)

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