Supported Indicators

Least Squares Moving Average

Introduction

The Least Squares Moving Average (LSMA) first calculates a least squares regression line over the preceding time periods, and then projects it forward to the current period. In essence, it calculates what the value would be if the regression line continued. source

To view the implementation of this indicator, see the LEAN GitHub repository.

Using LSMA Indicator

To create an automatic indicator for LeastSquaresMovingAverage, call the LSMAlsma helper method from the QCAlgorithm class. The LSMAlsma method creates a LeastSquaresMovingAverage 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 LeastSquaresMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private LeastSquaresMovingAverage _lsma;

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

    public override void OnData(Slice data)
    {

        if (_lsma.IsReady)
        {
            // The current value of _lsma is represented by itself (_lsma)
            // or _lsma.Current.Value
            Plot("LeastSquaresMovingAverage", "lsma", _lsma);
            // Plot all properties of abands
            Plot("LeastSquaresMovingAverage", "intercept", _lsma.Intercept);
            Plot("LeastSquaresMovingAverage", "slope", _lsma.Slope);
        }
    }
}
class LeastSquaresMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._lsma = self.lsma(self._symbol, 20)

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

        if self._lsma.is_ready:
            # The current value of self._lsma is represented by self._lsma.current.value
            self.plot("LeastSquaresMovingAverage", "lsma", self._lsma.current.value)
            # Plot all attributes of self._lsma
            self.plot("LeastSquaresMovingAverage", "intercept", self._lsma.intercept.current.value)
            self.plot("LeastSquaresMovingAverage", "slope", self._lsma.slope.current.value)

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

You can manually create a LeastSquaresMovingAverage 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 LeastSquaresMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private LeastSquaresMovingAverage _leastsquaresmovingaverage;

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

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

        if (_leastsquaresmovingaverage.IsReady)
        {
            // The current value of _leastsquaresmovingaverage is represented by itself (_leastsquaresmovingaverage)
            // or _leastsquaresmovingaverage.Current.Value
            Plot("LeastSquaresMovingAverage", "leastsquaresmovingaverage", _leastsquaresmovingaverage);
            // Plot all properties of abands
            Plot("LeastSquaresMovingAverage", "intercept", _leastsquaresmovingaverage.Intercept);
            Plot("LeastSquaresMovingAverage", "slope", _leastsquaresmovingaverage.Slope);
        }
    }
}
class LeastSquaresMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._leastsquaresmovingaverage = LeastSquaresMovingAverage(20)

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

        if self._leastsquaresmovingaverage.is_ready:
            # The current value of self._leastsquaresmovingaverage is represented by self._leastsquaresmovingaverage.current.value
            self.plot("LeastSquaresMovingAverage", "leastsquaresmovingaverage", self._leastsquaresmovingaverage.current.value)
            # Plot all attributes of self._leastsquaresmovingaverage
            self.plot("LeastSquaresMovingAverage", "intercept", self._leastsquaresmovingaverage.intercept.current.value)
            self.plot("LeastSquaresMovingAverage", "slope", self._leastsquaresmovingaverage.slope.current.value)

For more information about this indicator, see its referencereference.

Visualization

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

LeastSquaresMovingAverage line plot.

Indicator History

To get the historical data of the LeastSquaresMovingAverage 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 LeastSquaresMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private LeastSquaresMovingAverage _lsma;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _lsma = LSMA(_symbol, 20);

        var indicatorHistory = IndicatorHistory(_lsma, _symbol, 100, Resolution.Minute);
        var timeSpanIndicatorHistory = IndicatorHistory(_lsma, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
        var timePeriodIndicatorHistory = IndicatorHistory(_lsma, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);

        // Access all attributes of indicatorHistory
        var intercept = indicatorHistory.Select(x => ((dynamic)x).Intercept).ToList();
        var slope = indicatorHistory.Select(x => ((dynamic)x).Slope).ToList();
    }
}
class LeastSquaresMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._lsma = self.lsma(self._symbol, 20)

        indicator_history = self.indicator_history(self._lsma, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._lsma, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._lsma, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
    
        # Access all attributes of indicator_history
        indicator_history_df = indicator_history.data_frame
        intercept = indicator_history_df["intercept"]
        slope = indicator_history_df["slope"]

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