Supported Indicators

Squeeze Momentum

Introduction

The SqueezeMomentum indicator calculates whether the market is in a "squeeze" condition, determined by comparing Bollinger Bands to Keltner Channels. When the Bollinger Bands are inside the Keltner Channels, the indicator returns 1 (squeeze on). Otherwise, it returns -1 (squeeze off).

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

Using SM Indicator

To create an automatic indicator for SqueezeMomentum, call the SMsm helper method from the QCAlgorithm class. The SMsm method creates a SqueezeMomentum 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 SqueezeMomentumAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private SqueezeMomentum _sm;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _sm = SM(_symbol, 20, 2, 20, 1.5m);
    }

    public override void OnData(Slice data)
    {

        if (_sm.IsReady)
        {
            // The current value of _sm is represented by itself (_sm)
            // or _sm.Current.Value
            Plot("SqueezeMomentum", "sm", _sm);
            // Plot all properties of abands
            Plot("SqueezeMomentum", "bollingerbands", _sm.BollingerBands);
            Plot("SqueezeMomentum", "keltnerchannels", _sm.KeltnerChannels);
        }
    }
}
class SqueezeMomentumAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._sm = self.sm(self._symbol, 20, 2, 20, 1.5)

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

        if self._sm.is_ready:
            # The current value of self._sm is represented by self._sm.current.value
            self.plot("SqueezeMomentum", "sm", self._sm.current.value)
            # Plot all attributes of self._sm
            self.plot("SqueezeMomentum", "bollinger_bands", self._sm.bollinger_bands.current.value)
            self.plot("SqueezeMomentum", "keltner_channels", self._sm.keltner_channels.current.value)

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

You can manually create a SqueezeMomentum 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 SqueezeMomentumAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private SqueezeMomentum _squeezemomentum;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _squeezemomentum = new SqueezeMomentum("SPY", 20, 2m, 20, 1.5m);
    }

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

        if (_squeezemomentum.IsReady)
        {
            // The current value of _squeezemomentum is represented by itself (_squeezemomentum)
            // or _squeezemomentum.Current.Value
            Plot("SqueezeMomentum", "squeezemomentum", _squeezemomentum);
            // Plot all properties of abands
            Plot("SqueezeMomentum", "bollingerbands", _squeezemomentum.BollingerBands);
            Plot("SqueezeMomentum", "keltnerchannels", _squeezemomentum.KeltnerChannels);
        }
    }
}
class SqueezeMomentumAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._squeezemomentum = SqueezeMomentum(20, 2, 20, 1.5)

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

        if self._squeezemomentum.is_ready:
            # The current value of self._squeezemomentum is represented by self._squeezemomentum.current.value
            self.plot("SqueezeMomentum", "squeezemomentum", self._squeezemomentum.current.value)
            # Plot all attributes of self._squeezemomentum
            self.plot("SqueezeMomentum", "bollinger_bands", self._squeezemomentum.bollinger_bands.current.value)
            self.plot("SqueezeMomentum", "keltner_channels", self._squeezemomentum.keltner_channels.current.value)

For more information about this indicator, see its referencereference.

Visualization

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

SqueezeMomentum line plot.

Indicator History

To get the historical data of the SqueezeMomentum 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 SqueezeMomentumAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private SqueezeMomentum _sm;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _sm = SM(_symbol, 20, 2, 20, 1.5m);

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

        // Access all attributes of indicatorHistory
        var bollingerBands = indicatorHistory.Select(x => ((dynamic)x).BollingerBands).ToList();
        var keltnerChannels = indicatorHistory.Select(x => ((dynamic)x).KeltnerChannels).ToList();
    }
}
class SqueezeMomentumAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._sm = self.sm(self._symbol, 20, 2, 20, 1.5)

        indicator_history = self.indicator_history(self._sm, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._sm, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._sm, 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
        bollinger_bands = indicator_history_df["bollingerbands"]
        keltner_channels = indicator_history_df["keltnerchannels"]

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