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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 indicators for SqueezeMomentum, call the SM helper method from the QCAlgorithm class. The SM 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 initialize method.

Select Language: 
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)

The following reference table describes the SM method:

sm(symbol, bollinger_period=20, bollinger_multiplier=2.0, keltner_period=20, keltner_multiplier=1.5, resolution=None, selector=None)[source]

Creates a Squeeze Momentum indicator to identify market squeezes and potential breakouts. Compares Bollinger Bands and Keltner Channels to signal low or high volatility periods.

Parameters:
  • symbol (Symbol) — The symbol for which the indicator is calculated.
  • bollinger_period (int, optional) — The period for Bollinger Bands.
  • bollinger_multiplier (float, optional) — The multiplier for the Bollinger Bands' standard deviation.
  • keltner_period (int, optional) — The period for Keltner Channels.
  • keltner_multiplier (float, optional) — The multiplier for the Average True Range in Keltner Channels.
  • resolution (Resolution, optional) — The resolution of the data.
  • selector (Callable[IBaseData, IBaseDataBar], optional) — x.Value).
Returns:

The configured Squeeze Momentum indicator.

Return type:

SqueezeMomentum

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 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 update method with a TradeBar or QuoteBar. The indicator will only be ready after you prime it with enough data.

Select Language: 
class SqueezeMomentumAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._sm = SqueezeMomentum("SM", 20, 2, 20, 1.5)

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

To register a manual indicator for automatic updates with the security data, call the register_indicator method.

Select Language: 
class SqueezeMomentumAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._sm = SqueezeMomentum("SM", 20, 2, 20, 1.5)
        self.register_indicator(self._symbol, self._sm, Resolution.DAILY)

    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)

The following reference table describes the SqueezeMomentum constructor:

SqueezeMomentum

class QuantConnect.Indicators.SqueezeMomentum[source]

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).

get_enumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

reset()

Resets the state of the indicator, including all sub-indicators.

to_detailed_string()

Provides a more detailed string of this indicator in the form of {Name} - {Value}

Return type:

str

update(time, value)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
  • time (datetime)
  • value (float)
Return type:

bool

update(input)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
  • input (IBaseData)
Return type:

bool

property bollinger_bands

The Bollinger Bands indicator used to calculate the upper, lower, and middle bands.

Returns:

The Bollinger Bands indicator used to calculate the upper, lower, and middle bands.

Return type:

BollingerBands

property consolidators

The data consolidators associated with this indicator if any

Returns:

The data consolidators associated with this indicator if any

Return type:

ISet[IDataConsolidator]

property current

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property is_ready

Indicates whether the indicator is ready and has enough data for computation. The indicator is ready when both the Bollinger Bands and the Average True Range are ready.

Returns:

Indicates whether the indicator is ready and has enough data for computation. The indicator is ready when both the Bollinger Bands and the Average True Range are ready.

Return type:

bool

property item

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Returns:

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Return type:

IndicatorDataPoint

property keltner_channels

The Keltner Channels indicator used to calculate the upper, lower, and middle channels.

Returns:

The Keltner Channels indicator used to calculate the upper, lower, and middle channels.

Return type:

KeltnerChannels

property name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

str

property previous

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property samples

Gets the number of samples processed by this indicator

Returns:

Gets the number of samples processed by this indicator

Return type:

int

property warm_up_period

Gets the warm-up period required for the indicator to be ready. This is determined by the warm-up period of the Bollinger Bands indicator.

Returns:

Gets the warm-up period required for the indicator to be ready. This is determined by the warm-up period of the Bollinger Bands indicator.

Return type:

int

property window

A rolling window keeping a history of the indicator values of a given period

Returns:

A rolling window keeping a history of the indicator values of a given period

Return type:

RollingWindow[IndicatorDataPoint]

Visualization

The following image shows plot values of selected properties of SqueezeMomentum using the plotly library.

SqueezeMomentum line plot.

Indicator History

To get the historical data of the SqueezeMomentum indicator, call the self.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 indicator_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.

Select Language: 
class SqueezeMomentumAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        sm = self.sm(self._symbol, 20, 2, 20, 1.5)
        count_indicator_history = self.indicator_history(sm, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(sm, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(sm, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)

To make the indicator_history method update the indicator with an alternative price field instead of the close (or mid-price) of each bar, pass a selector argument.

Select Language: 
indicator_history = self.indicator_history(sm, 100, Resolution.MINUTE, lambda bar: bar.high)
indicator_history_df = indicator_history.data_frame

To access the properties of the indicator history, index the DataFrame with the property name.

Select Language: 
bollinger_bands = indicator_history_df["bollinger_bands"]
keltner_channels = indicator_history_df["keltner_channels"]

# Alternative way
# bollinger_bands = indicator_history_df.bollinger_bands
# keltner_channels = indicator_history_df.keltner_channels

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