book
Checkout our new book! Hands on AI Trading with Python, QuantConnect, and AWS Learn More arrow

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 indicators for LeastSquaresMovingAverage, call the LSMA helper method from the QCAlgorithm class. The LSMA 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 initialize method.

Select Language: 
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)

The following reference table describes the LSMA method:

lsma(symbol, period, resolution=None, selector=None)[source]

Creates and registers a new Least Squares Moving Average instance.

Parameters:
  • symbol (Symbol) — The symbol whose LSMA we seek.
  • period (int) — The LSMA period. Normally 14.
  • resolution (Resolution, optional) — The resolution.
  • selector (Callable[IBaseData, float], optional) — Selects a value from the BaseData to send into the indicator, if null defaults to casting the input value to a TradeBar.
Returns:

A LeastSquaredMovingAverage configured with the specified period

Return type:

LeastSquaresMovingAverage

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 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 update method with time/number pair or an IndicatorDataPoint. The indicator will only be ready after you prime it with enough data.

Select Language: 
class LeastSquaresMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._lsma = LeastSquaresMovingAverage(20)

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

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

Select Language: 
class LeastSquaresMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._lsma = LeastSquaresMovingAverage(20)
        self.register_indicator(self._symbol, self._lsma, Resolution.DAILY)

    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)

The following reference table describes the LeastSquaresMovingAverage constructor:

LeastSquaresMovingAverage

class QuantConnect.Indicators.LeastSquaresMovingAverage[source]

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: https://rtmath.net/assets/docs/finanalysis/html/b3fab79c-f4b2-40fb-8709-fdba43cdb363.htm

get_enumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

reset()

Resets this indicator and all sub-indicators (Intercept, Slope)

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 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 intercept

The point where the regression line crosses the y-axis (price-axis)

Returns:

The point where the regression line crosses the y-axis (price-axis)

Return type:

IndicatorBase[IndicatorDataPoint]

property is_ready

Gets a flag indicating when this indicator is ready and fully initialized

Returns:

Gets a flag indicating when this indicator is ready and fully initialized

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 name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

str

property period

Gets the period of this window indicator

Returns:

Gets the period of this window indicator

Return type:

int

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 slope

The regression line slope

Returns:

The regression line slope

Return type:

IndicatorBase[IndicatorDataPoint]

property warm_up_period

Required period, in data points, for the indicator to be ready and fully initialized.

Returns:

Required period, in data points, for the indicator to be ready and fully initialized.

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 LeastSquaresMovingAverage using the plotly library.

LeastSquaresMovingAverage line plot.

Indicator History

To get the historical data of the LeastSquaresMovingAverage 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 LeastSquaresMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        lsma = self.lsma(self._symbol, 20)
        count_indicator_history = self.indicator_history(lsma, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(lsma, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(lsma, 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(lsma, 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: 
intercept = indicator_history_df["intercept"]
slope = indicator_history_df["slope"]

# Alternative way
# intercept = indicator_history_df.intercept
# slope = indicator_history_df.slope

You can also see our Videos. You can also get in touch with us via Discord.

Did you find this page helpful?

Contribute to the documentation: