# Supported Indicators

## Linear Weighted Moving Average

### Introduction

This indicator represents the traditional Weighted Moving Average indicator. The weight are linearly distributed according to the number of periods in the indicator. For example, a 4 period indicator will have a numerator of (4 * window[0]) + (3 * window[1]) + (2 * window[2]) + window[3] and a denominator of 4 + 3 + 2 + 1 = 10 During the warm up period, IsReady will return False, but the LWMA will still be computed correctly because the denominator will be the minimum of Samples factorial or Size factorial and the computation iterates over that minimum value. The RollingWindow of inputs is created when the indicator is created. A RollingWindow of LWMAs is not saved. That is up to the caller.

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

### Using LWMA Indicator

To create an automatic indicators for LinearWeightedMovingAverage, call the LWMA helper method from the QCAlgorithm class. The LWMA method creates a LinearWeightedMovingAverage 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 LinearWeightedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private LinearWeightedMovingAverage _lwma;

public override void Initialize()
{
_lwma = LWMA(_symbol, 20);
}

public override void OnData(Slice data)
{
{
// The current value of _lwma is represented by itself (_lwma)
// or _lwma.Current.Value
Plot("LinearWeightedMovingAverage", "lwma", _lwma);

}
}
}
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._lwma = self.lwma(self._symbol, 20)

def on_data(self, slice: Slice) -> None:
# The current value of self._lwma is represented by self._lwma.current.value
self.plot("LinearWeightedMovingAverage", "lwma", self._lwma.current.value)



The following reference table describes the LWMA method:

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

Creates a new LinearWeightedMovingAverage indicator. This indicator will linearly distribute the weights across the periods.

Parameters:
• symbol (Symbol) — The symbol whose LWMA we want
• period (int) — The period over which to compute the LWMA
• resolution (Resolution, optional) — The resolution
• selector (Callable[IBaseData, float], optional) — x.Value)
Return type:

LinearWeightedMovingAverage

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

Creates a new LinearWeightedMovingAverage indicator. This indicator will linearly distribute the weights across the periods.

Parameters:
• symbol (Symbol) — The symbol whose LWMA we want
• period (Int32) — The period over which to compute the LWMA
• resolution (Resolution, optional) — The resolution
• selector (Func<IBaseData, Decimal>, optional) — x.Value)
Return type:

LinearWeightedMovingAverage

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.

You can manually create a LinearWeightedMovingAverage 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 with time/number pair or an IndicatorDataPoint. The indicator will only be ready after you prime it with enough data.

public class LinearWeightedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private LinearWeightedMovingAverage _lwma;

public override void Initialize()
{
_lwma = new LinearWeightedMovingAverage(20);
}

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

{
// The current value of _lwma is represented by itself (_lwma)
// or _lwma.Current.Value
Plot("LinearWeightedMovingAverage", "lwma", _lwma);

}
}
}
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._lwma = LinearWeightedMovingAverage(20)

def on_data(self, slice: Slice) -> None:
bar = slice.bars.get(self._symbol)
if bar:
self._lwma.update(bar.EndTime, bar.Close)
# The current value of self._lwma is represented by self._lwma.current.value
self.plot("LinearWeightedMovingAverage", "lwma", self._lwma.current.value)



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

public class LinearWeightedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private LinearWeightedMovingAverage _lwma;

public override void Initialize()
{
_lwma = new LinearWeightedMovingAverage(20);
RegisterIndicator(_symbol, _lwma, Resolution.Daily);
}

public override void OnData(Slice data)
{
{
// The current value of _lwma is represented by itself (_lwma)
// or _lwma.Current.Value
Plot("LinearWeightedMovingAverage", "lwma", _lwma);

}
}
}
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._lwma = LinearWeightedMovingAverage(20)
self.register_indicator(self._symbol, self._lwma, Resolution.DAILY)

def on_data(self, slice: Slice) -> None:
# The current value of self._lwma is represented by self._lwma.current.value
self.plot("LinearWeightedMovingAverage", "lwma", self._lwma.current.value)



The following reference table describes the LinearWeightedMovingAverage constructor:

#### LinearWeightedMovingAverage

class QuantConnect.Indicators.LinearWeightedMovingAverage[source]

Represents the traditional Weighted Moving Average indicator. The weight are linearly distributed according to the number of periods in the indicator. For example, a 4 period indicator will have a numerator of (4 * window[0]) + (3 * window[1]) + (2 * window[2]) + window[3] and a denominator of 4 + 3 + 2 + 1 = 10 During the warm up period, IsReady will return false, but the LWMA will still be computed correctly because the denominator will be the minimum of Samples factorial or Size factorial and the computation iterates over that minimum value. The RollingWindow of inputs is created when the indicator is created. A RollingWindow of LWMAs is not saved. That is up to the caller.

get_enumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

reset()

Resets this indicator to its initial state

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 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 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]

#### LinearWeightedMovingAverage

class QuantConnect.Indicators.LinearWeightedMovingAverage[source]

Represents the traditional Weighted Moving Average indicator. The weight are linearly distributed according to the number of periods in the indicator. For example, a 4 period indicator will have a numerator of (4 * window[0]) + (3 * window[1]) + (2 * window[2]) + window[3] and a denominator of 4 + 3 + 2 + 1 = 10 During the warm up period, IsReady will return false, but the LWMA will still be computed correctly because the denominator will be the minimum of Samples factorial or Size factorial and the computation iterates over that minimum value. The RollingWindow of inputs is created when the indicator is created. A RollingWindow of LWMAs is not saved. That is up to the caller.

GetEnumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

Reset()

Resets this indicator to its initial state

ToDetailedString()

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

Return type:

String

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 (decimal)
Return type:

Boolean

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:

Boolean

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 IsReady

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 Name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

string

property Period

Gets the period of this window indicator

Returns:

Gets the period of this window indicator

Return type:

Int32

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 WarmUpPeriod

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:

Int32

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>

property [System.Int32]

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

### Visualization

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

### Indicator History

To get the historical data of the LinearWeightedMovingAverage 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 LinearWeightedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;

public override void Initialize()
{
var lwma = LWMA(_symbol, 20);
var countIndicatorHistory = IndicatorHistory(lwma, _symbol, 100, Resolution.Minute);
var timeSpanIndicatorHistory = IndicatorHistory(lwma, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
var timePeriodIndicatorHistory = IndicatorHistory(lwma, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
}
}
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
lwma = self.lwma(self._symbol, 20)
count_indicator_history = self.indicator_history(lwma, self._symbol, 100, Resolution.MINUTE)
timedelta_indicator_history = self.indicator_history(lwma, self._symbol, timedelta(days=10), Resolution.MINUTE)
time_period_indicator_history = self.indicator_history(lwma, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)


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

var indicatorHistory = IndicatorHistory(lwma, 100, Resolution.Minute, (bar) => ((TradeBar)bar).High);
indicator_history = self.indicator_history(lwma, 100, Resolution.MINUTE, lambda bar: bar.high)
indicator_history_df = indicator_history.data_frame

If you already have a list of Slice objects, you can pass them to the IndicatorHistoryindicator_history method to avoid the internal history request.

var history = History(_symbol, 100, Resolution.Minute);
var historyIndicatorHistory = IndicatorHistory(lwma, history);

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