---
title: "Linear Weighted Moving Average"
description: "This indicator represents the traditional Weighted Moving Average indicator. The weight are linearly distributed according to t..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/linear-weighted-moving-average
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/linear-weighted-moving-average.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/060%20Linear%20Weighted%20Moving%20Average
modified: 2026-10-29
index: https://www.quantconnect.com/llms.txt
---

# Linear Weighted Moving Average

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Indicators > [Supported Indicators](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators.md) > Linear Weighted Moving Average

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## 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](https://github.com/QuantConnect/Lean/tree/master/Indicators/LinearWeightedMovingAverage.cs).

## Using LWMA Indicator

To create an automatic indicator 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 `initialize` method.

```python
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._lwma = self.lwma(self._symbol, 20)

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

        if self._lwma.is_ready:
            # The current value of self._lwma is represented by self._lwma.current.value
            self.plot("LinearWeightedMovingAverage", "lwma", self._lwma.current.value)
```

For more information about this method, see the [QCAlgorithm class](https://www.lean.io/docs/v2/lean-engine/class-reference/py/QuantConnect/Algorithm/QCAlgorithm/#QuantConnect.Algorithm.QCAlgorithm.lwma).

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 `update` method. The indicator will only be ready after you prime it with enough data.

```python
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._linearweightedmovingaverage = LinearWeightedMovingAverage(20)

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

        if self._linearweightedmovingaverage.is_ready:
            # The current value of self._linearweightedmovingaverage is represented by self._linearweightedmovingaverage.current.value
            self.plot("LinearWeightedMovingAverage", "linearweightedmovingaverage", self._linearweightedmovingaverage.current.value)
```

For more information about this indicator, see its [reference](https://www.lean.io/docs/v2/lean-engine/class-reference/py/QuantConnect/Indicators/LinearWeightedMovingAverage).

## Visualization

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

![LinearWeightedMovingAverage line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/linear-weighted-moving-average.png)

## Indicator History

To get the historical data of the `LinearWeightedMovingAverage` indicator, call the `self.indicator_history` method. This method resets your indicator, makes a [history request](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md), 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.

```python
class LinearWeightedMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._lwma = self.lwma(self._symbol, 20)

        indicator_history = self.indicator_history(self._lwma, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._lwma, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._lwma, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
```