---
title: "Zero Lag Exponential Moving Average"
description: "This indicator represents the zero lag moving average indicator (ZLEMA) ie a technical indicator that aims is to eliminate the ..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/zero-lag-exponential-moving-average
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/zero-lag-exponential-moving-average.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/133%20Zero%20Lag%20Exponential%20Moving%20Average
modified: 2026-11-01
index: https://www.quantconnect.com/llms.txt
---

# Zero Lag Exponential 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) > Zero Lag Exponential 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 zero lag moving average indicator (ZLEMA) ie a technical indicator that aims is to eliminate the inherent lag associated to all trend following indicators which average a price over time.

To view the implementation of this indicator, see the [LEAN GitHub repository](https://github.com/QuantConnect/Lean/tree/master/Indicators/ZeroLagExponentialMovingAverage.cs).

## Using ZLEMA Indicator

To create an automatic indicator for `ZeroLagExponentialMovingAverage`, call the `zlema` helper method from the `QCAlgorithm` class. The `zlema` method creates a `ZeroLagExponentialMovingAverage` 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 ZeroLagExponentialMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._zlema = self.zlema(self._symbol, 10)

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

        if self._zlema.is_ready:
            # The current value of self._zlema is represented by self._zlema.current.value
            self.plot("ZeroLagExponentialMovingAverage", "zlema", self._zlema.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.zlema).

You can manually create a `ZeroLagExponentialMovingAverage` 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 ZeroLagExponentialMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._zerolagexponentialmovingaverage = ZeroLagExponentialMovingAverage(10)

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

        if self._zerolagexponentialmovingaverage.is_ready:
            # The current value of self._zerolagexponentialmovingaverage is represented by self._zerolagexponentialmovingaverage.current.value
            self.plot("ZeroLagExponentialMovingAverage", "zerolagexponentialmovingaverage", self._zerolagexponentialmovingaverage.current.value)
```

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

## Visualization

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

![ZeroLagExponentialMovingAverage line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/zero-lag-exponential-moving-average.png)

## Indicator History

To get the historical data of the `ZeroLagExponentialMovingAverage` 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 ZeroLagExponentialMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._zlema = self.zlema(self._symbol, 10)

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