---
title: "Average Directional Index"
description: "This indicator computes Average Directional Index which measures trend strength without regard to trend direction. Firstly, it ..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/average-directional-index
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/average-directional-index.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/014%20Average%20Directional%20Index
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Average Directional Index

[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) > Average Directional Index

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

## Introduction

This indicator computes Average Directional Index which measures trend strength without regard to trend direction. Firstly, it calculates the Directional Movement and the True Range value, and then the values are accumulated and smoothed using a custom smoothing method proposed by Wilder. For an n period smoothing, 1/n of each period's value is added to the total period. From these accumulated values we are therefore able to derived the 'Positive Directional Index' (+DI) and 'Negative Directional Index' (-DI) which is used to calculate the Average Directional Index. Computation source: https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx

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

## Using ADX Indicator

To create an automatic indicator for `AverageDirectionalIndex`, call the `adx` helper method from the `QCAlgorithm` class. The `adx` method creates a `AverageDirectionalIndex` 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 AverageDirectionalIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._adx = self.adx(self._symbol, 20)

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

        if self._adx.is_ready:
            # The current value of self._adx is represented by self._adx.current.value
            self.plot("AverageDirectionalIndex", "adx", self._adx.current.value)
            # Plot all attributes of self._adx
            self.plot("AverageDirectionalIndex", "positive_directional_index", self._adx.positive_directional_index.current.value)
            self.plot("AverageDirectionalIndex", "negative_directional_index", self._adx.negative_directional_index.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.adx).

You can manually create a `AverageDirectionalIndex` 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 AverageDirectionalIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._averagedirectionalindex = AverageDirectionalIndex(20)

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

        if self._averagedirectionalindex.is_ready:
            # The current value of self._averagedirectionalindex is represented by self._averagedirectionalindex.current.value
            self.plot("AverageDirectionalIndex", "averagedirectionalindex", self._averagedirectionalindex.current.value)
            # Plot all attributes of self._averagedirectionalindex
            self.plot("AverageDirectionalIndex", "positive_directional_index", self._averagedirectionalindex.positive_directional_index.current.value)
            self.plot("AverageDirectionalIndex", "negative_directional_index", self._averagedirectionalindex.negative_directional_index.current.value)
```

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

## Visualization

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

![AverageDirectionalIndex line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/average-directional-index.png)

## Indicator History

To get the historical data of the `AverageDirectionalIndex` 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 AverageDirectionalIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._adx = self.adx(self._symbol, 20)

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

        # Access all attributes of indicator_history
        indicator_history_df = indicator_history.data_frame
        positive_directional_index = indicator_history_df["positivedirectionalindex"]
        negative_directional_index = indicator_history_df["negativedirectionalindex"]
```