---
title: "Mass Index"
description: "The Mass Index uses the high-low range to identify trend reversals based on range expansions. In this sense, the Mass Index is ..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/mass-index
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/mass-index.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/062%20Mass%20Index
modified: 2026-10-29
index: https://www.quantconnect.com/llms.txt
---

# Mass 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) > Mass Index

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

## Introduction

The Mass Index uses the high-low range to identify trend reversals based on range expansions. In this sense, the Mass Index is a volatility indicator that does not have a directional bias. Instead, the Mass Index identifies range bulges that can foreshadow a reversal of the current trend. Developed by Donald Dorsey.

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

## Using MASS Indicator

To create an automatic indicator for `MassIndex`, call the `mass` helper method from the `QCAlgorithm` class. The `mass` method creates a `MassIndex` 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 MassIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._mass = self.mass(self._symbol, 9, 25)

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

        if self._mass.is_ready:
            # The current value of self._mass is represented by self._mass.current.value
            self.plot("MassIndex", "mass", self._mass.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.mass).

You can manually create a `MassIndex` 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 MassIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._massindex = MassIndex(9, 25)

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

        if self._massindex.is_ready:
            # The current value of self._massindex is represented by self._massindex.current.value
            self.plot("MassIndex", "massindex", self._massindex.current.value)
```

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

## Visualization

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

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

## Indicator History

To get the historical data of the `MassIndex` 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 MassIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._mass = self.mass(self._symbol, 9, 25)

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