---
title: "Commodity Channel Index"
description: "This indicator represents the traditional commodity channel index (CCI) CCI = (Typical Price - 20-period SMA of TP) / (.015 * M..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/commodity-channel-index
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/commodity-channel-index.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/027%20Commodity%20Channel%20Index
modified: 2026-10-29
index: https://www.quantconnect.com/llms.txt
---

# Commodity Channel 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) > Commodity Channel Index

> 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 commodity channel index (CCI) CCI = (Typical Price - 20-period SMA of TP) / (.015 * Mean Deviation) Typical Price (TP) = (High + Low + Close)/3 Constant = 0.015 There are four steps to calculating the Mean Deviation, first, subtract the most recent 20-period average of the typical price from each period's typical price. Second, take the absolute values of these numbers. Third, sum the absolute values. Fourth, divide by the total number of periods (20).

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

## Using CCI Indicator

To create an automatic indicator for `CommodityChannelIndex`, call the `cci` helper method from the `QCAlgorithm` class. The `cci` method creates a `CommodityChannelIndex` 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 CommodityChannelIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._cci = self.cci(self._symbol, 20, MovingAverageType.SIMPLE)

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

        if self._cci.is_ready:
            # The current value of self._cci is represented by self._cci.current.value
            self.plot("CommodityChannelIndex", "cci", self._cci.current.value)
            # Plot all attributes of self._cci
            self.plot("CommodityChannelIndex", "typical_price_average", self._cci.typical_price_average.current.value)
            self.plot("CommodityChannelIndex", "typical_price_mean_deviation", self._cci.typical_price_mean_deviation.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.cci).

You can manually create a `CommodityChannelIndex` 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 CommodityChannelIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._commoditychannelindex = CommodityChannelIndex(20, MovingAverageType.SIMPLE)

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

        if self._commoditychannelindex.is_ready:
            # The current value of self._commoditychannelindex is represented by self._commoditychannelindex.current.value
            self.plot("CommodityChannelIndex", "commoditychannelindex", self._commoditychannelindex.current.value)
            # Plot all attributes of self._commoditychannelindex
            self.plot("CommodityChannelIndex", "typical_price_average", self._commoditychannelindex.typical_price_average.current.value)
            self.plot("CommodityChannelIndex", "typical_price_mean_deviation", self._commoditychannelindex.typical_price_mean_deviation.current.value)
```

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

## Visualization

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

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

## Indicator History

To get the historical data of the `CommodityChannelIndex` 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 CommodityChannelIndexAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._cci = self.cci(self._symbol, 20, MovingAverageType.SIMPLE)

        indicator_history = self.indicator_history(self._cci, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._cci, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._cci, 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
        typical_price_average = indicator_history_df["typicalpriceaverage"]
        typical_price_mean_deviation = indicator_history_df["typicalpricemeandeviation"]
```