---
title: "Correlation"
description: "The Correlation Indicator is a valuable tool in technical analysis, designed to quantify the degree of relationship between the..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/correlation
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/correlation.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/030%20Correlation
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Correlation

[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) > Correlation

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

## Introduction

The Correlation Indicator is a valuable tool in technical analysis, designed to quantify the degree of relationship between the price movements of a target security (e.g., a stock or ETF) and a reference market index. It measures how closely the target’s price changes are aligned with the fluctuations of the index over a specific period of time, providing insights into the target’s susceptibility to market movements. A positive correlation indicates that the target tends to move in the same direction as the market index, while a negative correlation suggests an inverse relationship. A correlation close to 0 implies a weak or no linear relationship. Commonly, the SPX index is employed as the benchmark for the overall market when calculating correlation, ensuring a consistent and reliable reference point. This helps traders and investors make informed decisions regarding the risk and behavior of the target security in relation to market trends. The indicator only updates when both assets have a price for a time step. When a bar is missing for one of the assets, the indicator value fills forward to improve the accuracy of the indicator.

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

## Using C Indicator

To create an automatic indicator for `Correlation`, call the `c` helper method from the `QCAlgorithm` class. The `c` method creates a `Correlation` 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 CorrelationAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._c = self.c(self._symbol, self._reference, 20, CorrelationType.PEARSON)

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

        if self._c.is_ready:
            # The current value of self._c is represented by self._c.current.value
            self.plot("Correlation", "c", self._c.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.c).

You can manually create a `Correlation` 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 CorrelationAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._correlation = Correlation(self._symbol, self._reference, 20, CorrelationType.PEARSON)

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

        if self._correlation.is_ready:
            # The current value of self._correlation is represented by self._correlation.current.value
            self.plot("Correlation", "correlation", self._correlation.current.value)
```

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

## Visualization

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

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

## Indicator History

To get the historical data of the `Correlation` 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 CorrelationAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._c = self.c(self._symbol, self._reference, 20, CorrelationType.PEARSON)

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