---
title: "Alpha"
description: "In financial analysis, the Alpha indicator is used to measure the performance of an investment (such as a stock or ETF) relativ..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/alpha
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/alpha.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/008%20Alpha
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Alpha

[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) > Alpha

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

## Introduction

In financial analysis, the Alpha indicator is used to measure the performance of an investment (such as a stock or ETF) relative to a benchmark index, often representing the broader market. Alpha indicates the excess return of the investment compared to the return of the benchmark index. The S P 500 index is frequently used as a benchmark in Alpha calculations to represent the overall market performance. Alpha is an essential tool for investors to understand the idiosyncratic returns of their investment that aren't caused by movement in the underlying benchmark.

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

## Using A Indicator

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

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

        if self._a.is_ready:
            # The current value of self._a is represented by self._a.current.value
            self.plot("Alpha", "a", self._a.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.a).

You can manually create a `Alpha` 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 AlphaAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._alpha = Alpha(self._symbol, self._reference, 20)

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

        if self._alpha.is_ready:
            # The current value of self._alpha is represented by self._alpha.current.value
            self.plot("Alpha", "alpha", self._alpha.current.value)
```

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

## Visualization

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

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

## Indicator History

To get the historical data of the `Alpha` 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 AlphaAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._a = self.a(self._symbol, self._reference, 20)

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