---
title: "On Balance Volume"
description: "This indicator computes the On Balance Volume (OBV). The On Balance Volume is calculated by determining the price of the curren..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/on-balance-volume
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/on-balance-volume.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/080%20On%20Balance%20Volume
modified: 2026-10-29
index: https://www.quantconnect.com/llms.txt
---

# On Balance Volume

[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) > On Balance Volume

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

## Introduction

This indicator computes the On Balance Volume (OBV). The On Balance Volume is calculated by determining the price of the current close price and previous close price. If the current close price is equivalent to the previous price the OBV remains the same, If the current close price is higher the volume of that day is added to the OBV, while a lower close price will result in negative value.

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

## Using OBV Indicator

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

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

        if self._obv.is_ready:
            # The current value of self._obv is represented by self._obv.current.value
            self.plot("OnBalanceVolume", "obv", self._obv.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.obv).

You can manually create a `OnBalanceVolume` 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 OnBalanceVolumeAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._onbalancevolume = OnBalanceVolume()

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

        if self._onbalancevolume.is_ready:
            # The current value of self._onbalancevolume is represented by self._onbalancevolume.current.value
            self.plot("OnBalanceVolume", "onbalancevolume", self._onbalancevolume.current.value)
```

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

## Visualization

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

![OnBalanceVolume line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/on-balance-volume.png)

## Indicator History

To get the historical data of the `OnBalanceVolume` 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 OnBalanceVolumeAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._obv = self.obv(self._symbol)

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