---
title: "New Highs New Lows"
description: "The NewHighsNewLows is a New Highs / New Lows indicator that accepts IBaseDataBar data as its input. This type is more of a shi..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/new-highs-new-lows
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/new-highs-new-lows.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/078%20New%20Highs%20New%20Lows
modified: 2026-11-07
index: https://www.quantconnect.com/llms.txt
---

# New Highs New Lows

[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) > New Highs New Lows

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

## Introduction

The NewHighsNewLows is a New Highs / New Lows indicator that accepts IBaseDataBar data as its input. This type is more of a shim/typedef to reduce the need to refer to things as NewHighsNewLows<IBaseDataBar>

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

## Using NHNL Indicator

To create an automatic indicator for `NewHighsNewLows`, call the `nhnl` helper method from the `QCAlgorithm` class. The `nhnl` method creates a `NewHighsNewLows` object, hooks it up for automatic updates, and returns it so you can use it in your algorithm. In most cases, you should call the helper method in the `initialize` method.

```python
class NewHighsNewLowsAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._nhnl = self.nhnl([self._symbol, self._reference], 2)

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

        if self._nhnl.is_ready:
            # The current value of self._nhnl is represented by self._nhnl.current.value
            self.plot("NewHighsNewLows", "nhnl", self._nhnl.current.value)
            # Plot all attributes of self._nhnl
            self.plot("NewHighsNewLows", "difference", self._nhnl.difference.current.value)
            self.plot("NewHighsNewLows", "ratio", self._nhnl.ratio.current.value)
```

To create an automatic indicator for `NewHighsNewLows` using universe constituents, call the `nhnl` helper method from the `QCAlgorithm` class. The `nhnl` method creates a `NewHighsNewLows` object, hooks it up for automatic updates, and returns it so you can use it in your algorithm. In this case, you should call the helper method in the `on_securities_changed` method.

```python
class NewHighsNewLowsAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024,1,1)
        self.set_end_date(2024,12,31)
        self.universe_settings.resolution = Resolution.DAILY
        self.universe_settings.schedule.on(self.date_rules.month_start())
        self._universe = self.add_universe(self.universe.etf('SPY'))

    def on_securities_changed(self, changes: SecurityChanges) -> None:
        self._nhnl = self.nhnl(list(self._universe.selected), 2)

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

        if self._nhnl.is_ready:
            # The current value of self._nhnl is represented by self._nhnl.current.value
            self.plot("NewHighsNewLows", "nhnl", self._nhnl.current.value)
            # Plot all attributes of self._nhnl
            self.plot("NewHighsNewLows", "difference", self._nhnl.difference.current.value)
            self.plot("NewHighsNewLows", "ratio", self._nhnl.ratio.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.nhnl).

You can manually create a `NewHighsNewLows` 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 NewHighsNewLowsAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._newhighsnewlows = NewHighsNewLows("NHNL", 2)
        self._newhighsnewlows.add(self._symbol);
        self._newhighsnewlows.add(self._reference);

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

        if self._newhighsnewlows.is_ready:
            # The current value of self._newhighsnewlows is represented by self._newhighsnewlows.current.value
            self.plot("NewHighsNewLows", "newhighsnewlows", self._newhighsnewlows.current.value)
            # Plot all attributes of self._newhighsnewlows
            self.plot("NewHighsNewLows", "difference", self._newhighsnewlows.difference.current.value)
            self.plot("NewHighsNewLows", "ratio", self._newhighsnewlows.ratio.current.value)
```

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

## Visualization

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

![NewHighsNewLows line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/new-highs-new-lows.png)

## Indicator History

To get the historical data of the `NewHighsNewLows` 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 NewHighsNewLowsAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._nhnl = self.nhnl([self._symbol, self._reference], 2)

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

        # Access all attributes of indicator_history
        indicator_history_df = indicator_history.data_frame
        difference = indicator_history_df["difference"]
        ratio = indicator_history_df["ratio"]
```