---
title: "Wave Trend Oscillator"
description: "The WaveTrend Oscillator (WTO) is a momentum indicator that highlights overbought and oversold conditions by measuring how far ..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/wave-trend-oscillator
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/wave-trend-oscillator.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/129%20Wave%20Trend%20Oscillator
modified: 2026-11-04
index: https://www.quantconnect.com/llms.txt
---

# Wave Trend Oscillator

[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) > Wave Trend Oscillator

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

## Introduction

The WaveTrend Oscillator (WTO) is a momentum indicator that highlights overbought and oversold conditions by measuring how far the typical price has deviated from a smoothed moving average, normalized by an exponentially smoothed mean absolute deviation. The oscillator's main line (WT1) is an EMA of this normalized channel index, and the signal line (WT2) is an SMA of WT1; crossovers between the two lines are commonly used as entry and exit signals. Formula: HLC3 = (High + Low + Close) / 3 ESA = EMA(HLC3, channelPeriod) D = EMA(|HLC3 - ESA|, channelPeriod) CI = (HLC3 - ESA) / (0.015 * D) WT1 = EMA(CI, averagePeriod) (the indicator's Current.Value) WT2 = SMA(WT1, signalPeriod) (exposed via `Signal`)

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

## Using WTO Indicator

To create an automatic indicator for `WaveTrendOscillator`, call the `wto` helper method from the `QCAlgorithm` class. The `wto` method creates a `WaveTrendOscillator` 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 WaveTrendOscillatorAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._wto = self.wto(self._symbol, 10, 21, 4)

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

        if self._wto.is_ready:
            # The current value of self._wto is represented by self._wto.current.value
            self.plot("WaveTrendOscillator", "wto", self._wto.current.value)
            # Plot all attributes of self._wto
            self.plot("WaveTrendOscillator", "channel_average", self._wto.channel_average.current.value)
            self.plot("WaveTrendOscillator", "channel_deviation", self._wto.channel_deviation.current.value)
            self.plot("WaveTrendOscillator", "channel_index_average", self._wto.channel_index_average.current.value)
            self.plot("WaveTrendOscillator", "signal", self._wto.signal.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.wto).

You can manually create a `WaveTrendOscillator` 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 WaveTrendOscillatorAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._wavetrendoscillator = WaveTrendOscillator(10, 21, 4)

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

        if self._wavetrendoscillator.is_ready:
            # The current value of self._wavetrendoscillator is represented by self._wavetrendoscillator.current.value
            self.plot("WaveTrendOscillator", "wavetrendoscillator", self._wavetrendoscillator.current.value)
            # Plot all attributes of self._wavetrendoscillator
            self.plot("WaveTrendOscillator", "channel_average", self._wavetrendoscillator.channel_average.current.value)
            self.plot("WaveTrendOscillator", "channel_deviation", self._wavetrendoscillator.channel_deviation.current.value)
            self.plot("WaveTrendOscillator", "channel_index_average", self._wavetrendoscillator.channel_index_average.current.value)
            self.plot("WaveTrendOscillator", "signal", self._wavetrendoscillator.signal.current.value)
```

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

## Visualization

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

![WaveTrendOscillator line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/wave-trend-oscillator.png)

## Indicator History

To get the historical data of the `WaveTrendOscillator` 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 WaveTrendOscillatorAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._wto = self.wto(self._symbol, 10, 21, 4)

        indicator_history = self.indicator_history(self._wto, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._wto, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._wto, 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
        channel_average = indicator_history_df["channelaverage"]
        channel_deviation = indicator_history_df["channeldeviation"]
        channel_index_average = indicator_history_df["channelindexaverage"]
        signal = indicator_history_df["signal"]
```