---
title: "High Wave Candle"
description: "The High-Wave Candle is a single candle with a short body and very long upper and lower shadows, which shows indecision. Create..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/candlestick-patterns/high-wave-candle
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/candlestick-patterns/high-wave-candle.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/00%20Candlestick%20Patterns/21%20High%20Wave%20Candle
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# High Wave Candle

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Indicators > Supported Indicators > [Candlestick Patterns](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/candlestick-patterns.md) > High Wave Candle

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

## Introduction

The High-Wave Candle is a single candle with a short body and very long upper and lower shadows, which shows indecision. Create a new High Wave Candle candlestick pattern to indicate the pattern's presence.

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

## Using HighWaveCandle Indicator

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

    def on_data(self, slice: Slice) -> None:
        if self._high_wave_candle.is_ready:
            # The current value of self._high_wave_candle is represented by self._high_wave_candle.current.value
            self.plot("HighWaveCandle", "high_wave_candle", self._high_wave_candle.current.value)
```

For more information about this method, see the [CandlestickPatterns class](https://www.lean.io/docs/v2/lean-engine/class-reference/py/QuantConnect/Algorithm/CandlestickPatterns/#QuantConnect.Algorithm.CandlestickPatterns.high_wave_candle).

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

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

        if self._high_wave_candle.is_ready:
            # The current value of self._high_wave_candle is represented by self._high_wave_candle.current.value
            self.plot("HighWaveCandle", "high_wave_candle", self._high_wave_candle.current.value)
```

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

## Indicator History

To get the historical data of the `HighWaveCandle` 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 HighWaveCandleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._high_wave_candle = self.candlestick_patterns.high_wave_candle(self._symbol)

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