---
title: "Morning Star"
description: "The Morning Star is a three-candle bullish pattern where a short body gaps down from a long black candle and a white candle fol..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/candlestick-patterns/morning-star
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/candlestick-patterns/morning-star.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/00%20Candlestick%20Patterns/37%20Morning%20Star
modified: 2026-10-29
index: https://www.quantconnect.com/llms.txt
---

# Morning Star

[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) > Morning Star

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

## Introduction

The Morning Star is a three-candle bullish pattern where a short body gaps down from a long black candle and a white candle follows. Create a new Morning Star 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/MorningStar.cs).

## Using MorningStar Indicator

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

    def on_data(self, slice: Slice) -> None:
        if self._morning_star.is_ready:
            # The current value of self._morning_star is represented by self._morning_star.current.value
            self.plot("MorningStar", "morning_star", self._morning_star.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.morning_star).

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

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

        if self._morning_star.is_ready:
            # The current value of self._morning_star is represented by self._morning_star.current.value
            self.plot("MorningStar", "morning_star", self._morning_star.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/MorningStar).

## Indicator History

To get the historical data of the `MorningStar` 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 MorningStarAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._morning_star = self.candlestick_patterns.morning_star(self._symbol)

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