---
title: "Stochastic"
description: "This indicator computes the Slow Stochastics %K and %D. The Fast Stochastic %K is is computed by (Current Close Price - Lowest ..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/stochastic
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/stochastic.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/103%20Stochastic
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Stochastic

[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) > Stochastic

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

## Introduction

This indicator computes the Slow Stochastics %K and %D. The Fast Stochastic %K is is computed by (Current Close Price - Lowest Price of given Period) / (Highest Price of given Period - Lowest Price of given Period) multiplied by 100. Once the Fast Stochastic %K is calculated the Slow Stochastic %K is calculated by the average/smoothed price of of the Fast %K with the given period. The Slow Stochastic %D is then derived from the Slow Stochastic %K with the given period.

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

## Using STO Indicator

To create an automatic indicator for `Stochastic`, call the `sto` helper method from the `QCAlgorithm` class. The `sto` method creates a `Stochastic` 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 StochasticAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._sto = self.sto(self._symbol, 20, 10, 20)

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

        if self._sto.is_ready:
            # The current value of self._sto is represented by self._sto.current.value
            self.plot("Stochastic", "sto", self._sto.current.value)
            # Plot all attributes of self._sto
            self.plot("Stochastic", "fast_stoch", self._sto.fast_stoch.current.value)
            self.plot("Stochastic", "stoch_k", self._sto.stoch_k.current.value)
            self.plot("Stochastic", "stoch_d", self._sto.stoch_d.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.sto).

You can manually create a `Stochastic` 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 StochasticAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._stochastic = Stochastic(20, 10, 20)

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

        if self._stochastic.is_ready:
            # The current value of self._stochastic is represented by self._stochastic.current.value
            self.plot("Stochastic", "stochastic", self._stochastic.current.value)
            # Plot all attributes of self._stochastic
            self.plot("Stochastic", "fast_stoch", self._stochastic.fast_stoch.current.value)
            self.plot("Stochastic", "stoch_k", self._stochastic.stoch_k.current.value)
            self.plot("Stochastic", "stoch_d", self._stochastic.stoch_d.current.value)
```

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

## Visualization

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

![Stochastic line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/stochastic.png)

## Indicator History

To get the historical data of the `Stochastic` 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 StochasticAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._sto = self.sto(self._symbol, 20, 10, 20)

        indicator_history = self.indicator_history(self._sto, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._sto, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._sto, 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
        fast_stoch = indicator_history_df["faststoch"]
        stoch_k = indicator_history_df["stochk"]
        stoch_d = indicator_history_df["stochd"]
```