Supported Indicators

Stochastic

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.

Using STO Indicator

To create an automatic indicator for Stochastic, call the STOsto helper method from the QCAlgorithm class. The STOsto 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 Initializeinitialize method.

public class StochasticAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Stochastic _sto;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _sto = STO(_symbol, 20, 10, 20);
    }

    public override void OnData(Slice data)
    {

        if (_sto.IsReady)
        {
            // The current value of _sto is represented by itself (_sto)
            // or _sto.Current.Value
            Plot("Stochastic", "sto", _sto);
            // Plot all properties of abands
            Plot("Stochastic", "faststoch", _sto.FastStoch);
            Plot("Stochastic", "stochk", _sto.StochK);
            Plot("Stochastic", "stochd", _sto.StochD);
        }
    }
}
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 classQCAlgorithm class.

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 Updateupdate method. The indicator will only be ready after you prime it with enough data.

public class StochasticAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Stochastic _stochastic;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _stochastic = new Stochastic(20, 10, 20);
    }

    public override void OnData(Slice data)
    {
        if (data.Bars.TryGetValue(_symbol, out var bar))
            _stochastic.Update(bar);

        if (_stochastic.IsReady)
        {
            // The current value of _stochastic is represented by itself (_stochastic)
            // or _stochastic.Current.Value
            Plot("Stochastic", "stochastic", _stochastic);
            // Plot all properties of abands
            Plot("Stochastic", "faststoch", _stochastic.FastStoch);
            Plot("Stochastic", "stochk", _stochastic.StochK);
            Plot("Stochastic", "stochd", _stochastic.StochD);
        }
    }
}
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 referencereference.

Visualization

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

Stochastic line plot.

Indicator History

To get the historical data of the Stochastic indicator, call the IndicatorHistoryself.indicator_history method. This method resets your indicator, makes a history request, and updates the indicator with the historical data. Just like with regular history requests, the IndicatorHistoryindicator_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. 

public class StochasticAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private Stochastic _sto;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _sto = STO(_symbol, 20, 10, 20);

        var indicatorHistory = IndicatorHistory(_sto, _symbol, 100, Resolution.Minute);
        var timeSpanIndicatorHistory = IndicatorHistory(_sto, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
        var timePeriodIndicatorHistory = IndicatorHistory(_sto, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);

        // Access all attributes of indicatorHistory
        var fastStoch = indicatorHistory.Select(x => ((dynamic)x).FastStoch).ToList();
        var stochK = indicatorHistory.Select(x => ((dynamic)x).StochK).ToList();
        var stochD = indicatorHistory.Select(x => ((dynamic)x).StochD).ToList();
    }
}
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"]

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