Supported Indicators

Auto Regressive Integrated Moving Average

Introduction

An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing times): Xₜ = c + εₜ + ΣᵢφᵢXₜ₋ᵢ + Σᵢθᵢεₜ₋ᵢ where the first sum has an upper limit of and the second .

To view the implementation of this indicator, see the LEAN GitHub repository.

Using ARIMA Indicator

To create an automatic indicator for AutoRegressiveIntegratedMovingAverage, call the ARIMAarima helper method from the QCAlgorithm class. The ARIMAarima method creates a AutoRegressiveIntegratedMovingAverage 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 AutoRegressiveIntegratedMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private AutoRegressiveIntegratedMovingAverage _arima;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _arima = ARIMA(_symbol, 1, 1, 1, 20);
    }

    public override void OnData(Slice data)
    {

        if (_arima.IsReady)
        {
            // The current value of _arima is represented by itself (_arima)
            // or _arima.Current.Value
            Plot("AutoRegressiveIntegratedMovingAverage", "arima", _arima);
        }
    }
}
class AutoRegressiveIntegratedMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._arima = self.arima(self._symbol, 1, 1, 1, 20)

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

        if self._arima.is_ready:
            # The current value of self._arima is represented by self._arima.current.value
            self.plot("AutoRegressiveIntegratedMovingAverage", "arima", self._arima.current.value)

For more information about this method, see the QCAlgorithm classQCAlgorithm class.

You can manually create a AutoRegressiveIntegratedMovingAverage 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 AutoRegressiveIntegratedMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private AutoRegressiveIntegratedMovingAverage _autoregressiveintegratedmovingaverage;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _autoregressiveintegratedmovingaverage = new AutoRegressiveIntegratedMovingAverage(1, 1, 1, 20, true);
    }

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

        if (_autoregressiveintegratedmovingaverage.IsReady)
        {
            // The current value of _autoregressiveintegratedmovingaverage is represented by itself (_autoregressiveintegratedmovingaverage)
            // or _autoregressiveintegratedmovingaverage.Current.Value
            Plot("AutoRegressiveIntegratedMovingAverage", "autoregressiveintegratedmovingaverage", _autoregressiveintegratedmovingaverage);
        }
    }
}
class AutoRegressiveIntegratedMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._autoregressiveintegratedmovingaverage = AutoRegressiveIntegratedMovingAverage(1, 1, 1, 20, True)

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

        if self._autoregressiveintegratedmovingaverage.is_ready:
            # The current value of self._autoregressiveintegratedmovingaverage is represented by self._autoregressiveintegratedmovingaverage.current.value
            self.plot("AutoRegressiveIntegratedMovingAverage", "autoregressiveintegratedmovingaverage", self._autoregressiveintegratedmovingaverage.current.value)

For more information about this indicator, see its referencereference.

Visualization

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

AutoRegressiveIntegratedMovingAverage line plot.

Indicator History

To get the historical data of the AutoRegressiveIntegratedMovingAverage 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 AutoRegressiveIntegratedMovingAverageAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private AutoRegressiveIntegratedMovingAverage _arima;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _arima = ARIMA(_symbol, 1, 1, 1, 20);

        var indicatorHistory = IndicatorHistory(_arima, _symbol, 100, Resolution.Minute);
        var timeSpanIndicatorHistory = IndicatorHistory(_arima, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
        var timePeriodIndicatorHistory = IndicatorHistory(_arima, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
    }
}
class AutoRegressiveIntegratedMovingAverageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._arima = self.arima(self._symbol, 1, 1, 1, 20)

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

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