Supported Indicators

Log Return

Introduction

This indicator represents the LogReturn indicator (LOGR) - log returns are useful for identifying price convergence/divergence in a given period - logr = log (current price / last price in period)

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

Using LOGR Indicator

To create an automatic indicator for LogReturn, call the LOGRlogr helper method from the QCAlgorithm class. The LOGRlogr method creates a LogReturn 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 LogReturnAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private LogReturn _logr;

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

    public override void OnData(Slice data)
    {

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

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

        if self._logr.is_ready:
            # The current value of self._logr is represented by self._logr.current.value
            self.plot("LogReturn", "logr", self._logr.current.value)

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

You can manually create a LogReturn 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 LogReturnAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private LogReturn _logreturn;

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

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

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

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

        if self._logreturn.is_ready:
            # The current value of self._logreturn is represented by self._logreturn.current.value
            self.plot("LogReturn", "logreturn", self._logreturn.current.value)

For more information about this indicator, see its referencereference.

Visualization

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

LogReturn line plot.

Indicator History

To get the historical data of the LogReturn 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 LogReturnAlgorithm : QCAlgorithm
{
    private Symbol _symbol;
    private LogReturn _logr;

    public override void Initialize()
    {
        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
        _logr = LOGR(_symbol, 20);

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

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

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