Supported Indicators

Advance Decline Ratio

Introduction

The advance-decline ratio (ADR) compares the number of stocks that closed higher against the number of stocks that closed lower than their previous day's closing prices.

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

Using ADR Indicator

To create an automatic indicator for AdvanceDeclineRatio, call the ADRadr helper method from the QCAlgorithm class. The ADRadr method creates a AdvanceDeclineRatio 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 AdvanceDeclineRatioAlgorithm : QCAlgorithm
{
    private Symbol _symbol,_reference;
    private AdvanceDeclineRatio _adr;

    public override void Initialize()
    {
        _symbol = AddEquity("QQQ", Resolution.Daily).Symbol;
        _reference = AddEquity("SPY", Resolution.Daily).Symbol;
        _adr = ADR([_symbol, _reference]);
    }

    public override void OnData(Slice data)
    {

        if (_adr.IsReady)
        {
            // The current value of _adr is represented by itself (_adr)
            // or _adr.Current.Value
            Plot("AdvanceDeclineRatio", "adr", _adr);
        }
    }
}
class AdvanceDeclineRatioAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._adr = self.adr([self._symbol, self._reference])

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

        if self._adr.is_ready:
            # The current value of self._adr is represented by self._adr.current.value
            self.plot("AdvanceDeclineRatio", "adr", self._adr.current.value)

To create an automatic indicator for AdvanceDeclineRatio using universe constituents, call the ADRadr helper method from the QCAlgorithm class. The ADRadr method creates a AdvanceDeclineRatio object, hooks it up for automatic updates, and returns it so you can used it in your algorithm. In this case, you should call the helper method in the OnSecuritiesChangedon_securities_changed method.

public class AdvanceDeclineRatioAlgorithm : QCAlgorithm
{
    private Universe _universe;
    private AdvanceDeclineRatio _adr;

    public override void Initialize()
    {
        UniverseSettings.Resolution = Resolution.Daily;
        UniverseSettings.Schedule.On(DateRules.MonthStart());
        _universe = AddUniverse(Universe.ETF("SPY"));
    }

    public override void OnSecuritiesChanged(SecurityChanges changes)
    {
        _adr = ADR(_universe.Selected);
    }

    public override void OnData(Slice data)
    {

        if (_adr.IsReady)
        {
            // The current value of _adr is represented by itself (_adr)
            // or _adr.Current.Value
            Plot("AdvanceDeclineRatio", "adr", _adr);
        }
    }
}
class AdvanceDeclineRatioAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.universe_settings.resolution = Resolution.DAILY
        self.universe_settings.schedule.on(self.date_rules.month_start())
        self._universe = self.add_universe(self.universe.etf('SPY'))

    def on_securities_changed(self, changes: SecurityChanges) -> None:
        self._adr = self.adr(list(self._universe.selected))

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

        if self._adr.is_ready:
            # The current value of self._adr is represented by self._adr.current.value
            self.plot("AdvanceDeclineRatio", "adr", self._adr.current.value)

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

You can manually create a AdvanceDeclineRatio 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 AdvanceDeclineRatioAlgorithm : QCAlgorithm
{
    private Symbol _symbol,_reference;
    private AdvanceDeclineRatio _advancedeclineratio;

    public override void Initialize()
    {
        _symbol = AddEquity("QQQ", Resolution.Daily).Symbol;
        _reference = AddEquity("SPY", Resolution.Daily).Symbol;
        _advancedeclineratio = new AdvanceDeclineRatio();
        _advancedeclineratio.Add(_symbol);
        _advancedeclineratio.Add(_reference);
    }

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

        if (_advancedeclineratio.IsReady)
        {
            // The current value of _advancedeclineratio is represented by itself (_advancedeclineratio)
            // or _advancedeclineratio.Current.Value
            Plot("AdvanceDeclineRatio", "advancedeclineratio", _advancedeclineratio);
        }
    }
}
class AdvanceDeclineRatioAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._advancedeclineratio = AdvanceDeclineRatio()
        self._advancedeclineratio.add(self._symbol);
        self._advancedeclineratio.add(self._reference);

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

        if self._advancedeclineratio.is_ready:
            # The current value of self._advancedeclineratio is represented by self._advancedeclineratio.current.value
            self.plot("AdvanceDeclineRatio", "advancedeclineratio", self._advancedeclineratio.current.value)

For more information about this indicator, see its referencereference.

Visualization

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

AdvanceDeclineRatio line plot.

Indicator History

To get the historical data of the AdvanceDeclineRatio 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 AdvanceDeclineRatioAlgorithm : QCAlgorithm
{
    private Symbol _symbol,_reference;
    private AdvanceDeclineRatio _adr;

    public override void Initialize()
    {
        _symbol = AddEquity("QQQ", Resolution.Daily).Symbol;
        _reference = AddEquity("SPY", Resolution.Daily).Symbol;
        _adr = ADR([_symbol, _reference]);

        var indicatorHistory = IndicatorHistory(_adr, new[] { _symbol, _reference }, 100, Resolution.Minute);
        var timeSpanIndicatorHistory = IndicatorHistory(_adr, new[] { _symbol, _reference }, TimeSpan.FromDays(10), Resolution.Minute);
        var timePeriodIndicatorHistory = IndicatorHistory(_adr, new[] { _symbol, _reference }, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
    }
}
class AdvanceDeclineRatioAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol
        self._reference = self.add_equity("SPY", Resolution.DAILY).symbol
        self._adr = self.adr([self._symbol, self._reference])

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

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