Supported Indicators
Zero Lag Exponential Moving Average
Introduction
This indicator represents the zero lag moving average indicator (ZLEMA) ie a technical indicator that aims is to eliminate the inherent lag associated to all trend following indicators which average a price over time.
To view the implementation of this indicator, see the LEAN GitHub repository.
Using ZLEMA Indicator
To create an automatic indicator for ZeroLagExponentialMovingAverage, call the ZLEMAzlema helper method from the QCAlgorithm class. The ZLEMAzlema method creates a ZeroLagExponentialMovingAverage 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 ZeroLagExponentialMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private ZeroLagExponentialMovingAverage _zlema;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_zlema = ZLEMA(_symbol, 10);
}
public override void OnData(Slice data)
{
if (_zlema.IsReady)
{
// The current value of _zlema is represented by itself (_zlema)
// or _zlema.Current.Value
Plot("ZeroLagExponentialMovingAverage", "zlema", _zlema);
}
}
} class ZeroLagExponentialMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self._zlema = self.zlema(self._symbol, 10)
def on_data(self, slice: Slice) -> None:
if self._zlema.is_ready:
# The current value of self._zlema is represented by self._zlema.current.value
self.plot("ZeroLagExponentialMovingAverage", "zlema", self._zlema.current.value)For more information about this method, see the QCAlgorithm classQCAlgorithm class.
You can manually create a ZeroLagExponentialMovingAverage 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 ZeroLagExponentialMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private ZeroLagExponentialMovingAverage _zerolagexponentialmovingaverage;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_zerolagexponentialmovingaverage = new ZeroLagExponentialMovingAverage(10);
}
public override void OnData(Slice data)
{
if (data.Bars.TryGetValue(_symbol, out var bar))
_zerolagexponentialmovingaverage.Update(bar.EndTime, bar.Close);
if (_zerolagexponentialmovingaverage.IsReady)
{
// The current value of _zerolagexponentialmovingaverage is represented by itself (_zerolagexponentialmovingaverage)
// or _zerolagexponentialmovingaverage.Current.Value
Plot("ZeroLagExponentialMovingAverage", "zerolagexponentialmovingaverage", _zerolagexponentialmovingaverage);
}
}
} class ZeroLagExponentialMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self._zerolagexponentialmovingaverage = ZeroLagExponentialMovingAverage(10)
def on_data(self, slice: Slice) -> None:
bar = slice.bars.get(self._symbol)
if bar:
self._zerolagexponentialmovingaverage.update(bar.end_time, bar.close)
if self._zerolagexponentialmovingaverage.is_ready:
# The current value of self._zerolagexponentialmovingaverage is represented by self._zerolagexponentialmovingaverage.current.value
self.plot("ZeroLagExponentialMovingAverage", "zerolagexponentialmovingaverage", self._zerolagexponentialmovingaverage.current.value)For more information about this indicator, see its referencereference.
Indicator History
To get the historical data of the ZeroLagExponentialMovingAverage 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 ZeroLagExponentialMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private ZeroLagExponentialMovingAverage _zlema;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_zlema = ZLEMA(_symbol, 10);
var indicatorHistory = IndicatorHistory(_zlema, _symbol, 100, Resolution.Minute);
var timeSpanIndicatorHistory = IndicatorHistory(_zlema, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
var timePeriodIndicatorHistory = IndicatorHistory(_zlema, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
}
} class ZeroLagExponentialMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self._zlema = self.zlema(self._symbol, 10)
indicator_history = self.indicator_history(self._zlema, self._symbol, 100, Resolution.MINUTE)
timedelta_indicator_history = self.indicator_history(self._zlema, self._symbol, timedelta(days=10), Resolution.MINUTE)
time_period_indicator_history = self.indicator_history(self._zlema, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
