Supported Indicators
Mean Absolute Deviation
Introduction
This indicator computes the n-period mean absolute deviation.
To view the implementation of this indicator, see the LEAN GitHub repository.
Using MAD Indicator
To create an automatic indicators for MeanAbsoluteDeviation, call the MAD helper method from the QCAlgorithm class. The MAD method creates a MeanAbsoluteDeviation 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 MeanAbsoluteDeviationAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private MeanAbsoluteDeviation _mad;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_mad = MAD(_symbol, 20);
}
public override void OnData(Slice data)
{
if (_mad.IsReady)
{
// The current value of _mad is represented by itself (_mad)
// or _mad.Current.Value
Plot("MeanAbsoluteDeviation", "mad", _mad);
// Plot all properties of mad
Plot("MeanAbsoluteDeviation", "mean", _mad.Mean);
}
}
} class MeanAbsoluteDeviationAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self._symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.mad = self.MAD(self.symbol, 20)
def on_data(self, slice: Slice) -> None:
if self.mad.IsReady:
# The current value of self.mad is represented by self.mad.Current.Value
self.plot("MeanAbsoluteDeviation", "mad", self.mad.Current.Value)
# Plot all attributes of self.mad
self.plot("MeanAbsoluteDeviation", "mean", self.mad.Mean.Current.Value)
The following reference table describes the MAD method:
MAD()1/1
MeanAbsoluteDeviation QuantConnect.Algorithm.QCAlgorithm.MAD (
Symbol symbol,
Int32 period,
*Nullable<Resolution> resolution,
*Func<IBaseData, Decimal> selector
)
Creates a new MeanAbsoluteDeviation indicator.
If you don't provide a resolution, it defaults to the security resolution. If you provide a resolution, it must be greater than or equal to the resolution of the security. For instance, if you subscribe to hourly data for a security, you should update its indicator with data that spans 1 hour or longer.
For more information about the selector argument, see Alternative Price Fields.
For more information about plotting indicators, see Plotting Indicators.
You can manually create a MeanAbsoluteDeviation 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 with time/number pair or an IndicatorDataPoint. The indicator will only be ready after you prime it with enough data.
public class MeanAbsoluteDeviationAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private MeanAbsoluteDeviation _mad;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_mad = new MeanAbsoluteDeviation(20);
}
public override void OnData(Slice data)
{
if (data.Bars.TryGetValue(_symbol, out var bar))
{
_mad.Update(bar.EndTime, bar.Close);
}
if (_mad.IsReady)
{
// The current value of _mad is represented by itself (_mad)
// or _mad.Current.Value
Plot("MeanAbsoluteDeviation", "mad", _mad);
// Plot all properties of mad
Plot("MeanAbsoluteDeviation", "mean", _mad.Mean);
}
}
} class MeanAbsoluteDeviationAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self._symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.mad = MeanAbsoluteDeviation(20)
def on_data(self, slice: Slice) -> None:
bar = slice.Bars.get(self.symbol)
if bar:
self.mad.Update(bar.EndTime, bar.Close)
if self.mad.IsReady:
# The current value of self.mad is represented by self.mad.Current.Value
self.plot("MeanAbsoluteDeviation", "mad", self.mad.Current.Value)
# Plot all attributes of self.mad
self.plot("MeanAbsoluteDeviation", "mean", self.mad.Mean.Current.Value)
To register a manual indicator for automatic updates with the security data, call the RegisterIndicator method.
public class MeanAbsoluteDeviationAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private MeanAbsoluteDeviation _mad;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_mad = new MeanAbsoluteDeviation(20);
RegisterIndicator(_symbol, _mad, Resolution.Daily);
}
public override void OnData(Slice data)
{
if (_mad.IsReady)
{
// The current value of _mad is represented by itself (_mad)
// or _mad.Current.Value
Plot("MeanAbsoluteDeviation", "mad", _mad);
// Plot all properties of mad
Plot("MeanAbsoluteDeviation", "mean", _mad.Mean);
}
}
} class MeanAbsoluteDeviationAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self._symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.mad = MeanAbsoluteDeviation(20)
self.RegisterIndicator(self.symbol, self.mad, Resolution.Daily)
def on_data(self, slice: Slice) -> None:
if self.mad.IsReady:
# The current value of self.mad is represented by self.mad.Current.Value
self.plot("MeanAbsoluteDeviation", "mad", self.mad.Current.Value)
# Plot all attributes of self.mad
self.plot("MeanAbsoluteDeviation", "mean", self.mad.Mean.Current.Value)
The following reference table describes the MeanAbsoluteDeviation constructor:
MeanAbsoluteDeviation()1/2
MeanAbsoluteDeviation QuantConnect.Indicators.MeanAbsoluteDeviation (
int period
)
Evaluates the mean absolute deviation of samples in the lookback period.
MeanAbsoluteDeviation()2/2
MeanAbsoluteDeviation QuantConnect.Indicators.MeanAbsoluteDeviation (
string name,
int period
)
Evaluates the mean absolute deviation of samples in the look-back period.
Visualization
The following image shows plot values of selected properties of MeanAbsoluteDeviation using the plotly library.
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