Supported Indicators
Regression Channel
Introduction
The Regression Channel indicator extends the
To view the implementation of this indicator, see the LEAN GitHub repository.
Using RC Indicator
To create an automatic indicators for RegressionChannel
, call the RC
helper method from the QCAlgorithm
class. The RC
method creates a RegressionChannel
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 Initialize
method.
public class RegressionChannelAlgorithm : QCAlgorithm { private Symbol _symbol; private RegressionChannel _rc; public override void Initialize() { _symbol = AddEquity("SPY", Resolution.Daily).Symbol; _rc = RC(_symbol, 20, 2); } public override void OnData(Slice data) { if (_rc.IsReady) { // The current value of _rc is represented by itself (_rc) // or _rc.Current.Value Plot("RegressionChannel", "rc", _rc); // Plot all properties of rc Plot("RegressionChannel", "linearregression", _rc.LinearRegression); Plot("RegressionChannel", "upperchannel", _rc.UpperChannel); Plot("RegressionChannel", "lowerchannel", _rc.LowerChannel); Plot("RegressionChannel", "intercept", _rc.Intercept); Plot("RegressionChannel", "slope", _rc.Slope); } } }
class RegressionChannelAlgorithm(QCAlgorithm): def Initialize(self) -> None: self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol self.rc = self.RC(self.symbol, 20, 2) def OnData(self, slice: Slice) -> None: if self.rc.IsReady: # The current value of self.rc is represented by self.rc.Current.Value self.Plot("RegressionChannel", "rc", self.rc.Current.Value) # Plot all attributes of self.rc self.Plot("RegressionChannel", "linearregression", self.rc.LinearRegression.Current.Value) self.Plot("RegressionChannel", "upperchannel", self.rc.UpperChannel.Current.Value) self.Plot("RegressionChannel", "lowerchannel", self.rc.LowerChannel.Current.Value) self.Plot("RegressionChannel", "intercept", self.rc.Intercept.Current.Value) self.Plot("RegressionChannel", "slope", self.rc.Slope.Current.Value)
The following reference table describes the RC
method:
RC()1/1
RegressionChannel QuantConnect.Algorithm.QCAlgorithm.RC (Symbol
symbol,Int32
period,Decimal
k,*Nullable<Resolution>
resolution,*Func<IBaseData, Decimal>
selector )
Creates a new RegressionChannel indicator which will compute the LinearRegression, UpperChannel and LowerChannel lines, the intercept and slope.
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 RegressionChannel
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 Update
method with time/number pair, or an IndicatorDataPoint
. The indicator will only be ready after you prime it with enough data.
public class RegressionChannelAlgorithm : QCAlgorithm { private Symbol _symbol; private RegressionChannel _rc; public override void Initialize() { _symbol = AddEquity("SPY", Resolution.Daily).Symbol; _rc = new RegressionChannel(20, 2); } public override void OnData(Slice data) { if (data.Bars.TryGeValue(_symbol, out var bar)) { _rc.Update(bar.EndTime, bar.Close); } if (_rc.IsReady) { // The current value of _rc is represented by itself (_rc) // or _rc.Current.Value Plot("RegressionChannel", "rc", _rc); // Plot all properties of rc Plot("RegressionChannel", "linearregression", _rc.LinearRegression); Plot("RegressionChannel", "upperchannel", _rc.UpperChannel); Plot("RegressionChannel", "lowerchannel", _rc.LowerChannel); Plot("RegressionChannel", "intercept", _rc.Intercept); Plot("RegressionChannel", "slope", _rc.Slope); } } }
class RegressionChannelAlgorithm(QCAlgorithm): def Initialize(self) -> None: self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol self.rc = RegressionChannel(20, 2) def OnData(self, slice: Slice) -> None: bar = slice.Bars.get(self.symbol) if bar: self.rc.Update(bar.EndTime, bar.Close) if self.rc.IsReady: # The current value of self.rc is represented by self.rc.Current.Value self.Plot("RegressionChannel", "rc", self.rc.Current.Value) # Plot all attributes of self.rc self.Plot("RegressionChannel", "linearregression", self.rc.LinearRegression.Current.Value) self.Plot("RegressionChannel", "upperchannel", self.rc.UpperChannel.Current.Value) self.Plot("RegressionChannel", "lowerchannel", self.rc.LowerChannel.Current.Value) self.Plot("RegressionChannel", "intercept", self.rc.Intercept.Current.Value) self.Plot("RegressionChannel", "slope", self.rc.Slope.Current.Value)
To register a manual indicator for automatic updates with the security data, call the RegisterIndicator
method.
public class RegressionChannelAlgorithm : QCAlgorithm { private Symbol _symbol; private RegressionChannel _rc; public override void Initialize() { _symbol = AddEquity("SPY", Resolution.Daily).Symbol; _rc = new RegressionChannel(20, 2); RegisterIndicator(_symbol, _rc, Resolution.Daily); } public override void OnData(Slice data) { if (_rc.IsReady) { // The current value of _rc is represented by itself (_rc) // or _rc.Current.Value Plot("RegressionChannel", "rc", _rc); // Plot all properties of rc Plot("RegressionChannel", "linearregression", _rc.LinearRegression); Plot("RegressionChannel", "upperchannel", _rc.UpperChannel); Plot("RegressionChannel", "lowerchannel", _rc.LowerChannel); Plot("RegressionChannel", "intercept", _rc.Intercept); Plot("RegressionChannel", "slope", _rc.Slope); } } }
class RegressionChannelAlgorithm(QCAlgorithm): def Initialize(self) -> None: self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol self.rc = RegressionChannel(20, 2) self.RegisterIndicator(self.symbol, self.rc, Resolution.Daily) def OnData(self, slice: Slice) -> None: if self.rc.IsReady: # The current value of self.rc is represented by self.rc.Current.Value self.Plot("RegressionChannel", "rc", self.rc.Current.Value) # Plot all attributes of self.rc self.Plot("RegressionChannel", "linearregression", self.rc.LinearRegression.Current.Value) self.Plot("RegressionChannel", "upperchannel", self.rc.UpperChannel.Current.Value) self.Plot("RegressionChannel", "lowerchannel", self.rc.LowerChannel.Current.Value) self.Plot("RegressionChannel", "intercept", self.rc.Intercept.Current.Value) self.Plot("RegressionChannel", "slope", self.rc.Slope.Current.Value)
The following reference table describes the RegressionChannel
constructor:
RegressionChannel()1/2
RegressionChannel QuantConnect.Indicators.RegressionChannel (string
name,int
period,decimal
k )
Initializes a new instance of the RegressionChannel
class.
RegressionChannel()2/2
RegressionChannel QuantConnect.Indicators.RegressionChannel (int
period,decimal
k )
Initializes a new instance of the LeastSquaresMovingAverage
class.
Visualization
The following image shows plot values of selected properties of RegressionChannel
using the plotly library.
