# Supported Indicators

## Regression Channel

### Introduction

The Regression Channel indicator extends the with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html

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 Initializeinitialize method.

public class RegressionChannelAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private RegressionChannel _rc;

public override void Initialize()
{
_rc = RC(_symbol, 20, 2);
}

public override void OnData(Slice data)
{
{
// 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._rc = self.rc(self._symbol, 20, 2)

def on_data(self, slice: Slice) -> None:
# 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", "linear_regression", self._rc.linear_regression.current.value)
self.plot("RegressionChannel", "upper_channel", self._rc.upper_channel.current.value)
self.plot("RegressionChannel", "lower_channel", self._rc.lower_channel.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(symbol, period, k, resolution=None, selector=None)[source]

Creates a new RegressionChannel indicator which will compute the LinearRegression, UpperChannel and LowerChannel lines, the intercept and slope

Parameters:
• symbol (Symbol) — The symbol whose RegressionChannel we seek
• period (int) — The period of the standard deviation and least square moving average (linear regression line)
• k (float) — The number of standard deviations specifying the distance between the linear regression and upper or lower channel lines
• resolution (Resolution, optional) — The resolution
• selector (Callable[IBaseData, float], optional) — x.Value)
Returns:

A Regression Channel configured with the specified period and number of standard deviation

Return type:

RegressionChannel

RC(symbol, period, k, resolution=None, selector=None)[source]

Creates a new RegressionChannel indicator which will compute the LinearRegression, UpperChannel and LowerChannel lines, the intercept and slope

Parameters:
• symbol (Symbol) — The symbol whose RegressionChannel we seek
• period (Int32) — The period of the standard deviation and least square moving average (linear regression line)
• k (decimal) — The number of standard deviations specifying the distance between the linear regression and upper or lower channel lines
• resolution (Resolution, optional) — The resolution
• selector (Func<IBaseData, Decimal>, optional) — x.Value)
Returns:

A Regression Channel configured with the specified period and number of standard deviation

Return type:

RegressionChannel

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.

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 Updateupdate 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()
{
_rc = new RegressionChannel(20, 2);
}

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

{
// 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._rc = RegressionChannel(20, 2)

def on_data(self, slice: Slice) -> None:
bar = slice.bars.get(self._symbol)
if bar:
self._rc.update(bar.EndTime, bar.Close)
# 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", "linear_regression", self._rc.linear_regression.current.value)
self.plot("RegressionChannel", "upper_channel", self._rc.upper_channel.current.value)
self.plot("RegressionChannel", "lower_channel", self._rc.lower_channel.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 RegisterIndicatorregister_indicator method.

public class RegressionChannelAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private RegressionChannel _rc;

public override void Initialize()
{
_rc = new RegressionChannel(20, 2);
RegisterIndicator(_symbol, _rc, Resolution.Daily);
}

public override void OnData(Slice data)
{
{
// 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._rc = RegressionChannel(20, 2)
self.register_indicator(self._symbol, self._rc, Resolution.DAILY)

def on_data(self, slice: Slice) -> None:
# 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", "linear_regression", self._rc.linear_regression.current.value)
self.plot("RegressionChannel", "upper_channel", self._rc.upper_channel.current.value)
self.plot("RegressionChannel", "lower_channel", self._rc.lower_channel.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

class QuantConnect.Indicators.RegressionChannel[source]

The Regression Channel indicator extends the LeastSquaresMovingAverage with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html

get_enumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

reset()

Resets this indicator and all sub-indicators (StandardDeviation, LowerBand, MiddleBand, UpperBand)

to_detailed_string()

Provides a more detailed string of this indicator in the form of {Name} - {Value}

Return type:

str

update(time, value)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
• time (datetime)
• value (float)
Return type:

bool

update(input)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
• input (IBaseData)
Return type:

bool

property consolidators

The data consolidators associated with this indicator if any

Returns:

The data consolidators associated with this indicator if any

Return type:

ISet[IDataConsolidator]

property current

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property intercept

The point where the regression line crosses the y-axis (price-axis)

Returns:

The point where the regression line crosses the y-axis (price-axis)

Return type:

IndicatorBase[IndicatorDataPoint]

property is_ready

Gets a flag indicating when this indicator is ready and fully initialized

Returns:

Gets a flag indicating when this indicator is ready and fully initialized

Return type:

bool

property item

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Returns:

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Return type:

IndicatorDataPoint

property linear_regression

Gets the linear regression

Returns:

Gets the linear regression

Return type:

LeastSquaresMovingAverage

property lower_channel

Gets the lower channel (linear regression - k * stdDev)

Returns:

Gets the lower channel (linear regression - k * stdDev)

Return type:

IndicatorBase[IndicatorDataPoint]

property name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

str

property previous

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property samples

Gets the number of samples processed by this indicator

Returns:

Gets the number of samples processed by this indicator

Return type:

int

property slope

The regression line slope

Returns:

The regression line slope

Return type:

IndicatorBase[IndicatorDataPoint]

property upper_channel

Gets the upper channel (linear regression + k * stdDev)

Returns:

Gets the upper channel (linear regression + k * stdDev)

Return type:

IndicatorBase[IndicatorDataPoint]

property warm_up_period

Required period, in data points, for the indicator to be ready and fully initialized.

Returns:

Required period, in data points, for the indicator to be ready and fully initialized.

Return type:

int

property window

A rolling window keeping a history of the indicator values of a given period

Returns:

A rolling window keeping a history of the indicator values of a given period

Return type:

RollingWindow[IndicatorDataPoint]

#### RegressionChannel

class QuantConnect.Indicators.RegressionChannel[source]

The Regression Channel indicator extends the LeastSquaresMovingAverage with the inclusion of two (upper and lower) channel lines that are distanced from the linear regression line by a user defined number of standard deviations. Reference: http://www.onlinetradingconcepts.com/TechnicalAnalysis/LinRegChannel.html

GetEnumerator()

Returns an enumerator that iterates through the history window.

Return type:

IEnumerator[IndicatorDataPoint]

Reset()

Resets this indicator and all sub-indicators (StandardDeviation, LowerBand, MiddleBand, UpperBand)

ToDetailedString()

Provides a more detailed string of this indicator in the form of {Name} - {Value}

Return type:

String

Update(time, value)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
• time (DateTime)
• value (decimal)
Return type:

Boolean

Update(input)

Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise

Parameters:
• input (IBaseData)
Return type:

Boolean

property Consolidators

The data consolidators associated with this indicator if any

Returns:

The data consolidators associated with this indicator if any

Return type:

ISet<IDataConsolidator>

property Current

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property Intercept

The point where the regression line crosses the y-axis (price-axis)

Returns:

The point where the regression line crosses the y-axis (price-axis)

Return type:

IndicatorBase<IndicatorDataPoint>

property IsReady

Gets a flag indicating when this indicator is ready and fully initialized

Returns:

Gets a flag indicating when this indicator is ready and fully initialized

Return type:

bool

property LinearRegression

Gets the linear regression

Returns:

Gets the linear regression

Return type:

LeastSquaresMovingAverage

property LowerChannel

Gets the lower channel (linear regression - k * stdDev)

Returns:

Gets the lower channel (linear regression - k * stdDev)

Return type:

IndicatorBase<IndicatorDataPoint>

property Name

Gets a name for this indicator

Returns:

Gets a name for this indicator

Return type:

string

property Previous

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Returns:

Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.

Return type:

IndicatorDataPoint

property Samples

Gets the number of samples processed by this indicator

Returns:

Gets the number of samples processed by this indicator

Return type:

int

property Slope

The regression line slope

Returns:

The regression line slope

Return type:

IndicatorBase<IndicatorDataPoint>

property UpperChannel

Gets the upper channel (linear regression + k * stdDev)

Returns:

Gets the upper channel (linear regression + k * stdDev)

Return type:

IndicatorBase<IndicatorDataPoint>

property WarmUpPeriod

Required period, in data points, for the indicator to be ready and fully initialized.

Returns:

Required period, in data points, for the indicator to be ready and fully initialized.

Return type:

Int32

property Window

A rolling window keeping a history of the indicator values of a given period

Returns:

A rolling window keeping a history of the indicator values of a given period

Return type:

RollingWindow<IndicatorDataPoint>

property [System.Int32]

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Returns:

Indexes the history windows, where index 0 is the most recent indicator value. If index is greater or equal than the current count, it returns null. If the index is greater or equal than the window size, it returns null and resizes the windows to i + 1.

Return type:

IndicatorDataPoint

### Visualization

The following image shows plot values of selected properties of RegressionChannel using the plotly library.

### Indicator History

To get the historical data of the RegressionChannel 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 RegressionChannelAlgorithm : QCAlgorithm
{
private Symbol _symbol;

public override void Initialize()
{
var rc = RC(_symbol, 20, 2);
var countIndicatorHistory = IndicatorHistory(rc, _symbol, 100, Resolution.Minute);
var timeSpanIndicatorHistory = IndicatorHistory(rc, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
var timePeriodIndicatorHistory = IndicatorHistory(rc, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
}
}
class RegressionChannelAlgorithm(QCAlgorithm):
def initialize(self) -> None:
rc = self.rc(self._symbol, 20, 2)
count_indicator_history = self.indicator_history(rc, self._symbol, 100, Resolution.MINUTE)
timedelta_indicator_history = self.indicator_history(rc, self._symbol, timedelta(days=10), Resolution.MINUTE)
time_period_indicator_history = self.indicator_history(rc, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)


To make the IndicatorHistoryindicator_history method update the indicator with an alternative price field instead of the close (or mid-price) of each bar, pass a selector argument.

var indicatorHistory = IndicatorHistory(rc, 100, Resolution.Minute, (bar) => ((TradeBar)bar).High);
indicator_history = self.indicator_history(rc, 100, Resolution.MINUTE, lambda bar: bar.high)
indicator_history_df = indicator_history.data_frame

If you already have a list of Slice objects, you can pass them to the IndicatorHistoryindicator_history method to avoid the internal history request.

var history = History(_symbol, 100, Resolution.Minute);
var historyIndicatorHistory = IndicatorHistory(rc, history);

To access the properties of the indicator history, invoke the property of each IndicatorDataPoint object.index the DataFrame with the property name.

var linearregression = indicatorHistory.Select(x => ((dynamic)x).LinearRegression).ToList();
var upperchannel = indicatorHistory.Select(x => ((dynamic)x).UpperChannel).ToList();
var lowerchannel = indicatorHistory.Select(x => ((dynamic)x).LowerChannel).ToList();
var intercept = indicatorHistory.Select(x => ((dynamic)x).Intercept).ToList();
var slope = indicatorHistory.Select(x => ((dynamic)x).Slope).ToList();

// Alternative way
// var linearregression = indicatorHistory.Select(x => x["linearregression"]).ToList();
// var upperchannel = indicatorHistory.Select(x => x["upperchannel"]).ToList();
// var lowerchannel = indicatorHistory.Select(x => x["lowerchannel"]).ToList();
// var intercept = indicatorHistory.Select(x => x["intercept"]).ToList();
// var slope = indicatorHistory.Select(x => x["slope"]).ToList();

linear_regression = indicator_history_df["linear_regression"]
upper_channel = indicator_history_df["upper_channel"]
lower_channel = indicator_history_df["lower_channel"]
intercept = indicator_history_df["intercept"]
slope = indicator_history_df["slope"]

# Alternative way
# linear_regression = indicator_history_df.linear_regression
# upper_channel = indicator_history_df.upper_channel
# lower_channel = indicator_history_df.lower_channel
# intercept = indicator_history_df.intercept
# slope = indicator_history_df.slope


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