Supported Indicators
Keltner Channels
Introduction
This indicator creates a moving average (middle band) with an upper band and lower band fixed at k average True range multiples away from the middle band.
To view the implementation of this indicator, see the LEAN GitHub repository.
Using KCH Indicator
To create an automatic indicators for KeltnerChannels
, call the KCH
helper method from the QCAlgorithm
class. The KCH
method creates a KeltnerChannels
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.
class KeltnerChannelsAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol self._kch = self.kch(self._symbol, 20, 2, MovingAverageType.Simple) def on_data(self, slice: Slice) -> None: if self._kch.is_ready: # The current value of self._kch is represented by self._kch.current.value self.plot("KeltnerChannels", "kch", self._kch.current.value) # Plot all attributes of self._kch self.plot("KeltnerChannels", "middle_band", self._kch.middle_band.current.value) self.plot("KeltnerChannels", "upper_band", self._kch.upper_band.current.value) self.plot("KeltnerChannels", "lower_band", self._kch.lower_band.current.value) self.plot("KeltnerChannels", "average_true_range", self._kch.average_True_range.current.value)
The following reference table describes the KCH
method:
kch(symbol, period, k, moving_average_type=0, resolution=None, selector=None)
[source]Creates a new Keltner Channels indicator. The indicator will be automatically updated on the given resolution.
- symbol (Symbol) — The symbol whose Keltner Channel we seek
- period (int) — The period over which to compute the Keltner Channels
- k (float) — from the middle band of the Keltner Channels
- moving_average_type (MovingAverageType, optional) — Specifies the type of moving average to be used as the middle line of the Keltner Channel
- resolution (Resolution, optional) — The resolution.
- selector (Callable[IBaseData, IBaseDataBar], optional) — Selects a value from the BaseData to send into the indicator, if null defaults to casting the input value to a TradeBar
The Keltner Channel indicator for the requested symbol.
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 KeltnerChannels
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 a TradeBar
or QuoteBar
. The indicator will only be ready after you prime it with enough data.
class KeltnerChannelsAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol self._kch = KeltnerChannels(20, 2, MovingAverageType.Simple) def on_data(self, slice: Slice) -> None: bar = slice.bars.get(self._symbol) if bar: self._kch.update(bar) if self._kch.is_ready: # The current value of self._kch is represented by self._kch.current.value self.plot("KeltnerChannels", "kch", self._kch.current.value) # Plot all attributes of self._kch self.plot("KeltnerChannels", "middle_band", self._kch.middle_band.current.value) self.plot("KeltnerChannels", "upper_band", self._kch.upper_band.current.value) self.plot("KeltnerChannels", "lower_band", self._kch.lower_band.current.value) self.plot("KeltnerChannels", "average_true_range", self._kch.average_True_range.current.value)
To register a manual indicator for automatic updates with the security data, call the register_indicator
method.
class KeltnerChannelsAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol self._kch = KeltnerChannels(20, 2, MovingAverageType.Simple) self.register_indicator(self._symbol, self._kch, Resolution.DAILY) def on_data(self, slice: Slice) -> None: if self._kch.is_ready: # The current value of self._kch is represented by self._kch.current.value self.plot("KeltnerChannels", "kch", self._kch.current.value) # Plot all attributes of self._kch self.plot("KeltnerChannels", "middle_band", self._kch.middle_band.current.value) self.plot("KeltnerChannels", "upper_band", self._kch.upper_band.current.value) self.plot("KeltnerChannels", "lower_band", self._kch.lower_band.current.value) self.plot("KeltnerChannels", "average_true_range", self._kch.average_True_range.current.value)
The following reference table describes the KeltnerChannels
constructor:
KeltnerChannels
This indicator creates a moving average (middle band) with an upper band and lower band fixed at k average true range multiples away from the middle band.
get_enumerator()
Returns an enumerator that iterates through the history window.
IEnumerator[IndicatorDataPoint]
reset()
Resets this indicator to its initial state
to_detailed_string()
Provides a more detailed string of this indicator in the form of {Name} - {Value}
str
update(time, value)
Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise
- time (datetime)
- value (float)
bool
update(input)
Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise
- input (IBaseData)
bool
average_true_range
Gets the average true range
Gets the average true range
IndicatorBase[IBaseDataBar]
consolidators
The data consolidators associated with this indicator if any
The data consolidators associated with this indicator if any
ISet[IDataConsolidator]
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.
Gets the current state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.
IndicatorDataPoint
is_ready
Gets a flag indicating when this indicator is ready and fully initialized
Gets a flag indicating when this indicator is ready and fully initialized
bool
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.
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.
IndicatorDataPoint
lower_band
Gets the lower band of the channel
Gets the lower band of the channel
IndicatorBase[IBaseDataBar]
middle_band
Gets the middle band of the channel
Gets the middle band of the channel
IndicatorBase[IndicatorDataPoint]
name
Gets a name for this indicator
Gets a name for this indicator
str
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.
Gets the previous state of this indicator. If the state has not been updated then the time on the value will equal DateTime.MinValue.
IndicatorDataPoint
samples
Gets the number of samples processed by this indicator
Gets the number of samples processed by this indicator
int
upper_band
Gets the upper band of the channel
Gets the upper band of the channel
IndicatorBase[IBaseDataBar]
warm_up_period
Required period, in data points, for the indicator to be ready and fully initialized.
Required period, in data points, for the indicator to be ready and fully initialized.
int
window
A rolling window keeping a history of the indicator values of a given period
A rolling window keeping a history of the indicator values of a given period
RollingWindow[IndicatorDataPoint]
Visualization
The following image shows plot values of selected properties of KeltnerChannels
using the plotly library.

Indicator History
To get the historical data of the KeltnerChannels
indicator, call the self.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 indicator_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.
class KeltnerChannelsAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol kch = self.kch(self._symbol, 20, 2, MovingAverageType.Simple) count_indicator_history = self.indicator_history(kch, self._symbol, 100, Resolution.MINUTE) timedelta_indicator_history = self.indicator_history(kch, self._symbol, timedelta(days=10), Resolution.MINUTE) time_period_indicator_history = self.indicator_history(kch, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
To make the indicator_history
method update the indicator with an alternative price field instead of the close (or mid-price) of each bar, pass a selector
argument.
indicator_history = self.indicator_history(kch, 100, Resolution.MINUTE, lambda bar: bar.high) indicator_history_df = indicator_history.data_frame
To access the properties of the indicator history, index the DataFrame with the property name.
middle_band = indicator_history_df["middle_band"] upper_band = indicator_history_df["upper_band"] lower_band = indicator_history_df["lower_band"] average_true_range = indicator_history_df["average_True_range"] # Alternative way # middle_band = indicator_history_df.middle_band # upper_band = indicator_history_df.upper_band # lower_band = indicator_history_df.lower_band # average_true_range = indicator_history_df.average_True_range