Supported Indicators
Commodity Channel Index
Introduction
This indicator represents the traditional commodity channel index (CCI) CCI = (Typical Price - 20-period SMA of TP) / (.015 * Mean Deviation) Typical Price (TP) = (High + Low + Close)/3 Constant = 0.015 There are four steps to calculating the Mean Deviation, first, subtract the most recent 20-period average of the typical price from each period's typical price. Second, take the absolute values of these numbers. Third, sum the absolute values. Fourth, divide by the total number of periods (20).
To view the implementation of this indicator, see the LEAN GitHub repository.
Using CCI Indicator
To create an automatic indicators for CommodityChannelIndex, call the CCI helper method from the QCAlgorithm class. The CCI method creates a CommodityChannelIndex 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 CommodityChannelIndexAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol self._cci = self.cci(self._symbol, 20, MovingAverageType.Simple) def on_data(self, slice: Slice) -> None: if self._cci.is_ready: # The current value of self._cci is represented by self._cci.current.value self.plot("CommodityChannelIndex", "cci", self._cci.current.value) # Plot all attributes of self._cci self.plot("CommodityChannelIndex", "typical_price_average", self._cci.typical_price_average.current.value) self.plot("CommodityChannelIndex", "typical_price_mean_deviation", self._cci.typical_price_mean_deviation.current.value)
The following reference table describes the CCI method:
cci(symbol, period, moving_average_type=0, resolution=None, selector=None)[source]Creates a new CommodityChannelIndex indicator. The indicator will be automatically updated on the given resolution.
- symbol (Symbol) — The symbol whose CCI we want
- period (int) — The period over which to compute the CCI
- moving_average_type (MovingAverageType, optional) — The type of moving average to use in computing the typical price average
- 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 CommodityChannelIndex indicator for the requested symbol over the specified period
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.
The following table describes the MovingAverageType enumeration members:
To avoid parameter ambiguity, use the resolution argument to set the Resolution.
class CommodityChannelIndexAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.HOUR).Symbol self._cci = self.cci(self._symbol, 20, MovingAverageType.Simple, resolution=Resolution.DAILY)
You can manually create a CommodityChannelIndex 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 CommodityChannelIndexAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol self._cci = CommodityChannelIndex(20, MovingAverageType.Simple) def on_data(self, slice: Slice) -> None: bar = slice.bars.get(self._symbol) if bar: self._cci.update(bar) if self._cci.is_ready: # The current value of self._cci is represented by self._cci.current.value self.plot("CommodityChannelIndex", "cci", self._cci.current.value) # Plot all attributes of self._cci self.plot("CommodityChannelIndex", "typical_price_average", self._cci.typical_price_average.current.value) self.plot("CommodityChannelIndex", "typical_price_mean_deviation", self._cci.typical_price_mean_deviation.current.value)
To register a manual indicator for automatic updates with the security data, call the register_indicator method.
class CommodityChannelIndexAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol self._cci = CommodityChannelIndex(20, MovingAverageType.Simple) self.register_indicator(self._symbol, self._cci, Resolution.DAILY) def on_data(self, slice: Slice) -> None: if self._cci.is_ready: # The current value of self._cci is represented by self._cci.current.value self.plot("CommodityChannelIndex", "cci", self._cci.current.value) # Plot all attributes of self._cci self.plot("CommodityChannelIndex", "typical_price_average", self._cci.typical_price_average.current.value) self.plot("CommodityChannelIndex", "typical_price_mean_deviation", self._cci.typical_price_mean_deviation.current.value)
The following reference table describes the CommodityChannelIndex constructor:
CommodityChannelIndex
Represents the traditional commodity channel index (CCI) CCI = (Typical Price - 20-period SMA of TP) / (.015 * Mean Deviation) Typical Price (TP) = (High + Low + Close)/3 Constant = 0.015 There are four steps to calculating the Mean Deviation, first, subtract the most recent 20-period average of the typical price from each period's typical price. Second, take the absolute values of these numbers. Third, sum the absolute values. Fourth, divide by the total number of periods (20).
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
consolidatorsThe data consolidators associated with this indicator if any
The data consolidators associated with this indicator if any
ISet[IDataConsolidator]
currentGets 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_readyGets a flag indicating when this indicator is ready and fully initialized
Gets a flag indicating when this indicator is ready and fully initialized
bool
itemIndexes 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
moving_average_typeGets the type of moving average
Gets the type of moving average
MovingAverageType
nameGets a name for this indicator
Gets a name for this indicator
str
previousGets 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
samplesGets the number of samples processed by this indicator
Gets the number of samples processed by this indicator
int
typical_price_averageKeep track of the simple moving average of the typical price
Keep track of the simple moving average of the typical price
IndicatorBase[IndicatorDataPoint]
typical_price_mean_deviationKeep track of the mean absolute deviation of the typical price
Keep track of the mean absolute deviation of the typical price
IndicatorBase[IndicatorDataPoint]
warm_up_periodRequired 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
windowA 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 CommodityChannelIndex using the plotly library.
Indicator History
To get the historical data of the CommodityChannelIndex 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 CommodityChannelIndexAlgorithm(QCAlgorithm): def initialize(self) -> None: self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol cci = self.cci(self._symbol, 20, MovingAverageType.Simple) count_indicator_history = self.indicator_history(cci, self._symbol, 100, Resolution.MINUTE) timedelta_indicator_history = self.indicator_history(cci, self._symbol, timedelta(days=10), Resolution.MINUTE) time_period_indicator_history = self.indicator_history(cci, 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(cci, 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.
typical_price_average = indicator_history_df["typical_price_average"] typical_price_mean_deviation = indicator_history_df["typical_price_mean_deviation"] # Alternative way # typical_price_average = indicator_history_df.typical_price_average # typical_price_mean_deviation = indicator_history_df.typical_price_mean_deviation
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