Supported Indicators
Auto Regressive Integrated Moving Average
Introduction
An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing
To view the implementation of this indicator, see the LEAN GitHub repository.
Using ARIMA Indicator
To create an automatic indicators for AutoRegressiveIntegratedMovingAverage, call the ARIMA helper method from the QCAlgorithm class. The ARIMA method creates a AutoRegressiveIntegratedMovingAverage 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 AutoRegressiveIntegratedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private AutoRegressiveIntegratedMovingAverage _arima;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_arima = ARIMA(_symbol, 1, 1, 1, 20);
}
public override void OnData(Slice data)
{
if (_arima.IsReady)
{
// The current value of _arima is represented by itself (_arima)
// or _arima.Current.Value
Plot("AutoRegressiveIntegratedMovingAverage", "arima", _arima);
// Plot all properties of arima
Plot("AutoRegressiveIntegratedMovingAverage", "arresidualerror", _arima.ArResidualError);
Plot("AutoRegressiveIntegratedMovingAverage", "maresidualerror", _arima.MaResidualError);
}
}
} class AutoRegressiveIntegratedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self.arima = self.ARIMA(self.symbol, 1, 1, 1, 20)
def on_data(self, slice: Slice) -> None:
if self.arima.is_ready:
# The current value of self.arima is represented by self.arima.current.value
self.plot("AutoRegressiveIntegratedMovingAverage", "arima", self.arima.current.value)
# Plot all attributes of self.arima
self.plot("AutoRegressiveIntegratedMovingAverage", "ar_residual_error", self.arima.ar_residual_error)
self.plot("AutoRegressiveIntegratedMovingAverage", "ma_residual_error", self.arima.ma_residual_error)
The following reference table describes the ARIMA method:
arima(symbol, ar_order, diff_order, ma_order, period, resolution=None, selector=None)[source]Creates a new ARIMA indicator.
- symbol (Symbol) — The symbol whose ARIMA indicator we want
- ar_order (int) — AR order (p) -- defines the number of past values to consider in the AR component of the model.
- diff_order (int) — Difference order (d) -- defines how many times to difference the model before fitting parameters.
- ma_order (int) — MA order (q) -- defines the number of past values to consider in the MA component of the model.
- period (int) — Size of the rolling series to fit onto
- resolution (Resolution, optional) — The resolution
- selector (Callable[IBaseData, float], optional) — x.Value)
The ARIMA indicator for the requested symbol over the specified period
ARIMA(symbol, arOrder, diffOrder, maOrder, period, resolution=None, selector=None)[source]Creates a new ARIMA indicator.
- symbol (Symbol) — The symbol whose ARIMA indicator we want
- arOrder (Int32) — AR order (p) -- defines the number of past values to consider in the AR component of the model.
- diffOrder (Int32) — Difference order (d) -- defines how many times to difference the model before fitting parameters.
- maOrder (Int32) — MA order (q) -- defines the number of past values to consider in the MA component of the model.
- period (Int32) — Size of the rolling series to fit onto
- resolution (Resolution, optional) — The resolution
- selector (Func<IBaseData, Decimal>, optional) — x.Value)
The ARIMA 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.
You can manually create a AutoRegressiveIntegratedMovingAverage 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 AutoRegressiveIntegratedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private AutoRegressiveIntegratedMovingAverage _arima;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_arima = new AutoRegressiveIntegratedMovingAverage(1, 1, 1, 20, True);
}
public override void OnData(Slice data)
{
if (data.Bars.TryGetValue(_symbol, out var bar))
{
_arima.Update(bar.EndTime, bar.Close);
}
if (_arima.IsReady)
{
// The current value of _arima is represented by itself (_arima)
// or _arima.Current.Value
Plot("AutoRegressiveIntegratedMovingAverage", "arima", _arima);
// Plot all properties of arima
Plot("AutoRegressiveIntegratedMovingAverage", "arresidualerror", _arima.ArResidualError);
Plot("AutoRegressiveIntegratedMovingAverage", "maresidualerror", _arima.MaResidualError);
}
}
} class AutoRegressiveIntegratedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self.arima = AutoRegressiveIntegratedMovingAverage(1, 1, 1, 20, True)
def on_data(self, slice: Slice) -> None:
bar = slice.bars.get(self._symbol)
if bar:
self.arima.update(bar.EndTime, bar.Close)
if self.arima.is_ready:
# The current value of self.arima is represented by self.arima.current.value
self.plot("AutoRegressiveIntegratedMovingAverage", "arima", self.arima.current.value)
# Plot all attributes of self.arima
self.plot("AutoRegressiveIntegratedMovingAverage", "ar_residual_error", self.arima.ar_residual_error)
self.plot("AutoRegressiveIntegratedMovingAverage", "ma_residual_error", self.arima.ma_residual_error)
To register a manual indicator for automatic updates with the security data, call the RegisterIndicatorregister_indicator method.
public class AutoRegressiveIntegratedMovingAverageAlgorithm : QCAlgorithm
{
private Symbol _symbol;
private AutoRegressiveIntegratedMovingAverage _arima;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_arima = new AutoRegressiveIntegratedMovingAverage(1, 1, 1, 20, True);
RegisterIndicator(_symbol, _arima, Resolution.Daily);
}
public override void OnData(Slice data)
{
if (_arima.IsReady)
{
// The current value of _arima is represented by itself (_arima)
// or _arima.Current.Value
Plot("AutoRegressiveIntegratedMovingAverage", "arima", _arima);
}
}
} class AutoRegressiveIntegratedMovingAverageAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self.arima = AutoRegressiveIntegratedMovingAverage(1, 1, 1, 20, True)
self.register_indicator(self._symbol, self.arima, Resolution.DAILY)
def on_data(self, slice: Slice) -> None:
if self.arima.is_ready:
# The current value of self.arima is represented by self.arima.current.value
self.plot("AutoRegressiveIntegratedMovingAverage", "arima", self.arima.current.value)
The following reference table describes the AutoRegressiveIntegratedMovingAverage constructor:
AutoRegressiveIntegratedMovingAverage
An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing _diffOrder times):
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
ar_residual_errorThe variance of the residuals (Var(ε)) from the first step of Double[]).
The variance of the residuals (Var(ε)) from the first step of Double[]).
float
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
ma_residual_errorThe variance of the residuals (Var(ε)) from the second step of Double[]).
The variance of the residuals (Var(ε)) from the second step of Double[]).
float
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
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]
ar_parametersFitted AR parameters (φ terms).
Fitted AR parameters (φ terms).
float[]
interceptFitted intercept (c term).
Fitted intercept (c term).
float
ma_parametersFitted MA parameters (θ terms).
Fitted MA parameters (θ terms).
float[]
AutoRegressiveIntegratedMovingAverage
An Autoregressive Intergrated Moving Average (ARIMA) is a time series model which can be used to describe a set of data. In particular,with Xₜ representing the series, the model assumes the data are of form (after differencing _diffOrder times):
GetEnumerator()Returns an enumerator that iterates through the history window.
IEnumerator[IndicatorDataPoint]
Reset()Resets this indicator to its initial state
ToDetailedString()Provides a more detailed string of this indicator in the form of {Name} - {Value}
String
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 (decimal)
Boolean
Update(input)Updates the state of this indicator with the given value and returns true if this indicator is ready, false otherwise
- input (IBaseData)
Boolean
ArResidualErrorThe variance of the residuals (Var(ε)) from the first step of Double[]).
The variance of the residuals (Var(ε)) from the first step of Double[]).
Double
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
IsReadyGets a flag indicating when this indicator is ready and fully initialized
Gets a flag indicating when this indicator is ready and fully initialized
bool
MaResidualErrorThe variance of the residuals (Var(ε)) from the second step of Double[]).
The variance of the residuals (Var(ε)) from the second step of Double[]).
Double
NameGets a name for this indicator
Gets a name for this indicator
string
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
WarmUpPeriodRequired 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.
Int32
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>
[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.
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
ArParametersFitted AR parameters (φ terms).
Fitted AR parameters (φ terms).
Double[]
InterceptFitted intercept (c term).
Fitted intercept (c term).
Double
MaParametersFitted MA parameters (θ terms).
Fitted MA parameters (θ terms).
Double[]
Visualization
The following image shows plot values of selected properties of AutoRegressiveIntegratedMovingAverage using the plotly library.
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