AutoRegressiveIntegratedMovingAverage

QuantConnect.Indicators.AutoRegressiveIntegratedMovingAverage

AutoRegressiveIntegratedMovingAverage(
    name: str,
    ar_order: int,
    diff_order: int,
    ma_order: int,
    period: int,
    intercept: bool = True,
)

AutoRegressiveIntegratedMovingAverage(
    ar_order: int,
    diff_order: int,
    ma_order: int,
    period: int,
    intercept: bool,
)

Bases: TimeSeriesIndicator

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):

Xₜ = c + εₜ + ΣᵢφᵢXₜ₋ᵢ +  Σᵢθᵢεₜ₋ᵢ

where the first sum has an upper limit of _arOrder and the second _maOrder.

Fits an ARIMA(ar_order,diff_order,ma_order) model of form (after differencing it _diffOrder times):

Parameters:

Name	Type	Description	Default
name	Optional[str]	The name of the indicator	None
ar_order	int	AR order (p) -- defines the number of past values to consider in the AR component of the model.	required
diff_order	int	Difference order (d) -- defines how many times to difference the model before fitting parameters.	required
ma_order	int	MA order -- defines the number of past values to consider in the MA component of the model.	required
period	int	Size of the rolling series to fit onto	required
intercept	bool	Whether or not to include the intercept term	True

handle_exceptions

handle_exceptions: bool

Whether or not to handle potential exceptions, returning a zero value. I.e, the values provided as input are not valid by the Normal Equations direct regression method

ar_parameters

ar_parameters: List[float]

Fitted AR parameters (φ terms).

ma_parameters

ma_parameters: List[float]

Fitted MA parameters (θ terms).

intercept

intercept: float

Fitted intercept (c term).

is_ready

is_ready: bool

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

warm_up_period

warm_up_period: int

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

ar_residual_error

ar_residual_error: float

The variance of the residuals (Var(ε)) from the first step of TwoStepFit.

ma_residual_error

ma_residual_error: float

The variance of the residuals (Var(ε)) from the second step of TwoStepFit.

consolidators

consolidators: ISet[IDataConsolidator]

The data consolidators associated with this indicator if any

current

current: IndicatorDataPoint

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

previous

previous: IndicatorDataPoint

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

name

name: str

Gets a name for this indicator

samples

samples: int

Gets the number of samples processed by this indicator

updated

updated: _EventContainer[
    Callable[[Object, IndicatorDataPoint], Any], Any
]

Event handler that fires after this indicator is updated

window

window: RollingWindow[IndicatorDataPoint]

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

compute_next_value

compute_next_value(input: IndicatorDataPoint) -> float

Forecasts the series of the fitted model one point ahead.

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
input	IndicatorDataPoint	The input given to the indicator	required

Returns:

Type	Description
float	A new value for this indicator.

reset

reset() -> None

Resets this indicator to its initial state

__eq__

__eq__(right: float) -> bool

__eq__(right: IndicatorBase) -> bool

__eq__(right: float) -> bool

__eq__(right: IndicatorBase) -> bool

__eq__(right: int) -> bool

__eq__(right: IndicatorBase) -> bool

__eq__(right: int) -> bool

__eq__(right: IndicatorBase) -> bool

Signature descriptions:

• Determines if the indicator's current value is equal to the specified value

• Determines if the specified value is equal to the indicator's current value

__ge__

__ge__(right: float) -> bool

__ge__(right: IndicatorBase) -> bool

__ge__(right: float) -> bool

__ge__(right: IndicatorBase) -> bool

__ge__(right: int) -> bool

__ge__(right: IndicatorBase) -> bool

__ge__(right: int) -> bool

__ge__(right: IndicatorBase) -> bool

Signature descriptions:

• Determines if the indicator's current value is greater than or equal to the specified value

• Determines if the specified value is greater than or equal to the indicator's current value

__gt__

__gt__(right: float) -> bool

__gt__(right: IndicatorBase) -> bool

__gt__(right: float) -> bool

__gt__(right: IndicatorBase) -> bool

__gt__(right: int) -> bool

__gt__(right: IndicatorBase) -> bool

__gt__(right: int) -> bool

__gt__(right: IndicatorBase) -> bool

Signature descriptions:

• Determines if the indicator's current value is greater than the specified value

• Determines if the specified value is greater than the indicator's current value

__le__

__le__(right: float) -> bool

__le__(right: IndicatorBase) -> bool

__le__(right: float) -> bool

__le__(right: IndicatorBase) -> bool

__le__(right: int) -> bool

__le__(right: IndicatorBase) -> bool

__le__(right: int) -> bool

__le__(right: IndicatorBase) -> bool

Signature descriptions:

• Determines if the indicator's current value is less than or equal to the specified value

• Determines if the specified value is less than or equal to the indicator's current value

__lt__

__lt__(right: float) -> bool

__lt__(right: IndicatorBase) -> bool

__lt__(right: float) -> bool

__lt__(right: IndicatorBase) -> bool

__lt__(right: int) -> bool

__lt__(right: IndicatorBase) -> bool

__lt__(right: int) -> bool

__lt__(right: IndicatorBase) -> bool

Signature descriptions:

• Determines if the indicator's current value is less than the specified value

• Determines if the specified value is less than the indicator's current value

__ne__

__ne__(right: float) -> bool

__ne__(right: IndicatorBase) -> bool

__ne__(right: float) -> bool

__ne__(right: IndicatorBase) -> bool

__ne__(right: int) -> bool

__ne__(right: IndicatorBase) -> bool

__ne__(right: int) -> bool

__ne__(right: IndicatorBase) -> bool

Signature descriptions:

• Determines if the indicator's current value is not equal to the specified value

• Determines if the specified value is not equal to the indicator's current value

compare_to

compare_to(obj: Any) -> int

compare_to(other: IIndicator) -> int

Signature descriptions:

• Compares the current instance with another object of the same type and returns an integer that indicates whether the current instance precedes, follows, or occurs in the same position in the sort order as the other object.

• Compares the current object with another object of the same type.

Parameters:

Name	Type	Description	Default
obj	Optional[Any]	An object to compare with this instance.	None
other	Optional[IIndicator]	An object to compare with this object.	None

Returns:

Type

Description

int

Depends on the signature used. Case 1: [A value that indicates the relative order of the objects being compared. The return value has these meanings: Value Meaning Less than zero This instance precedes obj in the sort order. Zero This instance occurs in the same position in the sort order as obj. Greater than zero This instance follows obj in the sort order.]; Case 2: [A value that indicates the relative order of the objects being compared. The return value has the following meanings: Value Meaning Less than zero This object is less than the other parameter.Zero This object is equal to other. Greater than zero This object is greater than other.]

update

update(input: IBaseData) -> bool

update(time: Union[datetime, date], value: float) -> bool

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

Parameters:

Name	Type	Description	Default
input	Optional[IBaseData]	The value to use to update this indicator	None
time	Optional[Union[datetime, date]]	The time associated with the value	None
value	Optional[float]	The value to use to update this indicator	None

Returns:

Type	Description
bool	True if this indicator is ready, false otherwise.

__getitem__

__getitem__(i: int) -> IndicatorDataPoint

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.

Parameters:

Name	Type	Description	Default
i	int	The index	required

Returns:

Type	Description
IndicatorDataPoint	the ith most recent indicator value.

__iter__

__iter__() -> Iterator[IndicatorDataPoint]

equals

equals(obj: Any) -> bool

Determines whether the specified object is equal to the current object.

Parameters:

Name	Type	Description	Default
obj	Any	The object to compare with the current object.	required

Returns:

Type	Description
bool	true if the specified object is equal to the current object; otherwise, false.

get_enumerator

get_enumerator() -> IEnumerator[IndicatorDataPoint]

Returns an enumerator that iterates through the history window.

Returns:

Type	Description
IEnumerator[IndicatorDataPoint]	A System.Collections.Generic.IEnumerator`1 that can be used to iterate through the history window.

get_hash_code

get_hash_code() -> int

Get Hash Code for this Object

Returns:

Type	Description
int	Integer Hash Code.

on_updated

on_updated(consolidated: IndicatorDataPoint) -> None

Event invocator for the Updated event

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
consolidated	IndicatorDataPoint	This is the new piece of data produced by this indicator	required

to_detailed_string

to_detailed_string() -> str

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

Returns:

Type	Description
str	A detailed string of this indicator's current state.

to_string

to_string() -> str

ToString Overload for Indicator Base

Returns:

Type	Description
str	String representation of the indicator.

validate_and_compute_next_value

validate_and_compute_next_value(
    input: QuantConnect_Indicators_IndicatorBase_T,
) -> IndicatorResult

Computes the next value of this indicator from the given state and returns an instance of the IndicatorResult class

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
input	QuantConnect_Indicators_IndicatorBase_T	The input given to the indicator	required

Returns:

Type	Description
IndicatorResult	An IndicatorResult object including the status of the indicator.

cumulative_sum

cumulative_sum(
    series: List[float], reverse: bool = False
) -> List[float]

Returns a series where each spot is taken by the cumulative sum of all points up to and including the value at that spot in the original series.

Parameters:

Name	Type	Description	Default
series	List[float]	Series to cumulatively sum over.	required
reverse	bool	Whether to reverse the series before applying the cumulative sum.	False

Returns:

Type	Description
List[float]	Cumulatively summed series.

difference_series

difference_series(
    d: int,
    series: List[float],
    diff_heads: Optional[List[float]],
) -> Tuple[List[float], List[float]]

Differences a time series d times.

Parameters:

Name	Type	Description	Default
series	List[float]	Series to difference	required
d	int	The differencing order	required
diff_heads	Optional[List[float]]	"Integration" constants	required

inverse_differenced_series

inverse_differenced_series(
    series: List[float], diff_heads: List[float]
) -> List[float]

Undoes the differencing of a time series which has been differenced using difference_series. https://github.com/statsmodels/statsmodels/blob/04f00006a7aeb1c93d6894caa420698400da6c33/statsmodels/tsa/tsatools.py#L758

Parameters:

Name	Type	Description	Default
series	List[float]	Series to un-difference	required
diff_heads	List[float]	Series of "integration" constants for un-differencing	required

lagged_series

lagged_series(
    p: int, series: List[float], include_t: bool = False
) -> List[List[float]]

Returns an array of lagged series for each of {1,...,p} lags.

Parameters:

Name	Type	Description	Default
p	int	Max lag order	required
series	List[float]	Series to calculate the lags of	required
include_t	bool	Whether or not to include t with its lags in the output array	False

Returns:

Type	Description
List[List[float]]	A list such that index i returns the series for i+1 lags.