Class TripleExponentialMovingAverageOscillator

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Import

from NitroFE import TripleExponentialMovingAverageOscillator

The Triple Exponential Moving Average Oscillator calculates the rate of change of Triple Exponential Moving Average

\[ Triple\_Exponential\_Moving\_Average\_Oscillator[t] = \frac{Triple\_Exponential\_Moving\_Average[t]-Triple\_Exponential\_Moving\_Average[t-1]}{Triple\_Exponential\_Moving\_Average[t-1]} \]

Methods

__init__(self, com=None, operation='mean', initialize_using_operation=False, initialize_span=None, span=None, halflife=None, alpha=None, min_periods=0, ignore_na=False, axis=0, times=None) special

Parameters:

Name	Type	Description	Default
alpha	float	Specify smoothing factor directly, by default None	None
operation	str	operation to be performed for the moving feature,available operations are 'mean','var','std', by default 'mean'	'mean'
initialize_using_operation	bool	If True, then specified 'operation' is performed on the first 'initialize_span' values, and then the exponential moving average is calculated, by default False	False
initialize_span	int	the span over which 'operation' would be performed for initialization, by default None	None
com	float	Specify decay in terms of center of mass, by default None	None
span	float	pecify decay in terms of span , by default None	None
halflife	float	Specify decay in terms of half-life, by default None	None
min_periods	int	Minimum number of observations in window required to have a value (otherwise result is NA)., by default 0	0
ignore_na	bool	Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior, by default False	False
axis	int	The axis to use. The value 0 identifies the rows, and 1 identifies the columns, by default 0	0
times	str	Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype, by default None	None

Source code in nitrofe\time_based_features\indicator_features\_tripleexponentialmovingaverageoscillator.py

def __init__(
    self,
    com: float = None,
    operation: str = "mean",
    initialize_using_operation: bool = False,
    initialize_span: int = None,
    span: float = None,
    halflife: float = None,
    alpha: float = None,
    min_periods: int = 0,
    ignore_na: bool = False,
    axis: int = 0,
    times: str = None,
):
    """
    Parameters
    ----------
    alpha : float, optional
        Specify smoothing factor  directly, by default None
    operation : str, {'mean','var','std'}
        operation to be performed for the moving feature,available operations are 'mean','var','std', by default 'mean'
    initialize_using_operation : bool, optional
        If True, then specified 'operation' is performed on the first 'initialize_span' values, and then the exponential moving average is calculated, by default False
    initialize_span : int, optional
        the span over which 'operation' would be performed for initialization, by default None
    com : float, optional
        Specify decay in terms of center of mass, by default None
    span : float, optional
        pecify decay in terms of span , by default None
    halflife : float, optional
        Specify decay in terms of half-life, by default None
    min_periods : int, optional
        Minimum number of observations in window required to have a value (otherwise result is NA)., by default 0
    ignore_na : bool, optional
        Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior, by default False
    axis : int, optional
        The axis to use. The value 0 identifies the rows, and 1 identifies the columns, by default 0
    times : str, optional
        Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype, by default None
    """
    self.com = com
    self.span = span
    self.halflife = halflife
    self.alpha = alpha
    self.min_periods = min_periods
    self.ignore_na = ignore_na
    self.axis = axis
    self.times = times
    self.operation = operation
    self.initialize_using_operation = initialize_using_operation
    self.initialize_span = initialize_span

fit(self, dataframe, first_fit=True)

For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False

Parameters:

Name	Type	Description	Default
dataframe	Union[pandas.core.frame.DataFrame, pandas.core.series.Series]	dataframe containing column values to create feature over	required
first_fit	bool	Indicator features require past values for calculation. Use True, when calculating for training data (very first fit) Use False, when calculating for subsequent testing/production data { in which case the values, which were saved during the last phase, will be utilized for calculation }, by default True	True

Source code in nitrofe\time_based_features\indicator_features\_tripleexponentialmovingaverageoscillator.py

def fit(
    self,
    dataframe: Union[pd.DataFrame, pd.Series],
    first_fit: bool = True,
):
    """

    For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False

    Parameters
    ----------
    dataframe : Union[pd.DataFrame, pd.Series]
        dataframe containing column values to create feature over
    first_fit : bool, optional
        Indicator features require past values for calculation.
        Use True, when calculating for training data (very first fit)
        Use False, when calculating for subsequent testing/production data { in which case the values, which
        were saved during the last phase, will be utilized for calculation }, by default True
    """

    if first_fit:
        self._osc_object = TripleExponentialMovingFeature(
            com=self.com,
            operation=self.operation,
            span=self.span,
            halflife=self.halflife,
            alpha=self.alpha,
            min_periods=self.min_periods,
            ignore_na=self.ignore_na,
            axis=self.axis,
            initialize_using_operation=self.initialize_using_operation,
            initialize_span=self.initialize_span,
            times=self.times,
        )
        self._difference_object = weighted_window_features()

    res = self._osc_object.fit(dataframe=dataframe, first_fit=first_fit)

    res = self._difference_object._template_feature_calculation(
        function_name="triple_exponential_moving_average_oscillator",
        win_function=_identity_window,
        first_fit=first_fit,
        dataframe=res,
        window=2,
        min_periods=None,
        symmetric=None,
        operation=self._ocs_value,
        operation_args=(),
    )
    return res

References

• investopedia, "triple-exponential-moving-average", https://www.investopedia.com/terms/t/triple-exponential-moving-average.asp