Class AroonOscillator

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Import

from NitroFE import AroonOscillator

The Aroon Oscillator is a trend-following indicator that uses aspects of the Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend and the likelihood that it will continue.

The Aroon Oscillator is calculated as

\[ Aroon\_Up[t] = \frac{'lookback\_period'- Period \ since \ 'lookback\_period' \ High }{'lookback\_period'} \]

\[ Aroon\_Down[t] = \frac{'lookback\_period'- Period \ since \ 'lookback\_period' \ Low }{'lookback\_period'} \]

\[ Aroon\_Oscillator[t] = Aroon\_Up[t] - Aroon\_Down[t] \]

Methods

__init__(self, lookback_period=4, min_periods=None) special

Parameters:

Name	Type	Description	Default
lookback_period	int	Size of the rolling window for lookback, by default 4	4
min_periods	int	Minimum number of observations in window required to have a value, by default None	None

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

def __init__(
    self,
    lookback_period: int = 4,
    min_periods: int = None,
):
    """
    Parameters
    ----------
    lookback_period : int, optional
        Size of the rolling window for lookback, by default 4
    min_periods : int, optional
        Minimum number of observations in window required to have a value, by default None
    """
    self.lookback_period = lookback_period
    self.min_periods = min_periods

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\_aroonoscillator.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._aroon_up_object = weighted_window_features()
        self._aroon_down_object = weighted_window_features()

    aroon_up = self._aroon_up_object._template_feature_calculation(
        function_name="aroon_up",
        win_function=_identity_window,
        first_fit=first_fit,
        dataframe=dataframe,
        window=self.lookback_period,
        min_periods=self.min_periods,
        symmetric=None,
        operation=self._calculate_aroon_up,
        operation_args=(self.lookback_period,),
    )
    aroon_down = self._aroon_up_object._template_feature_calculation(
        function_name="aroon_down",
        win_function=_identity_window,
        first_fit=first_fit,
        dataframe=dataframe,
        window=self.lookback_period,
        min_periods=self.min_periods,
        symmetric=None,
        operation=self._calculate_aroon_down,
        operation_args=(self.lookback_period,),
    )
    aroon_value = 100 * (aroon_up - aroon_down)

    return aroon_value

References

• investopedia, "Aroon", https://www.investopedia.com/terms/a/aroonoscillator.asp