Class KeltnerChannel

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Import

from NitroFE import KeltnerChannel

keltner channel (KC) is calculated as

\[ ATR = average \ true \ range \ of \ dataframe \]

\[ EMA = Exponential \ moving \ average \ of \ dataframe \ over \ 'ema\_span' \ span \]

\[ KC \ upperband = EMA + atr\_multiply * ATR \]

\[ KC \ lowerband = EMA - atr\_multiply * ATR \]

Methods

__init__(self, ema_span=8, initialize_using_operation=False, initialize_span=None, true_range_lookback=4, average_true_range_span=6, true_range_min_periods=None, average_true_range_periods=1, atr_multiply=2) special

Parameters:

Name	Type	Description	Default
ema_span	int	specify decay in terms of span for exponential moving average, by default 8	8
initialize_using_operation	bool	If True, then specified mean is performed over 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
true_range_lookback	int	Size of the rolling window for true range value calculation, by default 4	4
average_true_range_span	int	Size of the rolling window for average true range value calculation, by default 6	6
true_range_min_periods	int	Minimum number of observations in window required to have a value for true range calculation, by default None	None
average_true_range_periods	int	Minimum number of observations in window required to have a value for average true range calculation , by default 1	1
atr_multiply	int	multiplication factor for average true range, by default 2	2

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

def __init__(
    self,
    ema_span: int = 8,
    initialize_using_operation: bool = False,
    initialize_span: int = None,
    true_range_lookback: int = 4,
    average_true_range_span: int = 6,
    true_range_min_periods: int = None,
    average_true_range_periods: int = 1,
    atr_multiply: int = 2,
):
    """
    Parameters
    ----------
    ema_span : int, optional
        specify decay in terms of span for exponential moving average, by default 8
    initialize_using_operation : bool, optional
        If True, then specified mean is performed over 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
    true_range_lookback : int, optional
        Size of the rolling window for true range value calculation, by default 4
    average_true_range_span : int, optional
        Size of the rolling window for average true range value calculation, by default 6
    true_range_min_periods : int, optional
        Minimum number of observations in window required to have a value for true range calculation, by default None
    average_true_range_periods : int, optional
        Minimum number of observations in window required to have a value for average true range calculation , by default 1
    atr_multiply : int, optional
        multiplication factor for average true range, by default 2
    """
    self.ema_span = ema_span
    self.initialize_using_operation = initialize_using_operation
    self.initialize_span = initialize_span
    self.true_range_lookback = true_range_lookback
    self.average_true_range_span = average_true_range_span
    self.true_range_min_periods = true_range_min_periods
    self.average_true_range_periods = average_true_range_periods
    self.atr_multiply = atr_multiply

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\_keltnerchannel.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._atr_object = AverageTrueRange(
            true_range_lookback=self.true_range_lookback,
            average_true_range_span=self.average_true_range_span,
            true_range_min_periods=self.true_range_min_periods,
            average_true_range_periods=self.average_true_range_periods,
        )
        self._ema_object = ExponentialMovingFeature(
            span=self.ema_span,
            initialize_using_operation=self.initialize_using_operation,
            initialize_span=self.initialize_span,
        )

    atr_val = self._atr_object.fit(dataframe=dataframe, first_fit=first_fit)

    ema_val = self._ema_object.fit(dataframe=dataframe, first_fit=first_fit)

    positive_band = ema_val + self.atr_multiply * atr_val
    negative_band = ema_val - self.atr_multiply * atr_val

    if isinstance(dataframe, pd.Series):
        if dataframe.name is None:
            positive_band.name = "positive_band"
            negative_band.name = "negative_band"
        else:
            positive_band.name = positive_band.name + "_positive_band"
            negative_band.name = negative_band.name + "_negative_band"
    elif isinstance(dataframe, pd.DataFrame):
        positive_band.columns = positive_band.columns + "_positive_band"
        negative_band.columns = negative_band.columns + "_negative_band"

    return pd.concat([positive_band, negative_band], axis=1)