Class BollingerBands

• Jump right in for a hands-on

Import

from NitroFE import BollingerBands

Bollinger Bands are a type of statistical chart characterizing the prices and volatility over time of a financial instrument or commodity, using a formulaic method propounded by John Bollinger in the 1980s.

Bollinger Bands are caluclated as

\[ Typical\_Value[t] (TV)= \frac{ \max{(dataframe[t-lookback\_period \to t])} +\min{(dataframe[t-lookback\_period \to t])} + dataframe[t] }{3} \]

\[ MA \ of \ typical \ value \ (MATV) = Moving \ average \ of \ typical \ value \ with \ 'moving_average_typical_value_lookback_period' \ window \ size \]

\[ STD \ of \ typical \ value \ (STD TV)= Standard \ deviation \ of \ typical \ value \ with \ 'moving_average_typical_value_lookback_period' \ window \ size \]

\[ Positive \ band = (MATV) + 'standard\_deviation\_multiplier' * (STD TV) \]

\[ Negative band = (MATV) - 'standard\_deviation\_multiplier' * (STD TV) \]

Methods

__init__(self, typical_value_lookback_period=6, typical_value_min_periods=None, moving_average_typical_value_lookback_period=6, moving_average_typical_value_min_periods=None, standard_deviation_multiplier=2) special

Parameters:

Name	Type	Description	Default
typical_value_lookback_period	int	Size of the rolling window for typical value, by default 6	6
typical_value_min_periods	int	Minimum number of observations in window required to have a value for typical value, by default None	None
moving_average_typical_value_lookback_period	int	Size of the rolling window for moving average of typical value, by default 6	6
moving_average_typical_value_min_periods	int	Minimum number of observations in window required to have a value for moving average of typical value, by default None	None
standard_deviation_multiplier	int	standard deviation multiplier for upper and lower bollinger band, by default 2	2

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

def __init__(
    self,
    typical_value_lookback_period: int = 6,
    typical_value_min_periods: int = None,
    moving_average_typical_value_lookback_period: int = 6,
    moving_average_typical_value_min_periods: int = None,
    standard_deviation_multiplier: int = 2,
):
    """
    Parameters
    ----------
    typical_value_lookback_period : int, optional
        Size of the rolling window for typical value, by default 6
    typical_value_min_periods : int, optional
        Minimum number of observations in window required to have a value for typical value, by default None
    moving_average_typical_value_lookback_period : int, optional
        Size of the rolling window for moving average of typical value, by default 6
    moving_average_typical_value_min_periods : int, optional
        Minimum number of observations in window required to have a value for moving average of typical value, by default None
    standard_deviation_multiplier : int, optional
        standard deviation multiplier for upper and lower bollinger band, by default 2
    """
    self.typical_value_lookback_period = typical_value_lookback_period
    self.typical_value_min_periods = typical_value_min_periods

    self.moving_average_typical_value_lookback_period = (
        moving_average_typical_value_lookback_period
    )
    self.moving_average_typical_value_min_periods = (
        moving_average_typical_value_min_periods
    )

    self.standard_deviation_multiplier = standard_deviation_multiplier

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\_bollingerbands.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._typical_value_object = TypicalValue(
            lookback_period=self.typical_value_lookback_period,
            min_periods=self.typical_value_min_periods,
        )
        self._ma_bollinger_bands_object = weighted_window_features()
        self._std_bollinger_bands_object = weighted_window_features()

    _typical_value = self._typical_value_object.fit(
        dataframe=dataframe, first_fit=first_fit
    )

    _moving_average_typical_value = (
        self._ma_bollinger_bands_object._template_feature_calculation(
            function_name="moving_average_typical_value",
            win_function=_identity_window,
            first_fit=first_fit,
            dataframe=_typical_value,
            window=self.moving_average_typical_value_lookback_period,
            min_periods=self.moving_average_typical_value_min_periods,
            symmetric=None,
            operation=np.mean,
            operation_args=(),
        )
    )

    _std_typical_value = (
        self._std_bollinger_bands_object._template_feature_calculation(
            function_name="moving_average_typical_value",
            win_function=_identity_window,
            first_fit=first_fit,
            dataframe=_typical_value,
            window=self.moving_average_typical_value_lookback_period,
            min_periods=self.moving_average_typical_value_min_periods,
            symmetric=None,
            operation=np.std,
            operation_args=(),
        )
    )

    positive_band = (
        _moving_average_typical_value
        + self.standard_deviation_multiplier * _std_typical_value
    )

    negative_band = (
        _moving_average_typical_value
        - self.standard_deviation_multiplier * _std_typical_value
    )

    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)

References

• wikipedia, "Bollinger_Bands", https://en.wikipedia.org/wiki/Bollinger_Bands