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Class SmoothedMovingAverage

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Import

from NitroFE import SmoothedMovingAverage

SmoothedMovingAverage

The Smoothed Moving Average (SMMA) is a combination of a SMA and an EMA. It gives the recent values an equal weighting as the historic prices as it takes all available price data into account. The main advantage of a smoothed moving average is that it removes short-term fluctuations, and allows us to view the values trends much easier.

smoothed moving average (SMMA) is calculated as

\[ SMMA[t] = \frac{(SMMA[t-1]*(lookback\_period-1) + dataframe[t] )}{lookback\_period} \]

Methods

Provided dataframe must be in ascending order.

__init__(self, lookback_period=4) special

Parameters:

Name Type Description Default
lookback_period int

Size of the rolling window of lookback , by default 4

 4
Source code in nitrofe\time_based_features\moving_average_features\moving_average_features.py 
def __init__(self, lookback_period: int = 4):
    """
    Parameters
    ----------
    lookback_period : int, optional
        Size of the rolling window of lookback , by default 4
    """
    self.lookback_period = lookback_period

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

Moving 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\moving_average_features\moving_average_features.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
        Moving 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._first_object = weighted_window_features()

    if isinstance(dataframe, pd.Series):
        dataframe = dataframe.to_frame()

    sma = pd.DataFrame(
        np.zeros(dataframe.shape), columns=dataframe.columns, index=dataframe.index
    )
    if first_fit:
        sma.iloc[self.lookback_period - 1] = (
            dataframe.iloc[: (self.lookback_period)].sum() / self.lookback_period
        )
    else:
        sma = pd.concat([self.values_from_last_run, sma])

    sma["_iloc"] = np.arange(len(sma))
    ll = [x for x in sma.columns if x != "_iloc"]

    _start_frame = self.lookback_period if first_fit else 0
    _start_sma = self.lookback_period if first_fit else 1
    for r1, r2 in zip(
        dataframe[_start_frame:].iterrows(), sma[_start_sma:].iterrows()
    ):

        previous_kama = sma[sma["_iloc"] == (r2[1]["_iloc"] - 1)][ll]

        sma.loc[sma["_iloc"] == r2[1]["_iloc"], ll] = (
            (previous_kama * (self.lookback_period - 1) + r1[1])
            / self.lookback_period
        ).values[0]

    res = sma[ll] if first_fit else sma.iloc[1:][ll]

    self.values_from_last_run = res.iloc[-1:]
    return res

References