Return features and labels for the given equity data.

    Each row of the features returned contains `2 * n_sessions + 1` columns
    (or 1 less if the constant feature is excluded). After the constant feature,
    if present, there will be `n_sessions` columns derived from daily growth
    of the given price column, which defaults to 'Adj Close'. There will then
    follow another `n_sessions` columns representing current volume as
    a multiple of average volume over the previous 252 (or other value determined
    by the user) sessions. 
    
    The returned features are not centered or normalized because these
    adjustments need to be made after test or cross-validation data has
    been removed.

    The constant feature is prepended by default.

    The labels are derived from `eqdata` using `labelfunc`.

    Parameters
    ----------
    eqdata : DataFrame
        Expected is a dataframe as return by the `get()` function. A column
        labeled 'Volume' must be present.

    featurefunc : function
        Function taking a dataframe of simple equity data as argument
        and returning a dataframe of features and an integer representing
        the number of rows that had to be skipped at the beginning of
        the index of the input dataframe. The rows skipped are used
        to synchronize the indices of features and labels. For example,
        if the features are composed of 4 successive daily returns, then
        the date of row 0 of features would be the same as the date of row 3
        (counting from 0) of input data. So the corresponding `featurefunc`
        would return a dataframe and the value 3.

    labelfunc : function
        function for deriving labels from `eqdata`. `labelfunc` must
        take a single argument: `df`, a dataframe to which `labelfunc` will be applied.
        `labelfunc` should return a dataframe of labels followed by an int
        specifying the number of feature rows to skip at the end of the feature
        dataframe. For example, if features are relative prices 64 days out,
        these features will only be known up until 64 days before the data
        runs out. In order to properly align features and labels, the features
        should not include the last 64 rows that would otherwise be possible.

        Usage:
        `labels, skipatend = labelfunc(eqdata)`

    Returns
    -------
    features : DataFrame
        The features derived from the given parameters.

    labels : DataFrame
        The labels derived from the given parameters.

def labeledfeatures(eqdata, featurefunc, labelfunc):
    """
    Return features and labels for the given equity data.

    Each row of the features returned contains `2 * n_sessions + 1` columns
    (or 1 less if the constant feature is excluded). After the constant feature,
    if present, there will be `n_sessions` columns derived from daily growth
    of the given price column, which defaults to 'Adj Close'. There will then
    follow another `n_sessions` columns representing current volume as
    a multiple of average volume over the previous 252 (or other value determined
    by the user) sessions. 
    
    The returned features are not centered or normalized because these
    adjustments need to be made after test or cross-validation data has
    been removed.

    The constant feature is prepended by default.

    The labels are derived from `eqdata` using `labelfunc`.

    Parameters
    ----------
    eqdata : DataFrame
        Expected is a dataframe as return by the `get()` function. A column
        labeled 'Volume' must be present.

    featurefunc : function
        Function taking a dataframe of simple equity data as argument
        and returning a dataframe of features and an integer representing
        the number of rows that had to be skipped at the beginning of
        the index of the input dataframe. The rows skipped are used
        to synchronize the indices of features and labels. For example,
        if the features are composed of 4 successive daily returns, then
        the date of row 0 of features would be the same as the date of row 3
        (counting from 0) of input data. So the corresponding `featurefunc`
        would return a dataframe and the value 3.

    labelfunc : function
        function for deriving labels from `eqdata`. `labelfunc` must
        take a single argument: `df`, a dataframe to which `labelfunc` will be applied.
        `labelfunc` should return a dataframe of labels followed by an int
        specifying the number of feature rows to skip at the end of the feature
        dataframe. For example, if features are relative prices 64 days out,
        these features will only be known up until 64 days before the data
        runs out. In order to properly align features and labels, the features
        should not include the last 64 rows that would otherwise be possible.

        Usage:
        `labels, skipatend = labelfunc(eqdata)`

    Returns
    -------
    features : DataFrame
        The features derived from the given parameters.

    labels : DataFrame
        The labels derived from the given parameters.
    """
    _size = len(eqdata.index)
    _labels, _skipatend = labelfunc(eqdata)
    _features, _skipatstart = featurefunc(eqdata.iloc[:(_size - _skipatend), :])
    return _features, _labels.iloc[_skipatstart:, :]