code: def xmaup(f, c="close", pfast=20, pslow=50):
      r"""Determine those values of the dataframe that are below the
      moving average.
  
      Parameters
      ----------
      f : pandas.DataFrame
          Dataframe containing the column ``c``.
      c : str, optional
          Name of the column in the dataframe ``f``.
      pfast : int, optional
          The period of the fast moving average.
      pslow : int, optional
          The period of the slow moving average.
  
      Returns
      -------
      new_column : pandas.Series (bool)
          The array containing the new feature.
  
      References
      ----------
      *In the statistics of time series, and in particular the analysis
      of financial time series for stock trading purposes, a moving-average
      crossover occurs when, on plotting two moving averages each based
      on different degrees of smoothing, the traces of these moving averages
      cross* [WIKI_XMA]_.
  
      """
      sma = ma(f, c, pfast)
      sma_prev = sma.shift(1)
      lma = ma(f, c, pslow)
      lma_prev = lma.shift(1)
      new_column = (sma > lma) & (sma_prev < lma_prev)
      return new_column
  
imports: from alphapy.calendrical import biz_day_month
  from alphapy.calendrical import biz_day_week
  from alphapy.globals import NULLTEXT
  from alphapy.globals import BSEP, PSEP, USEP
  from alphapy.variables import vexec
  import itertools
  import logging
  import math
  import numpy
  import pandas
label_desc: Use image node_id 51 for calling a global function with example usage: xmaup(f, c, pfast, pslow) and returns: new_column