original_docstring: 
      the classifier method to get the labels for the training data
      :param data: dataframe
      :param frwd: the forward limit to look for extremums
      :param pos_limit:
      :param neg_limit:
      :return:
      
docstring: the classifier method to get the labels for the training data
docstring_tokens: the classifier method to get the labels for the training data
code: def classify(data, frwd, pos_limit=.001, neg_limit=-.001):
      """
      the classifier method to get the labels for the training data
      :param data: dataframe
      :param frwd: the forward limit to look for extremums
      :param pos_limit:
      :param neg_limit:
      :return:
      """
      temp = copy.deepcopy(data)
  
      temp['change'] = temp['Price'].diff() / temp['Price'].shift()
      return temp['change'].apply(lambda x: 1 if x > pos_limit else 0)
code_tokens: def classify ( data , frwd , pos_limit = .001 , neg_limit = - .001 ) : temp = copy . deepcopy ( data ) temp [ 'change' ] = temp [ 'Price' ] . diff ( ) / temp [ 'Price' ] . shift ( ) return temp [ 'change' ] . apply ( lambda x : 1 if x > pos_limit else 0 )
short_docstring: the classifier method to get the labels for the training data
short_docstring_tokens: the classifier method to get the labels for the training data
comment: """
      the classifier method to get the labels for the training data
      :param data: dataframe
      :param frwd: the forward limit to look for extremums
      :param pos_limit:
      :param neg_limit:
      :return:
      """