class_docstr: Provides methods for preprocessing the stock price data
  
  Attributes
  ----------
      use_technical_indicator : boolean
          we technical indicator or not
      tech_indicator_list : list
          a list of technical indicator names (modified from neofinrl_config.py)
      use_turbulence : boolean
          use turbulence index or not
      user_defined_feature:boolean
          use user defined features or not
  
  Methods
  -------
  preprocess_data()
      main method to do the feature engineering
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docstr:  add user defined features
  :param data: (df) pandas dataframe
  :return: (df) pandas dataframe
docstr_tok: ["add","user","defined","features",":","param","data",":","(","df",")","pandas","dataframe",":","return",":","(","df",")","pandas","dataframe"]
code: def add_user_defined_feature(self, data):
      """
       add user defined features
      :param data: (df) pandas dataframe
      :return: (df) pandas dataframe
      """
      df = data.copy()
      df["daily_return"] = df.close.pct_change(1)
      # df['return_lag_1']=df.close.pct_change(2)
      # df['return_lag_2']=df.close.pct_change(3)
      # df['return_lag_3']=df.close.pct_change(4)
      # df['return_lag_4']=df.close.pct_change(5)
      return df
  
imports: from __future__ import annotations
  import datetime
  from multiprocessing.sharedctypes import Value
  import numpy
  import pandas
  from stockstats import StockDataFrame
  from finrl import config
  from finrl.meta.preprocessor.yahoodownloader import YahooDownloader
label_desc: Use image node_id 5 for calling the FeatureEngineer obj's underlying member method code with example usage: obj.add_user_defined_feature(data) and returns: df