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 vix from yahoo finance
  :param data: (df) pandas dataframe
  :return: (df) pandas dataframe
docstr_tok: ["add","vix","from","yahoo","finance",":","param","data",":","(","df",")","pandas","dataframe",":","return",":","(","df",")","pandas","dataframe"]
code: def add_vix(self, data):
      """
      add vix from yahoo finance
      :param data: (df) pandas dataframe
      :return: (df) pandas dataframe
      """
      df = data.copy()
      df_vix = YahooDownloader(
          start_date=df.date.min(),
          end_date=df.date.max(),
          ticker_list=["^VIX"],
      ).fetch_data()
      vix = df_vix[["date", "close"]]
      vix.columns = ["date", "vix"]
  
      df = df.merge(vix, on="date")
      df = df.sort_values(["date", "tic"]).reset_index(drop=True)
      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 6 for calling the FeatureEngineer obj's underlying member method code with example usage: obj.add_vix(data) and returns: df