code: def clean_data(self, df):
      print("Data cleaning started")
      tic_list = np.unique(df.tic.values)
      n_tickers = len(tic_list)
  
      print("align start and end dates")
      grouped = df.groupby("timestamp")
      filter_mask = grouped.transform("count")["tic"] >= n_tickers
      df = df[filter_mask]
  
      # ... (generating 'times' series, same as in your existing code)
  
      trading_days = self.get_trading_days(
          start=self.start, end=self.end
      )
  
      # produce full timestamp index
      print("produce full timestamp index")
      times = []
      for day in trading_days:
          NY = "America/New_York"
          current_time = pd.Timestamp(day + " 09:30:00").tz_localize(NY)
          for i in range(390):
              times.append(current_time)
              current_time += pd.Timedelta(minutes=1)
  
      print("Start processing tickers")
  
      future_results = []
      for tic in tic_list:
          result = self.clean_individual_ticker((tic, df.copy(), times))
          future_results.append(result)
  
      print("ticker list complete")
  
      print("Start concat and rename")
      new_df = pd.concat(future_results)
      new_df = new_df.reset_index()
      new_df = new_df.rename(columns={"index": "timestamp"})
  
      print("Data clean finished!")
  
      return new_df
  
imports: from __future__ import annotations
  from concurrent.futures import ProcessPoolExecutor
  from concurrent.futures import ThreadPoolExecutor
  import alpaca_trade_api
  import exchange_calendars
  import numpy
  import pandas
  import pytz
  from stockstats import StockDataFrame
label_desc: Use image node_id 5 for calling the AlpacaProcessor obj's underlying member method code with example usage: obj.clean_data(df) and returns: new_df