code: def fetch_latest_data(
      self, ticker_list, time_interval, tech_indicator_list, limit=100
  ) -> pd.DataFrame:
      data_df = pd.DataFrame()
      for tic in ticker_list:
          barset = self.api.get_bars(
              [tic], time_interval, limit=limit
          ).df  # [tic]
          barset["tic"] = tic
          barset = barset.reset_index()
          data_df = pd.concat([data_df, barset])
  
      data_df = data_df.reset_index(drop=True)
      start_time = data_df.timestamp.min()
      end_time = data_df.timestamp.max()
      times = []
      current_time = start_time
      end = end_time + pd.Timedelta(minutes=1)
      while current_time != end:
          times.append(current_time)
          current_time += pd.Timedelta(minutes=1)
  
      df = data_df.copy()
      new_df = pd.DataFrame()
      for tic in ticker_list:
          tmp_df = pd.DataFrame(
              columns=["open", "high", "low", "close", "volume"],
              index=times,
          )
          tic_df = df[df.tic == tic]
          for i in range(tic_df.shape[0]):
              tmp_df.loc[tic_df.iloc[i]["timestamp"]] = tic_df.iloc[i][
                  ["open", "high", "low", "close", "volume"]
              ]
  
              if str(tmp_df.iloc[0]["close"]) == "nan":
                  for i in range(tmp_df.shape[0]):
                      if str(tmp_df.iloc[i]["close"]) != "nan":
                          first_valid_close = tmp_df.iloc[i]["close"]
                          tmp_df.iloc[0] = [
                              first_valid_close,
                              first_valid_close,
                              first_valid_close,
                              first_valid_close,
                              0.0,
                          ]
                          break
              if str(tmp_df.iloc[0]["close"]) == "nan":
                  print(
                      "Missing data for ticker: ",
                      tic,
                      " . The prices are all NaN. Fill with 0.",
                  )
                  tmp_df.iloc[0] = [
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                  ]
  
          for i in range(tmp_df.shape[0]):
              if str(tmp_df.iloc[i]["close"]) == "nan":
                  previous_close = tmp_df.iloc[i - 1]["close"]
                  if str(previous_close) == "nan":
                      previous_close = 0.0
                  tmp_df.iloc[i] = [
                      previous_close,
                      previous_close,
                      previous_close,
                      previous_close,
                      0.0,
                  ]
          tmp_df = tmp_df.astype(float)
          tmp_df["tic"] = tic
          new_df = pd.concat([new_df, tmp_df])
  
      new_df = new_df.reset_index()
      new_df = new_df.rename(columns={"index": "timestamp"})
  
      df = self.add_technical_indicator(new_df, tech_indicator_list)
      df["VIXY"] = 0
  
      price_array, tech_array, turbulence_array = self.df_to_array(
          df, tech_indicator_list, if_vix=True
      )
      latest_price = price_array[-1]
      latest_tech = tech_array[-1]
      turb_df = self.api.get_bars(["VIXY"], time_interval, limit=1).df
      latest_turb = turb_df["close"].values
      return latest_price, latest_tech, latest_turb
  
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 13 for calling the AlpacaProcessor obj's underlying member method code with example usage: obj.fetch_latest_data(ticker_list, time_interval, tech_indicator_list, limit) and returns: latest_price,  latest_tech,  latest_turb