code: def calculate_turbulence(self, data, time_period=252):
      # can add other market assets
      df = data.copy()
      df_price_pivot = df.pivot(
          index="date", columns="tic", values="close"
      )
      # use returns to calculate turbulence
      df_price_pivot = df_price_pivot.pct_change()
  
      unique_date = df.date.unique()
      # start after a fixed time period
      start = time_period
      turbulence_index = [0] * start
      # turbulence_index = [0]
      count = 0
      for i in range(start, len(unique_date)):
          current_price = df_price_pivot[
              df_price_pivot.index == unique_date[i]
          ]
          # use one year rolling window to calcualte covariance
          hist_price = df_price_pivot[
              (df_price_pivot.index < unique_date[i])
              & (df_price_pivot.index >= unique_date[i - time_period])
          ]
          # Drop tickers which has number missing values more than the "oldest" ticker
          filtered_hist_price = hist_price.iloc[
              hist_price.isna().sum().min() :
          ].dropna(axis=1)
  
          cov_temp = filtered_hist_price.cov()
          current_temp = current_price[
              [x for x in filtered_hist_price]
          ] - np.mean(filtered_hist_price, axis=0)
          temp = current_temp.values.dot(np.linalg.pinv(cov_temp)).dot(
              current_temp.values.T
          )
          if temp > 0:
              count += 1
              if count > 2:
                  turbulence_temp = temp[0][0]
              else:
                  # avoid large outlier because of the calculation just begins
                  turbulence_temp = 0
          else:
              turbulence_temp = 0
          turbulence_index.append(turbulence_temp)
  
      turbulence_index = pd.DataFrame(
          {"date": df_price_pivot.index, "turbulence": turbulence_index}
      )
      return turbulence_index
  
file_path: FinRL/finrl/meta/data_processors/processor_wrds.py
imports: from __future__ import annotations
  import datetime
  import exchange_calendars
  import numpy
  import pandas
  import pytz
  import wrds
  from stockstats import StockDataFrame
label_desc: Use image node_id 6 for calling the WrdsProcessor obj's underlying member method code with example usage: obj.calculate_turbulence(data, time_period) and returns: turbulence_index