code: def rolling_performances(self, timestamp='one_month'):
          ''' Filters self.perfs '''
          # TODO Study the impact of month choice
          # TODO Check timestamp in an enumeration
          # TODO Implement other benchmarks for perf computation
          # (zipline issue, maybe expected)
  
          if self.metrics:
              perfs = {}
              length = range(len(self.metrics[timestamp]))
              index = self._get_index(self.metrics[timestamp])
              perf_keys = self.metrics[timestamp][0].keys()
              perf_keys.pop(perf_keys.index('period_label'))
  
              perfs['period'] = np.array(
                  [pd.datetime.date(date) for date in index])
              for key in perf_keys:
                  perfs[key] = self._to_perf_array(timestamp, key, length)
          else:
              # TODO Get it from DB if it exists
              raise NotImplementedError()
  
          return pd.DataFrame(perfs, index=indexf rolling_performances(self, timestamp='one_month'):
          ''' Filters self.perfs '''
          # TODO Study the impact of month choice
          # TODO Check timestamp in an enumeration
          # TODO Implement other benchmarks for perf computation
          # (zipline issue, maybe expected)
  
          if self.metrics:
              perfs = {}
              length = range(len(self.metrics[timestamp]))
              index = self._get_index(self.metrics[timestamp])
              perf_keys = self.metrics[timestamp][0].keys()
              perf_keys.pop(perf_keys.index('period_label'))
  
              perfs['period'] = np.array(
                  [pd.datetime.date(date) for date in index])
              for key in perf_keys:
                  perfs[key] = self._to_perf_array(timestamp, key, length)
          else:
              # TODO Get it from DB if it exists
              raise NotImplementedError()
  
          return pd.DataFrame(perfs, index=index)
docs: Filters self.perfs