class_docstr: The algorithm's interface to the registered risk and performance
  metrics.
  
  Parameters
  ----------
  trading_calendar : TrandingCalendar
      The trading calendar used in the simulation.
  first_session : pd.Timestamp
      The label of the first trading session in the simulation.
  last_session : pd.Timestamp
      The label of the last trading session in the simulation.
  capital_base : float
      The starting capital for the simulation.
  emission_rate : {'daily', 'minute'}
      How frequently should a performance packet be generated?
  data_frequency : {'daily', 'minute'}
      The data frequency of the data portal.
  asset_finder : AssetFinder
      The asset finder used in the simulation.
  metrics : list[Metric]
      The metrics to track.
code: def _execution_open_and_close(calendar, session):
      open_, close = calendar.open_and_close_for_session(session)
      execution_open = calendar.execution_time_from_open(open_)
      execution_close = calendar.execution_time_from_close(close)
  
      return execution_open, execution_close
  
imports: from __future__ import division
  import logbook
  from ..ledger import Ledger
  from zipline.utils.exploding_object import NamedExplodingObject
label_desc: Use image node_id 1 for calling the MetricsTracker obj's underlying member method code with example usage: obj._execution_open_and_close(calendar, session) and returns: execution_open,  execution_close