docs: def can_trade(self, assets):
          """
          For the given asset or iterable of assets, returns true if all of the
          following are true:
          1) the asset is alive for the session of the current simulation time
            (if current simulation time is not a market minute, we use the next
            session)
          2) (if we are in minute mode) the asset's exchange is open at the
            current simulation time or at the simulation calendar's next market
            minute
          3) there is a known last price for the asset.
  
          Notes
          -----
          The second condition above warrants some further explanation.
          - If the asset's exchange calendar is identical to the simulation
          calendar, then this condition always returns True.
          - If there are market minutes in the simulation calendar outside of
          this asset's exchange's trading hours (for example, if the simulation
          is running on the CME calendar but the asset is MSFT, which trades on
          the NYSE), during those minutes, this condition will return false
          (for example, 3:15 am Eastern on a weekday, during which the CME is
          open but the NYSE is closed).
  
          Parameters
          ----------
          assets: Asset or iterable of assets
  
          Returns
          -------
          can_trade : bool or pd.Series[bool] indexed by asset.
          """
          dt = self.datetime
  
          if self._adjust_minutes:
              adjusted_dt = self._get_current_minute()
          else:
              adjusted_dt = dt
  
          data_portal = self.data_portal
  
          if isinstance(assets, Asset):
              return self._can_trade_for_asset(
                  assets, dt, adjusted_dt, data_portal
              )
          else:
              def fetch(asset):
                  return self._can_trade_for_asset(
                      asset, dt, adjusted_dt, data_portal
                  )
              tradeable = parallelize(fetch)(assets)
              return pd.Series(data=tradeable, index=assets, dtype=bool)