Module backtrader.observers.benchmark

Classes

class Benchmark

This observer stores the returns of the strategy and the return of a reference asset which is one of the datas passed to the system.

Params

• timeframe (default: None) If None then the complete return over the entire backtested period will be reported

• compression (default: None)

Only used for sub-day timeframes to for example work on an hourly timeframe by specifying "TimeFrame.Minutes" and 60 as compression

• data (default: None)

Reference asset to track to allow for comparison.

!!! note "Note: this data must have been added to a cerebro instance with" addata, resampledata or replaydata.

• _doprenext (default: False)

Benchmarking will take place from the point at which the strategy kicks in (i.e.: when the minimum period of the strategy has been met).

Setting this to True will record benchmarking values from the starting point of the data feeds

• firstopen (default: False)

Keepint it as False ensures that the 1st comparison point between the value and the benchmark starts at 0%, because the benchmark will not use its opening price.

See the TimeReturn analyzer reference for a full explanation of the meaning of the parameter

• fund (default: None)

If None the actual mode of the broker (fundmode - True/False) will be autodetected to decide if the returns are based on the total net asset value or on the fund value. See set_fundmode in the broker documentation

Set it to True or False for a specific behavior

Remember that at any moment of a run the current values can be checked by looking at the lines by name at index 0.

Expand source code

class Benchmark(TimeReturn):
    '''This observer stores the *returns* of the strategy and the *return* of a
    reference asset which is one of the datas passed to the system.

    Params:

      - ``timeframe`` (default: ``None``)
        If ``None`` then the complete return over the entire backtested period
        will be reported

      - ``compression`` (default: ``None``)

        Only used for sub-day timeframes to for example work on an hourly
        timeframe by specifying "TimeFrame.Minutes" and 60 as compression

      - ``data`` (default: ``None``)

        Reference asset to track to allow for comparison.

        .. note:: this data must have been added to a ``cerebro`` instance with
                  ``addata``, ``resampledata`` or ``replaydata``.


      - ``_doprenext`` (default: ``False``)

        Benchmarking will take place from the point at which the strategy kicks
        in (i.e.: when the minimum period of the strategy has been met).

        Setting this to ``True`` will record benchmarking values from the
        starting point of the data feeds

      - ``firstopen`` (default: ``False``)

        Keepint it as ``False`` ensures that the 1st comparison point between
        the value and the benchmark starts at 0%, because the benchmark will
        not use its opening price.

        See the ``TimeReturn`` analyzer reference for a full explanation of the
        meaning of the parameter

      - ``fund`` (default: ``None``)

        If ``None`` the actual mode of the broker (fundmode - True/False) will
        be autodetected to decide if the returns are based on the total net
        asset value or on the fund value. See ``set_fundmode`` in the broker
        documentation

        Set it to ``True`` or ``False`` for a specific behavior

    Remember that at any moment of a ``run`` the current values can be checked
    by looking at the *lines* by name at index ``0``.

    '''
    _stclock = True

    lines = ('benchmark',)
    plotlines = dict(benchmark=dict(_name='Benchmark'))

    params = (
        ('data', None),
        ('_doprenext', False),
        # Set to false to ensure the asset is measured at 0% in the 1st tick
        ('firstopen', False),
        ('fund', None)
    )

    def _plotlabel(self):
        labels = super(Benchmark, self)._plotlabel()
        labels.append(self.p.data._name)
        return labels

    def __init__(self):
        if self.p.data is None:  # use the 1st data in the system if none given
            self.p.data = self.data0

        super(Benchmark, self).__init__()  # treturn including data parameter
        # Create a time return object without the data
        kwargs = self.p._getkwargs()
        kwargs.update(data=None)  # to create a return for the stratey
        t = self._owner._addanalyzer_slave(bt.analyzers.TimeReturn, **kwargs)

        # swap for consistency
        self.treturn, self.tbench = t, self.treturn

    def next(self):
        super(Benchmark, self).next()
        self.lines.benchmark[0] = self.tbench.rets.get(self.treturn.dtkey,
                                                       float('NaN'))

    def prenext(self):
        if self.p._doprenext:
            super(TimeReturn, self).prenext()

Ancestors

• TimeReturn
• Observer
• ObserverBase
• DataAccessor
• LineIterator
• LineSeries
• LineMultiple
• LineRoot

Class variables

var alias

var aliased

var frompackages

var linealias

var packages

var params

var plotinfo

var plotlines

Inherited members

• TimeReturn:
  • addminperiod
  • incminperiod
  • lines
  • minbuffer
  • next
  • nextstart
  • once
  • oncestart
  • prenext
  • preonce
  • qbuffer
  • setminperiod
  • updateminperiod