backtrader.observers.benchmark module

Benchmark Observer Module - Strategy vs benchmark comparison.

This module provides the Benchmark observer for tracking strategy returns against a reference asset (benchmark).

Classes:

Benchmark: Observer that compares strategy performance to a benchmark.

Example

>>> cerebro = bt.Cerebro()
>>> data = bt.feeds.GenericCSVData(dataname='benchmark.csv')
>>> cerebro.adddata(data)
>>> cerebro.addobserver(bt.observers.Benchmark, data=data)

class backtrader.observers.benchmark.Benchmark

Bases: 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.

plotlines = <backtrader.metabase.plotlines_obj object>

__init__(*args, **kwargs)

Wrapped __init__ that properly handles observer initialization

next()

Update benchmark value for the current period.

Gets the benchmark return from the analyzer.

prenext()

Process bars before minimum period is reached.

Handles prenext phase for time tracking.

frompackages = ()

packages = ()