3. Perfomance of specific strategy in cerebro
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Perfomance of specific strategy in cerebro

  • O

    Hello,
    my code like this

        cerebro = bt.Cerebro()
    cerebro.broker.set_cash(100000.0)
    cerebro.broker.set_coc(True)
    cerebro.broker.addcommissioninfo(CommDivFrance())

    # --- Adding some data feeds ---
   
    # --- Observers ---
    cerebro.addobserver(bt.observers.Broker)
    cerebro.addobservermulti(bt.observers.BuySell)
    cerebro.addobserver(bt.observers.Benchmark,
                        data=benchdata,
                        _doprenext=True,
                        timeframe=TimeFrame.NoTimeFrame)

    # --- Analyzers ---
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, timeframe=TimeFrame.Years)
    cerebro.addanalyzer(bt.analyzers.Calmar, timeframe=TimeFrame.Years)
    cerebro.addanalyzer(bt.analyzers.DrawDown)
    cerebro.addanalyzer(bt.analyzers.AnnualReturn)
    cerebro.addanalyzer(bt.analyzers.PyFolio, timeframe=TimeFrame.Days, compression=1)
    cerebro.addanalyzer(bt.analyzers.Transactions)
    cerebro.addanalyzer(bt.analyzers.Returns)

    # --- Strategies ---
    cerebro.addstrategy(strategy=ValueBasket)
    cerebro.addstrategy(strategy=ValueBasket2)

    results = cerebro.run()

    for strat in results:
        print('='*79)
        print(strat.__class__.__name__)

        # Analyzers results
        strat.analyzers.annualreturn.print()
        strat.analyzers.drawdown.print()
        strat.analyzers.sharperatio.print()
        strat.analyzers.returns.print()

        # pyfolio
        pyfoliozer = strat.analyzers.getbyname('pyfolio')
        returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()

        # Perfomance
        pf.show_perf_stats(returns=returns)

    # 
    cerebro.plot(plotter=PlotterZoomed())

    I added 2 strategies, and would like to extract performance of each ones. But for each strat I get same perfomance (annualreturn, drawdown, profit, sharpe and so on), which comes from backtasting together (printing in section # Analyzers results). There are some capital allocation logic between strategies and they are related to each other.

    1. Is there any method to get perfomance of each strategies separate after finishing backtesting?
    2. Is there any way to get single strategy returns for past 6 months in next() method?
    0
    B
     1 Reply
  • B
    administrators

    @oalexandere said in Perfomance of specific strategy in cerebro:

    1. Is there any method to get perfomance of each strategies separate after finishing backtesting?

    The performance is for the account you have in the broker.

    0
    O
     1 Reply
  • O

    @backtrader ok. After much thought, one version (for example for calc pnl for each strategy) may be like this:

        def notify_order(self, order):

        if order.status in [order.Completed]:
            if hasattr(self, 'total_pnl'):
                self.total_pnl += order.executed.pnl - order.executed.comm
            else:
                self.total_pnl = order.executed.pnl - order.executed.comm
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  • O

    Solution: using analyzer.
    Maybe will be usefull for somebody: next analyzer calculate and save realized pnl and opened positions for each strategy date to date:

    import backtrader as bt
from collections import OrderedDict


class TotalReturns(bt.Analyzer):
    '''This analyzer reports the total pnl for strategy

    Params:

      - timeframe (default: ``None``)
        If ``None`` then the timeframe of the 1st data of the system will be
        used

      - 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

        If ``None`` then the compression of the 1st data of the system will be
        used

    Methods:

      - get_analysis

        Returns a dictionary with returns as values and the datetime points for
        each return as keys
    '''

    def __init__(self):
        self.total_pnl = 0  
        self.unrealized = 0  # Unrealized pnl for all positions all strategies
        self.positions = OrderedDict.fromkeys([d._name or 'Data%d' % i 
                                               for i, d in enumerate(self.datas)], 0)  # Current strategy positions

    def start(self):
        tf = min(d._timeframe for d in self.datas)
        self._usedate = tf >= bt.TimeFrame.Days

    def notify_order(self, order):

        if order.status in [order.Completed, order.Partial]:
            self.total_pnl += order.executed.pnl - order.executed.comm

            self.positions[order.data._name] += order.executed.size

    def next(self):
        if self._usedate:
            self.rets[self.strategy.datetime.date()] = self.total_pnl
        else:
            self.rets[self.strategy.datetime.datetime()] = self.total_pnl

    def stop(self):

        for dname in self.positions:
            self.unrealized += (self.strategy.dnames[dname].close[0] - 
                                self.strategy.positionsbyname[dname].price) * \
                               self.strategy.positionsbyname[dname].size
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