I guess by filters you mean indicators and not data filters. This is an example having a list of indicators (in this case SimpleMovingAverage applied on close), one per data feed.

class TestStrategy(bt.Strategy):

    params = (
        ('maperiod', 15),
    )

    def __init__(self):
        self.smas = []
        for d in self.datas:
            self.smas.append(bt.indicators.SimpleMovingAverage(d.lines.close, period=self.params.maperiod))

The strategy can turn on/off a parameter of the indicator according to whether in trade or not. Have a look at the dynamic indicator example
https://www.backtrader.com/blog/posts/2018-02-06-dynamic-indicator/dynamic-indicator.html

Another solution that does not use cheat on open. It is a cheat-on-close broker and you buy on open by disabling the coc and sell on close by enabling it.

class TestStrategy(bt.Strategy):

    def next(self):
        order1 = self.close(self.datas[0], coc=True)
        order2 = self.buy(self.datas[0], coc=False)

if __name__ == '__main__':
    cerebro = bt.Cerebro()
    cerebro.broker.set_coc(True)
    ....

Haven't tested it, but I think your csv file looks more like the following.

params = (
# ('nullvalue', float('NaN')),
('dtformat', '%Y-%m-%d %H:%M:%S'),
('datetime', 5),
('Open', 0),
('High', 1),
('Low', 2),
('Close', 3),
('Volume', 4),
)

I even like better the _nextforce = True pointed out to me by @backtrader some time ago.

class CustomIndicator(bt.Indicator):
    _nextforce = True
...

Hi,

when I am using SharpeRatio or PeriodStats analyzers with optimization, I get the following error

TypeError: __init__() missing 9 required positional arguments: 'status', 'dt', 'barlen', 'size', 'price', 'value', 'pnl', 'pnlcomm', and 'tz'

Both analyzers are based on TimeReturn which does not give any error if added as analyzer.

Any feedback would be really helpful.

Thanks

Without having tested it, perhaps you can use in next of strategy B self.cerebro.runningstrats[self._id ^ 1] to get access to strategy A

Hi,

if there are multiple trades on different products, then pnlplus and pnlminus in Trades observer will hold just the pnl of the last trade traversed.

    def next(self):
        for trade in self._owner._tradespending:
            if trade.data not in self.ddatas:
                continue

            if not trade.isclosed:
                continue

             if trade.pnl >= 0:
                self.lines.pnlplus[0] =  trade.pnl
            else:
                self.lines.pnlminus[0] =  trade.pnl

I guess something like this is needed

    def next(self):
        for trade in self._owner._tradespending:
            if trade.data not in self.ddatas:
                continue

            if not trade.isclosed:
                continue

            if trade.pnl >= 0:
                self.lines.pnlplus[0] = np.nansum([self.lines.pnlplus[0], trade.pnl])
            else:
                self.lines.pnlminus[0] = np.nansum([self.lines.pnlminus[0], trade.pnl])

I am using version 1.9.70.122 in case that is relevant.

Cheers

Hi,

when I am using SharpeRatio or PeriodStats analyzers with optimization, I get the following error

TypeError: __init__() missing 9 required positional arguments: 'status', 'dt', 'barlen', 'size', 'price', 'value', 'pnl', 'pnlcomm', and 'tz'

Both analyzers are based on TimeReturn which does not give any error if added as analyzer.

Any feedback would be really helpful.

Thanks

Hi,

I wonder why when runonce is False, then the data are not preloaded in optimization. I also wonder what happens when someone is using _nextforce = True instead. It seems to me that preloading will take place as the indicators that will call _disable_runonce are added afterwards.

Thanks

        iterstrats = itertools.product(*self.strats)
        if not self._dooptimize or self.p.maxcpus == 1:
            # If no optimmization is wished ... or 1 core is to be used
            # let's skip process "spawning"
            for iterstrat in iterstrats:
                runstrat = self.runstrategies(iterstrat)
                self.runstrats.append(runstrat)
                if self._dooptimize:
                    for cb in self.optcbs:
                        cb(runstrat)  # callback receives finished strategy
        else:
            if self.p.optdatas and self._dopreload and self._dorunonce:
                for data in self.datas:
                    data.reset()
                    if self._exactbars < 1:  # datas can be full length
                        data.extend(size=self.params.lookahead)
                    data._start()
                    if self._dopreload:
                        data.preload()

            pool = multiprocessing.Pool(self.p.maxcpus or None)
            for r in pool.imap(self, iterstrats):
                self.runstrats.append(r)
                for cb in self.optcbs:
                    cb(r)  # callback receives finished strategy

            pool.close()

You don't pass to the SimpleMovingAverage the ha_close line (self.hk.close) but rather a specific value of it (self.hk.close[0]).

If you look at the implementation of class HeikinAshi, it has line alias

    lines = ('ha_open', 'ha_high', 'ha_low', 'ha_close',)

    linealias = (
        ('ha_open', 'open',),
        ('ha_high', 'high',),
        ('ha_low', 'low',),
        ('ha_close', 'close',),
    )

The error has to do with division with zero, so I would suspect it has to do with the indicator. There is a parameter for RSI the safediv that you can set to True.

If you think that problem is with reading data, then I would suggest you check that bar(s) in next has the correct data

Hope this helps

Hi,

I am trying to change the rollover class to use it with input data feeds added in cerebro. I guess in this case next for the data feeds is also called by cerebro and thus no need to be called within rollover. I wonder how I can check for the end of the data feed without using the check for _next being False.

Many thanks

cerebro.setbroker(<instance of your broker class>)

Haven't tested it, but I think your csv file looks more like the following.

params = (
# ('nullvalue', float('NaN')),
('dtformat', '%Y-%m-%d %H:%M:%S'),
('datetime', 5),
('Open', 0),
('High', 1),
('Low', 2),
('Close', 3),
('Volume', 4),
)

Without having tested it, perhaps you can use in next of strategy B self.cerebro.runningstrats[self._id ^ 1] to get access to strategy A

I guess by filters you mean indicators and not data filters. This is an example having a list of indicators (in this case SimpleMovingAverage applied on close), one per data feed.

class TestStrategy(bt.Strategy):

    params = (
        ('maperiod', 15),
    )

    def __init__(self):
        self.smas = []
        for d in self.datas:
            self.smas.append(bt.indicators.SimpleMovingAverage(d.lines.close, period=self.params.maperiod))

I even like better the _nextforce = True pointed out to me by @backtrader some time ago.

class CustomIndicator(bt.Indicator):
    _nextforce = True
...