@klearner1 Did you find the way out?

@abhishek.anand @krispy have you tried to specify which asset should be sold / closed? In the code i see:

• self.buy() instead self.buy(data=d)
• self.close() instead self.close(data=d)

Let me know if works better.

Errata corr.:

@federico-bld said in Discrepancy with PnL in the Stop function vs TradeAnalyzer Net PnL:

 def stop(self):
        # if we're backtesting and out of data, close open positions so they get counted properly.
        # must be done on the 2nd to last, as market orders wait to get another candle, to execute based on open price.
        
        for data in self.datas:
            if self.getposition(data) and len(data) == data.buflen() - 1 and "live" not in self.environment:
                logger.debug("Closing open position to conclude backtesting")
                self.close(data=data)

 def stop(self):
        # if we're backtesting and out of data, close open positions so they get counted properly.
        # must be done on the 2nd to last, as market orders wait to get another candle, to execute based on open price.
        
        for data in self.datas:
            if self.position and len(self.data) == self.data.buflen() - 1 and "live" not in self.environment:
                logger.debug("Closing open position to conclude backtesting")
                self.close(data=d)

@manos would it make a better sense to put it in the stop() method?

Developing several strategies i have to combine them. So I wrote superclasses, i.e:

class BaseStrategy(bt.Strategy):
    params = (
        ('loShBo', 2),
        ('isBacktesting', False),       
        ("sizerType", None),  # Must be specified otherwise will be rised an Exception
        ("sizerDictParameters", {"amount": '', "retint": ""}), # Must be specified otherwise will be rised an Exception
    )

class OverBaseStrategy(BaseStrategy):
    params = (
        ('sma',26),
        ('roc',50),
        ('smaRoc',50),
        ('ema',26),
    )

class MyStrategy_01(OverBaseStrategy):
    params = dict(
        stop_loss=0.05,
        trail=0.07, 
    )

When I call:

cerebro = bt.Cerebro()
params = {
    'trail': 0.02,
    'smaRoc': 20,
    'isBacktesting': True}
        cerebro.addstrategy(strategy=MyStrategy_01, **params)
pprint(cerebro.strats[0][0][0].params.__dict__)

The output is:

mappingproxy({'__doc__': None,
              '__module__': 'backtrader.metabase',
              '_getpairs': <classmethod object at 0x0000024E510751F0>,
              '_getpairsbase': <classmethod object at 0x0000024E51075190>,
              '_getrecurse': <classmethod object at 0x0000024E51075250>,
              'stop_loss': 0.05,
              'trail': 0.07})

What can I do to retrive all params with superclasses included?
i.e. :

mappingproxy({
    '__doc__': None,
    '__module__': 'backtrader.metabase',
    '_getpairs': <classmethod object at 0x0000024E510751F0>,
    '_getpairsbase': <classmethod object at 0x0000024E51075190>,
    '_getrecurse': <classmethod object at 0x0000024E51075250>,
    'loShBo': 2,
    'isBacktesting': True,  # <== NOT False as default
    'sizerType': None,
    'sizerDictParameters': {"amount": '', "retint": ""},
    'sma': 26,
    'roc': 20,
    'smaRoc': 20,  # <== NOT 50 as default
    'ema': 26,
    'stop_loss': 0.05,
    'trail': 0.02,  # <== NOT 0.07 as default
})

I have to lunch strategies with serveral feeds with different timeframes.
I didn't find the way to get "minpediod" to calculate "hist_start_date" variable.

# My formula shuld be look like:
minutes_of_one_candle = get_compression_in_minutes_from_timeframe(assetTimeframe,assetCompression)
minperiod = # I don't know how to retrieve the "minperiod" from the strategy with custom 
timeAgoInMinutes = minperiod * minutes_of_one_candle 

Follows the complete code

strategies_list = [sma_200minperiod_strategy,
                   ema_5minperiod_strategy]
assets = [
    ['ticker1', 1, 'minutes',]
    ['ticker2', 30, 'minutes',]
    ['ticker3', 1, 'hours',]
    ['ticker4', 1, 'days',]
]
for strategy in strategies_list:
    cerebro.addstrategy(strategy=strategy, **get_strategyParams(strategy))

    for assetName in assets:
        dataname =assetName[0]

        assetTimeframe = get_bt_TimeFrame(assetName[2])
        assetCompression = get_bt_compression(assetName[1])
        assetCompression_in_Minutes = get_compression_in_minutes(assetTimeframe , assetCompression)

        minutes_of_one_candle = get_compression_in_minutes_from_timeframe(assetTimeframe,assetCompression)        
        minperiod = # I don't know how to retrieve the "minperiod" from the strategy with custom parameters
        timeAgoInMinutes = minperiod * minutes_of_one_candle         

        hist_start_date = datetime.utcnow() - timedelta(minutes=timeAgoInMinutes)

        data = store.getdata(
                             dataname=dataname,
                             timeframe=assetTimeframe,
                             compression=assetCompression,
                             fromdate=hist_start_date,  # <========
                             ohlcv_limit=250,
                             drop_newest=True)

        cerebro.adddata(data)

How can I get minperiod to set fromdate = hist_start_date before cerebro.run()?