def __init__(self):     
        self.dataclose = self.datas[0].close  
       
        # Order variable will contain ongoing order details/status
        self.order = None
   
        self.bb = bt.indicators.BBands(self.datas[0],
                                       period = self.params.bbperiod,
                                       devfactor = self.params.bbfactor,
                                       )

        self.ema = bt.indicators.EMA(self.datas[0], period=self.params.emaperiod)

This init has been used in other strategies and has no problems. I'm not sure what's going on.

    def __init__(self):     
        self.dataclose = self.datas[0].close  
       
        # Order variable will contain ongoing order details/status
        self.order = None
   
        self.bb = bt.indicators.BBands(self.datas[0],
                                       period = self.params.bbperiod,
                                       devfactor = self.params.bbfactor,
                                       )

        self.ema = bt.indicators.EMA(self.datas[0], period=self.params.emaperiod)

This init has been used in other strategies and has no problems. I'm not sure what's going on.

I've been trying to optimize a new strategy that I have based off of previous strategies. The previous strategies work, but the new one gives me an error saying "only size-1 arrays can be converted to Python scalars" from inside cerebro.run(). Does anybody have an idea of what would be causing this?

The optimizer works when I put a single value into the parameter instead of using np.arange. for bbfactor So it's most likely an issue with that, but I can't figure out what's going on.

import datetime
import backtrader as bt
from strategies import *
import numpy as np

cerebro = bt.Cerebro(optreturn=False)

data = bt.feeds.GenericCSVData(
    dataname=tickerSymbol + '_'+ Interval + '_data.csv',

    fromdate = datetime.datetime(2019, 10, 1),
    todate = datetime.date.today(),

    nullvalue=0.0,

    dtformat=('%Y-%m-%d'),

    datetime=0,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=5,
    openinterest=-1
)

cerebro.adddata(data)

#Add analyzer to Cerebro
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe_ratio')
cerebro.addanalyzer(bt.analyzers.SQN, _name='SQN')

#Add strategy to Cerebro
cerebro.optstrategy(BB_EMA, emaperiod=np.arange(20,40,5), bbperiod=np.arange(20,40,5),
                    bbfactor=np.arange(1,4,1))

#Default position size
cerebro.broker.setcash(10000.0)
cerebro.addsizer(bt.sizers.PercentSizerInt,percents = 99)

if __name__ == '__main__':
    optimized_runs = cerebro.run()

    final_results_list = []
        print('EMA Period|BB Period|BB factor|   PnL|               SQN') # for SQN analysis
    
    for run in optimized_runs:
        for strategy in run:
            PnL = round(strategy.broker.get_value() - 10000,2)
            sqn = strategy.analyzers.SQN.get_analysis()

                sqnF = '{:<10}|{:<9}|{:<9}|{:<6}|{:<18}'
                final_results_list.append([strategy.params.emaperiod,
                                           strategy.params.bbperiod,
                                           strategy.params.bbfactor,
                                           PnL, sqn['sqn']])

    sort_by_sharpe = sorted(final_results_list, key=lambda x: x[3], reverse=True)
    filter(None, sort_by_sharpe)
    #print(sort_by_sharpe)
    for line in sort_by_sharpe[:5]:
        if line is not None:
            print(sqnF.format(*line))
      
    print('\n1.6 - 1.9 Below average \n2.0 - 2.4 Average \n2.5 - 2.9 Good \n3.0 - 5.0 Excellent \n5.1 - 6.9 Superb \n7.0 - Holy Grail')

Error log:

---------------------------------------------------------------------------
RemoteTraceback                           Traceback (most recent call last)
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Users\David\Anaconda3\lib\multiprocessing\pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\cerebro.py", line 1007, in __call__
    return self.runstrategies(iterstrat, predata=predata)
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\cerebro.py", line 1293, in runstrategies
    self._runonce(runstrats)
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\cerebro.py", line 1652, in _runonce
    strat._once()
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\lineiterator.py", line 297, in _once
    indicator._once()
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\lineiterator.py", line 297, in _once
    indicator._once()
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\linebuffer.py", line 630, in _once
    self.oncestart(self._minperiod - 1, self._minperiod)
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\lineroot.py", line 165, in oncestart
    self.once(start, end)
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\linebuffer.py", line 758, in once
    self._once_val_op(start, end)
  File "C:\Users\David\Anaconda3\lib\site-packages\backtrader\linebuffer.py", line 793, in _once_val_op
    dst[i] = op(srca[i], srcb)
TypeError: only size-1 arrays can be converted to Python scalars
"""

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
<ipython-input-4-2f6d49262766> in <module>
     51 
     52 if __name__ == '__main__':
---> 53     optimized_runs = cerebro.run()
     54 
     55     final_results_list = []

~\Anaconda3\lib\site-packages\backtrader\cerebro.py in run(self, **kwargs)
   1141 
   1142             pool = multiprocessing.Pool(self.p.maxcpus or None)
-> 1143             for r in pool.imap(self, iterstrats):
   1144                 self.runstrats.append(r)
   1145                 for cb in self.optcbs:

~\Anaconda3\lib\multiprocessing\pool.py in next(self, timeout)
    733         if success:
    734             return value
--> 735         raise value
    736 
    737     __next__ = next                    # XXX

TypeError: only size-1 arrays can be converted to Python scalars