For the benefit of others, I have decided to share the full code of the datafeed extension solution that worked for me:

# Define Dynamic Class for Loading Data  

class GenericCSV(bt.feeds.GenericCSVData):
    
    lines = ('stock_p_change', 'predictions')
    
    params = (('nullvalue', float('NaN')),
              ('dtformat', '%m/%d/%Y'),
              ('stock_p_change', 7),
              ('predictions', 8))


# Trading Strategy
class SPYStrategy(bt.Strategy):
    
    def __init__(self):

        # The indicator autoregisters and will plot even if no obvious
        # reference is kept to it in the class
        pass 
        
    def next(self):
        if self.data.predictions > 0 and self.data.stock_p_change < -0.005:
            self.buy(stake=1000)
        
        else:
            if self.data.predictions < 1 and self.data.stock_p_change > 0:
                self.close()

    
    def notify_trade(self, trade):
        dt = self.data.datetime.date()
        if trade.isclosed:
            print('{} {} Closed: PnL Gross {}, Net {}'.format(
                                                dt,
                                                trade.data._name,
                                                round(trade.pnl,2),
                                                round(trade.pnlcomm,2)))

# Variable for our starting cash
startcash = 10000
    
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
    
# Set Commission - The Proportional Transaction Cost is Set as the Commission 
cerebro.broker.setcommission(commission=0.005199108990760269)
        
# Add a strategy
cerebro.addstrategy(SPYStrategy)

# Get Data
data = GenericCSV(dataname="event_based_backtesting.csv")

cerebro.adddata(data)

# Run over everything
cerebro.run()

# Get final portfolio Value
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash

# Print out the final result
print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

# Finally plot the end results
cerebro.plot(style='line')

To check if the added variables are being read in correctly--sometimes using the parameter "7" for your added variables may accidentally parse "openinterest" as an added variable--you can use this code:

# Define Dynamic Class for Loading Data  

class GenericCSV(bt.feeds.GenericCSVData):
    
    lines = ('stock_p_change', 'predictions')
    
    params = (('nullvalue', float('NaN')),
              ('dtformat', '%m/%d/%Y'),
              ('stock_p_change', 7),
              ('predictions', 8))


# Trading Strategy
class SPYStrategy(bt.Strategy):
    
    def __init__(self):

        # The indicator autoregisters and will plot even if no obvious
        # reference is kept to it in the class
        pass 
        
    def next(self):
        print('%03d %f %f, %f' % (
            len(self),
            self.data.close[0],
            self.data.stock_p_change[0],
            self.data.predictions[0],))


# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
    
# Add a strategy
cerebro.addstrategy(SPYStrategy)

# Get Data
data = GenericCSV(dataname="event_based_backtesting.csv")

cerebro.adddata(data)

# Run over everything
cerebro.run()

Resolved! The issue stemmed from my inclusion of BT pipeline OHLCV parameters in the "lines" object for added variable.

Works well now.

Regards,

I successfully managed to load an extended dataset with an indicator that was calculated outside the Backtrader coding pipeline. In my strategy, it is named: "predictions".

The problem comes when I try to use this variable as a trading indicator: No trades are effected. Can anyone help? My code is below:

# Define Dynamic Class for Loading Data   
lines = ('datetime', 'open', 'high', 'low', 'close', 'volume', 'openinterest', 'predictions')
    
params = (('datetime', -1),
              ('open', -1),
              ('high', -1),
              ('low', -1),
              ('close', -1),
              ('volume', -1),
              ('openinterest', -1),
              ('predictions', -1))
    
datafields = btfeeds.PandasData.datafields + (['datetime', 
                                                   'open', 
                                                   'high', 
                                                   'low', 
                                                   'close', 
                                                   'volume', 
                                                   'openinterest', 
                                                   'predictions'])
    
mydict = dict(lines=tuple(lines), params=params, datafields=bt.feeds.PandasData.datafields + list(lines),)

PandasSPY = type('SPY', (btfeeds.PandasData,), mydict)


# Trading Strategy
class SPYStrategy(bt.Strategy):
     
    def __init__(self):
        self.predictions = self.datas[0].predictions
 
    def next(self):
        if self.data.close[0] < self.data.close[-1] and self.predictions > 0:
            print("Signal Detected")
            self.buy(size=1000)
                
    
    def notify_trade(self, trade):
        if trade.isclosed:
            dt = self.data.datetime.date()
 
            print('---------------------------- TRADE ---------------------------------')
            print("1: Data Name:                            {}".format(trade.data._name))
            print("2: Bar Num:                              {}".format(len(trade.data)))
            print("3: Current date:                         {}".format(dt))
            print('4: Status:                               Trade Complete')
            print('5: Ref:                                  {}'.format(trade.ref))
            print('6: PnL:                                  {}'.format(round(trade.pnl,2)))
            print('--------------------------------------------------------------------')



    
# Variable for our starting cash
startcash = 10000
    
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
    
# Set Commission - The Proportional Transaction Cost is Set as the Commission 
cerebro.broker.setcommission(commission=0.004199108990760269)
        
# Add a strategy
cerebro.addstrategy(SPYStrategy)

# Get a pandas dataframe
datapath = ('event_based_backtesting.csv')

# Compile dataframe object
dataframe = pandas.read_csv(datapath,
                                parse_dates=True,
                                index_col=0)


# Pass it to the backtrader datafeed and add it to the cerebro
data = PandasSPY(dataname=dataframe)

cerebro.adddata(data)

# Run over everything
cerebro.run()

# Get final portfolio Value
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash

# Print out the final result
print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

# Finally plot the end results
cerebro.plot(style='candlestick')