@backtrader
Well, I'm just trying to port a strategy of mine from Pinescript. I'm sorry if my question seems ignorant?

Well, I have tried googling it now for the past few days. Still no luck. Perhaps @backtrader has any thoughtful input as to why indicators doesn't work?

neither bt.indicators.SimpleMovingAverage(self.datas[0].close, period = 40) or bt.ind.SMA(...) works. The indicators still dont get plotted. I tried this as well from the start but gave it a shot again, still no luck

Hello all!
Im new to backtrader and so far im trying to get a few simple indicators to work but without any luck.

I simply get an error on every indicator I try to use saying the following: Module 'backtrader' has no 'sma' memberpylint(no-member)

When plotting my strategy everything but the indicator plots. I have been looking around in the documentation for a while now and still can't seem to find a fix.

Code below:

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])
import backtrader as bt
import math

# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 15),
    )

    def __init__(self):
        self.startcash = self.broker.getvalue()
        self.sma = bt.sma(self.data.close, period=15)
        self.rsi = bt.RSI_SMA(self.data.close, period=14,)

    def next(self):
        if not self.position:
            if self.rsi < 30:
                self.buy()
        else:
            if self.rsi > 70:
                self.close()


class LongOnly(bt.Sizer):
	params = (('stake', 1),)
	def _getsizing(self, comminfo, cash, data, isbuy):
		if isbuy:
			divide = math.floor(cash/data.close[0])
			self.p.stake = divide

			return self.p.stake
        # Sell situation
		position = self.broker.getposition(data)
		if not position.size:
			return 0 # do not sell if nothing is open
		return self.p.stake

if __name__ == '__main__':
    #Variable for our starting cash
    startcash = 10000

    #Create an instance of cerebro
    cerebro = bt.Cerebro(optreturn=False)

    # Add Sizer
    cerebro.addsizer(LongOnly)

    # Activate optimization
    StrategyOptimizationEnabled = False

    #Add our strategy
    if StrategyOptimizationEnabled == True:
        cerebro.optstrategy(TestStrategy, maperiod=range(14,21))

    #Get Apple data from Yahoo Finance.
    data = bt.feeds.YahooFinanceData(
        dataname='AAPL',
        fromdate = datetime.datetime(2019,1,1),
        todate = datetime.datetime(2020,1,1),
        buffered= True
        )

    #Add the data to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(startcash)

    # Run over everything
    opt_runs = cerebro.run()

    # Set the commission
    cerebro.broker.setcommission(commission=0.005)

    # Generate results list
    if StrategyOptimizationEnabled == True:
        final_results_list = []
        for run in opt_runs:
            for strategy in run:
                value = round(strategy.broker.get_value(),2)
                PnL = round(value - startcash,2)
                maperiod = strategy.params.maperiod
                final_results_list.append([maperiod,PnL])

        #Sort Results List
        by_period = sorted(final_results_list, key=lambda x: x[0])
        by_PnL = sorted(final_results_list, key=lambda x: x[1], reverse=True)

        #Print results
        print('Results: Ordered by period:')
        for result in by_period:
            print('Period: {}, PnL: {}'.format(result[0], result[1]))
        print('Results: Ordered by Profit:')
        for result in by_PnL:
            print('Period: {}, PnL: {}'.format(result[0], result[1]))

    # Plot the result
    cerebro.plot()