Hi All,

I am facing issue to access for multi-data, please refer the code below:

    params = dict(
        selcperc=0.10,  # percentage of stocks to select from the universe
        rperiod=1,  # period for the returns calculation, default 1 period
        vperiod=36,  # lookback period for volatility - default 36 periods
        mperiod=12,  # lookback period for momentum - default 12 periods
        reserve=5,  # 5% reserve capital
    )
    
    def log(self, arg):
            print('{} {}'.format(self.datetime.date(), arg))   
            
    def __init__(self):
        # calculate 1st the amount of stocks that will be selected
        self.selnum = int(len(self.datas) * self.p.selcperc)
        print(int(len(self.datas) * self.p.selcperc))
        # allocation perc per stock
        # reserve kept to make sure orders are not rejected due to
        # margin. Prices are calculated when known (close), but orders can only
        # be executed next day (opening price). Price can gap upwards
        self.perctarget = (100 - self.p.reserve) / self.selnum
        print((100 - self.p.reserve) / self.selnum)
        # returns, volatilities and momentums
        rs = [bt.ind.PctChange(d, period=self.p.rperiod) for d in self.datas]
        vs = [bt.ind.StdDev(ret, period=self.p.vperiod) for ret in rs]
        ms = [bt.ind.ROC(d, period=self.p.mperiod) for d in self.datas]
        volume = [bt.indicators.MovingAverageSimple(d, period = 2) for d in self.datas[0].volume]
        
        # simple rank formula: (momentum * net payout) / volatility
        # the highest ranked: low vol, large momentum, large payout
        # self.ranks = {d: d.npy * m / v for d, v, m in zip(self.datas, vs, ms)}
        self.ranks = {d: bt.DivByZero(m, v) for d, v, m, volu in zip(self.datas, vs, ms, volume) if volu > 100000}

Above is giving error:

    self.ranks = {d: bt.DivByZero(m, v) for d, v, m, volu in zip(self.datas, vs, ms, volume) if volu > 100000}
TypeError: __bool__ should return bool, returned LineOwnOperation

I am not an expert, any pointers to fix the issue?

Thank you @run-out, makes sense.

Hi @vladisld ,

I am facing the same issue. Is there any fix for this yet?

Thank you @run-out, makes sense.

thanks @run-out , is there any other way? I am trying to filter out stocks whose average volume is greater than certain value.

Hi All,

I am facing issue to access for multi-data, please refer the code below:

    params = dict(
        selcperc=0.10,  # percentage of stocks to select from the universe
        rperiod=1,  # period for the returns calculation, default 1 period
        vperiod=36,  # lookback period for volatility - default 36 periods
        mperiod=12,  # lookback period for momentum - default 12 periods
        reserve=5,  # 5% reserve capital
    )
    
    def log(self, arg):
            print('{} {}'.format(self.datetime.date(), arg))   
            
    def __init__(self):
        # calculate 1st the amount of stocks that will be selected
        self.selnum = int(len(self.datas) * self.p.selcperc)
        print(int(len(self.datas) * self.p.selcperc))
        # allocation perc per stock
        # reserve kept to make sure orders are not rejected due to
        # margin. Prices are calculated when known (close), but orders can only
        # be executed next day (opening price). Price can gap upwards
        self.perctarget = (100 - self.p.reserve) / self.selnum
        print((100 - self.p.reserve) / self.selnum)
        # returns, volatilities and momentums
        rs = [bt.ind.PctChange(d, period=self.p.rperiod) for d in self.datas]
        vs = [bt.ind.StdDev(ret, period=self.p.vperiod) for ret in rs]
        ms = [bt.ind.ROC(d, period=self.p.mperiod) for d in self.datas]
        volume = [bt.indicators.MovingAverageSimple(d, period = 2) for d in self.datas[0].volume]
        
        # simple rank formula: (momentum * net payout) / volatility
        # the highest ranked: low vol, large momentum, large payout
        # self.ranks = {d: d.npy * m / v for d, v, m in zip(self.datas, vs, ms)}
        self.ranks = {d: bt.DivByZero(m, v) for d, v, m, volu in zip(self.datas, vs, ms, volume) if volu > 100000}

Above is giving error:

    self.ranks = {d: bt.DivByZero(m, v) for d, v, m, volu in zip(self.datas, vs, ms, volume) if volu > 100000}
TypeError: __bool__ should return bool, returned LineOwnOperation

I am not an expert, any pointers to fix the issue?

@backtrader thanks, can you please help an example? or would be great if you can share the reference, if this is already mentioned in any previous instance.

Hi,
I am a novice user with very limited knowledge of coding. Love the way all the examples are put up.
I further want to understand the process where the output of one strategy can be input to another, in this example:

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

import argparse
import datetime

import backtrader as bt


class Screener_SMA(bt.Analyzer):
    params = dict(period=10)

    def start(self):
        self.smas = {data: bt.indicators.SMA(data, period=self.p.period)
                     for data in self.datas}

    def stop(self):
        self.rets['over'] = list()
        self.rets['under'] = list()

        for data, sma in self.smas.items():
            node = data._name, data.close[0], sma[0]
            if data > sma:  # if data.close[0] > sma[0]
                self.rets['over'].append(node)
            else:
                self.rets['under'].append(node)


DEFAULTTICKERS = ['YHOO', 'IBM', 'AAPL', 'TSLA', 'ORCL', 'NVDA']


def run(args=None):
    args = parse_args(args)
    todate = datetime.date.today()
    # Get from date from period +X% for weekeends/bank/holidays: let's double
    fromdate = todate - datetime.timedelta(days=args.period * 2)

    cerebro = bt.Cerebro()
    for ticker in args.tickers.split(','):
        data = bt.feeds.YahooFinanceData(dataname=ticker,
                                         fromdate=fromdate, todate=todate)
        cerebro.adddata(data)

    cerebro.addanalyzer(Screener_SMA, period=args.period)
    cerebro.run(runonce=False, stdstats=False, writer=True)


def parse_args(pargs=None):

    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
        description='SMA Stock Screener')

    parser.add_argument('--tickers', required=False, action='store',
                        default=','.join(DEFAULTTICKERS),
                        help='Yahoo Tickers to consider, COMMA separated')

    parser.add_argument('--period', required=False, action='store',
                        type=int, default=10,
                        help=('SMA period'))

    if pargs is not None:
        return parser.parse_args(pargs)

    return parser.parse_args()


if __name__ == '__main__':
    run()

I am looking for a case where, if ticker data is above SMA then display over/under, feed over tickers to sma cross 20 over 50 strategy and then display over/under, again feed over tickers to RSI/MACD strategy, etc. Final strategy will give the result of tickers that has come over for all strategies. A solution will be to implement strategy for each and feed output to next strategy, that way, at the end I will only the ones that are over for all strategy tests. Is there a way this can be done?