Adding multiple data feeds in a for loop gives error

[A Former User]

When trying to add multiple data feeds I'm getting the following error:

❯ python3 optimize_all_indexes.py
Traceback (most recent call last):
  File "optimize_all_indexes.py", line 43, in <module>
    optimized_runs = cerebro.run()
  File "/home/user/.local/lib/python3.8/site-packages/backtrader/cerebro.py", line 1143, in run
    for r in pool.imap(self, iterstrats):
  File "/usr/lib/python3.8/multiprocessing/pool.py", line 868, in next
    raise value
multiprocessing.pool.MaybeEncodingError: Error sending result: '[<strategies.strategies.EMAcrossover object at 0x7f9dda1051f0>]'. Reason: 'PicklingError("Can't pickle <class 'backtrader.metabase.AutoInfoClass_95a5b7035dfc45a8b0580d4ca9a9ac62_AXJO.csv'>: attribute lookup AutoInfoClass_95a5b7035dfc45a8b0580d4ca9a9ac62_AXJO.csv on backtrader.metabase failed")'

The code works for a single data feed. This is what I've changed from the code:

    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, 'data/indexes/')
    for file in os.listdir(datapath):
        path = os.path.join(modpath, f'data/indexes/{file}')
        data = bt.feeds.YahooFinanceCSVData(
            dataname=path,
            name=file,
            reverse=False
        )
        cerebro.adddata(data)

The code for a single data feed:

sizers.py

import backtrader as bt

class MaxRiskSizer(bt.Sizer):
    params = (('risk', 0.98),)

    def __init__(self):
        if self.p.risk > 1 or self.p.risk < 0:
            raise ValueError('The risk parameter is a percentage which must be'
                'entered as a float. e.g. 0.5')

    def _getsizing(self, comminfo, cash, data, isbuy):
        position = self.broker.getposition(data)

        if not position:
            size = comminfo.getsize(data.close[0], cash * self.p.risk)
        else:
            size = position.size

        return size

commissions.py

import backtrader as bt

class DegiroCommission(bt.CommInfoBase):
    params = (('per_share', 0.004), ('flat', 0.5),)

    def _getcommission(self, size, price, pseudoexec):
        return self.p.flat + size * self.p.per_share

optimize.py

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

import datetime
import os.path
import sys
import math

import backtrader as bt

from strategies.strategies import *
from sizers import MaxRiskSizer
from commissions import DegiroCommission

STARTING_CASH = 1700


if __name__ == '__main__':
    cerebro = bt.Cerebro(optreturn=False)
    cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='annual_return')
    cerebro.optstrategy(EMAcrossover, fast=range(5, 7), slow=range(10,12))
    #cerebro.optstrategy(EMAcrossover, fast=range(5, 55, 5), slow=range(60, 300, 10))
    #cerebro.optstrategy(EMAcrossover, fast=range(15, 55), slow=range(40, 60))
    cerebro.broker.set_cash(STARTING_CASH)
    cerebro.broker.set_coc(True)

    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, 'data/SPY.csv')
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        reverse=False
    )
    cerebro.adddata(data)

    cerebro.addsizer(MaxRiskSizer)
    comminfo = DegiroCommission()
    cerebro.broker.addcommissioninfo(comminfo)

    optimized_runs = cerebro.run()

    final_results_list = []
    for run in optimized_runs:
        for strategy in run:
            PnL = round(strategy.broker.get_value() - STARTING_CASH, 2)
            my_dict = strategy.analyzers.annual_return.get_analysis()
            annual_returns = [v for _, v in my_dict.items() if v != 0]
            average_annual_return = sum(annual_returns) / len(annual_returns)
            final_results_list.append([strategy.params.fast,
            strategy.params.slow, PnL, round(average_annual_return*100, 2)])

    sort_by_return = sorted(final_results_list, key=lambda x: x[3],
                             reverse=True)
    print('\nSorted by Annualized Return:')
    for line in sort_by_return[:10]:
        print(line)

    sort_by_pnl = sorted(final_results_list, key=lambda x: x[2],
                             reverse=True)
    print('\nSorted by PnL:')
    for line in sort_by_pnl[:10]:
        print(line)

The indexes have different starting day for the datas. Maybe that affects the optimization somehow.

[A Former User]

The strategy was missing.

strategies.py

class EMAcrossover(bt.Strategy):
    params = (('fast', 20), ('slow', 50),)

    def log(self, txt, dt=None):
        dt = dt or self.datas[0].datetime.date(0)
        #print(f'{dt.isoformat()} {txt}') # Comment this line when running optimization

    def __init__(self):
        self.dataclose = self.datas[0].close
        self.order = None

        fast_ema, slow_ema = bt.ind.EMA(period=self.p.fast), bt.ind.EMA(period=self.p.slow)
        self.crossover = bt.indicators.CrossOver(fast_ema, slow_ema)
        #self.signal_add(bt.SIGNAL_LONGSHORT, bt.ind.CrossOver(ema1, ema2))

    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log(f'GROSS {trade.pnl:.2f}, NET {trade.pnlcomm:.2f}')

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # An active Buy/Sell order has been submitted/accepted - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(f'BUY EXECUTED, {order.executed.price:.2f}')
            elif order.issell():
                self.log(f'SELL EXECUTED, {order.executed.price:.2f}')
            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        self.order = None

    def next(self):
        if self.order:
            return

        if self.crossover > 0:
            self.log(f'BUY CREATE {self.dataclose[0]:.2f}')
            self.order = self.buy()
        elif self.crossover < 0:
            self.log(f'SELL CREATE {self.dataclose[0]:.2f}')
            self.order = self.sell()

[willt]

Same error, any help?

[run-out]

@willt Can you please elaborate on your error and start a new thread? Thanks.