Ah. I was on the right track-ish. Got it solved, thanks for your guidance!

For good order here is the code I used.

 def __init__(self):
        self.orderlst = []


def notify_order(self, order):


        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            #self.orderlst.extend(self.order.ref)
            #print(order.ref)
            pass
        if order.status in [order.Accepted]:
            self.orderlst.append(order)
 def next(self):
      
       #exit position
       if tme == datetime.time(14, 59, 0):
            if abs(self.position.size) > 0:
                #if in position and none of the brackets have executed then cancel all 3 orders and close position
                #after cancelling orders, make list empty
                #this also makes sure no orders are outstanding
                for ordnum in self.orderlst:
                    self.cancel(ordnum)
                self.orderlst = []
                print (self.orderlst)
                print('Canceles done')
                self.close(data)

Thanks for the reply. I should of realized that.

What would be the appropriate way to cancel all outstanding orders. I can only get the order numbers in Notify_Trade. Should I make a list and pass this list to the strategy section? Is there no cancel all?

Thanks again!

I am putting bracket orders on a futures and if the stops or take profits aren't hit I want to close at the end of the day.

            buytp = self.datas[0].close[0] + .5
            sellstp = self.datas[0].close[0] + .5
            selltp = self.datas[0].close[0] - .5

            if self.datas[0].FX_Idx_Slope[0] >= 0:
                #if element in rank (first in list = AD 2nd = BP etc) = 5 4 3 then sell currency otherwise do nothing
                if ranks[0] == 4 or ranks[0] == 3 or ranks[0] == 2:
                    brackets = self.sell_bracket(limitprice=selltp, stopprice=sellstp)

            if self.datas[0].FX_Idx_Slope[0] < 0:
                # if element in rank (first in list = AD 2nd = BP etc) = 5 4 3 then sell currency otherwise do nothing
                if ranks[0] == 4 or ranks[0] == 3 or ranks[0] == 2:
                    brackets = self.sell_bracket(limitprice=selltp, stopprice=sellstp)

        # close all at 4pm
        if self.datas[0].datetime.time(0) == datetime.time(15, 59, 0):
            if abs(self.position.size) > 0:
                self.close(data)
                self.broker.cancel(brackets)

I am getting the following error

  File "C:/Users/Dropbox/v1.py", line 309, in <module>
    results = cerebro.run()
  File "C:\Python27\lib\site-packages\backtrader\cerebro.py", line 1127, in run
    runstrat = self.runstrategies(iterstrat)
  File "C:\Python27\lib\site-packages\backtrader\cerebro.py", line 1290, in runstrategies
    self._runonce(runstrats)
  File "C:\Python27\lib\site-packages\backtrader\cerebro.py", line 1691, in _runonce
    strat._oncepost(dt0)
  File "C:\Python27\lib\site-packages\backtrader\strategy.py", line 287, in _oncepost
    self.next()
  File "C:/Users/Dropbox/All Devices/v1.py", line 208, in next
    self.broker.cancel(brackets)
UnboundLocalError: local variable 'brackets' referenced before assignment

I am missing something. I just don't know what. I am getting the first days orders off but the second day I am getting an error. Thanks for the help!

2018-02-02, SELL EXECUTED, Price: 79.88, Cost: 2000.00, Comm 1.00, Datetime: 2018-02-02 08:01:00
2018-02-02, BUY EXECUTED, Price: 79.37, Cost: 2000.00, Comm 1.00, Datetime: 2018-02-02 08:35:00
2018-02-02, Order Canceled
2018-02-02, OPERATION PROFIT, GROSS 510.00, NET 508.00
2018-02-05, SELL EXECUTED, Price: 79.30, Cost: 2000.00, Comm 1.00, Datetime: 2018-02-05 08:01:00

There are a few options.

1. You can use backtrader to actually resample the data (example here: https://www.backtrader.com/docu/data-multitimeframe/data-multitimeframe.html)

2. You can use a counter and resample every x amount of datapoints (ie. use the window of 5 min bars to get the ratio/std) https://backtest-rookies.com/2017/12/06/backtrader-data-replay/

3. Can "cheat" by using formatting and backfilling daily information into a minute custom dataframe and then sampling this minute data. (pandas is best for this IMO)

Not sure what a "ratio/std" means but if you provided a bit more information I am sure you would get a little more information.

I am trying to backtest Silver vs Gold and have no issues getting the feeds to work but am having an issue with the multipliers.

I use
cerebro.broker.setcommission(commission=2.0, margin=2000.0, mult=100.0)

But thats at the broker level.

Gold's multiplier per contract is 100x contract and Silver's is 5000x contract, can I extend the setcomission to each specific datafeed?

Also curious what is going on. I really like the platform and am really hoping this is not as serious as it seems.

I noticed the last line of my csv file had a formatted date that was an integer. I would check each line of your data first.

Re: Extending Datafeeds GenericCSV Attribute Error

Thanks Admin! I am an idiot!

I have looked at extending the GenericCSV and am trying to add an additional column (pe) like in the example and am getting the error

AttributeError: 'Lines_LineSeries_DataSeries_OHLC_OHLCDateTime_Abst' object has no attribute 'pe'

Hopefully i am doing something stupid but cannot seem to figure out why the GenericCSVpe isn't adding the pe parameter to the class?

Here is my complete test code.

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import backtrader as bt
import pandas as pd
from backtrader.feeds import GenericCSVData
import backtrader.feeds as btfeeds
import datetime as datetime
import backtrader.indicators as btind
import numpy as np

from backtrader.feeds import GenericCSVData

class GenericCSV_PE(GenericCSVData):

    # Add a 'pe' line to the inherited ones from the base class
    lines = ('pe',)

    # openinterest in GenericCSVData has index 7 ... add 1
    # add the parameter to the parameters inherited from the base class
    params = (('pe', 8),)

class MyStrategy(bt.Strategy):


    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = self.datas[0].datetime.date(0)
        dt_time = self.datas[0].datetime.time(0)
        dt_weekday = datetime.datetime.weekday(dt)
        print('%s, %s' % (dt.isoformat(), txt))


    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        # init stop loss and take profit order variables
        self.sl_order, self.tp_order = None, None
        self.datavolume = self.datas[0].volume
        self.pe = self.datas[0].pe


    def next(self):
        if self.datavolume[0] / np.mean(self.datavolume.get(size=10, ago=-1)) >= 1.25:
            self.buy()
            print(self.pe[0])



if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()


    data = btfeeds.GenericCSVData(
        dataname='C:\\Users\\oso\\Dropbox\\All Devices\\Datav1.csv',

        nullvalue=0.0,

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

        datetime=0,
        time=-1,
        high=2,
        low=3,
        open=1,
        close=4,
        volume=5,
        openinterest=6
    )

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

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Add a strategy
    cerebro.addstrategy(MyStrategy)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

    cerebro.plot(style='bar')

I am getting this weird error when running a backtest of minute data. There are 129k rows and it works fine for the most part until i start adding code for trailing stops and take profits and it runs out of memory.

I have a pretty powerful

Traceback (most recent call last):
  File "C:/Users/oso/Dropbox/All Devices/Backtest v1.1.py", line 328, in <module>
    cerebro.plot(style='bar')
  File "C:\Users\oso\AppData\Roaming\Python\Python27\site-packages\backtrader\cerebro.py", line 999, in plot
    start=start, end=end, use=use)
  File "C:\Users\oso\AppData\Roaming\Python\Python27\site-packages\backtrader\plot\plot.py", line 222, in plot
    self.plotdata(data, self.dplotsover[data])
  File "C:\Users\oso\AppData\Roaming\Python\Python27\site-packages\backtrader\plot\plot.py", line 725, in plotdata
    self.plotind(data, ind, subinds=self.dplotsover[ind], masterax=ax)
  File "C:\Users\oso\AppData\Roaming\Python\Python27\site-packages\backtrader\plot\plot.py", line 420, in plotind
    lplot = line.plotrange(self.pinf.xstart, self.pinf.xend)
  File "C:\Users\oso\AppData\Roaming\Python\Python27\site-packages\backtrader\linebuffer.py", line 332, in plotrange
    return self.array[start:end]
MemoryError

Here is the TP/SL code:

if (str(dt) in lst_ext) and tme == datetime.time(12,00,0,0):
                    self.close()
                    self.broker.cancel(self.sl_order)
                    self.broker.cancel(self.tp_order)


            else:

                # set stop loss and take profit prices
                # in case of trailing stops stop loss prices can be assigned based on current indicator value
                #Price Stop
                price_tp_long = self.position.price * 1.02
                price_tp_short = self.position.price * 0.98
                price_sl_long = .99
                price_sl_short = 1.01

                # cancel existing stop loss and take profit orders
                if self.tp_order:
                    self.broker.cancel(self.tp_order)

                #check & update stop loss order
                sl_price = 0
                # if price goes up and you are long move stop
                if self.position.size > 0 and (self.dataclose[0] - self.dataclose[-1]) > 0: sl_price = price_sl_long * self.position.price
                if self.position.size < 0 and (self.dataclose[0] - self.dataclose[-1]) < 0: sl_price = price_sl_short * self.position.price

                if sl_price != 0:
                    self.broker.cancel(self.sl_order)
                    self.sl_order = self.order_target_value(target=0.0, exectype=bt.Order.Stop, price=sl_price)


                # check & update take profit order
                tp_price = 0.0
                if self.position.size > 0 and price_tp_long != 0: tp_price = price_tp_long
                if self.position.size < 0 and price_tp_short != 0: tp_price = price_tp_short

                if tp_price != 0.0:
                    self.tp_order = self.order_target_value(target=0.0, exectype=bt.Order.Limit, price=tp_price)

and my main

if __name__ == '__main__':
    #Start the brain
    cerebro = bt.Cerebro()
    # Load Strat
    cerebro.addstrategy(Strat)

    #Start cash level
    cerebro.broker.setcash(10000.0)

    #Set multiplier and commish level
    cerebro.broker.setcommission(commission=0.79, margin=3000.0, mult=1000.0)




    #Get dataframe
    df = pd.DataFrame()

    #df = pd.DataFrame.from_csv('C:\\Users\\oso\\Dropbox\\Strats\\df.csv')

    data = bt.feeds.PandasData(dataname=df,timeframe=bt.TimeFrame.Minutes, compression=1)

    #Add Analyzers

    cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='tradeanalyizer')  # Analyzes individual trades
    cerebro.addanalyzer(bt.analyzers.SharpeRatio_A, _name='mysharpeA')  # Gets the annualized Sharpe ratio
    cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='myannualreturn')        # Annualized returns (does not work?)
    cerebro.addanalyzer(bt.analyzers.Returns, _name='myreturn')  # Returns
    cerebro.addanalyzer(bt.analyzers.DrawDown, _name='mydrawdown')  # Drawdown statistics
    cerebro.addanalyzer(bt.analyzers.VWR, _name='myvwr')  # Value at risk
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='mysharpe')  # Sharpe
    cerebro.addanalyzer(bt.analyzers.TimeDrawDown, _name='mydrawdowntime')
    cerebro.addanalyzer(bt.analyzers.SQN, _name='mysqn')
    cerebro.addanalyzer(bt.analyzers.Transactions, _name='trans')
    cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='myreturntme',timeframe=bt.TimeFrame.Months)

    #Load data to brain
    cerebro.adddata(data)

    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='mysharpe')

    results = cerebro.run()



    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())




    cerebro.plot(style='bar')

Any ideas how to solve?