There is no need for logic there and that's why there is none. The entry order (aka main side) is protected automatically with a stop-loss side and a take-profit side (the high/low side depends on the direction entry), which are only active if the entry order is executed. Once the stop-loss/take-profit sides become active, the execution of any of them automatically cancels the other. If you decide to exit the market with an unrelated order, you have to manually cancel any of the sides (the other will be cancelled automatically) as in self.cancel(order_stop_loss_side) # this automatically cancels the take-profit side too

@hiep-pham said in How is Getvalue() Calculated?: After spending a fair amount of time to make sense the relationship between order.executed.value vs (self.broker.getvalue() There isn't a relationship even if you look for one. One is for the order and the other is for the position. You can link them mentally, but they are not meant to be related. @hiep-pham said in How is Getvalue() Calculated?: that order.executed.value is the value of the current order position using the opening price of the NEXT period This is WRONG and this has nothing to do with broker.getvalue() order.executed.value is related to the execution price. If the execution price happens to be the opening price fine, but your definition is plainly wrong. @hiep-pham said in How is Getvalue() Calculated?: self.broker.getvalue() use the close price of the CURRENT period When you are in next, prices have already happened. The only price which can be used to calculate the last (and hence current) value is the last price, i.e.: the close

Thanks @backtrader. That will cover most cases. Maybe the SAREXT indicator should also be added? In my case I'm working with a very large datasheet so to avoid performance issues I've coded something like: def next(self): self.psar0._lookback = min(100, len(self.data0)) so I have a "dynamic" _lookback from 1 to 100 And why 100?... newbie here! just by observation, it seems to be a good value to calculate a real ParabolicSAR value.

@backtrader Very nice! One step closer to the quality of R's Quantstrat package. Will test it soon

In my particular case line._plotvalue = False helps. Thanks for flexible solution!

@backtrader Here is the link to the new post: link text

Not at the moment

@jacob said in Indicator using multi datafeed: operate only on overlapping bar: this is a great workaround for this issue/bug. This is not a workaround. It is clearly stated in the FAQ: Community - FAQ The system cannot know which timeframe/compression your data feed has unless you tell the system.

@Paska-Houso said in Genetic Optimization: The built-in optimization (cerebro.optstrategy) was always there from day one. This thread is about Genetic Optimization which implies not simply blindly traversing the cartesian product of all possibilities, but choosing and discarding paths based on outcomes of run. I.e.: If you have 3 parameters and one of the runs gives you a -50% profit, it is highly unlikely that slightly modifying one of the parameters for the next run is going to suddenly make you reach. It will likely deliver a result around -50%. You can therefore discard this part of the tree and focus on some other branches. Thanks!

@ab_trader @backtrader Thank you for your advises. I try to use BarReplayer_Open , but I can not understand how to use it. So I use cerebro.broker.set_coc(True), and I can get hopeful output. I put code and output in following. ・Code 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 the backtrader platform import backtrader as bt import backtrader.analyzers as btanalyzers import math import pandas as pd import talib import numpy as np PARAMS = ( ('maperiod', 15), #moving average period ('period', 15), # ('willperiod', 14), #moving average period ('sizer', None), ) class TestStrategy(bt.Strategy): params = PARAMS def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None # self.order = self.order_target_percent(target=0.1) self.buyprice = None self.buycomm = None self.sma = bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.maperiod) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enougth cash if order.status in [order.Completed, order.Canceled, order.Margin]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) # Write down: no pending order self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() class LongOnly(bt.Sizer): params = (('stake', 1),) def _getsizing(self, comminfo, cash, data, isbuy): if isbuy: divide = math.floor(cash/data.close[0]) # divide = math.floor(cash/data.open[1]) self.p.stake = divide # print(self.p.stake) # print(math.floor(cash/data.close[0])) 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__': cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Add Sizer cerebro.addsizer(LongOnly) # cerebro.broker.set_coc(True) # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join('datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # In order to buy on open you may want to # bt.filters.BarReplayer_Open(data) cerebro.broker.set_coc(True) # Set the commission cerebro.broker.setcommission(commission=0.0) # 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()) # Plot the result cerebro.plot() ・Output Starting Portfolio Value: 100000.00 2000-01-25, BUY CREATE, 26.61 2000-01-26, BUY EXECUTED, Price: 26.61, Cost: 99973.77, Comm 0.00 2000-01-27, SELL CREATE, 24.43 2000-01-28, SELL EXECUTED, Price: 24.43, Cost: 99973.77, Comm 0.00 2000-01-28, OPERATION PROFIT, GROSS -8190.26, NET -8190.26 2000-02-02, BUY CREATE, 25.61 2000-02-03, BUY EXECUTED, Price: 25.61, Cost: 91786.24, Comm 0.00 2000-02-18, SELL CREATE, 27.61 2000-02-22, SELL EXECUTED, Price: 27.61, Cost: 91786.24, Comm 0.00 2000-02-22, OPERATION PROFIT, GROSS 7168.00, NET 7168.00 2000-02-22, BUY CREATE, 27.97 2000-02-23, BUY EXECUTED, Price: 27.97, Cost: 98957.86, Comm 0.00 2000-03-30, SELL CREATE, 36.98 2000-03-31, SELL EXECUTED, Price: 36.98, Cost: 98957.86, Comm 0.00 2000-03-31, OPERATION PROFIT, GROSS 31877.38, NET 31877.38 2000-04-06, BUY CREATE, 38.75 2000-04-07, BUY EXECUTED, Price: 38.75, Cost: 130820.00, Comm 0.00 2000-04-11, SELL CREATE, 36.48 2000-04-12, SELL EXECUTED, Price: 36.48, Cost: 130820.00, Comm 0.00 2000-04-12, OPERATION PROFIT, GROSS -7663.52, NET -7663.52 2000-04-18, BUY CREATE, 37.22 2000-04-19, BUY EXECUTED, Price: 37.22, Cost: 123160.98, Comm 0.00 2000-04-19, SELL CREATE, 35.16 2000-04-20, SELL EXECUTED, Price: 35.16, Cost: 123160.98, Comm 0.00 2000-04-20, OPERATION PROFIT, GROSS -6816.54, NET -6816.54 2000-04-27, BUY CREATE, 36.45 2000-04-28, BUY EXECUTED, Price: 36.45, Cost: 116348.40, Comm 0.00 2000-05-04, SELL CREATE, 35.01 2000-05-05, SELL EXECUTED, Price: 35.01, Cost: 116348.40, Comm 0.00 2000-05-05, OPERATION PROFIT, GROSS -4596.48, NET -4596.48 2000-05-05, BUY CREATE, 36.22 2000-05-08, BUY EXECUTED, Price: 36.22, Cost: 111774.92, Comm 0.00 2000-05-08, SELL CREATE, 34.10 2000-05-09, SELL EXECUTED, Price: 34.10, Cost: 111774.92, Comm 0.00 2000-05-09, OPERATION PROFIT, GROSS -6542.32, NET -6542.32 2000-05-15, BUY CREATE, 36.31 2000-05-16, BUY EXECUTED, Price: 36.31, Cost: 105226.38, Comm 0.00 2000-05-18, SELL CREATE, 34.45 2000-05-19, SELL EXECUTED, Price: 34.45, Cost: 105226.38, Comm 0.00 2000-05-19, OPERATION PROFIT, GROSS -5390.28, NET -5390.28 2000-05-30, BUY CREATE, 34.98 2000-05-31, BUY EXECUTED, Price: 34.98, Cost: 99832.92, Comm 0.00 2000-06-22, SELL CREATE, 38.43 2000-06-23, SELL EXECUTED, Price: 38.43, Cost: 99832.92, Comm 0.00 2000-06-23, OPERATION PROFIT, GROSS 9846.30, NET 9846.30 2000-06-26, BUY CREATE, 38.99 2000-06-27, BUY EXECUTED, Price: 38.99, Cost: 109678.87, Comm 0.00 2000-06-27, SELL CREATE, 38.78 2000-06-28, SELL EXECUTED, Price: 38.78, Cost: 109678.87, Comm 0.00 2000-06-28, OPERATION PROFIT, GROSS -590.73, NET -590.73 2000-06-28, BUY CREATE, 39.11 2000-06-29, BUY EXECUTED, Price: 39.11, Cost: 109077.79, Comm 0.00 2000-06-29, SELL CREATE, 38.13 2000-06-30, SELL EXECUTED, Price: 38.13, Cost: 109077.79, Comm 0.00 2000-06-30, OPERATION PROFIT, GROSS -2733.22, NET -2733.22 2000-06-30, BUY CREATE, 39.64 2000-07-03, BUY EXECUTED, Price: 39.64, Cost: 106354.12, Comm 0.00 2000-07-03, SELL CREATE, 37.81 2000-07-05, SELL EXECUTED, Price: 37.81, Cost: 106354.12, Comm 0.00 2000-07-05, OPERATION PROFIT, GROSS -4909.89, NET -4909.89 2000-07-20, BUY CREATE, 36.84 2000-07-21, BUY EXECUTED, Price: 36.84, Cost: 101457.36, Comm 0.00 2000-07-21, SELL CREATE, 35.57 2000-07-24, SELL EXECUTED, Price: 35.57, Cost: 101457.36, Comm 0.00 2000-07-24, OPERATION PROFIT, GROSS -3497.58, NET -3497.58 2000-07-25, BUY CREATE, 35.83 2000-07-26, BUY EXECUTED, Price: 35.83, Cost: 97959.22, Comm 0.00 2000-07-27, SELL CREATE, 35.39 2000-07-28, SELL EXECUTED, Price: 35.39, Cost: 97959.22, Comm 0.00 2000-07-28, OPERATION PROFIT, GROSS -1202.96, NET -1202.96 2000-07-31, BUY CREATE, 35.45 2000-08-01, BUY EXECUTED, Price: 35.45, Cost: 96743.05, Comm 0.00 2000-08-01, SELL CREATE, 34.48 2000-08-02, SELL EXECUTED, Price: 34.48, Cost: 96743.05, Comm 0.00 2000-08-02, OPERATION PROFIT, GROSS -2647.13, NET -2647.13 2000-08-03, BUY CREATE, 36.51 2000-08-04, BUY EXECUTED, Price: 36.51, Cost: 94086.27, Comm 0.00 2000-09-08, SELL CREATE, 40.81 2000-09-11, SELL EXECUTED, Price: 40.81, Cost: 94086.27, Comm 0.00 2000-09-11, OPERATION PROFIT, GROSS 11081.10, NET 11081.10 2000-09-28, BUY CREATE, 38.42 2000-09-29, BUY EXECUTED, Price: 38.42, Cost: 105155.54, Comm 0.00 2000-09-29, SELL CREATE, 37.13 2000-10-02, SELL EXECUTED, Price: 37.13, Cost: 105155.54, Comm 0.00 2000-10-02, OPERATION PROFIT, GROSS -3530.73, NET -3530.73 2000-10-19, BUY CREATE, 34.30 2000-10-20, BUY EXECUTED, Price: 34.30, Cost: 101630.90, Comm 0.00 2000-10-30, SELL CREATE, 29.82 2000-10-31, SELL EXECUTED, Price: 29.82, Cost: 101630.90, Comm 0.00 2000-10-31, OPERATION PROFIT, GROSS -13274.24, NET -13274.24 2000-11-17, BUY CREATE, 27.17 2000-11-20, BUY EXECUTED, Price: 27.17, Cost: 88384.01, Comm 0.00 2000-11-20, SELL CREATE, 23.34 2000-11-21, SELL EXECUTED, Price: 23.34, Cost: 88384.01, Comm 0.00 2000-11-21, OPERATION PROFIT, GROSS -12458.99, NET -12458.99 2000-11-30, BUY CREATE, 24.99 2000-12-01, BUY EXECUTED, Price: 24.99, Cost: 75919.62, Comm 0.00 2000-12-14, SELL CREATE, 25.93 2000-12-15, SELL EXECUTED, Price: 25.93, Cost: 75919.62, Comm 0.00 2000-12-15, OPERATION PROFIT, GROSS 2855.72, NET 2855.72 2000-12-15, BUY CREATE, 26.93 2000-12-18, BUY EXECUTED, Price: 26.93, Cost: 78770.25, Comm 0.00 2000-12-20, SELL CREATE, 26.88 2000-12-21, SELL EXECUTED, Price: 26.88, Cost: 78770.25, Comm 0.00 2000-12-21, OPERATION PROFIT, GROSS -146.25, NET -146.25 2000-12-21, BUY CREATE, 27.82 2000-12-22, BUY EXECUTED, Price: 27.82, Cost: 78619.32, Comm 0.00 2000-12-29, SELL CREATE, 27.41 Final Portfolio Value: 77478.72

I don't know the size because I use order_target_percent.

See here for a longer post https://medium.com/@danjrod/custom-indicator-development-in-python-with-backtrader-bc775552dc3e

checksubmit controls if a submitted order can be accepted by the broker by simulating execution and with it checking if the current cash reserves are enough. It actually pseudo-executes all pending orders, because an order could actually be 1st opening a short and increasing the pseudo-cash reserves. But this is only pseudo-execution. When the actual execution takes place, the actual cash level is checked again and execution will be rejected if not possible.

My example was with daily bars, as I think is more close to the reality. At the end, forgetting about the -coc option for a moment, if you have a live order feed sending only daily bars, you could have that planned task at, for example, midday, that send the MOC order and gets executed with the close price of the daily bar. There is difference between backtesting with daily bars and having a live feed which delivers daily bars. The latter probably constantly updates the current daily bar and that's why you think that already at noon time it may be possible to send a MOC order. To achieve the same effect during backtesting you need one of this: intraday data and replay daily bars Set cheat-on-close and allow using the close price Use filters and break the feed into OHL + C steps (see the Pinkfish Challenge - and the sample in the sources) For the MOO I can send it to IB with kwargs as you´re proposing, but I cannot get the same effect with backtesting, basically because 1) Backtrader doesn´t support natively MOO orders (what about more order types support for the future? :slight_smile: ) or 2) as I don´t have "planning tasks capability" I should know which one is the last bar - 1 on the day, to send a Market Order, before it close, and simulate MOO behaviour... Yes you can. If you use daily bars, whenever you get a bar during backtesting, you are at the end of the session. Send for both cases (backtesting and live) a Market order and the extra kwargs. The standard backtesting broker will ignore it and the IB broker will overwrite the order type. if I do it with a background thread as proposed, I understand this is a real time capability, but no way to reproduce it on backtesting mode, isn´t? Not at the moment if implemented as points in lines (kind of time feed), wouldn´t be this more accurate to have same behaviour like in realtime with just 1 code? At least it would have the same behavior right now for both scenarios.

I see your use of get_ylim() and set_ylim() but could not find a parameter that would take advantage of this. Seems you use it for volume for this exact purpose.

@mula29 said in Oanda v20 API: Per Oanda support, the price needs to have not more than 5 decimal precision. In a market order, it works fine but requires more testing for Stoplimit and limit orders. I have commited today some updates. I hope, your issues are gone now. If not, please open a issue on github. For the precision, we have details for the traded instrument, so the price precision is known. We use these details for calculting position sizes. Thanks for the code!

Momentarily confused when trying out Kaber's code -- for those who care my code is python 2, and his code appears to be python 3. Changing super() to super(classname, self) successfully executes under python 2.

I bumped into similar situation. Looks like by default, backtrader is not touching DELAYED data and therefore inherits whatever "rightedge" convention from the original data feed, and in case of ibstore, it is "rightedge=False"; while for LIVE data part it will use the default convention of "rightedge=True", which causes the discrepancy. Many thanks for the tip from @dasch, now the bars match backteset/live trading.

@backtrader Oh man, you're awsome. Thanks for adding that code so quickly.

It's a blog post.