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@bernardlin
I have something critical to point out.

Backtesting forex pairs trading has a major flaw. It uses a different way to calculate pnl of the backtest because the assets involved rarely have the same quote or base currencies.

The only exception to this is if the assets involved have the same base currency, e.g USDCHF USDJPY. If this pair appear to be cointegrated, then the returns of the backtest would be the same as calculating pnl of, say, GLD GDX.

But if the forex assets involved are of the same quote currency or of completely different currency bases and quotes, then the calculation of the pnl is completely different.

If one proceeds and backtest these cointegrated pairs the same way one do regularly then one would get the wrong result.

I am really keen to know how you went about backtesting cointegrated forex pairs and getting the correct result using Backtrader. I am yet to do that.

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strat = results[0] pyfoliozer = strat.analyzers.getbyname('pyfolio') returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()

This is what the backtrader analyzer can return and then you can the returns, positions, transactions, gross_lev as arguments for the pyfolio library.

@Tomas
I am also new to Backtrader and I got the same issue.

I tried to check the code and did the following changes to work:

Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage(self.datas[0].close, period = self.params.myperiod)

or
self.sma = bt.indicators.SimpleMovingAverage(self.dataclose, period = self.params.myperiod)

Good luck!

Have the same issue. Since no one updates backtraders code anymore, I will install a python3.9 virtual environment using anaconda. On the long run, I need am actively maintained replacement for backtrader.

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Ok guys, thanks to some friends of mine, it came out that I was wrongly using bracket orders.
I should have used the following logic:

if buy_condition: self.buy(size=self.p.size) self.limitOrderSL = self.sell(size=self.p.size, exectype=Order.Stop, price=stop_price) self.limitOrderTP = self.sell(size=self.p.size, exectype=Order.Limit, price=profit_price) if close_condition: self.close() self.cancel(self.limitOrderSL) self.cancel(self.limitOrderTP)

Regards

in the:

def next(self) ... if self.position: print(self.position.price)

it works for me, also in a parallel environment.

I know this is over a year old, but I’m currently working on this problem and will try to make a post on how to backtest futures once I’m done figuring it out.

As for the comment about orders getting rejected, this is usually caused by a price difference when the order is submitted and when the order actually tries to execute, like this:

Buy Submitted on 2022-07-27 - Price $55.50, Value $1,000.00, Size 18.02 Buy Completed on 2022-07-27 - Price $55.43, Value $998.74, Size 18.02

You can see there if the price is higher when it’s executed you won’t have enough cash available so the order will be rejected. As far as I know, the easiest way to fix this is to change the order target percentage from 1.0 to something like 0.99 so that there will be enough cash available for any slight price changes.