If the data cannot be downloaded, there will an attempt to open a cached version from a file with the same name as the ticker, which obviously does not exist. This is probably a Yahoo problem.

The sample code is incomplete but the presence of

@Benjamin-Kesby said in Optstrategy and PivotPoint indicator pickle error:

if __name__ == "__main__":
    pass

does clearly point in the direction indicated by @vladisld and the scoping problems that pickle has. The solution would be the same as the one outlined in the referenced post.

The indicators are tightly coupled to support things like managing different timeframes, remain synchronized to the data, play well with replay and resample.

Using them outside of a strategy is not possible. You can follow the path recommended by @lampalork or wait a bit until things get settled with bta-lib: https://btalib.backtrader.com

@xyshell said in Crossover indicator doesn't plot as expected:

    self.adx_crossover = bt.ind.CrossOver(
        self.adx_fast, self.adx_slow, plot=True, subplot=True)

Indicators are plotted subordinated to its master. The crossover happens on two subindicators of an indicator, so the data ceased to be the master a couple of levels before the current point.

Use plotmaster=self.data

See: Docs - Plotting - https://www.backtrader.com/docu/plotting/plotting/

@xyshell said in Crossover indicator doesn't plot as expected:

but adx_crossover indicator doesn't show up as expected.

Understanding how something doesn't show up as expected without knowing what's expected and what's actually happening is a challenging problem. A real conundrum.

@xyshell said in Crossover indicator doesn't plot as expected:

What's going on here?

A picture would probably let us experience what's actually going on.

The resampler filter wouldn't know what to do with extra lines (add, max, min, sub, multiply, take the 3rd one, take the last ...) and works only with the fields for which it has a rule, the standard ones.

You can create a 2nd data feed, assign your desired fields to open (keep 1st), high/low (keep max/min), close (keep last), volume (add) and use that resampled to match the timeframe of your master data feed.

Or create your own resampling filter.

@Mark-Weaver said in strategy.getposition returning different values for same ticker on different timeframes:

I expected

You want to do "algotrading", remove then "expect" from your vocabulary. As soon as possible.

@Mark-Weaver said in strategy.getposition returning different values for same ticker on different timeframes:

if I have a DataFeed for FCEL on a 5m aggregation, and I send a buy order through the broker API, what is it actually buying, that's different than, say, the underlying instrument behind the FCEL datafeed on a 1M aggregration?

You know nothing is different. But the code treats each single data feed as an independent data feed. Hence accounting is different.

Why should the code be making efforts to try to identify things as being the same thing? That will fail in an epic manner when the data feed provider provides no identification means.

Your AI is still believing that there are many other AI entities in place.

@Mark-Weaver said in strategy.getposition returning different values for same ticker on different timeframes:

Is that how it would work in live trading?

I don't know, I didn't write the alpaca broker, you will have to ask alpaca, but I guess it will be so.

@Mark-Weaver said in strategy.getposition returning different values for same ticker on different timeframes:

I don't see it as a parameter in the buy function

The buy method has a clear parameter data which is the target of your acquisition. Track anything you want on 5min and buy the 1min feed always (and check its position)

@Mark-Weaver said in strategy.getposition returning different values for same ticker on different timeframes:

So why would the broker interface return different position values for FCEL 5M vs FCEL 1M?

Because they are different data feeds.

@exu said in Order modification feature request:

To simulate that in BT, one would have to cancel the OCO orders and issue new ones with the recent limit price levels

Which effectively is what happens in the market. You make a change and your order goes to the back of the queue (as if it had been cancelled and then re-issued. If brokers didn't do that, they would be giving you an unfair advantage)

@exu said in Order modification feature request:

can you do that with only daily data0?

The timeframe is not relevant and is not looked into.

Since your other compression is 60*24 and 24 > 20, the answer is 0 (and depending on the stage of the resampling even partially 1, which is actually the future)

Your (A)I is far more advanced that the rest of the AIs in algotrading (non-existent AIs in fact) which makes you think that things are understood as you understand them.

@Mark-Weaver said in strategy.getposition returning different values for same ticker on different timeframes:

the posSize value is retrieved via

for i, d in enumerate(self.datas):
            posSize = self.getposition(d).size

Even if you know that they are the same "ticker" because you have personally added them, the code cannot know it. They are different data feeds, full stop. If they are the same or not is something you have to know and manage.

And for each data feed a separate position accounting is kept, hence your result.