Problem with resampling 1m data to 5m/15m

ppawel

I have 1m OHLC data and would like to compress that into 5m and 15m data. I know Backtrader has resampling and I tried different options but I still can't get it to align correctly with my data and charts at TradingView.

What I want to achieve:

1m bars 17:00-17:04 (inclusive) -> 5m bar dated 17:00

What Backtrader does by default:

1m bars 17:01-17:05 (inclusive) -> 5m bar dated 17:05

Obviously this gives me OHLC data that is not matching what I want so I used the boundoff=1 option. This results in correct OHLC for the 5m bars but they are dated incorrectly for my needs, like this:

1m bars 17:00-17:04 (inclusive) -> 5m bar dated 17:04

I tried other resampling options but perhaps I don't fully understand them and I could not get the result I want.

Could you give me some hints if it can be achieved before I dive into the Backtrader code?

backtrader

You want rightedge=False. Docs - Data Resampling

But what you probably really want is to resample your data outise of backtrader and thenn lod it, to avoid a problem with the convention.

ppawel

@backtrader I tried that already and it does not seem to have any effect - I get the same results with either rightedge=false or true (default I guess).

Might that be a conflict of this option with boundoff=1?

In any case, I would prefer to avoid processing the data outside of Backtrader because I already am storing 1m bar data so no point storing 5m as well. So I'm thinking about writing a data source for Backtrader which converts this automatically or adding an option to the resampling method that outputs correct bars for my use case. If I would make it work with resampling, does it make sense to put it in a PR?

backtrader

There were several patches to resampling, which probably had an impact on rightedge.

Use this code as a template to achieve your desired effect.

    rd = cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=5)

    def _new_load(self):
        ret = self._old_load()
        if ret:
            dt = self.datetime.datetime(0)
            self.datetime[0] = bt.date2num(dt - datetime.timedelta(minutes=5))

        return ret

    rd._old_load, rd._load = rd._load, _new_load.__get__(rd, rd.__class__)

lexar

Has there been any update to this code? I tried the suggested code and it still doesn't work.

Basically I am trying to have resampling of 1m to 1h.
So as an example the close of 1m at 10:59 should be the 1h close of 10:00.

However I see that the 1m close at 10:59 becomes the 1h close of 11:00.

How can I fix this?

bigdavediode

Lexar, having a minute bar from 10:59 go back in time to generate a result at 10:00 is a good way to make a system that won't work.

balibou

Thanks for this solution @backtrader it works like a charm !
I was wondering how you can pass an argument to the new_load function in order to deal with a variable minute (for dealing with multiple timeframes in a strategy).

I looked on stack overflow and I tried this (but got this error AttributeError: 'Lines_LineSeries_DataSeries_OHLC_OHLCDateTime_Abst' object has no attribute '_old_load'):

def _new_load(self, minutes):
    ret = self._old_load()
    if ret:
        datetime = self.datetime.datetime(0)
        self.datetime[0] = bt.date2num(datetime - timedelta(minutes=minutes - 1))

    return ret

minutes = 240
rd = cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=minutes)
rd._old_lo
ad, rd._load = rd._load, _new_load.__get__(rd, rd.__class__)(minutes)

Thanks :)