@brettelliot I would think the easiest way is to just pass start and end date arguments to data feed calls for each data set.

data = btfeeds.GenericCSVData( dataname=filename, fromdate=start_date, todate=end_date, ... )

@pradeepofc1
I have the same problem. There is no logs making by "next" method of the TestStrategy.

@new_trader Please share all of your code. Thanks.

@you Normally one would use boundoff as follows:

cerebro.resampledata( data, timeframe=bt.TimeFrame.Minutes, compression=240, boundoff=180 )

But sadly when I try to execute this in IB if doesn't work.

@ab_trader Ah of course - I was trying to spend more money than I had. Thanks for the pointer.

@atmps Sorry - what I meant in the last part is that you'll need to extend PandasData like so:

class PandasDataRev(bt.feeds.PandasData): lines=('rev',) params=( ('rev', None), )

Then you can use it like this:

for tkr, grp in df.groupby('ticker'): data = PandasDataRev( dataname=grp, datetime='date', close='close', rev='rev', open=None, high=None, low=None, volume=None, openinterest=None, ) cerebro.adddata(data, name=tkr)

You will need to figure out how to reshape your data from how you have it in Excel to a form where it's easy to iterate through it ticker by ticker. There are many ways you can do that. You could load it in Pandas and "unpivot" the data or just use it in the form you have but the indexing will be different.

And to your last question, you would access each dataset as self.datas[n].close[0] or self.datas[n].rev[0] where n is an integer for the n-th ticker. (You are loading one data feed for each ticker so you have n feeds now).

@run-out Thanks I must have missed that.

@punajunior My guess is that this error is thrown when it's trying to parse the "Open" field which defaults to column 1, so after parsing the datetime correctly, it's then trying to parse that same field as a float. Try adding explicit column indicators for all fields in your call to GenericCSVData:
datetime=1,open=2,high=3,low=4,close=5,volume=6,openinterest=-1,

BTW, it looks like you created the CSV file using Pandas .to_csv() method without the index=False parameter. That's why it has that unnamed first column. If you can recreate the file without the index, this probably would have just worked.

@run-out By both sides I mean before and after.

@kuky You can resample your data to the other edge. Keep timeframe and compression the same and just resample to rightedge = true. At least I think this would work.

@adamb032 When I was reading the docs today it says that the first bar will be open to close if open is available, otherwise not data exist. Not sure if this works but try setting firstopen to False.

- ``firstopen`` (default: ``True``) When tracking the returns of a ``data`` the following is done when crossing a timeframe boundary, for example ``Years``: - Last ``close`` of previous year is used as the reference price to see the return in the current year The problem is the 1st calculation, because the data has** no previous** closing price. As such and when this parameter is ``True`` the *opening* price will be used for the 1st calculation. This requires the data feed to have an ``open`` price (for ``close`` the standard [0] notation will be used without reference to a field price) Else the initial close will be used.

@davidavr You can definitely load data in that way. The performance of course will depend on many factors. Let us know how goes.

@heinrich
Here the corresponding data: https://drive.google.com/file/d/1jWMhpJGeXE8i_mU7rpgWEZ6Dk3uyiD8y/view?usp=sharing in case somebody would like to have look.
The data should be continues. I got them via one of the ccxt examples.
I started directly with the IB and the CCXTStore in live mode and I am impressed how easy it is to implement multivariate and multi-frequency models. Something around these lines,

store = CCXTStore(exchange='binance', currency='BNB', config=config, retries=5, debug=False) data = store.getdata(dataname="BTC/USDT", name="BTCUSDT", timeframe=timeframe, fromdate=hist_start_date, compression=5, ohlcv_limit=ohlcv_limit, drop_newest=False) data1 = store.getdata(dataname="BTC/USDT", name="BTCUSDT", timeframe=timeframe, fromdate=hist_start_date, compression=15, ohlcv_limit=ohlcv_limit, drop_newest=False)

works nicely(Besides the given granularity and the reliance on the OHLCV bars).
But I struggle a bit with the import from text.
Related to this, shouldn't in the provided data-resample.py example,

python data-resample/data-resample.py --timeframe daily --compression 7
and
python data-resample/data-resample.py --timeframe weekly --compression 1
yield the same result?res_backtrader.png

@yacc2000 That code is just expanding the buffer with empty values (NAN). Later in the routine, the value is appended to the buffer. If you notice, even if PANDIZE is false, it's still just expanding the buffer with NAN values, this time using a numpy array vs. a Pandas Series.

@davidavr great