@violet-mewtwo Thanks, you are right and we will work in that direction. Best regards

@violet-mewtwo Dear violet-mewtwo, your submissions are processed correctly, it just needs some more time because of the big submission queue currently on our side. Thank you for your patience.

@support Ohh, I understand. Thank you for your support.

@support Thank you very much. Please delete all my strategies in the deleted section.

@magenta-kabuto Thank you very much for your advice. I will research to apply your suggestions to the algorithm

@magenta-kabuto Hi, yes, sorry for late answer. For the moment we can support only the default panda version you mention, sorry

@support
Thanks a lot !

@yonasbo Hi, sorry for delay, we will start soon a new contest, in the next 2 weeks

@cross_platform-zebra Hi, there is no other limitation regarding local development. It is already configured to be exactly the same datasets for Nasdaq100 stocks, and returns the same statistics for trading system running locally or online.

@scalpingaf Correct, all trades (buy or sell) are taken at the open of the next day you take the decision.

@algotime Dear Algotime, all three strategies will participate in the contest. We will update the contest leaderboard once all eligible strategies have finished processing. Thank you for your patience.

Dear magenta.kabuto, it will be done at the open price in t+1 time.

@cyan-gloom Hi, we are busy with checking the submissions which are coming, as we are close to the end of the Q20 contest, sorry if we were not responsive so fast. We will inform as soon as we are ready.

@buyers_are_back We reprocessed the submission, it is formally correct and passes all the filters. Sorry for the issue, evidently on our side.

@cyan-gloom

Hello. The provided code is insufficient to understand the problem.

I assume that a certain function might not be returning the required value (for instance, the function where your model is being created).

I recommend that you check all return values of functions, using tools like display or print. Then, compare them with what is returned in properly working examples.

The state allows you to use data from previous iterations. You can find an example here:
https://github.com/quantiacs/toolbox/blob/2f4c42e33c7ce789dfad5d170444fd542e28c8ae/qnt/examples/004-strategy-futures-multipass-stateful.py

@magenta-grimer hi, you can find one very simple example here:

https://quantiacs.com/documentation/en/data/fundamental.html

best regards

@support Thanks!

@captain-nidoran Fixed, sorry for issue

@theflyingdutchman

it can help you. See topic 3) Not enough bid information.

https://github.com/quantiacs/strategy-q20-nasdaq100-quick-start/blob/master/strategy.ipynb

def get_enough_bid_for(data_, weights_): time_traded = weights_.time[abs(weights_).fillna(0).sum('asset') > 0] is_strategy_traded = len(time_traded) if is_strategy_traded: return xr.where(weights_.time < time_traded.min(), data_.sel(field="is_liquid"), weights_) return weights_ weights_new = get_enough_bid_for(data, weights) weights_new = weights_new.sel(time=slice("2006-01-01",None))