@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))

@eddiee

Hello.

This code looks to the future.
It is needed to train the model.
Pay attention to the name of the variable.

@magenta-kabuto thx a lot bro for your support and pointing out the mistakes👍 🙂
I will try the revised code now.
Good luck for the competition 👍

@support
Thanks !

@EDDIEE
Hello, sorry for late answer, for now it is best to use indicators shown in the table at https://quantiacs.com/documentation/en/data/fundamental.html. Recent qnt library version has obsolete methods for getting the list of all available fundamentals, it will be fixed in next qnt update.

@cyan-gloom
Hi, sorry for late answer. This number shows the total number of instruments that weights have been allocated to, at any point of entire time period. In your case, it means that 87 different instruments have been ranked in top5 or bottom5 by Sharpe Ratio.

@steel-camel Sorry for the issue, it has been fixed.