@illustrious-felice Hi, just insist and test other ideas, it is not easy but you will manage!

@support Hello. My strategy has the id #16934018 and was submitted in early May, but pnl OS has not been updated yet. Please check this issue. Thank you.

@support Thanks for your respond. Now I understand the cause and fixed it

@support Ok, I see, thanks

@auxiliary-snail Hi,

unfortunately, this is not allowed and in accordance with the rules. Using hard-coded time periods in which trading algorithm will work differently, is not a quantitative method (just like manual asset selection, e.g. "trade only Apple or Microsoft"). We still haven't implemented a mechanism for automatic recognition of such behaviors in trading strategies, and even though a strategy could be successfully submitted, it will not be eligible for prize winning.
What we are searching for, is well performing strategy over entire in_sample period (SR>0.7), robust to all market movements 2006-2025, so we can expect it will perform well in future, too.

@newbiequant96 Hi, the template is a "working code" still to be finalized and published among the templates in the account area, however the logic behind is strictly multi-pass and a conversion to single pass is not really so straightforward.

@support I see, need to push them to submit then....

@eddiee Hi, Mr. Eddie.

I am new to building strategies using ML/DL on Quantiacs and am very impressed with the OS performance of your ML strategies. I hope you can give me your contact (mail, limkedin,...) so I can learn from your experience in building an ML/DL strategy.

Sincerely thank.

@dark-pidgeot Yes, of course. Please note that we do not implement leverage, and the sum of the absolute values of the weights has to be equal or smaller than 1. If it is larger, they will be rescaled down.

@support Thank you so much. I have resolved this error

@support yes coarse heuristics work well as long as you are conservative. For shorter term models I have started using minute bars despite the computational hit, because it helps in a lot of other ways.

I may enter this contest, I am pretty rusty on predictive modelling and I am not sure I can do a good job using just daily prices, there is not a lot of data. I used to work at a CTA and I feel like we wasted a lot of man years using only prices, hoping better models would acheive more alpha. in the end the sharpe is similar to the S&P but uncorrelated, but you have gotten there with some simpler models and enjoyed life.

I have some other questions about the platform and the contest that I will post here.

Best
P.M.

@buyers_are_back Hello.
Here is a new example of stock prediction using index data.
I recommend using the single-pass version.
https://quantiacs.com/documentation/en/data/indexes.html

@support Thanks for ur help

@anthony_m we patched with spot BTC data see answer: https://quantiacs.com/community/topic/6/btc-contest-start-date

@rezhak21 Rules are defined at: https://quantiacs.com/contest and more details for the current contests (submission time till end of May) can be found at: https://quantiacs.com/contest/15

For Futures the in sample period starts on January 1st 2006, for the BTC Futures on January 1st, 2014

Thanks !

@vyacheslav_b Apologies for the late response. Thanks for the assistance, all is now well. Cheers

@support
Thanks a lot !

@nosaai Hello

Spyder should be run under conda environment

an alternative way is to clone the library from https://github.com/quantiacs/toolbox
and develop strategies inside qnt. But I recommend using the approach from the documentation.

@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