@multi_byte-wildebeest Hi, these limitations refer to the processing time per point in time, not for the full strategy.

If it takes 10 minutes per historical day, and the simulation has to take into account 250 days for let us say 10 years, the multi-pass simulation would process 6 days per hour, 144 days per real day, that means 2 weeks of processing time for the full submission, it is a lot of time.

@support

Thank you, I noticed some changes on the rules so I have to reimplement the strategy anyway.

@newbiequant9696 Hello. Everything works correctly in the online environment. Where do you run your code? Try recloning your strategy.

import qnt.data as qndata import qnt.stats as qns import qnt.graph as qngraph data = qndata.stocks.load_ndx_data(min_date="2005-01-01", assets=['NAS:GOOGL'])

12689361-ffcc-4ea5-b1b6-1c1704399adc-image.png

Dear @quani42,

Your submissions are in the queue and will be processed. Also, all submissions that are sent to the contest before the deadline will be eligible to take part in it.

Regards

@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.

@support
Actually, I've write the weights to the output function.
螢幕擷取畫面 2024-04-24 235034.png
I think the reason might be that the data was out-of-date when the strategy received at the weekend. After the data update in the next day, it failed to pass the test.

@newbiequant96 no problem.
I think the issue now is unrelated to the the previous issue. If you can show what is written above return code 1, I can maybe help.
It seems to be an issue in the code.
Regards

Dear @lookman,

Thank you for pointing at a problem with fundamental strategies. We are working on implementing a fix as soon as possible.

Regards

@buyers_are_back Hi,
the update was related to market_capitalization field availability and correctness, as vyacheslav_b described. Unfortunately, at the moment we cannot provide missing fundamental data, like number of ordinary shares, for some stocks (e.g. META). We are going to investigate potential new data sources in order to improve our datasets.
Regards,

@stefanm said in Collections has no attribute Iterable:

pip install progressbar2==3.55.0

Thank you so much @stefanm for support. Yes, it did work. Although on installation I did get these complaints:
qnt 0.0.306 requires tabulate>=0.9.0, but you have tabulate 0.8.10 which is incompatible.
qnt 0.0.306 requires xarray==0.20.2, but you have xarray 0.20.1 which is incompatible.

Also, my apologies for the late response. I had to put things aside for a day or so. Once again, thank you.

@machesterdragon Hello. Yes, it can never have too many examples 🙂

Inside the documentation sections, there are examples of strategies and technical indicators.
It's better to keep track of new strategy examples or updates on GitHub: https://github.com/quantiacs

@machesterdragon said in Cannot cast ufunc 'multiply' output from dtype('float64') to dtype('int64') with casting rule 'same_kind':

weights *= asset_filter

Hello. Change

weights *= asset_filter

to

weights = weights * asset_filter

You can see usage examples here:
https://quantiacs.com/documentation/en/user_guide/dynamic_assets_selection.html

Everything is working correctly now, try cloning the strategy.

@vg2001 Dear vg2001,
There were some problems with the servers, thank you for your patience.
Regards

@support Thank you !

@commanderangle Hi, the reason is that the algorithm is generating non-negative weights for assets which are not liquid at some point in time, in other words they are not part of the Nasdaq index at that time.

@multi_byte-wildebeest is it taking 2-3 minutes for each point in time?

@dark-pidgeot Hi, the question is a bit too generic, what do you need exactly to speed up?

@vyacheslav_b Thank you so much.

I have one more question for you to answer. I ran the precheck and the result was nan value the first time, but I set the min_date to 2005 - 01 - 01. I would like to ask, why is there a nan value problem? Is it because the ticker I chose had some companies that weren't listed at that time? My strategy id code is # 16767242. Thank you so much

Screenshot 2024-04-09 173002.png
Screenshot 2024-04-09 173012.png

@vg2001 of course you are right, thanks for pointing out the typo in the formula!

I finally resolved the issue, after lots of struggle. The custom layers, custom loss function and the function had to be serialized and deserialized correctly in order to save the architecture and weights as Json, rather than in a dictionary, like is suggested for pytorch in the Neural netowork template.
It seems Pytorch is way more user friendly when it comes to saving and loading models.