@omohyoid Dear omohyoid,

Yes, that's right. After submitting your strategy shouldn't override environment variables.

Regards

@eddiee

Hello.

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

@sheikh It is fine, please just submit, check the result and let us know if you see any issue. It should work fine.

@support Great help! Thank you so much!

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

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

@antinomy
Thanks for your advice !

@support Thanks for the detailed answer, that seems to be it, here is the final code:

import xarray as xr import qnt.stats as qns import qnt.output as qnout import qnt.data as qndata # single-stock trading data = qndata.futures.load_data(min_date="2005-01-01", assets=["F_ES"]) # attempting an optimal (unrealistic) long-only strategy # by looking at future prices, and investing only if there will be profit next_price_open = data.sel(field="open").shift(time=-1) next2_price_open = data.sel(field="open").shift(time=-2) weights = xr.where(next_price_open < next2_price_open, 1.0, 0.0) # sell short when optimal: # weights = xr.where(next_price_open > next2_price_open, -1.0, weights) weights = qnout.clean(weights, data) qnout.check(weights, data) qnout.write(weights) stats = qns.calc_stat( data, weights, # ignoring slippage for simplicity slippage_factor=0, roll_slippage_factor=0) stats.loc[:, "equity"].plot.step();

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

@support no worries, I've been able to recover the strategy thank you!

@theflyingdutchman Yes, we are integrating new data sources for a new asset class, once we are done (next week) the data and leaderboard updates will start again.

@algotime Hello, on 1st November allocations will start, you will receive a mail soon today!

@sun-73 @RoyPalo, Hi,

Q23 Leaderboard was updated several days ago, all eligible submissions are there now, sorry for late notice. Please let us know if you find any submission that is missing.

@lookman Hello. Try cloning your strategy and running it again. It should work correctly with the new version of the qnt library.

import qnt.data as qndata import qnt.data.secgov_fundamental as fundamental market_data = qndata.stocks.load_spx_data(min_date="2005-01-01") indicators_data = fundamental.load_indicators_for(market_data, indicator_names=['roe']) display(indicators_data.sel(field="roe").to_pandas().tail(2)) display(indicators_data.sel(asset='NAS:AAPL').to_pandas().tail(2)) display(indicators_data.sel(asset=['NAS:AAPL']).sel(field="roe").to_pandas().tail(2))

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

@eddiee

This code works for me. I can give you ideas on what to try.

Update the qnt library or reinstall.

If it doesn't help, clone the repository

https://github.com/quantiacs/toolbox

git clone https://github.com/quantiacs/toolbox.git

run
qnt/examples/005-01-optimizer.py
and other examples.

You may need to specify API_KEY

You might be able to see exactly where the error occurs in the code.
And you can modify the library code by adding logging for optimize_strategy

@illustrious-felice Hello. please look at this post
https://quantiacs.com/community/topic/515/what-is-forward-looking-and-why-it-s-effective-badly-to-strategy/6?_=1711712434795

@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

@omohyoid Hi, we do not have such calls, sorry

@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.

@gjhernandezp Thank you for sharing your solution!