@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

@omohyoid Dear omohyoid,

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

Regards

@eddiee Dear eddiee, no, please, for the moment do not resubmit. The timed out submissions are stored as timed out submissions and we can reprocess them. In case you need resubmission, we will let you know.

@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

@support Thank you !

@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

@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

And what if a strategy uses the following rules to select assets to trade:

the primary exchange is NAS the sector is not finance has price data for at least the previous 3 months has an average daily trading volume of at least 200 k based on the previous 3 months belongs to the top 100 of the thus far selected assets in terms of market capitalization

Would you say any of these rules violate the contest rules?

Because these are the selection criteria for the N100 constituents. The only difference in my strategy is that I'm using qnt.data.stocks_load_ndx_data.sel(field='is_liquid') instead.
Sure, the first of the rules above manually selects the exchange and the second one manually excludes a sector. But still none of these manually select assets and neither does the filter is_liquid from another dataset.

Also, lets take a look why you prohibited manual asset selection in the first place. Wasn't this to avoid lookahead bias? And isn't this also the reason for the existence of the field is_liquid in any of your datasets? Are you saying that the exact field you introduced to avoid lookahead bias is now the reason you disqualify a strategy because of lookahead bias just because it's from a dataset other than the one for the contest?

@xiaolan Ok, but please note that you can work all the time with xarray, the documentation is very good:

http://xarray.pydata.org/en/stable/

@magenta-grimer Hi, we cannot provide the list of strategies we are still trading and the payouts. However, all the statistics are public, the new ones (since Q15) and the old ones at:
https://legacy.quantiacs.com/Systems.aspx

@gjhernandezp yes, correct, 2/(n+1), sorry for the typo, thanks for correcting

@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.

@eddiee

Hello.

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

@gjhernandezp Thank you for sharing your solution!

@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

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

@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();

@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 !