Dear magenta.kabuto,

It appears to be a pandas version mismatch. You can try using different pandas version but it's not documented nor supported and our library could behave in an unexpected manner.

@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.

@violet-mewtwo Dear violet-mewtwo, your submissions are processed correctly, it just needs some more time because of the big submission queue currently on our side. Thank you for your patience.

@support ok, I see, all my systems look processed btw

@commanderangle Dear commanderangle,

Your strategies are processed in a correct manner, but the reason why you see 0 out-of-sample score is due to the fact that your strategies generate zero weights for all assets for out-of-sample time period. You can check your weights for any strategy by downloading them. There is a download button in the submission logs section.

Regards

@support The webpage is fine now. Thank you for the prompt response!

@support yes, they have

@antinomy Hello, sorry for the delay in the answer, the problem has been fixed in the meanwhile. If correlations are too high, you will get a warning. Note that correlations will be checked against template code and submissions to past contests of the same kind (which are not present currently as this is the 1st crypto long-only contest we run).

@multi_byte-wildebeest Hello.

How to get the Sharpe Ratio is in the Quick Start template.
https://github.com/quantiacs/strategy-q20-nasdaq100-quick-start/blob/master/strategy.ipynb

import qnt.stats as qnstats def get_sharpe(market_data, weights): rr = qnstats.calc_relative_return(market_data, weights) sharpe = qnstats.calc_sharpe_ratio_annualized(rr).values[-1] return sharpe sharpe = get_sharpe(data, weights) # weights.sel(time=slice("2006-01-01",None))

or

import qnt.output as qnout qnout.check(weights, data, "stocks_nasdaq100")

or

stat = qnstats.calc_stat(data, weights) display(stat.to_pandas().tail())

or

import qnt.graph as qngraph statistics = qnstats.calc_stat(data, weights) display(statistics.to_pandas().tail()) performance = statistics.to_pandas()["equity"] qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log") display(statistics[-1:].sel(field=["sharpe_ratio"]).transpose().to_pandas()) qnstats.print_correlation(weights, data)

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

@support Hello,

got it, thanks for the reply,

Thank you for the report. The template has been updated.

@support Thank you for your advise, it's ok

@support thanks, yes....

@anshul96go Hello, when you submit, you have to select the competition type.

If you want to submit a Bitcoin system, you have to select "Cryptofutures".

If you want to submit a Futures system not including Bitcoin at all, you have to select "Futures".

@support Thank you for the prompt response. Time to get on with it. Thanks again.

@support Thank you for your feedback. I also hope Quantiacs updates new strategy examples on how to use technical analysis (besides sma, trix_ema, atr_lwma,...), and strategies on using ML/DL models effectively (not an example that strategy forward-looking),...

Hopefully in the future Quantiacs will release new data sets such as news, sentiment, macro, options,... Create new contests that allow merging strategies to build portfolios,...

Hopefully, Quantiacs will continue to grow. Sincere thanks to Quantiacs for creating extremely high-quality contests.

@jeppe_and Ok, thanks for the quick reply!

@support that is really useful! thank you very much!

@magenta-grimer thank you @support 🙇 🙇 🙇 🙇 🙇 🙇
My compliments four your exceptional python skills

@support
Thanks a lot !