@anshul96go
To get the actual statistics you currently have to calculate them like so:

import qnt.stats as qns data = qndata.cryptodaily_load_data(min_date="2014-01-01") # or whenever your backtest started stats = qns.calc_stat(data, weights)

And if you really need them as xls file you can do:

stats.to_pandas().to_excel('stats.xls') # I got a ModuleNotFoundError the first time - pip install did the trick.

Allthough I can't recommend xls because at least LibreOffice becomes very slow / unresponsive when handling such a file.

Getting the statistics after a backtest could be a little simpler, which brings me to a feature request:
@support
Do you think you could add a parameter to the backtester which makes it return the statistics? They get calculated anyway by default, but we only see a truncated printout or the plots and can't use them for further analysis.
.
In my local environment I did it like this in qnt.backtester.py:

Add the parameter return_stats: bool = False to the parameters of the backtest function From line 353 onward my backtester now looks like this: qnout.write(result) qnstate.write(state) if return_stats: analyze = True out = [result] if analyze: log_info("---") stats = analyze_results(result, data, competition_type, build_plots, start_date) if return_stats: out.append(stats) if args_count > 1: out.append(state) if len(out) == 1: out = out[0] return out finally: qndc.set_max_datetime(None) And of course I made analyze_results return the statistics like so (line 458 in the original): if not build_plots: log_info(stat_global.to_pandas().tail()) return stat_global # here log_info("---") log_info("Calc stats per asset...") stat_per_asset = qnstat.calc_stat(data, output, per_asset=True) stat_per_asset = stat_per_asset.loc[output.time.values[0]:] if is_notebook(): build_plots_jupyter(output, stat_global, stat_per_asset) else: build_plots_dash(output, stat_global, stat_per_asset) return stat_global # and there

This might not be the most elegant solution but you get the idea.
Now I can get the statistics immediately after the backtest with

weights, stats = backtest(...return_stats=True)

and can do further analysis.
For instance, I started to calculate the correlations between my strategies to avoid uploading more of the same to the contest.

It would be nice to have this feature in a future version, so I don't have to mess with the backtester after each update 😉

Best regards

@cespadilla It is fine now. We announced the Q15 winners and changed some details, sorry for the problems.

@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.

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

@anshul96go Dear Anshul, it means that the weights for each day have to be generated in less than 10 minutes of time per day.

Note that all submissions are processed on the server after submission using a muti-pass approach (not single-pass).

10 minutes per day, times 250 days, times 10 years, that is more than 400 hours of running time.

@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 yes, they have

We managed to reproduce this issue and we fixed it.

@support Hello,

got it, thanks for the reply,

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

@stefanm Thank you for the details

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.

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

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

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

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

@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".

@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).

@support thanks, yes....

@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.