@support Thank you, yes, this is what I was looking for

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.

@steel-camel Sorry for the issue, it has been fixed.

@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.

@magenta-kabuto thx a lot bro for your support and pointing out the mistakes👍 🙂
I will try the revised code now.
Good luck for the competition 👍

@support Thank you very much. Please delete all my strategies in the deleted section.

@anshul96go Hi Anshul, the IS period starts on January 1st 2014. You can use a strategy with the following logic:

if Volume/OI is zero, then take this decision:

Otherwise, take this decision:

But yous algorithm should produce results also in the period when Volume/OI data were not available.

@support
Thanks !

@vyacheslav_b Thank you very much for your support.

I would like to ask, if I want to filter out the crypto codes with the highest sharpness, what should I do? Thank you. I tried using the get_best_instruments function but it didn't work

import qnt.stats as qnstats # data = qndata.stocks.load_ndx_data(tail = 17*365, dims = ("time", "field", "asset")) data = qndata.stocks.load_ndx_data(min_date="2005-01-01") def get_best_instruments(data, weights, top_size): # compute statistics: stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True) # calculate ranks of assets by "sharpe_ratio": ranks = (-stats_per_asset.sel(field="sharpe_ratio")).rank("asset") # select top assets by rank "top_period" days ago: top_period = 1 rank = ranks.isel(time=-top_period) top = rank.where(rank <= top_size).dropna("asset").asset # select top stats: top_stats = stats_per_asset.sel(asset=top.values) # print results: print("SR tail of the top assets:") display(top_stats.sel(field="sharpe_ratio").to_pandas().tail()) print("avg SR = ", top_stats[-top_period:].sel(field="sharpe_ratio").mean("asset")[-1].item()) display(top_stats) return top_stats.coords["asset"].values get_best_instruments(data, weights, 10)

19ae499c-71f3-4702-bba3-81d20fb6c5ac-image.png

Dear @angusslq,

Allocations are set at the end of the contest, and they take into account the strategies ranking at that time. We update the rankings as we process the participating strategies with the new available data, which happens 5 times a week for 5 working days. The allocations that you refer are probably in the systems page and they take into account the current ranking in the contest but that doesn't mean anything until the contest ends and prizes are given.

Regards

@magenta-kabuto Hi, yes, sorry for late answer. For the moment we can support only the default panda version you mention, sorry

@support Great news, thanks!

@support thanks, yes....

@antinomy Yes, it is fine, let us know if you see anomalies!

Hi @support, thank you! The box is now working fine!

I have a simple suggestion regarding the charts created for each strategy:

Besides the equity curve (strategy, SPX and Nasdaq100), Logarithmic scale, Long/Short, Underwater, and Bias, it would be nice to see an additonal chart (with two lines) showing the number of assets in each day that have a positive weight (long position), as well as the number of assets in each day that have a negative weight (short position).

This will help us developing the strategies, since we can better track the outcomes generated by different algorithms being constructed.

Also, this can help you identify asset hand-picking with buy-and-hold positions.

Thanks!

@joshua408 We allow development on our cloud or local development on user's machines. No need to open any port.

@support great! i can see the latest result. Do you know when Quantiacs will announce the Q22 result formally? Thanks.

@damnedlies Hello:

we are taking relative daily returns, in other words the ratio: [price(t)-price(t-1)]/price(t-1)

we are using no benchmark.

Note also that we apply reinvesting when computing the numerator of the Sharpe ratio (geometric mean)

Thanks 🙏