Topics created by omohyoid

@antinomy
Hi! Don’t worry about leverage — it isn’t allowed on the Quantiacs platform: all user-supplied weights are automatically normalized when your strategy is saved. Here’s how that works with two instruments.
Source code of the normalize function https://github.com/quantiacs/toolbox/blob/main/qnt/output.py:

def normalize(output, per_asset=False): from qnt.data.common import ds output = output.where(np.isfinite(output)).fillna(0) if ds.TIME in output.dims: output = output.transpose(ds.TIME, ds.ASSET) output = output.loc[ np.sort(output.coords[ds.TIME].values), np.sort(output.coords[ds.ASSET].values) ] if per_asset: output = xr.where(output > 1, 1, output) output = xr.where(output < -1, -1, output) else: s = abs(output).sum(ds.ASSET) if ds.TIME in output.dims: s[s < 1] = 1 else: s = 1 if s < 1 else s output = output / s try: output = output.drop_vars(ds.FIELD) except ValueError: pass return output

Example with two assets

import xarray as xr from qnt.data.common import ds from qnt.output import normalize times = ['2025-06-16'] assets = ['Asset1', 'Asset2'] out1 = xr.DataArray([[1.5, 0.5]], coords={ds.TIME: times, ds.ASSET: assets}, dims=[ds.TIME, ds.ASSET]) print(normalize(out1).values) out2 = xr.DataArray([[0.3, -0.2]], coords={ds.TIME: times, ds.ASSET: assets}, dims=[ds.TIME, ds.ASSET]) print(normalize(out2).values)

Console output

[[0.75 0.25]] [[ 0.3 -0.2 ]]

Example 1: The absolute exposure is 2 > 1, so every weight is divided by 2, yielding 0.75 and 0.25.

Example 2: The exposure is 0.5 < 1, so the scaling factor is set to 1 and the weights stay 0.3 and –0.2.

In short, even if your strategy outputs more than 100 % exposure, normalize always scales it back so the total absolute exposure never exceeds 1—preventing leverage on the Quantiacs platform.

@support Thx for ur reply

@support I got it. Thanks for ur reply

Dear @omohyoid,

Currently we are facing some data issues, the error is not caused by changes in qnt library. At the moment, only current index members of S&P500 index are available, but Nasdaq100 stocks are temporarily unavailable. Sorry for the inconvenience, we are working on a solution.

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

@omohyoid Dear omohyoid,

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

Regards

@support I got it
Thanks for ur reply

@omohyoid Dear omohyoid,

You can send correlated strategies to the same contest but you cannot send a strategy that is highly correlated with the templates, your strategies from the past contests or prize winning strategies from previous contests.

Regards

@support Thx for ur reply
I'll try it

@omohyoid Dear omohyoid,

You don't need to specify the output path nor any other environment variable when submitting your code. If you're using local development option to create a strategy, you should remove setting environment variables manually since those are created automatically after you submit your strategy so no need to worry about that and it is actually advisable to do it that way.

Regards

@support Thanks for ur help

@support
Actually, I've write the weights to the output function.
螢幕擷取畫面 2024-04-24 235034.png
I think the reason might be that the data was out-of-date when the strategy received at the weekend. After the data update in the next day, it failed to pass the test.

Dear @quani42,

Your submissions are in the queue and will be processed. Also, all submissions that are sent to the contest before the deadline will be eligible to take part in it.

Regards