@dark-pidgeot Yes, of course. Please note that we do not implement leverage, and the sum of the absolute values of the weights has to be equal or smaller than 1. If it is larger, they will be rescaled down.

@captain-nidoran Fixed, sorry for issue

@support Got it, thanks a lot!

@support yes coarse heuristics work well as long as you are conservative. For shorter term models I have started using minute bars despite the computational hit, because it helps in a lot of other ways.

I may enter this contest, I am pretty rusty on predictive modelling and I am not sure I can do a good job using just daily prices, there is not a lot of data. I used to work at a CTA and I feel like we wasted a lot of man years using only prices, hoping better models would acheive more alpha. in the end the sharpe is similar to the S&P but uncorrelated, but you have gotten there with some simpler models and enjoyed life.

I have some other questions about the platform and the contest that I will post here.

Best
P.M.

@support Ok, I see, thanks

@support Thank you so much. I have resolved this error

@nosaai Hello

Spyder should be run under conda environment

conda activate qntdev conda install spyder spyder

an alternative way is to clone the library from https://github.com/quantiacs/toolbox
and develop strategies inside qnt. But I recommend using the approach from the documentation.

@multi_byte-wildebeest Hello. I don't use machine learning models in trading.

@support
Thanks.
You guys are the best!🏆

@support ok, thank you!

@support

This is a possible fix, but no gurantee. You have to adjust also the prediction function.

def train_model(data):
"""Create and train the models working on an asset-by-asset basis."""

models = dict()

asset_name_all = data.coords['asset'].values

data = data.sel(time=slice('2013-05-01',None)) # cut the noisy data head before 2013-05-01

features_all = get_features(data)
target_all = get_target_classes(data)

model = create_model()

for asset_name in asset_name_all:

# drop missing values: target_cur = target_all.sel(asset=asset_name).dropna('time', 'any') features_cur = features_all.sel(asset=asset_name).dropna('time', 'any') # align features and targets: target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner') if len(features_cur.time) < 10: # not enough points for training continue try: model.fit(feature_for_learn_df.values, target_for_learn_df) models[asset_name] = model except KeyboardInterrupt as e: raise e except: logging.exception('model training failed')

return models

@buyers_are_back Hi,

Regarding your first question, yes, that is correct. As we look more into the past, it is more difficult to get data for companies which have been index members but don't exist anymore, for example. This is also related to your second question - symbols with '~1' are in almost all cases, the same companies with the same ticker symbol, but with different ISIN (International Securities Identification Number). For instance, SanDisk company ("NAS:SNDK") was standalone public company until 2016, when Western Digital acquired SanDisk. In 2025 company spinoff, SanDisk re-emerged on the Nasdaq as an independent public company, with the same ticker as it was ('SNDK'), but with different ISIN (considered as different company).
Those symbol pairs, should not have an intersection in membership ("is_liquid" field should not be 1.0 for both at the same time), otherwise it could be mistake by provider.

@newbiequant96 Hi, the template is a "working code" still to be finalized and published among the templates in the account area, however the logic behind is strictly multi-pass and a conversion to single pass is not really so straightforward.

@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.

@support thanks, yes....

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

@eddiee Dear Eddiee, yes, the rules and the universe are the same. We will need some more time to extend the universe and the data set, so we decided to run a new contest with the same rules.

Please note that according to the rules at https://quantiacs.com/contest/19

A Trading System will be deemed to be a “unique“ Trading System if it was not submitted by the same user to a previous Contest and it was not published by the Sponsor itself and it was not submitted by another user to a previous Contest or to the current Contest. The Sponsor will run on submissions a correlation filter and will have to right to disqualify submissions which are not deemed to be unique.

So re-submitting the same system will result into a system which is not eligible for a prize.

@magenta-grimer

Hello.

Remove .isel(time=-1).

ma_slow = close.rolling(time=parameter1).mean() #.isel(time=-1) ma_fast = close.rolling(time=parameter2).mean()#.isel(time=-1)

It selects the last day, you need an entire series.

Regards.

@magenta-grimer

Hello.

I confirm this bug.
It is fixed now.
If you clone this template again, it will work ok.

Thank you very much for your report.

@antinomy wow, thank you so much, this is awesome!