@cespadilla Hello.

The reason is in "train_model" function.

def train_model(data): asset_name_all = data.coords['asset'].values features_all = get_features(data) target_all = get_target_classes(data) models = dict() 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') target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner') if len(features_cur.time) < 10: continue model = get_model() try: model.fit(feature_for_learn_df.values, target_for_learn_df) models[asset_name] = model except: logging.exception('model training failed') return models

If there are less than 10 features for training the model, then the model is not created (if len(features_cur.time) < 10).

This condition makes sense. I would not remove it.

The second thing that can affect is the retraining interval of the model ("retrain_interval").

weights = qnbt.backtest_ml( train=train_model, predict=predict_weights, train_period=2 *365, # the data length for training in calendar days retrain_interval=10 *365, # how often we have to retrain models (calendar days) retrain_interval_after_submit=1, # how often retrain models after submission during evaluation (calendar days) predict_each_day=False, # Is it necessary to call prediction for every day during backtesting? # Set it to true if you suspect that get_features is looking forward. competition_type='crypto_daily_long_short', # competition type lookback_period=365, # how many calendar days are needed by the predict function to generate the output start_date='2014-01-01', # backtest start date analyze = True, build_plots=True # do you need the chart? )

@vyacheslav_b Apologies for the late response. Thanks for the assistance, all is now well. Cheers

@xiaolan Yes, allocations are translate to orders internally, it is enough to check the variation in the allocations and transform it into number of contracts bought/sold. When we designed the toolbox the goal was to simplify development as much as possible for the users.

@vg2001 Hello, the Q19 is a replica of the Q18, you ccan use the same examples.

@buyers_are_back Hello.
Here is a new example of stock prediction using index data.
I recommend using the single-pass version.
https://quantiacs.com/documentation/en/data/indexes.html

It's safe to ignore these notices but if they bother you, you can set the variables together with your API key using the defaults and the messages go away:

import os os.environ['API_KEY'] = 'YOUR-API-KEY' os.environ['DATA_BASE_URL'] = 'https://data-api.quantiacs.io/' os.environ['CACHE_RETENTION'] = '7' os.environ['CACHE_DIR'] = 'data-cache'

@cross_platform-zebra Hi, there is no other limitation regarding local development. It is already configured to be exactly the same datasets for Nasdaq100 stocks, and returns the same statistics for trading system running locally or online.

@theflyingdutchman Hello, before the end of the week the update will be ready, sorry for the delay

@support thanks!!!

Thanks !

@magenta-kabuto The weights generated are simply the daily allocations to the various assets.

@support Thanks for ur help

@anthony_m we patched with spot BTC data see answer: https://quantiacs.com/community/topic/6/btc-contest-start-date

@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!