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?
)