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 = "stocks_nasdaq100", # competition type
lookback_period = 365, # how many calendar days are needed by the predict function to generate the output
start_date = "2005-01-01", # backtest start date
analyze = True,
build_plots = True # do you need the chart?
)

Questions

1. It seems that the models are trained in the train_period of the final data and that trained models are used in all the chunks of data, is that correct ?

2. Is the re-training after every retrain_interval done with incremental learning of the type that the fitting that partial_fit does (see following link) ?

https://datascience.stackexchange.com/questions/68599/incremental-learning-with-sklearn-warm-start-partial-fit-fit?

3. How can i provide qnbt.backtest_ml with and already trained model have the model trained with incremental learning every retrain_interval (would train_period = 0 and have train_model retruning the trained models work for this)