Hi,
Your Machine Learning - LSTM strategy seems to be forward-looking. You train the model using feature_for_learn_df, then you calculate the prediction for features_cur (with timestamp in the past), and then you use the predictions as the weights for those same past timestamps (weights.loc[dict(asset=asset_name, time=features_cur.time.values)] = prediction). In this way you have weights as values from a model that has seen values from the future (from the point of view of the timestamp of the weight).
Thank you

Thank you for your prompt reply. The model is trained correctly, by shifting the label/target column, but if you use the predictions (for the training set timestamps) as the weights for those timestamps, they use information that would not be available in reality.
Because the training set contains data up to the latest timestamps for the slice, while any weight should be derived by looking exclusively at previous timestamps data.
However, I do not know the details of how the qnbt.backtest_ml function does slice the data, but if it uses the weights as they are returned from the predict(models, data) function, then they might be forward-looking. I will look at the single-pass version , that may be different.

Hi,
Your Machine Learning - LSTM strategy seems to be forward-looking. You train the model using feature_for_learn_df, then you calculate the prediction for features_cur (with timestamp in the past), and then you use the predictions as the weights for those same past timestamps (weights.loc[dict(asset=asset_name, time=features_cur.time.values)] = prediction). In this way you have weights as values from a model that has seen values from the future (from the point of view of the timestamp of the weight).
Thank you