Best posts made by black.magmar

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