I'm trying to understand the rationale behind using information drive bars over traditional time bars and specifically when it comes to practically feeding those in to a machine learning model to run a regression.
I was wondering, how could one feed alternative bars into a machine learning model? If we use time bars, when we run prediction, we will get a prediction on n number of time steps, but we already know our time step, whether it's a minute, hourly or daily prices.
If for example we use dollar bars instead, our sampling is at non fixed time intervals, but rather on the dollar volume traded. So we don't know what the time step is.
Therefore if we feed this into our model and then run to predict, what will the output be? I'm guessing that each 'step' will be every time a certain amount of dollar value is traded. Correct me if I'm wrong.
How then would the trader know when to enter or exit a position? Is the idea to constantly monitor the trading activity? i.e whenever the specified dollar value is traded? How would that work practically?