Now it's getting really interesting, thank you very much for sharing your knowledge with us/me.

To summarize your explanations:

1.0) Training:
1.1) - you open a new trade on every bar (no TP / SL), Zorro saves the returns of every trade/bar
1.2) - in your examples you convert the trade return results to binary 0/1
1.3) - you train your ml-model with the binary(0/1) signal as target
1.4) - the lifetime of the trade is regulated only by the parameter LifeTime

2.0) Testing / Trading:
2.1) - a far away stop outside of the "normal working area" is used as an emergency anchor
2.2) - the distance (TP/SL/both?) is then determined by average volatility
2.3) - the LifeTime parameter is ...?
2.4) - you close the trade by ...?

Questions:
I am really sorry but some parts of this approach are still unclear, if it is ok - I would like to ask more?

Q-1.2) you convert the trade returns to a binary outcome and train that 0/1 result.
But - in this way, you remove exactly the information as to how successful the predicted trade may be or am I wrong? Why?

Q-2.2) The distance (determined by average volatility) of the target / stop or both?

Q-2.3) The LifeTime parameter is now enabled/disabled in Testing / Trading?

Q-2.4) You close the trade by TP/SL or on opposite signal only?

The most confusing thing is the conversion into a 0/1 signal. If you were going to use different
classes (<= 10Pip || >10Pip/>20Pip...) as a target or make a regression on the trade return - I
would understand that. You already mentioned something similar.

Very interesting to talk about it, thank you!