The idea is that the machine would learn what level to put stops at, and where to put take profits. For example, the machine gets input about security ABC and learns the best place to put a stop. It then modifies this stop, based on the risk (volatility maybe?) of other positions it is taking.
The point is that a machine learning algorithm would analyze its risk and learn where to put stops based on its strategy (any sort of basic strategy, this not what my question is about). It would find that it, for example, has an expectancy of +2% per trade that it takes. Unfortunately, it also learns that it can only reach such a high expectancy when it places its stops and take profits very far from the current price. This makes the trades take significantly longer than is optimal. The machine would then modify its risk management to optimize its expectancy to the optimal amount of time that it is holding positions. It is trying to find the best way to place stops to have a short position holding time, while still trying to achieve a high expectancy.
The formula to evaluate the success of an expectancy on certain holding period would be something like this:
Expectancy / Average_Holding_Period = Evaluated_Expectancy
Where the program is attempting to optimize
Does a system exist that works like this? If not, would it be easy to create, and would it be effective?