If you make AI "predict" prices, you’ll lose. So I limited it to three answers

When people hear AI trading, many imagine price-prediction type that guesses “what it will be worth next.” We did not adopt this method. Here is why.

Reason 1: The definition of “hit” can be made ambiguous.Performance for prediction types is shown as something like “hit within X pips error,” but this expression leaves a lot of room for adjustment. What point is used as the baseline? How many pips is the error range? What percentage of all predictions fall within that range (and that’s a matter of the denominator). Depending on the baseline and how you set the range, you can manufacture different “hit rates” from the same prediction results. The structure that creates numbers is the very target this series has repeatedly warned about.

Reason 2: Even if the prediction is correct, it does not provide a trading answer.Even a flawless forecast like “up 50 pips in 24 hours” has a path where the price retraces −80 pips in the meantime; your position would hit stop loss and exit with a loss. There is a gap between price prediction and the trading decision of when to enter and exit.

Therefore, the output of ‘GENSEN-AI’ focuses on action, not price. Buy, sell, or pass. Each signal comes with take-profit and stop-loss from the start, and the result is always settled as win or loss. Because it settles, you can perform machine-quantification with the number of settlements as the denominator. The “hit” of a prediction can be manufactured by definition, but fixed TP/SL outcomes cannot be manufactured. It can be said that the design pushes you into a form that can be validated.