The product that purchasers learn with has low reproducibility, so I will organize it.

Regarding products that learners train themselves, there are criticisms that results vary by person and reproducibility is low. Our ‘GENSEN-AI’ is exactly a product that the buyer trains, so we will answer directly. This criticism uses the word “reproducibility” in only one sense. There are two meanings.

Meaning 1: Reproducibility of results.Do all buyers achieve similar results? Fixed-logic products run the same calculations for everyone, so this is high. I acknowledge that. However, fixed-logic reproduces the behavior of the market at the time it was created. As the market always changes, after changeseveryone’s results deteriorate and become the same. Including this deterioration, calling everyone identical as “high reproducibility” is Meaning 1. When deterioration occurs, buyers of fixed logic have two choices: wait for the seller’s update or repurchase.

Meaning 2: Reproducibility of the process.Can the same thing be produced with the same steps? We design it this way. The three steps—learning → filtering → placement—are fixed and shared by all buyers; what changes is the input data, namely your broker’s candlesticks. With the same data and the same steps, you get the same result.

So is it a flaw that each buyer ends up with different surviving AIs? As mentioned in a previous article, it is, in fact, the opposite. The AI that survives under your spread and rates is more accurate for your account than AI chosen under someone else’s environment. “All the same for everyone” and “best for you” cannot both be achieved. We choose the latter. If you want the former, buy fixed logic. However, in that case, everyone waits together from the day the market changes.