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This week’s research focused on whether strategies created measurable growth and outperformed simple holding, not whether they generated many trades. The strongest evidence came from CONVERT rotation, where unit growth remained the clearest advantage, although some results were concentrated in only one active plan. BUY/SELL strategies showed a different lesson: frequent trades and many wins can still fail when exits are too early or when losses are larger than gains. Several adaptive changes therefore focused on SELL or StopLoss logic rather than adding more entries. One proposed change was blocked by validation, which is an important reminder that AI assistance must remain auditable, scenario tested and reversible. The next review will focus on whether revised exits reduce quick recovery after exit and improve benchmark-relative performance.

This week’s CoinRobot research highlights the value of auditable trading logic. The strongest supplied evidence came from a CONVERT rotation strategy designed to move between relatively stronger assets, while several BUY/SELL strategies showed that a high win rate alone is not enough when exit timing and loss size are unfavorable. StopLoss behavior was a repeated concern: multiple strategies showed exits that recovered quickly, so the applied changes generally added confirmation or widened thresholds while preserving emergency exits. The key lesson is not that AI guarantees improvement. It is that users can express trading ideas as formulas, and AI-assisted revisions can remain visible, scenario-tested and measurable. The next review should focus on whether the new StopLoss confirmations reduce bounce exits and whether rotation frequency improves without weakening switch quality.