🔥$VIRTUAL $KITE $FET On December 13, according to CoinDesk, industry insiders noted that machine learning in the crypto trading space has not yet reached a widespread adoption phase akin to an "iPhone moment," but AI-driven automated trading agents are rapidly approaching this tipping point. As algorithm customization and reinforcement learning capabilities improve, a new generation of AI trading models is beginning to move beyond simply pursuing absolute profit and loss (P&L), instead incorporating risk-adjusted metrics such as the Sharpe ratio, maximum drawdown, and value at risk (VaR) to dynamically balance risk and return across different market conditions.

Michael Sena, Chief Marketing Officer at Recall Labs, said that in recent AI trading competitions, specially customized and optimized trading agents have clearly outperformed general large models, which only slightly beat the market when executing trades autonomously. The results show that specialized trading agents—enhanced with additional logic, reasoning, and data sources—are gradually surpassing base models.

However, the "democratization" of AI trading has also raised concerns about whether alpha advantages might be quickly eroded. Sena pointed out that those who will truly benefit in the long term will still be institutions and individuals with the resources to develop private, specialized tools. The most promising future form may be an AI-driven "smart portfolio manager" that still allows users to set their own strategy preferences and risk parameter