According to BlockBeats, industry experts highlight that machine learning in the crypto trading sector has not yet reached a widespread adoption phase akin to an 'iPhone moment.' However, AI-driven automated trading agents are rapidly approaching this critical juncture. With advancements in algorithm customization and reinforcement learning capabilities, the new generation of AI trading models is shifting focus from absolute profit and loss (P&L) to incorporating risk-adjusted metrics such as the Sharpe ratio, maximum drawdown, and value at risk (VaR) to dynamically balance risk and reward across various market conditions.
Michael Sena, Chief Marketing Officer at Recall Labs, noted that in recent AI trading competitions, specially customized and optimized trading agents significantly outperformed general large models, which only slightly surpassed the market when executing trades autonomously. The results indicate that specialized trading agents, enhanced with additional logic, reasoning, and data sources, are gradually surpassing basic models.
Nonetheless, the democratization of AI trading raises concerns about whether the advantage of Alpha will be quickly exhausted. Sena emphasized that those who can develop proprietary, specialized tools will continue to benefit in the long term. The most promising future form may be an AI-driven 'smart portfolio manager' that still allows users to set strategy preferences and risk parameters.