BlockBeats News, December 13 — According to CoinDesk, industry insiders point out that machine learning in the crypto trading sector has not yet reached a stage of widespread adoption comparable to an “iPhone moment,” but AI-driven automated trading agents are rapidly approaching this critical point. As algorithm customization and reinforcement learning capabilities improve, the new generation of AI trading models are no longer solely focused on absolute profit and loss (P&L), but are incorporating risk-adjusted metrics such as Sharpe ratio, maximum drawdown, and value at risk (VaR) to dynamically balance risk and return across different market environments. Recall Labs Chief Marketing Officer Michael Sena stated that in recent AI trading competitions, specially customized and optimized trading agents significantly outperform general large models, which only marginally beat the market when executing trades autonomously. Results show that dedicated trading agents with added logic, reasoning, and data sources are gradually surpassing basic models. However, the “democratization” of AI trading also raises concerns about whether the alpha edge will be quickly depleted. Sena pointed out that those who will truly benefit in the long term are institutions and individuals with resources to develop privatized, specialized tools. The most promising future form may be an “intelligent portfolio manager” driven by AI, yet still allowing users to set strategic preferences and risk parameters.
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AI agents accelerate their entry, and the "iPhone moment" in the crypto trading market is approaching
BlockBeats News, December 13 — According to CoinDesk, industry insiders point out that machine learning in the crypto trading sector has not yet reached a stage of widespread adoption comparable to an “iPhone moment,” but AI-driven automated trading agents are rapidly approaching this critical point. As algorithm customization and reinforcement learning capabilities improve, the new generation of AI trading models are no longer solely focused on absolute profit and loss (P&L), but are incorporating risk-adjusted metrics such as Sharpe ratio, maximum drawdown, and value at risk (VaR) to dynamically balance risk and return across different market environments. Recall Labs Chief Marketing Officer Michael Sena stated that in recent AI trading competitions, specially customized and optimized trading agents significantly outperform general large models, which only marginally beat the market when executing trades autonomously. Results show that dedicated trading agents with added logic, reasoning, and data sources are gradually surpassing basic models. However, the “democratization” of AI trading also raises concerns about whether the alpha edge will be quickly depleted. Sena pointed out that those who will truly benefit in the long term are institutions and individuals with resources to develop privatized, specialized tools. The most promising future form may be an “intelligent portfolio manager” driven by AI, yet still allowing users to set strategic preferences and risk parameters.