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AI Trading Models Enter Decisive Adoption Stage
According to BlockBeats, industry experts point out that the use of machine learning in cryptocurrency trading has not yet reached a mass adoption comparable to an "iPhone moment." Nevertheless, AI-based automated trading agents are rapidly advancing towards this turning point. With improvements in algorithm customization and reinforcement learning, this new generation of models prioritizes performance metrics adjusted for risk — such as Sharpe ratio, maximum drawdown, and Value at Risk (VaR) — instead of focusing solely on absolute profit and loss, allowing for a more dynamic balance between risk and return in different market scenarios.
Michael Sena, CMO of Recall Labs, highlighted that in recent AI trading competitions, highly specialized and fine-tuned agents significantly outperformed large generalist models. The latter only achieved slightly above-market results when operating autonomously. Data suggests that specific agents, reinforced with their own logic, reasoning ability, and multiple data sources, are progressively surpassing more generic solutions.
Still, the popularization of AI trading raises questions about the rapid dilution of competitive advantage (alpha). Sena emphasizes that participants capable of creating proprietary and highly specialized tools tend to maintain sustainable benefits in the long term. In this context, the most promising path seems to be the development of an AI-based "smart portfolio manager" that preserves user autonomy in defining strategies and risk limits.
#IA #BlockBeats $AI
According to BlockBeats, industry experts point out that the use of machine learning in cryptocurrency trading has not yet reached a mass adoption comparable to an "iPhone moment." Nevertheless, AI-based automated trading agents are rapidly advancing towards this turning point. With improvements in algorithm customization and reinforcement learning, this new generation of models prioritizes performance metrics adjusted for risk — such as Sharpe ratio, maximum drawdown, and Value at Risk (VaR) — instead of focusing solely on absolute profit and loss, allowing for a more dynamic balance between risk and return in different market scenarios.
Michael Sena, CMO of Recall Labs, highlighted that in recent AI trading competitions, highly specialized and fine-tuned agents significantly outperformed large generalist models. The latter only achieved slightly above-market results when operating autonomously. Data suggests that specific agents, reinforced with their own logic, reasoning ability, and multiple data sources, are progressively surpassing more generic solutions.
Still, the popularization of AI trading raises questions about the rapid dilution of competitive advantage (alpha). Sena emphasizes that participants capable of creating proprietary and highly specialized tools tend to maintain sustainable benefits in the long term. In this context, the most promising path seems to be the development of an AI-based "smart portfolio manager" that preserves user autonomy in defining strategies and risk limits.
#IA #BlockBeats $AI
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