AI-powered trading is evolving but hasn’t yet hit its “iPhone moment.” Experts suggest we are approaching a time when algorithmic portfolio managers become widely accessible. Unlike self-driving AI, trading is an adversarial landscape, making model refinement complex. Traditional success metrics focus on profit and loss (P&L), but new developments allow for more sophisticated models that consider risk-adjusted metrics like the Sharpe Ratio. As Michael Sena from Recall Labs points out, customizing algorithms to user preferences enhances trading effectiveness.
Recent competitions on decentralized exchanges, such as Hyperliquid, showcased various large language models (LLMs), revealing that specialized trading agents often outperform base models. However, this democratization of AI raises questions about sustaining alpha in a crowded marketplace. The future likely belongs to those who can invest in custom trading tools, protecting their unique strategies and insights. The ideal AI trading product would blend robust portfolio management with user-defined parameters for a personalized trading experience.
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