AI-powered trading hasn’t yet reached the “iPhone moment,” where everyone carries an algorithmic and reinforcement learning portfolio manager in their pocket, but something like that is coming, experts say.
In fact, the power of AI finds its match when faced with the dynamic and conflictive scenario of trading markets. Unlike an AI agent informed by endless loops of self-driving cars that learn to accurately recognize traffic signs, no amount of data and models will be able to predict the future.
This makes perfecting AI business models a complex and demanding process. The measure of success has typically been measuring profit and loss (P&L). But advances in how to customize algorithms are producing agents that continually learn to balance risk and reward when faced with a multitude of market conditions.
Allowing risk-adjusted metrics, such as the Sharpe ratio, to inform the learning process multiplies the sophistication of a test, said Michael Sena, chief marketing officer at Recall Labs, a company that has run about 20 AI trading arenas, where a community sends AI trading agents, and those agents compete over a period of four or five days.
“When it comes to exploring the market for alpha, the next generation of builders is exploring customization and specialization of algorithms, taking into account user preferences,” Sena said in an interview. “Being optimized for a particular index and not just gross P&L is more like the way leading financial institutions work in traditional markets. So considering things like, what is your maximum drawdown, how much was your value at risk to achieve these P&L?”
Taking a step back, a recent trading competition on the decentralized exchange Hyperliquid, involving several large language models (LLMs), such as GPT-5, DeepSeek, and Gemini Pro, laid the foundation for where AI is in the trading world. All of these LLMs were given the same message and ran autonomously, making decisions. But they weren’t that good, according to Sena, and barely outperformed the market.
“We took the AI models used in the Hyperliquid contest and let people submit their trading agents that they had created to compete against those models. We wanted to see if the trading agents are better than the fundamental models, with that extra specialization,” Sena said.
The top three places in the Recall competition were occupied by custom models. “Some models were unprofitable and underperforming, but it became clear that specialized trading agents taking these models and applying additional logic and inference, data sources and so on, are outperforming base AI,” he said.
The democratization of AI-based trading raises interesting questions about whether there will be any alpha left to cover if everyone uses the same level of sophisticated machine learning technology.
“If everyone uses the same agent and that agent executes the same strategy for everyone, does that collapse on itself?” Sena said. “Does the alpha you’re detecting disappear because you’re trying to run it at scale for everyone else?”
That’s why those who are best positioned to benefit from the advantage that AI trading will eventually bring are those who have the resources to invest in the development of custom tools, Sena said. As in traditional finance, the highest quality tools that generate the most alpha are typically not public, he added.
“People want to keep these tools as private as possible, because they want to protect that alpha,” Sena said. “They paid a lot for it. This was seen with hedge funds buying data sets. You can see it with some proprietary ones developed by family offices.
“I think the magic point will be when there is a product that is a portfolio manager, but the user still has a say in their strategy. They can say, ‘This is how I like to trade and these are my parameters, let’s implement something similar, but let’s make it better.'”
