INTERVIEW
How Trading Is Becoming Autonomous
Five Minutes with Irene Aldridge, AbleMarkets CEO & HFT Author
Irene Aldridge wrote the book on high-frequency trading—literally, the Wiley textbook that's become the industry standard.
Now she says that AI agents are the next step in trading evolution.
Her new paper, "Agentic Artificial Intelligence in Finance: A Comprehensive Survey," argues that we've crossed a threshold from AI that predicts to AI that acts. She published the new research with her students from Cornell University, where she's been a visiting professor for the last nine years.
AI agents are autonomous systems with objectives, memory, and the capacity to adapt—agents that decide what to trade, execute, and learn from the outcome. This differs from “traditional” AI which has typically relied on human-written code.
The practical result, she argues, is systems that mirror how trading floors actually work: one agent maximizing profit, another minimizing risk, a third handling compliance. "Each agent has a different objective, but together they make the whole stronger because they control each other's behavior."
In a recent conversation, we dive into what this means for the future of markets:
AI Agents are real: AI agents are no longer just trial-and-error systems—they have objectives and optimize dynamically, drawing on math that's been in academic departments but never applied to production systems. (Aldridge runs her own AI agent trading strategy with promising results.)
Multi-agent systems mirror organizational structure: Just like trading floors have traders, risk managers, and compliance, AI systems now deploy multiple agents with competing objectives that control each other's behavior.
Regulators are unprepared: They need purpose-built AI systems to police AI traders, but have been distracted by crypto debates while the real transformation happens.
Current agents have an advantage: While most trading is still done by humans, agents can learn from rational, profit-maximizing behavior. When more agents enter, the environment becomes much more chaotic.
This interview has been edited for clarity and length.
The Shift from "Trial and Error" to Optimization
Matt Robinson: Your background's with HFT using deterministic rules for trading. How is generative AI different?
Irene Aldridge: Up to about 10 years ago, machine learning was much about trial and error: "Let's try this route. If it doesn't work, let's switch our machine to this route." Computer scientists were mostly thinking about speeding up machine learning by getting faster hardware.
Recently, optimization got involved. Optimization is a different department from Computer Science ... It is Operations Research. Now, Operations Research works on dramatically speeding up algorithms and finding high-precision solutions -- something I have always been very passionate about.
Matt Robinson: How does that change the approach?
Irene Aldridge: Instead of just having computers run different paths to try to find optimal solutions, now you set an objective: "This is what I want to accomplish." And let's find the optimal path to fulfilling that objective. Let's optimize it across all the different dimensions. And this is what really gives birth to these agents.
