How Jump Trading is scaling quant research with ChatGPT

As a quantitative trading firm, Jump Trading creates predictive models that use market data, news and events, and a range of alternative data sources to make the best possible predictions about asset prices. Because markets are complex, noisy, and changing over time, it is rarely possible to anticipate exactly what will happen. But according to Lucas Baker, Head of LLM R&D at Jump, predicting even slightly better than a coin flip at scale is enough to result in a successful strategy.

Baker leads agentic research and development, and he’s focused on building the agents, harnesses, and infrastructure that let quantitative researchers explore their ideas in greater breadth and depth. Adding GPT‑6 Astra has dramatically expanded the scale and complexity of workflows that can be handed off to agents, from day-to-day coding to advanced quantitative studies to validate new hypotheses.

“With the GPT-6 series, especially GPT-6 Astra, OpenAI has unlocked a new tier of autonomy for long-horizon tasks that require flexible agent coordination and extreme persistence on complex workflows. Where we used to require frequent human guidance and intervention, we can now focus fully on defining a secure and well-monitored environment with clear goals and letting the agents find their own way.”

—Lucas Baker, Head of LLM R&D, Jump Trading

From short snippets of code to comprehensive analysis

Over the past year, AI has transformed from a helpful tool, useful for writing one-off code snippets or finding small bugs into a capable, versatile system that can develop entire codebases and services by itself. Now, Baker and his team find that AI works best when treated more like a colleague. Researchers can define a key problem, a work environment, and a way of evaluating the quality and significance of results, then steer one or many agents in real time about where to focus the analysis or which job to run next.