Jump Trading uses GPT‑6 Astra to take on longer, more ambiguous research problems.

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.

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.

Baker says that with GPT‑6 Astra, agents are now capable of not only finding meaningful and practical changes, but merging and stacking those wins together in a process of recursive improvement.

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