research
Ten Flow Steps Rebuilt the Order Book
A conditional flow-matching generator produced controllable market trajectories with fewer solver evaluations than a matched diffusion baseline.
Summary
A conditional flow-matching generator produced controllable market trajectories with fewer solver evaluations than a matched diffusion baseline.
FlowLOB trains on several Hong Kong Exchange symbols at three sampling frequencies and represents prices relative to ticks so the generator can transfer to an unseen instrument. Under matched data, architecture and training budget, flow matching reached its best reported quality with ten ODE steps; diffusion required more evaluations to approach it. Most distributional metrics improved at the two finer frequencies, and counterfactual controls transferred to the held-out symbol. Synthetic realism does not establish trading profitability or faithful behavior during every market regime.
Why it matters
A conditional flow-matching generator produced controllable market trajectories with fewer solver evaluations than a matched diffusion baseline.
Limits and context
- Synthetic realism does not establish trading profitability or faithful behavior during every market regime.
Key claims
A conditional flow-matching generator produced controllable market trajectories with fewer solver evaluations than a matched diffusion baseline.
Qualification: Synthetic realism does not establish trading profitability or faithful behavior during every market regime.
Evidence: source-2026-08-14-015
Sources
- arXiv preprint 2608.13096arXiv · primary research
Corrections
No corrections have been recorded for this story.