MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model

The paper introduces a Large Market Model, an order-level generative foundation model, and builds the MarS engine on top of it to make order generation realistic, interactive, and controllable. The authors report scalability across data size and model complexity, and realism in controlled generation with market impact. MarS is positioned as multi-purpose infrastructure — a forecast tool, a detection system, an analysis platform, and a training environment where trading agents can be exercised without risking capital. It was accepted as an ICLR 2025 poster, verified on ICLR’s own proceedings and virtual-conference pages.

The released code is MIT-licensed (Microsoft Corporation) and readable: order generation in market_simulation/models/order_model.py, market state in states/order_state.py, and the order-generating agent in agents/background_agent.py, plus four worked examples — an 11-characteristic stylized-facts report, simulation-as-forecasting, market-impact analysis, and a Streamlit demo. Note that the MIT license covers the code only; the paper itself is published under CC BY-NC-SA 4.0, which is non-commercial and share-alike.

The model the engine depends on has never been released, which is the caveat that governs everything else here. download.py fetches Hugging Face repository microsoft/mars-order-model, which returns HTTP 401; the README concedes the model “is currently set to private awaiting final review approval” and that the examples’ full functionality depends on its release. MarS therefore cannot be run end to end today. Open issue #20, filed in March 2026, asks for the plan after more than a year and has no maintainer reply; the OneDrive fallback the README offers for prerequisite files is separately reported broken. The main branch has not moved since May 2025, and the pinned dependency stack is roughly a year behind. Running what does work is Linux-only and CUDA-bound, with Docker effectively mandatory.