[2602.23784] TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure

Computer Science > Machine Learning arXiv:2602.23784 (cs) [Submitted on 27 Feb 2026] Title:TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure Authors:Maxime Kawawa-Beaudan, Srijan Sood, Kassiani Papasotiriou, Daniel Borrajo, Manuela Veloso View a PDF of the paper titled TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure, by Maxime Kawawa-Beaudan and 4 other authors View PDF HTML (experimental) Abstract:Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-parameter generative Transformer that brings this paradigm to market microstructure, learning directly from billions of trade events across >9K equities. To enable cross-asset generalization, we develop scale-invariant features and a universal tokenization scheme that map the heterogeneous, multi-modal event stream of order flow into a unified discrete sequence -- eliminating asset-specific calibration. Integrated with a deterministic market simulator, TradeFM-generated rollouts reproduce key stylized facts of financial returns, including heavy tails, volatility clustering, and absence of return autocorrelation. Quantitatively, TradeFM achieves 2-3x lower distributional error than Compound Hawkes baselines and generalizes zero-shot to geographically out-of-distribution APAC markets with moderate perplexity degradation. Together, these result...