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

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  • Maxime Kawawa-Beaudan
  • Srijan Sood
  • Kassiani Papasotiriou
  • Daniel Borrajo
  • Manuela Veloso

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 results suggest that scale-invariant trade representations capture transferable structure in market microstructure, opening a path toward synthetic data generation, stress testing, and learning-based trading agents.

Suggested Citation

  • Maxime Kawawa-Beaudan & Srijan Sood & Kassiani Papasotiriou & Daniel Borrajo & Manuela Veloso, 2026. "TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure," Papers 2602.23784, arXiv.org.
  • Handle: RePEc:arx:papers:2602.23784
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