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

TL;DR

TradeFM is introduced, a 524M-parameter generative Transformer that brings this paradigm to market microstructure, learning directly from billions of trade events across>9K equities, and suggests that scale-invariant trade representations capture transferable structure in market microstructure.

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.

Figures (17)

• Figure 1: Trade feature distributions. Canonical distributions for core trade features, conditioned on liquidity. Price features are leptokurtic (Laplace); volume follows a heavy-tailed power-law; and interarrival time is exponential.
• Figure 2: Calibrated bin edges. Price features (top) use quantile-based binning for high resolution near the mean; volume and time (bottom) use logarithmic bins to capture their wide dynamic range.
• Figure 3: Closed-loop simulation architecture. TradeFM predicts a trade, the Market Simulator executes it, and the updated market state is fed back to the model.
• Figure 4: TradeFM model validation. Simulated returns exhibit: (Left) near-zero autocorrelation, (Middle) slowly decaying autocorrelation of absolute returns (volatility clustering), and (Right) heavy tails and aggregational Gaussianity.
• Figure 5: Zero-shot geographic generalization. Perplexity distributions for TradeFM (trained on US equities) evaluated on held-out US, China, and Japan data (January 2025) demonstrate cross-geography robustness of scale-invariant features.