Title: TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
Primary area: foundation or frontier models, including LLMs
Keywords: Foundation Model, Generative Model, Market Microstructure, Reinforcement Learning, Algorithmic Trading, Trade-flow, Financial Time Series, Multi-Agent Simulation, Transformer, TradeFM

Abstract: Learning generalizable representations from the high-frequency, heterogeneous event streams of financial markets is a significant challenge. We introduce TradeFM, a foundation model that learns the universal dynamics of market microstructure. Pre-trained on billions of equities transactions, TradeFM uses a novel scale-invariant feature representation and a universal tokenization scheme to form a unified representation, enabling generalization without asset-specific calibration. We validate the quality of the learned representations by demonstrating that model-generated rollouts in a closed-loop simulator successfully reproduce canonical stylized facts of financial returns. TradeFM provides a high-fidelity engine for synthetic data generation and downstream agent-based modeling.

TL;DR: We introduce TradeFM, a generative Transformer-based foundation model that learns the "language" of financial markets from billions of trades and simulates realistic order-level behavior across assets and liquidity regimes for downstream tasks.