Probabilistic Limit Order Book Forecasting with Conditional Diffusion Models
We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator.
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Key points
- Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation.
- While diffusion models achieve strong empirical performance, existing LOB snapshot models typically lack a learned recurrent encoder and formal statistical guarantees.
- In particular, we establish an excess-risk oracle inequality for the one-step DDPM noise-prediction objective under exponential mixing and regularity assumptions.
- Experiments on BTC/USDT, GOOGL, and META show that LOBGrad improves on its encoder-free diffusion ablation across all reported queue metrics and obtains competitive results against other baselines.
Sources (1)
- [1]Probabilistic Limit Order Book Forecasting with Conditional Diffusion ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:34 PM
We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator.
Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation.
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