标题： 端到端限价订单簿建模的生成式人工智能：使用深度状态空间网络的消息流分 token 自回归生成模型 显示英文标题

标题： Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network

摘要： 开发一个金融市场上现实的订单流生成模型是一个具有挑战性的开放问题，对市场参与者有许多应用。 针对这一问题，我们提出了第一个端到端的自回归生成模型，该模型生成分词的限价单簿（LOB）消息。 这些消息由一个Jax-LOB模拟器解释，该模拟器更新LOB状态。 为了高效处理长序列，该模型采用简化的结构化状态空间层来处理订单簿状态和分词消息的序列。 使用NASDAQ股票LOB的LOBSTER数据，我们为消息数据开发了一个自定义分词器，将连续的数字组转换为标记，类似于大型语言模型中的分词。 样本外结果表明，模型在近似数据分布方面表现出色，这由低模型困惑度证明。 此外，从生成的订单流计算出的中间价格收益与数据有显著相关性，表明模型具有出色的条件预测性能。 由于生成数据的粒度和模型的准确性，它为未来的工作提供了新的应用领域，超越了预测，例如 在高频金融强化学习应用中作为世界模型。 总体而言，我们的结果鼓励在自回归大型金融模型的方向上使用和扩展该模型，以生成高频金融数据，我们承诺开源代码以促进未来的研究。 显示英文摘要

摘要： Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we propose the first end-to-end autoregressive generative model that generates tokenized limit order book (LOB) messages. These messages are interpreted by a Jax-LOB simulator, which updates the LOB state. To handle long sequences efficiently, the model employs simplified structured state-space layers to process sequences of order book states and tokenized messages. Using LOBSTER data of NASDAQ equity LOBs, we develop a custom tokenizer for message data, converting groups of successive digits to tokens, similar to tokenization in large language models. Out-of-sample results show promising performance in approximating the data distribution, as evidenced by low model perplexity. Furthermore, the mid-price returns calculated from the generated order flow exhibit a significant correlation with the data, indicating impressive conditional forecast performance. Due to the granularity of generated data, and the accuracy of the model, it offers new application areas for future work beyond forecasting, e.g. acting as a world model in high-frequency financial reinforcement learning applications. Overall, our results invite the use and extension of the model in the direction of autoregressive large financial models for the generation of high-frequency financial data and we commit to open-sourcing our code to facilitate future research.