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AntoBr96 committed Mar 20, 2024
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# LOBFrame

We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data. This framework integrates all the latest cutting-edge insights from scientific research (see [Lucchese et al.](https://www.sciencedirect.com/science/article/pii/S0169207024000062), [Prata et al.](https://arxiv.org/pdf/2308.01915.pdf)) into a cohesive system. Its strength lies in the comprehensive nature of the implemented pipeline, which includes the data transformation and processing stage, an ultra-fast implementation of the training, validation, and testing steps, as well as the evaluation of the quality of a model's outputs through trading simulations. Moreover, it offers flexibility by accommodating the integration of new models, ensuring adaptability to future advancements in the field.
We release `LOBFrame' (see the [paper](https://arxiv.org/abs/2403.09267v1)), a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data. This framework integrates all the latest cutting-edge insights from scientific research (see [Lucchese et al.](https://www.sciencedirect.com/science/article/pii/S0169207024000062), [Prata et al.](https://arxiv.org/pdf/2308.01915.pdf)) into a cohesive system. Its strength lies in the comprehensive nature of the implemented pipeline, which includes the data transformation and processing stage, an ultra-fast implementation of the training, validation, and testing steps, as well as the evaluation of the quality of a model's outputs through trading simulations. Moreover, it offers flexibility by accommodating the integration of new models, ensuring adaptability to future advancements in the field.

## Introduction

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