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---
pipeline_tag: time-series-forecasting
library_name: pytorch
license: mit # TODO: Verify and replace with the actual license from the repository's LICENSE file
---
# TLOB: A Novel Transformer Model with Dual Attention for Stock Price Trend Prediction with Limit Order Book Data
This is the official repository for the paper [TLOB: A Novel Transformer Model with Dual Attention for Stock Price Trend Prediction with Limit Order Book Data](https://hf.co/papers/2502.15757).
## Abstract
Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data is a fundamental challenge in financial markets. Despite advances in deep learning, existing models fail to generalize across different market conditions and struggle to predict short-term trends reliably. Surprisingly, by adapting a simple MLP-based architecture to LOB, we show that we surpass SoTA performance, thus challenging the necessity of complex architectures. Unlike past work that shows robustness issues, we propose TLOB, a transformer-based model that uses a dual attention mechanism to capture spatial and temporal dependencies in LOB data. This allows it to adaptively focus on the market microstructure, making it particularly effective for longer-horizon predictions and volatile market conditions. We also introduce a new labeling method that improves on previous ones, removing the horizon bias. We evaluate LOB’s effectiveness using the established FI-2010 benchmark, which exceeds the state-of-the-art by an average of 3.7 F1-score(%). Additionally, TLOB shows improvements on Tesla and Intel with a 1.3 and 7.7 increase in F1-score(%), respectively. Additionally, we empirically show how stock price predictability has declined over time (-6.68 absolute points in F1-score(%)), highlighting the growing market efficiencies. Predictability must be considered in relation to transaction costs, so we experimented with defining trends using an average spread, reflecting the primary transaction cost. The resulting performance deterioration underscores the complexity of translating trend classification into profitable trading strategies. We argue that our work provides new insights into the evolving landscape of stock price trend prediction and sets a strong foundation for future advancements in financial AI.
## How to Use
For code and sample usage, please refer to the GitHub repository: [https://github.com/LeonardoBerti00/TLOB](https://github.com/LeonardoBerti00/TLOB)
## Citation
```
@misc{berti2025tlobnoveltransformermodel,
title={TLOB: A Novel Transformer Model with Dual Attention for Stock Price Trend Prediction with Limit Order Book Data},
author={Leonardo Berti and Gjergji Kasneci},
year={2025},
eprint={2502.15757},
archivePrefix={arXiv},
primaryClass={q-fin.ST},
url={https://arxiv.org/abs/2502.15757},
}
``` |