Deep Limit Order Book Forecasting: A microstructural guide
By Antonio Briola, Silvia Bartolucci, Tomaso Aste
Rating
1496
Battle Count: 76
Relevance
9/10
Highly relevant for high-frequency trading and market microstructure analysis. Provides insights into stock predictability based on microstructural properties.
Implementation Complexity
7/10
Requires sophisticated deep learning models and high-quality LOB data. The provided 'LOBFrame' framework may reduce implementation complexity.
Reproducibility
4/5
The paper provides detailed methodology, data sources, and introduces an open-source framework 'LOBFrame' for reproducibility. However, access to specific LOBSTER data may require purchase.
About this paper
Methodology: Deep Learning for Limit Order Book Forecasting. Problem types: Time Series Forecasting, Classification.
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