Modelling high-frequency limit order book dynamics with support vector machines

Alec N. Kercheval, Yuan Zhang · 2015 · Quantitative Finance

IntermediateHFTEmpirical

Hand-crafted LOB features (spreads, imbalances, arrival intensities) fed to SVMs. The feature list is still worth borrowing.

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At a glance

Level: Intermediate: Comfortable with statistics, regressions and basic probability models
Horizon: HFT
Data needed: L2 depth (snapshots) Set what data you have
Themes: Machine learning Order-flow alpha
Assets: Equities
Market: Global

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Deep learning on the order book (with a reality check)

Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods →

Cite

@article{kercheval2015modelling,
  author = {Kercheval, Alec N. and Zhang, Yuan},
  title = {{Modelling high-frequency limit order book dynamics with support vector machines}},
  journal = {Quantitative Finance},
  year = {2015},
  doi = {10.1080/14697688.2015.1032546}
}

Kercheval, A. N., & Zhang, Y. (2015). Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance. https://doi.org/10.1080/14697688.2015.1032546