Design of High-Frequency Trading Algorithm Based on Machine Learning

Boyue Fang; Yutong Feng

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arXiv · 1912.10343

Design of High-Frequency Trading Algorithm Based on Machine Learning

Boyue Fang·Yutong Feng

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Abstract

Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading ($VPIN$), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market-making transactions, uncovering the relationship between discrete dimensional data from the projection of high-dimensional time-series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return.

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BibTeX RIS

Boyue Fang, Yutong Feng. 2019-12-21. Design of High-Frequency Trading Algorithm Based on Machine Learning. https://arxiv.org/abs/1912.10343

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