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Design of High-Frequency Trading Algorithm Based on Machine Learning

Boyue Fang and 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.

Date: 2019-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-mst
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Handle: RePEc:arx:papers:1912.10343