LM jump detection-high frequency grid trading model based on LSTM prediction

Haowei Sun; Wei Li; Chenghuan Ou

doi:10.54097/hbem.v12i.8355

Authors

Haowei Sun
Wei Li
Chenghuan Ou

DOI:

https://doi.org/10.54097/hbem.v12i.8355

Keywords:

High Frequency Quantitative Trading, LSTM Model, Grid Trading Strategies.

Abstract

In the computer era, high frequency trading is one of the hottest topics in the financial investment market. In order to develop a high-frequency trading strategy model with specific validity for different types of stocks, this paper proposes a LM jump detection-grid trading model based on LSTM prediction to construct a trading strategy for three types of stocks: blue chips, volatile stocks and stable stocks. The proposed model is a combination of a black-box model and a white-box model, which not only has the powerful learning capability of LSTM Model and but also the excellent scalability of LM Jump Detection Model and Grid Trading Model. These allow the model to have greater advantages in terms of trading strategies and the convenience of optimizing anomaly detection.

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