A novel hybrid method for direction forecasting and trading of Apple Futures

Shangkun Deng*, Xiaoru Huang, Zhaohui Qin, Zhe Fu, Tianxiang Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this research, a novel hybrid method MCXGBoost–Bagging–RegPSO​ is proposed for direction forecasting of the high-frequency Apple Futures’ price and simulation trading. First, a multi-classification method based on the eXtreme Gradient Boosting (XGBoost) is established for Apple Futures price direction classification, while the Regrouping Particle Swarm Optimization (RegPSO) is adopted to optimize the parameters of the movement magnitude levels, XGBoost, and the pre-designed trading rules. Next, a Bagging method is incorporated into the proposed approach to solve the overfitting problem. Then, the proposed method predicts the price movement direction and magnitude level, and a one-year high-frequency trading simulation is executed based on the price direction forecasting results. Finally, several evaluation indicators are used to assess the direction prediction and profitability performances of the proposed method. Experimental results demonstrate that the proposed approach successfully achieved outstanding performance in terms of hit ratio, accumulated return, maximum drawdown, and return–risk ratio. As far as it is concerned, the proposed method could be considered as a useful reference for both intraday investors engaged in high-frequency trading and regulators of the Apple Futures market.

Original languageEnglish
Article number107734
JournalApplied Soft Computing
Volume110
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • Apple Futures
  • High-frequency trading
  • Hybrid approach
  • Parameter optimization
  • Trading rule

ASJC Scopus subject areas

  • Software

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