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A High Winning Opportunities Intraday Volatility Trading Method Using Artificial Immune Systems

  • Theo Raymond Chan
  • Kwun-wing Chan
  • Steve Luk
  • Chun-ho Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

This paper introduces a quantitative forecasting trading mechanism which captures intraday volatility and at the same time enjoying the Index directional trading profit. The method applies Artificial Immune Network (AIN) to adjust the Index Equilibrium Point Forecasting (IEPF) and Mean Reversion Grid Trading (MRGT) method to maximize its winning opportunity. In practice, a system has been developed over the Hang Seng China Enterprises Index (HSCEI) Futures market. We have applied 9-years real market historical data, approximately 160 Terabytes Bid-Ask and Done Trade full book records, to training up the AIN to enhance the index forecasting result. The performance of the proposed method in backward test appear to be promising, and therefore, a real-time intraday trading system is currently under deployment for a further pilot experiment with the real market trading test.

Keywords

AIS Artificial immune system Financial forecasting Stock future market Optimization FinTech 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Theo Raymond Chan
    • 1
  • Kwun-wing Chan
    • 1
  • Steve Luk
    • 1
  • Chun-ho Lee
    • 1
  1. 1.Chan’s Research Company LimitedHong KongChina

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