Earticle

초록

영어

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

목차

Abstract
1. Introduction
2. Background
2.1 Pair Trading Strategy
2.2 Triple Barrier Labeling Method (TBM)
3. Method
3.1 Selecting Asset Pairs
3.2 Calculating Pairs Spread
3.3 Generating Labels Using Triple Barrier Labeling method (TBM)for Pair Trading Strategy
3.4 Optimizing by GA
3.5 Back Testing
4. Experiment Setup and Result
4.1 Dataset
4.2 Hypermeters
4.3 Result
5. Conclusion
Acknowledgement
References

키워드

저자

• Ning Fu [ Dept. of Software Engineering, Jeonbuk National University, Korea ]
• Suntae Kim [ Dept. of Software Engineering, Jeonbuk National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380