Acquiring a Government Bond Trading Strategy Using Reinforcement Learning

被引：6
|
作者：
Matsui, Tohgoroh [1 ]
Goto, Takashi [2 ]
Lzumi, Kiyoshi [3 ]
机构：
[1] Tohgoroh Machine Learning Res Inst, 4-112-133-406 Nishi Hatsuishi, Chiba 2700121, Japan
[2] Bank Tokyo Mitsubishi UFJ Ltd, Chiyoda Ku, Tokyo 1006417, Japan
[3] Natl Inst Adv Ind Sci & Technol, Koto Ku, Tokyo 1350064, Japan
来源：
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2009年 / 13卷 / 06期
关键词：
machine learning; reinforcement learning; finance; trading strategy;
D O I：
10.20965/jaciii.2009.p0691
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes using reinforcement learning to acquire a government bond trading strategy. We applied this method to the 10-year Japanese government bond (JGB) market and confirmed that it acquires profitable trading even in extrapolation.
引用
收藏
页码：691 / 696
页数：6
相关论文
共 50 条
  • [1] Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change
    Aloud, Monira Essa
    Alkhamees, Nora
    [J]. IEEE ACCESS, 2021, 9 : 114659 - 114671
  • [2] Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach
    Fallahpour, Saeid
    Hakimian, Hasan
    Taheri, Khalil
    Ramezanifar, Ehsan
    [J]. SOFT COMPUTING, 2016, 20 (12) : 5051 - 5066
  • [3] A Reliable Energy Trading Strategy in Intelligent Microgrids Using Deep Reinforcement Learning
    Cao, Man
    Yin, Zhiyong
    Wang, Yajun
    Yu, Le
    Shi, Peiran
    Cai, Zhi
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2023, 110
  • [4] Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach
    Saeid Fallahpour
    Hasan Hakimian
    Khalil Taheri
    Ehsan Ramezanifar
    [J]. Soft Computing, 2016, 20 : 5051 - 5066
  • [5] A Stock Trading Strategy Based on Deep Reinforcement Learning
    Khemlichi, Firdaous
    Chougrad, Hiba
    Khamlichi, Youness Idrissi
    El Boushaki, Abdessamad
    Ben Ali, Safae El Haj
    [J]. ADVANCED INTELLIGENT SYSTEMS FOR SUSTAINABLE DEVELOPMENT (AI2SD'2020), VOL 2, 2022, 1418 : 920 - 928
  • [6] Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries
    Kim, Taewook
    Kim, Ha Young
    [J]. COMPLEXITY, 2019, 2019
  • [7] Improved pairs trading strategy using two-level reinforcement learning framework
    Xu, Zhizhao
    Luo, Chao
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 126
  • [8] Structural break-aware pairs trading strategy using deep reinforcement learning
    Jing-You Lu
    Hsu-Chao Lai
    Wen-Yueh Shih
    Yi-Feng Chen
    Shen-Hang Huang
    Hao-Han Chang
    Jun-Zhe Wang
    Jiun-Long Huang
    Tian-Shyr Dai
    [J]. The Journal of Supercomputing, 2022, 78 : 3843 - 3882
  • [9] Structural break-aware pairs trading strategy using deep reinforcement learning
    Lu, Jing-You
    Lai, Hsu-Chao
    Shih, Wen-Yueh
    Chen, Yi-Feng
    Huang, Shen-Hang
    Chang, Hao-Han
    Wang, Jun-Zhe
    Huang, Jiun-Long
    Dai, Tian-Shyr
    [J]. JOURNAL OF SUPERCOMPUTING, 2022, 78 (03): : 3843 - 3882
  • [10] Using Reinforcement Learning in the Algorithmic Trading Problem
    Ponomarev, E. S.
    Oseledets, I. V.
    Cichocki, A. S.
    [J]. JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS, 2019, 64 (12) : 1450 - 1457
12345