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An intelligent financial portfolio trading strategy using deep Q-learning


  • Hyungjun Park
  • Min Kyu Sim
  • Dong Gu Choi


Portfolio traders strive to identify dynamic portfolio allocation schemes so that their total budgets are well allocated through the investment horizon. This study proposes a novel portfolio trading strategy in which an intelligent agent is trained to identify an optimal trading action by using an algorithm called deep Q-learning. This study formulates a portfolio trading process as a Markov decision process in which the agent can learn about the financial market environment, and it identifies a deep neural network structure as an approximation of the Q-function. To ensure applicability to real-world trading, we devise three novel techniques that are both reasonable and implementable. First, the agent's action space is modeled as a combinatorial action space of trading directions with prespecified trading sizes for each asset. Second, we introduce a mapping function that can replace an initially-determined action that may be infeasible with a feasible action that is reasonably close to the original, ideal action. Last, we introduce a technique by which an agent simulates all feasible actions in each state and learns about these experiences to derive a multi-asset trading strategy that best reflects financial data. To validate our approach, we conduct backtests for two representative portfolios and demonstrate superior results over the benchmark strategies.

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  • Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665,, revised Aug 2019.
  • Handle: RePEc:arx:papers:1907.03665

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    5. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059,, revised Jul 2017.
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    8. Kouwenberg, Roy, 2001. "Scenario generation and stochastic programming models for asset liability management," European Journal of Operational Research, Elsevier, vol. 134(2), pages 279-292, October.
    9. Papailias, Fotis & Thomakos, Dimitrios D., 2015. "An improved moving average technical trading rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 458-469.
    10. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
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    12. repec:eee:ejores:v:270:y:2018:i:2:p:654-669 is not listed on IDEAS
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