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Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network

Author

Listed:
  • Frensi Zejnullahu
  • Maurice Moser
  • Joerg Osterrieder

Abstract

This paper presents a Double Deep Q-Network algorithm for trading single assets, namely the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for our environment with multiple extensions. The features of our trading agent are constantly being expanded to include additional assets such as commodities, resulting in four models. We also respond to environmental conditions, including costs and crises. Our trading agent is first trained for a specific time period and tested on new data and compared with the long-and-hold strategy as a benchmark (market). We analyze the differences between the various models and the in-sample/out-of-sample performance with respect to the environment. The experimental results show that the trading agent follows an appropriate behavior. It can adjust its policy to different circumstances, such as more extensive use of the neutral position when trading costs are present. Furthermore, the net asset value exceeded that of the benchmark, and the agent outperformed the market in the test set. We provide initial insights into the behavior of an agent in a financial domain using a DDQN algorithm. The results of this study can be used for further development.

Suggested Citation

  • Frensi Zejnullahu & Maurice Moser & Joerg Osterrieder, 2022. "Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network," Papers 2206.14267, arXiv.org.
    • Handle: RePEc:arx:papers:2206.14267
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    File URL: http://arxiv.org/pdf/2206.14267
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    References listed on IDEAS

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    1. Kumar Yashaswi, 2021. "Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module," Papers 2102.06233, arXiv.org.
    2. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    3. Uta Pigorsch & Sebastian Schafer, 2021. "High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning," Papers 2112.04755, arXiv.org.
    4. Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Data Normalization for Bilinear Structures in High-Frequency Financial Time-series," Papers 2003.00598, arXiv.org, revised Jul 2020.
    5. Chien Yi Huang, 2018. "Financial Trading as a Game: A Deep Reinforcement Learning Approach," Papers 1807.02787, arXiv.org.
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    Cited by:

    1. Adrian Millea & Abbas Edalat, 2022. "Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization," IJFS, MDPI, vol. 11(1), pages 1-16, December.

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