IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics0360544223027883.html
   My bibliography  Save this article

A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning

Author

Listed:
  • Du, Xiaoxu
  • Tang, Zhenpeng
  • Chen, Kaijie

Abstract

With the deepening financialization of crude oil, an increasing number of investors are becoming involved in trading crude oil futures. However, various factors influence crude oil futures price fluctuations, presenting complex characteristics. For this reason, a trading strategy plays a crucial role in investment. In this paper, we construct an ensemble trading strategy by combining time-frequency feature extraction of crude oil volume-price series with deep reinforcement learning. We decompose the original oil volume-price series by using the optimized variational mode decomposition. Additionally, taking into account the long memory characteristics of the crude oil time series, we employ the Sharpe ratio to filter the appropriate agents corresponding to market volatility. Moreover, the best features of the three agents are effectively combined and used in the next phase of trading, thus robustly adapting to different market volatility situations. Finally, we tested our strategy using Brent crude oil futures. Compared with other benchmark models, the proposed OVMD(V–P)-DRL-Ensemble strategy is suitable for the highly volatile crude oil market and performs well in terms of the corresponding performance evaluation metrics, showing excellent return performance and stability.

Suggested Citation

  • Du, Xiaoxu & Tang, Zhenpeng & Chen, Kaijie, 2023. "A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027883
    DOI: 10.1016/j.energy.2023.129394
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223027883
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129394?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    2. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    3. Cerqueti, Roy & Fanelli, Viviana & Rotundo, Giulia, 2019. "Long run analysis of crude oil portfolios," Energy Economics, Elsevier, vol. 79(C), pages 183-205.
    4. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    5. Han, Yufeng & Huang, Dashan & Huang, Dayong & Zhou, Guofu, 2022. "Expected return, volume, and mispricing," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1295-1315.
    6. George Varghese & Vinodh Madhavan, 2019. "Nonlinear dynamics in crude oil benchmarks: an AMH perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 26(21), pages 1798-1801, December.
    7. Benk, Szilard & Gillman, Max, 2020. "Granger predictability of oil prices after the Great Recession," Journal of International Money and Finance, Elsevier, vol. 101(C).
    8. Syamala, Sudhakara Reddy & Wadhwa, Kavita, 2020. "Trading performance and market efficiency: Evidence from algorithmic trading," Research in International Business and Finance, Elsevier, vol. 54(C).
    9. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
    10. Xingrui Jiao & Yuping Song & Yang Kong & Xiaolong Tang, 2022. "Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 933-944, August.
    11. Pop, Ionuț Daniel, 2022. "COVID-19 crisis, voters’ drivers, and financial markets consequences on US presidential election and global economy," Finance Research Letters, Elsevier, vol. 44(C).
    12. Zhu, Pengfei & Tang, Yong & Wei, Yu & Lu, Tuantuan, 2021. "Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: Evidence during the COVID-19 epidemic," Energy, Elsevier, vol. 231(C).
    13. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    14. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
    15. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    16. Li, Xiafei & Wei, Yu, 2018. "The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method," Energy Economics, Elsevier, vol. 74(C), pages 565-581.
    17. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).
    18. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    19. Ederington, Louis H. & Fernando, Chitru S. & Hoelscher, Seth A. & Lee, Thomas K. & Linn, Scott C., 2019. "A review of the evidence on the relation between crude oil prices and petroleum product prices," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 1-15.
    20. Bianchi, Robert J. & Fan, John Hua & Todorova, Neda, 2020. "Financialization and de-financialization of commodity futures: A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    21. Yin, Libo & Wang, Yang, 2019. "Forecasting the oil prices: What is the role of skewness risk?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    22. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
    23. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
    24. Zhao, Wan-Li & Fan, Ying & Ji, Qiang, 2022. "Extreme risk spillover between crude oil price and financial factors," Finance Research Letters, Elsevier, vol. 46(PA).
    25. Zhang, Yue-Jun & Yao, Ting, 2016. "Interpreting the movement of oil prices: Driven by fundamentals or bubbles?," Economic Modelling, Elsevier, vol. 55(C), pages 226-240.
    26. Khan, Zeeshan & Haouas, Ilham & Trinh, Hai Hong & Badeeb, Ramez Abubakr & Zhang, Changyong, 2023. "Financial inclusion and energy poverty nexus in the era of globalization: Role of composite risk index and energy investment in emerging economies," Renewable Energy, Elsevier, vol. 204(C), pages 382-399.
    27. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    28. Zhu, Pengfei & Lu, Tuantuan & Chen, Shenglan, 2022. "How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    29. Lahmiri, Salim & Bekiros, Stelios, 2018. "Chaos, randomness and multi-fractality in Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 28-34.
    30. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    31. Rodriguez, E. & Alvarez-Ramirez, J., 2021. "Time-varying cross-correlation between trading volume and returns in US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    32. Qiu-Su Wang & Chi-Wei Su & Yu-Fei Hua & Muhammad Umar, 2021. "Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 34(1), pages 1727-1751, January.
    33. Ghazani, Majid Mirzaee & Ebrahimi, Seyed Babak, 2019. "Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices," Finance Research Letters, Elsevier, vol. 30(C), pages 60-68.
    34. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
    2. Xiao, Jihong & Wen, Fenghua & He, Zhifang, 2023. "Impact of geopolitical risks on investor attention and speculation in the oil market: Evidence from nonlinear and time-varying analysis," Energy, Elsevier, vol. 267(C).
    3. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    4. Zhu, Pengfei & Lu, Tuantuan & Chen, Shenglan, 2022. "How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Mohsin, Muhammad & Jamaani, Fouad, 2023. "Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning," Resources Policy, Elsevier, vol. 85(PA).
    6. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    7. Tian, Maoxi & Alshater, Muneer M. & Yoon, Seong-Min, 2022. "Dynamic risk spillovers from oil to stock markets: Fresh evidence from GARCH copula quantile regression-based CoVaR model," Energy Economics, Elsevier, vol. 115(C).
    8. Li, Ye & Chen, Yiyan & Lean, Hooi Hooi, 2024. "Geopolitical risk and crude oil price predictability: Novel decomposition ensemble approach based ternary interval number series," Resources Policy, Elsevier, vol. 92(C).
    9. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    10. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
    11. Rui Luo & Jinpei Liu & Piao Wang & Zhifu Tao & Huayou Chen, 2024. "A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 366-390, March.
    12. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    13. Giancarlo Corsetti & Romain Lafarguette & Arnaud Mehl, 2019. "Fast Trading and the Virtue of Entropy: Evidence from the Foreign Exchange Market," Discussion Papers 1914, Centre for Macroeconomics (CFM).
    14. Borgards, Oliver & Czudaj, Robert L. & Hoang, Thi Hong Van, 2021. "Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact," Resources Policy, Elsevier, vol. 71(C).
    15. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    16. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    17. Liu, Xueyong & Jiang, Cheng, 2020. "The dynamic volatility transmission in the multiscale spillover network of the international stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    18. Kristoufek, Ladislav & Vosvrda, Miloslav, 2016. "Gold, currencies and market efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 27-34.
    19. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    20. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027883. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.