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Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach


  • Wang, Lijun
  • An, Haizhong
  • Liu, Xiaojia
  • Huang, Xuan


Strategies to increase profit from investments in crude oil futures markets are an important issue for investors in energy finance. This paper proposes an approach to generate dynamic moving average trading rules in crude oil futures markets. An adaptive moving average calculation is used to better describe the fluctuations, and trading rules can be adjusted dynamically in the investment period based on the performance of four reference rules. We use genetic algorithms to select optimal dynamic moving average trading rules from a large set of possible parameters. Our results indicate that dynamic trading rules can help traders make profit in the crude oil futures market and are more effective than the BH strategy in the price decrease process. Moreover, dynamic moving average trading rules are more favorable to traders than static trading rules, and the advantage becomes more obvious over long investment cycles. The lengths of the two periods of dynamic moving average trading rules are closely associated with price volatility. The dynamic trading rules will have outstanding performance when market is shocked by significant energy related events. Investment advices are given out and these advices are helpful for traders when choosing technical trading rules in actual investments.

Suggested Citation

  • Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
  • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:1608-1618
    DOI: 10.1016/j.apenergy.2015.08.132

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    Cited by:

    1. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    2. Liu, Xueyong & An, Haizhong & Huang, Shupei & Wen, Shaobo, 2017. "The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 374-383.
    3. Gong, Xu & Wen, Fenghua & Xia, X.H. & Huang, Jianbai & Pan, Bin, 2017. "Investigating the risk-return trade-off for crude oil futures using high-frequency data," Applied Energy, Elsevier, vol. 196(C), pages 152-161.
    4. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
    5. Taylor, Nick, 2017. "Timing strategy performance in the crude oil futures market," Energy Economics, Elsevier, vol. 66(C), pages 480-492.
    6. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
    7. Liu, Xiaojia & An, Haizhong & Wang, Lijun & Guan, Qing, 2017. "Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 444-457.


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