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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 101808, 10 pages
Research Article

Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

1School of Humanities and Economic Management, China University of Geosciences, Beijing 100083, China
2Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources, Beijing 100083, China
3Lab of Resources and Environmental Management, China University of Geosciences, Beijing 100083, China
4Institute of China’s Economic Reform and Development, Renmin University of China, Beijing 100872, China

Received 19 February 2014; Revised 4 May 2014; Accepted 7 May 2014; Published 26 May 2014

Academic Editor: Wei Chen

Copyright © 2014 Lijun Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The crude oil futures market plays a critical role in energy finance. To gain greater investment return, scholars and traders use technical indicators when selecting trading strategies in oil futures market. In this paper, the authors used moving average prices of oil futures with genetic algorithms to generate profitable trading rules. We defined individuals with different combinations of period lengths and calculation methods as moving average trading rules and used genetic algorithms to search for the suitable lengths of moving average periods and the appropriate calculation methods. The authors used daily crude oil prices of NYMEX futures from 1983 to 2013 to evaluate and select moving average rules. We compared the generated trading rules with the buy-and-hold (BH) strategy to determine whether generated moving average trading rules can obtain excess returns in the crude oil futures market. Through 420 experiments, we determine that the generated trading rules help traders make profits when there are obvious price fluctuations. Generated trading rules can realize excess returns when price falls and experiences significant fluctuations, while BH strategy is better when price increases or is smooth with few fluctuations. The results can help traders choose better strategies in different circumstances.