Boboc Ioana-Andreea, Dinică Mihai-Cristian
Financial Engineering Section, Swiss Finance Institute at École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
PLoS One. 2013 Oct 29;8(10):e78177. doi: 10.1371/journal.pone.0078177. eCollection 2013.
The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).
本研究的目的是通过应用一个交易系统来检验欧元兑美元市场的效率。该系统使用一种基于技术分析指标的遗传算法,这些指标包括指数移动平均线(EMA)、移动平均收敛背离(MACD)、相对强弱指数(RSI)以及为投资者提供买卖建议的过滤器。该算法通过动态搜索在训练期提高盈利能力的参数来优化策略。然后将最佳规则集应用于测试期。结果表明,很难找到一组在两个时期都表现良好的交易规则。在训练期获得非常好回报的策略在测试期难以获得正收益,这与有效市场假说(EMH)一致。