This paper proposes non-dominated sorting genetic algorithm-II (NSGA-II ) in the context of technical indicator-based stock trading, by finding optimal combinations of technical indicators to generate buy and sell strategies such that the objectives, namely, Sharpe ratio and Maximum Drawdown are maximized and minimized respectively. NSGA-II is chosen because it is a very popular and powerful bi-objective evolutionary algorithm. The training and testing used a rolling-based approach (two years training and a year for testing) and thus the results of the approach seem to be considerably better in stable periods without major economic fluctuations. Further, another important contribution of this study is to incorporate the transaction cost and domain expertise in the whole modeling approach.

翻译：本文件建议,在技术指标型股票交易中采用非主要分类的遗传算法II(NSGA-II),办法是找到最佳的技术指标组合,以产生买卖战略,从而分别最大限度地实现和尽量减少目标,即:Sharpe比率和最大减速率;选择第二个基因算法,是因为这是一个非常受欢迎和强大的双目标演进算法;培训和测试采用滚动方法(两年培训和一年测试),因此,在稳定时期,在没有重大经济波动的情况下,该方法的结果似乎好得多;此外,这项研究的另一个重要贡献是将交易成本和领域专门知识纳入整个建模方法。