Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors: the market in which it operates, the size of the time window, and others. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series. We propose the combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike other approaches, this paper applies a set of different MOEAs, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of non-dominated solutions obtained with different MOEAs simultaneously. Experimental results show that this technique increases the returns of the commonly used Buy \& Hold strategy and other multi-objective strategies, even for daily operations.
翻译:技术指标采用各种数学公式对财务时序价格进行财务时序计算,从而对数据集进行图形表达。这些公式包括一套规则和参数,其价值不一定为人所知,并取决于许多因素:其运作的市场、时间窗口的大小及其他因素。本文件侧重于实时优化用于分析数据时间序列的参数。特别是,我们优化技术和财务指标的参数,并提议其他应用,如葡萄糖时间序列。我们提议将若干多目标进化阿尔高氏(MOEAs)组合在一起。与其他方法不同,本文采用一套不同的规则和参数,合作构建一套全球性的Pareto解决方案。解决财务问题的办法寻求高回报,风险最小。优化过程是连续的,并且与投资时间间隔同步发生。这种技术允许同时应用不同MOEAs获得的非主导性解决方案。实验结果显示,这种技术增加了常用的买入“持有”战略和其他多目标战略的回报,甚至用于日常操作。