基于RPNN-SA-PSO混沌时间序列预测模型的程序化交易研究
Programming trading Research based on the RPNN-SA-PSO chaotic time series prediction model
Abstract
本研究以混沌理论为基础,以金融市场的混沌特征为依据,以人工神经网络和智能优化算法为技术手段,建立了混沌时间序列预测模型。更进一步地,借助于MetaTrader交易平台,以混沌时间序列预测模型为核心,编写了外汇程序化交易策略,从交易结果中检验预测模型的有效性。本研究做出的主要工作及结论如下: 首先,本研究以CiteSpaceII文献分析软件为工具,以文献计量研究方法对金融时间序列预测研究的发展历程做了梳理。着重探讨了其非线性动力学方法的研究现状、热点及趋势,并以可视化图表的形式将结果呈现出来。引入非线性研究范式对金融变量进行建模,通过非线性迭代、学习模型近似描述金融混沌动力系统,是金融市场理论... Based on the chaos theory and the chaotic feature in financial markets, this research constructed a chaotic time series prediction model with artificial neural network and intelligent optimization algorithms. Further, aiming at testing the validity of the chaotic time series prediction model, this research took the model as core to program a forex trading strategy with the support of MetaTrader tr...