基于博弈动态影响图的股市趋势推理算法  

Stock Market Trend Reasoning Algorithm Based on Game Dynamic Influence Diagram

作　　者：姚宏亮[1] 尹致远 杨静[1] 俞奎[1] YAO Hongliang;YIN Zhiyuan;YANG Jing;YU Kui(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]合肥工业大学计算机与信息学院,合肥230601

出　　处：《计算机科学》2023年第S02期593-599,共7页Computer Science

基　　金：国家重点研发计划(2020AAA0106100);国家自然科学基金面上项目(61876206,62176082)。

摘　　要：股票市场是一个复杂非线性动态系统,具有高度不确定性和多变性,股市趋势预测是数据挖掘领域的一个研究热点。针对基于数据驱动方法所生成的模型鲁棒性差,训练良好的模型不适应实际需要的问题,提出了一种多Agent博弈动态影响图模型(Mulit-Agent Game Dynamic Influence Diagrams,MAGDIDs)。首先,从博弈的角度引入多方和空方作为股市的行为主体(Agent),提取行为主体的相关特征;然后,利用能量表示博弈主体的力量大小,并对行为主体特征进行量化融合;进而引入博弈策略,构建多Agent博弈动态影响图模型,对于股市行为主体的博弈过程进行建模;最后,利用联合树的自动推理技术,预测股市趋势。在实际数据上进行实验,实验结果表明多空博弈趋势预测算法具有良好性能。The stock market is a complex nonlinear dynamic system with high uncertainty and variability.Stock market trend prediction is a research hotspot in the field of data mining.Aiming at the problem that the model based on the data-driven method has poor robustness and the well-trained model does not meet the actual needs,Multi-agent game dynamic influence diagrams(MAGDIDs)is proposed.First of all,from the perspective of the game,the long side and the short side are introduced as the behavior subjects(Agent)of the stock market,and the relevant characteristics of the behavioral subjects are extracted.Next,the power of the game subjects is represented by energy,and the characteristics of the behavioral subjects are quantified and integra-ted.Then,the game strategy is introduced to build a multi-agent game dynamic influence graph model,and model the game process of the stock market actors.Finally,the automatic reasoning technology of the junction tree is used to predict the stock market trend.Experiments are carried out on actual data,and the results show that the trend prediction algorithm of long-short game has good performance.

关 键 词：博弈 多AGENT 鲁棒性 联合树 动态影响图 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]