基金数据可视分析:基于交互式层次树设计与动态排序探索的基金数据可视分析-ChinaVis2022
Interactive tree and ranking-based fund data visual analysis-ChinaVis2022
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摘要: 采用国内公募混合型基金数据, 与领域专家合作, 本文提炼对高维时序基金数据的基本分析任务, 提出一种交互可视分析方法, 并实现一个可视分析系统, 支持投资者自上而下地探索分析, 从上千支基金挑选最为适宜的产品. 用户通过时间趋势图及展示业绩评价指标的平行坐标图预选基金, 并在产品聚类图和重要维度散点图上进一步比较和决策; 在此基础上, 通过一种新颖的交互层次可视化设计, 用树形结构展示用户挑选基金时的内在逻辑; 借此交互树形图, 用户可动态地分析基金数据, 构建个性化投资决策层次树与多属性排序规则, 最终选出合适的基金. 通过2个案例的结果验证了该方法及可视化设计在筛选优质基金、探索市场概况等方面的可用性和有效性.
Abstract:
Taking Chinese public fund data as an instance, we collaborate with domain experts and distill basic tasks for analyzing multidimensional temporal fund data. We propose a visual analytics solution for the problems and implement an interactive system which supports investors to explore the data from an overview to details, and thereby choose the most appropriate fund products from thousands of options. Generally, the user can filter for a pre-liminary set of candidates through a line plot showing the overall performance and a parallel coordinate showing various indices. They may compare specific funds with others under a global clutter view, and further analyze on the scatter plot for important data dimensions. In particular, we design a novel hierarchical interac-tive tree that surfaces the underlying investment principle when the user interacts with the system, where users can analyze the fund data dynamically and build their personalized tree and rank criteria for such multidimen-sional data. Through two case studies based on real-life data, we demonstrate the usability and effectiveness of our method.