Financial data visualization analysis method based on multi-factor group histogram

Abstract

The invention discloses a financial data visualization analysis method based on a multi-factor group histogram. The analysis method comprises the following steps: constructing a multi-factor group data model; establishing a visual configuration interface; establishing and realizing an interaction function, and obtaining multiple groups of data; and performing data integration processing on the multiple groups of data. Data can be extracted, cleaned and integrated from a plurality of financial data sources with different formats and sources, and can be converted into a data format suitable for drawing a histogram by D3.

Description

Technical Field

[0001] The present invention relates to the field of financial data analysis, and particularly to a financial data visualization analysis method based on a multi-factor group bar chart. Background Art

[0002] In the financial field, the amount of data is huge and complex, covering various dimensions such as transaction data, financial data, and macroeconomic data of various financial products such as stocks, bonds, funds, and futures. With the rapid development and increasing competition in the financial market, in-depth analysis and accurate interpretation of financial data have become increasingly crucial. Traditional data analysis tools and visualization methods have gradually revealed many deficiencies when dealing with multi-factor financial data.

[0003] On the one hand, most existing financial data analysis software provides chart displays in fixed formats, lacking flexibility in the combined display of multi-factor data. For example, when analyzing the investment value of stocks, it is difficult to present multiple key factors such as price-to-earnings ratio, price-to-book ratio, dividend yield, operating income growth rate, and net profit growth rate in an intuitive and flexibly configurable manner on the same visualization interface. Often, it is necessary to switch between multiple charts to view, which not only reduces the analysis efficiency but also easily ignores the potential correlation relationships between factors.

[0004] On the other hand, poor interactivity is also a major pain point in traditional financial data visualization. Users can usually only passively view the pre-set chart content and cannot perform operations such as filtering, zooming, and switching display dimensions on the data in real time according to their own analysis ideas and concerns. For example, when studying the multi-factor performance of stocks in different industry sectors, if it is not convenient to filter out specific industries or zoom in on the factors of interest to view details, it will greatly limit the in-depth mining and accurate analysis of the data.

[0005] In addition, when dealing with large-scale financial data, traditional visualization technologies are prone to performance bottlenecks, resulting in slow or even stuck chart rendering, unable to meet the requirements of real-time data analysis in the rapidly changing financial market. Moreover, the formats of financial data from different sources are diverse, and traditional methods often require a large amount of manual intervention in the data integration and cleaning process, which is error-prone and inefficient.

[0006] Currently, common visualization tools in the financial field, such as Excel, Echarts, etc., provide basic bar chart drawing functions. When dealing with multi-factor data, usually only a single dimension can be represented, and other dimensions are represented by the colors of the bars. The display of dimensions is not intuitive, and there is a lack of effective organization and flexible configuration of factor groups. Although some professional financial analysis software can draw more complex bar charts, they are often customized for specific financial scenarios and data formats, and it is difficult to meet the requirements of users to flexibly adjust the display method, analysis dimensions, chart styles, etc. of multi-factor groups according to their own needs. For example, when it is necessary to compare the performance of multiple financial factors (such as price-earnings ratio, price-to-book ratio, yield, etc.) under different time periods and different market segments, the existing technologies are difficult to quickly and conveniently achieve the dynamic configuration and visual display of multi-factor groups, and there is also a lack of interactivity in the charts, making it impossible for users to deeply explore data details. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide a financial data visualization analysis method based on a multi-factor group bar chart that overcomes the above problems or at least partially solves the above problems.

[0008] According to one aspect of the present invention, there is provided a financial data visualization analysis method based on a multi-factor group bar chart, and the analysis method includes:

[0009] Construct a multi-factor group data model;

[0010] Establish a visualization configuration interface;

[0011] Establish and implement an interaction function to obtain multiple groups of data;

[0012] Perform data integration processing on the multiple groups of data.

[0013] Optionally, the constructing of the multi-factor group data model specifically includes:

[0014] Construct a hierarchical data structure to organize multi-factor financial data;

[0015] Establish a multi-dimensional data index and create an index structure based on key information such as factor name, factor group name, and time range.

[0016] Optionally, the constructing of a hierarchical data structure to organize multi-factor financial data specifically includes:

[0017] Define a top-level factor group set object, including multiple factor group objects;

[0018] Each factor group object includes multiple financial factor objects. Each financial factor object includes a factor name, a factor value, a data time, an affiliated sector, and data source identification attribute information, so as to record and manage factor data in detail.

[0019] Optionally, the establishment of the visual configuration interface specifically includes:

[0020] A factor selection module, where the user selects the factors to be displayed from a predefined factor library through a dropdown menu, a search box, etc., and adds them to a multi-factor group. The user is supported to perform custom naming and grouping operations on the factor group;

[0021] A chart style setting module, which allows the user to adjust the color, width, spacing, font style and appearance attributes of the bar chart, and allows the user to select a preset theme style;

[0022] An axis setting function, where the user sets the label, scale range and scale interval parameters of the axis.

[0023] Optionally, the establishment and implementation of the interaction function and the acquisition of multiple groups of data specifically include:

[0024] A data screening function, which provides a screening condition input box on the visual interface. The user screens the data in the bar chart of the multi-factor group according to the time range, sector name, and factor value range conditions, and the chart will be updated in real time to display the screened results;

[0025] By right-clicking on a certain bar of the bar chart and selecting the sorting direction in the right-click menu, sort according to the corresponding factor;

[0026] A zoom function, which supports the user to zoom in and out of the bar chart through the mouse wheel or gesture operations; during the zooming process, the axis scales and data labels will be adjusted accordingly to maintain the readability of the chart;

[0027] Tooltip dynamic display, when the user hovers the mouse over the bar chart, a tooltip window will pop up to display the detailed information of the data represented by the bar chart, so as to obtain the key points of the data;

[0028] Click event response, when the user clicks on the bar chart, a corresponding event is triggered, such as popping up a detailed data panel, jumping to a relevant data details page or performing specific data analysis operations, enhancing the interactivity between the user and the chart, and the content of the pop-up window can also be customized through drag-and-drop operations.

[0029] Optionally, the detailed data information specifically includes the factor name, factor value, corresponding time and sector information.

[0030] Optionally, the data integration and processing of the multiple groups of data specifically include:

[0031] Data source connection management, supporting connection to a variety of common financial data sources and financial data interfaces;

[0032] Develop corresponding connection adapters according to the data sources, establish connections, obtain data and perform preliminary data format conversion;

[0033] Data cleaning and conversion, performing cleaning operations on the data obtained from the data sources, removing noise data and handling missing values; converting the cleaned data into a format suitable for D3 to draw bar charts according to a predefined data model;

[0034] Adopt a data caching strategy, cache the bar chart data that has been drawn, and when the user performs interactive operations, read the data from the cache and update the chart.

[0035] A financial data visualization analysis method based on a multi-factor group bar chart provided by the present invention, the analysis method includes: constructing a multi-factor group data model; establishing a visualization configuration interface; establishing and implementing an interactive function to obtain multiple groups of data; performing data integration processing on the multiple groups of data. It can extract, clean and integrate data from multiple financial data sources in different formats and sources, and convert it into a data format suitable for D3 to draw bar charts.

[0036] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically given below. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a flowchart of a financial data visualization analysis method based on a multi-factor group bar chart provided by an embodiment of the present invention;

[0039] Figure 2 It is a flowchart of performing data integration processing on the multiple groups of data provided by an embodiment of the present invention;

[0040] Figure 3 It is a multi-factor group model structure diagram provided by an embodiment of the present invention;

[0041] Figure 4 It is a schematic diagram of the visualization configuration interface provided by an embodiment of the present invention;

[0042] Figure 5 This is the data integration flowchart provided by the embodiments of the present invention. Detailed implementation manners

[0043] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0044] The terms "including" and "having" and any variations thereof in the description, claims and drawings of the present invention are intended to cover non-exclusive inclusion. For example, a series of steps or units are included.

[0045] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0046] As Figures 1-5 shown, a financial data visualization analysis method based on a configurable multi-factor group bar chart of D3 includes:

[0047] 1. Multi-factor group data model design:

[0048] Construct a hierarchical data structure to organize multi-factor financial data. Define a top-level "factor group set" object, which contains multiple "factor group" objects below. Each "factor group" object contains several "financial factor" objects. Each "financial factor" object contains attribute information such as factor name, factor value, data time, affiliated sector, data source identifier, etc., so as to record and manage factor data in detail. For example, in the stock analysis scenario, the "factor group set" can be the "stock comprehensive analysis factor group", which contains a "valuation factor group" (including factors such as price-to-earnings ratio and price-to-book ratio), an "earnings factor group" (including factors such as net profit and operating income growth rate), a "risk factor group" (including factors such as volatility and beta coefficient), etc.

[0049] Establish a multi-dimensional data index, create an index structure based on key information such as factor name, factor group name, time range, etc., to facilitate quick retrieval and screening of data for specific factors or factor groups, and improve data query efficiency.

[0050] 2. Visualization configuration interface:

[0051] Provide a factor selection module. Users can select the factors to be displayed from a predefined factor library through methods such as dropdown menus and search boxes, and add them to the multi-factor group. At the same time, users are supported to perform custom naming and grouping operations on the factor group.

[0052] Chart style setting module, which allows users to adjust the appearance attributes of bar charts, such as color, width, spacing, font style, etc., and select different chart themes (such as minimalist style, financial professional style, etc.) to meet personalized visualization needs.

[0053] Axis setting function. Users set parameters such as axis labels, scale ranges, and scale intervals to ensure that the axes can clearly and accurately reflect the data distribution.

[0054] 3. Implementation of interactive functions:

[0055] Data filtering function. Provide a filtering condition input box on the visualization interface. Users filter the data in the bar chart of the multi-factor group according to conditions such as time range, sector name, and factor value range, and the chart will be updated in real time to display the filtered results. By right-clicking on a bar in the bar chart, sorting can be performed according to the corresponding factor.

[0056] Zoom function, which supports users to zoom in and out of the bar chart through mouse wheel or gesture operations to view the details or overall trends of the data. During the zooming process, the axis scales and data labels will be adjusted accordingly to maintain the readability of the chart.

[0057] Tooltip dynamic display. When the user hovers the mouse over the bar chart, a tooltip window pops up, showing the detailed information of the data represented by the bar chart, including factor name, factor value, corresponding time, sector, etc., to help users quickly obtain the key points of the data.

[0058] Click event response. When the user clicks on the bar chart, corresponding events are triggered, such as popping up a detailed data panel, jumping to the relevant data details page, or performing specific data analysis operations, enhancing the interaction between the user and the chart. The content of the pop-up window can also be customized through drag-and-drop operations.

[0059] 4. Data integration module:

[0060] Data source connection management, which supports connecting to various common financial data sources, such as databases (SQL Server, Oracle, etc.), files (CSV, Excel, etc.), and financial data interfaces (such as Bloomberg, Wind, etc.). For different data sources, corresponding connection adapters are developed to be responsible for establishing connections, obtaining data, and performing preliminary data format conversions.

[0061] Data cleaning and transformation involve cleaning the data obtained from the data source, removing noisy data, handling missing values, etc. According to the predefined data model, the cleaned data is transformed into a format suitable for D3 to draw bar charts. For example, the data is transformed into JSON format, which contains necessary data structures such as factor groups, factor values, axis information, etc.

[0062] Adopt a data caching strategy to cache the bar chart data that has been drawn. When the user performs interactive operations (such as filtering and zooming without substantial changes in the data), directly read the data from the cache and update the chart to reduce the time overhead of data reloading and drawing.

[0063] Beneficial effects:

[0064] 1. A flexible configurable multi-factor group bar chart data model based on D3, which can effectively organize multi-factor data and support dynamic configuration.

[0065] 2. The multi-factor bar chart can intuitively display multi-dimensional data information. The close combination of the visual configuration interface and the multi-factor group bar chart enables users to conveniently set chart parameters and manage factor groups.

[0066] 3. Implement rich interactive functions, including data filtering, zooming, tooltip display, and click event response, etc., to enhance users' exploration and analysis capabilities of financial data.

[0067] 4. An efficient data integration module that can seamlessly connect to various financial data sources and perform data cleaning and transformation to ensure the accuracy and availability of the data.

[0068] 5. D3 drawing performance optimization strategies, including data caching and rendering algorithm optimization, to achieve fast visualization of large-scale financial data.

[0069] The above specific implementation manners further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A financial data visualization analysis method based on a multi-factor group bar chart, characterized in that: The analysis method comprises: Construct multi-factor group data models; Establish a visual configuration interface; Establish and implement interactive functions to obtain multiple sets of data; The multiple sets of data are subjected to data integration processing.

2. The method for visualizing and analyzing financial data based on a multi-factor group bar chart according to claim 1, characterized in that: The construction of the multi-factor group data model specifically includes: Build a hierarchical data structure to organize multi-factor financial data; Establish multi-dimensional data indexes and create index structures based on factor names, factor group names, and time range key information.

3. The method for visualizing and analyzing financial data based on a multi-factor group bar chart according to claim 2, characterized in that: The construction of a hierarchical data structure to organize multi-factor financial data specifically includes: Define a top-level factor group collection object, including multiple factor group objects; Each factor group object includes multiple financial factor objects, and each financial factor object includes factor name, factor value, data time, sector, and data source identification attribute information, so as to record and manage factor data in detail.

4. The method for visualizing and analyzing financial data based on a multi-factor group bar chart according to claim 1, characterized in that: The establishment of a visual configuration interface specifically includes: Factor selection module: users can select factors to be displayed from the predefined factor library through drop-down menus, search boxes, etc., and add them to the multi-factor group. Users can also perform custom naming and grouping operations on factor groups. The chart style setting module allows users to adjust the color, width, spacing, font style appearance attributes of the bar chart, and allows users to select preset theme styles; The axis setting function allows users to set the axis label, scale range and scale interval parameters.

5. The method for visualizing and analyzing financial data based on a multi-factor group bar chart according to claim 1, characterized in that: The establishment and realization of the interactive function and the acquisition of multiple sets of data specifically include: Data filtering function, providing a filtering condition input box on the visual interface, users can filter the data in the multi-factor group bar chart according to the time range, sector name, and factor value range conditions, and the chart will be updated in real time to display the filtered results; By right-clicking a column in the bar chart, select the sorting direction in the right-click menu and sort according to the corresponding factor; The zoom function allows users to zoom in and out of the bar chart using the mouse wheel or gestures. During the zooming process, the axis scales and data labels will be adjusted accordingly to maintain the readability of the chart. Tooltip is displayed dynamically. When the user hovers the mouse over the bar chart, a tooltip window pops up to display the detailed information of the data represented by the bar chart, so as to quickly obtain the key points of the data; Click event response: when the user clicks on a bar chart, a corresponding event is triggered, such as popping up a detailed data panel, jumping to the relevant data details page, or performing specific data analysis operations, thereby enhancing the interactivity between the user and the chart. The content of the pop-up window can also be customized by dragging and dropping.

6. The method for visualizing and analyzing financial data based on a multi-factor group bar chart according to claim 1, characterized in that: The detailed data information specifically includes factor name, factor value, corresponding time and section information.

7. The method for visualizing and analyzing financial data based on a multi-factor group bar chart according to claim 1, characterized in that: The data integration process of the multiple sets of data specifically includes: Data source connection management, supporting connection to a variety of common financial data sources and financial data interfaces; According to the data source, develop the corresponding connection adapter, establish the connection, obtain data and perform preliminary data format conversion; Data cleaning and conversion: clean the data obtained from the data source, remove noise data, and process missing values; according to the pre-defined data model, convert the cleaned data into a format suitable for D3 to draw a bar chart; A data caching strategy is adopted to cache the bar chart data that has been drawn. When the user performs interactive operations, the data is read from the cache and the chart is updated.

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