Graph generation system and method based on Highcharts chart library

Abstract

The invention discloses a chart generation system and method based on a Highcharts chart library. The system comprises a data acquisition module, wherein the data acquisition module comprises an index library data acquisition unit and a prediction index library data acquisition unit; the chart generation module comprises a date and value drawing unit, a combined algorithm drawing unit, a chart obtaining drawing unit and a chart picture obtaining unit; the chart theme configuration module comprises a default theme configuration unit, a personalized theme configuration unit and an independent field configuration unit; and the chart output module comprises a research report calling chart unit, a PPT calling chart unit, a logic deduction calling chart unit and a chart library framework calling chart unit. The system can improve usability and individuation of chart generation, and meets complex business requirements in a financial investment research system.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and particularly to a chart generation system and method based on the Highcharts chart library. Background Art

[0002] In today's digital age, data visualization has become an important tool for information transmission and decision-making support. Especially in the field of financial investment research, analysts need to support investment decisions through complex data analysis and chart display. However, there are many limitations in the existing technology for data visualization:

[0003] High technical threshold: Traditional data visualization tools, such as Tableau and Power BI, although powerful, usually require users to have certain programming or data processing knowledge. This makes it difficult for non-technical users (such as financial analysts, market researchers, etc.) to use, restricting their wide application.

[0004] Insufficient customization: Existing tools have limitations in chart customization and are difficult to meet specific business requirements. The financial industry has high requirements for the professionalism of charts. For example, complex correlation analysis, cross-variety analysis, etc. Existing tools often cannot directly support these needs, resulting in a large amount of manual adjustment and secondary development required by users.

[0005] Complex data processing: In financial investment research, data often needs to go through complex calculations and processing before it can be used for chart generation. For example, seasonal analysis, fitting equation calculation, correlation analysis, etc. When existing tools handle these complex calculations, they usually require manual operation by users, which is time-consuming and error-prone.

[0006] Insufficient resource sharing and management: Existing tools have deficiencies in the storage, management, and resource sharing functions of the results. For example, it is difficult for team members to efficiently share and manage chart resources, affecting the team's collaboration efficiency and the inheritance of the results.

[0007] Therefore, there is an urgent need to propose a chart generation method based on the Highcharts chart library to improve the usability and personalization of chart generation and meet the complex business requirements in the financial investment research system. Summary of the Invention

[0008] Aiming at some problems existing in the prior art, the purpose of the present invention is to provide a chart generation system and method based on the Highcharts chart library, aiming to provide an efficient, convenient, and personalized data visualization solution for users in the financial industry.

[0009] To achieve the above object, on the one hand, the present invention provides a chart generation system based on the Highcharts chart library, including:

[0010] A data acquisition module for acquiring index data, where the index data includes data indexes and prediction indexes; the data acquisition module includes an index library data acquisition unit and a prediction index library data acquisition unit; the index library data acquisition unit is used to process the basic index data in the index library to obtain the data indexes; the prediction index library data acquisition unit is used to call the data indexes and generate the prediction indexes according to prediction rules;

[0011] A chart generation module for generating charts according to the index data, where the charts include basic charts and business charts; the chart generation module includes a date and value plotting unit, a combined algorithm plotting unit, a chart acquisition plotting unit, and a chart image acquisition unit; the date and value plotting unit is used to generate the basic charts according to the corresponding relationship between the date and value of the index data; the combined algorithm plotting unit is used to combine business requirements and generate the business charts by using the corresponding business calculation models for the index data; the chart acquisition plotting unit is used to implement the plotting of the charts by calling the core functions of the Highcharts chart library; the chart image acquisition unit is used to acquire the image format data of the charts;

[0012] A chart theme configuration module for performing personalized settings on the generated charts;

[0013] A chart output module for applying the generated charts to different output scenarios.

[0014] Preferably, the chart theme configuration module includes a default theme configuration unit, a personalized theme configuration unit, and an independent field configuration unit; the default theme configuration unit is used to maintain the default styles during the chart generation process; the personalized theme configuration unit is used for users to perform custom settings on the visual elements of the charts; the independent field configuration unit is used to associate independent configuration fields with the structure of each chart;

[0015] The chart output module includes a research report chart calling unit, a PPT chart calling unit, a logical deduction chart calling unit, and a gallery framework chart calling unit; the research report chart calling unit is used to embed the charts in the investment research reports; the PPT chart calling unit is used to insert the chart data into the PPT presentation; the logical deduction chart calling unit is used to call the charts during the logical deduction process; the gallery framework chart calling unit is used to store the charts in the gallery framework.

[0016] On the other hand, the present invention also proposes a chart generation method based on the Highcharts chart library, using the system as described above. The method includes the steps of:

[0017] Obtain index data: After processing the basic index data in the index library and the prediction index library, obtain the processed basic index data and prediction index data;

[0018] Generate a chart: Based on the correspondence between the date and value of the index data, use the date and value plotting unit to generate a basic chart; in combination with business requirements, call the characteristic algorithm and calculation model through the combined algorithm plotting unit to generate a business chart with complex business logic; call the obtain chart plotting unit to render and draw the chart using the Highcharts chart library; through the obtain chart picture unit, convert the drawn chart into picture format data;

[0019] Chart theme configuration: Perform personalized settings on the generated chart;

[0020] Preferably, the added basic index data includes at least one of third-party data sources, manual data, and terminal interface data.

[0021] Preferably, in the step of obtaining index data, the calculation model adopted by the index library data acquisition unit encapsulates at least one model algorithm including index operation, cumulative value, year-on-year and month-on-month difference, moving average calculation, N-value month-on-month and year-on-year difference, rise and fall frequency, fitting residual, super-seasonality, and daily average value.

[0022] Preferably, the prediction index data is generated in the prediction index library according to the prediction rules based on the basic index data in the index library; the prediction rules include at least one of using the latest value, fixed value, year-on-year and month-on-month difference, month-on-month and year-on-year difference, linear extrapolation, fixed final value, and annual backward deduction.

[0023] Preferably, the basic chart includes at least one of a line chart, a stacked bar chart, and a combination chart.

[0024] Preferably, the business chart includes at least one of a seasonal chart, a time series scatter plot, a cross-sectional scatter plot, a cross-sectional combination chart, a bar chart, a radar chart, a commodity price chart, a profit price chart, a correlation analysis chart, a fitting equation chart, a statistical feature chart, and a cross-variety analysis chart.

[0025] Preferably, the personalized settings include customizing the styles and colors of the lines, legends, titles, identifiers, horizontal and vertical axes, and plotting areas of the chart.

[0026] Preferably, after performing personalized settings on the generated chart, the method further includes:

[0027] Output charts: Apply the generated charts to different output scenarios, including embedding in research reports, inserting into PPT presentations, for logical deduction, and storing in the gallery framework.

[0028] Compared with the prior art, the present invention has at least the following technical effects:

[0029] First of all, existing data visualization tools such as Tableau, Power BI, etc. usually require users to have certain programming or data processing knowledge, which limits the use of non-technical background users. By encapsulating business requirements, the present invention can realize the function of generating highly available charts without programming knowledge, enabling non-technical users to easily create charts. For example, the index library data acquisition unit and the prediction index library data acquisition unit in the data acquisition module can automatically complete the secondary cleaning, calculation and generation of data, and users can obtain the required index data without manually processing the data, greatly reducing the usage threshold.

[0030] In addition, existing tools do not support the generation of highly customized charts in some cases and are difficult to meet specific business requirements. Based on the extension of Highcharts, the present invention can realize various line types, custom configurations and style configurations to meet the personalized needs in different business scenarios. For example, the chart theme configuration module includes a default theme configuration unit, a personalized theme configuration unit and an independent field configuration unit. Users can make detailed custom settings for the visual elements of the chart according to their personal preferences. At the same time, an independent configuration field can be associated with the structure of each chart to meet the personalized needs of specific charts.

[0031] At the same time, when existing tools process complex calculations and data processing, users usually need to operate manually, which is time-consuming and error-prone. The present invention integrates complex calculation models such as correlation charts, cross-variety analysis charts and fitting equation charts, and automatically executes tasks such as index calculation, data extraction and frequency conversion. For example, the combined algorithm drawing unit can generate business charts with complex business logic through characteristic algorithms and calculation models in combination with business requirements. Users can quickly generate the required charts without manually performing complex calculations.

[0032] Finally, the resource sharing and result retention functions of existing tools may be imperfect, affecting the team collaboration efficiency. Through intelligent design, the present invention can realize the resource management, result retention and resource sharing of charts. For example, the chart output module includes a research report call chart unit, a PPT call chart unit, a logical deduction call chart unit and a gallery framework call chart unit, which can apply the generated charts to different research result output scenarios, such as research reports, PPT presentations, logical deductions and gallery frameworks, facilitating team members to share and reuse chart resources, thereby improving the team collaboration efficiency and ensuring the storage and inheritance of results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as a limitation of the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0034] Figure 1 It is a system module structure diagram in a specific embodiment of the present invention;

[0035] Figure 2 It is the overall flowchart of the method in a specific embodiment of the present invention;

[0036] Figure 3 It is the detailed flowchart of the method in a specific embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will describe in more detail a chart generation system and method based on the Highcharts chart library of the present invention with reference to the schematic diagrams, which show the preferred embodiments of the present invention. It should be understood that those skilled in the art can modify the present invention described herein while still achieving the advantageous effects of the present invention. Therefore, the following description should be understood as a broad guidance for those skilled in the art and not as a limitation of the present invention.

[0038] For clarity, not all features of the actual embodiments are described. In the following description, well-known functions and structures are not described in detail because they would obscure the present invention with unnecessary details. It should be considered that in the development of any actual embodiment, a large number of implementation details must be made to achieve the specific goals of the developer, such as changing from one embodiment to another according to the relevant system or business limitations. Additionally, it should be considered that such development work may be complex and time-consuming, but it is only routine work for those skilled in the art.

[0039] In the following paragraphs, the present invention will be described more specifically by way of example with reference to the drawings. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are all in a very simplified form and use non-precise scales, only for the purpose of facilitating and clearly assisting in explaining the objectives of the embodiments of the present invention.

[0040] As mentioned in the background art, there are many deficiencies in existing traditional data visualization tools (such as Tableau, Power BI, etc.): on the one hand, they have relatively high technical requirements for users, restricting the use of non-technical background users; on the other hand, they perform poorly in aspects such as chart customization, complex calculation and data processing automation, resource sharing and management, and deep integration with investment research business scenarios, making it difficult to meet specific business needs, affecting the efficiency of team collaboration and the inheritance of results, and also unable to fully adapt to the diverse output requirements in the investment research system. In view of this, this embodiment provides a chart generation system and method based on the Highcharts chart library.

[0041] Highcharts is an open-source chart library based on JavaScript and is widely used in the field of data visualization. It provides a rich variety of chart types (such as line charts, bar charts, pie charts, scatter plots, etc.) and supports highly customizable configurations and style settings. Through a simple and easy-to-use API, developers can quickly generate interactive charts to meet various data display needs. In addition, in the field of financial investment research, data visualization is an important tool for information transmission and decision support. The functional characteristics of Highcharts make it an ideal basic library for chart generation, capable of helping users efficiently present complex data in an intuitive and easy-to-understand chart form. Through extension and encapsulation, Highcharts can further meet the complex business needs of the financial industry and provide powerful visualization support for financial analysts and researchers.

[0042] On the one hand, as Figure 1 shown, this embodiment provides a chart generation system based on the Highcharts chart library, including a data acquisition module, a chart generation module, a chart theme configuration module, and a chart output module.

[0043] The data acquisition module is used to acquire metric data, and the metric data includes data metrics and prediction metrics. The data acquisition module includes a metric library data acquisition unit and a prediction metric library data acquisition unit; the metric library data acquisition unit is used to process the basic metric data in the metric library to obtain the data metrics; the prediction metric library data acquisition unit is used to call the data metrics generated in the metric library and generate the prediction metrics according to the prediction rules;

[0044] A chart generation module, which is used to generate charts according to the index data. The charts include a basic chart and a business chart. The chart generation module includes a date and value plotting unit, a combined algorithm plotting unit, a chart acquisition plotting unit, and a chart image acquisition unit. The date and value plotting unit is used to generate the basic chart according to the corresponding relationship between the date and value of the index data. The combined algorithm plotting unit is used to combine business requirements and generate the business chart according to the corresponding business calculation model of the index data. The chart acquisition plotting unit is used to implement the plotting of the chart by calling the core functions of the Highcharts chart library. The chart image acquisition unit is used to acquire the image format data of the chart.

[0045] A chart theme configuration module, which is used to perform personalized settings on the generated charts. The chart theme configuration module includes a default theme configuration unit, a personalized theme configuration unit, and an independent field configuration unit. The default theme configuration unit is used to maintain the default style during the chart generation process. The personalized theme configuration unit is used for users to customize the visual elements of the chart according to their personal preferences. The independent field configuration unit is used to associate independent configuration fields with the structure of each chart to meet the personalized needs of specific charts.

[0046] A chart output module, which is used to apply the generated charts to different investment research result output scenarios. The chart output module includes a research report chart calling unit, a PPT chart calling unit, a logical deduction chart calling unit, and a gallery framework chart calling unit. The research report chart calling unit is used to embed the chart in the investment research report. The PPT chart calling unit is used to insert the chart data into the PPT presentation. The logical deduction chart calling unit is used to call the chart during the logical deduction process. The gallery framework chart calling unit is used to store the chart in the gallery framework.

[0047] On the other hand, as Figure 2 and Figure 3 shown, this embodiment also provides a chart generation method based on the Highcharts chart library. Using the system as described above, the method includes the steps:

[0048] S1: Obtain index data: After processing the basic index data in the index library and the prediction index library, obtain the data index and the prediction index.

[0049] S2: Generate charts: Based on the obtained metric data, through the correspondence between dates and values, use the date and value plotting unit to generate a basic chart; in combination with business requirements, call the feature algorithms and calculation models through the combined algorithm plotting unit to generate business charts with complex business logics; call the obtain chart plotting unit to render and draw the chart using the Highcharts library; through the obtain chart image unit, convert the drawn chart into picture format data.

[0050] S3: Chart theme configuration: Perform personalized settings on the generated charts, including selecting the default theme, customizing the personalized theme, and setting independent field configurations.

[0051] S4: Chart output: Apply the generated charts to different investment research result output scenarios, including embedding in investment research reports, inserting into PPT presentations, for logical deduction, and storing in the gallery framework.

[0052] It should be noted that this embodiment realizes various chart types that fit the business and the financial industry, as well as the settings of high-definition chart configurations and styles based on the Highcharts chart library. The ETA metric library in this embodiment is a data storage and processing module related to the financial investment research system, mainly used for storing and managing the metric data that has been cleaned, calculated, and generated.

[0053] Specifically, for step S1, the sources of the added basic metric data include at least one of third-party data sources, manual data, and terminal interface data. All the basic metric data will finally be secondarily cleaned and calculated in the ETA metric library to generate the final ETA metric data. The metric data is either an ETA data metric or an ETA prediction metric and can be used in the ETA system; among them, the ETA metric library and the ETA prediction metric library contain multiple calculation models for secondary cleaning of data.

[0054] Specifically, the common calculation models in the ETA metric library include at least one model algorithm encapsulation of metric operations, cumulative values, year-on-year and month-on-month differences, moving average calculations, N-value month-on-month and year-on-year differences, up and down frequencies, fitting residuals, super-seasonality, daily averages.

[0055] In addition, the ETA prediction metric is generated in the prediction metric library according to the prediction rules based on the data metrics in the metric library. That is to say, the ETA prediction metric library depends on the data metrics in the ETA metric library, and multiple prediction rules include using at least two combinations or single rules of the latest value, fixed value, year-on-year and month-on-month differences, month-on-month and year-on-year differences, linear extrapolation, fixed final value, annual backward deduction, etc. to predict the future data trend. At the same time, the calculation models in the ETA metric library can also be called to perform secondary processing and calculation on the ETA prediction metric.

[0056] In a specific embodiment, for step S2, the generation of various data charts is implemented based on Highcharts, and the specific idea is as follows:

[0057] It should be noted that in this embodiment, the principle of chart drawing is to implement chart drawing through the core initialization function Highcharts.chart(container, options) of Highcharts. Among them, container is the target DOM container for chart drawing, usually an HTML element with a specific id, which is used to specify the position where the chart is displayed; options is a JavaScript object that contains various configuration items and data of the chart and is used to define the type, style, data series, and other functional characteristics of the chart. The Dom container is an area for chart drawing, and the id of the Dom needs to be passed in; the configuration item option is a key parameter for chart drawing; the main core components include xAxis (X-axis configuration), yAxis (Y-axis configuration), series [data column configuration], and chart [global configuration].

[0058] The Highstock stock chart control is used to develop stock trend or timeline charts with a large amount of data. Through the correspondence between dates and values, a basic chart with a time line on the X-axis and values on the Y-axis is generated. The basic chart includes at least one of a line chart, a stacked column chart, and a combination chart. The implementation of specific basic chart types is as follows:

[0059] Line chart: The generation method and drawing idea of the line chart are mainly generated through the API of Highstock. Generate a chart based on the correspondence between index data and dates and values. Set type in series to change the style of the line. The value of series.data has a structure of [[date1, value1], [date2, value2],...]. This is the basic drawing method of the chart and does not require secondary processing logic. Similarly, the generation logic of the stacked column chart is the same as that of the line chart, and the generation style type of series is column. Similarly, the generation logic of the combination chart is the same as that of the line chart, and the generation style type of series can be freely combined. Currently, the types that can be combined are smooth lines, columns, and non-smooth lines.

[0060] Furthermore, by combining the indicator data with a characteristic algorithm and a calculation model, a method for processing data is encapsulated to generate charts for characteristic services, eliminating the need for users to repeatedly process data. By combining the usability and professionalism of financial industry charts and integrating with the business, business charts with complex business logics are developed. The business charts include at least one of a seasonal chart, a time series scatter plot, a cross-sectional scatter plot, a cross-sectional combination chart, a bar chart, a radar chart, a commodity price chart, a profit-price chart, a correlation analysis chart, a fitting equation chart, a statistical feature chart, and a cross-variety analysis chart. The specific implementation methods for each business chart are as follows: (The indicators mentioned below refer to indicator data, and the indicator data includes either data indicators or prediction indicators).

[0061] Seasonal chart: Generate a trend chart of the past N years for this indicator based on the indicator data, and compare the data trends of each year with those of other years to visually observe seasonal fluctuations. By grouping the same indicator data by different years, the time interval of half a year, one year, or across years can be determined by defining the X-axis time range, and 5-year or 10-year data can be displayed by selecting the years.

[0062] Time series scatter plot: Generate a scatter plot style, select at least two indicator data, and generate a chart with numerical values on both the X-axis and Y-axis for the numerical coordinates on the same date. The specific implementation logic is as follows: First, obtain all the data of the two indicator data, then traverse the two sets of data and extract the items with the same date. Use the value of the first indicator data as the value of X and the value of the second indicator data as the value of Y, and render it.

[0063] Cross-sectional scatter plot: This chart is generated by calculating the coordinate points through the linear regression equations of multiple sets of indicator data and generating multiple scatter points in a plane. The specific implementation logic is as follows: Select multiple indicators for the X-axis and the corresponding number of indicators for the Y-axis, as well as the data dates of the X and Y axes, which is called a series. First, obtain all the data of the series of indicators, traverse to find the values corresponding to the data dates, and use the values of the X and Y axes as X and Y in the linear regression equation for calculation. The calculation results can determine a scatter point coordinate, and this chart is rendered by one or more sets of calculated scatter point coordinates.

[0064] Combined Section Chart: The combined section chart can freely combine curve charts, line charts, and bar charts. The specific implementation is as follows: Multiple series can be added. For each series, N indicators can be selected and a chart type can be specified. The indicator can choose the latest date of the indicator or the reference date. The latest date of the indicator means obtaining the latest data. If a date shift has been performed, then the data of the shifted date is taken. If the reference date is selected, then the data of the indicator will be obtained in the way specified in the reference date. The reference date setting includes three methods: taking the latest date of a certain indicator, taking the system date, and taking a fixed date. Through the above settings, the data of the indicator is obtained to generate the chart type of the series, and finally a chart composed of multiple series combined is rendered.

[0065] Bar Chart: Generate a bar chart style. Multiple indicators and multiple dates can be selected. The X-axis is the selected indicators and the corresponding dates, and the Y-axis is the data corresponding to the indicator dates. The date options are the latest date, N days ago, and a fixed date. The specific implementation is as follows: First, obtain all the data of multiple indicators, then traverse the array, take the corresponding values according to each selected date, arrange the indicators and dates in sequence as the X-axis information, and the corresponding values as the Y-axis, and then render it.

[0066] Radar Chart: Generate an N-sided polygon chart, where N is the number of indicators, and at least 3 indicators are required. Multiple dates can be selected. The specific implementation logic is as follows: First, obtain all the data of the selected indicators, then traverse the data of each indicator, take the corresponding value of the selected date from it as the length of one side of the date, and finally find the values of each date of all indicators and render them into an N-sided polygon with the indicator values as the side lengths. Multiple dates will form multiple N-sided polygon charts.

[0067] Commodity Price Chart: Generate a price curve chart of futures based on the spot price indicator. The specific implementation logic is as follows: Select a futures indicator and multiple spot price indicators, and select one of the spot price indicators as the date benchmark, and then configure the date. The date options are the latest date, N days ago, and a fixed date. Obtain the data corresponding to the futures indicator according to the configured date, and then obtain the values of the spot indicators corresponding to the date. Finally, generate a curve chart. The X-axis is the spot indicators in sequence, from the selected date month + 1 to the selected date month + N2, where N2 changes according to the data months found for the futures indicator, and the Y-axis is the corresponding values of the futures indicator and the spot indicator dates.

[0068] Profit - Price Chart: The profit - price curve has similarities with the commodity - price curve. The generated style is also a price curve graph. The specific implementation logic is as follows: Configure a formula for calculating the profit on the trading panel. Multiple futures indicators can be selected in the formula for arithmetic operations such as addition, subtraction, multiplication, and division. Then, select multiple spot - price indicators, and choose one of them as the date benchmark. Next, configure the date. Based on the date, obtain the futures indicators involved in the profit setting on the trading panel and perform formula calculations. For the finally generated curve graph, the X - axis is successively the spot indicator, the selected date month + 1 to date + N2, and the Y - axis is the monthly data value of the futures indicator obtained according to the calculation formula.

[0069] Correlation Analysis Chart: The correlation chart is drawn using two analysis modes, namely the single - factor mode and the multi - factor mode. Specifically as follows:

[0070] Single - factor mode: The specific implementation logic is as follows: Select the target indicator A and another indicator B. Calculate the number of days that the benchmark date needs to be shifted according to the calculation window and analysis period. Obtain the data of the target indicator from the number of days pushed forward to the latest date. Then, obtain all the data of indicator B. If the frequencies of indicator B and A are inconsistent, linear - equation interpolation method will be used to supplement the data to make the frequencies of the two indicators consistent. Then, calculate the correlation coefficient between the shifted days of indicator B and each date of indicator A. The generated curve graph has the positive and negative analysis - period intervals on the X - axis and the correlation coefficient on the Y - axis.

[0071] Multi - factor mode: Select a target indicator, and then select multiple indicators as a set of series indicators. Multiple series can be selected. Then, according to the calculation window and analysis period, calculate the correlation - coefficient matrix between the target indicator and one or more series indicators. By selecting the calculation results in the matrix, a set of curve graphs is generated.

[0072] Fitting - Equation Chart: The fitting - equation chart consists of three parts, and the generated styles are all curve graphs. The three parts are the elasticity - coefficient curve graph, the intercept curve graph, and the correlation curve graph. The specific implementation logic is as follows: Select at least two groups of X - axis and Y - axis indicators. Based on the first indicators of the selected X - axis and Y - axis, use the lower frequency of the two as the benchmark date. Based on the benchmark - date sequence, respectively extract the indicator data on the X - axis and Y - axis as the X and Y values for participating in the calculation of the fitting linear equation, and calculate R 2 . For the finally calculated results, the X - axis of the three graphs is the benchmark date, the gradient value calculated by the linear equation is the Y - axis of the elasticity - coefficient curve graph, the calculated intercept is the Y - axis of the intercept curve graph, and the value of R 2 is the Y - axis of the correlation curve graph.

[0073] Statistical - Feature Chart: The statistical feature consists of four parts: a curve graph, standard deviation, percentile, and frequency distribution. The generated styles of the four parts are all curve graphs. The specific implementation is as follows:

[0074] Line graph: Select an indicator and a time range, obtain the indicator data within that time range. The X-axis represents time, and the Y-axis represents the data value.

[0075] Standard deviation graph: Set a rolling period, calculate the sample standard deviation s of the indicator data within the selected time range, s = sqrt(((x1 - x)^2+(x2 - x)^2+......+(xn - x)^2) / (n - 1)), where n represents the number of data points. The X-axis represents the date of the indicator data, and the Y-axis represents the standard deviation calculated for the corresponding date.

[0076] Percentile graph: Select a time length and an algorithm to calculate the percentile of the data within the selected time range. There are two algorithms for percentiles. The first is the percentile algorithm based on the number of data points. That is, for the first (earlier date) and the last data points (later date) in the data range, they are (T1, S1) and (T2, S2) respectively, where T represents time and S represents the corresponding value. The percentile based on the number of data points = (n - 1) / (N - 1), and when N = 1, it is not calculated. N is the number of indicator data points in the time period from T1 to T2, and n is the number of data points less than or equal to S2. The second is the percentile algorithm for the data range. That is, for the data within the selected time range, take the maximum value Max and the minimum value Min, calculate Max - Min, and the percentile = (current value - Min) / (Max - Min). When Max = Min, it is not calculated. The generated graph has the date of the indicator data on the X-axis and the percentile value calculated by the corresponding algorithm on the Y-axis.

[0077] Frequency distribution graph: Select a time range and specify the number of frequencies for the frequency graph. The algorithm is to take the maximum and minimum values within the selected time range, divide them into multiple intervals with the same spacing according to the number of frequency bands (left-closed and right-open, and the last interval is left-closed and right-closed), count the number of data points falling into each interval, the frequency is the number of data points falling into a certain interval divided by the total number of data points within the selected time period, and the cumulative frequency is accumulated starting from the frequency corresponding to the interval where the minimum value is located. The generated graph consists of two curves for frequency and cumulative frequency. The X-axis represents the minimum value obtained by the algorithm for the time range, and the Y-axis represents the frequency and cumulative frequency values obtained by the algorithm.

[0078] Cross-variety analysis graph: Set multiple varieties and multiple labels. Each label is associated with a specified indicator for each variety. By selecting one label for each of the X-axis and Y-axis, and selecting N1 varieties and N2 labels, use the percentile algorithm in the above statistical characteristic graphs to calculate the percentile of the indicator data within the selected range. The percentile value of the indicator in the label selected on the X-axis is used as the X-axis coordinate of the scatter point, and the label selected on the Y-axis is used as the Y-axis coordinate of the scatter point. The generated graph shows N1 scatter points for the selected number of varieties. The X-axis range is from the minimum value to the maximum value of the X coordinates of the scatter points, and the Y-axis range is from the minimum value to the maximum value of the Y coordinates of the scatter points.

[0079] In a specific embodiment, to ensure operational convenience and fast chart export, the ETA system provides a copy chart button that can be directly clicked and pasted into an office or WeChat window. The implementation is as follows:

[0080] Obtain the chart image; obtain the svg code of the chart through Chart.getSvg(), and then convert the chart into a Blob object through canvas.toBlob();

[0081] Define the clipboard; create a paste content through new ClipboardItem() of BOM, and write the content into the clipboard through navigator.clipboard.write; finally, realize clicking to copy the chart and pasting out the exported image of the chart.

[0082] In this embodiment, for step S3, the combination of the theme style and configuration items realizes the application of highly personalized charts. On the basis of the outer layer of various chart business logics, the functions of chart themes and chart configurations are added, thus enriching the diversity and customization of chart theme colors. In the logic of drawing the graph, it follows the priority of chart details configuration > global chart theme configuration > default configuration. The specific idea is as follows:

[0083] First, maintain a set of default configurations defaultOptions at the bottom layer, which are used to define the default chart styles, including line thickness, scatter point size, column stacking, color, display rules of floating window tooltip, default styles of XY axes, and the position of the legend.

[0084] In addition, maintain the theme styles ChartTheme of each chart in the ETA chart configuration module, which defines the line thickness, scatter point size, column stacking, color, display rules of floating window tooltip, default styles of XY axes, and further details including line types, text colors, and sizes. After selecting the chart theme ChartTheme associated with the chart during chart addition and editing, the styles of the corresponding theme will be applied.

[0085] Finally, on the structure of each chart, independent configuration fields will also be associated. The configuration fields include the left and right axes, identification line area, chart source, line thickness and color. It should be noted that when drawing the style, the configuration fields on the chart are given the first priority, and then the configurations of the associated chart theme ChartTheme are taken. This design can not only facilitate the unified application of the same theme style globally but also take into account the needs of some charts for personalized customization.

[0086] When rendering a chart, distinguish the chart type and call the API of Highcharts.chart or Highcharts.stockChart to draw the chart. Merge {...defaultOptions,...ChartTheme,...options} all configuration items to generate the final configuration of the chart for rendering.

[0087] In this embodiment, for step S4, after performing personalized settings on the generated chart, the method further includes outputting the chart: applying the generated chart to different output scenarios of investment research results. The generated data chart is applied to modules such as the research report, PPT, deduction logic, and gallery framework produced. Specifically, it includes embedding in the investment research report, inserting into the PPT presentation, being used for logical deduction, and storing in the gallery framework, thereby realizing the combination of the chart and the products of the research results.

[0088] In summary, a chart generation system and method based on the Highcharts chart library are provided in the embodiments of the present invention. First, existing data visualization tools such as Tableau, PowerBI, etc. usually require users to have certain programming or data processing knowledge, which limits the use of non-technical background users. The present invention can realize the function of generating highly available charts without programming knowledge by encapsulating business requirements, enabling non-technical users to easily create charts. For example, the index library data acquisition unit and the prediction index library data acquisition unit in the data acquisition module can automatically complete the secondary cleaning, calculation, and generation of data. Users can obtain the required index data without manually processing the data, greatly reducing the usage threshold.

[0089] In addition, existing tools do not support the generation of highly customized charts in some cases and are difficult to meet specific business requirements. Based on the extension of Highcharts, the present invention can realize various line types, custom configurations, and style configurations to meet the personalized needs in different business scenarios. For example, the chart theme configuration module includes a default theme configuration unit, a personalized theme configuration unit, and an independent field configuration unit. Users can make detailed custom settings for the visual elements of the chart according to their personal preferences. At the same time, each chart can also be associated with independent configuration fields in terms of structure to meet the personalized needs of specific charts.

[0090] Meanwhile, when existing tools handle complex calculations and data processing, they usually require manual operations by users, which are time-consuming and error-prone. The present invention integrates complex calculation models such as correlation charts, cross-variety analysis charts, and fitting equation charts, and automatically executes tasks such as index calculation, data extraction, and frequency conversion. For example, the combined algorithm drawing unit can generate business charts with complex business logics according to business requirements through characteristic algorithms and calculation models. Users can quickly generate the required charts without manually performing complex calculations.

[0091] Finally, the resource sharing and result retention functions of existing tools may not be perfect, affecting the efficiency of team collaboration. Through intelligent design, the present invention can achieve resource management, result retention, and resource sharing of charts. For example, the chart output module includes a research report calling chart unit, a PPT calling chart unit, a logical deduction calling chart unit, and a gallery framework calling chart unit, which can apply the generated charts to different investment research result output scenarios, such as investment research reports, PPT presentations, logical deductions, and gallery frameworks, facilitating team members to share and reuse chart resources, thereby improving the efficiency of team collaboration and ensuring the storage and inheritance of results.

[0092] The above are only the preferred embodiments of the present invention and do not impose any limitation on the present invention. Any person skilled in the art within the technical field, without departing from the technical solution of the present invention, makes any form of equivalent substitution or modification and other changes to the technical solution and technical content disclosed by the present invention, all of which fall within the content of the technical solution of the present invention and still belong to the protection scope of the present invention.

Claims

1. A chart generation system based on Highcharts chart library, characterized in that: include: A data acquisition module is used to acquire indicator data, wherein the indicator data includes data indicators and prediction indicators; the data acquisition module includes an indicator library data acquisition unit and a prediction indicator library data acquisition unit; the indicator library data acquisition unit is used to process the basic indicator data in the indicator library to obtain the data indicators; the prediction indicator library data acquisition unit is used to call the data indicators and generate the prediction indicators according to the prediction rules; A chart generation module is used to generate charts according to the indicator data, and the charts include basic charts and business charts; the chart generation module includes a date and value drawing unit, a combined algorithm drawing unit, a chart acquisition drawing unit and a chart image acquisition unit; the date and value drawing unit is used to generate the basic chart according to the corresponding relationship between the date and value of the indicator data; the combined algorithm drawing unit is used to generate the business chart according to the corresponding business calculation model based on the indicator data in combination with business needs; the chart acquisition drawing unit is used to realize the drawing of the chart by calling the core function of the Highcharts chart library; The chart image acquisition unit is used to acquire the image format data of the chart; Chart theme configuration module, used to personalize the generated charts; The chart output module is used to apply the generated charts to different output scenarios.

2. The system according to claim 1, characterized in that The chart theme configuration module includes a default theme configuration unit, a personalized theme configuration unit and an independent field configuration unit; the default theme configuration unit is used to maintain the default style in the chart generation process; the personalized theme configuration unit is used for users to customize the visual elements of the chart; the independent field configuration unit is used to associate independent configuration fields with the structure of each chart; The chart output module includes a research report calling chart unit, a PPT calling chart unit, a logic deduction calling chart unit and a gallery framework calling chart unit; the research report calling chart unit is used to embed charts into investment research reports; the PPT calling chart unit is used to insert chart data into PPT presentations; the logic deduction calling chart unit is used to call charts during the logic deduction process; and the gallery framework calling chart unit is used to store charts in the gallery framework.

3. A method for generating a chart based on the Highcharts chart library, implemented using the system as described in any one of claims 1-2, characterized in that: The method comprises the steps of: Obtaining indicator data: After processing the basic indicator data in the indicator library and the forecast indicator library, data indicators and forecast indicators are obtained; Generate charts: Based on the acquired indicator data, basic charts are generated using the date and value drawing unit through the correspondence between date and value; combined with business needs, characteristic algorithms and calculation models are called by combining the algorithm drawing unit to generate business charts with complex business logic; Call to obtain the chart drawing unit, and use the Highcharts library to render and draw the chart; by obtaining the chart image unit, convert the drawn chart into image format data; Chart theme configuration: Personalize the generated chart.

4. The method according to claim 3, characterized in that The added basic indicator data includes at least one of a third-party data source, manual data and terminal interface data.

5. The method according to claim 3, characterized in that: In the step of obtaining the indicator data, the calculation model adopted by the indicator library data acquisition unit includes at least one model algorithm encapsulated from indicator operation, cumulative value, year-on-year difference, moving average calculation, N-value month-on-month difference, frequency increase and decrease, fitting residual, super seasonality, and daily average.

6. The method according to claim 3, characterized in that The prediction indicators are generated in the prediction indicator library according to prediction rules based on the data indicators in the indicator library; the prediction rules include adopting at least one of the latest value, fixed value, year-on-year difference, quarter-on-quarter difference, linear extrapolation, final value, and annual reverse calculation.

7. The method according to claim 3, characterized in that The basic chart includes at least one of a curve chart, a stacked bar chart, and a combination chart.

8. The method according to claim 3, characterized in that The business charts include at least one of seasonal charts, time series scatter charts, cross-sectional scatter charts, cross-sectional combination charts, bar charts, radar charts, commodity price charts, profit price charts, correlation analysis charts, fitting equation charts, statistical feature charts, and cross-variety analysis charts.

9. The method according to claim 3, characterized in that: The personalized settings include customizing the styles and colors of the chart's lines, legends, titles, logos, horizontal and vertical axes, and drawing areas.

10. The method according to claim 3, characterized in that After personalizing the generated chart, the method further includes: Output charts: Apply the generated charts to different output scenarios, including embedding in investment research reports, inserting into PPT presentations, using for logical deduction, and storing in the gallery framework.

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