A market index data interface display method and system

Abstract

The application relates to a market index data interface display method and system, and relates to the technical field of data processing, which comprises the following steps: collecting market index original data, user input information and terminal screen configuration parameters; calculating and generating visual benchmark data; calling target index categories, calling market time granularity and historical data loading numbers; determining a demand estimation scene in combination with the target index categories, the market time granularity and the historical data loading numbers; determining whether the user input information contains a user-specified display scene; if yes, determining a display selected scene as a user current analysis scene; if no, taking the demand estimation scene as the user current analysis scene; obtaining a height proportion configuration according to the user current analysis scene; and dynamically adjusting the visual benchmark data by using the height proportion configuration and the terminal screen configuration parameters to form a market index display graph and outputting the market index display graph. The application has the effect of facilitating market analysis of investors, traders and analysts and the like.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for displaying market indicator data interfaces. Background Technology

[0002] In order to transform messy, non-standard, and unusable raw data into clear, standardized, interpretable, and practically useful information or data, it is necessary to process the collected raw data, which involves a series of operations such as collection, cleaning, organization, transformation, calculation, analysis, storage, and output.

[0003] Market data refers to the raw data and derived calculation results used in financial markets to reflect asset prices, trading volume, and the overall market status. Currently, the raw market data is generally cleaned, outliers are removed, and indicators are calculated to form a series with a unified time period. Then, the display dimensions and interface layout are determined, and prices, trading volumes, and various technical indicators are presented in the main and sub-charts in the form of candlestick charts, bar charts, etc., to ensure that the time axis is synchronized. Subsequently, interactive functions such as hover viewing, period switching, zooming and panning are configured to refresh the data in real time and mark key signals such as golden crosses and divergences, thereby completing the visualization of indicator data and facilitating market analysis and trading decisions for investors, traders, and analysts.

[0004] Currently, when displaying market indicator data, a fixed interface partitioning structure is generally used, with a fixed ratio between the main chart and sub-charts. When multiple indicators need to be viewed simultaneously, the sub-chart area is too narrow, causing indicator curves to overlap and making it difficult to distinguish between values ​​and signals. Meanwhile, the unused interface space cannot be effectively utilized, making it inconvenient for investors, traders, analysts, and other users to conduct market analysis. Summary of the Invention

[0005] To facilitate market analysis for investors, traders, analysts, and other users, this invention provides a method and system for displaying market indicator data interfaces.

[0006] Firstly, the present invention provides a method for displaying market indicator data interface, employing the following technical solution: A method for displaying market indicator data interface, comprising: Collect raw market data, user-entered information, and terminal screen configuration parameters of the user's display terminal; The raw data of market indicators is formatted and the basic display elements are calculated to generate visual benchmark data. The target indicator category is retrieved based on the user-entered information, and the market data time granularity and the number of historical data entries are retrieved based on the visualized benchmark data. By combining the target indicator category, market time granularity, and the number of historical data entries loaded, the scenario is determined to identify the expected demand scenario; Determine whether the user-entered information includes the user-specified display scenario; If so, the display selection scenario is determined based on the consistency between the user-specified display scenario and the expected demand scenario, and the display selection scenario is taken as the user's current analysis scenario; If not, then the demand forecast scenario will be taken as the user's current analysis scenario; Based on the user's current analysis scenario, the corresponding height ratio configuration of the main image and sub-image is obtained from the preset scenario ratio mapping relationship; The visualization benchmark data is dynamically adjusted using height ratio configuration and terminal screen configuration parameters to form a market index display chart, which is then output to the user's display terminal for layout display.

[0007] By adopting the above technical solution, raw market indicator data, user-entered information, and terminal screen configuration parameters are collected, the data is cleaned and organized to generate visualized benchmark data. Then, the target indicator category, market time granularity, and number of historical data entries are obtained through user-entered information and visualized benchmark data to determine the expected demand scenario. Depending on whether the user has entered a user-specified display scenario, the selected scenario or the expected demand scenario is displayed as the user's current analysis scenario. The height ratio of the main and sub-charts is matched according to the scenario, and the display layout is dynamically adjusted to form a market indicator display chart and output it. This enables precise adaptation of the market indicator display chart to user analysis needs and display terminal environment, avoiding indicator crowding or wasted space caused by a fixed display layout, improving the rationality and applicability of market indicator display, and facilitating market analysis for investors, traders, analysts, and other users.

[0008] Optional methods for determining demand forecasting scenarios include: Determine the period comparison value based on the market time granularity and the preset benchmark period; The number of indicator types and the baseline length of each type are retrieved based on the target indicator category; The length comparison value is determined based on the number of historical data entries and the type baseline length. The number of indicator types, period comparison values, and length comparison values ​​are input into a preset scenario prediction model to determine multiple candidate prediction scenarios; The estimated number of scenarios is retrieved based on multiple candidate prediction scenarios; Based on the number of predicted scenarios, multiple candidate predicted scenarios are selected to obtain the selected predicted scenario, and the selected predicted scenario is used as the demand predicted scenario.

[0009] By adopting the above technical solution, the market time granularity, the number of indicator types, and the number of historical data entries are converted into period comparison values ​​and length comparison values ​​and input into the scenario prediction model to determine multiple candidate prediction scenarios. Then, the number of prediction scenarios is retrieved and selected to obtain the selected prediction scenario as the demand prediction scenario. This can quantify the impact of multi-dimensional features on the scenario, realize the standardization and intelligent judgment of demand prediction scenarios, and improve the accuracy of the obtained demand prediction scenarios.

[0010] Optionally, the selection method for the predicted scenario includes: Determine if the estimated number of scenarios is only one; If so, then the multiple candidate prediction scenarios will be selected as the prediction scenarios; If not, the scene selection coefficient is determined based on the user's input information; The period sorting value is determined by sorting the period comparison values ​​from largest to smallest; the number sorting value is determined by sorting the number of indicator types from largest to smallest; and the length sorting value is determined by sorting the length comparison values ​​from largest to smallest. The scenario ranking value is obtained by weighting the period ranking value, the number ranking value, and the length ranking value according to the selected coefficients. Multiple candidate prediction scenarios are selected based on the scenario ranking value and used as the selected prediction scenario.

[0011] By adopting the above technical solution, a scene ranking value is obtained by weighting multiple candidate prediction scenes with periodic ranking value, number ranking value, and length ranking value. This enables the selection of the best candidate scene, making the final selected prediction scene more in line with the user's actual analysis habits and improving the accuracy of scene recognition.

[0012] Optionally, methods for determining the scene selection coefficient include: User identification information is retrieved based on user-entered information, and queries are performed based on user identification information to obtain the user's historical query data; The frequency of data retrieval is adjusted based on the user's historical query data retrieval cycle, the number of data types, the loading length, and the historical query frequency. The single frequency coefficient is determined by combining the adjustment frequency of the cycle, the adjustment frequency of the number of types, and the adjustment frequency of the loading length; The deviation baseline frequency is determined based on the historical query frequency. The adjustment deviation frequency is determined by calculating the adjustment frequency based on the periodic adjustment frequency, the adjustment frequency based on the number of types, and the adjustment frequency based on the loading length. Determine whether the adjustment deviation frequency is greater than the deviation reference frequency; If so, the deviation frequency coefficient is determined by combining the adjustment deviation frequency, the deviation reference frequency, and the single frequency coefficient, and the deviation frequency coefficient is used as the scenario selection coefficient; If not, then the single frequency coefficient will be used as the scene selection coefficient.

[0013] By adopting the above technical solution, the frequency of periodic adjustments, the frequency of adjustments for the number of types, and the frequency of adjustments for loading length are determined by using users' historical query data. The system also distinguishes whether there are any abnormal adjustment deviations to determine the scene selection coefficients accordingly. This allows for dynamic weight adaptation based on users' historical behavior, making scene selection more in line with users' personalized usage preferences and enhancing the fit of scene judgment.

[0014] Optional methods for determining a single frequency coefficient include: The sum of the adjustment frequency of the cycle, the adjustment frequency of the number of types, and the adjustment frequency of the loading length is calculated and used as the overall adjustment frequency. The initial coefficients of period preference, number preference, and length preference are retrieved based on the user's historical query data. Calculate the ratio between the periodic adjustment frequency and the overall adjustment frequency, and determine the periodic adjustment coefficient by combining the initial periodic preference coefficient; Calculate the ratio between the frequency of type number adjustment and the overall adjustment frequency, and determine the number adjustment coefficient by combining the initial number preference coefficient; Calculate the ratio between the loading length adjustment frequency and the overall adjustment frequency, and determine the length adjustment coefficient by combining the initial length preference coefficient; The period adjustment coefficient, the number adjustment coefficient, and the length adjustment coefficient are combined and used as a single frequency coefficient.

[0015] By adopting the above technical solution, the proportion of each adjustment frequency to the overall adjustment frequency is calculated, and the single frequency coefficient is determined in combination with the initial preference coefficient. This enables a quantitative expression of user cycle, number of indicators, and loading length preferences, providing an objective and stable weight basis for subsequent weighted ranking and improving the rationality of scenario selection.

[0016] Optional methods for determining the frequency coefficient of deviation include: When there is only one adjustment deviation frequency, select the adjustment deviation frequency that is greater than the deviation reference frequency and use it as the additional deviation frequency, and select the closest adjustment deviation frequency as the neighboring deviation frequency based on the additional deviation frequency. Additional deviation types are retrieved based on the frequency of additional deviations, and neighboring deviation types are retrieved based on the frequency of neighboring deviations. Calculate the difference between the frequency of the additional deviation and the frequency of the deviation reference and use it as the frequency outlier; The anomaly adjustment factor is determined by combining the frequency of outliers with the types of additional deviations; Calculate the difference between the nearest deviation frequency and the deviation reference frequency and use it as the frequency deviation value; The proximity adjustment factor is determined by combining the frequency deviation value with the type of proximity deviation; The deviation frequency coefficient is obtained by adjusting and updating the single frequency coefficient based on the anomaly adjustment coefficient and the neighbor adjustment coefficient.

[0017] By adopting the above technical solution, when there is a single additional deviation type, the single frequency coefficient is updated by calculating the abnormal adjustment coefficient and the neighboring adjustment coefficient. This can specifically correct abnormal deviations in user behavior, weaken the impact of accidental operations on the scene selection coefficient, and ensure the stability of weight calculation.

[0018] Optionally, methods for determining the frequency coefficient of deviation also include: When there is more than one adjustment deviation frequency, the adjustment deviation frequencies are sorted from largest to smallest and defined as the larger deviation frequency and the smaller deviation frequency, respectively. Determine the type of normal deviation based on the type of additional deviation; Calculate the difference between the frequency of large deviations and the reference frequency of deviation, and use it as the high deviation magnitude; Calculate the difference between the frequency of smaller deviations and the reference frequency of deviation, and use it as the magnitude of the smaller deviation. The normal adjustment coefficient is determined by combining the magnitude of high deviation, the magnitude of low deviation, and the types of normal deviation; The deviation frequency coefficient is obtained by adjusting and updating the single frequency coefficient based on the normal adjustment coefficient.

[0019] By adopting the above technical solution, when there are multiple additional types of deviations, the single frequency coefficient can be updated by sorting the deviation frequency and calculating the normal adjustment coefficient. This can adapt to scenarios with multi-dimensional behavioral deviations, comprehensively correct users' historical behavioral deviations, and further improve the accuracy of scenario selection coefficients.

[0020] Optionally, the methods for determining the selected scene include: Determine the scene similarity benchmark value based on user identification information; Determine whether the user-specified display scenario matches the estimated demand scenario; If so, the user-specified display scenario will be used as the selected display scenario; If not, then the scene similarity value is determined by combining the user-specified display scene and the expected demand scene; When the scene similarity value is greater than the scene similarity benchmark value, the scene with the estimated demand will be selected for display. When the scene similarity value is not greater than the scene similarity benchmark value, the scene specified by the user will be used as the selected scene for display.

[0021] By adopting the above technical solution, the consistency between the user-specified display scenario and the expected demand scenario is judged by the scenario similarity benchmark value, so as to realize the intelligent selection between automatic scenario and user-specified scenario. This not only respects the user's active choice, but also adopts a more reasonable expected scenario when the scenario difference is large, thus taking into account both personalized needs and display rationality.

[0022] Secondly, this invention provides a market data indicator interface display system, which adopts the following technical solution: A market data indicator interface display system, comprising: The data acquisition module is used to collect raw market data, user-entered information, and terminal screen configuration parameters. The memory stores a program for implementing a market indicator data interface display method as described in any one of the first aspects; The processor loads and executes programs stored in memory.

[0023] In summary, the present invention has at least one of the following beneficial technical effects: 1. By collecting raw market indicator data, user-entered information, and terminal screen configuration parameters, the data is cleaned, organized, and visualized benchmark data is generated. Then, by using user-entered information and visualized benchmark data, the target indicator category, market time granularity, and number of historical data entries are obtained to determine the expected demand scenario. Depending on whether the user has entered a specified display scenario, the selected scenario or the expected demand scenario is displayed as the user's current analysis scenario. The height ratio of the main and sub-charts is matched according to the scenario, and the display layout is dynamically adjusted to form a market indicator display chart and output it. This enables the market indicator display chart to be accurately adapted to the user's analysis needs and the display terminal environment, avoiding indicator crowding or wasted space caused by a fixed display layout, improving the rationality and applicability of market indicator display, and facilitating market analysis for investors, traders, analysts, and other users. 2. By using the scenario similarity benchmark value to determine the consistency between the user-specified display scenario and the expected demand scenario, the system can intelligently select between automatic scenarios and user-specified scenarios. This respects the user's active choice and can adopt a more reasonable expected scenario when there are significant differences between scenarios, thus balancing personalized needs and display rationality. 3. By weighting multiple candidate prediction scenarios with periodicity, number, and length ranking values, a scenario ranking value is obtained. This enables the selection of the best candidate scenario, making the final selected prediction scenario more in line with the user's actual analysis habits and improving the accuracy of scenario recognition. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the method for displaying market indicator data on the interface. Figure 2This is a flowchart illustrating the method for determining demand forecasting scenarios. Detailed Implementation

[0025] A method for displaying market indicator data interfaces involves collecting raw market indicator data, user-entered information, and terminal screen configuration parameters. After data cleaning and regularization, visual benchmark data is generated. Then, the target indicator category, market time granularity, and number of historical data entries are extracted from the user-entered information. Based on the adjustment frequency in the user's historical query data, normal and abnormal deviations are calculated and distinguished to determine the scene selection coefficient. A scene prediction model is used to determine the demand prediction scene. At the same time, the final analysis scene is determined by whether the user specifies a scene. Finally, the height ratio of the main and sub-charts is matched according to the identified scene, and the layout is dynamically adjusted to form a market indicator display chart and output it. By combining real-time user needs and historical behavior, the method achieves intelligent identification of analysis scenes and adaptive optimization of interface layout, effectively solving the problems of crowded indicator display and wasted space. It balances objectivity and personalization, improves display adaptability and user experience, and facilitates market analysis for investors, traders, analysts, and other users.

[0026] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0027] Reference Figure 1 This invention discloses a method for displaying market indicator data interface, which includes: S100: Collects raw market data, user-entered information, and terminal screen configuration parameters of the user's display terminal.

[0028] The raw market data refers to the basic data generated by financial market transactions, including opening price, closing price, highest price, lowest price, trading volume, trading value, and source data for calculating various technical indicators. The raw market data is retrieved in real-time or periodically through a connection to a market data source interface.

[0029] User-entered information refers to the viewing-related instructions actively input or selected by the user. This information includes the user's account details, viewing preferences, and other input. User-entered information is obtained after the operator has pre-entered it.

[0030] The user display terminal refers to the terminal on which the user is currently viewing market indicators. This terminal can be a computer, mobile phone, tablet, or other similar device. Terminal screen configuration parameters refer to parameters such as screen resolution and window size. These parameters are obtained by querying the user display terminal.

[0031] S101: Formats and standardizes the raw data of market indicators, calculates basic display elements to generate visual benchmark data.

[0032] The basic display elements refer to fundamental units such as candlestick data, moving average values, indicator values, and coordinate scales that can be directly used for drawing. The visualization benchmark data refers to standardized datasets that, after cleaning and normalization, can be directly used for interface rendering.

[0033] The raw market data is cleaned by removing suspended stocks, missing candlesticks, and extreme abnormal prices, and then standardized into fixed period sequences such as 1 minute and daily charts. Based on the standardized data, moving averages (MA), moving average of the divergence (MACD), trading volume, and other basic display elements are calculated according to formulas. Finally, the price, indicator, and time axis data are integrated into structured data to form visualized benchmark data.

[0034] S102: Retrieve the target indicator category based on user-entered information, and retrieve the market data time granularity and the number of historical data entries based on the visualized benchmark data.

[0035] Here, "Target Indicator Category" refers to the types of technical indicators the user wishes to view. "Market Time Granularity" refers to the length of the time unit represented by the candlestick chart, including 1-minute, daily, and weekly charts. "Number of Historical Data Loaded" refers to the number of candlesticks in the historical market data currently displayed.

[0036] By parsing the user-entered information, the system extracts and retrieves the target indicator category selected by the user, reads the current market time granularity from the visualized benchmark data, and counts and retrieves the number of K-lines covered in this data display to obtain the number of historical data entries loaded, which is convenient for subsequent use.

[0037] S103: Combine the target indicator category, market time granularity, and number of historical data entries to determine the scenario and identify the expected demand scenario.

[0038] Among them, demand forecasting scenarios refer to the estimated potential user analysis scenarios.

[0039] By comprehensively judging the number of target indicator categories, the length of market time granularity, and the number of historical data entries as judgment features, the demand prediction scenario can be determined, which will facilitate subsequent use.

[0040] S104: Determine whether the user-entered information includes the user-specified display scenario. If yes, proceed to S105; if no, proceed to S106.

[0041] Among them, the user-specified display scenario refers to the analysis scenario type that the user actively and explicitly specifies and wishes to use directly.

[0042] By performing key field retrieval and semantic analysis on user-entered information, it can be determined whether there are explicit scene identifiers or scene selection instructions.

[0043] When a clear scene identifier or scene selection instruction exists, it is determined that the user-entered information contains the user-specified display scene, and the scene corresponding to the scene identifier or the scene selection instruction is taken as the user-specified display scene. When no clear scene identifier or scene selection instruction exists, it is determined that the user-entered information does not contain the user-specified display scene.

[0044] S105: Based on the consistency between the user-specified display scenario and the expected demand scenario, determine the display selection scenario and use the display selection scenario as the user's current analysis scenario.

[0045] The selected display scenario refers to the scenario ultimately chosen after comprehensively comparing the user-specified display scenario with the estimated demand scenario. The user's current analysis scenario refers to the type of market analysis the user is currently performing. The user's current analysis scenario includes short-term market monitoring, medium- to long-term analysis, and multi-indicator resonance, etc.

[0046] When the information entered by the user includes a user-specified display scenario, the consistency between the user-specified display scenario and the estimated demand scenario is analyzed. Based on the consistency results, a display scenario is selected from the user-specified display scenario and the estimated demand scenario is used as the user's current analysis scenario.

[0047] S106: Use the demand forecast scenario as the user's current analysis scenario.

[0048] In cases where the information entered by the user does not include the user-specified display scenario, the estimated demand scenario will be directly used as the user's current analysis scenario.

[0049] S107: Based on the user's current analysis scenario, obtain the corresponding height ratio configuration of the main image and sub-image from the preset scenario ratio mapping relationship.

[0050] The scene proportion mapping relationship refers to a pre-configured rule base in the system that corresponds one-to-one with the display size of the main and sub-charts for different analysis scenarios. The scene proportion mapping relationship is obtained after pre-input by the operator. The height proportion configuration refers to the numerical parameter used to specify the vertical display ratio of the main chart area and the indicator sub-chart area.

[0051] The system matches the user's current analysis scene against a preset scene ratio mapping relationship, directly retrieving the corresponding height ratio configuration for convenient subsequent use.

[0052] For example, the scene ratio mapping relationship can be preset to a height ratio configuration of 3:2 for short-term multi-indicator scenes and 5:1 for medium- and long-term trend scenes.

[0053] S108: Dynamically adjust the visualization benchmark data using height ratio configuration and terminal screen configuration parameters to form a market indicator display chart, and output the market indicator display chart to the user display terminal for layout display.

[0054] Among them, the market indicator display chart refers to the final visual chart after the size, elements, and area division are completed according to the layout ratio.

[0055] By retrieving screen resolution and window size from terminal screen configuration parameters, and calculating the actual pixel height of the main chart and each sub-chart based on the height ratio configuration, the system adapts and scales the coordinate axes, curve density, and element size of the visualized benchmark data, and allocates regions accordingly. This generates a market indicator display chart that conforms to the layout, and then outputs the chart to the user's display terminal interface for rendering and display according to the calculated region position. This achieves precise adaptation of the market indicator display chart to the user's analysis needs and display terminal environment, avoiding indicator crowding or wasted space caused by a fixed display layout, improving the rationality and applicability of market indicator display, and facilitating market analysis for investors, traders, analysts, and other users.

[0056] To further ensure the rationality of the demand forecast scenario, it is necessary to conduct a further separate analysis and calculation of the demand forecast scenario, which will be explained in detail through the steps shown below.

[0057] Reference Figure 2 The method for determining demand forecasting scenarios includes the following steps: S200: Determines the period comparison value based on the market time granularity and the preset benchmark period.

[0058] The benchmark period refers to a standard period preset by the system for comparison and reference. The benchmark period is preset by the operator according to actual needs. The period comparison value is a quantitative judgment value obtained by comparing the actual market time granularity with the benchmark period.

[0059] The system calculates the ratio between the current market data time granularity and the system's preset benchmark period, and uses the calculation result as the period comparison value.

[0060] S201: Retrieve the number of indicator types and the baseline length of each type based on the target indicator category.

[0061] The number of indicator types refers to the number of technical indicator categories selected by the user for display. The type baseline length refers to the pre-defined data length standard for reference and comparison between different indicator categories.

[0062] The number of indicator types is obtained by counting the target indicator categories selected by the user. Then, the corresponding type baseline length is retrieved from the preset indicator type parameter library according to the indicator classification to which each target indicator category belongs, which facilitates subsequent use.

[0063] The indicator type parameter library pre-stores a table of different target indicator categories and their corresponding type baseline lengths. The indicator type parameter library is obtained after the operator pre-inputs the data.

[0064] S202: Determine the length comparison value based on the number of historical data entries and the type baseline length.

[0065] Among them, the length comparison value refers to the quantitative judgment value obtained by comparing the number of historical data entries loaded with the type baseline length.

[0066] By calculating the ratio between the number of historical data entries loaded and the type baseline length, and using the calculation result as a length comparison value, it is convenient for subsequent use.

[0067] S203: Combine the number of indicator types, period comparison value, and length comparison value into the preset scenario prediction model to determine multiple candidate prediction scenarios.

[0068] The scenario prediction model refers to the pre-configured computational rule model used for scenario classification and determination. It is obtained by pre-analyzing different multi-candidate prediction scenarios and extracting the corresponding number of indicator types, period comparison values, and length comparison values. A multi-candidate prediction scenario refers to one or more candidate analysis scenarios output by the model based on multi-dimensional input features.

[0069] By inputting the number of indicator types, period comparison value, and length comparison value as feature parameters into the preset scene prediction model, the model performs calculations and classification matching according to the built-in judgment logic, and outputs the corresponding multi-candidate prediction scene.

[0070] S204: Retrieve the estimated number of scenarios based on multiple candidate prediction scenarios.

[0071] The number of predicted scenarios refers to the number of scenarios corresponding to multiple candidate prediction scenarios.

[0072] By counting the number of multiple candidate prediction scenarios and using the count results as the estimated number of scenarios, it is convenient for subsequent use.

[0073] S205: Select multiple candidate prediction scenarios based on the number of prediction scenarios to obtain the selected prediction scenario, and use the selected prediction scenario as the demand prediction scenario.

[0074] Among them, selecting the estimated scenario refers to selecting the optimal scenario from the candidate scenarios based on the number of scenarios.

[0075] By estimating the number of scenarios, multiple candidate scenarios are selected, and the selected scenario is used as the estimated scenario. Then, the estimated scenario is used as the demand estimation scenario, thereby improving the accuracy of the obtained demand estimation scenario.

[0076] To further ensure the rationality of the selected prediction scenarios, it is necessary to conduct further separate analysis and calculation on the selected prediction scenarios, which will be explained in detail through the steps shown below.

[0077] The method for selecting the predicted scenario includes the following steps: S300: Determine if the estimated number of scenarios is only one. If yes, proceed to S301; if no, proceed to S302.

[0078] Specifically, by determining whether there is only one estimated scenario, it can be determined whether further selection of multiple candidate scenarios is needed.

[0079] S301: Select the prediction scenario from multiple candidate prediction scenarios.

[0080] When there is only one predicted scenario, it means that there is no need to further select multiple candidate predicted scenarios. Therefore, the multiple candidate predicted scenarios are selected as the predicted scenarios.

[0081] S302: Determine the scene selection coefficient based on the information entered by the user.

[0082] Among them, the scenario selection coefficient refers to the weighting coefficient used to calculate the sorting values ​​of period, number of indicators, and data length when selecting the best scenario from multiple candidates.

[0083] When the estimated number of scenarios is more than one, it means that further selection of multiple candidate scenarios is needed. Therefore, by analyzing the user's input information, the scenario selection coefficient can be determined to facilitate subsequent use.

[0084] S303: Determine the period sorting value by sorting from largest to smallest based on the period comparison value, determine the number sorting value by sorting from largest to smallest based on the number of indicator types, and determine the length sorting value by sorting from largest to smallest based on the length comparison value.

[0085] Among them, the period sort value refers to the sorted value after sorting the period comparison values. The number sort value refers to the sorted value after sorting the number of indicator types. The length sort value refers to the sorted value after sorting the length comparison values.

[0086] By sorting the three sets of data—period comparison value, number of indicator types, and length comparison value—in descending order, and using the corresponding ranking numbers as the period ranking value, number ranking value, and length ranking value, respectively, the results are determined.

[0087] S304: Based on the scenario selection coefficient, the period sort value, the number sort value, and the length sort value are weighted and calculated to obtain the scenario sort value.

[0088] The scene ranking value is a comprehensive score used to measure the suitability of candidate scenes. The higher the scene ranking value, the better the suitability of the candidate scene.

[0089] By multiplying the period sort value, the number sort value, and the length sort value by their respective scene selection coefficients, and then adding the products together, the final value is the scene sort value, which is convenient for subsequent use.

[0090] S305: Select multiple candidate prediction scenarios based on scenario ranking values ​​and use them as the selected prediction scenarios.

[0091] In this method, the accuracy of the selected predicted scenarios is improved by selecting the candidate predicted scenarios with the highest scenario ranking value.

[0092] To further ensure the rationality of the scene selection coefficients, it is necessary to perform further separate analysis and calculation on the scene selection coefficients, which will be explained in detail through the steps shown below.

[0093] The method for determining the scene selection coefficient includes the following steps: S400: Retrieve user identification information based on user-entered information, and perform a query based on the user identification information to obtain the user's historical query data.

[0094] User identification information refers to unique identifiers such as account ID and device identifier. User historical query data refers to the stored records of the user's past operations when using the market data function, including the indicators, periods, and scenarios they selected.

[0095] The system extracts user IDs, login credentials, and other identity identifiers from the user's input information. Then, using these user identifiers as search criteria, it performs a matching query in the system's user behavior database to retrieve the corresponding user's historical query data for later use.

[0096] S401: Adjust the frequency of data retrieval based on the user's historical query data retrieval cycle, the frequency of data type count, the frequency of loading length, and the frequency of historical queries.

[0097] Among these, the frequency of period adjustment refers to the frequency with which users modify the time granularity of market data in their historical data. The frequency of indicator type adjustment refers to the frequency with which users adjust the number of indicator types. The frequency of load length adjustment refers to the frequency with which users change the number of historical data entries loaded. The frequency of historical query refers to the total frequency with which users utilize the market data query function.

[0098] Behavioral statistics are performed on users' historical query data. The modification frequency of market data time granularity, number of indicators, and number of historical data loading records is extracted and calculated to obtain the corresponding adjustment frequency. At the same time, the total number of user queries is counted to obtain the historical query frequency, which is convenient for subsequent use.

[0099] S402: Determine the single frequency coefficient by combining the periodic adjustment frequency, the number of types adjustment frequency, and the loading length adjustment frequency.

[0100] Among them, the single frequency coefficient refers to a set of quantitative weight coefficients that reflect the operational preferences in terms of cycle, number of types, and loading length.

[0101] By combining and analyzing the frequency of period adjustment, the frequency of type number adjustment, and the frequency of loading length adjustment, a single frequency coefficient is determined to facilitate subsequent use.

[0102] S403: Calculate and determine the deviation baseline frequency based on the historical query frequency.

[0103] Among them, the deviation reference frequency refers to the reference value used to determine whether there is an abnormal deviation in the adjustment frequency of each dimension.

[0104] The product of historical query frequency and preset baseline frequency ratio is calculated, and the result is used as the deviation baseline frequency for convenient subsequent use.

[0105] The baseline frequency ratio coefficient refers to the ratio coefficient used to convert historical query frequencies into deviation baseline frequencies. The baseline frequency ratio coefficient is preset by the operator according to actual needs.

[0106] For example, the baseline frequency ratio coefficient can be set to 0.15. When the historical query frequency is 80, the deviation baseline frequency is 12.

[0107] S404: The adjustment deviation frequency is determined by calculating the adjustment frequency based on the periodic adjustment frequency, the adjustment frequency based on the number of types, and the adjustment frequency based on the loading length.

[0108] Among them, the adjustment deviation frequency refers to the deviation frequency between the periodic adjustment frequency, the type number adjustment frequency, and the loading length adjustment frequency.

[0109] By sequentially calculating the deviations of two of the following adjustments—the cycle adjustment frequency, the type number adjustment frequency, and the loading length adjustment frequency—and using the calculation results as the adjustment deviation frequency, it becomes convenient for subsequent use.

[0110] S405: Determine if the adjustment deviation frequency is greater than the deviation reference frequency. If yes, proceed to S406; if no, proceed to S407.

[0111] Specifically, by judging whether the frequency of adjustment deviation is greater than the deviation reference frequency, it is determined whether further adjustment of a single frequency coefficient is needed.

[0112] S406: Combine the adjustment deviation frequency, the deviation reference frequency, and the single frequency coefficient to determine the deviation frequency coefficient, and use the deviation frequency coefficient as the scenario selection coefficient.

[0113] Among them, the deviation frequency coefficient refers to the quantization weight coefficient corresponding to the adjustment of a single frequency coefficient.

[0114] When the adjustment deviation frequency is greater than the deviation reference frequency, it indicates that further adjustment of the single frequency coefficient is required. Therefore, by combining the adjustment deviation frequency, the deviation reference frequency, and the single frequency coefficient, the deviation frequency coefficient is determined and used as the scene selection coefficient to improve the accuracy of the obtained scene selection coefficient.

[0115] S407: Use a single frequency coefficient as the scene selection coefficient.

[0116] When the frequency of the adjustment deviation does not exceed the frequency of the deviation reference, it means that no further adjustment of the single frequency coefficient is needed. Therefore, the single frequency coefficient is used as the scene selection coefficient to improve the accuracy of the obtained scene selection coefficient.

[0117] To further ensure the rationality of individual frequency coefficients, it is necessary to perform further separate analysis and calculation on individual frequency coefficients, which will be explained in detail through the steps shown below.

[0118] The method for determining a single frequency coefficient includes the following steps: S500: Calculate the sum of the adjustment frequency of the cycle, the adjustment frequency of the number of types, and the adjustment frequency of the loading length, and use it as the overall adjustment frequency.

[0119] Among them, the overall adjustment frequency refers to the total adjustment frequency of the cycle, number of types, and loading length.

[0120] The sum of the adjustment frequency of the cycle, the adjustment frequency of the number of types, and the adjustment frequency of the loading length is calculated, and the calculation result is used as the comprehensive adjustment frequency for convenient subsequent use.

[0121] S501: Retrieve the initial coefficients of period preference, number preference, and length preference based on the user's historical query data.

[0122] Among them, the initial coefficient of period preference refers to the basic preference weight value pre-calculated based on the user's historical selection preference in terms of market time granularity. The initial coefficient of number preference refers to the basic preference weight value pre-calculated based on the user's historical selection preference in terms of the number of indicator types. The initial coefficient of length preference refers to the basic preference weight value pre-calculated based on the user's historical selection preference in terms of the number of historical data entries loaded.

[0123] By performing preference statistics on users' historical query data, the distribution characteristics of users' choices in terms of cycle, number of indicators, and loading length are extracted. Then, the corresponding initial coefficients of cycle preference, number preference, and length preference are matched and retrieved from the system's preference parameter library.

[0124] The preference parameter library pre-stores a table showing the selection distribution characteristics for different periods, number of indicators, and loading lengths, along with the corresponding initial coefficients for the period preference, the number preference, and the length preference. The preference parameter library can be pre-set by the operator according to actual needs.

[0125] S502: Calculate the ratio between the periodic adjustment frequency and the overall adjustment frequency, and determine the periodic adjustment coefficient in combination with the initial periodic preference coefficient.

[0126] Among them, the periodic adjustment coefficient refers to the quantified weight of the periodic dimension obtained after the fusion ratio and initial preference.

[0127] The ratio between the periodic adjustment frequency and the overall adjustment frequency is calculated, and then the product of this ratio and the initial periodic preference coefficient is calculated as the periodic adjustment coefficient for convenient subsequent use.

[0128] S503: Calculate the ratio between the adjustment frequency of the number of types and the overall adjustment frequency, and determine the number adjustment coefficient in combination with the initial coefficient of number preference.

[0129] Among them, the number adjustment coefficient refers to the quantitative weight of the number of types obtained after the fusion ratio and the initial preference.

[0130] The ratio between the adjustment frequency of the number of types and the overall adjustment frequency is calculated, and then the product of this ratio and the initial coefficient of the number preference is calculated as the number adjustment coefficient for convenient use later.

[0131] S504: Calculate the ratio between the loading length adjustment frequency and the overall adjustment frequency, and determine the length adjustment coefficient in combination with the initial length preference coefficient.

[0132] Among them, the length adjustment coefficient refers to the quantified weight of the loading length dimension obtained after the fusion ratio and the initial preference.

[0133] The ratio between the frequency of loading length adjustment and the frequency of overall adjustment is calculated, and then the product of this ratio and the initial coefficient of length preference is calculated as the length adjustment coefficient for convenient subsequent use.

[0134] S505: Combines the period adjustment coefficient, the number adjustment coefficient, and the length adjustment coefficient as a single frequency coefficient.

[0135] In this process, the period adjustment coefficient, the number adjustment coefficient, and the length adjustment coefficient are combined to form a set of coefficients, which are then used as a single frequency coefficient for convenient subsequent use.

[0136] To further ensure the rationality of the deviation frequency coefficient, it is necessary to perform a further separate analysis and calculation of the deviation frequency coefficient, which will be explained in detail through the steps shown below.

[0137] The method for determining the frequency coefficient of deviation includes the following steps: S600: When there is only one adjustment deviation frequency, select the adjustment deviation frequency that is greater than the deviation reference frequency and use it as the additional deviation frequency, and select the closest adjustment deviation frequency as the neighboring deviation frequency based on the additional deviation frequency.

[0138] The additional deviation frequency refers to the adjustment deviation frequency that is greater than the deviation reference frequency. The adjacent deviation frequency refers to the adjustment deviation frequency with the smallest difference from the additional deviation frequency among all adjustment deviation frequencies.

[0139] When there is only one adjustment deviation frequency, the additional deviation frequency and the adjacent deviation frequency can be selected and defined to facilitate subsequent use.

[0140] S601: Retrieve additional deviation types based on the frequency of additional deviations, and retrieve neighboring deviation types based on the frequency of neighboring deviations.

[0141] Here, "Extra Deviation Type" refers to the dimension type (period / number of indicator types / load length) corresponding to the frequency of extra deviations. "Nearest Deviation Type" refers to the dimension type (period / number of indicator types / load length) corresponding to the frequency of nearby deviations.

[0142] Additional deviation types are retrieved by the frequency of additional deviations, and neighboring deviation types are retrieved by the frequency of neighboring deviations, which facilitates subsequent use.

[0143] S602: Calculate the difference between the extra deviation frequency and the deviation reference frequency and use it as the frequency outlier.

[0144] Among them, frequency outliers refer to the difference between the frequency of extra deviation and the frequency of deviation reference.

[0145] Calculating the frequency outliers facilitates subsequent use.

[0146] S603: Determine the anomaly adjustment factor by combining the frequency outliers with the types of additional deviations.

[0147] The anomaly adjustment coefficient is a weighting coefficient calculated for the deviation dimension by combining the frequency of outliers with the types of additional deviations.

[0148] The additional deviation categories retrieve the category coefficient corresponding to the deviation dimension from the single frequency coefficient, then calculate the product between the frequency outlier and the preset outlier adjustment ratio coefficient, and use it as the outlier adjustment value. Finally, calculate the product between the category coefficient and the outlier adjustment value, and use it as the outlier adjustment coefficient for subsequent use.

[0149] The abnormal adjustment ratio coefficient refers to the ratio coefficient used to convert frequency abnormal values ​​into abnormal adjustment values. The abnormal adjustment ratio coefficient is preset by the operator according to actual needs.

[0150] S604: Calculate the difference between the nearest deviation frequency and the deviation reference frequency and use it as the frequency deviation value.

[0151] The frequency deviation value refers to the difference between the nearest deviation frequency and the deviation reference frequency.

[0152] Calculating the frequency deviation value facilitates subsequent use.

[0153] S605: Determine the proximity adjustment coefficient by combining the frequency deviation value and the proximity deviation type.

[0154] The proximity adjustment coefficient is a weighting coefficient calculated for the deviation dimension by combining the frequency deviation value with the type of proximity deviation.

[0155] The corresponding category coefficient for the deviation dimension is retrieved from the single frequency coefficient by the neighboring deviation category. Then, the product value between the frequency deviation value and the preset deviation adjustment ratio coefficient is calculated and used as the deviation adjustment value. Finally, the product value between the category coefficient and the deviation adjustment value is calculated and used as the neighboring adjustment coefficient for convenient subsequent use.

[0156] The deviation adjustment ratio coefficient is a ratio coefficient used to convert frequency deviation values ​​into deviation adjustment values. The deviation adjustment ratio coefficient is preset by the operator according to actual needs.

[0157] S606: Adjust and update a single frequency coefficient based on the abnormal adjustment coefficient and the neighboring adjustment coefficient to obtain the deviation frequency coefficient.

[0158] Specifically, the category coefficients corresponding to the deviation dimension of a single frequency coefficient are updated and replaced by anomaly adjustment coefficients and neighboring adjustment coefficients, and the updated single frequency coefficients are used as deviation frequency coefficients to improve the accuracy of the obtained deviation frequency coefficients.

[0159] To further ensure the rationality of the deviation frequency coefficient, it is necessary to perform a further separate analysis and calculation of the deviation frequency coefficient, which will be explained in detail through the steps shown below.

[0160] The method for determining the frequency coefficient of deviation also includes the following steps: S700: When there is more than one adjustment deviation frequency, sort them from largest to smallest according to the adjustment deviation frequency, and define them as the larger deviation frequency and the smaller deviation frequency respectively.

[0161] The term "frequencies with larger deviations" refers to the frequency of the largest adjustment deviation. The term "frequencies with smaller deviations" refers to the frequency of the smallest adjustment deviation.

[0162] When there is more than one adjustment deviation frequency, the adjustment deviation frequencies are sorted from largest to smallest. Based on the sorting results, the adjustment deviation frequency with the largest frequency is taken as the larger frequency, and the adjustment deviation frequency with the smallest frequency is taken as the smaller frequency, which facilitates subsequent use.

[0163] S701: Determine the type of normal deviation based on the type of additional deviation.

[0164] Among them, the normal deviation type refers to the dimension type that does not exceed the deviation baseline frequency after removing additional deviation types from all three dimensions.

[0165] By removing the identified additional bias types from the total dimension set and treating the remaining dimensions as normal bias types, it is easier to use them later.

[0166] S702: Calculate the difference between the frequency of larger deviations and the reference frequency of deviation, and use it as the high deviation magnitude.

[0167] Among them, the high deviation range refers to the difference between the frequency of larger deviations and the reference frequency of deviations.

[0168] Calculating the magnitude of high deviation facilitates subsequent use.

[0169] S703: Calculate the difference between the frequency of smaller deviation and the reference frequency of deviation and use it as the small deviation magnitude.

[0170] Among them, the low-amplitude deviation refers to the difference between the frequency of the smaller deviation and the deviation reference frequency.

[0171] Calculating the magnitude of low-amplitude deviations facilitates subsequent use.

[0172] S704: Determine the normal adjustment coefficient by combining the high deviation range, the low deviation range, and the type of normal deviation.

[0173] Among them, the normal adjustment coefficient refers to the conventional adjustment weight coefficient calculated from the normal deviation dimension.

[0174] A normal adjustment coefficient is obtained by weighting the high and low deviation amplitudes for convenient subsequent use. The specific weights in the weighting calculation are preset by the operator according to actual needs.

[0175] S705: Adjust and update the single frequency coefficient based on the normal adjustment coefficient to obtain the deviation frequency coefficient.

[0176] Specifically, the category coefficients corresponding to the deviation dimensions of a single frequency coefficient are updated and replaced by normal adjustment coefficients, and the updated single frequency coefficients are used as deviation frequency coefficients to improve the accuracy of the obtained deviation frequency coefficients.

[0177] To further ensure the rationality of the selected display scene, it is necessary to perform further separate analysis and calculation on the selected display scene, which will be explained in detail through the steps shown below.

[0178] The method for determining the selected scene includes the following steps: S800: Determine the scene similarity benchmark value based on user identification information.

[0179] Among them, the scenario similarity benchmark value refers to the basic reference value used to measure the similarity between each estimated scenario and user needs.

[0180] By querying a pre-set user database from user identification information, a scenario similarity benchmark value is obtained, which facilitates subsequent use.

[0181] The user database pre-stores a table that compares different user identification information with corresponding scenario-similar benchmark values. The user database is obtained after the operator pre-inputs the information.

[0182] S801: Determine whether the user-specified display scenario matches the estimated demand scenario. If yes, proceed to S802; otherwise, proceed to S803.

[0183] Specifically, the system determines whether the user-specified display scenario can be used directly by judging whether it matches the expected demand scenario.

[0184] S802: Use the user-specified display scene as the selected display scene.

[0185] When the user-specified display scenario matches the expected demand scenario, it means that the user-specified display scenario can be used directly. Therefore, the user-specified display scenario is used as the display selection scenario to improve the accuracy of the obtained display selection scenario.

[0186] S803: Determine the scene similarity value by combining the user-specified display scene and the expected demand scene.

[0187] The scene similarity value refers to a calculated value used to quantify the degree of matching and similarity between the user-specified display scene and the expected demand scene.

[0188] When the user-specified display scenario differs from the estimated demand scenario, it means that the user-specified display scenario cannot be used directly. Therefore, core features such as cycle type, number of indicators, number of historical data entries, and analysis mode are extracted from the user-specified display scenario and the estimated demand scenario, respectively. Then, the features of the two scenarios are matched and compared item by item. The similarity is calculated according to the proportion of the number of matched features to the total number of features, and finally, the scenario similarity value is obtained for subsequent use.

[0189] S804: When the scene similarity value is greater than the scene similarity benchmark value, the scene with the estimated demand will be selected as the scene to be displayed.

[0190] When the scene similarity value is greater than the scene similarity benchmark value, it means that the automatically generated estimated scene can be used. Therefore, the estimated scene is used as the display selection scene to improve the accuracy of the obtained display selection scene.

[0191] S805: When the scene similarity value is not greater than the scene similarity benchmark value, the scene specified by the user will be used as the selected scene for display.

[0192] When the scene similarity value is not greater than the scene similarity benchmark value, it means that the scene needs to be selected according to the customer's needs. Therefore, the scene specified by the user is used as the display selection scene to improve the accuracy of the obtained display selection scene.

[0193] Based on the same inventive concept, embodiments of the present invention provide a market indicator data interface display system, including: The data acquisition module is used to collect raw market data, user-entered information, and terminal screen configuration parameters. The memory stores a program for implementing a market indicator data interface display method as described above; The processor loads and executes programs stored in memory.

[0194] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0195] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for displaying market indicator data interface, characterized in that, include: Collect raw market data, user-entered information, and terminal screen configuration parameters of the user's display terminal; The raw data of market indicators is formatted and the basic display elements are calculated to generate visual benchmark data. The target indicator category is retrieved based on the user-entered information, and the market data time granularity and the number of historical data entries are retrieved based on the visualized benchmark data. By combining the target indicator category, market time granularity, and the number of historical data entries loaded, the scenario is determined to identify the expected demand scenario; Determine whether the user-entered information includes the user-specified display scenario; If so, the display selection scenario is determined based on the consistency between the user-specified display scenario and the expected demand scenario, and the display selection scenario is taken as the user's current analysis scenario; If not, then the demand forecast scenario will be taken as the user's current analysis scenario; Based on the user's current analysis scenario, the corresponding height ratio configuration of the main image and sub-image is obtained from the preset scenario ratio mapping relationship; The visualization benchmark data is dynamically adjusted using height ratio configuration and terminal screen configuration parameters to form a market index display chart, which is then output to the user's display terminal for layout display.

2. The method for displaying market indicator data interface according to claim 1, characterized in that, Methods for determining demand forecasting scenarios include: Determine the period comparison value based on the market time granularity and the preset benchmark period; The number of indicator types and the baseline length of each type are retrieved based on the target indicator category; The length comparison value is determined based on the number of historical data entries and the type baseline length. The number of indicator types, period comparison values, and length comparison values ​​are input into a preset scenario prediction model to determine multiple candidate prediction scenarios; The estimated number of scenarios is retrieved based on multiple candidate prediction scenarios; Based on the number of predicted scenarios, multiple candidate predicted scenarios are selected to obtain the selected predicted scenario, and the selected predicted scenario is used as the demand predicted scenario.

3. The method for displaying market indicator data interface according to claim 2, characterized in that, Methods for selecting the predicted scenario include: Determine if the estimated number of scenarios is only one; If so, then the multiple candidate prediction scenarios will be selected as the prediction scenarios; If not, the scene selection coefficient is determined based on the user's input information; The period sorting value is determined by sorting the period comparison values ​​from largest to smallest; the number sorting value is determined by sorting the number of indicator types from largest to smallest; and the length sorting value is determined by sorting the length comparison values ​​from largest to smallest. The scenario ranking value is obtained by weighting the period ranking value, the number ranking value, and the length ranking value according to the selected coefficients. Multiple candidate prediction scenarios are selected based on the scenario ranking value and used as the selected prediction scenario.

4. The method for displaying market indicator data interface according to claim 3, characterized in that, The methods for determining the scene selection coefficient include: User identification information is retrieved based on user-entered information, and queries are performed based on user identification information to obtain the user's historical query data; The frequency of data retrieval is adjusted based on the user's historical query data retrieval cycle, the number of data types, the loading length, and the historical query frequency. The single frequency coefficient is determined by combining the adjustment frequency of the cycle, the adjustment frequency of the number of types, and the adjustment frequency of the loading length; The deviation baseline frequency is determined based on the historical query frequency. The adjustment deviation frequency is determined by calculating the adjustment frequency based on the periodic adjustment frequency, the adjustment frequency based on the number of types, and the adjustment frequency based on the loading length. Determine whether the adjustment deviation frequency is greater than the deviation reference frequency; If so, the deviation frequency coefficient is determined by combining the adjustment deviation frequency, the deviation reference frequency, and the single frequency coefficient, and the deviation frequency coefficient is used as the scenario selection coefficient; If not, then the single frequency coefficient will be used as the scene selection coefficient.

5. The method for displaying market indicator data interface according to claim 4, characterized in that, Methods for determining a single frequency coefficient include: The sum of the adjustment frequency of the cycle, the adjustment frequency of the number of types, and the adjustment frequency of the loading length is calculated and used as the overall adjustment frequency. The initial coefficients of period preference, number preference, and length preference are retrieved based on the user's historical query data. Calculate the ratio between the periodic adjustment frequency and the overall adjustment frequency, and determine the periodic adjustment coefficient by combining the initial periodic preference coefficient; Calculate the ratio between the frequency of type number adjustment and the overall adjustment frequency, and determine the number adjustment coefficient by combining the initial number preference coefficient; Calculate the ratio between the loading length adjustment frequency and the overall adjustment frequency, and determine the length adjustment coefficient by combining the initial length preference coefficient; The period adjustment coefficient, the number adjustment coefficient, and the length adjustment coefficient are combined and used as a single frequency coefficient.

6. The method for displaying market indicator data interface according to claim 5, characterized in that, Methods for determining the frequency coefficient of deviation include: When there is only one adjustment deviation frequency, select the adjustment deviation frequency that is greater than the deviation reference frequency as the additional deviation frequency, and select the closest adjustment deviation frequency as the neighboring deviation frequency based on the additional deviation frequency. Additional deviation types are retrieved based on the frequency of additional deviations, and neighboring deviation types are retrieved based on the frequency of neighboring deviations. Calculate the difference between the frequency of the additional deviation and the frequency of the deviation reference and use it as the frequency outlier; The anomaly adjustment factor is determined by combining the frequency of outliers with the types of additional deviations; Calculate the difference between the nearest deviation frequency and the deviation reference frequency and use it as the frequency deviation value; The proximity adjustment factor is determined by combining the frequency deviation value with the type of proximity deviation; The deviation frequency coefficient is obtained by adjusting and updating the single frequency coefficient based on the anomaly adjustment coefficient and the neighbor adjustment coefficient.

7. The method for displaying market indicator data interface according to claim 6, characterized in that, Methods for determining the frequency coefficient of deviation also include: When there is more than one adjustment deviation frequency, the adjustment deviation frequencies are sorted from largest to smallest and defined as the larger deviation frequency and the smaller deviation frequency, respectively. Determine the type of normal deviation based on the type of additional deviation; Calculate the difference between the frequency of large deviations and the reference frequency of deviation, and use it as the high deviation magnitude; Calculate the difference between the frequency of smaller deviations and the reference frequency of deviation, and use it as the small deviation magnitude; The normal adjustment coefficient is determined by combining the magnitude of high deviation, the magnitude of low deviation, and the types of normal deviation; The deviation frequency coefficient is obtained by adjusting and updating the single frequency coefficient based on the normal adjustment coefficient.

8. The method for displaying market indicator data interface according to claim 4, characterized in that, The methods for determining the selected scene include: Determine the scene similarity benchmark value based on user identification information; Determine whether the user-specified display scenario matches the estimated demand scenario; If so, the user-specified display scenario will be used as the selected display scenario; If not, then the scene similarity value is determined by combining the user-specified display scene and the expected demand scene; When the scene similarity value is greater than the scene similarity benchmark value, the scene with the estimated demand will be selected for display. When the scene similarity value is not greater than the scene similarity benchmark value, the scene specified by the user will be used as the selected scene for display.

9. A market data indicator interface display system, characterized in that, include: The data acquisition module is used to collect raw market data, user-entered information, and terminal screen configuration parameters. The memory stores a program for implementing a market data indicator interface display method as described in any one of claims 1 to 8; The processor loads and executes programs stored in memory.

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