WO2019119626A1

WO2019119626A1 - Method for collecting performance statistics, apparatus, terminal, and computer-readable storage medium

Info

Publication number: WO2019119626A1
Application number: PCT/CN2018/075211
Authority: WO
Inventors: 薛晨; 韩亮亮; 刘禹; 陆修远
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-12-21
Filing date: 2018-02-05
Publication date: 2019-06-27
Also published as: CN107862489A

Abstract

Disclosed by the present application is a method for collecting performance statistics, which comprises: when receiving a performance statistics collection instruction, determining assessment objects according to the performance statistics collection instruction; querying product information managed by the assessment objects, and configuring a time axis according to management time information contained within the product information; along the time axis, collecting statistics on performance information of the assessment objects according to the product information, and generating and displaying a performance assessment diagram of the assessment objects. Further disclosed by the present application are a performance statistics collection apparatus, a terminal, and a computer-readable storage medium. The present application solves the technical problem wherein a sole factor is used to assess the performance of fund management.

Description

Performance statistics method, device, terminal and computer readable storage medium

This application claims the priority of the Chinese Patent Application filed on Dec. 21, 2017, the Chinese Patent Application No. 201711398746.2, entitled "Performance Statistics Method, Apparatus, Terminal, and Computer Readable Storage Medium", the entire contents of which are hereby incorporated by reference. The citation is incorporated in the application.

Technical field

The present application relates to the field of computer technology, and in particular, to a performance statistics method, apparatus, terminal, and computer readable storage medium.

Background technique

When investing in a fund, investors usually choose a fund manager to assist in the investment. The fund manager is mainly responsible for the fund product portfolio and investment strategy, and its level of competence is very important.

At present, the performance appraisal of fund managers is mainly based on the performance of each fund managed by them, but this leads to a relatively simple assessment of the fund manager's perspective and benchmarks, which does not reflect the comprehensive ability of the fund manager. The result may be less than fair.

Summary of the invention

The main purpose of the present application is to provide a performance statistics method, apparatus, terminal, and computer readable storage medium, which are aimed at solving a single technical problem of a performance evaluation factor for a fund manager.

To achieve the above objective, the present application provides a performance statistics method, and the performance statistics method includes the following steps:

When receiving the performance statistics instruction, the assessment object is determined according to the performance statistics instruction;

Querying product information managed by the assessment object, and configuring a time axis according to management time information included in the product information;

A performance evaluation map of the assessment object is generated according to the product information, and a performance assessment map of the assessment object is generated and displayed along the time axis.

In addition, in order to achieve the above object, the present application further provides a performance statistics device, where the performance statistics device includes:

An object module, configured to determine an assessment object according to the performance statistics instruction when receiving the performance statistics instruction;

a product module, configured to query product information managed by the assessment object, and configure a time axis according to management time information included in the product information;

The statistics module is configured to calculate, according to the product information, the performance information of the assessment object according to the product information, and generate a performance assessment map of the assessment object and display the performance assessment map.

In addition, in order to achieve the above object, the present application further provides a terminal, where the terminal includes: a memory, a processor, and a performance statistics program stored on the memory and operable on the processor, the performance statistics program The following steps are implemented when executed by the processor:

In addition, in order to achieve the above object, the present application further provides a computer readable storage medium, where the performance statistic program is stored, and when the performance statistics program is executed by the processor, the following steps are implemented:

The performance statistics method, device, terminal and computer readable storage medium proposed by the embodiment of the present application determine the assessment object according to the performance statistics instruction when receiving the performance statistics instruction, that is, the personnel targeted for the performance statistics Then, query the management time information of the assessment object, configure the timeline according to the management time information according to the management time information; then, query the product information managed by the assessment object, and configure the time according to the management time information included in the product information. The axis is used to establish an assessment timeline; then, along the time axis, the performance information of the assessment object is calculated according to the product information, and the performance assessment map of the assessment object is generated and displayed. The embodiment of the present application adds the management time information of the assessment object, and the obtained performance information includes the performance performance of the assessment object in the process of managing the product, and the obtained performance is an overall embodiment of the assessment object in the process of managing the product, thereby being more comprehensive, The performance capability of the complete and long-term feedback appraisers can reflect the performance of the appraisers in the whole product management process, whether they have improvement, overall control ability, etc., rather than just watching the performance of a fund or a fund. It is easy to draw conclusions about the performance ability of the appraisers, and eliminate cheating behaviors that take only a period of time or a product to be evaluated to deliberately increase or degrade performance. Therefore, the embodiment of the present application combines the management time information of the assessment object with the management product information to analyze the performance of the assessment object from the time limit of the appointment, instead of the traditional one, only the income of each fund of the assessment object. Performance, solved the technical problem of a single factor for the performance evaluation of fund managers.

DRAWINGS

1 is a schematic structural diagram of a terminal in a hardware operating environment involved in a solution according to an embodiment of the present application;

2 is a schematic flow chart of a first embodiment of a performance statistics method according to the present application;

3 is a schematic flowchart of a second embodiment of a performance statistics method according to the present application;

4 is a schematic flow chart of a third embodiment of a performance statistics method according to the present application;

FIG. 5 is a schematic flowchart of a fourth embodiment of a performance statistics method according to the present application; FIG.

6 is a schematic flowchart of a fifth embodiment of a performance statistics method according to the present application;

7 is a schematic diagram of functional modules of the performance statistics device of the present application;

FIG. 8 is a schematic diagram of a custom reference in the embodiment of the present application.

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

Detailed ways

It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

As shown in FIG. 1 , FIG. 1 is a schematic structural diagram of a terminal in a hardware operating environment involved in an embodiment of the present application.

The terminal in this embodiment may be a terminal device such as a PC or a server. This embodiment uses a terminal as a server for illustration.

As shown in FIG. 1, the server may include a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 can include a display, an input unit such as a keyboard, and the optional user interface 1003 can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 1005 may be a high speed RAM memory or a non-volatile memory such as a disk memory. The memory 1005 can also optionally be a storage device independent of the aforementioned processor 1001. It will be understood by those skilled in the art that the terminal structure shown in FIG. 1 does not constitute a limitation to the terminal, and may include more or less components than those illustrated, or a combination of certain components, or different component arrangements.

As shown in FIG. 1, an operating system, a network communication module, a user interface module, and a performance statistics program may be included in the memory 1005 as a computer storage medium.

In the terminal shown in FIG. 1, the network interface 1004 is mainly used to connect to a background server, and performs data communication with a background server such as an attendance system and a product management system; the user interface 1003 is mainly used to connect a client (user end) with a client. The terminal performs data communication and receives the input operation instruction. The processor 1001 can be used to call the performance statistics program stored in the memory 1005, and execute the performance statistics method provided by the embodiment of the present invention. The specific embodiment of the application terminal is basically the same as the following embodiments of the performance statistics method, and details are not described herein.

Referring to FIG. 2, a first embodiment of the performance statistics method of the present application provides a performance statistics method, where the performance statistics method includes:

Step S10: When receiving the performance statistics instruction, determine the assessment object according to the performance statistics instruction.

This embodiment enriches the evaluation factors and evaluates the fund managers in a multi-faceted manner, thereby obtaining fair and comprehensive assessment results.

Specifically, as an implementation manner, when a user such as a human resource or a manager needs to know the performance of the fund manager, the user can log in to the performance statistics system and click the performance statistics button on the system page. Then, the user inputs or searches for the identity information of the fund manager's name, job number, etc., and determines the fund manager who needs statistics; of course, the user can also select the fund manager that needs statistics in the candidate interface provided by the server of the performance statistics system. After the user determines the fund manager that needs statistics, click the OK button on the page to enter the performance statistics command. The performance statistics instruction carries the user-defined fund manager.

When receiving the input performance statistics instruction, the server parses the specified field in the performance statistics instruction, and obtains the fund manager determined by the user to be counted as the assessment object.

Step S20: Query product information managed by the assessment object, and configure a time axis according to management time information included in the product information.

After obtaining the assessment object, the server may send a query request to the product management system, where the query request includes the assessment object and the requested information. After receiving the inquiry request, the product management system counts the products managed by the assessment object according to the query request, the management time information of each product, the purchase cost of each product, and the market value of each product in each period of the management period, and each product is in the The daily rate of return during the management period, the transaction records of each product, etc., and packaged back to the server.

It should be noted that since the same fund manager may manage multiple fund products at the same time, the management time information of each product may be different. For example, a fund product has a full management cycle of 10 years, and a fund manager may only manage a few of them for a few years, and then temporarily hand it over to others for management. For example, if there is no management fund after one year of interruption, the fund will be re-managed one year later. Product or other fund products.

Thus, the management time information for each product includes the management date for managing the product during the period of the fund manager's employment and the non-management day for which the product is not managed.

Thus, the server obtains product information managed by the assessment object. After obtaining the product information, the server configures the timeline used for this performance statistics based on the management time information of each product in the product information.

Specifically, the first day of the entry of the assessment object is the start date, and the time interval of the time axis is set with the latest departure date or the statistical date as the termination date. Then, set the timeline scale in units of time, day, and time. Each scale corresponds to a specific date, so as to count the performance of each date, which can be flexibly set according to actual needs. This embodiment is exemplified by the scale of the time axis in units of days. For example, the assessment target was hired on January 1, 2016, and left on February 3, 2016. The time unit is Japan. The timeline is from January 1, 2016 to February 3, 2016. The scale includes each day between January 1, 2016 and February 3, 2016.

Then, the server marks the management day corresponding to each product on the time axis according to the management time information of each product. Of course, further, non-management days of each product may also be included. Thus, the timeline for this performance evaluation is obtained.

In step S30, along the time axis, the performance information of the assessment object is calculated according to the product information, and a performance assessment map of the assessment object is generated and displayed.

After obtaining the product information managed by the assessment object, the server counts the performance corresponding to each scale on the time axis according to the product information along the time axis.

Specifically, as an implementation manner, the server separately extracts the daily return rate of each product in the current assessment object management period as the product daily return rate of the product in the product information. If there is a management interval for a product, that is, the assessment object does not continuously manage the fund product, the daily yield of the product corresponding to the non-management day for which the product is not managed is set to zero. Of course, the daily yield of products on non-administrative days can also be set to the average daily rate of return of the product during the management period, which can be flexibly set according to actual needs. Thereby, the product daily yield corresponding to each tick date of the same product on the time axis is obtained. As a result, the server obtains the daily yield of each product corresponding to each scale on the time axis, and uses the daily yield of each product corresponding to each scale as the performance information of the assessment target.

Then, the server uses the time axis as the abscissa, and the performance information, that is, the daily yield of each product corresponding to each scale, is taken as the ordinate, and a line graph is generated, which is displayed as a performance evaluation map of the assessment object. Among them, the product daily yield line in the line chart is connected in series with the product daily rate of return corresponding to each scale, and the product daily rate of tandem in a product yield line is the daily yield of the same product, therefore, when there are multiple products There may be multiple product daily yield lines. Of course, when there are multiple products, the daily yields of the products corresponding to the same scale can be added to obtain the daily total return rate, and correspondingly, the daily yield of the products in series in the product daily yield line is corresponding to each scale. Total daily rate of return.

Further, as an implementation manner, the performance information includes a cumulative yield of the product, and the step S30 includes:

Calculating a daily cumulative rate of return of the product in the timeline according to the product daily rate of return included in the product information;

A performance assessment map of the assessment object is generated and displayed according to the cumulative rate of return of the product along the time axis.

Specifically, the server extracts the daily yield of each product on the date corresponding to each scale of the timeline from the product information, and then calculates the cumulative yield of each product on each date using the following formula:

Where x _i is the cumulative rate of return of a product on date a. Since date a is the ith day from the date when the assessment object starts managing the product, x _i is the cumulative income from the ith day from the start date. The rate, y _{i is} the daily yield of the _i- th day from the start date of the slave product, and x _{i-1 is} the cumulative yield of the i-th day from the start date of the slave product. The start date is the date on which the assessment object begins to manage the product.

It should be noted that if the management object of the assessment object is interrupted, the daily return rate of the non-management date of the product is set to 0; of course, the product daily income of the previous management day of the non-management date of the product may also be used. Rate, set the product daily rate of return for this non-management day, thereby obtaining a continuous product daily yield. Thus, the cumulative yield of each product on the time axis is calculated.

Then, the server uses the time axis as the abscissa, and the performance information, that is, the cumulative yield of each product corresponding to each scale, is taken as the ordinate, and a line graph is generated, which is displayed as a performance evaluation map of the assessment object. Wherein, the cumulative yield line of the product in the line chart is connected in series with the cumulative yield of products corresponding to each scale, and the cumulative yield of products in series in the cumulative yield line of one product is the cumulative yield of the same product, therefore, when there are multiple products There may be multiple product cumulative yield lines. Of course, when there are multiple products, the cumulative yield of products corresponding to the same scale can be added to obtain the cumulative total return rate, and correspondingly, the cumulative yield of products in series in the cumulative yield line of the product corresponds to each scale. Cumulative total rate of return.

Further, as an implementation manner, the performance information further includes the number of products managed by the assessment object and the product market value, and the step S30 includes:

In step S31, along the time axis, the number of products managed by the assessment object and the market value of the product are counted according to the product information.

In order to understand the changes in the product scale of the assessment object management, the performance information also includes the number of products managed by the assessment object and the market value of the product.

Specifically, the server counts the products managed by the assessment object management on each date according to the product transaction records in the product information and/or the market value of each product on each date. For example, if the market value of the product on a date is 0, it is determined that the management object does not manage the product on this date. If the market value is greater than 0, it is determined that the management object manages the product on this date, and thus, the server obtains the assessment object on each date. The number of products managed corresponds to the number of products corresponding to each scale. Of course, it is also possible to determine the number of products managed by the assessment object on each date according to the management time information of each product, and respectively correspond to the number of products corresponding to each scale.

Moreover, the server counts the total market value of the products managed by the assessment object management on each date according to the market value of each product in the product information on each date. Specifically, the server adds the market values of the products corresponding to the respective dates to obtain the total market value of each date, which is respectively used as the product market value corresponding to the corresponding scale. As a result, the server obtains the product market value and the number of products corresponding to each scale on the time axis, and the product market value and the number of products corresponding to each scale are the performance information of the evaluation target.

Then, the server uses the time axis as the abscissa, and uses the performance information, that is, the product daily yield, the product market value, and the number of products corresponding to each scale as the ordinate, respectively, to generate a line chart, which is the performance evaluation chart of the assessment object, and displays .

It should be noted that the line drawing includes the product yield line, the product market value line, and the product number line. The product yield line is connected in series with the product yield corresponding to each scale. The product market value line is connected in series with the product market value corresponding to each scale, and the product number line is connected in series with the number of products corresponding to each scale. As a result, the user can see the overall performance of the assessment object during his or her career.

In this embodiment, when the performance statistics instruction is received, the assessment object is determined according to the performance statistics instruction, that is, the person targeted by the performance statistics; and then the management time information of the assessment object is queried according to the management time information. Configure the timeline on which the assessment is based; then, query the product information managed by the assessment object, and configure the timeline based on the management time information contained in the product information to establish an assessment timeline; then, along the timeline, based on the product information The performance information of the assessment object is counted, and the performance assessment diagram of the assessment object is generated and displayed. The embodiment adds the management time information of the assessment object, and the obtained performance information includes the performance performance of the assessment object in the process of managing the product, and the obtained performance is the overall embodiment of the assessment object in the process of managing the product, thereby being more comprehensive and complete. The long-term feedback appraisers' performance ability can reflect the performance of the appraisers in the whole product management process, whether they have improvement, overall control ability, etc., rather than just watching the performance of a fund or a fund. The performance ability of the appraisers is easily concluded, and the cheating behavior that only takes a period of time or a product to be evaluated to deliberately increase or degrade performance is eliminated. Therefore, in the embodiment, by combining the management time information of the assessment object and the plurality of factors of the managed product information, the performance analysis of the assessment object is performed from the time limit of the appointment, and the income performance of each fund of the assessment object is replaced by the traditional one. It solves the technical problem of a single factor for the performance evaluation of fund managers.

Further, referring to FIG. 3, a second embodiment of the performance statistics method of the present application provides a performance statistics method. According to the embodiment shown in FIG. 2, the performance information includes a cumulative profit rate of the assessment object, and the step is performed. S30 includes:

Step S32: Calculate a daily daily return rate of the assessment object in the time axis according to a product daily yield and a current scale included in the product information.

Specifically, the server analyzes the product information to obtain the products that are managed by the assessment object, the daily daily rate of return of each product during the current assessment object management period, and the current scale. Among them, the current scale of the product is the market value of the products that are released regularly, for example, every three months. Then, the date between the current update and the date before the next update, the current scale of the product corresponds to the current scale of the update. The server can calculate the daily daily rate of return of the assessment object in the timeline using the following formula:

Where R is the daily return rate of the date a of the assessment object in the time axis, n is the number of products managed by the assessment object on the date a, v _j is the current scale of the product j on the date a, and y _j is the product j on the date a Daily rate of return, j=1...n.

It should be noted that regarding the daily yield of the product, there are two cases:

Case 1: If the product managed by the assessment object has been managed by the assessment object within the time interval of the timeline, there is no interruption in management time, then the daily yield of each product obtained by the server during the current assessment object management period is also That is, the daily product yield of a product in the timeline can be directly calculated by the above company.

Case 2: If there is a management interval between one or more products in the products managed by the assessment object, that is, on the time axis, the assessment object does not continuously manage all the fund products, as an implementation manner, the time axis includes The management date of the assessment object management product and the non-administrative date of the management of the product, the step S32 includes:

Step S321: Acquire, according to the product information, a product daily return rate of the management day of the product in the time axis.

The server determines the daily yield of the product corresponding to each product on each management day according to the daily daily yield of each product during the management period of the current assessment object, corresponding to the scale in the time axis.

Step S322: Set a daily return rate of the non-management day of the product in the time axis according to a daily return rate of a previous management day of the product non-management date.

After obtaining the daily yield of each product on each management day, the server screens out the products with the management gap period. Products that have a management gap period, that is, products that have an unmanaged day on the timeline. The server then sets the daily yield on the non-administrative day for the product with the gap period. Specifically, the daily yield of the product corresponding to the previous management day of the non-management day is set to the product daily yield of the non-management day according to the time sequence of the time axis. If there are multiple non-administrative days, the previous management days of each non-administrative day are respectively determined, and the daily yields of the products corresponding to the previous management days are respectively set as the product daily yields of the non-administrative days. Get the product daily yield for this product on the non-management day in the timeline. Thus, each product in the timeline is obtained, that is, the daily product yield per day.

When there are multiple products with management interruptions, the server automatically determines the daily product daily yield of each product in the timeline according to the above method. From this calculation, the daily return rate of the assessment object is continuous, which can fully reflect the trend of his entire career, and grasp the business capability of the assessment object as a whole. If there is no management day before the non-management date of the product, the daily return rate of the non-management day of the product in the timeline is set according to the daily yield of the next management day of the non-management date of the product.

In another embodiment, the step S322 further includes:

The product daily return rate of the product on the non-management day is set to zero.

That is, the server sets the yield of each product on the non-management day in the timeline to 0, and regards the empty window period of the object management product as two independent and independent performances, which can avoid the performance trend of “anticlimactic”. . Thereby, the daily yield corresponding to each scale of each product in the time axis is obtained.

Step S323: Calculate a daily daily return rate of the assessment object in the time axis according to a daily return rate of the product on a management day and a non-management day, and a current scale of the product.

After obtaining the daily yield corresponding to each scale of the product in the time axis, the server calculates the daily daily yield of the assessment object in the time axis according to the following formula.

Step S33: Calculate a daily cumulative rate of return of the assessment object in the time axis according to the daily return rate of the assessment object.

After obtaining the daily rate of return corresponding to each scale in the timeline of the assessment object, the server needs to calculate the daily cumulative rate of return of the assessment object in the timeline. Specifically, as an embodiment, the following formula can be used to calculate:

Where P _i is the cumulative rate of return of the assessment object on date a, date a is the ith day from the date of the start date of the time axis; R _i is the ith day from the start date of the time axis The daily rate of return, P _i-1 is the cumulative rate of return on the i-1th day from the start date of the timeline.

Thereby, the server separately calculates the cumulative rate of return of the assessment object corresponding to each scale of the time axis, and uses the cumulative rate of return of the assessment object as its performance information.

Step S34: Generate and display a performance assessment map of the assessment object according to the time axis and the performance information.

After obtaining the performance information of the assessment object, the server generates its performance assessment map and displays it. Specifically, the server uses the time axis as the abscissa, and uses the performance information, for example, the cumulative yield of the evaluation object corresponding to each scale, the number of products, and the market value of the product as the respective ordinates, and generates a line graph as a performance evaluation map of the assessment object. And display.

In this embodiment, according to the assessment object, the daily return rate and scale of the plurality of products are managed, and the cumulative rate of return of the assessment object is comprehensively calculated, which can comprehensively reflect the performance capability of the assessment object, thereby avoiding one-sided and single-time time from a single product. Ability evaluation of the assessment object. In addition, the embodiment of this city considers the gap period generated by the unmanaged management products of the assessment object, and configures various countermeasures to avoid the zero return rate of the non-management day affecting the performance of the assessment object and guarantee the fairness of the assessment results.

Referring to FIG. 4, a third embodiment of the performance statistical method of the present application provides a performance statistics method. Based on the embodiment shown in FIG. 3, the performance information further includes a daily excess return rate of the statistical object, and the step S34 is performed. Previously included:

Step S35: Calculate a daily excess return rate of the assessment object according to a preset benchmark daily return rate and a daily return rate of the assessment object.

In order to measure the profitability of the assessment object, the server calculates the daily excess return rate of the assessment object after obtaining the daily return rate of the assessment object. Specifically, as an embodiment, the following formula can be used to calculate:

C _a =R _a -O _a

Among them, C _a is the daily excess return rate of date a, R _a is the daily return rate of the assessment object on date a, and O _a is the base daily yield of date a.

It should be noted that the benchmark daily yield can be a common standard in the industry, such as the benchmarks of Shanghai and Shenzhen, Shanghai Stock Exchange, etc., which can be flexibly set according to actual needs. The server can query the historical record of the benchmark daily yield, and obtain the benchmark daily yield corresponding to the date on each scale on the time axis to calculate the daily excess return.

After obtaining the daily excess return rate, the server generates a performance assessment map of the assessment object according to the time axis and the performance information, and displays the performance information including the daily return rate, the daily excess return rate, the number of products, and the market value of the product. Further, the server may also generate and display a performance assessment map of the assessment object according to the time axis, the performance information, and the preset benchmark daily return rate.

In the present embodiment, the performance assessment chart obtained by the configuration includes the performance of the assessment object and the assessment benchmark, so that the user can clearly compare the assessment object with the industry benchmark, and have a more fair and objective understanding of the comprehensive ability of the assessment object.

Further, referring to FIG. 5, the fourth embodiment of the performance statistics method of the present application provides a performance statistics method. Based on the foregoing embodiment shown in FIG. 4, the step S35 includes:

Step S351: When receiving the reference custom instruction, obtain the selected preset reference and the set weight according to the reference custom instruction, and configure the custom reference.

In order to more accurately compare performance trends, the server also supports user-defined benchmarks.

Specifically, the user can click on the button of the custom reference in the page, and the server provides a plurality of preset industry benchmarks for selection. Then, the user selects one or more preset benchmarks, inputs the corresponding weights, and clicks the confirm button to input the reference custom command. As an application scenario, referring to FIG. 8, the preset reference selected by the user includes the CSI 300 and the SSE 50, and the weights corresponding to the settings are 60% and 40%, respectively.

When the server receives the reference custom instruction, it obtains the preset reference and the weight of the setting carried in the reference custom instruction. Then, based on the daily benchmark rate of return and the corresponding weight of the preset benchmark, a custom baseline is calculated, which can be calculated using the following formula:

T _a =X _a1 ×Y _a1 +...X _an ×Y _an

Where T _a is a custom reference corresponding to the date a; X _a1 ...... X _an are the reference daily returns of the preset reference 1, ..., n on the date a; Y _a1 , ..., Y _an are respectively Set the weights corresponding to the benchmarks 1, ..., and n. It should be noted that n is an integer greater than 1, and the sum of Y _a1 , . . . , Y _an is 1. That is, after the user selects one or more preset benchmarks, the sum of the weights set for each benchmark is 1.

Thus, the server calculates the custom daily rate of return corresponding to each readable time on the time axis as a custom benchmark.

Step S352: Calculate a daily excess return rate of the assessment object according to the custom reference and the daily return rate of the assessment object.

After obtaining the custom baseline, the server uses the following formula to calculate or update the daily excess return rate of the assessment object:

C _a =R _a -T _a

Among them, C _a is the daily excess return rate of date a, R _a is the daily return rate of the assessment object on date a, and T _a is the custom daily return rate of date a. Thereby, the daily excess return rate of the assessment object is obtained.

Then, the server updates or generates a performance assessment map of the assessment object according to the obtained daily excess return rate and displays it.

In this embodiment, supporting the user's self-selection benchmark and setting the weight can more accurately compare and analyze the fund performance trend.

Further, referring to FIG. 6, the fifth embodiment of the performance statistics method of the present application provides a performance statistics method, which is based on the embodiment shown in any of the above-mentioned FIGS. 2-4 (this embodiment takes FIG. 2 as an example). Before S30, it also includes:

Step S40: screening the flagship product in the product managed by the assessment object.

In order to eliminate the performance impact of the non-primary manager fund, this embodiment supports the user to select one or several products as the flagship product to measure the performance and comprehensive ability of the assessment object. Specifically, as an implementation manner, after obtaining the product information managed by the assessment object, the server selects one or more products with the highest market value in the preset time interval according to the product information, as the flagship product. Of course, the server can also select the flagship product based on other product information and preset conditions, which can be flexibly set according to actual needs. As another implementation manner, the user can also click the button of selecting the flagship product in the page, and the server provides the product of the object management for selection, and the user can select one or more products as the flagship product, and then confirm and input the flagship product. Select the instruction.

The server receives the flagship product selection instruction input by the user, and acquires the flagship product selected by the user. So, the server gets the flagship product.

Correspondingly, step S30 includes:

S36. The performance information of the assessment object is calculated according to the information of the flagship product along the time axis, and a performance assessment diagram of the assessment object is generated and displayed.

After obtaining the flagship product, the server generates the performance assessment map of the assessment object according to the information of the flagship product along the time axis and displays the performance assessment map of the assessment object. For details, refer to the first to fourth embodiments of the foregoing performance statistics method of the present application, and details are not repeatedly described herein.

In this embodiment, the performance appraisal of the appraisers is carried out by screening the flagship products of the appraisers, which is more representative and typical, except as the performance of the non-main manager products.

Referring to FIG. 7, the performance statistics apparatus of the present application provides a performance statistics apparatus, and the performance statistics apparatus includes:

The object module 10 is configured to determine an assessment object according to the performance statistics instruction when receiving the performance statistics instruction;

The product module 20 is configured to query product information managed by the assessment object, and configure a time axis according to management time information included in the product information;

The statistics module 30 is configured to calculate, according to the product information, the performance information of the assessment object according to the product information, and generate a performance assessment map of the assessment object and display the performance assessment map.

The foregoing embodiment of the performance statistics device is substantially the same as the foregoing embodiment of the performance statistics method, and the performance statistics method provided by the embodiment of the present invention is implemented, and details are not described herein.

In addition, the embodiment of the present application further provides a computer readable storage medium, where the performance statistic program is stored, and the performance statistics program is executed by the processor to implement performance statistics provided by the embodiments of the present invention. method,.

The specific embodiment of the computer readable storage medium of the present application is substantially the same as the foregoing embodiments of the performance statistics method, and details are not described herein.

It is to be understood that the term "comprises", "comprising", or any other variants thereof, is intended to encompass a non-exclusive inclusion, such that a process, method, article, or It also includes other elements that are not explicitly listed, or elements that are inherent to such a process, method, item, or system. An element defined by the phrase "comprising a ..." does not exclude the presence of additional equivalent elements in a process, method, article, or system that includes the element, without further limitation.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

A performance statistics method, characterized in that the performance statistics method comprises the following steps:

When receiving the performance statistics instruction, the assessment object is determined according to the performance statistics instruction;

Querying product information managed by the assessment object, and configuring a time axis according to management time information included in the product information;

A performance evaluation map of the assessment object is generated according to the product information, and a performance assessment map of the assessment object is generated and displayed along the time axis.
The performance statistics method according to claim 1, wherein the performance information includes a cumulative rate of return of the assessment object, and the performance information of the assessment object is counted according to the product information along the time axis. The steps of generating the performance assessment map of the assessment object and displaying the steps include:

Calculating a daily daily rate of return of the assessment object in the time axis according to a product daily yield and a current scale included in the product information;

Calculating a daily cumulative rate of return of the assessment object in the time axis according to a daily return rate of the assessment object;

And generating, according to the time axis and the performance information, a performance assessment map of the assessment object and displaying the performance assessment map.
The performance statistics method according to claim 2, wherein the time axis includes a management date of the assessment object management product and a non-management day in which the product is not managed, the content included in the product information The daily yield of the product and the current scale, the steps of calculating the daily return rate of the assessment object in the time axis include:

Obtaining a product daily return rate of the product on the management day in the time axis according to the product information;

Setting a daily rate of return of the product on the non-management day in the timeline according to a daily rate of return of the previous management day of the product non-management date;

The daily return rate of the assessment object in the time axis is calculated according to the daily return rate of the product on the management day and the non-management day, and the current scale of the product.
The performance statistics method according to claim 3, wherein the performance statistics method further comprises:

The product daily return rate of the product on the non-management day is set to zero.
The performance statistics method according to claim 2, wherein the daily yield of the assessment object in the time axis is calculated as follows:

Where R is the daily return rate of the date of the assessment object in the time axis, n is the number of products managed by the assessment object on the date a, v j is the current scale of the product j on the date a, and y j is the product j Daily rate of return on date a, j=1...n;

The daily cumulative yield calculation formula of the assessment object in the timeline is as follows:

Wherein, P i is the cumulative rate of return of the assessment object on the date a, the date a is the ith day from the date of the start date of the time axis of the assessment object; R i is the assessment object from the time axis The daily return rate on the first day from the start of the day, P i-1 is the cumulative rate of return on the i-1th day from the start date of the time axis.
The performance statistics method according to claim 2, wherein the performance information further includes a daily excess return rate of the statistical object, and the generating the assessment object according to the time axis and the performance information Before the performance review chart and the steps shown, it also includes:

Calculating the daily excess return rate of the assessment object according to the preset benchmark daily return rate and the daily return rate of the assessment object, and the calculation formula is as follows:

C a =R a -O a ,

Where, Ca is the daily excess return rate of date a, R a is the daily return rate of the assessment object on date a, and O a is the base daily yield of date a.
The performance statistics method according to claim 6, wherein the performance statistics method further comprises:

A performance assessment map of the assessment object is generated and displayed according to the time axis, performance information, and a preset benchmark daily yield.
The performance statistics method according to claim 6, wherein the step of calculating the daily excess return rate of the assessment object according to the preset reference daily return rate and the daily return rate of the assessment object comprises:

When receiving the reference custom instruction, acquiring the selected preset reference and the set weight according to the reference custom instruction, and configuring the custom reference;

Calculating a daily excess return rate of the assessment object according to the custom benchmark and the daily return rate of the assessment object.
The performance statistics method according to claim 8, wherein the sum of the weights corresponding to the selected preset reference is 1.
The performance statistics method according to claim 1, wherein the performance statistics method further comprises:

Screening out the flagship product in the products managed by the assessment object;

The step of generating, according to the product information, the performance information of the assessment object according to the product information, generating a performance assessment map of the assessment object, and displaying the method includes:

Along the time axis, the performance information of the assessment object is calculated according to the information of the flagship product, and a performance assessment map of the assessment object is generated and displayed.
The performance statistics method according to claim 1, wherein the performance information further includes a number of products managed by the assessment object and a product market value, and the performance statistics method further includes:

Along the time axis, the number of products managed by the assessment object and the market value of the product are counted according to the product information.
A performance statistics device, wherein the performance statistics device comprises:

An object module, configured to determine an assessment object according to the performance statistics instruction when receiving the performance statistics instruction;

The attendance module is configured to query management time information of the assessment object, and configure a time axis according to the management time information;

a product module, configured to query product information managed by the assessment object according to the time axis;

The statistics module is configured to calculate, according to the product information, the performance information of the assessment object according to the product information, and generate a performance assessment map of the assessment object and display the performance assessment map.
A terminal, comprising: a memory, a processor, and a performance statistics program stored on the memory and operable on the processor, when the performance statistics program is executed by the processor Implement the following steps:

When receiving the performance statistics instruction, the assessment object is determined according to the performance statistics instruction;

Querying product information managed by the assessment object, and configuring a time axis according to management time information included in the product information;

A performance evaluation map of the assessment object is generated according to the product information, and a performance assessment map of the assessment object is generated and displayed along the time axis.
The terminal according to claim 14, wherein said performance statistics program is further executed by said processor when:

Calculating a daily daily rate of return of the assessment object in the time axis according to a product daily yield and a current scale included in the product information;

Calculating a daily cumulative rate of return of the assessment object in the time axis according to a daily return rate of the assessment object;

And generating, according to the time axis and the performance information, a performance assessment map of the assessment object and displaying the performance assessment map.
The terminal according to claim 15, wherein said performance statistics program is further executed by said processor when:

Obtaining a product daily return rate of the product on the management day in the time axis according to the product information;

Setting a daily rate of return of the product on the non-management day in the timeline according to a daily rate of return of the previous management day of the product non-management date;

The daily return rate of the assessment object in the time axis is calculated according to the daily return rate of the product on the management day and the non-management day, and the current scale of the product.
A computer readable storage medium, wherein the computer readable storage medium stores a performance statistics program, and the performance statistics program is executed by the processor to implement the following steps:

When receiving the performance statistics instruction, the assessment object is determined according to the performance statistics instruction;

Querying product information managed by the assessment object, and configuring a time axis according to management time information included in the product information;

A performance evaluation map of the assessment object is generated according to the product information, and a performance assessment map of the assessment object is generated and displayed along the time axis.
The computer readable storage medium of claim 17, wherein the performance statistics program is further executed by the processor to:

Calculating a daily daily rate of return of the assessment object in the time axis according to a product daily yield and a current scale included in the product information;

Calculating a daily cumulative rate of return of the assessment object in the time axis according to a daily return rate of the assessment object;

And generating, according to the time axis and the performance information, a performance assessment map of the assessment object and displaying the performance assessment map.
The computer readable storage medium of claim 18, wherein the performance statistics program is further executed by the processor to:

Obtaining a product daily return rate of the product on the management day in the time axis according to the product information;

Setting a daily rate of return of the product on the non-management day in the timeline according to a daily rate of return of the previous management day of the product non-management date;

The daily return rate of the assessment object in the time axis is calculated according to the daily return rate of the product on the management day and the non-management day, and the current scale of the product.