Data analysis method, device and equipment based on fund company, medium and program product

Abstract

The invention provides a fund company-based data analysis method, which can be applied to the technical field of big data. The method comprises the following steps: obtaining index sample data and product net value sample data in a preset period range of a fund company; obtaining a product net value change rate corresponding to the fund product according to the product net value sample data of the fund product in the predetermined period range, and / or obtaining an index change rate corresponding to the fund product according to the index sample data of the fund product in the predetermined period range; a cumulative distribution function is obtained based on the same type of to-be-analyzed data of the fund company, the to-be-analyzed data comprises an index change rate, a product net value change rate or a difference value between the product net value change rate and the index change rate, and the same type of to-be-analyzed data comprises to-be-analyzed data of at least two fund products; and determining the score of the fund company by using the cumulative distribution function. The invention further provides a fund company-based data analysis device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and more specifically to a data analysis method, device, equipment, medium and program product based on a fund company. Background Art

[0002] As an investment product, funds can bring huge investment returns, but huge investment returns are also accompanied by huge risks. How to analyze and monitor fund risks has become a difficult problem for investors in the investment and trading process. Different investors have different values ​​on the investment returns and risks of fund products. In order to recommend suitable fund products to different investors, the existing scenario designs a series of questions based on the user side to assess the user's risk level. The fund product side also sets the product risk level according to the relevant fluctuations, and recommends fund products with corresponding product risk levels to users according to the user's risk level.

[0003] However, the current analysis and evaluation of funds are mostly limited to the product level, lacking data comparison and corresponding evaluation at the fund company level. The existing scenario is: cooperate with multiple fund companies to conduct regular forecasts of popular industry tracks, and associate the company's recommended products on the track. For example, the fund company can select several tracks from multiple tracks for forecasting within a certain period, and associate the company's fund products on the track. After several periods, the forecast settlement is carried out, and the index change rate and product net value change rate of the track are counted separately. In the conventional statistical method, all fund products of the fund company are comprehensively evaluated, which requires large amount of calculation, high cost of data analysis, and low pertinence of analysis; investors can only see the increase or decrease of the index and product net value, but cannot form an effective concept of the increase or decrease. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a data analysis method, device, equipment, medium and program product based on a fund company.

[0005] According to a first aspect of the present disclosure, a data analysis method based on a fund company is provided, comprising: obtaining index sample data and product net asset value sample data within a predetermined period range of the fund company; obtaining the product net asset value change rate corresponding to the fund product based on the product net asset value sample data within the predetermined period range, and / or obtaining the index change rate corresponding to the fund product based on the index sample data of the fund product within the predetermined period range; obtaining a cumulative distribution function based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed include the index change rate, the product net asset value change rate, or the difference between the product net asset value change rate and the index change rate, and the same type of data to be analyzed include data to be analyzed of at least two fund products; and determining the score of the fund company by using the cumulative distribution function.

[0006] According to an embodiment of the present disclosure, the fund companies include a first fund company and a second fund company, wherein obtaining a cumulative distribution function based on the same type of data to be analyzed of the fund companies includes: obtaining a cumulative distribution function based on the data to be analyzed of the first fund company and the data to be analyzed of the second fund company, wherein the data to be analyzed of the first fund company and the data to be analyzed of the second fund company are of the same type, wherein determining the scores of the fund companies using the cumulative distribution function includes: determining the scores of the first fund company and the scores of the second fund company using the cumulative distribution function.

[0007] According to an embodiment of the present disclosure, obtaining an index change rate corresponding to a fund product based on index sample data of the fund product within a predetermined period range includes: in response to the fact that the track of the first fund product is the same as the track of the second fund product, calculating the index change rate of the first fund product within the predetermined period range as the index change rate of the second fund product within the predetermined period range.

[0008] According to an embodiment of the present disclosure, obtaining a cumulative distribution function based on the same type of data to be analyzed of a fund company includes: obtaining a probability mass function based on the same type of data to be analyzed of the fund company; and calculating the cumulative distribution function using the probability mass function.

[0009] According to an embodiment of the present disclosure, determining the score of a fund company using a cumulative distribution function includes: determining a numerical value corresponding to the same type of data to be analyzed based on the cumulative distribution function; and obtaining a score corresponding to the numerical value through a preset standard.

[0010] According to an embodiment of the present disclosure, it also includes: adding recommendation tags for fund companies according to the scores, wherein the recommendation tags include at least one of a track tag, an increase tag, a comprehensive score tag, and a product tag.

[0011] According to an embodiment of the present disclosure, adding a recommendation label for a fund company based on a score includes: determining the recommendation label by horizontally comparing the scores of the same forecast period within a predetermined period range; and / or determining the recommendation label by comparing the scores of multiple forecast periods within a predetermined period range.

[0012] According to an embodiment of the present disclosure, determining a recommendation label by horizontally comparing scores of the same forecast period within a predetermined period range includes: horizontally comparing scores of different fund products of a fund company in the same forecast period to obtain the recommendation label.

[0013] According to an embodiment of the present disclosure, determining a recommendation label by comparing the scores of multiple forecast periods within a predetermined period range includes: obtaining a recommendation label based on the score changes of the same fund product of a fund company in multiple forecast periods; and / or obtaining a recommendation label by comparing the comprehensive scores of different fund products of the fund company in multiple forecast periods, wherein the comprehensive score depends on the score of each forecast period within the predetermined period range.

[0014] A second aspect of the present disclosure provides a data analysis device based on a fund company, including: a sample module, used to obtain index sample data and product net asset value sample data within a predetermined period range of the fund company; a change rate module, used to obtain the product net asset value change rate corresponding to the fund product based on the product net asset value sample data of the fund product within the predetermined period range, and / or to obtain the index change rate corresponding to the fund product based on the index sample data of the fund product within the predetermined period range; a cumulative distribution module, used to obtain a cumulative distribution function based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed includes the index change rate, the product net asset value change rate, or the difference between the product net asset value change rate and the index change rate, and the same type of data to be analyzed includes data to be analyzed of at least two fund products; and a score module, used to determine the score of the fund company using the cumulative distribution function.

[0015] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0016] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0017] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0019] Figure 1 A diagram schematically illustrates an application scenario of a data analysis method, device, equipment, medium, and program product based on a fund company according to an embodiment of the present disclosure;

[0020] Figure 2 A flowchart of a data analysis method based on a fund company according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 A flowchart of a data analysis method based on a fund company according to another embodiment of the present disclosure is schematically shown;

[0022] Figure 4 A schematic diagram schematically shows a probability mass function of the difference between the product net value change rate and the index change rate according to an embodiment of the present disclosure;

[0023] Figure 5 A schematic diagram schematically shows a cumulative distribution function of the difference between the product net value change rate and the index change rate according to an embodiment of the present disclosure;

[0024] Figure 6 A schematic diagram of a structure of a data analysis device based on a fund company according to an embodiment of the present disclosure is shown; and

[0025] Figure 7 A block diagram of an electronic device suitable for implementing a data analysis method based on a fund company according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0028] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] Currently, the analysis and evaluation of funds are mostly limited to the fund product level, and there is a lack of data comparison and corresponding evaluation at the fund company level.

[0031] An embodiment of the present disclosure provides a data analysis method based on a fund company, the method comprising: obtaining index sample data and product net asset value sample data within a predetermined period range of the fund company; obtaining the product net asset value change rate corresponding to the fund product based on the product net asset value sample data within the predetermined period range, and / or obtaining the index change rate corresponding to the fund product based on the index sample data of the fund product within the predetermined period range; obtaining a cumulative distribution function based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed includes the index change rate, the product net asset value change rate, or the difference between the product net asset value change rate and the index change rate, and the same type of data to be analyzed includes data to be analyzed of at least two fund products; and determining the score of the fund company by using the cumulative distribution function.

[0032] The data analysis method based on fund companies provided in the embodiments of the present disclosure uses a cumulative distribution function to determine the score of a fund company, which is simple to calculate and can reduce the amount of calculation, improve analysis efficiency, and reduce the cost of data analysis. It performs data analysis on at least one of the index change rate, the product net value change rate, or the difference between the product net value change rate and the index change rate to enhance the targeted analysis, reduce unnecessary analysis calculations, and further improve analysis efficiency. It performs analysis and evaluation at the fund company level and presents it to users in the form of summarized scores, which can help users intuitively judge the advantages of the fund company, understand the volatility of the fund market, improve the quality of analysis and evaluation, and enhance user experience.

[0033] In order to better understand the data analysis method based on fund companies in the embodiment of the present disclosure, the relevant professional terms involved in the embodiment of the present disclosure are introduced as follows:

[0034] Index: An indicator that comprehensively reflects the market average situation of a certain type or location of financial transaction products, usually used to calculate the average price of a certain type of financial transaction products.

[0035] Product Net Value: also known as unit net value, refers to the total assets of the fund minus total liabilities, divided by the total number of fund shares.

[0036] Index change rate: refers to the degree of change of an index over a certain period of time, which can be expressed as a percentage. It is used to measure the trend of changes in an index relative to a certain period in the past.

[0037] Product net asset value change rate: refers to the percentage change in the net asset value of a fund unit within a certain period of time, which reflects the change in the fund value relative to the previous period.

[0038] Track: usually refers to hot industries that have been particularly good in the past and are generally sought after by funds, such as medical care, liquor, new energy, technology, chips, etc.

[0039] Figure 1 The application scenario diagram of the data analysis method based on a fund company according to an embodiment of the present disclosure is schematically shown.

[0040] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a terminal device, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0041] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0042] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0043] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0044] It should be noted that the data analysis method based on the fund company provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data analysis device based on the fund company provided in the embodiment of the present disclosure can generally be set in the server 105. The data analysis method based on the fund company provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the data analysis device based on the fund company provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0045] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0046] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The data analysis method based on the fund company of the disclosed embodiment is described in detail.

[0047] Figure 2 The flowchart of the data analysis method based on the fund company according to the embodiment of the present disclosure is schematically shown.

[0048] like Figure 2 As shown, the data analysis method based on the fund company of this embodiment includes operations S210 to S240, and the method can be executed by a terminal device.

[0049] In operation S210, index sample data and product net asset value sample data within a predetermined period of the fund company are obtained.

[0050] Generally, fund companies are more inclined to recommend their advantages and popular funds to users. Therefore, some unpopular products of fund companies can be filtered out, and the index sample data and / or product net value sample data of advantageous funds can be used for data analysis. The obtained index sample data and product net value sample data can be sample data of some fund products of the fund company, for example, sample data of popular fund products, so as to reduce the cost of data analysis and improve the pertinence of analysis.

[0051] In some embodiments, index sample data and product net asset value sample data within a predetermined period range are obtained, and the predetermined period range includes multiple forecast periods. Specifically, T represents the forecast period; t represents the current time, which is generally an integer multiple of T. t=T represents that the current time is the first forecast period, and t=2T represents that the current time is the second forecast period. When n is a positive integer, nT≤t<(n+1)T represents that the current time is within the nth forecast period. To ensure the simplicity of description, t=nT is used below to represent any moment within the time period of nT≤t<(n+1)T. For example, the predetermined period range can be from the Tth forecast period to the 30Tth forecast period, 30 forecast periods. The data analysis method based on the fund company in this embodiment believes that sufficient sample data has been obtained within the predetermined period range, which can be used for statistical scoring and data analysis.

[0052] Exemplarily, the forecast results of the nth forecast period are settled at t=(n+10)T, that is, the change rate corresponding to the nth forecast period is calculated, and then the score of the fund company is calculated according to the track index change rate and the product net value change rate. For the predetermined period range from T to qT, t=(q-10)T forecast periods are included. It should be noted that the time for settling the forecast results of the nth forecast period is only exemplary and not a limitation of the embodiments of the present disclosure. In other embodiments, the forecast results of the nth forecast period can be settled at other forecast periods, for example, the forecast results of the nth forecast period are settled at t=(n+8)T, t=(n+20)T, and t=(n+16)T. For example, q is any positive integer greater than or equal to 11. Since the forecast results of the nth forecast period are settled at t=(n+10)T, the forecast results of t=(q-10)T forecast periods can be collected at t=qT, and the forecast results include the index change rate and the product net value change rate.

[0053] The index sample data and product net value sample data of the embodiment of the present disclosure refer to the index sample data and product net value sample data corresponding to the fund products of the fund company. Specifically, the index sample data and product net value sample data of the first fund product of the fund company, the index sample data and product net value sample data of the second fund product, etc. are obtained. For example, the index sample data and product net value sample data of the popular fund products of the fund company are obtained. Here, there is no restriction on the number and track of the fund products obtained. The fund products of the fund company can be selected according to the characteristics and advantages of the fund products, the fund products of the fund company can be selected according to the requirements of the user, and the fund products of the fund company can be selected according to the development trend of the fund industry.

[0054] In operation S220, the product net asset value change rate corresponding to the fund product is obtained based on the product net asset value sample data of the fund product within a predetermined period range, and / or the index change rate corresponding to the fund product is obtained based on the index sample data of the fund product within a predetermined period range.

[0055] In some embodiments, the corresponding change rate is calculated based on the product net asset value sample data and index sample data obtained within a predetermined period range. The sample data corresponds to the fund product, and therefore, it is equivalent to calculating the net asset value change rate and / or index change rate of the fund company's fund products. In the fund field, the index change rate on the same track is the same, so the index change rate of the fund product is the index change rate of the track.

[0056] The calculation method of the product net value change rate is: the product net value in the later forecast period is divided by the product net value in the previous forecast period, and then subtract 100% after obtaining the corresponding ratio; or the difference between the product net value in the later forecast period and the product net value in the previous forecast period is divided by the product net value in the previous forecast period. Taking the settlement of the forecast results of the nth forecast period at t=(n+10)T as an example, the product net value change rate is: the product net value at t=(n+10)T is divided by the product net value at t=nT, and then subtract 100% after obtaining the corresponding ratio.

[0057] The index change rate is calculated as follows: the index of the next forecast period is divided by the index of the previous forecast period, and then the corresponding ratio is subtracted from 100%; or the difference between the index of the next forecast period and the index of the previous forecast period is divided by the index of the previous forecast period. Taking the settlement of the forecast results of the nth forecast period at t=(n+10)T as an example, the index change rate is: the difference between the index at t=(n+10)T and the index at t=nT divided by the index at t=nT.

[0058] In some embodiments, Means: when t=(n+10)T, calculate the index change rate of the m track predicted by the p fund company at t=nT; Means: when t=(n+10)T, calculate the product net asset value change rate of the fund product of track m predicted by fund company p at t=nT; It means: when t=(n+10)T, calculate the difference between the net asset value change rate of the fund product of track m predicted by company p at t=nT and the index change rate (keep the positive and negative signs in the calculation).

[0059] The method of the disclosed embodiment can calculate only the index change rate, only the product net value change rate, or both the index change rate and the product net value change rate for the fund products of the fund company. For fund products in the same track, only the product net value change rate of different fund products can be calculated; for comparisons between different tracks, only the index change rate of the track can be calculated; for comparisons of the comprehensive strengths between fund products or fund companies, both the track index change rate and the fund product net value change rate can be calculated. Targeted calculation of the index change rate and / or product net value change rate for different scenarios can reduce the amount of calculation and improve the efficiency of data analysis.

[0060] In some embodiments, obtaining an index change rate corresponding to a fund product based on index sample data of the fund product within a predetermined period range includes: in response to the fact that the track of the first fund product is the same as the track of the second fund product, calculating the index change rate of the first fund product within the predetermined period range as the index change rate of the second fund product within the predetermined period range.

[0061] The index reflects the overall performance of the track. Fund products in the same track have the same index in each forecast period. Therefore, the index change rate of different fund products in the same track is the same. When conducting data analysis and evaluation on fund companies, it is only necessary to calculate the index change rate once for fund products in the same track, thereby reducing the amount of calculation and the cost of data analysis.

[0062] In operation S230, a cumulative distribution function is obtained based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed includes the index change rate, the product net asset value change rate, or the difference between the product net asset value change rate and the index change rate, and the same type of data to be analyzed includes data to be analyzed of at least two fund products.

[0063] The index change rate reflects the rise and fall of the track, the product net value change rate reflects the change in the net value of the fund product, and the difference between the product net value change rate and the index change rate is more statistically significant for the comparison between different tracks. The index change rate, product net value change rate, or the difference between the product net value change rate and the index change rate are used as the data to be analyzed to obtain the corresponding cumulative distribution function. The obtained cumulative distribution function represents the cumulative probability corresponding to the independent variable (for example, the difference between the product net value change rate and the index change rate), which can reflect the characteristics and advantages of the fund company's track or fund products, and help investors choose the fund company that suits them and choose the right fund products.

[0064] In some embodiments, obtaining the cumulative distribution function using the same type of data to be analyzed includes: obtaining the cumulative distribution function based on the index change rate within a predetermined period range of multiple fund products of a fund company, and the multiple fund products are fund products in different tracks; obtaining the cumulative distribution function based on the product net asset value change rate within a predetermined period range of multiple fund products of the fund company; or obtaining the cumulative distribution function based on the difference between the product net asset value change rate and the index change rate within a predetermined period range of multiple fund products of the fund company.

[0065] Further, obtaining the cumulative distribution function based on the same type of data to be analyzed of the fund company includes: obtaining a probability mass function based on the same type of data to be analyzed of the fund company; and calculating the cumulative distribution function using the probability mass function.

[0066] In some embodiments, by collecting historical sample data of the fund company, the obtained index sample data and product net value sample data are discontinuous, and the index change rate of the index sample and the product net value change rate of the product net value sample can be calculated, so as to draw the probability mass function (PMF) of the product net value change rate, the index change rate, or the difference between the product net value change rate and the index change rate within a predetermined period range. Specifically, within the predetermined period range, the occurrence probability of each data to be analyzed in the same type of data to be analyzed is calculated, and the probability mass function is constructed using the same type of data to be analyzed and the corresponding occurrence probability. The probability mass function reflects the corresponding relationship between the predicted data within the predetermined period range (for example, the difference between the product net value change rate and the index change rate) and the occurrence probability of the predicted data. For example, refer to the following Figure 4 The probability mass function of the difference between the product net asset value change rate and the index change rate is shown. The difference between the product net asset value change rate and the index change rate is 8.81%, and the probability of occurrence of this difference is approximately 0.04.

[0067] The same type of data to be analyzed is used as the independent variable of the probability mass function, including data to be analyzed of at least two fund products. When the tracks of the at least two fund products are different, it is equivalent to including data to be analyzed of at least two tracks. The data analysis method based on fund companies in the disclosed embodiment obtains probability mass functions corresponding to the data to be analyzed of multiple fund products or multiple tracks for analysis and evaluation, for example, obtaining data to be analyzed of different fund products or different tracks of a fund company to construct a probability mass function, or obtaining data to be analyzed of fund products or tracks of different fund companies to construct a probability mass function.

[0068] The data set corresponding to the probability mass function is a discrete random variable, which is a discrete random variable composed of the same type of data to be analyzed. The cumulative distribution function (CDF) of the same type of data to be analyzed is calculated based on these discrete random variables. Specifically, for a random variable, the cumulative distribution function is defined as:

[0069] (1)

[0070] Where X is a random variable, P represents the probability, and the cumulative distribution function is It gives the probability that a random variable will take on a value less than or equal to a specific value x.

[0071] Exemplarily, the cumulative distribution function calculated using the probability mass function represents the cumulative probability corresponding to each data to be analyzed of the same type of data to be analyzed within a predetermined period. In the cumulative distribution function, the larger the value of the independent variable, the higher the cumulative probability corresponding to the independent variable. The cumulative distribution function of the same type of data to be analyzed represents the probability that the value of the independent variable is less than or equal to one of the data to be analyzed, for example, referring to the accompanying drawings of the specification Figure 5 When the difference between the product net asset value change rate and the index change rate is 4.72%, the cumulative probability is 1 / 3, and 4.72% exceeds 1 / 3 of the difference between the product net asset value change rate and the index change rate within the predetermined period.

[0072] According to the embodiments of the present disclosure, through the cumulative distribution function of the index change rate, the product net value change rate, or the difference between the product net value change rate and the index change rate, the characteristics and advantages of the fund company can be displayed from different angles such as products, tracks, and ups and downs, thereby improving the quality of analysis and evaluation: the cumulative distribution function is calculated using the probability mass function, which reduces the amount of calculation and improves data analysis efficiency.

[0073] After the cumulative distribution function of the same type of data to be analyzed is obtained, in operation S240, the score of the fund company is determined using the cumulative distribution function. Specifically, determining the score of the fund company using the cumulative distribution function includes: determining a value corresponding to the same type of data to be analyzed based on the cumulative distribution function; and obtaining a score corresponding to the value through a preset standard.

[0074] For each of the same type of data to be analyzed of the fund company, the cumulative probability value corresponding to the data to be analyzed can be determined from the cumulative distribution function, and the cumulative probability value is used as the corresponding numerical value. For example, referring to the accompanying drawings Figure 5 When the difference between the product net value change rate and the index change rate is 4.72%, the cumulative probability value of 1 / 3 is the value corresponding to 4.72%.

[0075] Exemplarily, one of the same type of data to be analyzed within a predetermined period range (e.g., index change rate, product net value change rate, or the difference between product net value change rate and index change rate) is substituted into the corresponding cumulative distribution function to obtain its corresponding ordinate. The ordinate of the cumulative distribution function of the exponential change rate is marked as The vertical coordinate of the cumulative distribution function of the product net value change rate is The vertical coordinate of the cumulative distribution function of the difference between the product net value change rate and the index change rate is , the vertical coordinate of the data to be analyzed is the corresponding value.

[0076] Exemplarily, the determined numerical value is converted into a score by a preset standard. Based on the acquired ordinate (ie, numerical value), the score corresponding to the numerical value is calculated by formula induction, and a scoring result within the range of 4 to 10 points is given by formula induction. The formula is as follows:

[0077] (2)

[0078] in, It means: when t=(n+10)T, the settlement score of the index change rate of track m predicted by company p; It means: when t=(n+10)T, the settlement score of the net value change rate of the fund product of track m predicted by company p; It means: at t=(n+10)T, the settlement score of the difference between the net asset value change rate of the fund product in track m and the index change rate predicted by company p.

[0079] By converting the relevant data to be analyzed into scores, users can intuitively understand the relevant information of the fund company and improve the quality of analysis and evaluation. It should be noted that the above-mentioned method of calculating the score is only exemplary and not a limitation of the embodiments of the present disclosure. In other embodiments, other scoring methods can be used. For example, a scoring result between 1 and 100 points is given by formula induction. The score calculation formula can be a quadratic function or other function type. In some embodiments, if the final statistical scores differ greatly, the gap can be reduced by the square root method.

[0080] The data analysis method based on fund companies in the embodiments of the present disclosure uses a cumulative distribution function to determine the score of a fund company, which can reduce the amount of calculation, improve analysis efficiency, and reduce the cost of data analysis; calculate and analyze at least one of the index change rate, the product net value change rate, or the difference between the product net value change rate and the index change rate, thereby enhancing the targeted nature of the analysis, reducing unnecessary analysis and calculations, and further improving analysis efficiency; perform analysis and evaluation at the fund company level, and present the results to users in the form of summarized scores, which can help users intuitively judge the advantages of the fund company, understand the volatility of the fund market, and improve the quality of analysis and evaluation.

[0081] Data comparison and corresponding evaluation at the fund company level may involve comparative analysis of multiple fund companies. In another embodiment of the present disclosure, a method for analyzing data of the first fund company and the second fund company is described by taking the first fund company and the second fund company as examples. The data analysis of multiple fund companies is similar and will not be described in detail here.

[0082] Figure 3 The flowchart of a data analysis method based on a fund company according to another embodiment of the present disclosure is schematically shown.

[0083] like Figure 3 As shown, the data analysis method based on fund companies in this embodiment includes operations S310 to S340, and the method can be executed by a terminal device. In this embodiment, the fund companies include a first fund company and a second fund company.

[0084] In operation S310, index sample data and product net asset value sample data within a predetermined period range of the first fund company and the second fund company are obtained.

[0085] The first fund company and the second fund company may be more inclined to recommend their advantages and popular funds to users. Therefore, some unpopular products of the fund companies can be filtered out, and the index sample data and / or product net value sample data of the advantageous funds can be used for data analysis. The index sample data and product net value sample data refer to the index sample data and product net value sample data corresponding to the fund products of the first fund company or the fund products of the second fund company. The predetermined period range and the forecast period are the same as above. Figure 2 The relevant description is consistent with that of , and will not be elaborated here.

[0086] In operation S320, the product net asset value change rate corresponding to the fund product is obtained based on the product net asset value sample data of the fund product within a predetermined period range, and / or the index change rate corresponding to the fund product is obtained based on the index sample data of the fund product within a predetermined period range.

[0087] Exemplarily, the fund product refers to the fund product of the first fund company or the fund product of the second fund company. For the fund products of the first fund company and the fund products of the second fund company, only the index change rate, only the product net value change rate, or the index change rate and the product net value change rate can be calculated. The index change rates of different fund products in the same track are the same, and the index change rate corresponding to the fund product is obtained according to the index sample data of the fund product within a predetermined period range, including: in response to the track of the first fund product being the same as the track of the second fund product, the index change rate of the first fund product within the predetermined period range is calculated as the index change rate of the second fund product within the predetermined period range. For fund products on the same track, the index change rate only needs to be calculated once, thereby reducing the amount of calculation and reducing the cost of data analysis.

[0088] To better understand the embodiments of the present disclosure, Tables 1 and 2 respectively show examples of the index change rate, product net value change rate, and the difference between the product net value change rate and the index change rate corresponding to different fund companies within a predetermined period. In Tables 1 and 2, the current forecast period is t=22T, that is, the predetermined period range is T to 22T. Considering that the forecast result of the nth forecast period is settled at t=(n+10)T, settlement of 12 forecast periods has been performed.

[0089] Table 1 (i.e. p = ) Forecast data within the company's scheduled period

[0090]

[0091] In Table 1, the forecast data for Track 2 within the predetermined period are shown. These forecast data indicate that the company The index change rate, product net value change rate, and the difference between the product net value change rate and the index change rate of the fund products in Track 2 from the Tth forecast period to the 12Tth forecast period; the forecast data of different tracks in the Tth forecast period are also shown.

[0092] Table 2 b (i.e. p=b) forecast data within the company's scheduled period

[0093]

[0094] Table 2 shows the forecast data on track 2 within the predetermined period, that is, the index change rate, product net value change rate, and the difference between the product net value change rate and the index change rate of the fund products of company b in track 2 from the Tth forecast period to the 12th forecast period. Referring to Tables 1 and 2, the index change rate of the same track is the same in each forecast period. For example, in the 11th forecast period, The index change rates of Company A and Company B on Track 2 were both 0.19%.

[0095] After obtaining the forecast data of the first fund company and the second fund company, in operation S330, a cumulative distribution function is obtained based on the data to be analyzed of the first fund company and the data to be analyzed of the second fund company, wherein the data to be analyzed of the first fund company and the data to be analyzed of the second fund company are of the same type. Exemplarily, the data to be analyzed include the index change rate, the product net value change rate, or the difference between the product net value change rate and the index change rate.

[0096] In some embodiments, obtaining a cumulative distribution function based on the data to be analyzed by the first fund company and the data to be analyzed by the second fund company includes: obtaining a cumulative distribution function based on the index change rate within a predetermined period of the fund products of the first fund company and the index change rate within a predetermined period of the fund products of the second fund company, wherein the fund products of the first fund company and the fund products of the second fund company are fund products of different tracks; obtaining a cumulative distribution function based on the net asset value change rate of the products within the predetermined period of the fund products of the first fund company and the net asset value change rate of the products within the predetermined period of the fund products of the second fund company; or obtaining a cumulative distribution function based on the difference between the net asset value change rate of the products within the predetermined period of the fund products of the first fund company and the index change rate, and the difference between the net asset value change rate of the products within the predetermined period of the fund products of the second fund company and the index change rate.

[0097] Further, obtaining a cumulative distribution function based on the data to be analyzed of the first fund company and the data to be analyzed of the second fund company includes: obtaining a probability mass function based on the data to be analyzed of the first fund company and the data to be analyzed of the second fund company; and calculating the cumulative distribution function using the probability mass function.

[0098] In some embodiments, a probability mass function of the data to be analyzed within a predetermined period is drawn based on the data to be analyzed of the first fund company and the data to be analyzed of the second fund company. Specifically, the probability of occurrence of each data to be analyzed of the first fund company and the second fund company is calculated, and the probability mass function is constructed using the data to be analyzed and the corresponding probability of occurrence. The data to be analyzed of the first fund company and the data to be analyzed of the second fund company include the same type of data to be analyzed, specifically, data to be analyzed of at least two fund products. The cumulative distribution function of the data to be analyzed of the first fund company and the data to be analyzed of the second fund company is calculated based on the obtained probability mass function. The cumulative distribution function represents the cumulative probability corresponding to each data to be analyzed of the relevant fund companies within a predetermined period. In order to intuitively understand the probability mass function and the cumulative distribution function obtained based on the data to be analyzed of different fund companies, Figure 4 and Figure 5Examples of a probability mass function and a cumulative distribution function according to an embodiment of the present disclosure are respectively shown.

[0099] In Table 1 and Table 2, the submissions of the two fund companies are mostly concentrated in Track 2. For the same forecast period, the index change rate of the same track is the same. Therefore, the main comparison is the net asset value change rate of all products of the two fund companies in Track 2 of the statistical forecast period, that is, the net asset value change rate of the product, or the difference between the net asset value change rate of the product and the index change rate. For comparisons between different tracks, it is more statistically significant to compare the difference between the net asset value change rate of the product and the index change rate. The following embodiment analyzes the difference between the net asset value change rate of the product and the index change rate, first draws the probability mass function, and then draws the cumulative distribution function based on the probability mass function.

[0100] Figure 4 A schematic diagram schematically shows a probability mass function of the difference between the product net value change rate and the exponential change rate according to an embodiment of the present disclosure.

[0101] Figure 4 The difference between the product net asset value change rate and the index change rate shown comes from the difference between the product net asset value change rate and the index change rate on track 2 shown in Table 1 and Table 2. Figure 4 The vertical axis represents the difference between the net value change rate of each product and the index change rate. The probability of occurrence of fund a and fund b in all forecast periods (a total of 24 forecast periods), for example, The difference between the product net value change rate and the index change rate in the company's 9T forecast period is 13.52%, and the probability of occurrence in 24 forecast periods is about 0.0417. Figure 4 The probability of occurrence of the difference between the product net value change rate and the index change rate of 11.03% is about 0.0833. The emergence of the 10T forecast cycle of company A and the 5T forecast cycle of company B.

[0102] Figure 5 A schematic diagram schematically shows a cumulative distribution function of the difference between the product net value change rate and the index change rate according to an embodiment of the present disclosure.

[0103] Figure 5 is based on Figure 4 The cumulative distribution function plotted from the probability mass function in Figure 5 The vertical axis represents: the sum of the occurrence probabilities of the difference between the net value change rate and the index change rate of all products less than a specific value in all forecast periods listed in Tables 1 and 2. The specific value can be the difference between the net value change rate and the index change rate of any product in all forecast periods. For example, The difference between the product net asset value change rate and the index change rate in the company's 9T forecast period is 13.52%, and the cumulative probability in 24 forecast periods is approximately 0.6667. In other words, the probability that the difference between the product net asset value change rate and the index change rate is less than 13.52% is 0.6667.

[0104] In operation S340, the score of the first fund company and the score of the second fund company are determined using the cumulative distribution function. Specifically, determining the score of the first fund company and the score of the second fund company using the cumulative distribution function includes: determining a numerical value corresponding to the data to be analyzed of the first fund company and the data to be analyzed of the second fund company based on the cumulative distribution function; and obtaining a score corresponding to the numerical value through a preset standard. For each data to be analyzed in the data to be analyzed of the first fund company and the data to be analyzed of the second fund company, a cumulative probability value corresponding to each data to be analyzed can be determined from the cumulative distribution function, and the cumulative probability value is the corresponding numerical value.

[0105] The fund company-based data analysis method of the disclosed embodiment can improve the pertinence of analysis and reduce data analysis costs by performing analysis and evaluation among different fund companies; it helps users understand the characteristics and advantages of fund companies and facilitates investors to make investments.

[0106] In some embodiments, after determining the score of the fund company, the data analysis method based on the fund company according to the embodiment of the present disclosure also includes: adding a recommended label for the fund company according to the score, wherein the recommended label includes at least one of a track label, an increase label, a comprehensive score label, and a product label.

[0107] In order to intuitively show the characteristics and advantages of fund companies to users and improve the quality of analysis and evaluation, we add recommended tags to fund companies based on the scores of fund companies, and implement fund companies and their corresponding recommended tags in the software program. Users can filter by tags to obtain suitable fund companies. For example, if users pay more attention to the comprehensive score of track prediction, they can check the "Track Prediction Comprehensive Score TOP Tag" in the software program to filter out fund companies with the TOP track prediction comprehensive score.

[0108] Adding recommendation labels for fund companies based on scores includes: determining the recommendation labels by horizontally comparing scores of the same forecast period within a predetermined period range; and / or determining the recommendation labels by comparing scores of multiple forecast periods within a predetermined period range.

[0109] In some embodiments, determining the recommendation tag by performing a horizontal comparison of the scores of the same forecast period within a predetermined period range includes: performing a horizontal comparison of the scores of different fund products of the fund company in the same forecast period to obtain the recommendation tag.

[0110] Exemplarily, different fund products of the same fund company can be compared horizontally in the same forecast period to obtain recommendation labels, and fund products of different fund companies can also be compared horizontally in the same forecast period to obtain recommendation labels. Specifically, based on the scores obtained through the cumulative distribution function, a horizontal comparison of the scores of the same forecast period is performed to screen out the "TOP products", "TOP tracks" and "the company's predicted track with the largest increase" in the forecast period, and add corresponding recommendation labels to the corresponding fund products, tracks and fund companies, so as to screen out excellent products in the same forecast period and improve the quality of analysis and evaluation. For example, referring to Table 1, in the Tth forecast period, the scores of the index change rates of Track 1, Track 2, and Track 3 are compared. Based on the size of the scores, The company adds the label "Company TOP Track"; for example, referring to Table 1 and Table 2, in the 2T forecast period, compare The score of the product net asset value change rate of the fund products of company track 2 and the score of the product net asset value change rate of the fund products of company B track 2 are based on the size of the score. A fund company or B fund company adds the "TOP product" label.

[0111] In some embodiments, determining a recommended label by comparing the scores of multiple forecast periods within a predetermined period range includes: obtaining a recommended label based on the changes in scores of the same fund product of a fund company in multiple forecast periods; and / or obtaining a recommended label by comparing the comprehensive scores of different fund products of the fund company in multiple forecast periods, wherein the comprehensive score depends on the score of each forecast period within the predetermined period range.

[0112] In some embodiments, the multiple prediction periods may be all prediction periods within a predetermined period range, or may be part of the prediction periods within the predetermined period range. For example, as described in Table 1, the multiple prediction periods may be the 6Tth prediction period to the 12Tth prediction period.

[0113] The score change of the same fund product of a fund company in multiple forecast periods refers to: for the same fund product, the comprehensive score corresponding to the forecast data of the next K forecast periods is compared vertically with the comprehensive score corresponding to the forecast data of the previous K forecast periods, where K ≥ 1, and K is less than the number of forecast periods within the predetermined period range (for example, for the case of Table 1, K is less than 12). The comprehensive score can be the weighted average of the scores of each forecast period within the predetermined period range. For example, referring to Table 1, the score of the product net asset value change rate of the fund products of track 2 in each forecast period is calculated, and the average scores corresponding to the last four forecast periods, the average scores corresponding to the middle four forecast periods (5T forecast periods to 8T forecast periods), and the average scores corresponding to the first four forecast periods are compared and analyzed to determine the increase or decrease of the product net asset value change rate of the fund products, thereby adding a recommendation label to the fund company.

[0114] Comparing the comprehensive scores of different fund products of a fund company in multiple forecast periods means: calculating the comprehensive scores of different fund products of the same fund company in multiple forecast periods respectively, and making a comprehensive comparison of different fund products of the same fund company; or calculating the comprehensive scores of fund products of different fund companies in multiple forecast periods respectively, and making a comprehensive comparison of fund products of different fund companies. The comprehensive score can be the weighted average of the scores of each forecast period within a predetermined period. For example, if the average score of the index change rate of all tracks of a fund company in multiple periods is high, a label of "high track forecast comprehensive score" or "TOP3 track forecast comprehensive score" can be added; if the average score of the net value change rate of products in a certain track is high, a label of "high prediction score of products in a certain track" or "TOP3 prediction score of products in a certain track" can be added to attract investors in the track to invest; for some tracks with lower indexes, if the comprehensive score of the net value change rate of products minus the index change rate is very high, it means that the company still has advantageous products in these tracks, and a "superior product label" can be added for them.

[0115] For example, refer to Table 1, Table 2 and Figure 5 This example describes how to calculate the comprehensive scores of fund products of different fund companies in multiple forecast periods and add recommendation labels. At t=8T, fund company b is in track 2. The value is 4.72%. In the current scenario, when the horizontal axis is 4.72%, the cumulative probability in the cumulative distribution function is 1 / 3, and when t=8T, company B The score is 6. By calculating all the statistical forecast periods in track 2, it can be calculated that the score set of fund company b in each forecast period is {5.75, 5.25, 5.5, 9.75, 7, 5, 4.5, 6, 9.5, 4.75, 9, 4.25}, with an average score of 6.35. The company's score set is {10, 6.25, 7.5, 9.25, 6.5, 7.25, 7.75, 8.25, 8, 7,8.75, 8.5}, with an average score of 7.92, so for all forecast periods so far, A fund company’s better forecast of the net asset value of its products on Track 2 can label the company as having high scores for its Track 2 products.

[0116] Illustratively, by comparing multiple data horizontally or over multiple periods, the data analysis method based on fund companies in the disclosed embodiments can help users understand the advantages of fund companies in the market, improve analysis accuracy, and improve analysis quality. A comprehensive comparison of the scores of multiple forecast periods can highlight the comprehensive advantages of fund companies. A fund company's high long-term comprehensive evaluation score indicates that the fund company understands its advantageous products and market conditions, which is beneficial to investors. The fluctuations in the fund company's scores can reflect the fund company's investment risk tendency. Users can make investments based on the data of this evaluation method combined with their own risk level assessment. For example, ordinary investors may be more inclined to invest in products of companies with smaller score fluctuations.

[0117] The data analysis method based on fund companies in the disclosed embodiment cooperates with multiple fund companies. Each fund company regularly predicts changes in track market conditions and recommends the most promising fund products of the company on the track during the current period. After a period of time, the collected data is further analyzed and scored based on the changes in track index and product rise and fall predicted by the fund company, that is, whether there is a rise or fall and the extent of the rise or fall, and recommendation tags are added to the corresponding fund companies, thereby providing data of fund companies on corresponding tracks according to different tracks preferred by users.

[0118] Based on the above-mentioned data analysis method based on fund companies, the present disclosure also provides a data analysis device based on fund companies. Figure 6 The device is described in detail.

[0119] Figure 6 The structural block diagram of the data analysis device based on a fund company according to an embodiment of the present disclosure is schematically shown.

[0120] like Figure 6 As shown, the fund company-based data analysis device 600 of this embodiment includes a sample module 610 , a change rate module 620 , a cumulative distribution module 630 and a score module 640 .

[0121] The sample module 610 is used to obtain the index sample data and product net value sample data within the predetermined period of the fund company. In one embodiment, the sample module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0122] The change rate module 620 is used to obtain the product net value change rate corresponding to the fund product based on the product net value sample data of the fund product within a predetermined period range, and / or obtain the index change rate corresponding to the fund product based on the index sample data of the fund product within a predetermined period range. In one embodiment, the change rate module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0123] The cumulative distribution module 630 is used to obtain a cumulative distribution function based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed includes the index change rate, the product net value change rate, or the difference between the product net value change rate and the index change rate, and the same type of data to be analyzed includes data to be analyzed of at least two fund products. In one embodiment, the cumulative distribution module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0124] The scoring module 640 is used to determine the score of the fund company using the cumulative distribution function. The scoring module 630 can be used to perform the operation S230 described above, which will not be described in detail here.

[0125] According to an embodiment of the present disclosure, the fund companies include a first fund company and a second fund company, and the cumulative distribution module 630 is further used to obtain a cumulative distribution function based on the data to be analyzed of the first fund company and the data to be analyzed of the second fund company, wherein the data to be analyzed of the first fund company and the data to be analyzed of the second fund company are of the same type. The score module 640 is further used to determine the score of the first fund company and the score of the second fund company using the cumulative distribution function.

[0126] According to an embodiment of the present disclosure, the rate of change module 620 is also used to calculate the index change rate of the first fund product within a predetermined period range as the index change rate of the second fund product within a predetermined period range in response to the fact that the track of the first fund product is the same as the track of the second fund product. The cumulative distribution module 630 is also used to: obtain a probability mass function based on the same type of data to be analyzed of the fund company; and calculate the cumulative distribution function using the probability mass function. The score module 640 is also used to: determine the numerical value corresponding to the same type of data to be analyzed based on the cumulative distribution function; and obtain the score corresponding to the numerical value through a preset standard.

[0127] The fund company-based data analysis device 600 further includes a label module 650. The label module 650 is used to add a recommended label for the fund company according to the score, wherein the recommended label includes at least one of a track label, an increase label, a comprehensive score label, and a product label.

[0128] According to an embodiment of the present disclosure, the label module 650 is also used to: determine the recommended label by performing a horizontal comparison of the scores of the same forecast period within a predetermined period range; and / or determine the recommended label by comparing the scores of multiple forecast periods within a predetermined period range. Determining the recommended label by performing a horizontal comparison of the scores of the same forecast period within a predetermined period range includes: performing a horizontal comparison of the scores of different fund products of a fund company in the same forecast period to obtain the recommended label. Determining the recommended label by comparing the scores of multiple forecast periods within a predetermined period range includes: obtaining the recommended label based on the changes in the scores of the same fund product of a fund company in multiple forecast periods; and / or obtaining the recommended label by comparing the comprehensive scores of different fund products of a fund company in multiple forecast periods, wherein the comprehensive score depends on the score of each forecast period within the predetermined period range.

[0129] According to an embodiment of the present disclosure, any multiple modules of the sample module 610, the rate of change module 620, the cumulative distribution module 630, the score module 640 and the label module 650 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the sample module 610, the rate of change module 620, the cumulative distribution module 630, the score module 640 and the label module 650 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the sample module 610, the change rate module 620, the cumulative distribution module 630, the score module 640 and the label module 650 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0130] Figure 7 A block diagram of an electronic device suitable for implementing a data analysis method based on a fund company according to an embodiment of the present disclosure is schematically shown.

[0131] like Figure 7As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0132] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0133] According to an embodiment of the present disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 may further include one or more of the following components connected to the input / output (I / O) interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0134] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0135] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0136] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the data analysis method based on fund companies provided by the embodiment of the present disclosure.

[0137] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 701. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0138] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0139] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0140] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0141] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0142] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0143] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data analysis method based on a fund company, characterized in that: The method comprises: Obtain index sample data and product net asset value sample data within the period predetermined by the fund company; Obtaining the product net asset value change rate corresponding to the fund product according to the product net asset value sample data within the predetermined period, and / or obtaining the index change rate corresponding to the fund product according to the index sample data within the predetermined period; Obtaining a cumulative distribution function based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed includes an index change rate, a product net value change rate, or a difference between a product net value change rate and an index change rate, and the same type of data to be analyzed includes data to be analyzed of at least two fund products; and The cumulative distribution function is used to determine the score of the fund company.

2. The method according to claim 1, characterized in that The fund companies include the first fund company and the second fund company. Wherein, the obtaining of the cumulative distribution function based on the same type of to-be-analyzed data of the fund company comprises: A cumulative distribution function is obtained based on the data to be analyzed by the first fund company and the data to be analyzed by the second fund company, wherein the data to be analyzed by the first fund company and the data to be analyzed by the second fund company are of the same type, Wherein, determining the score of the fund company by using the cumulative distribution function comprises: The cumulative distribution function is used to determine the score of the first fund company and the score of the second fund company.

3. The method according to claim 1, characterized in that The step of obtaining the index change rate corresponding to the fund product according to the index sample data of the fund product within the predetermined period includes: In response to the fact that the track of the first fund product is the same as that of the second fund product, the index change rate of the first fund product within the predetermined period range is calculated as the index change rate of the second fund product within the predetermined period range.

4. The method according to claim 1, characterized in that: The obtaining of the cumulative distribution function based on the same type of to-be-analyzed data of the fund company comprises: Obtaining a probability mass function based on the same type of to-be-analyzed data of the fund company; and The cumulative distribution function is calculated using the probability mass function.

5. The method according to claim 1, characterized in that Determining the score of the fund company by using the cumulative distribution function includes: Determining a value corresponding to the same type of data to be analyzed based on the cumulative distribution function; and A score corresponding to the numerical value is obtained according to a preset standard.

6. The method according to claim 1, characterized in that The method further comprises: A recommendation label is added for the fund company according to the score, wherein the recommendation label includes at least one of a track label, an increase label, a comprehensive score label, and a product label.

7. The method according to claim 6, characterized in that Adding a recommendation tag to the fund company according to the score includes: Determining the recommended tag by comparing the scores of the same prediction period within the predetermined period range; and / or The recommended label is determined by comparing the scores of multiple prediction periods within the predetermined period range.

8. The method according to claim 7, characterized in that The determining the recommended tag by horizontally comparing the scores of the same prediction period within the predetermined period range includes: The recommendation label is obtained by performing a horizontal comparison on the scores of different fund products of the fund company in the same forecast period.

9. The method according to claim 7, characterized in that: The step of determining the recommended tag by comparing the scores of a plurality of prediction periods within the predetermined period range includes: Acquiring the recommendation tag based on the score changes of the same fund product of the fund company in the multiple forecast periods; and / or The recommendation label is obtained by comparing the comprehensive scores of different fund products of the fund company in the multiple forecast periods. The comprehensive score depends on the score of each prediction period within the predetermined period range.

10. A data analysis device based on a fund company, characterized in that: The device comprises: The sample module is used to obtain the index sample data and product net value sample data within the period predetermined by the fund company; A change rate module, used for obtaining a product net asset value change rate corresponding to the fund product according to the product net asset value sample data within the predetermined period, and / or obtaining an index change rate corresponding to the fund product according to the index sample data within the predetermined period; a cumulative distribution module, configured to obtain a cumulative distribution function based on the same type of data to be analyzed of the fund company, wherein the data to be analyzed includes an index change rate, a product net value change rate, or a difference between a product net value change rate and an index change rate, and the same type of data to be analyzed includes data to be analyzed of at least two fund products; and The scoring module is used to determine the score of the fund company by using the cumulative distribution function.

11. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

13. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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