Virtual resource processing method, processing device and computer readable storage medium

Abstract

The application discloses a virtual resource processing method, a processing device and a computer readable storage medium. The processing method comprises the following steps: selecting data from a database of virtual resources, processing the selected data to obtain a data set; selecting a plurality of first data and a plurality of second data from the data set, substituting the first data and the second data into a first preset function, and obtaining related parameters of the first preset function through a fitting function; obtaining a predicted data set based on the related parameters, the first data and the first preset function; calculating an outlier based on the data set and the predicted data set, and screening the data set based on the outlier; and substituting the screened data set into a preset model function to fit a rating curve of the virtual resource. Through the method, the class function and the statistics of the outlier are introduced, the abnormal data actually existing is removed, the reliability of the selected data is improved, and the accuracy of the curve fitting is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, and particularly relates to a virtual resource processing method, a processing device and a computer readable storage medium. BACKGROUND

[0002] With the continuous development of Internet technology, there are more and more data resources in the Internet, which are called virtual resources, such as fund, stock, bond and other financial products. Taking the bond as an example, the changing relationship between the bond yield and the maturity period within a certain period of time is called the yield curve structure, and the curve representing the relationship is called the yield curve. The yield curve can reflect the interest rate level of the bond market and serve as an important reference for bond transaction pricing.

[0003] At present, the yield curve of the bond is obtained by Hermite interpolation model accordinging to the yield of the key period point, but the selection of the key period point requires higher experience of the related personnel, and lacks support for some bond types, such as private placement, exchange ABS (Asset-Backed Securities) and PPN (Private Placement Note).

[0004] The current yield curve fitting method usually adopts Nelson-Siegel (N-S) method, which is a parameter fitting model. The model has low requirement on the sample quantity, needs less parameters to be estimated, and the economic meaning of the parameters is clear. The fitted curve has strong economic meaning and meets the interest rate expectation theory. However, the N-S model focuses on the description of the overall shape of the yield curve, resulting in low yield curve fitting accuracy. SUMMARY

[0005] In order to solve the above problems, the present application provides a virtual resource processing method, a processing device and a computer readable storage medium.

[0006] To solve the above technical problems, the first technical solution provided by the present application is to provide a virtual resource processing method, which comprises the following steps: selecting data from a database of virtual resources and processing the selected data to obtain a data set; selecting a plurality of first data and a plurality of second data from the data set, substituting the first data and the second data into a first preset function, and obtaining relevant parameters of the first preset function through a fitting function; obtaining a predicted data set based on the relevant parameters, the first data and the first preset function; calculating outliers based on the data set and the predicted data set, and screening the data set based on the outliers; substituting the screened data set into a preset model function, and fitting a rating curve of the virtual resource.

[0007] To solve the above technical problems, another technical solution provided by the present application is to provide a virtual resource processing device, which comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to realize the processing method as described above.

[0008] To solve the above technical problems, another technical solution provided by the present application is to provide a computer readable storage medium, which stores a computer program, and the computer program is used to realize the processing method as described above when executed by a processor.

[0009] The present application provides a virtual resource processing method, which comprises the following steps: selecting data from a database of virtual resources and processing the selected data to obtain a data set; selecting a plurality of first data and a plurality of second data from the data set, substituting the first data and the second data into a first preset function, and obtaining relevant parameters of the first preset function through a fitting function; obtaining a predicted data set based on the relevant parameters, the first data and the first preset function; calculating outliers based on the data set and the predicted data set, and screening the data set based on the outliers; substituting the screened data set into a preset model function, and fitting a rating curve of the virtual resource. Based on the above method, by introducing a class function and the statistics of outliers, some actually existing abnormal data are removed, the reliability of the selected data is improved, and the accuracy of curve fitting is improved. BRIEF DESCRIPTION OF DRAWINGS

[0010] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application and illustrate the illustrative embodiments of the present application and their description serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0011] Figure 1 is a flow chart of an embodiment of the virtual resource processing method provided by the present application;

[0012] Figure 2 isFigure 1 Figure 1 is a flow chart of an embodiment of the method for processing virtual resources according to the present application;

[0013] Figure 3 Figure 1 Figure 2 is a flow chart of an embodiment of the method for processing selected data according to the present application;

[0014] Figure 4 Figure 3 is a flow chart of another embodiment of the method for processing virtual resources according to the present application;

[0015] Figure 5 Figure 4 is a schematic block diagram of an embodiment of the processing device for virtual resources according to the present application;

[0016] Figure 6 Figure 5 is a schematic structural diagram of an embodiment of the computer readable storage medium according to the present application. DETAILED DESCRIPTION

[0017] In order to make the above objectives, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that only the parts related to the present application are shown in the accompanying drawings, but not all the structures. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0018] The terms "first", "second", and the like in the present application are used to distinguish different objects, but not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally includes other steps or units not listed or optionally includes other steps or units inherent to the process, method, product or device.

[0019] In this document, the term "embodiment" means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it mutually exclusive or alternative to other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0020] ​With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. First, some nouns or terms appearing in the description of the embodiments of the present application are explained as follows.

[0021] Maturity: The maturity of a virtual resource refers to the period of time from the issuance of the virtual resource to the repayment of the principal. The issuer of the virtual resource must repay the principal at the maturity, and the holder of the virtual resource will receive the principal at the maturity, i.e. the time from the interest calculation date of the virtual resource to the date of repayment of the principal and interest.

[0022] Valuation yield: The valuation yield refers to the income obtained by the holder of the virtual resource from the issuance of the virtual resource to the repayment period.

[0023] Implicit rating: The implicit rating is a credit rating of a virtual resource based on market price signals, public information of the issuer, and other factors. For example, the implicit rating can be divided into six grades, specifically AAA, AAA-, AA+, AA, AA(2), and AA-. The implicit rating can be a Zhongde implicit rating or a YY rating. The Zhongde implicit rating is a credit rating of a virtual resource by Zhongde Financial Valuation Center, and the YY rating is a credit rating of a virtual resource by a RuiTeng organization.

[0024] Please refer to Figure 1 , Figure 1 is a flowchart of an embodiment of a virtual resource processing method provided by the present application. The virtual resource processing method in the embodiment is applied to a processing device. The processing device can be a server, a computer, a tablet, or the like. The processing device in the present application can be a server. The virtual resource can be a fund, a stock, a bond, or the like. The present application takes a bond as an example for illustration.

[0025] The virtual resource processing method in the embodiment includes the following steps:

[0026] S11: Select data from a database of virtual resources, and process the selected data to obtain a data set.

[0027] The processing device has a database of virtual resources in advance. The database can be an existing WIND database or a same flower iFinD database. Alternatively, the database is a database of a certain company for virtual resources.

[0028] The database has various data information of the virtual resources, and various data of the virtual resources are selected from the data table in the database of the virtual resources, the selected data is processed, screened and classified, and then combined into a data set, the data set including first data, second data, third data and fourth data. Each virtual resource includes at least one first data, at least one second data, at least one third data and at least one fourth data.

[0029] S12: selecting a plurality of first data and a plurality of second data from the data set, substituting the first data and the second data into a first preset function, and obtaining the related parameters of the first preset function through a fitting function.

[0030] The related parameters of the first preset function include a first parameter, a second parameter and a third parameter.

[0031] The related parameters of the first preset function include a first parameter, a second parameter and a third parameter.

[0032] S13: obtaining a predicted data set based on the related parameters, the first data and the first preset function.

[0033] The related parameters include the first parameter, the second parameter and the third parameter in step S12.

[0034] The related parameters include the first parameter, the second parameter and the third parameter in step S12.

[0035] S14: calculating an outlier based on the data set and the predicted data set, and screening the data set based on the outlier.

[0036] The outlier is obtained by calculating based on the second data in the data set and the predicted value of the second data in the predicted data set. Based on the outlier, the data set is screened to obtain a screened data set.

[0037] The outlier is a part of actually existing abnormal data, and is mostly a point with a long remaining life at the tail end of a yield curve and a low valuation. The reliability of the selected data can be improved by removing this part of the point.

[0038] S15: substituting the screened data set into a preset model function to fit the rating curve of the virtual resource.

[0039] The preset function model is an N-S model, and the N-S model function includes the first data, the second data, the first parameter, the second parameter, the third parameter and the fourth parameter. The rating curve is a yield curve of a virtual resource with a matching implied rating, a data set of a specific virtual resource with an implied rating is selected, and a rating curve of the virtual resource is fitted.

[0040] The first parameter, the second parameter, the third parameter and the fourth parameter of the N-S model function are obtained by substituting the plurality of first data and the plurality of second data corresponding to the virtual resource in the screened data set into the N-S model function, that is, the first parameter, the second parameter, the third parameter and the fourth parameter of the rating curve are obtained, and the rating curve of the virtual resource is obtained.

[0041] In this embodiment, data is selected from a database of virtual resources, and the selected data is processed to obtain a data set. A plurality of first data and a plurality of second data are selected from the data set, and the first data and the second data are substituted into a first preset function to obtain related parameters of the first preset function by fitting the function. A predicted data set is obtained based on the related parameters, the first data and the first preset function. Outliers are calculated based on the data set and the predicted data set, and the data set is screened based on the outliers. The screened data set is substituted into a preset model function, and a rating curve of the virtual resource is fitted. Based on the above method, by introducing a class function and statistics of outliers, some actually existing abnormal data is removed, the reliability of the selected data is improved, and the accuracy of curve fitting is improved.

[0042] Optionally, the first preset function is a class function, and the step of substituting the first data and the second data into the first preset function includes: substituting the first data and the corresponding second data into the class function; and the step of obtaining the related parameters of the first preset function by fitting the function includes: obtaining a first parameter value, a second parameter value and a third parameter value of the class function by fitting the function.

[0043] The class function is an N-S model function, and the class function includes the first data, the second data, the first parameter, the second parameter and the third parameter. In the data set, each virtual resource includes corresponding first data and second data, and the first data and the second data of each virtual resource correspond one by one.

[0044] The first data and the corresponding second data of a plurality of virtual resources are substituted into the class function, and the first parameter value, the second parameter value and the third parameter value of the class function are obtained by fitting the function.

[0045] Further, the step of obtaining a predicted data set based on the related parameters, the first data and the first preset function includes: substituting the first parameter value, the second parameter value and the third parameter value into the class function, and substituting a plurality of first data into the class function in sequence to obtain a plurality of predicted second data, so as to obtain the predicted data set.

[0046] The first parameter value, the second parameter value and the third parameter value obtained in the above steps are substituted into the class function, and the plurality of first data of the virtual resource is substituted into the class function, to obtain a plurality of predicted second data, thereby obtaining a prediction data set of the second data.

[0047] The plurality of predicted second data is obtained by calculation, and compared with the actual second data, to obtain a prediction error of the model. If the prediction error is within a preset error range, it indicates that the prediction effect of the model is good and conforms to the actual situation. Points outside the preset error range are outliers. Screening and deleting the outliers can improve the prediction effect of the model and improve the reliability of the selected data.

[0048] Further, the step of calculating the outliers based on the data set and the prediction data set comprises: subtracting the corresponding predicted second data from the plurality of second data in the data set in sequence to obtain a result set; substituting the result set into a preset formula to calculate the outliers not in the interval; and the step of screening the data set based on the outliers comprises: deleting the outliers in the data set.

[0049] The first data and the second data of each virtual resource in the data set correspond to each other, and the first data and the predicted value of the second data of each virtual resource in the prediction data set correspond to each other. Based on the above correspondence, the second data of each virtual resource and the predicted value of the second data correspond to each other. The preset formula is a formula that can calculate the outliers not in the interval.

[0050] The second data of each virtual resource in the data set is subtracted from the corresponding predicted value of the second data to obtain a result set. The result set is substituted into a preset formula to calculate the outliers not in the interval, and the outliers are deleted from the data set.

[0051] Further, the preset formula is:

[0052] |x-median(x)|>2*quantile(x,0.75)-2*quantile(x,0.25);

[0053] Wherein, x is the result set; the interval is 0.25-0.75. median(x) calculates the median of the result set, quantile(x, 0.75) calculates the larger quartile of the result set, and quantile(x, 0.25) calculates the smaller quartile of the result set. The outliers not in the interval 0.25-0.75 are calculated.

[0054] Therefore, by the method of the embodiment, the class function and the statistics of outliers are introduced, the abnormal data actually existing in part is removed, the reliability of the selected data is improved, and the accuracy of the curve fitting is improved.

[0055] Referring to Figure 2 , Figure 2 is Figure 1 a flowchart of an embodiment of fitting a rating curve of a virtual resource.

[0056] The data set comprises a plurality of ratings, and the step of selecting the first data and the second data from the data set comprises: selecting the first data and the second data of the same rating from the data set.

[0057] The data set comprises a plurality of ratings, i.e. implicit ratings of the virtual resource. In order to fit the curve of the implicit ratings of the virtual resource, the first data and the second data of the same rating of the virtual resource are selected from the data set, and then the steps S12-S14 in the method are performed. Figure 1

[0058] The step of substituting the screened data set into the preset model function comprises: fitting the first parameter value, the second parameter value, the third parameter value and the fourth parameter value of the preset model function, and the preset model function is the Nielson-Singer function.

[0059] The preset function model is the N-S model, and the preset model function is:

[0060]

[0061] wherein y t,t+x represents the estimated yield rate of the t period, x represents the repayment period, a, b, c and d are respectively the first parameter value, the second parameter value, the third parameter value and the fourth parameter value of the function.

[0062] The first data in the data set is the repayment period of the virtual resource, and the second data is the estimated yield rate of the virtual resource. The first data and the second data in the screened data set, i.e. the plurality of repayment periods and the plurality of estimated yield rates of the virtual resource, are substituted into the N-S model function to obtain the first parameter value a, the second parameter value b, the third parameter value c and the fourth parameter value d.

[0063] As shown in Figure 2 , the step of fitting the rating curve of the virtual resource comprises:

[0064] S21: determining whether the second parameter value is positive.

[0065] It is determined whether the second parameter value b is positive; if yes, step S22 is performed; if no, step S25 is performed.

[0066] S22: determining whether the third parameter value is in a decreasing change.

[0067] ​determining whether the third parameter value c is decreasing; if yes, executing step S23; if no, executing step S25.

[0068] S23: determining whether the fourth parameter value is less than 2.5.

[0069] determining whether the fourth parameter value d is less than 2.5; if yes, executing step S24; if no, executing step S25.

[0070] S24: the rating curve is the standard curve of the virtual resource;

[0071] In the embodiment, the order of determining the three parameter values is not required, and the order of determining does not affect the determination result. If the three parameter values all meet the requirements, the rating curve is the standard curve of the virtual resource.

[0072] S25: switching other rating data in the data set to repeat the fitting.

[0073] If any one of the three parameter values does not meet the determination requirement, i.e., does not meet the determination requirement of the standard curve of the virtual resource, returning to step S25, switching other rating data in the data set to repeat steps S12-S15 to re-fit.

[0074] After the step of the rating curve being the standard curve of the virtual resource, the processing method further comprises: selecting a plurality of first data and a plurality of second data of other same rating from the data set to process to obtain a rating curve of other rating; and calculating the first parameter value, the second parameter value, the third parameter value and the fourth parameter value corresponding to each rating curve based on the standard curve and the rating curve of other rating.

[0075] The data set contains a plurality of ratings, and the fitting is performed in sequence for each rating. After a rating curve is determined to be the standard curve of the virtual resource, the second parameter value, the third parameter value and the fourth parameter value of the standard curve are obtained. A plurality of first data and a plurality of second data of other same rating are selected from the data set, and the plurality of first data and the plurality of second data, and the second parameter value, the third parameter value and the fourth parameter value of the above standard curve are substituted into the N-S model function to obtain the first parameter value of other rating. Thus, the first parameter value, the second parameter value, the third parameter value and the fourth parameter value corresponding to each rating curve are obtained, i.e., the rating curves of all ratings of the virtual resource are obtained.

[0076] Therefore, by the method of the embodiment, the standard curve of the virtual resource is obtained by determining the parameter value, and then the rating curves of all ratings of the virtual resource are obtained by fitting the curves of other ratings in the data set.

[0077] Optionally, after obtaining the rating curve of the virtual resource, the following steps can also be included: generating a to-be-repaid period segment such as [0.1, 0.2…9.8, 9.9, 10]; substituting the to-be-repaid period segment into the N-S model function of each rating curve to obtain the estimated value data corresponding to each rating curve [6.6123, 6.5123…2.2212, 2.2111, 2.1234]; obtaining a series of

to-be-repaid period, estimated value

to-be-repaid period, estimated value

[0078] Referring to Figure 3 , Figure 3 is Figure 1 a flowchart of an embodiment of step S11. Step S11 includes the following steps:

[0079] S100: selecting data from the database of virtual resources.

[0080] The database includes a rating table of virtual resources, a period and estimated value table of virtual resources, a classification table of virtual resources, and a preset table of virtual resources. The step of selecting data from the database of virtual resources includes: obtaining the code of the virtual resource and the corresponding rating information from the rating table of virtual resources; obtaining the code of the virtual resource, the period of the virtual resource, the estimated value yield of the virtual resource, and the preset credibility of the virtual resource from the period and estimated value table of virtual resources; obtaining the code of the virtual resource and the corresponding one or more classification levels from the classification table of virtual resources; and obtaining the code of the preset virtual resource from the preset table of virtual resources.

[0081] Further, the step of processing the selected data includes:

[0082] S101: screening the virtual resources according to the preset credibility of the virtual resources.

[0083] The credibility of the virtual resource is used to identify the comparative tendency of the virtual resource with multiple estimated values. The credibility evaluation of the virtual resource is included in the database, and the credibility evaluation can be recommended or blank. In this embodiment, the credibility is preset as “recommended”. The virtual resources are screened according to the preset credibility of the virtual resources, and only the virtual resources with the credibility of “recommended” are selected.

[0084] S102: screening all preset virtual resource codes from the virtual resource codes obtained from the classification table of virtual resources and the preset table of virtual resources, and classifying them separately.

[0085] Part of the virtual resources (for example, perpetual bonds) have no explicit repayment period, and cannot get the principal at a certain point in time, but can get interest regularly. Set such virtual resources as preset virtual resources, filter out the code of all preset virtual resources from the code of the obtained virtual resources, and classify them separately.

[0086] S103: Classify the virtual resources according to the classification level of the virtual resources.

[0087] The classification level of the virtual resources can be a one-level classification, a two-level classification or a multi-level classification, and the virtual resources are classified according to the classification level of the virtual resources. In this embodiment, the virtual resources can be subdivided according to the attributes of the virtual resources, for example, according to the city investment attribute of the virtual resources (for example, bonds), the virtual resources can be divided into city investment type virtual resources and non-city investment type virtual resources. In addition, in this embodiment, the curve of the virtual resources not supported in the prior art can also be manually supplemented to enrich the rating curve of the virtual resources.

[0088] All the processed virtual resources are combined to obtain a data set, and the data set includes first data, second data, third data and fourth data. The first data, the second data, the third data and the fourth data of the data set are the repayment period, the estimated yield, the code and the implied rating of the virtual resources, respectively.

[0089] Therefore, by the method of this embodiment, the virtual resources are selected and data processed, the virtual resources are filtered according to the preset credibility of the virtual resources, and the reliability of the data is improved. At the same time, the categories of the virtual resources are carefully divided, and the yield curve not supported in the prior art is supplemented, and the rating curve of the virtual resources is enriched.

[0090] Please refer to Figure 4 , Figure 4 is a flowchart of another embodiment of the virtual resource processing method provided by the present application, and in this embodiment, the virtual resources are taken as bonds as an example. The virtual resource processing method of this embodiment can first adopt steps S100-S103 as shown in Figure 3 , which will not be repeated here.

[0091] Among them, as shown in Figure 3As shown in step S100, the database of virtual resources (e.g., bonds) can be a WIND database, and the data of virtual resources (e.g., bonds) is selected from the WIND database, and the WIND source tables involved include the WIND implicit rating table of China Bond, the WIND remaining term and valuation table of China Bond, the WIND classification table of China Bond, and the perpetual bond table. The code of the bond and the corresponding China Bond implicit rating are obtained from the WIND implicit rating table of China Bond. The code of the bond and the corresponding repayment term, valuation yield, and reliability are obtained from the WIND remaining term and valuation table of China Bond. The code of the bond and the corresponding first-level classification and second-level classification are obtained from the WIND classification table of China Bond. The codes of all perpetual bonds are obtained from the perpetual bond table.

[0092] In step S101, the classification table of virtual resources (e.g., bonds) and the preset table of virtual resources (e.g., bonds) are the WIND classification table of China Bond and the perpetual bond table, respectively, the preset virtual resource (e.g., bond) is a perpetual bond, and the codes of all perpetual bonds are selected from the bonds obtained from the WIND classification table of China Bond and the perpetual bond table for separate classification.

[0093] In step S103, the virtual resources (e.g., bonds) are subdivided according to the attributes of the virtual resources (e.g., bonds), for example, according to the municipal bond attribute of the bond, the bond can be divided into a municipal bond class and a non-municipal bond class. In addition, in this embodiment, the curve of a virtual resource (e.g., bond) that is not supported in the prior art can also be manually supplemented, such as a private placement bond, an exchange ABS, a PPN, etc. All processed virtual resources (e.g., bonds) are combined to obtain a data set, and the data set includes first data, second data, third data, and fourth data. The first data, the second data, the third data, and the fourth data of the data set are the repayment term, the valuation yield, the code, and the implicit rating of the virtual resource (e.g., bond), respectively.

[0094] As shown in step S103, the processing method of the virtual resource of the present embodiment further includes the following steps: Figure 4

[0095] S401: Selecting a plurality of first data and a plurality of second data of the same rating from the data set.

[0096] The data of the same rating is selected from the data set, and the data can be selected according to the classification level, the municipal bond attribute, and the implicit rating of the virtual resource (e.g., bond). For example, data with an implicit rating of AAA in the municipal bond class of the first-level classification private placement bond is selected. The duration of the virtual resource is an index of the sensitivity of the price of the virtual resource to the yield rate, and further, the number of virtual resources (e.g., bonds) with a duration is determined. If the number is greater than 10, a plurality of first data and a plurality of second data under the same rating are selected; otherwise, it indicates that the sample data is too small, and the fitting of the curve is meaningless, and the fitting of the curve under the same rating is cancelled.

[0097] ​S402: Substitute the first data and the corresponding second data into the class function.

[0098] The first data is the repayment period of the virtual resource (e.g., a bond), and the second data is the estimated yield of the virtual resource (e.g., a bond).

[0099] The class function is:

[0100]

[0101] The class function is:

[0102] Substitute the repayment period of the virtual resource (e.g., a bond) and the corresponding repayment period of the virtual resource (e.g., a bond) into the class function.

[0103] S403: Obtain the first parameter value, the second parameter value, and the third parameter value of the class function by fitting the function.

[0104] S404: Substitute the first parameter value, the second parameter value, and the third parameter value into the class function, and substitute the multiple first data into the class function in turn to obtain multiple predicted second data, thereby obtaining a prediction data set.

[0105] S405: Subtract the corresponding predicted second data from the multiple second data in the data set in turn to obtain a result set.

[0106] S406: Substitute the result set into a preset formula to calculate outliers not in the interval.

[0107] The preset formula is:

[0108] |x-median(x)|>2*quantile(x,0.75)-2*quantile(x,0.25);

[0109] The x is the result set; the interval is 0.25-0.75. The median(x) calculates the median of the result set, the quantile(x,0.75) calculates the larger quartile of the result set, and the quantile(x,0.25) calculates the smaller quartile of the result set. The outliers not in the interval 0.25-0.75 are obtained by calculation.

[0110] S407: Delete the outliers in the data set.

[0111] S408: Fit the first parameter value, the second parameter value, the third parameter value, and the fourth parameter value of the preset model function, and the preset model function is the Nelson-Siegel function.

[0112] wherein the preset function model is an N-S model, and the model function is:

[0113]

[0114] wherein y t,t+x represents the estimated yield rate of the t-th period, x represents the repayment period, a, b, c, and d are respectively the first parameter value, the second parameter value, the third parameter value, and the fourth parameter value of the function.

[0115] S409: fitting the rating curve of the virtual resource.

[0116] This step is the same as the step shown in the above Figure 2 , and will not be described here again.

[0117] S410: selecting a plurality of first data and a plurality of second data of the same rating from the data set to process the rating curve of the other rating.

[0118] S411: based on the standard curve and the rating curve of the other rating, calculating the first parameter value, the second parameter value, the third parameter value, and the fourth parameter value corresponding to each rating curve.

[0119] Therefore, by the method of the embodiment, the types of virtual resources (such as bonds) are finely divided, the yield curve not supported in the China Bond Curve is supplemented, and the perpetual bond is separately classified and processed, further subdivided into the first-level classification, the second-level classification, and the city investment attribute of the bond, and matched with the China Bond implicit rating to generate the curve under different ratings. For the rating curve, the class function and the statistics of outliers are introduced to remove part of the actual existing abnormal data, improve the reliability of the selected data, and improve the accuracy of the curve fitting.

[0120] Please refer to Figure 5 , Figure 5 is a schematic diagram of the framework of an embodiment of the virtual resource processing device provided by the present application. As Figure 5 shown, the virtual resource processing device 100 includes a memory 101 and a processor 102 connected with the memory 101. The memory 101 is used to store a computer program, and the processor 102 is used to execute the computer program to realize the processing method described above.

[0121] The processor 102 can also be referred to as a CPU (Central Processing Unit). The processor 102 can be an electronic chip with signal processing capability. The processor 102 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0122] The memory 101 can be a memory stick, a TF card, etc., and can store all information in the virtual resource processing device 100, including input raw data, computer programs, intermediate running results and final running results. The information is saved in the memory 101. It is stored and retrieved according to the location specified by the processor 102. With the memory 101, the virtual resource processing device 100 has a memory function and can work normally. The memory 101 of the virtual resource processing device 100 can be divided into main memory (memory) and auxiliary memory (external memory) according to the purpose, and there is also a classification method of external memory and internal memory. The external memory is usually a magnetic medium or an optical disc, etc., which can store information for a long time. The memory refers to the storage component on the motherboard, which is used to store the data and programs currently being executed, but only for temporary storage of programs and data. When the power is off or the power is off, the data will be lost.

[0123] Please refer to Figure 6 , Figure 6 is an embodiment of the computer readable storage medium provided by the application. As Figure 6 shown, the computer readable storage medium 110 stores program instructions 111 capable of realizing all the methods described above.

[0124] The units integrated in each functional unit in the embodiments of the present application can be realized in the form of a software functional unit and sold or used as an independent product when the units are realized in the form of a software functional unit. Based on such understanding, the technical solutions of the present application or the whole or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer readable storage medium 110 includes a plurality of instructions in a program instruction 111 to make a computer device (which can be a personal computer, a system server, or a network device, etc.), an electronic device (such as MP3, MP4, etc., which can also be a mobile terminal such as a mobile phone, a tablet computer, a wearable device, etc., or a desktop computer, etc.) or a processor to execute all or part of the steps of the method of each embodiment of the present application.

[0125] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, a system or a computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) having computer usable program code embodied therein.

[0126] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable storage media 110. The computer readable storage media 110 can be provided to a processor of a general purpose computer, special purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions that execute via the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks.

[0127] The computer readable storage media 110 can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks.

[0128] The computer readable storage media 110 can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks.

[0129] Any processes or methods described in the flow charts or otherwise described herein can be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or steps, and the preferred embodiments of the present application include additional implementations in which the functions are performed in a different order, in substantially simultaneous fashion, or in reverse order, and the scope of the embodiments of the present application should not be limited to the order of execution of the functions as shown or discussed.

[0130] The logic and / or steps represented in the flow charts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a personal computer, server, network device, or other processing device, that can fetch the instructions from the instruction execution system, apparatus, or device, and execute the instructions.

[0131] The above is merely the preferred embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation made by using the content of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method of processing virtual resources, characterized by, The method comprises the following steps: selecting data from the database of virtual resources and processing the selected data to obtain a data set; selecting a plurality of first data and a plurality of second data from the data set, substituting the first data and the second data into a first preset function, and obtaining the related parameters of the first preset function through a fitting function; obtaining a predicted data set based on the related parameters, the first data and the first preset function; calculating outliers based on the data set and the predicted data set, and screening the data set based on the outliers; substituting the screened data set into a preset model function to fit the rating curve of the virtual resource.

2. The treatment method according to claim 1, characterized in that, The first preset function is a class function, and the step of substituting the first data and the second data into the first preset function comprises: substituting the first data and the corresponding second data into the class function; The step of obtaining the related parameters of the first preset function through a fitting function comprises: obtaining the first parameter value, the second parameter value and the third parameter value of the class function through a fitting function.

3. The treatment method according to claim 2, characterized in that, The step of obtaining a predicted data set based on the related parameters, the first data and the first preset function comprises: substituting the first parameter value, the second parameter value and the third parameter value into the class function, and substituting a plurality of the first data into the class function in turn to obtain a plurality of predicted second data, so as to obtain the predicted data set.

4. The treatment method according to claim 3, characterized in that, The step of calculating outliers based on the data set and the predicted data set comprises: subtracting the corresponding predicted second data from a plurality of the second data in the data set in turn to obtain a result set; substituting the result set into a preset formula to calculate outliers not in the interval; The step of screening the data set based on the outliers comprises: deleting the outliers in the data set.

5. The treatment method according to claim 4, characterized in that, The preset formula is: |x-median(x)|>2*quantile(x,0.75)-2*quantile(x,0.25); The x is the result set; and the interval is 0.25-0.

75.

6. The treatment method according to any one of claims 1 to 5, characterized in that, The data set comprises a plurality of ratings, and the step of selecting a plurality of first data and a plurality of second data from the data set comprises: selecting a plurality of the first data and a plurality of the second data of the same rating from the data set; The step of substituting the screened data set into a preset model function comprises: fitting the first parameter value, the second parameter value, the third parameter value and the fourth parameter value of the preset model function, and the preset model function is the Nernst-Site function.

7. The treatment method according to claim 6, characterized in that, The step of fitting the rating curve of the virtual resource comprises: determining whether the second parameter value is positive; if yes, determining whether the third parameter value decreases; if yes, determining whether the fourth parameter value is less than 2.5; if yes, the rating curve is the standard curve of the virtual resource.

8. The treatment method according to claim 7, characterized in that, After the step of determining that the rating curve is the standard curve of the virtual resource, the processing method further comprises: selecting other first data and second data of the same rating from the data set to obtain a rating curve of other ratings; calculating the first parameter value, the second parameter value, the third parameter value and the fourth parameter value corresponding to each rating curve based on the standard curve and the rating curve of other ratings.

9. The treatment method according to any one of claims 1 to 5, characterized in that, The database includes a rating table of virtual resources, a term and valuation table of virtual resources, a classification table of virtual resources and a preset table of virtual resources, and the step of selecting data from the database of virtual resources includes: obtaining the code of the virtual resource and the corresponding rating information from the rating table of virtual resources; obtaining the code of the virtual resource, the term of the virtual resource, the valuation yield of the virtual resource and the preset credibility of the virtual resource from the term and valuation table of virtual resources; obtaining the code of the virtual resource and the corresponding one or more classification levels from the classification table of virtual resources; obtaining the code of the preset virtual resource from the preset table of virtual resources.

10. The processing method according to claim 9, characterized in that, The step of processing the selected data includes: screening the virtual resources according to the preset credibility of the virtual resources; screening all the codes of the preset virtual resources from the codes of the virtual resources obtained from the classification table of virtual resources and the preset table of virtual resources, and classifying them separately; classifying the virtual resources according to the classification levels of the virtual resources; The step of obtaining the data set includes: merging all the virtual resources to obtain the data set, which includes first data, second data, third data and fourth data.

11. A processing device of a virtual resource, characterized by, The processing device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program to realize the processing method according to any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is used to realize the processing method according to any one of claims 1-10 when executed by a processor.

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