CN116228433A - Method, apparatus, device and readable storage medium for returning bond combination performance - Google Patents
Method, apparatus, device and readable storage medium for returning bond combination performance Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a readable storage medium for returning bond combination performance, wherein the method comprises the following steps: acquiring cash flow data of a bond body, wherein the cash flow data comprises basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least comprise the same attribute category; preprocessing basic platform data and auxiliary platform data to obtain characteristic data; acquiring historical transaction data of a bond body, and calculating a weight coefficient of the feature data through the historical transaction data, wherein the feature data is identical to the bond body of the historical transaction data; and calculating target holding information of the bond main body according to the characteristic data and the weight coefficient, judging the numerical difference between the target holding information and the actual holding information, and finishing the return test of the bond combination performance through the numerical difference.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for measuring bond combination performance.
Background
With the rapid development of the financial market, more and more people choose to buy financial products such as funds, bonds or stocks to increase their own income. In the transaction process, the yield of the bond product is often one of factors which are important to the operator, and the verification of the bond combination performance is directly bound with the determination of the final yield, so that a technical scheme capable of accurately calculating the real-time bond combination performance is urgently needed in the bond market.
In the prior art, the quantitative investment is mainly applied to stock trading, the application in the field of bonds is less, mainly because of the problems of huge data volume of bonds, uneven data quality, various behaviors of bond subjects, insufficient market mobility, lack of effective tools for quantitative tracking difference and the like, the factor research adopted in the traditional quantization needs to group index components according to the size of the factors, the compiling components of bond indexes are unknown, the sources of the factors are seriously influenced, the components are commonly influenced by macroscopic factors, the indexes are difficult to group according to the factors, and the practical bond combination performance cannot be verified and measured.
Aiming at the technical problem that the prior art has no quantification tool of bond products, so that the practical bond combination performance cannot be verified and measured, no effective solution exists at present.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a readable storage medium for back measuring bond combination performance, which can solve the technical problem that the actual bond combination performance cannot be verified and measured due to the fact that no quantization tool of bond products is available.
One aspect of the present invention provides a method for returning bond composite performance, the method comprising: acquiring cash flow data of a bond body, wherein the cash flow data comprises basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least comprise the same attribute category; preprocessing basic platform data and auxiliary platform data to obtain characteristic data; acquiring historical transaction data of a bond body, and calculating a weight coefficient of the feature data through the historical transaction data, wherein the feature data is identical to the bond body of the historical transaction data; and calculating target holding information of the bond main body according to the characteristic data and the weight coefficient, judging the numerical difference between the target holding information and the actual holding information, and finishing the return test of the bond combination performance through the numerical difference.
Optionally, acquiring cash flow data of the bond principal includes: judging attribute dimensions contained in the basic platform data; if the attribute dimension of the basic platform data is larger than or equal to a first preset threshold value, acquiring the basic platform data of the bond main body; and if the attribute dimension of the basic platform data is smaller than a first preset threshold value, acquiring auxiliary platform data of the bond main body, and replacing the basic platform data with the auxiliary platform data of the bond main body.
Optionally, preprocessing the basic platform data and the auxiliary platform data to obtain feature data, including: respectively determining the behavior identification of cash flow data of each bond body; determining whether the cash flow data is the last cash flow data according to the behavior identification; if yes, date adjustment is carried out on the last cash flow data; if not, calculating through cash flow data and company behavior data to obtain the last cash flow data; and integrating the cash flow data adjusted by all bond subjects to obtain characteristic data.
Optionally, calculating the weight coefficient of the feature data from the historical transaction data includes: determining a category identification of historical transaction data; traversing preset behavior rules stored in a database, matching the category identification with key fields of the preset behavior rules, and calling the preset behavior rules to process historical transaction data if the category identification is consistent with the key fields of the preset behavior rules, so as to obtain weight coefficients of feature data of the bond main body.
Optionally, the target holding information of the bond principal is calculated by the following formula:
X i_input /leverage_long+X∑w j y j_input ＝position
wherein position represents the bin, w, of the combined equity j Representing the corresponding guaranty proportion, x i_input Representing the bond subject's input spot multi-head weight, y j_input Representing bond principal input futures weights, lever_long represents leverage ratio, and X is scaling factor.
Optionally, determining a numerical difference between the target holding information and the actual holding information, and completing the return test of the bond combination performance through the numerical difference, including: if the target bin holding result is smaller than or equal to a third preset threshold value, outputting the target bin holding result; if the target bin holding result is larger than the third preset threshold value, correcting the target bin holding result through the actual bin holding information.
Optionally, after acquiring cash flow data of the bond subject, the method further comprises: the acquired cash flow data are input to storage nodes and processed through metadata servers, storage spaces of all the storage nodes are managed in a unified mode through a distributed clustering technology, all the storage nodes are formed into a unified storage node cluster, and all metadata servers are formed into a unified metadata server cluster; the metadata server cluster caches the intermediate calculation results of each granularity for direct use by the next time granularity calculation, thereby reducing the data processing magnitude.
Another aspect of the present invention provides a bond combination performance review device, comprising: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring cash flow data of a bond main body, the cash flow data comprises basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least comprise the same attribute category; the preprocessing module is used for preprocessing the basic platform data and the auxiliary platform data to obtain characteristic data; the first calculation module is used for acquiring historical transaction data of the bond main body and calculating a weight coefficient of the characteristic data through the historical transaction data, wherein the characteristic data is the same as the bond main body of the historical transaction data; and the second calculation module is used for calculating the target holding information of the bond main body according to the characteristic data and the weight coefficient, judging the numerical difference between the target holding information and the actual holding information, and completing the return test of the bond combination performance through the numerical difference.
Yet another aspect of the present invention provides a computer device comprising: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and is characterized in that the processor realizes the method for returning the bond combination performance of any of the above embodiments when executing the computer program.
A further aspect of the present invention provides a computer storage medium having stored thereon a computer program which when executed by a processor implements the method of return of bond combination performance of any of the embodiments described above. Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the invention, cash flow data of a bond main body are firstly obtained, and then basic platform data and auxiliary platform data are preprocessed to obtain characteristic data; and acquiring historical transaction data of the bond main body, calculating a weight coefficient of the characteristic data through the historical transaction data, calculating target holding information of the bond main body according to the characteristic data and the weight coefficient after acquiring required data, judging the numerical difference between the target holding information and the actual holding information, and completing the return test of the combined performance of the bond main body through the numerical difference. Based on the application, the technical problem that the actual bond combination performance cannot be verified due to the fact that no quantification tool of bond products exists in the market is solved, the basic platform data are taken as a main acquisition mode, corresponding auxiliary platform data are acquired according to the state of the basic platform data to be supplemented, and the efficiency of acquiring the data can be effectively achieved while the comprehensiveness of historical transaction data is maintained. In addition, the statistics of historical transaction data is combined, target holding information is obtained through calculation, and the calculation result of the target holding information and the actual holding information is used for recalling the bond combination performance, so that the accuracy of bond combination performance verification is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart showing an alternative method for returning bond combination performance provided in accordance with an embodiment of the present invention;
fig. 2 is a block diagram showing a structure of a device for returning bond combination performance according to a second embodiment of the present invention; and
fig. 3 is a block diagram of a computer device adapted to implement a method for returning bond combination performance according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Example 1
The present embodiment provides a method for returning bond combination performance, fig. 1 shows a flowchart of the method for returning bond combination performance, and as shown in fig. 1, the method for returning bond combination performance may include steps S101 to S104, where:
step S101, cash flow data of a bond main body is obtained, wherein the cash flow data comprises basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least comprise the same attribute category;
the cash flow data is data formed by bond bodies when trading on different financial platforms, and can be obtained according to screening conditions of annual, quaternary, monthly or daily trading behaviors. The base platform data may be sink system data; the auxiliary platform data may be wind system data, and the two types of platform data may also be obtained from a conventional financial transaction system, which is not limited herein.
Preferably, step S101 may include steps S1011 to S1013, wherein:
step S1011, judging attribute dimension contained in the basic platform data;
the attribute dimension may be a number of transaction data categories corresponding to each bond principal in the base platform.
Step S1012, if the attribute dimension of the basic platform data is greater than or equal to a first preset threshold value, acquiring the basic platform data of the bond main body;
the first preset threshold may be 1, or may be another value, which is determined by the actual requirement, and is not limited herein. When the attribute dimension of the basic platform data is larger than or equal to a first preset threshold value, the corresponding bond main body is in a complete state in the transaction data category of the basic platform, and the data of other platforms are not required to be additionally acquired.
Step S1013, if the attribute dimension of the basic platform data is smaller than the first preset threshold, the auxiliary platform data of the bond body is obtained, and the auxiliary platform data of the bond body is replaced with the basic platform data.
When the attribute dimension of the basic platform data is smaller than a first preset threshold value, the situation that the corresponding bond main body has a deficiency or an error in the transaction data of the basic platform is indicated, and the data is flaw data, and auxiliary platform data are required to be acquired for correction. In specific implementation, the identity (may be ID) of the bond principal of the flaw data is determined, and relevant data of the bond principal is obtained in the auxiliary platform by using the identity as an index, and the flaw data of the basic platform is replaced by the relevant data. And after all flaw data of the basic platform are corrected, the cash flow data of the bond main body are acquired.
Judging attribute dimensions of cash flow data of each bond main body of the basic platform data to determine whether the auxiliary platform data are required to be acquired or not, so that workload of data acquisition and processing is reduced, and propulsion efficiency of a back-testing process is accelerated; the general way of acquiring cash flow data has the following problems: the defect that the data acquired by a single channel is insufficient in data quantity and inaccurate in return measurement results exists, and the situation that data redundancy exists in multi-channel simultaneous acquisition is likely to occur, so that the difficulty of data processing is increased.
Step S102, preprocessing basic platform data and auxiliary platform data to obtain feature data;
the cash flow data is important basic data for the combined performance return of the bonds, and the original cash flow data has a large number of conditions of missing, mismoney, date error and the like, and the accuracy of the historical return can be ensured only by carrying out corresponding cleaning work.
Preferably, step S102 may include steps S1021 to S1025, wherein:
step S1021, respectively determining the behavior identification of cash flow data of each bond subject;
the behavior mark may be expressed as 02 or 03, or may be other values, where if the behavior mark is expressed as 02 or 03, it indicates that the cash flow data of the current bond body is the last cash flow data, and if the behavior mark is expressed as a value other than 02 or 03, it indicates that the cash flow data of the current bond body is not the last cash flow data. In particular, the setting of the behavior identifier of the last cash flow data may be represented by another symbol or number, which is not limited herein.
Step S1022, determining whether the cash flow data is the last cash flow data according to the behavior identification;
step S1023, if yes, performing date adjustment on the last piece of cash flow data;
the date adjustment is divided into two cases, one is according to the front and back interval date adjustment of the target transaction day, wherein the interval date can be + -5 days. For example, the cash flow data behavior flag of the advance return is displayed as 02 or 03, which indicates that the advance return is the last cash flow data, the advance return recording date (target transaction date) is within t+5 transaction dates, but not on T days, and the return recording date is changed to T days. The other is to pre-set the target trade date, wherein, the pre-set date can be the stock change date, namely the next trade date of the last estimated value date. For example, the cash flow data behavior flag of the drawn ticket is displayed as 02 or 03, indicating that the drawn ticket has the last cash flow data, but the last cash flow data has a target transaction date greater than the stock change date, and the last cash flow data is modified to the stock change date. In particular, the date adjustment type of the last cash flow data is determined by the company behavior data to which the cash flow data belongs, and is not limited herein.
Step S1023, if not, calculating through cash flow data and company behavior data to obtain final cash flow data;
if the behavior identification of the cash flow data is not 02 or 03, the cash flow data is not the last cash flow data, and corresponding operation is performed according to the auditing relation between the cash flow data and the company behavior data to obtain the last cash flow data.
Step S1025, integrating the cash flow data after adjustment of all bond subjects to obtain characteristic data.
Step S103, historical transaction data of bond subjects are obtained, and weight coefficients of the feature data are calculated through the historical transaction data, wherein the feature data are identical to the bond subjects of the historical transaction data;
historical transaction data may be bond incorporations, advance payouts (derates/reduction bins), payouts, expiration, violations, resale, advance redemption, interest rate compensation, violations, and the like.
Preferably, step S103 may include steps S1031 to S1032, wherein:
step S1031, determining category identification of historical transaction data;
the category of historical transaction data (i.e., corporate behavior data) corresponding to each bond principal is not always fixed and is determined based on the user's interaction at the front end of the system. And determining the category identification of the historical transaction data of the user through the clicking operation of the user on the screen.
Step S1032, traversing the preset behavior rules stored in the database, matching the category identification with the key fields of the preset behavior rules, and if the category identification is consistent with the key fields of the preset behavior rules, invoking the preset behavior rules to process the historical transaction data, so as to obtain the weight coefficient of the feature data of the bond main body.
The preset behavior rules may be a way to process historical transaction data. The preset behavior rules uniquely correspond to the category identifiers of the historical transaction data. By identifying the category identification of the front-end historical transaction data, traversing preset behavior rules stored in a database, matching the category identification with key fields of the preset behavior rules, and calling the corresponding rules to process the real-time behavior data so as to obtain the characteristic data weight coefficient of the bond main body.
Taking a payoff event as an example, payoff event refers to the generation of a usable cash to combination, and if there is an advance payoff of the denomination reduction mode, the payoff is correspondingly adjusted. For example, if a ticket in the database returns to 6 yuan per hundred yuan, corresponding to 6 yuan/sheet, the ticket changes to 50 yuan due to the advance return of the denomination, corresponding to 3 yuan/sheet.
Step S104, calculating target holding information of the bond body according to the characteristic data and the weight coefficient, judging the numerical difference between the target holding information and the actual holding information, and completing the return test of the bond body combination performance through the numerical difference.
Preferably, the target holding information of the bond body is calculated by:
X i_input /leverage_long+X∑w j y j_input ＝position
where position represents the bin of the combined equity (i.e., target taken information), w j Representing the corresponding guaranty proportion, x i_input Representing the bond principal's input spot (real-time behavioral data) multi-headed weights, y j_input Representing bond principal input futures weights, lever_long represents leverage ratio, and X is scaling factor.
In the formula, the corresponding deposit proportion of the future and the input future weight of the bond main body belong to the characteristic data, and the input spot multi-head weight of the bond main body is the weight coefficient obtained by calculation according to the real-time behavior data.
Preferably, step S104 may include steps S1041 to S1042, wherein:
step S1041, if the target bin holding result is smaller than or equal to a third preset threshold value, outputting the target bin holding result.
Step S1042, if the target bin holding result is greater than the third preset threshold, correcting the target bin holding result by the actual bin holding information.
The numerical difference between the target holding information and the actual holding information can be a scaling factor, and the scaling factor can intuitively reflect the return measurement result of the transaction risk, and is specifically determined by the ratio of the target holding information to the actual holding information. The third preset threshold may be a standard scaling factor, where the standard scaling factor may be determined according to a business transaction scenario, and is not limited herein.
When the numerical value difference is smaller than or equal to a third preset threshold value, the target bin holding information is indicated to be in an error range, and the current return measurement result accords with an actual transaction scene. When the numerical difference is larger than a third preset threshold, the target holding information is indicated to exceed the error range, and the historical transaction data related to the target holding result is corrected through the actual holding information, so that the target holding result accords with the actual bond combination performance. The modification of this stage may refer to the preprocessing stage, and will not be described herein.
After obtaining the historical transaction data of the bond body, the method further comprises steps A1 to A2, wherein:
step A1, inputting acquired cash flow data into storage nodes and processing the cash flow data through a metadata server, uniformly managing storage spaces of all the storage nodes through a distributed cluster technology, forming all the storage nodes into a uniform storage node cluster, and forming all the metadata servers into a uniform metadata server cluster;
and step A2, the metadata server cluster caches the intermediate calculation results of each granularity for the next time granularity calculation to be directly used, so that the data processing magnitude is reduced.
The acquired historical transaction data is firstly cached, and then the corresponding calculation flow is carried out, so that the reading operation on the database is reduced, and the pressure of the database is reduced; and meanwhile, the response speed is increased, and the efficiency and stability of data processing are ensured.
In this embodiment, the cleaning work of cash flow data, the processing of historical transaction data and the solving of target holding information are integrated in a bond return platform, so that a user can trace back the risk and income performances of the investment strategy under different historical market environments, and meanwhile, the self cognition of the strategy can be deepened through the functions of combined analysis, performance attribution and the like, the continuous iterative optimization is performed, and the return accuracy of bond combined performance is improved. In particular, the bond return platform can also be applied to the fields of stocks, funds, futures and other types of financial products, and has various application ranges.
In the embodiment, cash flow data of a bond main body is firstly obtained, and then basic platform data and auxiliary platform data are preprocessed to obtain characteristic data; and acquiring historical transaction data of the bond main body, calculating a weight coefficient of the characteristic data through the historical transaction data, calculating target holding information of the bond main body according to the characteristic data and the weight coefficient after acquiring required data, judging the numerical difference between the target holding information and the actual holding information, and completing the return test of the combined performance of the bond main body through the numerical difference. Based on the application, the technical problem that the actual bond combination performance cannot be verified due to the fact that no quantification tool of bond products exists in the market is solved, the basic platform data are taken as a main acquisition mode, corresponding auxiliary platform data are acquired according to the state of the basic platform data to be supplemented, and the efficiency of acquiring the data can be effectively achieved while the comprehensiveness of historical transaction data is maintained. In addition, the statistics of historical transaction data is combined, target holding information is obtained through calculation, and the calculation result of the target holding information and the actual holding information is used for recalling the bond combination performance, so that the accuracy of bond combination performance verification is improved.
Example two
The second embodiment of the present invention also provides a device for returning the performance of bond combination, which corresponds to the method for returning the performance of bond combination provided in the first embodiment, and the corresponding technical features and technical effects are not described in detail in this embodiment, and the relevant places can be referred to the first embodiment. Specifically, fig. 2 shows a block diagram of the structure of the bond combination performance return device. As shown in fig. 2, the bond combination performance return device 200 includes an acquisition module 201, a preprocessing module 202, a first calculation module 203, and a second calculation module 204, where:
an obtaining module 201, configured to obtain cash flow data of a bond body, where the cash flow data includes basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least include one same attribute category;
the preprocessing module 202 is connected with the acquisition module 201 and is used for preprocessing the basic platform data and the auxiliary platform data to obtain characteristic data;
the first calculating module 203 is connected with the preprocessing module 202, and is used for acquiring historical transaction data of the bond main body, and calculating a weight coefficient of the characteristic data through the historical transaction data, wherein the characteristic data is the same as the bond main body of the historical transaction data;
the second calculating module 204, together with the first calculating module 203, is configured to calculate target holding information of the bond body according to the feature data and the weight coefficient, determine a numerical difference between the target holding information and the actual holding information, and complete the return of the bond combination performance through the numerical difference.
Optionally, the obtaining module is specifically configured to: judging attribute dimensions contained in the basic platform data; if the attribute dimension of the basic platform data is larger than or equal to a first preset threshold value, acquiring the basic platform data of the bond main body; and if the attribute dimension of the basic platform data is smaller than a first preset threshold value, acquiring auxiliary platform data of the bond main body, and replacing the basic platform data with the auxiliary platform data of the bond main body.
Optionally, the preprocessing module is specifically configured to: respectively determining the behavior identification of cash flow data of each bond body; determining whether the cash flow data is the last cash flow data according to the behavior identification; if yes, date adjustment is carried out on the last cash flow data; if not, calculating through cash flow data and company behavior data to obtain the last cash flow data; and integrating the cash flow data adjusted by all bond subjects to obtain characteristic data.
Optionally, the first determining module is specifically configured to: determining a category identification of historical transaction data; traversing preset behavior rules stored in a database, matching the category identification with key fields of the preset behavior rules, and calling the preset behavior rules to process historical transaction data if the category identification is consistent with the key fields of the preset behavior rules, so as to obtain weight coefficients of feature data of the bond main body.
Optionally, the second determining module is specifically configured to: calculating target holding information of the bond body by the following formula:
X i_input /leverage_long+X∑w j y j_input ＝position
wherein position represents the bin, w, of the combined equity j Representing the corresponding guaranty proportion, x i_input Representing the bond subject's input spot multi-head weight, y j_input Representing bond principal input futures weights, lever_long represents leverage ratio, and X is scaling factor.
Optionally, the second determining module is specifically configured to: if the target bin holding result is smaller than or equal to a third preset threshold value, outputting the target bin holding result; if the target bin holding result is larger than the third preset threshold value, correcting the target bin holding result through the actual bin holding information.
Optionally, the device further comprises a storage module, specifically configured to: the acquired cash flow information is input to storage nodes and processed through metadata servers, storage spaces of all the storage nodes are uniformly managed through a distributed cluster technology, all the storage nodes are formed into a uniform storage node cluster, and all the metadata servers are formed into a uniform metadata server cluster; the metadata server cluster caches the intermediate calculation results of each granularity for direct use by the next time granularity calculation, thereby reducing the data processing magnitude.
Example III
Fig. 3 is a block diagram of a computer device adapted to implement a method for returning bond combination performance according to a third embodiment of the present invention. In this embodiment, the computer device 300 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack-mounted server, a blade server, a tower server, or a rack-mounted server (including a stand-alone server or a server cluster formed by a plurality of servers) for executing programs, etc. As shown in fig. 3, the computer device 300 of the present embodiment includes at least, but is not limited to: a memory 301, a processor 302, and a network interface 303, which may be communicatively connected to each other via a system bus. It is noted that FIG. 3 only shows a computer device 300 having components 301-303, but it should be understood that not all of the illustrated components are required to be implemented, and that more or fewer components may alternatively be implemented.
In this embodiment, the memory 303 includes at least one type of computer readable storage medium, including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 301 may be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 301 may also be an external storage device of the computer device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 300. Of course, the memory 301 may also include both internal storage units of the computer device 300 and external storage devices. In the present embodiment, the memory 301 is typically used to store an operating system installed in the computer device 300 and various types of application software, such as program codes of a return method for bond combination performance.
The processor 302 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 302 is generally used to control the overall operation of the computer device 300. Such as performing control and processing related to data interaction or communication with the computer device 300. In this embodiment, the processor 302 is configured to execute a program code for executing steps of a method for returning bond combination performance stored in the memory 301.
In this embodiment, the method for returning the bond combination performance stored in the memory 301 may be further divided into one or more program modules and executed by one or more processors (the processor 302 in this embodiment) to complete the present invention.
The network interface 303 may include a wireless network interface or a wired network interface, which network interface 303 is typically used to establish a communication link between the computer device 300 and other computer devices. For example, the network interface 303 is used to connect the computer device 300 to an external terminal through a network, establish a data transmission channel and a communication link between the computer device 300 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a global system for mobile communications (Global System of Mobile communication, abbreviated as GSM), wideband code division multiple access (Wideband Code Division Multiple Access, abbreviated as WCDMA), a 4G network, a 5G network, bluetooth (Bluetooth), wi-Fi, etc.
Example IV
The present embodiment also provides a computer readable storage medium including a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., having stored thereon a computer program that when executed by a processor implements the steps of the bond combining performance return method.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be separately fabricated into individual integrated circuit modules, or a plurality of modules or steps in them may be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
It should be noted that, the embodiment numbers of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A method of return of bond composite performance, the method comprising:
acquiring cash flow data of a bond body, wherein the cash flow data comprises basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least comprise the same attribute category;
preprocessing the basic platform data and the auxiliary platform data to obtain characteristic data;
acquiring historical transaction data of the bond main body, and calculating a weight coefficient of the characteristic data through the historical transaction data, wherein the characteristic data is identical to the bond main body of the historical transaction data;
and calculating target holding information of the bond main body according to the characteristic data and the weight coefficient, judging the numerical value difference between the target holding information and the actual holding information, and completing the return test of the bond combination performance through the numerical value difference.
2. The method of claim 1, wherein the acquiring cash flow data of the bond subject comprises:
judging attribute dimensions contained in the basic platform data;
if the attribute dimension of the basic platform data is larger than or equal to a first preset threshold value, acquiring the basic platform data of the bond main body;
and if the attribute dimension of the basic platform data is smaller than a first preset threshold value, acquiring auxiliary platform data of the bond main body, and replacing the basic platform data by the auxiliary platform data of the bond main body.
3. The method of claim 1, wherein preprocessing the base platform data and the auxiliary platform data to obtain feature data comprises:
respectively determining the behavior identification of cash flow data of each bond body;
determining whether the cash flow data is the last cash flow data according to the behavior identification;
if yes, carrying out date adjustment on the last piece of cash flow data;
if not, calculating through the cash flow data and the company behavior data to obtain the last cash flow data;
and integrating the cash flow data adjusted by all bond subjects to obtain characteristic data.
4. The method of claim 1, wherein said calculating the weighting coefficients of the characteristic data from the historical transaction data comprises:
determining a category identification of the historical transaction data;
traversing preset behavior rules stored in a database, matching the category identification with key fields of the preset behavior rules, and calling the preset behavior rules to process the historical transaction data if the category identification is matched with the key fields of the preset behavior rules, so as to obtain weight coefficients of the feature data of the bond main body.
5. The method of any one of claims 1-4, wherein the targeted holding information of the bond principal is calculated by the formula:
X∑x i_input /leverage_long+X∑w j y j_input ＝position
wherein position represents the bin, w, of the combined equity j Representing the corresponding guaranty proportion, x i_input Representing the bond subject's input spot multi-head weight, y j_input Representing bond principal input futures weights, lever_long represents leverage ratio, and X is scaling factor.
6. The method of any one of claims 1-4, wherein said determining a numerical difference between said target holding information and actual holding information, and performing a return of said bond combination performance from said numerical difference comprises:
if the target bin holding result is smaller than or equal to the third preset threshold value, outputting the target bin holding result;
and if the target bin holding result is larger than the third preset threshold value, correcting the target bin holding result through the actual bin holding information.
7. The method of claim 1, wherein after acquiring cash flow data of the bond subject, the method further comprises:
the acquired cash flow data are input to storage nodes and processed through metadata servers, storage spaces of all the storage nodes are managed in a unified mode through a distributed clustering technology, all the storage nodes are formed into a unified storage node cluster, and all metadata servers are formed into a unified metadata server cluster;
the metadata server cluster caches the intermediate calculation results of each granularity for direct use in the next time granularity calculation, so that the data processing magnitude is reduced.
8. A bond combination performance return device, the device comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring cash flow data of a bond main body, the cash flow data comprise basic platform data and auxiliary platform data, and the basic platform data and the auxiliary platform data at least comprise the same attribute category;
the preprocessing module is used for preprocessing the basic platform data and the auxiliary platform data to obtain characteristic data;
the first calculation module is used for acquiring historical transaction data of the bond main body and calculating a weight coefficient of the characteristic data through the historical transaction data, wherein the characteristic data is the same as the bond main body of the historical transaction data;
and the second calculation module is used for calculating target holding information of the bond main body according to the characteristic data and the weight coefficient, judging the numerical value difference between the target holding information and the actual holding information, and completing the return test of the bond combination performance through the numerical value difference.
9. A computer device, the computer device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.
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