CN113609192B - Service data processing method, device and server - Google Patents
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Abstract
The specification provides a service data processing method, a device and a server. Based on the method, a server can firstly acquire and respond to the identification information carrying the target service and the data processing requests of a plurality of target time periods initiated by a user, and inquire a preset database according to the identification information of the target service so as to acquire the definition data of the target curve of the target service; determining a plurality of matched target time points according to the definition data of the target curve of the target service and a plurality of target time periods; further, according to a preset construction rule, automatically acquiring and utilizing the service data of the plurality of target time points to construct a target curve of the target service; and then processing target data related to the target service according to the target curve of the target service. Therefore, the user operation can be effectively simplified, and the target curve of the required target service can be automatically and efficiently generated so as to perform target data processing related to the target service.
Description
Technical Field
The specification belongs to the technical field of big data processing, and particularly relates to a processing method, a processing device and a server of business data.
Background
In some more complex business data processing scenarios (e.g., financial transaction scenarios, etc.), it is often necessary to acquire and utilize huge amounts of business data to construct a relevant curve model; and then carrying out specific business data processing according to the curve model.
However, based on the existing method, the user often has complicated and complex operation when constructing the curve model, and further influences the overall data processing efficiency of the service data.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The specification provides a method, a device and a server for processing service data, so that user operation is effectively simplified, a target curve of target service required by a user can be automatically and efficiently generated, further target data processing related to the target service can be rapidly performed, and data processing efficiency is improved.
The embodiment of the specification provides a method for processing service data, which comprises the following steps:
acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods;
responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve;
Determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods;
acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule;
and processing target data related to the target service according to the target curve of the target service.
In some embodiments, the target traffic includes at least one of: foreign exchange losing period business, futures trading business and noble metal trading business.
In some embodiments, prior to obtaining the data processing request for the target service, the method further comprises:
acquiring definition data of a target curve of a target service which is user-defined and set by a user based on a preset setting rule through a preset setting interface;
and storing the definition data of the target curve of the target service in a preset database, and establishing a corresponding relation between the definition data of the target curve and the identification information of the target service in the preset database.
In some embodiments, the data source comprises: money trade market, interest rate interchange market.
In some embodiments, the target curve comprises: a first segment curve with time less than or equal to the segment time, and a second segment curve with time greater than the segment time; the data source of the business data of the first section curve is a money transaction market, and the data source of the business data of the second section curve is an interest exchange market.
In some embodiments, determining a plurality of target time points matched according to the definition data of the target curve of the target service and the plurality of target time periods includes:
determining the area of a data source of service data and acquiring the time rule of the area; the time rule comprises holiday information and working time information of the area;
and determining a matched target time point in a segmentation curve corresponding to a data source of the service data according to the time rule and the target event segments.
In some embodiments, according to a preset construction rule, acquiring and constructing a target curve of a target service according to service data of a plurality of target time points includes:
acquiring and generating target data points in a target curve according to the definition data of the target curve of the target service through corresponding data sources and service data of a target time point in a segmented curve corresponding to the data sources;
and generating other data points in the target curve by utilizing the target data points according to a preset interpolation processing rule so as to obtain a target curve of the target service.
In some embodiments, performing target data processing related to the target service according to the target curve of the target service includes:
Calculating a discount factor according to the target curve of the target service;
and generating an estimated value evaluation report aiming at the target service according to the discount factors.
The embodiment of the specification also provides a processing device of service data, which comprises:
the acquisition module is used for acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods;
the query module is used for responding to the data processing request, querying a preset database according to the identification information of the target service, and obtaining definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve;
the determining module is used for determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods;
the construction module is used for acquiring and constructing a target curve of the target service according to service data of a plurality of target time points according to a preset construction rule;
and the processing module is used for processing target data related to the target service according to the target curve of the target service.
The embodiments of the present specification also provide a server, including a processor and a memory for storing instructions executable by the processor, where the processor implements: acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods; responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve; determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods; acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule; and processing target data related to the target service according to the target curve of the target service.
The present description also provides a computer-readable storage medium having stored thereon computer instructions that when executed perform the steps of: acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods; responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve; determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods; acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule; and processing target data related to the target service according to the target curve of the target service.
The present specification provides a method, an apparatus, and a server for processing service data, where the server may first obtain and respond to a data processing request initiated by a user and carrying at least identification information of a target service and a plurality of target time periods specified by the user, and query a preset database according to the identification information of the target service, so as to obtain definition data of a target curve of the target service; determining a target time point for triggering the matching of the acquired service data according to the definition data of the target curve of the target service and a plurality of target time periods; furthermore, according to a preset construction rule, the service data of the multiple target time points can be automatically collected and utilized to construct a target curve of the target service; and then processing target data related to the target service according to the target curve of the target service. Therefore, the user operation can be effectively simplified, the target curve of the target service required by the user can be automatically and efficiently generated, further, the target data processing related to the target service can be rapidly and automatically performed according to the target curve of the target service, and the data processing efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure, the drawings that are required for the embodiments will be briefly described below, and the drawings described below are only some embodiments described in the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a flow chart of a method for processing service data according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of the structural composition of a server according to one embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a processing device for service data according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of an embodiment of a method for processing service data according to the embodiments of the present disclosure, in a scenario example;
FIG. 5 is a schematic diagram of an embodiment of a method for processing service data according to the embodiments of the present disclosure, in a scenario example;
fig. 6 is a schematic diagram of an embodiment of a processing method of service data provided by the embodiment of the present disclosure, in one scenario example.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
Referring to fig. 1, an embodiment of the present disclosure provides a method for processing service data. The method is particularly applied to the server side of the business service platform. In particular implementations, the method may include the following:
s101: acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods;
s102: responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve;
s103: determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods;
s104: acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule;
s105: and processing target data related to the target service according to the target curve of the target service.
Through the embodiment, the server can acquire and respond to the data processing request about the target service initiated by the user, and accurately determine a plurality of matched target time points according to the definition data of the target curve of the corresponding target service and a plurality of target time periods designated by the user; according to a preset construction rule, automatically acquiring through corresponding data sources and constructing a target curve of a target service according to service data of a plurality of time points; and then the target data processing about the target service can be automatically completed according to the target curve of the service. Therefore, the user operation can be effectively simplified, and the target curve of the target service required by the user can be automatically and efficiently generated; and according to the target curve of the target service, the target data processing related to the target service is rapidly realized, and the overall data processing efficiency of the target data processing is improved.
In some embodiments, embodiments of the present disclosure may be specifically applied to a server of a business service platform.
The business service platform can be specifically understood as a network platform for providing relevant business services for users. Specifically, for example, the business service platform may be a financial service platform, and may provide financial services such as valuation evaluation, investment advice, and the like to the investor user.
The server can specifically comprise a background server which is applied to one side of a business service platform and can realize functions of data transmission, data processing and the like. Specifically, the server may be, for example, an electronic device having data operation, storage function and network interaction function. Alternatively, the server may be a software program running in the electronic device that provides support for data processing, storage, and network interactions. In the present embodiment, the number of servers included in the server is not particularly limited. The server may be one server, several servers, or a server cluster formed by several servers.
In some embodiments, the target traffic includes at least one of: foreign exchange blackout transactions (e.g., foreign exchange far blackout transactions), futures transactions, noble metal transactions (e.g., gold transactions), and the like.
Of course, it should be noted that the above-listed target service is only a schematic illustration. In the implementation, according to specific application scenes and processing requirements, the processing method of the service data provided by the embodiment of the specification can be expanded and applied to other services of other application scenes. The present specification is not limited to this.
Through the above embodiment, the service data processing method provided by the embodiment of the present specification can be applied to perform corresponding processing on service data in a plurality of different application scenarios efficiently.
The following specifically describes an example of applying the processing method of service data provided in the embodiments of the present disclosure to a foreign exchange period service in a financial transaction scenario. For the case that the processing method of the service data is applied to other services of other application scenarios, reference may be made to the following embodiments of the foreign exchange period service applied to financial transaction scenarios, which are not described in detail in this specification.
The foreign exchange period business can specifically refer to buying and selling a certain amount of foreign exchange money at the same time on two different interest days in a financial transaction scene.
In some embodiments, when a user needs to perform target data processing on service data of a certain period of a target service (for example, performing evaluation on service data of a certain period of foreign exchange period service), a client arranged at the user side can be used to initiate a data processing request on the target service data. The data request may at least carry identification information of the target service and a plurality of target time periods.
The client side specifically comprises a front-end electronic device which is applied to a user side and can realize functions of data acquisition, data transmission and the like. Specifically, the client may be, for example, a desktop computer, a tablet computer, a notebook computer, a smart phone, etc. Alternatively, the client may be a software application capable of running in the electronic device described above. For example, it may be some APP installed and running on a smart phone, etc.
The identification information may be indicative information such as a service name, a service number, and a service ID of the target service.
The plurality of target time periods may be specifically specified by a user or a system default time interval range for extracting service data from a corresponding data source. For example, one month (may be written as 1M), two years (may be written as 2Y), and the like.
In some embodiments, the data processing request may further carry a start time point and an end time point of the data processing.
In some embodiments, the server may first parse the data processing request for the target service to extract the identification information of the target service and the plurality of target time periods. And responding to the data processing request, and firstly querying a preset database according to the identification information of the target service to find definition data corresponding to the identification information of the target service in a matching way, wherein the definition data is used as target curve definition data of the target service. The preset database stores a plurality of definition data of curves which are preset for a plurality of different services.
In some embodiments, before acquiring the data processing request about the target service, the method may further include the following when implemented:
s1: acquiring definition data of a target curve of a target service which is user-defined and set by a user based on a preset setting rule through a preset setting interface;
s2: and storing the definition data of the target curve of the target service in a preset database, and establishing a corresponding relation between the definition data of the target curve and the identification information of the target service in the preset database.
Through the embodiment, the server can firstly acquire the definition data of the curves which are set by the user in a self-defining way and are aimed at a plurality of different services, and the definition data are stored in the preset database, so that the subsequent user can directly inquire and utilize the definition data existing in the preset database to efficiently construct the target curve of the specific target service.
In some embodiments, the defining data of the target curve includes at least: the segment time of the segment curve in the target curve, the data source of the traffic data of the segment curve in the target curve.
The segment time may be a preset time limit for the target service. The target curve may be divided into a plurality of segment curves (e.g., a first segment curve, a second segment curve, etc.) or a plurality of reference line segments based on the segment times described above.
The data source of the service data may specifically be a data source of the service data for generating each segment curve. Specifically, the data source includes: money trade markets (e.g., CNYSH, USDLB, etc.), interest rate interchange markets, and the like.
By the above embodiment, the definition data of the relatively necessary target curve can be obtained and stored in advance, so that the target curve of the required target service can be accurately generated based on the definition data.
In some embodiments, the target curve may specifically include: a first segment curve with time less than or equal to the segment time, and a second segment curve with time greater than the segment time; the data source of the business data of the first section curve is a money transaction market, and the data source of the business data of the second section curve is an interest exchange market.
Specifically, taking the target service as an example of the foreign exchange period service, the preset time limit (trade period) is usually one year. Thus, one year can be determined as the segment time. Correspondingly, a one-year section curve in the target curve can be marked as a first section curve by taking one year as a demarcation point, and a section curve corresponding to one year later can be marked as a second section curve. Further, the business data for generating the first segment curve is determined to be data from a money transaction market, and the business data for generating the second segment curve is determined to be data from a interest rate interchange market.
With the above embodiment, the target curve may be split into two or more different segment curves according to the segment time; and determining and acquiring corresponding service data according to the data sources corresponding to the segmented curves to generate specific segmented curves, so as to obtain complete target curves.
In some embodiments, the defining data of the target curve may further include information data of each segment curve, for example, a name, a currency, a manner of information of each segment curve, and the like.
In some embodiments, the definition data of the target curve may further include floating point interest rate information corresponding to each target time period, for example, short, full scale, data source (e.g. real-time market data or default value, etc.), type (e.g. loan interest rate, central ticket interest rate, interchange interest rate, etc.), segment curve to which the target curve belongs, term, external information code, quotation information (e.g. buying price, selling price, intermediate price, fixed disk price, etc.), etc.
In some embodiments, the defining data of the target curve may further include maintenance information of the target curve, for example, abbreviation, full scale, currency, curve type, on-demand delay, date convention, discount form, construction method, interpolation mode, zero interest rate ZC interest mode, money market-related line segment, interest rate interchange market-related line segment, calculation mode, priority market, etc.
Through the embodiment, the definition data of the relatively rich and comprehensive target curve can be obtained and stored in advance, so that the target curve of the finer target service can be accurately generated later.
In some embodiments, the determining a plurality of matched target time points according to the defining data of the target curve of the target service and the plurality of target time periods may include the following when implemented:
s1: determining the area of a data source of service data and acquiring the time rule of the area; the time rule comprises holiday information and working time information of the area;
s2: and determining a matched target time point in a segmentation curve corresponding to a data source of the service data according to the time rule and the target event segments.
Through the embodiment, the target time period which accords with the specification of the user can be accurately determined according to the definition data of the target curve and a plurality of target time periods and by combining the working time characteristics of the region to which the data source belongs, and meanwhile, the target time point which is matched with the working time characteristics of the region to which the data source belongs can be obtained from the data source smoothly based on the target time point.
Specifically, for example, for the first segment curve, the area to which the data source (for example, the money transaction market in the area a) corresponding to the segment curve belongs is determined as the area a, and then the time rule a corresponding to the area a may be found from a plurality of time rules stored in the preset database. And determining a matched target time point according to the time rule A by combining the starting time point and the target time period carried in the data processing request. For example, the starting time point is 2021, 4 months and 1 day, and the first target period is 1M. At this time, based on the existing method, the preliminarily determined target time point corresponding to the first target time period is 2021, 5 months and 2 days. However, according to the time rule a, legal holidays with the time of 5 months and 2 days being the area a can be identified, and the fake-putting time is 3 days, and the money transaction market in the area a is in a closed state in the period of time, so that corresponding business data cannot be acquired. Further, according to the time rule a, the time period may be extended on the basis of 2021, 5, and 2, and 2021, 5, may be determined as the target time point of the match corresponding to the first target time period specified by the user.
Accordingly, the server may monitor the date, and when the monitored date is 2021, 5, and trigger collection of relevant market data of the money transaction market in the current day a region (e.g., market interest rate, market price, etc. of the money transaction market in the current day a region) as the business data of the target time point.
In some embodiments, the method for obtaining and constructing the target curve of the target service according to the service data of the plurality of target time points according to the preset construction rule may include the following steps:
s1: acquiring and generating target data points in a target curve according to the definition data of the target curve of the target service through corresponding data sources and service data of a target time point in a segmented curve corresponding to the data sources;
s2: and generating other data points in the target curve by utilizing the target data points according to a preset interpolation processing rule so as to obtain a target curve of the target service.
Through the embodiment, the corresponding target data point can be acquired in a targeted manner and determined in the target curve according to the service data of the target time point; and then according to the target data points, other data points are determined in an interpolation mode, so that a target curve of the target service is obtained efficiently.
In some embodiments, the generating, according to the preset interpolation processing rule, other data points in the target curve by using the target data points to obtain a target curve of the target service may include:
Detecting the position types of other data points; and determining and utilizing corresponding target data points in a matching mode according to the position types of other data points, and determining other data points.
In particular, in the case where the position type of the other data point is determined to be the data point corresponding to the start time point, the data of the next target data point that is most adjacent to the data point may be determined and taken as the data of the data point.
In the case where the position type of the other data point is determined to be the data point corresponding to the end time point, the data of the last target data point that is most adjacent to the data point may be determined and taken as the data of the data point.
In the case where the location type of the other data point is determined to be between two determined target data points, the data of the last target data point nearest to the data point and the data of the next target data point can be determined and utilized, and the data of the data point can be determined by performing Linear interpolation or Hermit interpolation processing.
In some embodiments, the processing of the target data related to the target service according to the target curve of the target service may include the following when implemented:
S1: calculating a discount factor according to the target curve of the target service;
s2: and generating an estimated value evaluation report aiming at the target service according to the discount factors.
Through the embodiment, the evaluation report of the evaluation for the target service with higher reference value for investment decision of the user can be generated according to the target curve of the target service, and the evaluation report is timely fed back to the user, so that target data processing related to the target service can be efficiently realized.
In some embodiments, when calculating the discount factor specifically, the data source types corresponding to the segment curves can be distinguished, and according to different data source types, different modes are used to calculate the discount factor of different segment curves.
Specifically, taking the target service as the foreign exchange period service as an example, for the first segment curve, since the data source corresponding to the first segment curve is the money transaction market, the discount factor of the first segment curve can be calculated according to the following formula: the discount factor = 1/(1 + market interest rate number of days/365). Wherein, market interest rate is a business data collected by a money transaction market.
For the second section curve, since the data source corresponding to the second section curve is the interest rate interchange market, the discount factor of the second section curve can be calculated by adopting a bootstrap mode.
In some embodiments, after generating the valuation assessment report for the target traffic according to the discount factor, the method further comprises: and displaying the estimated evaluation report aiming at the target service to the user through the client, so that reference can be provided for investment decision of the user in time.
From the above, based on the method for processing service data provided in the embodiments of the present disclosure, the method may first obtain and respond to a data processing request initiated by a user and carrying identification information of a target service and a plurality of target time periods specified by the user, and query a preset database according to the identification information of the target service carried by the data processing request, so as to obtain definition data of a target curve of the target service; determining a matched target time point according to definition data of a target curve of the target service and a plurality of target time periods; further, according to a preset construction rule, automatically acquiring and utilizing the service data of the plurality of target time points to construct a target curve of the target service; and then processing target data related to the target service according to the target curve of the target service. Therefore, the user operation can be effectively simplified, and the target curve of the required target service can be automatically and efficiently generated. And further, the target data processing related to the target service can be rapidly and automatically performed according to the target curve of the target service, so that the data processing efficiency is improved.
The embodiment of the specification also provides a server, which comprises a processor and a memory for storing instructions executable by the processor, wherein the processor can execute the following steps according to the instructions when being implemented: acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods; responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve; determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods; acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule; and processing target data related to the target service according to the target curve of the target service.
In order to more accurately complete the above instructions, referring to fig. 2, another specific server is provided in this embodiment of the present disclosure, where the server includes a network communication port 201, a processor 202, and a memory 203, and the above structures are connected by an internal cable, so that each structure may perform specific data interaction.
Wherein, the network communication port 201 may be specifically configured to obtain a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods.
The processor 202 may be specifically configured to respond to the data processing request, query a preset database according to the identification information of the target service, and obtain definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve; determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods; acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule; and processing target data related to the target service according to the target curve of the target service.
The memory 203 may be used for storing a corresponding program of instructions.
In this embodiment, the network communication port 201 may be a virtual port that binds with different communication protocols, so that different data may be sent or received. For example, the network communication port may be a port responsible for performing web data communication, a port responsible for performing FTP data communication, or a port responsible for performing mail data communication. The network communication port may also be an entity's communication interface or a communication chip. For example, it may be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it may also be a Wifi chip; it may also be a bluetooth chip.
In this embodiment, the processor 202 may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor, and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic controller, and an embedded microcontroller, among others. The description is not intended to be limiting.
In this embodiment, the memory 203 may include a plurality of layers, and in a digital system, the memory may be any memory as long as it can hold binary data; in an integrated circuit, a circuit with a memory function without a physical form is also called a memory, such as a RAM, a FIFO, etc.; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card, and the like.
The embodiments of the present specification also provide a computer readable storage medium storing computer program instructions that when executed implement a method for processing service data as described above: acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods; responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve; determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods; acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule; and processing target data related to the target service according to the target curve of the target service.
In the present embodiment, the storage medium includes, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.
In this embodiment, the functions and effects of the program instructions stored in the computer readable storage medium may be explained in comparison with other embodiments, and are not described herein.
Referring to fig. 3, on a software level, the embodiment of the present disclosure further provides a service data processing apparatus, where the apparatus may specifically include the following structural modules:
the acquiring module 301 may be specifically configured to acquire a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods;
the query module 302 may be specifically configured to respond to the data processing request, query a preset database according to the identification information of the target service, and obtain definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve;
The determining module 303 may be specifically configured to determine a plurality of target time points that are matched according to the definition data of the target curve of the target service and the plurality of target time periods;
the construction module 304 may be specifically configured to acquire and construct a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule;
the processing module 305 may be specifically configured to perform target data processing related to the target service according to the target curve of the target service.
It should be noted that, the units, devices, or modules described in the above embodiments may be implemented by a computer chip or entity, or may be implemented by a product having a certain function. For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when the present description is implemented, the functions of each module may be implemented in the same piece or pieces of software and/or hardware, or a module that implements the same function may be implemented by a plurality of sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
From the above, the processing device based on the service data provided by the embodiments of the present disclosure can effectively simplify the user operation, and automatically and efficiently generate the target curve of the required target service; and further, the target data processing related to the target service can be rapidly and automatically performed according to the target curve of the target service, so that the data processing efficiency is improved.
In a specific example scenario, the processing method of business data provided in the embodiments of the present disclosure may be applied to generate a derivative yield curve (a target curve of a target business) based on market price.
In this scenario example, applying the above method may implement a rate of return curve definition and construction rule, and the valuation manager user (e.g., user) may generate a corresponding rate of return curve for different currency and trading market settings as needed. According to the activity level of the market trade, a quotation information code is set for a plurality of standard deadlines (for example, target data points) on the curve, and definition information of the yield curve and deadline point (for example, data points on the target curve) information are stored together. When the fair value measurement needs to generate a yield curve, a user can grasp real-time market quotations of the deadlines with the quotation information codes, complement all deadlines through interpolation, and automatically construct discount factors and zero interest rates corresponding to all deadlines of the complete yield curve. The user can modify the information code or the quotation of the deadline according to the requirement, and recalculate the discount factor and the zero interest rate according to the modified quotation so as to meet the evaluation requirement of the financial market derivative. Thus, the user does not need to collect and sort numerous excel templates or maintain data in different systems, but can automatically construct and obtain a yield curve through unified rules to support the estimated needs of various derivatives (such as target businesses) such as foreign exchange long-term, interest rate long-term, currency exchange long-term, commodity long-term, structural financial products and the like so as to cover financial tools of the banking system for measuring the fair value.
In this scenario example, referring to fig. 4, the definition of the yield curve may be first performed according to the following steps (for example, the definition data of the target curve of the target service set by the user is obtained and stored).
401: the settings are defined with reference to line segments. Since the typical term of foreign exchange long-term trade in the market is within 1 year (e.g., segment time), the time of setting the yield curve needs to be divided into two segments to be defined respectively. The market quote calculations (e.g., first segment curve) are exchanged by interest rate over a 1 year period and the market quote calculations (e.g., second segment curve) are exchanged by interest rate over a 1 year period. Definition information is respectively set for two sections of the yield curve. The system includes abbreviations of reference line segments, currencies (CNY, USD, etc.), trade markets (money markets, interest exchange markets), information recording modes (ACT/365, ACT/360), information recording frequencies (month, season, half year, year), and reference index groups (USDOS, EURLB, etc. reference markets).
402: floating point interest rate definition settings. The information maintained may include floating point interest rate short, full scale, source (real-time market data, default value), type (lending interest rate, central ticket interest rate, interchange interest rate), name, belonging reference line segment (reference line segment set in 101), term (standard term point of all ON to 30Y), external information code, offer edge (buy price, sell price, intermediate price, offer price), default value, when the source is real-time market data, the external information code and offer edge need to be maintained, real-time market data is fetched according to the maintained information code, when the source is default value, the corresponding default value needs to be maintained.
403: definition and maintenance of a yield curve. The information to be maintained comprises curve definition abbreviation, full scale, currency, curve type, time delay, date convention, discount form, construction method, interpolation mode, zero interest rate ZC information counting mode, currency market association line segment, interest rate exchange market association line segment, calculation mode and priority market. And finishing the setting of the information and storing the information into a database, namely finishing the setting of the definition of the yield curve. All the set parameters can be modified again, and the curve data is constructed according to the definition information of the latest version when being generated.
After the definition is completed, referring to fig. 5, a specific yield curve (e.g., a target curve of a target service) may be constructed as follows.
501: and retrieving the definition information of the yield curve maintained in the previous step from the database.
502: the starting and ending dates (e.g., determining the matching target time points) for each deadline are calculated according to the market and date rules (e.g., time rules) defined by the curve. For different markets, a holiday parameter table can be additionally maintained, and work calendars corresponding to different markets need to be considered when calculating the deadlines. For example, the market is CNYSH, the curve generating date is 2021-3-31. For period 1M, its day of rest is 2021-4-1 (delay of rest is 1 Beijing working day), plus 1 month and working day adjustment is made to obtain the expiration date as 2021-5-6, corresponding to days (2021/5/6) - (2012-4-1) =36 days. The other curve tools were calculated as the corresponding days.
503: the captured market data is filled into the flat rate field (e.g., business data at the target point in time is obtained). Wherein, the system can grasp the quotation of road penetration in real time through the interface, and update every half hour in the trade day. The system determines if all deadlines have acquired market offers.
If there is a miss, then 504, interpolating the deadline point of the completion miss. Specifically, the days are ordered from left to right by due date-current date. If the left side of the first valued deadline has a defect, flat inserting the quotation of the first valued deadline as the quotation of the left missing deadline; if the right side of the last valued deadline point has a defect, using the bid adjustment of the last valued deadline point as the bid of the right missing deadline point; the missing prices for the deadlines are interpolated from the two preceding and following quotes (Linear or Hermit interpolation).
505: the discount factor for each term is calculated in turn from left to right. Specifically, if there is a monetary market quote, the discount factor = 1/(1+market rate x days/365). If the market quotations are the interest rate interchange, the floating interest rate F is collected by sequentially calculating by means of bootstrap, for example, paying a fixed interest rate X j The specific interchange interest rate calculation formula may be the following:
wherein R represents interchange interest rate, F j Representing the floating interest rate of the floating segment time limit j,n represents the number of floating segments, τ j Time t representing the time period of the floating segment time period j i Represents the duration of the fixed segment time period i, m represents the segment number of the fixed segment time period i, df (τ) j ) A discount factor representing the time limit j of the floating segment, df (t i ) Representing the discount factor of the fixed segment time limit i.
Specifically, for example, the deadlines by which the market has quoted for the USDLB curve are 6M, 9M, 1Y, 2Y, 3Y, 4Y, 5Y. Assuming that the payment frequency is Semi, the exchange of 1 year has one cash flow exchange at 6 months and 1 year respectively, assuming that principal is $ 1, the exchange fixed end pays the fixed end interest according to market exchange quotation, and the floating end calculates the first-stage floating end interest according to the 6M discount factor calculated by the previous currency market. According to the rule without arbitrage, the discount factor of 1Y can be obtained by solving. The discount factors of the following 2Y deadlines are also obtained by solving in the same way. And solving the discount factors of each term by the system through Newton iteration method, and finally calculating the discount factors of all the terms. And then calculating the zero interest rate after conversion of the discount factors, wherein the zero interest rate is = (1-discount factor)/(discount factor by annual days), and the results are stored in a database. And obtaining the corresponding discount factors of each date through fixed-term interpolation on the curve in the subsequent estimation calculation. Accordingly, the resulting yield curve for the further plotting of the bid for the constructed curve parameters may be seen in FIG. 6.
Through the scene example, the processing method of the business data provided by the embodiment of the specification is verified, and the derivative yield curve construction can be automatically realized through the system. Furthermore, the method for constructing the yield curve can be widely applied to various derivative estimation values, curve definition and increase and decrease of quotation deadlines can be adjusted at any time along with business changes, and data can be visually displayed through a line graph. Based on the method, the original complex estimation curve construction work can be solved through a system one-key. After the initial definition maintenance, the user can complete the construction of all the yield curves for estimation by clicking one button each time, so that the user operation is simplified, the daily metering management work of the user is facilitated, and the requirement of the user on the construction of the derivative yield curve is met.
Although the present description provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented by an apparatus or client product in practice, the methods illustrated in the embodiments or figures may be performed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment). 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, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. The terms first, second, etc. are used to denote a name, but not any particular order.
Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer-readable storage media including memory storage devices.
From the above description of embodiments, it will be apparent to those skilled in the art that the present description may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present specification may be embodied essentially in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and include several instructions to cause a computer device (which may be a personal computer, a mobile terminal, a server, or a network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the present specification.
Various embodiments in this specification are described in a progressive manner, and identical or similar parts are all provided for each embodiment, each embodiment focusing on differences from other embodiments. The specification is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
Although the present specification has been described by way of example, it will be appreciated by those skilled in the art that there are many variations and modifications to the specification without departing from the spirit of the specification, and it is intended that the appended claims encompass such variations and modifications as do not depart from the spirit of the specification.
Claims (11)
1. A method for processing service data, comprising:
acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods;
responding to the data processing request, inquiring a preset database according to the identification information of the target service, and acquiring definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve;
determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods;
acquiring and constructing a target curve of a target service according to service data of a plurality of target time points according to a preset construction rule;
And processing target data related to the target service according to the target curve of the target service.
2. The method of claim 1, wherein the target traffic comprises at least one of: foreign exchange losing period business, futures trading business and noble metal trading business.
3. The method of claim 2, wherein prior to obtaining the data processing request for the target service, the method further comprises:
acquiring definition data of a target curve of a target service which is user-defined and set by a user based on a preset setting rule through a preset setting interface;
and storing the definition data of the target curve of the target service in a preset database, and establishing a corresponding relation between the definition data of the target curve and the identification information of the target service in the preset database.
4. The method of claim 2, wherein the data source comprises: money trade market, interest rate interchange market.
5. The method of claim 4, wherein the target curve comprises: a first segment curve with time less than or equal to the segment time, and a second segment curve with time greater than the segment time; the data source of the business data of the first section curve is a money transaction market, and the data source of the business data of the second section curve is an interest exchange market.
6. The method of claim 5, wherein determining a plurality of target time points for matching based on the definition data of the target curve of the target service and the plurality of target time periods comprises:
determining the area of a data source of service data and acquiring the time rule of the area; the time rule comprises holiday information and working time information of the area;
and determining a matched target time point in a segmentation curve corresponding to a data source of the service data according to the time rule and the target time periods.
7. The method of claim 6, wherein obtaining and constructing a target curve of the target service from service data of a plurality of target time points according to a preset construction rule comprises:
acquiring and generating target data points in a target curve according to the definition data of the target curve of the target service through corresponding data sources and service data of a target time point in a segmented curve corresponding to the data sources;
and generating other data points in the target curve by utilizing the target data points according to a preset interpolation processing rule so as to obtain a target curve of the target service.
8. The method of claim 2, wherein performing target data processing associated with the target traffic based on the target profile of the target traffic comprises:
calculating a discount factor according to the target curve of the target service;
and generating an estimated value evaluation report aiming at the target service according to the discount factors.
9. A service data processing apparatus, comprising:
the acquisition module is used for acquiring a data processing request about a target service; the data processing request at least carries identification information of target service and a plurality of target time periods;
the query module is used for responding to the data processing request, querying a preset database according to the identification information of the target service, and obtaining definition data of a target curve of the target service; wherein, the definition data of the target curve at least comprises: the segmentation time of the segmentation curve in the target curve and the data source of the business data of the segmentation curve in the target curve;
the determining module is used for determining a plurality of matched target time points according to the definition data of the target curve of the target service and the plurality of target time periods;
The construction module is used for acquiring and constructing a target curve of the target service according to service data of a plurality of target time points according to a preset construction rule;
and the processing module is used for processing target data related to the target service according to the target curve of the target service.
10. A server comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor implement the steps of the method of any one of claims 1 to 8.
11. A computer readable storage medium having stored thereon computer instructions which when executed implement the steps of the method of any of claims 1 to 8.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110321240A (en) * | 2019-06-28 | 2019-10-11 | 阿里巴巴集团控股有限公司 | A kind of business impact assessment method and apparatus based on time series forecasting |
| CN110674180A (en) * | 2019-09-26 | 2020-01-10 | 腾讯科技（深圳）有限公司 | Business data processing method and device and readable storage medium |
| CN113093958A (en) * | 2021-04-06 | 2021-07-09 | 中国工商银行股份有限公司 | Data processing method and device and server |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN110674180A (en) * | 2019-09-26 | 2020-01-10 | 腾讯科技（深圳）有限公司 | Business data processing method and device and readable storage medium |
| CN113093958A (en) * | 2021-04-06 | 2021-07-09 | 中国工商银行股份有限公司 | Data processing method and device and server |
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