CN114529396A - Data processing method and device and electronic equipment - Google Patents
Data processing method and device and electronic equipment Download PDFInfo
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Abstract
A data processing method, a device and an electronic device are provided, wherein the method comprises the following steps: the interest rate method comprises the steps of obtaining an interest rate curve and all time nodes on the interest rate curve, screening out time nodes with preset quantity values from all the time nodes, using the time nodes as interest rate risk nodes, and calculating interest rate risk values and target profit and loss values corresponding to the interest rate risk nodes according to a preset weighting algorithm. According to the method, part of time nodes are selected from the interest rate curve to serve as interest rate risk nodes, the interest rate risk value and the target profit and loss value corresponding to each interest rate risk node are calculated through a preset weighting algorithm, the interest rate risk and the profit and loss value are redistributed according to the weight of the time nodes on the interest rate curve, the weight value can be adjusted according to actual conditions, and the accuracy of the calculated interest rate risk value and the target profit and loss value is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, and an electronic device.
Background
In the current economic market of China, interest rate interchange products become important components in the economic market, common interest rate interchange products comprise bonds, stocks and the like, and the interest rate interchange is the interest exchange of the same kind of currency with different interest rates, such as: in the transaction process of the interest rate interchange product, the amount A has two ways of calculating the interest of the amount A, one way is to calculate the interest of the amount A according to a fixed interest rate, the other way is to calculate the interest of the amount A according to a floating interest rate, if the interest of the amount A is calculated to be a by adopting the fixed interest rate, the interest of the amount A is calculated to be b by adopting the floating interest rate, the amount A is calculated to be b by adopting the fixed interest rate, the amount A is held by the client 1, the amount B is held by the client 2, and after the client 1 and the client 2 carry out transaction, the amount B is held by the client 1, the amount A is held by the client 2, the transaction process described above is a transaction process of the interest rate interchange product, and in the transaction process of the interest rate interchange product, in order to ensure the fairness of the transaction, the price of the interest rate interchange product needs to be priced in advance.
At present, the pricing of the interest rate interchange products in the Chinese economic market is realized by comparing the floating rate of return and the fixed rate of return of the interest rate interchange products to obtain the risk of interest rate of the interest rate interchange products, and then the risk of interest rate is compared with the actual expectation of investors to judge the correctness of the pricing of the interest rate interchange products.
According to the method, the interest rate risk is determined by collecting the floating and fixed yield rates of the existing interest rate interchange product, so that the profit value corresponding to the time node in the statistical time is calculated based on the interest rate risk, however, the interest rate risk and the profit value determined according to the method are only obtained by dividing the interest rate risk and the profit value in each statistical time period to the time node closest to the statistical time period and adding the interest rate risk value and the profit value on the time node, and when the interest rate risk value and the profit value on the delivery date are not considered between the time nodes, the influence of the interest rate risk value and the profit value on the time nodes at the two ends is not considered, so that the obtained interest rate risk and the profit data are inaccurate.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device and electronic equipment, wherein an interest rate risk value and a target profit and loss value corresponding to an interest rate risk node are calculated through a preset weighting algorithm, the interest rate risk and the target profit and loss value corresponding to each time node on an interest rate curve are redistributed, the weight value of each time node between each interest rate risk node can be adjusted according to actual conditions, the influence of the delivery date of each transaction amount on the interest rate risk and the target profit and loss value is reduced, and therefore the accuracy of the interest rate risk value and the target profit and loss value is improved.
In a first aspect, the present application provides a data processing method, including:
obtaining an interest rate curve and all time nodes on the interest rate curve;
screening time nodes with preset quantity values from all the time nodes, and taking the time nodes as interest rate risk nodes;
and calculating interest rate risk values and target profit and loss values respectively corresponding to the interest rate risk nodes according to a preset weighting algorithm.
By the aid of the method, interest rate risk nodes are screened from all time nodes on the interest rate curve, the weighted values corresponding to all time nodes among the interest rate risk nodes are calculated through a preset weighting algorithm, the weighted values corresponding to all time nodes among the interest rate risk nodes can be adjusted according to actual conditions, the interest rate risk values and the target profit and loss values corresponding to the interest rate risk nodes are calculated based on the weighted values, influences of inconsistency between the statistical date and the delivery date on the calculated interest rate risk values and the target profit and loss values corresponding to all time nodes are reduced, and accuracy of the calculated interest rate risk values and the target profit and loss values is improved.
In one possible design, the calculating the interest rate risk value corresponding to each interest rate risk node according to a preset weighting algorithm includes:
obtaining each interest rate risk node and a weight value corresponding to each time node;
moving the interest rate curve according to a preset moving mode and the weight value to obtain a first cash flow value and a second cash flow value corresponding to each interest rate risk node of the interest rate curve respectively;
and substituting the first cash flow value and the second cash flow value into a preset interest rate risk algorithm, and calculating the interest rate risk value corresponding to each interest rate risk node.
In one possible design, obtaining the weight value corresponding to each interest rate risk node and each time node includes:
arranging all time nodes according to a time sequence, and performing weighted calculation according to the interval positions between adjacent time nodes to obtain each interest rate risk node and a weight value corresponding to each time node; or
And arranging all time nodes according to a time sequence, and performing weighted calculation according to a time difference value between adjacent time nodes to obtain each interest rate risk node and a weighted value corresponding to each time node, or calculating the weighted value corresponding to each time node based on a preset weighted function to obtain each interest rate risk node and the weighted value corresponding to each time node.
In one possible design, shifting the interest rate curve according to a preset shifting manner and the weight value includes:
reading the weight values corresponding to all time nodes on the interest rate curve according to the time sequence;
and respectively translating the target unit value upwards and downwards according to the weight value corresponding to each time node to obtain an interest rate curve of the target unit value translated upwards and an interest rate curve of the target unit value translated downwards.
In a possible design, before calculating the target profit-and-loss values respectively corresponding to the interest rate risk nodes according to a preset weighting algorithm, the method includes:
and obtaining a delivery date corresponding to each transaction amount in the interest rate curve, and calculating an initial profit-loss value of each transaction amount when the transaction amount is subjected to transaction on the delivery date.
In one possible design, calculating the target profit-and-loss values respectively corresponding to the interest rate risk nodes according to a preset weighting algorithm includes:
calculating a time difference value between adjacent interest rate risk nodes on the interest rate curve, and performing weighted calculation according to the time difference value to obtain each interest rate risk node and a weight value corresponding to each time node;
extracting an initial loss and gain value corresponding to each interest rate risk node, and distributing the initial loss and gain values according to the weight values;
and counting a total initial profit and loss value corresponding to each interest rate risk node, and taking the initial profit and loss value as a target profit and loss value to obtain the target profit and loss value corresponding to each interest rate risk node.
In a second aspect, the present application provides a data processing apparatus, the apparatus comprising:
the interest rate obtaining module is used for obtaining an interest rate curve and all time nodes on the interest rate curve;
the screening module is used for screening out time nodes with preset quantity values from all the time nodes and taking the time nodes as interest rate risk nodes;
and the calculation module is used for calculating interest rate risk values and target profit and loss values corresponding to the interest rate risk nodes according to a preset weighting algorithm.
In a possible design, the calculating module is specifically configured to obtain each interest rate risk node and a weight value corresponding to each time node, move the interest rate curve according to a preset moving mode and the weight value, obtain a first cash flow value and a second cash flow value corresponding to each interest rate risk node of the interest rate curve, bring the first cash flow value and the second cash flow value into a preset interest rate risk algorithm, and calculate an interest rate risk value corresponding to each interest rate risk node.
In a possible design, the calculation module is further configured to arrange all the time nodes according to a time sequence, perform weighting calculation according to an interval position between adjacent time nodes, to obtain a weight value corresponding to each interest rate risk node and each time node, or arrange all the time nodes according to the time sequence, perform weighting calculation according to a time difference between adjacent time nodes, to obtain a weight value corresponding to each interest rate risk node and each time node, or calculate a weight value corresponding to each time node based on a preset weighting function, to obtain a weight value corresponding to each interest rate risk node and each time node.
In a possible design, the calculating module is further configured to read weight values corresponding to respective time nodes on the interest rate curve according to a time sequence, and translate the target unit value upward and downward according to the weight values corresponding to the respective time nodes, to obtain an interest rate curve of translating the target unit value upward and an interest rate curve of translating the target unit value downward.
In a possible design, the calculation module is further configured to obtain a delivery date corresponding to each transaction amount in the interest rate curve, and calculate an initial profit-and-loss value of each transaction amount when the transaction amount is transacted on the delivery date.
In a possible design, the calculating module is further configured to calculate a time difference between adjacent interest rate risk nodes on the interest rate curve, perform weighting calculation according to the time difference, obtain each interest rate risk node and a weight value corresponding to each time node, extract an initial loss value corresponding to each interest rate risk node, allocate the initial loss value according to the weight value, calculate a total initial loss value corresponding to each interest rate risk node, and obtain a target loss value corresponding to each interest rate risk node by using the initial loss value as a target loss value.
In a third aspect, the present application provides an electronic device, comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the data processing method when executing the computer program stored in the memory.
In a fourth aspect, a computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements a data processing method step as described above.
In a fifth aspect, the present application provides a computer program product for causing a computer to perform the above-mentioned data processing method steps when the computer program product is run on the computer.
For each of the first aspect to the fifth aspect and possible technical effects of each aspect, please refer to the above description of the possible technical effects of the first aspect or each possible solution of the first aspect, and no repeated description is given here.
Drawings
FIG. 1 is a flow chart of steps of a data processing method provided herein;
FIG. 2 is a schematic structural diagram of a data processing apparatus provided in the present application;
fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be further described in detail with reference to the accompanying drawings. The particular methods of operation in the method embodiments may also be applied to apparatus embodiments or system embodiments. It should be noted that "a plurality" is understood as "at least two" in the description of the present application. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. A is connected with B and can represent: a and B are directly connected and A and B are connected through C. In addition, in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not intended to indicate or imply relative importance nor order to be construed.
In the prior art, the interest rate risk is determined by collecting the floating and fixed interest rates of the existing interest rate interchange product, and the profit value corresponding to the statistical time node is calculated based on the interest rate risk, but the calculated interest rate risk value and profit value are obtained by dividing the interest rate risk value and profit value to the nearest time node and adding all the interest rate risk values and profit values corresponding to the time node.
In order to solve the above-described problems, embodiments of the present application provide a data processing method for improving the accuracy of interest rate risk values and target profit and loss values. The method and the device in the embodiment of the application are based on the same technical concept, and because the principles of the problems solved by the method and the device are similar, the device and the embodiment of the method can be mutually referred, repeated parts are not repeated, and the data acquisition, storage, use, processing and the like in the technical scheme of the application all conform to relevant regulations of national laws and regulations.
The embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the present application provides a data processing method, which may calculate a weight value corresponding to each time between each interest rate risk node, and calculate an interest rate risk value and a target profit-and-loss value corresponding to each interest rate risk node based on the weight value, so as to improve the accuracy of the interest rate risk value and the target profit-and-loss value, and an implementation flow of the method is as follows:
step S1: an interest rate curve is obtained, along with all time nodes on the interest rate curve.
Currently, interest rate curves are used for counting interest rates corresponding to the interest rate interchange products on each time node when the products are traded on different delivery dates, it should be noted that the delivery dates and the time nodes may be consistent or inconsistent, the time nodes are statistical time limits of transaction data, the interest rate curves generated based on the time nodes and the interest rates can be used for reflecting transaction conditions of the time nodes, and in order to obtain interest rate risk values and target benefit values corresponding to the time nodes based on the interest rate curves, the interest rate curves and all the time nodes on the interest rate curves need to be obtained.
Step S2: and screening time nodes with preset quantity values from all the time nodes, and taking the time nodes as interest rate risk nodes.
Because the interest rate curve is obtained according to the time nodes for counting the transaction data, the time nodes for counting the transaction data cannot be completely consistent with the delivery dates of all interest rate interchange products, the basket of the transaction data to a certain time node influences the interest rate risk value and the target profit and loss value of the time node, in practical situations, a large number of delivery dates of the interest rate interchange products are between time periods formed by the time nodes, and how the transaction data generated by the interest rate interchange products in the time periods are basket to a certain time node becomes a problem to be solved.
After the importance of correct basket return of transaction data is explained, because interest rate risk values corresponding to time nodes on an interest rate curve are influenced by fixed interest rates and floating interest rates, and transaction data among the time nodes have a large difference, in order to avoid the situation that the calculated interest rate risk values and target profit and loss values of the time nodes are inaccurate due to the fact that the transaction data among the time nodes are completely basket returned to a certain time node, time nodes with preset number values need to be screened out from all the time nodes of the interest rate curve, the preset number values can be adjusted according to actual situations, and finally, the screened time nodes are used as interest rate risk nodes.
It should be further noted that the interest rate risk nodes may be set based on a fixed term mode, or may be set based on a curve mode, when the interest rate risk nodes are set based on the fixed term, the fixed term may be 3 months and 6 months, all the interest rate risk nodes in the fixed term mode are 3 months and 6 months, and the fixed term may be adjusted according to actual situations, so that the description is not repeated here.
When setting up interest rate risk nodes based on the description of interest rate curves, each interest rate curve does not influence each other, and does not influence each other between the interest rate risk nodes that each interest rate curve corresponds.
Such as: by taking dollars and renminbi as examples, time nodes on the interest rate curve are 1 month, 2 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months and 10 months in sequence, when the interest rate risk nodes are set in a fixed term mode, the dollars are consistent with the interest rate risk nodes corresponding to the renminbi, the time nodes of 1 month, 4 months, 5 months and 10 months can be used as the interest rate risk nodes, when the interest rate risk nodes are set in a curve mode, the interest rate risk nodes corresponding to the dollars can be 2 months, 5 months, 7 months and 9 months, and the interest rate risk nodes corresponding to the renminbi can be 1 month, 4 months, 5 months and 10 months.
By adopting the method, the time nodes with the preset quantity value are screened out from all the time nodes of the interest rate curve, and the time nodes are used as the interest rate risk nodes, so that the quantity of the interest rate risk nodes can be adjusted according to the actual condition, the influence of extreme data on the interest rate risk nodes is avoided, and the accuracy of the calculated interest rate risk nodes is improved.
Step S3: and calculating interest rate risk values and target profit and loss values respectively corresponding to the interest rate risk nodes according to a preset weighting algorithm.
Continuing with the above example of determining interest rate risk nodes from all time nodes of the interest rate curve, after the interest rate risk nodes are determined, in order to make the interest rate risk values and the target profit and loss values corresponding to the time nodes between the interest rate risk nodes more accurate, it is necessary to accurately obtain the interest rate risk values and the target profit and loss values corresponding to the interest rate risk nodes.
In order to obtain an accurate interest rate risk value and a target profit and loss value corresponding to the interest rate risk node, the interest rate curve needs to be moved up and down according to the weight value, and then the interest rate risk value corresponding to each interest rate risk node is calculated.
In the embodiment of the application, three ways are adopted to calculate the weight values of all time nodes between interest rate risk nodes, the first way is to perform weighting calculation based on the interval positions between all time nodes of an interest rate curve, the second way is to perform weighting calculation based on the time difference values between all time nodes of the interest rate curve, the third way is to calculate the weight values corresponding to all time nodes based on a preset weighting function to obtain each interest rate risk node and the weight values corresponding to all time nodes, the first way and the second way of calculating the weight values of all time nodes can be used as a basket-returning calculation method corresponding to interest rate risks, and the third way of calculating the weight values can be used as a basket-returning calculation method corresponding to target damage values.
The specific process of performing weighting calculation based on the interval positions between all time nodes of the interest rate curve is as follows:
continuing with the above example, the determined interest rate risk node corresponds to a weight value as shown in table 1:
| 1 | 2 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| interest rate risk node 1 | 1 | ||||||||
| Interest rate risk node 4 | 1 | ||||||||
| Interest rate risk node 5 | 1 | ||||||||
| Interest rate risk node 10 | 1 |
TABLE 1
As can be seen from the above example, the determined interest rate risk nodes are 1 month, 4 months, 5 months, and 10 months, each interest rate risk node corresponds to one interest rate risk value, and therefore, the interest rate risk values need to be calculated 4 times, and further, the interest rate curve needs to be translated up and down 4 times, respectively, because only the interval position between the adjacent time nodes of the interest rate curve is considered, and therefore, the weight value of each time node on the interest rate curve in calculating the interest rate risk value of each interest rate risk node is shown in table 2:
| 1 | 2 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| interest rate risk node 1 | 1 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 4 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 5 | 0 | 0 | 0 | 1 | 0.8 | 0.6 | 0.4 | 0.2 | 0 |
| Interest rate risk node 10 | 0 | 0 | 0 | 0 | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
TABLE 2
In table 2, the total sum of the weight values corresponding to each time node is 1, and when the interest rate risk value corresponding to the interest rate risk node 1 is calculated, although the weight values between month 1 and month 2 are 1 month apart, 2 months between month 2 and month 4 apart, and 5 months between month 5 and month 10 apart, but the weight values between month 1 and month 2, and month 2 and month 4 are equal, it can be known from table 1 that the weight value corresponding to the interest rate risk node 1 is 1, and in order to calculate the interest rate risk value of the interest rate risk node 1, it is necessary to shift the point on the interest rate curve where the time node is 1 by one unit upward, calculate the first cash flow value based on the interest rate curve after the upward shift, further shift the interest rate curve by one unit downward, calculate the second cash flow value based on the interest rate curve after the downward shift, and calculate the cash flow value based on the interest rate curve, which is well known to those skilled in the art, therefore, it will not be explained herein too much.
After obtaining the first cash flow value and the second cash flow value, substituting the first cash flow value and the second cash flow value into the following formula:
DV01 represents the interest rate risk value for the interest rate risk node, PV 'represents the first cash flow value calculated after the interest rate curve is translated upward by the target unit value, PV' represents the second cash flow value calculated after the interest rate curve is translated downward by the target unit value, and x bump represents the target unit value of the movement of the interest rate curve.
Based on the above formula, the interest rate risk value of the interest rate risk node 1 can be calculated, and since the processes corresponding to the calculated interest rate risk values of the interest rate risk nodes are the same, the processes for acquiring the interest rate risk values of other interest rate risk nodes can refer to the process for acquiring the interest rate risk value of the interest rate risk node 1, which is not described herein.
The specific process of performing weighting calculation based on the time difference between all time nodes of the interest rate curve is as follows:
continuing to explain with the determined interest rate risk nodes, calculating the weight values of the time nodes according to the time difference values among the time nodes as shown in table 3:
| 1 | 2 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| interest rate risk node 1 | 1 | 0.67 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 4 | 0 | 0.33 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 5 | 0 | 0 | 0 | 1 | 0.8 | 0.6 | 0.4 | 0.2 | 0 |
| Interest rate risk node 10 | 0 | 0 | 0 | 0 | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
TABLE 3
In table 3, it is known that the interest rate risk nodes are 1 month, 4 months, 5 months and 10 months, 1 month is separated from 1 month to 2 months, 2 months is separated from 2 months to 4 months, 5 months is separated from 5 months to 10 months, the probability value from the weight value of 2 months to the interest rate risk node 1 is 0.67, the probability value from the weight value of 2 months to the interest rate risk node 4 is 0.33, it can be known that, similarly, the difference between 5 months and 10 months is 6, 7, 8 and 9 months, the weight value equally divided to each interest rate risk node is 0.2, since the closer to the interest rate risk node, the higher the probability of basket the time node close to the interest rate risk node, therefore, the weight values of the time nodes 6, 7, 8 and 9 corresponding to the interest rate risk node 5 are decreased in sequence, and the weight values of the time nodes 6, 7, 8 and 9 corresponding to the interest rate risk node 10 are increased in sequence.
After the weight values of the time nodes are calculated based on the time difference values between the time nodes, the interest rate curves are translated up and down according to the weight values, a first cash flow value and a second cash flow value corresponding to each interest rate risk node are calculated, and finally, the interest rate risk value corresponding to each interest rate risk node is calculated based on the formula.
Calculating the weight value corresponding to each time node based on a preset weighting function, wherein the calculation process of obtaining each interest rate risk node and the weight value corresponding to each time node is as follows:
continuing to explain the determined interest rate risk nodes, and calculating the weight values corresponding to the time nodes according to a preset weighting algorithm, wherein the weight values are shown in a table 4:
| 1 | 2 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| interest rate risk node 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 4 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Interest rate risk node 10 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 |
TABLE 4
In table 4, the weighted values of the time nodes are calculated based on a preset weighting function, the weighted values corresponding to the interest rate risk nodes 1, 4, 5, and 10 are 1, and the preset weighting function used in the embodiment of the present application is a step weighting function.
In order to obtain the target profit and loss value corresponding to each interest rate risk node, it is necessary to calculate a third cash flow value when each transaction amount in each transaction data corresponding to the interest rate curve is transacted on the delivery date, and calculate an initial profit and loss value corresponding to each transaction data based on the cash flow value, and calculating the initial profit and loss value of the transaction data based on the interest rate curve is a technique known to those skilled in the art, and therefore, it is not described here.
After the initial profit-and-loss value of each transaction data corresponding to the interest rate curve is obtained, in order to calculate the target profit-and-loss value corresponding to each interest rate risk node, the profit-and-loss values corresponding to each time node between each interest rate risk node still need to be grouped to the interest rate risk node, and since table 4 described above describes that the weight value corresponding to each time node is calculated based on the step weighting function, no excessive explanation is made here.
After the initial profit and loss values of the time nodes are obtained, the initial profit and loss values of the time nodes among the interest rate risk nodes are grouped to the interest rate risk nodes according to the calculated weight values, the sum of the grouped initial profit and loss values of each interest rate risk node is calculated, and the sum of the initial profit and loss values is used as a target profit and loss value corresponding to the interest rate risk nodes.
According to the method, the weight values corresponding to the interest rate risk values and the weight values corresponding to the initial loss value corresponding to the interest rate risk values corresponding to the time nodes among the interest rate risk nodes are calculated in different modes, the interest rate risk values on the time nodes are grouped to the interest rate risk nodes based on the calculated weight values, the initial loss values on the time nodes are grouped to the interest rate risk nodes, meanwhile, the calculated weight values can be adjusted according to actual conditions, the problem that the interest rate risk values and the target loss value are grouped inaccurately is solved, and the accuracy of the calculated interest rate risk values and the target loss value is improved.
Based on the same inventive concept, an embodiment of the present application further provides a data processing apparatus, where the data processing apparatus is configured to implement a function of a data processing method, and with reference to fig. 2, the apparatus includes:
an obtaining module 201, configured to obtain an interest rate curve and all time nodes on the interest rate curve;
the screening module 202 is configured to screen out time nodes with a preset quantity value from all the time nodes, and use the time nodes as interest rate risk nodes;
the calculating module 203 is configured to calculate interest rate risk values and target profit and loss values corresponding to the interest rate risk nodes according to a preset weighting algorithm.
In a possible design, the calculating module 203 is specifically configured to obtain each interest rate risk node and a weight value corresponding to each time node, move the interest rate curve according to a preset moving mode and the weight value, obtain a first cash flow value and a second cash flow value corresponding to each interest rate risk node of the interest rate curve, bring the first cash flow value and the second cash flow value into a preset interest rate risk algorithm, and calculate an interest rate risk value corresponding to each interest rate risk node.
In a possible design, the calculating module 203 is further configured to arrange all the time nodes according to a time sequence, perform weighting calculation according to an interval position between adjacent time nodes, to obtain a weight value corresponding to each interest rate risk node and each time node, or arrange all the time nodes according to the time sequence, perform weighting calculation according to a time difference between adjacent time nodes, to obtain a weight value corresponding to each interest rate risk node and each time node, or calculate a weight value corresponding to each time node based on a preset weighting function, to obtain a weight value corresponding to each interest rate risk node and each time node.
In a possible design, the calculating module 203 is further configured to read weight values corresponding to each time node on the interest rate curve according to a time sequence, and translate the target unit value upwards and downwards according to the weight values corresponding to each time node, so as to obtain an interest rate curve of translating the target unit value upwards and an interest rate curve of translating the target unit value downwards.
In a possible design, the calculating module 203 is further configured to obtain a delivery date corresponding to each transaction amount in the interest rate curve, and calculate an initial profit-and-loss value of each transaction amount when the transaction amount is transacted on the delivery date.
In a possible design, the calculating module 203 is further configured to calculate a time difference between adjacent interest rate risk nodes on the interest rate curve, perform weighting calculation according to the time difference, obtain each interest rate risk node and a weight value corresponding to each time node, extract an initial loss value corresponding to each interest rate risk node, distribute the initial loss values according to the weight values, calculate a total initial loss value corresponding to each interest rate risk node, and obtain a target loss value corresponding to each interest rate risk node by using the initial loss value as a target loss value.
Based on the same inventive concept, an embodiment of the present application further provides an electronic device, where the electronic device can implement the function of the foregoing data processing apparatus, and with reference to fig. 3, the electronic device includes:
at least one processor 301 and a memory 302 connected to the at least one processor 301, in this embodiment, a specific connection medium between the processor 301 and the memory 302 is not limited in this application, and fig. 3 illustrates an example where the processor 301 and the memory 302 are connected through a bus 300. The bus 300 is shown in fig. 3 by a thick line, and the connection between other components is merely illustrative and not limited thereto. The bus 300 may be divided into an address bus, a data bus, a control bus, etc., and is shown with only one thick line in fig. 3 for ease of illustration, but does not represent only one bus or type of bus. Alternatively, the processor 301 may also be referred to as a controller, without limitation to name a few.
In the embodiment of the present application, the memory 302 stores instructions executable by the at least one processor 301, and the at least one processor 301 can execute one of the data processing methods discussed above by executing the instructions stored in the memory 302. The processor 301 may implement the functions of the various modules in the apparatus shown in fig. 2.
The processor 301 is a control center of the apparatus, and may connect various parts of the entire control device by using various interfaces and lines, and perform various functions of the apparatus and process data by operating or executing instructions stored in the memory 302 and calling up data stored in the memory 302, thereby performing overall monitoring of the apparatus.
In one possible design, processor 301 may include one or more processing units, and processor 301 may integrate an application processor that primarily handles operating systems, user interfaces, application programs, and the like, and a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 301. In some embodiments, the processor 301 and the memory 302 may be implemented on the same chip, or in some embodiments, they may be implemented separately on separate chips.
The processor 301 may be a general-purpose processor, such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like, that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a data processing method disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
By programming the processor 301, the code corresponding to a data processing method described in the foregoing embodiments may be solidified into the chip, so that the chip can execute a data processing method step of the embodiment shown in fig. 1 when running. How to program the processor 301 is well known to those skilled in the art and will not be described herein.
Based on the same inventive concept, the present application also provides a storage medium storing computer instructions, which when executed on a computer, cause the computer to execute a data processing method as discussed above.
In some possible embodiments, the present application provides that the various aspects of a data processing method may also be implemented in the form of a program product comprising program code means for causing a control device to perform the steps of a data processing method according to various exemplary embodiments of the present application described above in this specification, when the program product is run on an apparatus.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The program product of the data processing method provided in the embodiments of the present invention may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a computing device. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., over the internet using an internet service provider).
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the units described above may be embodied in one unit, according to embodiments of the invention. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Moreover, while the operations of the method of the invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (15)
1. A data processing method, comprising:
obtaining an interest rate curve and all time nodes on the interest rate curve;
screening time nodes with preset quantity values from all the time nodes, and taking the time nodes as interest rate risk nodes;
and calculating interest rate risk values and target profit and loss values respectively corresponding to the interest rate risk nodes according to a preset weighting algorithm.
2. The method of claim 1, wherein calculating the interest rate risk value corresponding to each interest rate risk node according to a predetermined weighting algorithm comprises:
obtaining each interest rate risk node and a weight value corresponding to each time node;
moving the interest rate curve according to a preset moving mode and the weight value to obtain a first cash flow value and a second cash flow value corresponding to each interest rate risk node of the interest rate curve respectively, wherein the first cash flow value is obtained by calculation based on the interest rate curve of the target unit value translated upwards, and the second cash flow value is obtained by calculation based on the interest rate curve of the target unit value translated downwards;
and substituting the first cash flow value and the second cash flow value into a preset interest rate risk algorithm, and calculating the interest rate risk value corresponding to each interest rate risk node.
3. The method of claim 2, wherein obtaining a weight value for each interest rate risk node and for each time node comprises:
arranging all time nodes according to a time sequence, and performing weighted calculation according to the interval positions between adjacent time nodes to obtain each interest rate risk node and a weight value corresponding to each time node; or
Arranging all time nodes according to a time sequence, and performing weighted calculation according to a time difference value between adjacent time nodes to obtain each interest rate risk node and a weight value corresponding to each time node; or
And calculating a weight value corresponding to each time node based on a preset weighting function, and obtaining each interest rate risk node and the weight value corresponding to each time node.
4. The method of claim 2, wherein shifting the interest rate curve according to a preset shifting pattern and the weight value comprises:
reading the weight values corresponding to all time nodes on the interest rate curve according to the time sequence;
and respectively translating the target unit value upwards and downwards according to the weight value corresponding to each time node to obtain an interest rate curve of the target unit value translated upwards and an interest rate curve of the target unit value translated downwards.
5. The method of claim 1, wherein before calculating the target profit-and-loss values corresponding to the interest rate risk nodes according to a predetermined weighting algorithm, the method comprises:
and obtaining a delivery date corresponding to each transaction amount in the interest rate curve, and calculating an initial profit-loss value of each transaction amount when the transaction amount is subjected to transaction on the delivery date.
6. The method of claim 1, wherein calculating the target profit-and-loss values corresponding to the interest rate risk nodes according to a predetermined weighting algorithm comprises:
calculating a time difference value between adjacent interest rate risk nodes on the interest rate curve, and performing weighted calculation according to the time difference value to obtain each interest rate risk node and a weight value corresponding to each time node;
extracting an initial loss and gain value corresponding to each interest rate risk node, and distributing the initial loss and gain values according to the weight values;
and counting a total initial profit and loss value corresponding to each interest rate risk node, and taking the initial profit and loss value as a target profit and loss value to obtain the target profit and loss value corresponding to each interest rate risk node.
7. A data processing apparatus, comprising:
the interest rate obtaining module is used for obtaining an interest rate curve and all time nodes on the interest rate curve;
the screening module is used for screening out time nodes with preset quantity values from all the time nodes and taking the time nodes as interest rate risk nodes;
and the calculation module is used for calculating interest rate risk values and target profit and loss values corresponding to the interest rate risk nodes according to a preset weighting algorithm.
8. The apparatus according to claim 7, wherein the calculating module is specifically configured to obtain a weight value corresponding to each interest rate risk node and each time node, move the interest rate curve according to a preset moving manner and the weight value, obtain a first cash flow value and a second cash flow value corresponding to each interest rate risk node of the interest rate curve, and substitute the first cash flow value and the second cash flow value into a preset interest rate risk algorithm to calculate the interest rate risk value corresponding to each interest rate risk node.
9. The apparatus according to claim 7, wherein the calculating module is further configured to arrange all time nodes in a time sequence, perform weighting calculation according to the interval positions between adjacent time nodes, to obtain a weight value corresponding to each interest rate risk node and each time node, or arrange all time nodes in a time sequence, perform weighting calculation according to the time difference between adjacent time nodes, to obtain a weight value corresponding to each interest rate risk node and each time node, or calculate a weight value corresponding to each time node based on a preset weighting function, to obtain a weight value corresponding to each interest rate risk node and each time node.
10. The apparatus of claim 7, wherein the computing module is further configured to read the weight values corresponding to the time nodes on the interest rate curve in a time sequence, and shift the target unit value up and down according to the weight values corresponding to the time nodes, respectively, to obtain an interest rate curve shifted by the target unit value up and an interest rate curve shifted by the target unit value down.
11. The apparatus of claim 7, wherein the calculation module is further configured to obtain a delivery date corresponding to each transaction amount in the interest rate curve, and calculate an initial profit-loss value for each transaction amount when the transaction amount is transacted on the delivery date.
12. The apparatus according to claim 7, wherein the calculating module is further configured to calculate a time difference between adjacent interest rate risk nodes on the interest rate curve, and perform weighting calculation according to the time difference to obtain each interest rate risk node and a weight value corresponding to each time node;
extracting an initial loss and gain value corresponding to each interest rate risk node, and distributing the initial loss and gain values according to the weight values;
and counting a total initial profit and loss value corresponding to each interest rate risk node, and taking the initial profit and loss value as a target profit and loss value to obtain the target profit and loss value corresponding to each interest rate risk node.
13. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1-6 when executing the computer program stored on the memory.
14. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1-6.
15. A computer program product, characterized in that, when run on a computer, causes the computer to perform the method according to any one of claims 1-6.
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| CN202210141927.1A CN114529396A (en) | 2022-02-16 | 2022-02-16 | Data processing method and device and electronic equipment |
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| CN202210141927.1A CN114529396A (en) | 2022-02-16 | 2022-02-16 | Data processing method and device and electronic equipment |
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| CN114529396A true CN114529396A (en) | 2022-05-24 |
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| CN202210141927.1A Pending CN114529396A (en) | 2022-02-16 | 2022-02-16 | Data processing method and device and electronic equipment |
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