CN112232953A - Bond transaction prepayment method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and discloses a bond transaction prepayment method, a device, equipment and a storage medium, wherein the method comprises the following steps: optimizing parameters in a pre-payment calculation model used in the bond transaction process by using historical bond transaction data; and calculating the prepayment amount of the user for initiating the purchase according to the public sale quantity and the actual total number of the purchase in the current bond transaction process through the optimized prepayment calculation model, and sending a notice for deducting the prepayment amount to the client of the user. By utilizing historical IPO subscription and pricing big data statistics, parameters in the subscription prepayment calculation formula are optimized, prepayment cost of a user is reduced as far as possible, and occupation of funds of the user is further reduced. In addition, the invention also relates to a block chain technology, and historical bond transaction data can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, and relates to a bond transaction prepayment method, a bond transaction prepayment device, bond transaction equipment and a storage medium.
Background
At present, in the IPO (Initial Public Offering) process of the port transit security industry, a specific price is not given, but a price interval is given, after a user buys an IPO, corresponding amount of money needs to be paid in advance, but due to the existence of a drawing and pricing mechanism, the actual buyout number of the final user may be smaller than the Initial buyout number, and the amount of money to be paid in advance is deducted according to the Initial buyout number and the highest price, so that if the actual buyout number is smaller than the Initial buyout number, funds are idle, and part of the funds are occupied.
Disclosure of Invention
The invention aims to provide a bond transaction prepayment method, a device, equipment and a storage medium aiming at the defects of the prior art, and the aim is realized by the following technical scheme.
A first aspect of the present invention proposes a method for prepaid bond transactions, said method comprising:
optimizing parameters in a pre-payment calculation model used in the bond transaction process by using historical bond transaction data;
and calculating the prepayment amount of the user for initiating the purchase according to the public sale quantity and the actual total number of the purchase in the current bond transaction process through the optimized prepayment calculation model, and sending a notice for deducting the prepayment amount to the client of the user.
Optionally, the optimizing parameters in the pre-payment calculation model used in the bond transaction process by using the historical bond transaction data may include: taking the initial purchase number, price interval of each share, public sale number and actual purchase total number of each user in the historical bond transaction data as input parameters of the pre-payment calculation model; taking the actual purchase number and the actual payment amount of each user in the historical bond transaction data as tag data of input parameters; and optimizing the parameters in the pre-payment calculation model according to the pre-payment amount and the actual procurement amount output by the pre-payment calculation model, and the actual procurement amount and the actual payment amount of each user in the label data.
Optionally, the calculating, by the optimized prepaid calculation model, the prepaid amount of the user initiating the subscription according to the public sale quantity and the actual subscription total number in the current bond transaction process may include: determining the sale quantity after callback according to the open sale quantity, the actual procurement total number and the callback parameter; grouping the initial purchase requisition number of each user according to a grouping critical value, determining a callback adjustment multiple of each group according to an excess subscription parameter and determining the available number of each group according to the callback adjustment multiple and the sale number after callback; and aiming at each user, determining the actual purchase number of the user according to the initial purchase number of the user and the available number of the group to which the user belongs, and determining and outputting the prepayment amount of the user according to the actual purchase number and the price interval of each share.
Optionally, the determining the sale quantity after callback according to the public sale quantity, the actual procurement total number and the callback parameter may include: calculating the excess procurement proportion according to the public sale quantity and the actual procurement total quantity; determining a callback proportion according to the callback parameters and the excess purchase ratio; and determining the back-dialled sales number according to the back-dialling proportion and the total sales number of the whole bond transaction.
Optionally, the determining the callback adjustment multiple of the group according to the over-subscription parameter may include: determining an excess multiple of the initial number of purchases for all users in the group; and determining a callback adjusting multiple corresponding to the excess multiple according to the excess subscription parameter.
Optionally, the determining the available number of the group according to the callback adjustment multiple and the number of sales after callback may include: distributing a distribution quota for the group according to the distribution quantity after callback; and determining the available quantity of the packet according to the selling quota and the callback adjustment multiple of the packet.
Optionally, the determining the actual number of buys for the user according to the initial number of buys for the user and the available number of the group to which the user belongs may include: determining the share of the initial purchase number of the user in all the users in the group; and determining the actual purchase requisition quantity of the user according to the share and the available quantity of the belonged group.
A second aspect of the present invention proposes a bond transaction prepayment device, comprising:
the model optimization module is used for optimizing parameters in a pre-payment calculation model used in the bond transaction process by utilizing historical bond transaction data;
and the calculation module is used for calculating the prepayment amount of the procurement initiated by the user according to the public sale quantity and the actual procurement total number in the current bond transaction process through the optimized prepayment calculation model, and sending a notice of deducting the prepayment amount to the client of the user.
A third aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for prepaid bond transactions as described in the first aspect above.
A fourth aspect of the present invention proposes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for prepaid bond transactions according to the first aspect described above.
The bond transaction prepayment method based on the first aspect has the following beneficial effects:
by utilizing historical IPO subscription and pricing big data statistics, parameters in the subscription prepayment calculation formula are optimized, prepayment cost of a user is reduced as far as possible, and occupation of funds of the user is further reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart illustrating an embodiment of a method for prepayment of bond transactions according to an exemplary embodiment of the present invention;
FIG. 2 is a diagram illustrating a hardware configuration of a computer device in accordance with an illustrative embodiment of the present invention;
fig. 3 is a flow chart illustrating an embodiment of a bond transaction prepayment device according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
During the bond transaction, the bond party will allocate a public sale quantity in the public sale stage, and the quantity of the end user subscription may exceed the allocated public sale quantity, so that the actual quantity of the purchase can be allocated for each user in the drawing stage, and usually the quantity of the middle-drawing subscription is less than or equal to the quantity of the initial subscription.
Before drawing a lot, the user needs to pay fees to the bond party after making a purchase, in the prior art, the fees are usually deducted according to the amount of the prepaid fee calculated according to the maximum price in each price interval and the initial amount of the user making a purchase, after drawing a lot, the actual amount of the sale may be smaller than the initial amount of the purchase, and the final price may not be the maximum price, so that part of the funds of the user is occupied.
For example, each stock price interval is 9-10 yuan, the initial purchase amount of the user is 100 stocks, the deducted prepaid amount is 10 × 100 × 1000 yuan, the actual purchase amount of the user after drawing is 80 stocks, the final price is 9.5, and the amount of money required to be returned to the user in the subscription stage is 1000- (80 × 9.5) × 240 yuan, so that the 240 yuan is occupied in the whole period of the IPO.
In order to solve the problem that funds of users are occupied, the invention provides a method for prepaying for bond transaction, which optimizes parameters in a prepayment calculation model used in the bond transaction process by using historical bond transaction data, then calculates the prepayment amount of each user for initiating a subscription according to the public sale quantity and the actual subscription total number in the current bond transaction process by using the optimized prepayment calculation model, and notifies each user of a notice for deducting the prepayment amount.
Based on the description, the parameters in the calculation formula of the advance payment fee of the purchase subscription are optimized by utilizing the historical IPO subscription and the big data statistics of pricing, so that the advance payment fee of the user is reduced as much as possible, and the occupation of funds of the user is further reduced.
Fig. 1 is a flowchart illustrating an embodiment of a method for prepayment of a bond transaction according to an exemplary embodiment of the present invention, which may be applied to a computer device (e.g., a terminal, a server, etc.). As shown in fig. 1, the method for prepayment of bond transaction comprises the following steps:
step 101: parameters in a pre-payment calculation model used during a bond transaction are optimized using historical bond transaction data.
The historical bond transaction data comprises initial purchase numbers, actual purchase numbers, pre-payment amounts, actual payment amounts, price intervals of each share, pricing of each share, open sale amounts and the like of each user in a certain IPO process.
Illustratively, historical bond transaction data may be stored in nodes of the blockchain.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In this embodiment, the prepayment calculation model simulates an allocation principle of a drawing stage, inputs of the prepayment calculation model include an initial number of purchases of a user, a price interval of each stock, a number of open sales, and an actual total number of purchases, outputs include an estimated prepayment amount and an actual number of purchases of the user, and parameters included in the prepayment calculation model include a grouping critical value, a callback parameter, an over subscription parameter, and the like.
The initial subscription number of the user refers to the subscription number submitted according to the actual subscription requirement when the user performs subscription. The open sale quantity refers to the sale quantity pre-distributed by the bond party for the open sale stage in the bond transaction process. The actual procurement totals refer to the sum of the actual procurement amounts of all customers throughout the open distribution period.
In one embodiment, the prepay computing model may be implemented using a deep learning neural network architecture.
For example, each formula function involved in the prepayment calculation mode is taken as a calculation layer forming the neural network, coefficients in the formula functions are taken as weights to be optimized in the calculation layer, and the output of the formula function of the previous calculation layer is taken as the input of the next calculation layer.
Based on this, in the optimization process for the parameters in the pre-payment calculation model, the initial purchase number, the price interval of each share, the open sale number and the actual purchase total number of each user in the historical bond transaction data can be used as input parameters of the pre-payment calculation model, the actual purchase number and the actual payment amount of each user are used as label data of the input parameters, the input parameters are input into the pre-payment calculation model, and the parameters in the pre-payment calculation model are optimized according to the pre-payment amount and the actual purchase number output by the pre-payment calculation model and the actual purchase total number and the actual payment amount of each user in the label data.
According to the prepaid amount and the actual subscription amount output by the prepaid calculation model, and the actual subscription amount and the actual payment amount in the corresponding label data, the parameters in the prepaid calculation model can be optimized by adopting a preset optimization algorithm, for example, an Adam optimization algorithm.
Step 102: and calculating the prepayment amount of the user for initiating the purchase according to the open sale amount and the actual total number of the purchase payment in the current bond transaction process through the optimized prepayment calculation model, and sending a notice for deducting the prepayment amount to the client of the user.
In an embodiment, in a process of calculating a prepaid amount of a user initiating a subscription according to an optimized prepaid calculation model and an open number of offers and an actual total number of offers, the number of offers after callback may be determined according to the open number of offers, the actual total number of offers and a callback parameter, an initial number of offers of each user may be grouped according to a grouping threshold, a callback adjustment multiple of each group may be determined according to an excess subscription parameter for each group, an available number of each group may be determined according to the callback adjustment multiple and the number of offers after callback, an actual number of offers of the user may be determined according to the initial number of offers of the user and the available number of the group to which the user belongs for each user, and a prepaid amount of the user may be determined and output according to the actual number of offers and a price interval of each group.
In order to equalize the subscription requirements of each user and limit the users with a large subscription number, the users with a small subscription number and the users with a large subscription data may be classified into different pool sub-groups, so the group critical value in this embodiment refers to a division limit value of the subscription number, and the group critical value may be a stock number or a money amount, which is not specifically limited in this application.
In an embodiment, in the process of determining the returned sales number according to the open sales number, the actual total number of purchases and the callback parameter, the excess purchase proportion may be calculated according to the open sales number and the actual total number of purchases, the callback proportion may be determined according to the callback parameter and the excess purchase proportion, and the returned sales number may be determined according to the callback proportion and the total number of sales of the whole bond transaction.
The callback parameters refer to corresponding relations between different excess procurement proportion intervals and callback proportions, the whole bond transaction process comprises a public selling stage and a distributing stage, the sum of the public selling quantity of the public selling stage and the distributing quantity of the distributing stage is the total selling quantity of the whole bond transaction, and when the total actual procurement quantity of users in the public selling stage exceeds the public selling quantity, a part of stock quantity can be recalled to the public selling stage according to the excess procurement proportions.
For example, the open sale amount is 10000, the proportion of the total sale amount of the whole bond transaction is 10%, the actual total number of the bargaining is 450000, the excess bargaining proportion is 45 times, and if the callback proportion determined according to the callback parameter is 30%, the sale amount after the callback can be determined to be 30000 according to the callback proportion of 30% and the total sale amount of the whole bond transaction.
In an embodiment, in the process of determining the callback adjustment multiple of each group according to the excess subscription parameter, the excess multiple of the initial number of purchases of all users in the group may be determined first, and then the callback adjustment multiple corresponding to the excess multiple is determined according to the excess subscription parameter.
The excess subscription parameter refers to a corresponding relation between different excess multiple intervals and callback adjustment multiples.
As another example, assume that two packets are divided according to a packet critical value: the group A and the group B, and the selling quota of each group is half of the public selling quantity (namely 10000 × 0.5), the total number of the initial purchase requests of all the users in the group A is 50000, so that the excess multiple of the group A is 50000/(10000 × 0.5) ═ 10, and if the callback adjustment multiple corresponding to the excess multiple interval of 5-15 in the excess purchase parameter is 7, the callback adjustment multiple corresponding to the group A can be determined to be 7; the total number of the initial subscription numbers of all the users in the group B is 400000, so that the excess multiple of the group B is 400000/(10000 × 0.5) ═ 80, and if the callback adjustment multiple corresponding to the excess multiple interval of 50-100 in the excess subscription parameter is 12.5, the callback adjustment multiple corresponding to the group B can be determined to be 12.5.
In an embodiment, for the process of determining the available number of the packet according to the callback adjustment multiple and the number of sold packets after callback, an allocation quota is allocated to the packet according to the number of sold packets after callback, and the available number of the packet is determined according to the allocation quota of the packet and the callback adjustment multiple.
For another example, the number of sold shares after callback is 30000 shares, and the allocated quota for both packet a and packet B is 15000 shares, if the callback adjustment multiple of packet a is 7, then the available number of packet a may be determined to be 7 × 15000 — 105000 shares; if the dial-back adjustment multiple of packet B is 12.5, then the available number of packets B may be determined to be 12.5 × 15000 — 187500 strands.
In an embodiment, for each user, the process of determining the actual number of buys for the user according to the initial number of buys for the user and the available number of the group to which the user belongs may determine the share of the initial number of buys for the user to the initial number of buys for all users in the group to which the user belongs, and determine the actual number of buys for the user according to the share and the available number of the group to which the user belongs.
For example, the initial subscription number of the user 1 is 50, the group to which the user belongs is the group a, and since the initial subscription number of all the users in the group a is 50000, the share of the user 1 is 50/50000 ═ 0.001.
For another example, the initial number of purchases of the user 2 is 2000 shares, the group to which the user belongs is the group B, and the initial number of purchases of all the users in the group B is 400000 shares, so the share of the user 2 is 2000/400000 ═ 0.005.
In an embodiment, for the process of determining the actual subscription number of the user according to the share and the available number of the affiliated group, if the estimated number according to the share and the available number of the affiliated group is less than the initial subscription number of the user, determining the estimated number as the actual subscription number; and if the quantity estimated according to the share and the available quantity of the group is larger than the initial procurement quantity of the user, determining the initial procurement quantity as the actual procurement quantity.
For example, for user 2, the actual number of purchases for user 2 is 938, since the number estimated from the share of 0.005 and the available number 187500 for the group B is 0.005 × 187500 ═ 937.5 ≦ the initial number of purchases 2000.
For another example, for the user 1, since the number estimated from the share of 0.001 and the available number 105000 of the group a is 0.001 × 105000 ═ 105 ═ 50 pieces of the initial subscription number, the actual subscription number of the user 1 is 50 pieces.
It follows that the amount ultimately allocated to a user is typically equal to or less than the initial number of purchases by that user.
In an embodiment, in the process of determining the prepaid amount of the user according to the actual number of the requests and the price interval of each share, the prepaid amount of the user may be determined according to the highest price and the actual request data in the price interval of each share.
Even if the prepayment cost of the user is still calculated according to the highest price, the prepayment cost of the user can be reduced as far as possible because the number of the user who makes a subscription is adjusted to more practically conform to the number of the final actual subscription.
So far, the prepayment process shown in fig. 1 is completed, and the parameter in the calculation formula of the prepayment fee is optimized by utilizing the big data statistics of historical IPO procurement and pricing, so that the prepayment fee of the user is reduced as much as possible, and the occupation of funds of the user is further reduced.
Fig. 2 is a schematic diagram illustrating a hardware structure of a computer device according to an exemplary embodiment of the present invention. As shown in fig. 2, the computer device includes a processor, a storage medium, a memory, and a network interface connected through a system bus. The storage medium may be non-volatile or volatile. The storage medium of the computer device stores an operating system, a database, and computer readable instructions, the database may store a control information sequence, and the computer readable instructions, when executed by the processor, may cause the processor to implement the method for prepaid bond transactions described above. The processor of the computer device is used for providing calculation and control capability and supporting the operation of the whole computer device. The memory of the computer device may have computer readable instructions stored therein, which when executed by the processor, implement the steps of: optimizing parameters in a pre-payment calculation model used in the bond transaction process by using historical bond transaction data; and calculating the prepayment amount of the user for initiating the purchase according to the public sale quantity and the actual total number of the purchase in the current bond transaction process through the optimized prepayment calculation model, and sending a notice for deducting the prepayment amount to the client of the user.
In one embodiment, the step of optimizing parameters in a pre-payment calculation model used in the course of bond transactions using historical bond transaction data performed by the processor comprises: taking the initial purchase number, price interval of each share, public sale number and actual purchase total number of each user in the historical bond transaction data as input parameters of the pre-payment calculation model; taking the actual purchase number and the actual payment amount of each user in the historical bond transaction data as tag data of input parameters; and optimizing the parameters in the pre-payment calculation model according to the pre-payment amount and the actual procurement amount output by the pre-payment calculation model, and the actual procurement amount and the actual payment amount of each user in the label data.
In one embodiment, the step of calculating the advance payment amount of the user for initiating the purchase according to the public sale amount and the actual purchase payment amount in the current bond transaction process by the optimized advance payment calculation model executed by the processor comprises the following steps: determining the sale quantity after callback according to the open sale quantity, the actual procurement total number and the callback parameter; grouping the initial purchase requisition number of each user according to a grouping critical value, determining a callback adjustment multiple of each group according to an excess subscription parameter and determining the available number of each group according to the callback adjustment multiple and the sale number after callback; and aiming at each user, determining the actual purchase number of the user according to the initial purchase number of the user and the available number of the group to which the user belongs, and determining and outputting the prepayment amount of the user according to the actual purchase number and the price interval of each share.
In one embodiment, the step performed by the processor of determining the number of offers after callback based on the number of open offers, the actual number of purchases, and the callback parameter comprises: calculating the excess procurement proportion according to the public sale quantity and the actual procurement total quantity; determining a callback proportion according to the callback parameters and the excess purchase ratio; and determining the back-dialled sales number according to the back-dialling proportion and the total sales number of the whole bond transaction.
In one embodiment, the processor performs the step of determining the callback adjustment factor for the packet based on the oversubscription parameter comprises: determining an excess multiple of the initial number of purchases for all users in the group; and determining a callback adjusting multiple corresponding to the excess multiple according to the excess subscription parameter.
In one embodiment, the step performed by the processor for determining the available number of the packets according to the callback adjustment multiple and the number of sales after callback includes: distributing a distribution quota for the group according to the distribution quantity after callback; and determining the available quantity of the packet according to the selling quota and the callback adjustment multiple of the packet.
In one embodiment, the step performed by the processor of determining the actual number of purchases by the user based on the initial number of purchases by the user and the available number of groups to which the user belongs comprises: determining the share of the initial purchase number of the user in all the users in the group; and determining the actual purchase requisition quantity of the user according to the share and the available quantity of the belonged group.
Those skilled in the art will appreciate that the configuration shown in fig. 2 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing devices to which aspects of the present invention may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Corresponding to the embodiment of the bond transaction prepayment method, the invention also provides an embodiment of a bond transaction prepayment device.
Fig. 3 is a flowchart illustrating an embodiment of a bond transaction prepayment device according to an exemplary embodiment of the present invention, which can be applied to a computer device. As shown in fig. 3, the bond transaction prepayment device includes:
the model optimization module 310 is used for optimizing parameters in a pre-payment calculation model used in the bond transaction process by using historical bond transaction data;
and the calculation module 320 is configured to calculate a prepaid amount of the subscription initiated by the user according to the public sale quantity and the actual total number of the subscription in the current bond transaction process through the optimized prepaid calculation model, and send a notification of deducting the prepaid amount to the client of the user.
In an optional implementation manner, the model optimization module 310 is specifically configured to use the initial number of purchases of each user, the price interval of each share, the number of open sales, and the actual total number of purchases in the historical bond transaction data as input parameters of the pre-payment calculation model; taking the actual purchase number and the actual payment amount of each user in the historical bond transaction data as tag data of input parameters; and optimizing the parameters in the pre-payment calculation model according to the pre-payment amount and the actual procurement amount output by the pre-payment calculation model, and the actual procurement amount and the actual payment amount of each user in the label data.
In an optional implementation manner, the calculating module 320 is specifically configured to determine the sale quantity after callback according to the public sale quantity, the actual procurement total number and the callback parameter in the process of calculating the advance payment amount of the user initiated procurement according to the public sale quantity and the actual procurement total number in the current bond transaction process through the optimized advance payment calculating model; grouping the initial purchase requisition number of each user according to a grouping critical value, determining a callback adjustment multiple of each group according to an excess subscription parameter and determining the available number of each group according to the callback adjustment multiple and the sale number after callback; and aiming at each user, determining the actual purchase number of the user according to the initial purchase number of the user and the available number of the group to which the user belongs, and determining and outputting the prepayment amount of the user according to the actual purchase number and the price interval of each share.
In an optional implementation manner, the calculating module 320 is specifically configured to calculate the excess procurement ratio according to the public sales number and the actual procurement total number in the process of determining the retrograded sales number according to the public sales number, the actual procurement total number, and the retrograded parameter; determining a callback proportion according to the callback parameters and the excess purchase ratio; and determining the back-dialled sales number according to the back-dialling proportion and the total sales number of the whole bond transaction.
In an optional implementation manner, the calculating module 320 is specifically configured to determine an excess multiple of the initial purchase requisition quantity of all users in the group in the process of determining the callback adjustment multiple of the group according to the excess subscription parameter; and determining a callback adjusting multiple corresponding to the excess multiple according to the excess subscription parameter.
In an optional implementation manner, the calculating module 320 is specifically configured to, in the process of determining the available quantity of the group according to the callback adjustment multiple and the number of sales after callback, allocate a sales quota to the group according to the number of sales after callback; and determining the available quantity of the packet according to the selling quota and the callback adjustment multiple of the packet.
In an optional implementation manner, the calculating module 320 is specifically configured to determine, in the process of determining the actual number of the users in terms of the initial number of the users and the available number of the group to which the users belong, the share of the initial number of the users in the group to which the users belong; and determining the actual purchase requisition quantity of the user according to the share and the available quantity of the belonged group.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
The present invention also provides another embodiment, which is to provide a computer-readable storage medium having a computer program stored thereon, the computer program being executable by at least one processor to cause the at least one processor to perform the steps of any one of the bond transaction prepayment methods described above.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of prepaid bond transactions, the method comprising:
optimizing parameters in a pre-payment calculation model used in the bond transaction process by using historical bond transaction data;
and calculating the prepayment amount of the user for initiating the purchase according to the public sale quantity and the actual total number of the purchase in the current bond transaction process through the optimized prepayment calculation model, and sending a notice for deducting the prepayment amount to the client of the user.
2. The method of claim 1, wherein the optimizing parameters in a pre-payment calculation model used during the bond transaction using historical bond transaction data comprises:
taking the initial purchase number, price interval of each share, public sale number and actual purchase total number of each user in the historical bond transaction data as input parameters of the pre-payment calculation model;
taking the actual purchase number and the actual payment amount of each user in the historical bond transaction data as tag data of input parameters;
and optimizing the parameters in the pre-payment calculation model according to the pre-payment amount and the actual procurement amount output by the pre-payment calculation model, and the actual procurement amount and the actual payment amount of each user in the label data.
3. The method of claim 1, wherein the calculating the prepayment amount of the user for initiating the purchase according to the public sale amount and the actual purchase payment amount in the current bond transaction process through the optimized prepayment calculation model comprises:
determining the sale quantity after callback according to the open sale quantity, the actual procurement total number and the callback parameter;
grouping the initial purchase requisition number of each user according to a grouping critical value, determining a callback adjustment multiple of each group according to an excess subscription parameter and determining the available number of each group according to the callback adjustment multiple and the sale number after callback;
and aiming at each user, determining the actual purchase number of the user according to the initial purchase number of the user and the available number of the group to which the user belongs, and determining and outputting the prepayment amount of the user according to the actual purchase number and the price interval of each share.
4. The method of claim 1, wherein determining the number of offers after callback based on the number of open offers, the total number of actual purchases, and the callback parameters comprises:
calculating the excess procurement proportion according to the public sale quantity and the actual procurement total quantity;
determining a callback proportion according to the callback parameters and the excess purchase ratio;
and determining the back-dialled sales number according to the back-dialling proportion and the total sales number of the whole bond transaction.
5. The method of claim 1, wherein determining the callback adjustment factor for the group based on the oversubscription parameter comprises:
determining an excess multiple of the initial number of purchases for all users in the group;
and determining a callback adjusting multiple corresponding to the excess multiple according to the excess subscription parameter.
6. The method of claim 1, wherein determining the available number of packets based on the call-back adjustment factor and the number of sales after call-back comprises:
distributing a distribution quota for the group according to the distribution quantity after callback;
and determining the available quantity of the packet according to the selling quota and the callback adjustment multiple of the packet.
7. The method of claim 1, wherein determining the actual number of purchases of the user based on the initial number of purchases of the user and the available number of groups to which the user belongs comprises:
determining the share of the initial purchase number of the user in all the users in the group;
and determining the actual purchase requisition quantity of the user according to the share and the available quantity of the belonged group.
8. A bond transaction advance payment apparatus, the apparatus comprising:
the model optimization module is used for optimizing parameters in a pre-payment calculation model used in the bond transaction process by utilizing historical bond transaction data;
and the calculation module is used for calculating the prepayment amount of the procurement initiated by the user according to the public sale quantity and the actual procurement total number in the current bond transaction process through the optimized prepayment calculation model, and sending a notice of deducting the prepayment amount to the client of the user.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method according to any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for prepaid bond transactions according to any one of claims 1 to 7.
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| CN202011099365.6A CN112232953A (en) | 2020-10-14 | 2020-10-14 | Bond transaction prepayment method, device, equipment and storage medium |
| PCT/CN2021/123556 WO2022078395A1 (en) | 2020-10-14 | 2021-10-13 | Bond trading advance payment method and apparatus, computer device and storage medium |
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| CN202011099365.6A CN112232953A (en) | 2020-10-14 | 2020-10-14 | Bond transaction prepayment method, device, equipment and storage medium |
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| WO2022078395A1 (en) * | 2020-10-14 | 2022-04-21 | 深圳壹账通智能科技有限公司 | Bond trading advance payment method and apparatus, computer device and storage medium |
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