CN116402618A - Funds distribution method, funds distribution device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a funds distribution method, a funds distribution device, electronic equipment and a storage medium. The method comprises the following steps: receiving expected yield of various types of assets input by a user, and establishing a first equation representing the expected yield sum of the various types of assets under all accounts and a second equation representing the expected risk sum of the various types of assets under all accounts; establishing a quadratic programming expression of the first equation and the second equation, and solving the quadratic programming expression to obtain a corresponding evaluation result and an allocation result corresponding to the evaluation result; and generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user. According to the scheme, the user only needs to input the expected yield of various assets, the pareto curve can be automatically generated based on the optimal result of the quadratic programming, and the fund distribution result is determined from the pareto curve.
Description
Technical Field
The present disclosure relates to the field of mathematical statistics, and in particular, to a method and apparatus for distributing funds, an electronic device, and a storage medium.
Background
For the annual fee investment, the operation department collects the annual fee fund information, the financial department carries out financial accounting according to the annual fee fund information, the checked-in fund is handed to the investment department, and the investment department distributes the checked-in fund to each investment account. When the fund amount is large and the investment amount is large, timeliness cannot be ensured only by manually calculating the fund distribution scheme.
Therefore, how to improve the efficiency of the funds distribution plays an important role in improving the investment management capability of annual funds.
Disclosure of Invention
The application provides a fund distribution method, a device, electronic equipment and a storage medium, which are used for improving fund distribution efficiency.
In a first aspect, the present application provides a funds distribution method comprising: receiving an allocation request of a user, wherein the allocation request comprises expected profitability of various assets input by the user; establishing a first equation representing the sum of expected benefits of all types of assets under all accounts and a second equation representing the sum of expected risks of all types of assets under all accounts according to the expected benefits of all types of assets; establishing a quadratic programming expression of the first equation and the second equation, and acquiring a plurality of funds distribution coefficients and funds to be distributed, which are input by a user; according to the multiple fund distribution coefficients, solving the quadratic programming expression to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result, wherein the evaluation result comprises the expected sum of benefits of all types of assets under all accounts and the expected sum of risks of all types of assets under all accounts, and the distribution result comprises the distribution fund of each account; wherein, the constraint condition of the quadratic programming expression is that the sum of the allocated funds of each account is equal to the funds to be allocated; and obtaining a target result input by a user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user.
In one possible implementation, the establishing a first equation characterizing the sum of expected benefits of the assets under all accounts according to the expected benefits rates of the assets, includes: acquiring the current funds of each type of asset under each account from a data system; establishing the first equation by taking the allocated funds of each type of asset under each account as independent variables according to the expected yield of each type of asset, wherein the first equation comprises: the sum of expected benefits of all accounts is the sum of expected benefits of each type of asset under each account, wherein the expected benefits of each type of asset are the product of the sum of the allocated funds of the type of asset and the expected benefit rate of the type of asset accumulated for the current funds of the type of asset.
In one possible implementation, the establishing a second equation characterizing the sum of expected benefits of the assets under all accounts according to the expected benefits rates of the assets, includes: acquiring the historical yield of each type of asset from a data system; according to the historical profitability of each type of asset and the expected profitability of each type of asset, calculating to obtain covariance of the profitability between any two types of assets; selecting one type of asset from any account, selecting one type of asset from another arbitrary account, and enabling the types of the two types of assets to be different; establishing the second equation based on the covariance of the profitability between the two types of assets using the allocated funds of the two types of assets as arguments, the second equation comprising: the sum of the expected risks for all accounts is the sum of the expected risks for each type of asset under each account, where the expected risk for each two types of assets is the product of the sum of the allocated funds for the two types of assets and the covariance of the rate of return between the two types of assets added to the current funds for the two types of assets.
In one possible implementation manner, the calculating the covariance of the profitability between any two types of assets according to the historical profitability of each type of asset and the expected profitability of each type of asset includes: calculating the risk value of each type of asset according to the historical yield of each type of asset; the risk value characterizes the loss rate of the asset under a preset confidence level; calculating to obtain the variance of the expected profitability of each type of asset according to the risk value of each type of asset and the expected profitability of the corresponding asset; calculating to obtain the average value of the historical yield of various assets at each moment of the history; aiming at any two types of assets, synthesizing the discrete degree of the average value of the historical yield of each type of asset at each time of the history and the historical yield of each type of asset at each time of the history to obtain the correlation coefficient between any two types of assets; and calculating the covariance of the profitability between any two types of assets according to the variance of the expected profitability of any two types of assets and the correlation coefficient between the two types of assets.
In one possible embodiment, the method further comprises: if a confirmation instruction of the user to the first allocation result is received, allocating the funds to be allocated to each account according to the first allocation result; and if a cancel instruction of the user for the first allocation result is received, pushing a second allocation result corresponding to an evaluation result adjacent to the evaluation result corresponding to the first allocation result in the pareto curve to the user until a confirm instruction of the user for the second allocation result is received, and allocating the funds to be allocated to each account according to the second allocation result.
In a second aspect, the present application provides a funds dispensing apparatus comprising: the receiving module is used for receiving a distribution request of a user, wherein the distribution request comprises expected profitability of various assets input by the user; the establishing module is used for establishing a first equation representing the sum of expected benefits of all types of assets under all accounts and a second equation representing the sum of expected risks of all types of assets under all accounts according to the expected benefits of all types of assets; the establishing module is further used for establishing a quadratic programming expression of the first equation and the second equation and acquiring a plurality of fund distribution coefficients and funds to be distributed, which are input by a user; the distribution module is used for solving the quadratic programming expression according to the plurality of fund distribution coefficients to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result, wherein the evaluation result comprises the expected total sum of benefits of various assets under all accounts and the expected total sum of risks of various assets under all accounts, and the distribution result comprises the distribution fund of each account; wherein, the constraint condition of the quadratic programming expression is that the sum of the allocated funds of each account is equal to the funds to be allocated; the pushing module is used for obtaining a target result input by a user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user.
In one possible implementation, the establishing module is specifically configured to obtain, from the data system, a current fund of each type of asset under each account; the establishing module is specifically further configured to establish the first equation according to an expected yield of each type of asset under each account by using allocated funds of each type of asset as an argument, where the first equation includes: the sum of expected benefits of all accounts is the sum of expected benefits of each type of asset under each account, wherein the expected benefits of each type of asset are the product of the sum of the allocated funds of the type of asset and the expected benefit rate of the type of asset accumulated for the current funds of the type of asset.
In one possible implementation, the establishing module is further configured to obtain a historical rate of return for each type of asset from the data system; the establishing module is further used for calculating covariance of the profitability between any two types of assets according to the historical profitability of each type of assets and the expected profitability of each type of assets; the building module is also used for selecting one type of asset from any account and selecting one type of asset from another arbitrary account, and the types of the two types of selected assets are different; establishing the second equation based on the covariance of the profitability between the two types of assets using the allocated funds of the two types of assets as arguments, the second equation comprising: the sum of the expected risks for all accounts is the sum of the expected risks for each type of asset under each account, where the expected risk for each two types of assets is the product of the sum of the allocated funds for the two types of assets and the covariance of the rate of return between the two types of assets added to the current funds for the two types of assets.
In one possible embodiment, the apparatus further comprises: the calculation module is used for calculating the risk value of each type of asset according to the historical yield of each type of asset; the risk value characterizes the loss rate of the asset under a preset confidence level; the calculation module is also used for calculating and obtaining the variance of the expected profitability of each type of asset according to the risk value of each type of asset and the expected profitability of the corresponding asset; the calculation module is also used for calculating and obtaining the average value of the historical yield of various assets at each moment of the history; the calculation module is further used for integrating the historical yield of each type of asset at each time of history and the discrete degree of the average value of the historical yield of the type of asset at each time of history according to any two types of assets to obtain a correlation coefficient between any two types of assets; the calculation module is further configured to calculate, according to the variance of the expected profitability of any two types of assets and the correlation coefficient between the two types of assets, a covariance of the profitability between any two types of assets.
In one possible embodiment, the apparatus further comprises: the execution module is used for distributing the funds to be distributed to each account according to the first distribution result if a confirmation instruction of the user to the first distribution result is received; and the execution module is further configured to, if a cancellation instruction of the user on the first allocation result is received, push a second allocation result corresponding to an evaluation result adjacent to the evaluation result corresponding to the first allocation result in the pareto curve to the user until a confirmation instruction of the user on the second allocation result is received, and allocate the funds to be allocated to each account according to the second allocation result.
In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory to implement the method of any one of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for performing the method of any of the first aspects by a processor.
The fund distribution method, the device, the electronic equipment and the storage medium provided by the application receive a distribution request of a user, wherein the distribution request comprises expected profitability of various assets input by the user; establishing a first equation representing the expected total gain of all types of assets under all accounts and a second equation representing the expected total risk of all types of assets under all accounts according to the expected yield of all types of assets; establishing a quadratic programming expression of the first equation and the second equation, and acquiring a plurality of funds distribution coefficients and funds to be distributed, which are input by a user; according to the multiple fund distribution coefficients, solving the quadratic programming expression to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result; and obtaining a target result input by the user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user. According to the scheme, the user only needs to input the expected yield of various assets, the pareto curve can be automatically generated based on the optimal result of the quadratic programming, and the fund distribution result is determined from the pareto curve.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is an application scenario schematic diagram of a fund distribution method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a funds distribution method according to a first embodiment of the present application;
FIG. 3 is an example of a data system structure provided in an embodiment of the present application;
fig. 4 is an example of a covariance calculation method provided in an embodiment of the present application;
fig. 5 is an example of pareto curves provided in an embodiment of the present application;
fig. 6 is a diagram illustrating a structure of a funds distribution device according to a second embodiment of the present application;
fig. 7 is a block diagram of a funds distribution device according to a third embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
First, the terms involved are explained:
and (5) secondary planning: the objective function is a quadratic function, the constraint condition is a nonlinear programming of linear constraint, and the nonlinear programming is applied to the optimization problem;
in the risk value: the loss rate of an asset at a certain confidence level (e.g., 95%) over a certain period of time.
Fig. 1 is a schematic application scenario diagram of a fund distribution method according to an embodiment of the present application, and examples are illustrated with reference to the illustrated scenario: and the user only initiates a fund distribution request, a first equation representing the expected profit sum and a second equation representing the expected risk sum can be established according to the distribution request, a quadratic programming expression is established according to the first equation and the second equation, the quadratic programming expression is used for solving the distribution result with the minimum risk and the maximum profit, and the distribution result obtained by solving the quadratic programming expression is pushed to the user. According to the scheme, the fund distribution scheme can be automatically calculated only by a user initiating the fund distribution request, so that the fund distribution efficiency is improved.
The technical scheme of the present application and the technical scheme of the present application are described in detail below with specific examples. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. In the description of the present application, the terms are to be construed broadly in the art, unless explicitly stated or defined otherwise. Embodiments of the present application will be described below with reference to the accompanying drawings.
Example 1
Fig. 2 is a flow chart of a funds distribution method according to an embodiment of the present application, the method includes the following steps:
s201, receiving a distribution request of a user, wherein the distribution request comprises expected profitability of various assets input by the user;
s202, according to expected yield of the various assets, establishing a first equation representing expected yield sum of the various assets under all accounts and a second equation representing expected risk sum of the various assets under all accounts;
s203, establishing a quadratic programming expression of the first equation and the second equation, and acquiring a plurality of funds distribution coefficients and funds to be distributed, which are input by a user;
s204, solving the quadratic programming expression according to the plurality of fund distribution coefficients to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result, wherein the evaluation result comprises the expected sum of benefits of various assets under all accounts and the expected sum of risks of various assets under all accounts, and the distribution result comprises the distribution fund of each account; wherein, the constraint condition of the quadratic programming expression is that the sum of the allocated funds of each account is equal to the funds to be allocated;
S205, acquiring a target result input by a user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user.
As an example, the execution body of this embodiment may be a funds distribution device, and there are various implementations of the funds distribution device. For example, the program may be software, or a medium storing a related computer program, such as a usb disk; alternatively, the apparatus may be a physical device, such as a chip, a smart terminal, a computer, a server, etc., in which the relevant computer program is integrated or installed.
In one example, S202 specifically includes: acquiring the current funds of each type of asset under each account from a data system; establishing the first equation by taking the allocated funds of each type of asset under each account as independent variables according to the expected yield of each type of asset, wherein the first equation comprises: the sum of expected benefits of all accounts is the sum of expected benefits of each type of asset under each account, wherein the expected benefits of each type of asset are the product of the sum of the allocated funds of the type of asset and the expected benefit rate of the type of asset accumulated for the current funds of the type of asset.
As shown in fig. 3 in combination with the scene example, fig. 3 is a data system structure example. The data system comprises a plurality of accounts which are independent from each other, each account comprises different types of assets, and each type of assets comprises current funds and allocated funds. The current funds are the existing account value under the account.
As one implementation, a first equation is established as shown in formula (1):
where r is the sum of expected benefits of all types of assets under all accounts, w a,i For class i assets assigned to account aDispensing funds, v a,i The current fund of the i-th type asset held by the account a, M is the number of the accounts, N is the asset class number, r i Is the expected yield of the class i asset.
For example, w a,i And v a,i The sum is the total funds of the class i assets of account a, (w) a,i +v a,i )r i And summing the expected benefits of the assets of the ith class of the account a for all accounts to obtain the expected benefits of all accounts. It will be appreciated that the current funds are fixed values, the expected return rate is a preset fixed value, only the allocated funds are independent variables, and the change relation of the expected return sum of all accounts to the allocated funds is established through the first way.
Based on the above embodiments, by the first approach, expected revenue for all accounts may be quantified by the expected revenue rate as an argument.
In one example, S202 specifically includes: acquiring the historical yield of each type of asset from a data system; according to the historical profitability of each type of asset and the expected profitability of each type of asset, calculating to obtain covariance of the profitability between any two types of assets; selecting one type of asset from any account, selecting one type of asset from another arbitrary account, and enabling the types of the two types of assets to be different; establishing the second equation based on the covariance of the profitability between the two types of assets using the allocated funds of the two types of assets as arguments, the second equation comprising: the sum of the expected risks for all accounts is the sum of the expected risks for each type of asset under each account, where the expected risk for each two types of assets is the product of the sum of the allocated funds for the two types of assets and the covariance of the rate of return between the two types of assets added to the current funds for the two types of assets.
As one implementation, a second equation is established as shown in equation (2):
wherein sigma 2 For all accounts Sum of expected risks for various types of assets, w b,j Funds allocated for assets of class j allocated to account b, v b,j Current funds for the j-th class asset held by account b, c i,j For covariance of the profitability of asset i and asset j, M is the total number of accounts and N is the total number of asset types.
For example, w b,j And v b,j The sum is the total funds of the j-th type assets of the account b, and the total funds of the i-th type assets of the account a and the total funds of the j-th type assets of the account b are uniformly quantified through covariance of the yield, so that the allocated funds of the two types of assets are taken as independent variables.
Based on the above embodiment, the covariance can be used for integrally evaluating and managing a plurality of assets, the predicted total risk accuracy is higher, and the fund distribution accuracy is improved.
Specifically, regarding the covariance calculation method, in one example, the funds distribution method further includes: calculating the risk value of each type of asset according to the historical yield of each type of asset; the risk value characterizes the loss rate of the asset under a preset confidence level; calculating to obtain the variance of the expected profitability of each type of asset according to the risk value of each type of asset and the expected profitability of the corresponding asset; calculating to obtain the average value of the historical yield of various assets at each moment of the history; aiming at any two types of assets, synthesizing the discrete degree of the average value of the historical yield of each type of asset at each time of the history and the historical yield of each type of asset at each time of the history to obtain the correlation coefficient between any two types of assets; and calculating the covariance of the profitability between any two types of assets according to the variance of the expected profitability of any two types of assets and the correlation coefficient between the two types of assets.
As an implementation manner, the equation established in the risk value is shown in the formula (3):
∑ t 1(s i,t ≤var i )＝T(1-α)··············(3)
wherein 1 (-) is an indication function, s i,t For the historical profitability of asset i at time t, var i For the risk value of asset i, α is confidenceDegree.
It should be noted that, the corresponding confidence level may be set according to specific service requirements.
As one implementation, an equation that establishes the variance of the expected profitability of the various types of assets based on the normal distribution relationship of the revenue and risk is shown in equation (4):
wherein sigma i As the variance of the profitability of asset i,
the inverse of the distribution function is accumulated for a standard normal.
As one implementation, the correlation coefficient between any two classes of assets is calculated as shown in equation (5):
wherein ρ is i,j For a correlation coefficient between any two classes of assets,
for asset i mean of historical profitability, +.>
Is the average value of the historical yield of the asset j, s j,t Is the historical rate of return of asset j at time t.
For example, the correlation coefficient between asset i and asset j is calculated from the historic rate of return dispersion of asset i and asset j at each moment in the history.
As one implementation, the covariance of the profitability between any two classes of assets is calculated as shown in equation (6):
c i,j ＝σ i σ j ρ i,j ··············(6)
Wherein c i,j Between asset i and asset jCovariance of the yield of (c).
For example, the product of the variance of the profitability of asset i, the variance of the profitability of asset j, and the correlation coefficient between asset i and asset j is calculated to obtain the covariance of the profitability between asset i and asset j. The covariance evaluates the overall error of the profitability of asset i and the profitability of asset j. In connection with equation (4), covariance can be used to calculate an expected risk from funds of an account.
For ease of understanding, fig. 4 is an example of a covariance calculation method, as shown in fig. 4: historical rates of return are obtained from the data system and expected rates of return are obtained from the allocation requests. And calculating a historical yield average value based on the historical yield, and calculating a correlation coefficient based on the historical yield average value. The on-risk value is calculated based on the historical profitability, and the expected profitability variance can be calculated through the on-risk value and the expected profitability. The covariance of the profitability can be calculated from the correlation coefficient and the expected profitability variance.
Based on the embodiment, different confidence degrees can be set according to actual demands by evaluating risk in risk value, application is more flexible, any two types of assets can be associated through the correlation coefficient, and calculated covariance is more accurate.
As one example, a quadratic programming expression is established as shown in equation (7):
where x is the optimized argument, i.e., the allocated funds for each type of asset under each account. The parameters are as follows:
x＝(w 1,1 ,....,w 1,N ,...,w a,1 ,...,w a,N ,...,w M,1 ,...,w M,N ) T
A＝1 1*MN
b＝w 0
G＝-E MN*MN
h＝0 MN*1
wherein lambda is a preset fund distribution coefficient, 1 is an all-1 matrix, 0 is an all-0 matrix, E is an identity matrix,
cronecker product of matrices, used for computation between matrices, w 0 Funds are to be allocated.
For example, a set of funds distribution coefficients is received. P is calculated from the covariance of the profitability of the various types of assets. Q is calculated according to the current funds of each type of assets under each account, the covariance of the profitability of each type of assets and the expected profitability of each type of assets. B is determined based on the funds to be allocated. A, G, h are determined based on the number of accounts and asset classes. Substituting a group of fund distribution coefficients into the solution of the quadratic programming problem to obtain a fund distribution result w= (w) a,i ) A*N . Substituting the distribution result into the formula (1) to obtain an expected profit sum r, and substituting the distribution result into the formula (2) to obtain an expected risk sum sigma 2 。
It should be noted that, the quadratic programming solution may be a method such as an interior point method, which is not limited in this application.
In one example, the funds distribution method further comprises: if a confirmation instruction of the user to the first allocation result is received, allocating the funds to be allocated to each account according to the first allocation result; and if a cancel instruction of the user for the first allocation result is received, pushing a second allocation result corresponding to an evaluation result adjacent to the evaluation result corresponding to the first allocation result in the pareto curve to the user until a confirm instruction of the user for the second allocation result is received, and allocating the funds to be allocated to each account according to the second allocation result.
As an implementation, sum of expected benefits r and pre-determinedSum of phase risks sigma 2 Establishing a pareto curve, referring to fig. 5, fig. 5 is an example of a pareto curve. And pushing the distribution result which accords with the target result in the pareto curve to the user according to the target result of the user. If the user confirms, the pushing is finished, and if the user cancels, the distribution results adjacent to the pareto curve in the pareto curve are continuously pushed.
Based on the above embodiment, by generating the pareto curve, the association relationship between the expected revenue sum and the expected risk sum can be quantified, so that an accurate funds distribution result can be selected.
In the fund distribution method provided by the embodiment, a distribution request of a user is received, wherein the distribution request comprises expected yield of various assets input by the user; establishing a first equation representing the expected total gain of all types of assets under all accounts and a second equation representing the expected total risk of all types of assets under all accounts according to the expected yield of all types of assets; establishing a quadratic programming expression of the first equation and the second equation, and acquiring a plurality of funds distribution coefficients and funds to be distributed, which are input by a user; according to the multiple fund distribution coefficients, solving the quadratic programming expression to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result; and obtaining a target result input by the user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user. According to the scheme, the user only needs to input the expected yield of various assets, the pareto curve can be automatically generated based on the optimal result of the quadratic programming, and the fund distribution result is determined from the pareto curve.
Example two
Fig. 6 is a schematic structural diagram of a funds distribution device according to a second embodiment of the present application, as shown in fig. 6, where the funds distribution device includes:
a receiving module 61, configured to receive a distribution request of a user, where the distribution request includes expected profitability of various assets input by the user;
a building module 62, configured to build a first equation representing a sum of expected benefits of the assets under all accounts and a second equation representing a sum of expected risks of the assets under all accounts according to expected benefits rates of the assets under all accounts;
the establishing module 62 is further configured to establish a quadratic programming expression of the first equation and the second equation, and obtain a plurality of funds distribution coefficients and funds to be distributed input by a user;
the distribution module 63 is configured to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result by solving the quadratic programming expression according to the multiple funds distribution coefficients, where the evaluation result includes a sum of expected benefits of various assets under all accounts and a sum of expected risks of various assets under all accounts, and the distribution result includes a distributed funds of each account; wherein, the constraint condition of the quadratic programming expression is that the sum of the allocated funds of each account is equal to the funds to be allocated;
The pushing module 64 is configured to obtain a target result input by a user, generate a pareto curve according to the evaluation result, and push a first allocation result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user.
In one example, the creation module 62 is specifically configured to obtain, from the data system, the current funds for each type of asset under each account; the establishing module 62 is specifically further configured to establish the first equation according to the expected yield of the assets of each type, with the allocated funds of each type under each account as an argument, where the first equation includes: the sum of expected benefits of all accounts is the sum of expected benefits of each type of asset under each account, wherein the expected benefits of each type of asset are the product of the sum of the allocated funds of the type of asset and the expected benefit rate of the type of asset accumulated for the current funds of the type of asset.
As shown in fig. 3 in combination with the scene example, fig. 3 is a data system structure example. The data system comprises a plurality of accounts which are independent from each other, each account comprises different types of assets, and each type of assets comprises current funds and allocated funds. The current funds are the existing account value under the account.
As one implementation, a first equation is established as shown in formula (1):
where r is the sum of expected benefits of all types of assets under all accounts, w a,i Allocating funds for an i-th class of assets allocated to account a, v a,i The current fund of the i-th type asset held by the account a, M is the number of the accounts, N is the asset class number, r i Is the expected yield of the class i asset.
For example, w a,i And v a,i The sum is the total funds of the class i assets of account a, (w) a,i +v a,i )r i And summing the expected benefits of the assets of the ith class of the account a for all accounts to obtain the expected benefits of all accounts. It will be appreciated that the current funds are fixed values, the expected return rate is a preset fixed value, only the allocated funds are independent variables, and the change relation of the expected return sum of all accounts to the allocated funds is established through the first way.
Based on the above embodiments, by the first approach, expected revenue for all accounts may be quantified by the expected revenue rate as an argument.
In one example, the build module 62 is further configured to obtain a historical rate of return for each type of asset from the data system; the establishing module 62 is further configured to calculate, according to the historical profitability of each type of asset and the expected profitability of each type of asset, a covariance of the profitability between any two types of assets; the establishing module 62 is further configured to select one type of asset from any account, and select one type of asset from another arbitrary account, where the types of the two types of assets are different; establishing the second equation based on the covariance of the profitability between the two types of assets using the allocated funds of the two types of assets as arguments, the second equation comprising: the sum of the expected risks for all accounts is the sum of the expected risks for each type of asset under each account, where the expected risk for each two types of assets is the product of the sum of the allocated funds for the two types of assets and the covariance of the rate of return between the two types of assets added to the current funds for the two types of assets.
As one implementation, a second equation is established as shown in equation (2):
wherein sigma 2 Sum of expected risks of various assets under all accounts, w b,j Funds allocated for assets of class j allocated to account b, v b,j Current funds for the j-th class asset held by account b, c i,j For covariance of the profitability of asset i and asset j, M is the total number of accounts and N is the total number of asset types.
For example, w b,j And v b,j The sum is the total funds of the j-th type assets of the account b, and the total funds of the i-th type assets of the account a and the total funds of the j-th type assets of the account b are uniformly quantified through covariance of the yield, so that the allocated funds of the two types of assets are taken as independent variables.
Based on the above embodiment, the covariance can be used for integrally evaluating and managing a plurality of assets, the predicted total risk accuracy is higher, and the fund distribution accuracy is improved.
Specifically, in one example, the funds distribution apparatus further includes: a calculating module 65, configured to calculate the risk value of each type of asset according to the historical yield of the various types of assets; the risk value characterizes the loss rate of the asset under a preset confidence level; the calculating module 65 is further configured to calculate, according to the risk value of each type of asset and the expected yield of the corresponding asset, a variance of the expected yield of each type of asset; the calculating module 65 is further configured to calculate a mean value of historical yield rates of various types of assets at each moment of the history; the calculation module 65 is further configured to integrate, for any two types of assets, a degree of dispersion of a mean value of a historical yield of each type of asset at each time of the history and a historical yield of each type of asset at each time of the history, to obtain a correlation coefficient between any two types of assets; the calculating module 65 is further configured to calculate a covariance of the profitability between the two types of assets according to the variance of the expected profitability of the two types of assets and the correlation coefficient between the two types of assets.
As an implementation manner, the equation established in the risk value is shown in the formula (3):
∑ t 1(s i,t ≤var i )＝T(1-α)··············(3)
wherein 1 (-) is an indication function, s i,t For the historical profitability of asset i at time t, var i Is the risk value of asset i, α is the confidence level.
It should be noted that, the corresponding confidence level may be set according to specific service requirements.
As one implementation, an equation that establishes the variance of the expected profitability of the various types of assets based on the normal distribution relationship of the revenue and risk is shown in equation (4):
wherein sigma i As the variance of the profitability of asset i,
the inverse of the distribution function is accumulated for a standard normal.
As one implementation, the correlation coefficient between any two classes of assets is calculated as shown in equation (5):
wherein ρ is i,j For a correlation coefficient between any two classes of assets,
for asset i mean of historical profitability, +.>
Is the average value of the historical yield of the asset j, s j,t Is the historical rate of return of asset j at time t.
For example, the correlation coefficient between asset i and asset j is calculated from the historic rate of return dispersion of asset i and asset j at each moment in the history.
As one implementation, the covariance of the profitability between any two classes of assets is calculated as shown in equation (6):
c i,j ＝σ i σ j ρ i,j ··············(6)
Wherein c i,j Is the covariance of the yield between asset i and asset j.
For example, the product of the variance of the profitability of asset i, the variance of the profitability of asset j, and the correlation coefficient between asset i and asset j is calculated to obtain the covariance of the profitability between asset i and asset j. The covariance evaluates the overall error of the profitability of asset i and the profitability of asset j. In connection with equation (4), covariance can be used to calculate an expected risk from funds of an account.
For ease of understanding, fig. 4 is an example of a covariance calculation method, as shown in fig. 4: historical rates of return are obtained from the data system and expected rates of return are obtained from the allocation requests. And calculating a historical yield average value based on the historical yield, and calculating a correlation coefficient based on the historical yield average value. The on-risk value is calculated based on the historical profitability, and the expected profitability variance can be calculated through the on-risk value and the expected profitability. The covariance of the profitability can be calculated from the correlation coefficient and the expected profitability variance.
Based on the embodiment, different confidence degrees can be set according to actual demands by evaluating risk in risk value, application is more flexible, any two types of assets can be associated through the correlation coefficient, and calculated covariance is more accurate.
As one example, a quadratic programming expression is established as shown in equation (7):
where x is the optimized argument, i.e., the allocated funds for each type of asset under each account. The parameters are as follows:
x＝(w 1,1 ,....,w 1,N ,...,w a,1 ,...,w a,N ,...,w M,1 ,...,w M,N ) T
A＝1 1*MN
b＝w 0
G＝-E MN*MN
h＝0 MN*1
wherein lambda is a preset fund distribution coefficient, 1 is an all-1 matrix, 0 is an all-0 matrix, E is an identity matrix,
cronecker product of matrices, used for computation between matrices, w 0 Funds are to be allocated.
For example, a set of funds distribution coefficients is received. P is calculated from the covariance of the profitability of the various types of assets. Q is calculated according to the current funds of each type of assets under each account, the covariance of the profitability of each type of assets and the expected profitability of each type of assets. B is determined based on the funds to be allocated. A, G, h are determined based on the number of accounts and asset classes. Substituting a group of fund distribution coefficients into the solution of the quadratic programming problem to obtain a fund distribution result w= (w) a,i ) A*N . Substituting the distribution result into the formula (1) to obtain an expected profit sum r, and substituting the distribution result into the formula (2) to obtain an expected risk sum sigma 2 。
It should be noted that, the quadratic programming solution may be a method such as an interior point method, which is not limited in this application.
In one example, the funds distribution apparatus further comprises: the execution module 66 is configured to, if a confirmation instruction of the user to the first allocation result is received, allocate the funds to be allocated to each account according to the first allocation result; the execution module 66 is further configured to, if a cancel instruction of the user on the first allocation result is received, push a second allocation result corresponding to an evaluation result adjacent to the evaluation result corresponding to the first allocation result in the pareto curve to the user until a confirm instruction of the user on the second allocation result is received, and allocate the funds to be allocated to each account according to the second allocation result.
As one implementation, the expected benefit sum r and the expected risk sum sigma are calculated 2 Establishing a pareto curve, referring to fig. 5, fig. 5 is an example of a pareto curve. And pushing the distribution result which accords with the target result in the pareto curve to the user according to the target result of the user. If the user confirms, the pushing is finished, and if the user cancels, the distribution results adjacent to the pareto curve in the pareto curve are continuously pushed.
Based on the above embodiment, by generating the pareto curve, the association relationship between the expected revenue sum and the expected risk sum can be quantified, so that an accurate funds distribution result can be selected.
In the funds distribution device provided by the embodiment, the receiving module is used for receiving a distribution request of a user, wherein the distribution request comprises expected profitability of various assets input by the user; the establishing module is used for establishing a first equation representing the sum of expected benefits of all types of assets under all accounts and a second equation representing the sum of expected risks of all types of assets under all accounts according to the expected benefits of all types of assets; the establishing module is also used for establishing a quadratic programming expression of the first equation and the second equation and acquiring a plurality of funds distribution coefficients and funds to be distributed which are input by a user; the distribution module is used for obtaining a corresponding evaluation result and a distribution result corresponding to the evaluation result by solving the quadratic programming expression according to the plurality of fund distribution coefficients; the pushing module is used for obtaining a target result input by a user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user. According to the scheme, the user only needs to input the expected yield of various assets, the pareto curve can be automatically generated based on the optimal result of the quadratic programming, and the fund distribution result is determined from the pareto curve.
Example III
Fig. 7 is a block diagram of an apparatus, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, etc., of a funds dispensing apparatus according to an exemplary embodiment.
The apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the apparatus 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phonebook data, messages, pictures, videos, and the like. The Memory 804 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random-Access Memory (SRAM), electrically erasable programmable Read-Only Memory (EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The power supply component 806 provides power to the various components of the device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 800.
The multimedia component 808 includes a screen between the device 800 and the user that provides an output interface. In some embodiments, the screen may include a liquid crystal display (Liquid Crystal Display, LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the apparatus 800 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.
Input/output interface 812 provides an interface between processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the apparatus 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, an orientation or acceleration/deceleration of the device 800, and a change in temperature of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a photosensor, such as a complementary metal oxide semiconductor (Complementary Metal Oxide Semiconductor, CMOS) image sensor or Charge-coupled Device (CCD), for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the apparatus 800 and other devices, either in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, a second generation mobile communication technology (2 nd-Generation Communication Technology, 2G for short), a third generation mobile communication technology (3 rd-Generation Communication Technology, 3G for short), a fourth generation mobile communication technology (4 th-Generation Communication Technology, 4G for short), or a fifth generation mobile communication technology (5 th-Generation Communication Technology, 5G for short), or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (Near Field Communication, NFC for short) module to facilitate short range communications. For example, the NFC module may be implemented based on radio frequency identification (Radio Frequency Identification, RFID) technology, infrared data association (Infrared Data Association, irDA) technology, ultra Wide Band (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated ASIC), digital signal processors (Digital Signal Process, abbreviated DSP), digital signal processing devices (Digital Signal Process Device, abbreviated DSPD), programmable logic devices (Programmable Logic Device, abbreviated PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of apparatus 800 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random access memory (Random Access Memory, RAM for short), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
Example IV
Fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, as shown in fig. 8, where the electronic device includes:
A processor 291, the electronic device further comprising a memory 292; a communication interface (Communication Interface) 293 and bus 294 may also be included. The processor 291, the memory 292, and the communication interface 293 may communicate with each other via the bus 294. Communication interface 293 may be used for information transfer. The processor 291 may call logic instructions in the memory 292 to perform the methods of the above-described embodiments.
Further, the logic instructions in memory 292 described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product.
The memory 292 is a computer readable storage medium, and may be used to store a software program, a computer executable program, and program instructions/modules corresponding to the methods in the embodiments of the present application. The processor 291 executes functional applications and data processing by running software programs, instructions and modules stored in the memory 292, i.e., implements the methods of the method embodiments described above.
Embodiments of the present application provide a non-transitory computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement a method as described in the previous embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method of funds distribution, comprising:
receiving an allocation request of a user, wherein the allocation request comprises expected profitability of various assets input by the user;
Establishing a first equation representing the sum of expected benefits of all types of assets under all accounts and a second equation representing the sum of expected risks of all types of assets under all accounts according to the expected benefits of all types of assets;
establishing a quadratic programming expression of the first equation and the second equation, and acquiring a plurality of funds distribution coefficients and funds to be distributed, which are input by a user;
according to the multiple fund distribution coefficients, solving the quadratic programming expression to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result, wherein the evaluation result comprises the expected sum of benefits of all types of assets under all accounts and the expected sum of risks of all types of assets under all accounts, and the distribution result comprises the distribution fund of each account; wherein, the constraint condition of the quadratic programming expression is that the sum of the allocated funds of each account is equal to the funds to be allocated;
and obtaining a target result input by a user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user.
2. The method of claim 1, wherein establishing a first equation characterizing a sum of expected revenue for each type of asset under all accounts based on expected revenue rates for each type of asset comprises:
acquiring the current funds of each type of asset under each account from a data system;
establishing the first equation by taking the allocated funds of each type of asset under each account as independent variables according to the expected yield of each type of asset, wherein the first equation comprises: the sum of expected benefits of all accounts is the sum of expected benefits of each type of asset under each account, wherein the expected benefits of each type of asset are the product of the sum of the allocated funds of the type of asset and the expected benefit rate of the type of asset accumulated for the current funds of the type of asset.
3. The method of claim 2, wherein establishing a second equation characterizing a sum of expected revenue for each type of asset under all accounts based on expected revenue rates for each type of asset comprises:
acquiring the historical yield of each type of asset from a data system;
according to the historical profitability of each type of asset and the expected profitability of each type of asset, calculating to obtain covariance of the profitability between any two types of assets;
Selecting one type of asset from any account, selecting one type of asset from another arbitrary account, and enabling the types of the two types of assets to be different; establishing the second equation based on the covariance of the profitability between the two types of assets using the allocated funds of the two types of assets as arguments, the second equation comprising: the sum of the expected risks for all accounts is the sum of the expected risks for each type of asset under each account, where the expected risk for each two types of assets is the product of the sum of the allocated funds for the two types of assets and the covariance of the rate of return between the two types of assets added to the current funds for the two types of assets.
4. A method according to claim 3, wherein said calculating a covariance of the profitability between any two classes of assets based on said historical profitability of each class of assets and said expected profitability of each class of assets comprises:
calculating the risk value of each type of asset according to the historical yield of each type of asset; the risk value characterizes the loss rate of the asset under a preset confidence level;
calculating to obtain the variance of the expected profitability of each type of asset according to the risk value of each type of asset and the expected profitability of the corresponding asset;
Calculating to obtain the average value of the historical yield of various assets at each moment of the history;
aiming at any two types of assets, synthesizing the discrete degree of the average value of the historical yield of each type of asset at each time of the history and the historical yield of each type of asset at each time of the history to obtain the correlation coefficient between any two types of assets;
and calculating the covariance of the profitability between any two types of assets according to the variance of the expected profitability of any two types of assets and the correlation coefficient between the two types of assets.
5. The method according to any one of claims 1-4, further comprising:
if a confirmation instruction of the user to the first allocation result is received, allocating the funds to be allocated to each account according to the first allocation result;
and if a cancel instruction of the user for the first allocation result is received, pushing a second allocation result corresponding to an evaluation result adjacent to the evaluation result corresponding to the first allocation result in the pareto curve to the user until a confirm instruction of the user for the second allocation result is received, and allocating the funds to be allocated to each account according to the second allocation result.
6. A funds distribution apparatus, comprising:
the receiving module is used for receiving a distribution request of a user, wherein the distribution request comprises expected profitability of various assets input by the user;
the establishing module is used for establishing a first equation representing the sum of expected benefits of all types of assets under all accounts and a second equation representing the sum of expected risks of all types of assets under all accounts according to the expected benefits of all types of assets;
the establishing module is further used for establishing a quadratic programming expression of the first equation and the second equation and acquiring a plurality of fund distribution coefficients and funds to be distributed, which are input by a user;
the distribution module is used for solving the quadratic programming expression according to the plurality of fund distribution coefficients to obtain a corresponding evaluation result and a distribution result corresponding to the evaluation result, wherein the evaluation result comprises the expected total sum of benefits of various assets under all accounts and the expected total sum of risks of various assets under all accounts, and the distribution result comprises the distribution fund of each account; wherein, the constraint condition of the quadratic programming expression is that the sum of the allocated funds of each account is equal to the funds to be allocated;
The pushing module is used for obtaining a target result input by a user, generating a pareto curve according to the evaluation result, and pushing a first distribution result corresponding to the evaluation result corresponding to the target result in the pareto curve to the user.
7. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the establishing module is specifically used for acquiring the current funds of each type of asset under each account from the data system;
the establishing module is specifically further configured to establish the first equation according to an expected yield of each type of asset under each account by using allocated funds of each type of asset as an argument, where the first equation includes: the sum of expected benefits of all accounts is the sum of expected benefits of each type of asset under each account, wherein the expected benefits of each type of asset are the product of the sum of the allocated funds of the type of asset and the expected benefit rate of the type of asset accumulated for the current funds of the type of asset.
8. The apparatus of claim 7, wherein the device comprises a plurality of sensors,
the building module is also used for obtaining the historical yield of each type of asset from the data system;
the establishing module is further used for calculating covariance of the profitability between any two types of assets according to the historical profitability of each type of assets and the expected profitability of each type of assets;
The building module is also used for selecting one type of asset from any account and selecting one type of asset from another arbitrary account, and the types of the two types of selected assets are different; establishing the second equation based on the covariance of the profitability between the two types of assets using the allocated funds of the two types of assets as arguments, the second equation comprising: the sum of the expected risks for all accounts is the sum of the expected risks for each type of asset under each account, where the expected risk for each two types of assets is the product of the sum of the allocated funds for the two types of assets and the covariance of the rate of return between the two types of assets added to the current funds for the two types of assets.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-5.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-5.
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| CN113742080A (en) * | 2020-09-10 | 2021-12-03 | 吕戈 | Efficient construction method and device for immutable object execution environment |
| CN113742080B (en) * | 2020-09-10 | 2024-03-01 | 吕戈 | Efficient method and device for constructing immutable object execution environment |
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