CN115829368A - Transaction strategy evaluation method based on multiple factors - Google Patents
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- CN115829368A CN115829368A CN202211377477.2A CN202211377477A CN115829368A CN 115829368 A CN115829368 A CN 115829368A CN 202211377477 A CN202211377477 A CN 202211377477A CN 115829368 A CN115829368 A CN 115829368A
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Abstract
The application discloses a transaction strategy evaluation method based on multiple factors, which comprises the following steps: constructing a database required by transaction strategy evaluation; constructing a strategy model required by transaction strategy evaluation; selecting and setting model parameters of the strategy model; setting the parameter weight of the model parameter according to the transaction environment; optimizing the strategy model according to the transaction strategy output after the transaction data of the database is input into the strategy model; and feeding back the transaction strategy and the evaluation result of the transaction strategy to the user by using the optimized strategy model. The method has the advantages that the transaction strategy evaluation method based on multiple factors comprehensively considering the professional ability and the system computing ability of the user is provided.
Description
Technical Field
The application relates to the technical field of computers, in particular to a transaction strategy evaluation method based on multiple factors.
Background
The existing system for generating or evaluating the transaction strategy has the defect of high requirement on the expertise of a user. Alternatively, the model of the trading strategy itself is computationally expensive.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present application provide a multi-factor based transaction policy evaluation method to solve the technical problems mentioned in the above background.
As one aspect of the present application, some embodiments of the present application provide a multi-factor based transaction policy evaluation method, including:
constructing a database required by transaction strategy evaluation;
constructing a strategy model required by transaction strategy evaluation;
selecting and setting model parameters of the strategy model;
setting the parameter weight of the model parameter according to the transaction environment;
optimizing the strategy model according to the transaction strategy output after the transaction data of the database is input into the strategy model;
and feeding back the transaction strategy and the evaluation result of the transaction strategy to the user by using the optimized strategy model.
Further, the multi-factor based transaction policy evaluation method further includes:
and providing an operation interface using the strategy model.
Further, the multi-factor based transaction policy evaluation method further includes:
and feeding back the evaluation result and the transaction party list corresponding to the evaluation structure to the user through the operation interface.
Further, the building of the policy model required for transaction policy evaluation includes:
and screening and constructing the strategy model according to the type of the transaction environment.
Further, the types of transaction environments include:
preferred for all weather, bear and cattle.
Further, the building of the policy model required for transaction policy evaluation includes:
and classifying the strategy model to a preset strategy type according to a strategy object.
Further, the policy types include: at least one of stock excess, firm collection arbitrage, management of systematic trading of futures, management of free adjudication of futures, and convertible arbitrage.
Further, the policy model includes:
Black-Literman model or/and FOF model.
Further, the method for optimizing the Black-Litterman model comprises the following steps:
and (3) carrying out sparse representation on the posterior covariance matrix in the Black-Litterman model.
Further, wherein, the optimization method of the FOF model comprises the following steps:
on the basis of actively selecting fund, the input items of the model are added;
the input items include, but are not limited to: expected total return, standard deviation, transaction skewness, and transaction kurtosis.
The beneficial effect of this application lies in: a multi-factor based transaction strategy evaluation method comprehensively considering the professional ability and the system computing ability of a user is provided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it.
Further, throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
In the drawings:
FIG. 1 is a schematic diagram illustrating the main steps of a multi-factor based transaction policy evaluation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a device relied on by the multi-factor based transaction policy evaluation method according to an embodiment of the application.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a" or "an" in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will appreciate that references to "one or more" are intended to be exemplary and not limiting unless the context clearly indicates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, the multi-factor based transaction policy evaluation method of the present application includes the following steps:
s1, constructing a database required by transaction strategy evaluation;
s2, constructing a strategy model required by transaction strategy evaluation;
s3, selecting and setting model parameters of the strategy model;
s4, setting the parameter weight of the model parameter according to the transaction environment;
s5, inputting the transaction data of the database into a strategy model and then outputting the transaction strategy optimization strategy model;
and S6, feeding back the trading strategy and the evaluation result of the trading strategy to the user by using the optimized strategy model.
Specifically, the multi-factor based transaction policy evaluation method further includes:
an operator interface using the policy model is provided.
Specifically, the multi-factor based transaction policy evaluation method further includes:
and feeding back the evaluation result and the transaction party list corresponding to the evaluation structure to the user through an operation interface.
Specifically, the method for constructing the policy model required for transaction policy evaluation includes:
and screening and constructing a strategy model according to the type of the transaction environment.
Specifically, the types of transaction environments include:
preferred for all weather, bear and cattle.
Specifically, the method for constructing the policy model required for transaction policy evaluation includes:
and classifying the strategy model to a preset strategy type according to the strategy purpose.
Specifically, the policy types include: at least one of stock excess, firm collection arbitrage, management of systematic trading of futures, management of free adjudication of futures, and convertible arbitrage.
Specifically, the policy model includes:
Black-Literman model or/and FOF model.
Specifically, the optimization method of the Black-Litterman model comprises the following steps:
and (3) carrying out sparse representation on the posterior covariance matrix in the Black-Litterman model.
Specifically, the optimization method of the FOF model comprises the following steps:
on the basis of actively selecting the fund, the input items of the model are added;
input items include, but are not limited to: expected total return, standard deviation, transaction skewness, and transaction kurtosis.
As a more specific scheme, the Black-Litterman asset allocation model has various advantages over the 'mean-variance' method, but the influence in actual combat is limited, and one of the main reasons is that the posterior covariance matrix generated by the model brings significant computational challenges to any quadratic optimizer.
This problem is solved by using a feature analysis method as a Black-Litterman model optimization solution.
According to the feature analysis method, the following can be obtained:
Approximating the original matrix using the first p (p < n) p feature components and collecting the residuals, one can obtain:
where Λ is the diagonal matrix subtracted from D by retaining the first p eigenvalues; Θ is the matrix subtracted from E by retaining the first p eigenvectors; r represents a residual matrix.
Where Δ is a diagonal matrix obtained from R by setting all its diagonal elements to 0.
By such approximation, one can obtain
Is sparse (i.e. Θ is just one n × p]Matrix, Λ is a small p × p]Matrix and delta is a diagonal matrix). This will greatly reduce the amount of computation and help the optimizer to work. Under the condition of selecting the same p, the method can well approximate the posterior covariance matrix, and the obtained approximate value has the same accuracy as a Principal Component Analysis (PCA) risk model.
Meanwhile, the "mean-variance" method adopted by the investment fund combination model of the FOF proposed by Waring in the optimal asset allocation combination focuses on the attention to the first two moments (mean and variance) of the profit, and the variance considers both upward fluctuation and downward fluctuation of the profit as risks, which do not conform to the actual model. Meanwhile, the optimization of the model introduces the factors such as the relevance of the asset classes, the standard deviation of the asset classes, the risk exposure of the asset classes of managers and the like, but lacks the consideration of the factors such as the income, the risk and the like of a specific fund.
The specific scheme for solving the FOF model is as follows: the CVaR, namely the Conditional Value at Risk is introduced, the calculation of the asset classification skewness and kurtosis is increased, the tail Risk is considered more fully, the secondary additivity is met, and the method has convexity and the like. In the aspect of fund selection optimization of fund, the new model combines qualitative analysis with quantitative model, and increases multiple inputs of fund expected total return, fund standard deviation, fund skewness, fund kurtosis and the like on the basis of actively selecting fund so as to obtain the optimal alpha benefit. By validating the 1997-2011 data, the new model can achieve higher revenue and is more sensitive to revenue capture for fund configurations involving alternative investments than the passively selected beta revenue, alpha revenue of Waring et al, (2000).
As shown in fig. 2, the electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.: output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 808 including, for example, magnetic tape, hard disk, etc.: and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 2 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may be alternatively implemented or provided. Each block shown in fig. 2 may represent one device or may represent multiple devices, as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through communications device 809, or installed from storage device 808, or installed from ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (hypertext transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be one contained in the electronic device: or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: constructing a database required by transaction strategy evaluation; establishing a strategy model required by transaction strategy evaluation; selecting and setting model parameters of the strategy model; setting the parameter weight of the model parameter according to the transaction environment; optimizing the strategy model according to the transaction strategy output after the transaction data of the database is input into the strategy model; and feeding back the transaction strategy and the evaluation result of the transaction strategy to the user by using the optimized strategy model.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, or the like, including the conventional procedural programming languages: such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combinations of the above-mentioned features, and other embodiments in which the above-mentioned features or their equivalents are combined arbitrarily without departing from the spirit of the invention are also encompassed. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A multi-factor based transaction policy evaluation method, comprising:
constructing a database required by transaction strategy evaluation;
constructing a strategy model required by transaction strategy evaluation;
selecting and setting model parameters of the strategy model;
setting the parameter weight of the model parameter according to the transaction environment;
optimizing the strategy model according to the transaction strategy output after the transaction data of the database is input into the strategy model;
and feeding back the transaction strategy and the evaluation result of the transaction strategy to the user by using the optimized strategy model.
2. The multi-factor based transaction policy evaluation method of claim 1 wherein:
the multi-factor based transaction policy evaluation method further comprises:
and providing an operation interface using the strategy model.
3. The multi-factor based transaction policy evaluation method of claim 1 wherein:
the multi-factor based transaction policy evaluation method further comprises:
and feeding back the evaluation result and the transaction party list corresponding to the evaluation structure to the user through the operation interface.
4. The multi-factor based transaction policy evaluation method according to any one of claims 1 to 3, wherein:
the method for constructing the strategy model required by the transaction strategy evaluation comprises the following steps:
and screening and constructing the strategy model according to the type of the transaction environment.
5. The multi-factor based transaction policy evaluation method of claim 4 wherein:
the types of transaction environments include:
preferred for all weather, bear and cattle.
6. The multi-factor based transaction policy evaluation method according to any one of claims 1 to 3, wherein:
the method for constructing the strategy model required by the transaction strategy evaluation comprises the following steps:
and classifying the strategy model to a preset strategy type according to the strategy purpose.
7. The multi-factor based transaction policy evaluation method of claim 6, wherein:
the policy types include: at least one of stock excess, firm collection arbitrage, management of systematic trading of futures, management of free adjudication of futures, and convertible arbitrage.
8. The multi-factor based transaction policy evaluation method of claim 1 wherein:
the policy model includes:
Black-Literman model or/and FOF model.
9. The multi-factor based transaction policy evaluation method of claim 8 wherein:
the optimization method of the Black-Litterman model comprises the following steps:
and (3) carrying out sparse representation on the posterior covariance matrix in the Black-Litterman model.
10. The multi-factor based transaction policy evaluation method of claim 9 wherein:
the FOF model optimization method comprises the following steps:
on the basis of actively selecting the fund, the input items of the model are added;
the input items include, but are not limited to: expected total return, standard deviation, transaction skewness, and transaction kurtosis.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070124230A1 (en) * | 2005-11-30 | 2007-05-31 | Goldman, Sachs & Co. | Model-based selection of trade execution strategies |
| CN106650992A (en) * | 2016-10-10 | 2017-05-10 | 北京极派客科技有限公司 | Quantitative investment strategy generating method and apparatus |
| CN107833137A (en) * | 2017-11-03 | 2018-03-23 | 上海宽全智能科技有限公司 | Quantization trading strategies generation method and device, equipment and storage medium based on multiple-objection optimization |
| CN110046997A (en) * | 2019-01-31 | 2019-07-23 | 阿里巴巴集团控股有限公司 | A kind of transaction risk appraisal procedure, device and electronic equipment |
| CN110517142A (en) * | 2019-08-28 | 2019-11-29 | 中国银行股份有限公司 | The output method and device of Policy evaluation information |
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- 2022-11-04 CN CN202211377477.2A patent/CN115829368A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070124230A1 (en) * | 2005-11-30 | 2007-05-31 | Goldman, Sachs & Co. | Model-based selection of trade execution strategies |
| CN106650992A (en) * | 2016-10-10 | 2017-05-10 | 北京极派客科技有限公司 | Quantitative investment strategy generating method and apparatus |
| CN107833137A (en) * | 2017-11-03 | 2018-03-23 | 上海宽全智能科技有限公司 | Quantization trading strategies generation method and device, equipment and storage medium based on multiple-objection optimization |
| CN110046997A (en) * | 2019-01-31 | 2019-07-23 | 阿里巴巴集团控股有限公司 | A kind of transaction risk appraisal procedure, device and electronic equipment |
| CN110517142A (en) * | 2019-08-28 | 2019-11-29 | 中国银行股份有限公司 | The output method and device of Policy evaluation information |
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