CN109447814A - Financial asset analysis method and device - Google Patents
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- CN109447814A CN109447814A CN201811238185.4A CN201811238185A CN109447814A CN 109447814 A CN109447814 A CN 109447814A CN 201811238185 A CN201811238185 A CN 201811238185A CN 109447814 A CN109447814 A CN 109447814A
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- 230000009286 beneficial effect Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 4
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 claims description 4
- 239000010931 gold Substances 0.000 claims description 4
- 229910052737 gold Inorganic materials 0.000 claims description 4
- 230000005055 memory storage Effects 0.000 claims description 3
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Abstract
This application discloses a kind of financial asset analysis method and devices, are related to financial investment, for being analyzed according to risk income classification financial asset, carry out financial asset configuration for investor and provide decision support.The financial asset analysis method, comprising: the price of M financial asset of acquisition and corresponding time；The risk income quota of M financial asset is obtained according to the price of M financial asset and corresponding time；M financial asset is clustered according to the risk income quota of M financial asset to obtain N number of cluster, N < M according to risk income classification.The embodiment of the present application is analyzed applied to financial asset.
Description
Technical field
This application involves financial investment field more particularly to a kind of financial asset analysis method and devices.
Background technique
In financial investment field, investor requires income and risk that target is invested to intention before doing investment decision
Understood, than if any investor can receive risky financial asset and invest, some investors can only connect
It is invested by the financial asset of the low income of low-risk.
Summary of the invention
Embodiments herein provides a kind of financial asset analysis method and device, for receiving to financial asset according to risk
Beneficial classification is analyzed, and is carried out financial asset configuration for investor and is provided decision support.
In order to achieve the above objectives, embodiments herein adopts the following technical scheme that
In a first aspect, embodiments herein provides a kind of financial asset analysis method, the financial asset analysis method
Include:
The price series data of M financial asset are obtained, the price series data include the valence of the M financial asset
Lattice and corresponding time；
Referred to according to the risk income that the price of the M financial asset and corresponding time obtain the M financial asset
Mark；
According to the risk income quota of the M financial asset to the M financial asset according to risk income classification into
Row cluster obtains N number of cluster, N < M.
Second aspect, embodiments herein provide a kind of financial asset analytical equipment, comprising:
Acquiring unit, for obtaining the price series data of M financial asset, the price series data include the M
The price of a financial asset and corresponding time；
The acquiring unit is also used to obtain the M gold according to the price and corresponding time of the M financial asset
The risk income quota that financing produces；
Cluster cell, for the risk income quota according to the M financial asset to the M financial asset according to wind
Dangerous income classification is clustered to obtain N number of cluster, N < M.
The third aspect, provides a kind of computer readable storage medium for storing one or more programs, it is one or
Multiple programs include instruction, and described instruction makes the finance of the computer execution as described in relation to the first aspect when executed by a computer
Assets Analyst method.
Fourth aspect provides a kind of computer program product comprising instruction, when described instruction is run on computers
When, so that computer executes financial asset analysis method as described in relation to the first aspect.
5th aspect, provides a kind of financial asset analytical equipment, comprising: processor and memory, memory is for storing
Program, processor calls the program of memory storage, to execute financial asset analysis method described in above-mentioned first aspect.
The financial asset analysis method and device that embodiments herein provides, obtain the price series of M financial asset
Data, price series data include M financial asset price and the corresponding time；According to the price of the M financial asset
The risk income quota of the M financial asset is obtained with the corresponding time；Referred to according to the risk income of the M financial asset
Mark is clustered to obtain N number of cluster, N < M according to risk income classification to the M financial asset.Realize to financial asset according to
Risk income classification is analyzed, and is carried out financial asset configuration for investor and is provided decision support.
Detailed description of the invention
Fig. 1 is the flow diagram one for the financial asset analysis method that embodiments herein provides；
Fig. 2 is the flow diagram two for the financial asset analysis method that embodiments herein provides；
Fig. 3 is the structural schematic diagram for the financial asset analytical equipment that embodiments herein provides.
Specific embodiment
Embodiment 1,
The embodiment of the present application provides a kind of financial asset analysis method, referring to fig. 1, the Assets Analyst method packet
It includes:
S101, the price series data for obtaining M financial asset.
Price series data include M financial asset price and the corresponding time.
Data file is read in the path that can specify from user, includes the price of M financial asset in data file and right
The time answered.The format of the data file of support includes: .csv .xls .json .txt etc..
The time of financial asset can be day, the moon, season, year etc..
It, can be by the financial asset in previous number if the price of financial asset or there are missing values in the corresponding time
According to price or corresponding time missing values are filled.If the price of financial asset exists abnormal in the corresponding time
Value can be then filled with the average value that preceding A data and rear B data occurs in exceptional value.
S102, the risk income quota of M financial asset is obtained according to the price and corresponding time of M financial asset.
Risk income quota include average return, volatitle revenue dynamic rate, Sharpe Ratio, maximum is withdrawn, maximum withdraws the period,
At least one of in normotopia ratio or negative position ratio.Wherein, Sharpe Ratio, maximum withdraw the period and have both risk and return relationship between Characterizations energy
Power.
The earning rate of t-th of time are as follows:
The average return of continuous n time are as follows:
The volatitle revenue dynamic rate of continuous n time are as follows:
Sharpe Ratio are as follows:
Wherein, E (rf) it is risk free return, it is parameter preset.
Maximum is withdrawn are as follows:
MXDD=max (Pi-Pj/Pi) formula 5
Wherein, PiFor the price of the financial asset of i-th of time, PjFor the price of the financial asset of j-th of time.
Maximum withdraws the longest period not hit new peak for asset price in the period.
Normotopia ratio are as follows:
Wherein, Max (Pi, L) indicate i-th of time financial asset price PiMaximum value within the L time of past.
Negative position ratio are as follows:
Wherein, Min (Pi, L) indicate i-th of time financial asset price PiMinimum value within the L time of past.
Earnings season distributional difference are as follows:
Earnings season distributional difference for quarterly counting financial assets' incoming rate respectively year by year, determine earning rate whether be in
Now significant seasonal variation.The method for judging whether there is seasonal variation are as follows: the annual same season average yield is higher than
On two standard deviations of annual fourth quater average yield.
S103, M financial asset is gathered according to risk income classification according to the risk income quota of M financial asset
Class obtains N number of cluster, N < M.
Financial asset can be clustered to obtain N number of cluster using unsupervised machine learning algorithm, the financial asset in cluster
Risk income quota similarity with higher, and the similarity of the risk income quota of the financial asset between cluster is lower.Show
Example property, N can be 5, i.e., clustered financial asset according to 5 kinds of risk income classifications to obtain 5 clusters.
Optionally, it is illustrated so that unsupervised machine learning algorithm is clustered as an example, as shown in Figure 2, step S103
Include:
S1, from the risk income quota of N number of financial asset is randomly choosed in M financial asset respectively as in N number of cluster
Center value.
S2, a financial asset is selected from the surplus assets of M financial asset, calculate separately selected financial asset
Risk income quota and N number of cluster central value Euclidean distance.
S3, selected financial asset is included into the smallest cluster of Euclidean distance and updates the central value of corresponding cluster.
Wherein, the central value of corresponding cluster is the flat of the Euclidean distance of the risk income quota of all financial assets in corresponding cluster
Mean value.
S4, S2 is repeated until criterion function is restrained.
Criterion function can be square error criterion, and criterion function convergence refers to that square error criterion is minimum.
Wherein, E is the summation of the square error of all financial assets in database, and p is the point in space, miIt is i-th
The central value of cluster.The square error criterion makes the cluster independence as compact as possible generated, and the distance metric used is Euclidean distance, when
Other distance metrics can also so be used.
Financial asset analysis method provided by the embodiments of the present application obtains the price series data of M financial asset, price
Sequence data include M financial asset price and the corresponding time；According to the price of the M financial asset and it is corresponding when
Between obtain the risk income quota of the M financial asset；According to the risk income quota of M financial asset to the M gold
Financing produces and is clustered to obtain N number of cluster, N < M according to risk income classification.It realizes to financial asset according to risk income classification
It is analyzed, facilitates investor's investment decision.
Optionally, which can also include: that the risk of all financial assets in each cluster of display is received
The average value of beneficial index.
Show that average return, volatitle revenue dynamic rate, Sharpe Ratio, the maximum of financial asset are withdrawn, maximum is returned according to cluster
Remove the average value of at least one in period, normotopia ratio or negative position ratio.Each index of financial asset can also be shown in all gold
Sorting position in financing production.The practical significance of each index can also be shown to user.
Embodiment 2,
The embodiment of the present application provides a kind of financial asset analytical equipment, is applied to above-mentioned financial asset analysis method, such as
Shown in Fig. 3, which includes:
Acquiring unit 301, for obtaining the price series data of M financial asset, price series data include M finance
The price of assets and corresponding time.
Acquiring unit 301 is also used to obtain M financial asset according to the price and corresponding time of M financial asset
Risk income quota.
Cluster cell 302, for being received to M financial asset according to risk according to the risk income quota of M financial asset
Beneficial classification is clustered to obtain N number of cluster, N < M.
Optionally, cluster cell 302 is specifically used for:
S1, from the risk income quota of N number of financial asset is randomly choosed in M financial asset respectively as in N number of cluster
Center value.
S2, a financial asset is selected from the surplus assets of M financial asset, calculate separately selected financial asset
Risk income quota and N number of cluster central value Euclidean distance.
S3, selected financial asset is included into the smallest cluster of Euclidean distance and updates the central value of corresponding cluster, wherein is right
The central value for answering cluster is the average value for corresponding to the Euclidean distance in cluster between the risk income quota of all financial assets.
S4, S2 is repeated until criterion function is restrained.
Optionally, financial asset analytical equipment 300 further includes display unit 303, for showing all finance in each cluster
The average value of the risk income quota of assets.
Optionally, risk income quota includes average return, volatitle revenue dynamic rate, Sharpe Ratio, maximum is withdrawn, maximum is returned
Remove at least one in period, normotopia ratio or negative position ratio.
Embodiments herein provides a kind of computer readable storage medium for storing one or more programs, one
Or multiple programs include instruction, described instruction makes computer execute the finance as described in Fig. 1-Fig. 2 when executed by a computer
Assets Analyst method.
Embodiments herein provides a kind of computer program product comprising instruction, when instruction is run on computers
When, so that computer executes the financial asset analysis method as described in Fig. 1-Fig. 2.
Embodiments herein provides a kind of speech recognition equipment, comprising: processor and memory, memory is for storing
Program, processor calls the program of memory storage, to execute the financial asset analysis method as described in Fig. 1-Fig. 2.
By financial asset analytical equipment, computer readable storage medium, computer program in the case of this application
Product can be applied to above-mentioned financial asset analysis method, therefore, can be obtained technical effect see also the above method
Embodiment, details are not described herein for embodiments herein.
It should be noted that above-mentioned each unit can be the processor individually set up, also can integrate controller certain
It is realized in one processor, in addition it is also possible to be stored in the form of program code in the memory of controller, by controller
Some processor calls and executes the function of the above each unit.Processor described here can be a central processing unit
(Central Processing Unit, CPU) or specific integrated circuit (Application Specific
Integrated Circuit, ASIC), or be arranged to implement one or more integrated circuits of the embodiment of the present application.
It should be understood that magnitude of the sequence numbers of the above procedures are not meant to execute suitable in the various embodiments of the application
Sequence it is successive, the execution of each process sequence should be determined by its function and internal logic, the implementation without coping with the embodiment of the present application
Process constitutes any restriction.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
Scope of the present application.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed system, apparatus and method, it can be with
It realizes by another way.For example, apparatus embodiments described above are merely indicative, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of equipment or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
Claims (11)
1. a kind of financial asset analysis method characterized by comprising
Obtain M financial asset price series data, the price series data include the M financial asset price with
The corresponding time；
The risk income quota of the M financial asset is obtained according to the price of the M financial asset and corresponding time；
The M financial asset is gathered according to risk income classification according to the risk income quota of the M financial asset
Class obtains N number of cluster, N < M.
2. financial asset analysis method according to claim 1, which is characterized in that described according to the M financial asset
Risk income quota the M financial asset is clustered to obtain N number of cluster according to risk income classification, comprising:
S1, the risk income quota of N number of financial asset is randomly choosed from the M financial asset respectively as in N number of cluster
Center value；
S2, a financial asset is selected from the remaining financial asset of the M financial asset, calculate separately selected finance
The Euclidean distance of the central value of the risk income quota of assets and N number of cluster；
S3, selected financial asset is included into the smallest cluster of Euclidean distance and updates the central value of corresponding cluster, wherein is described right
Answer the central value of cluster for the average value of the Euclidean distance of the risk income quota of all financial assets in the corresponding cluster；
S4, S2 is repeated until criterion function is restrained.
3. financial asset analysis method according to claim 1, which is characterized in that the financial asset analysis method is also wrapped
It includes:
Show the average value of the risk income quota of all financial assets in each cluster.
4. financial asset analysis method according to claim 1-3, which is characterized in that the risk income quota
It is withdrawn including average return, volatitle revenue dynamic rate, Sharpe Ratio, maximum, maximum is withdrawn in period, normotopia ratio or negative position ratio extremely
One item missing.
5. a kind of financial asset analytical equipment characterized by comprising
Acquiring unit, for obtaining the price series data of M financial asset, the price series data include the M gold
It finances the price produced and corresponding time；
The acquiring unit is also used to obtain the M finance money according to the price and corresponding time of the M financial asset
The risk income quota of production；
Cluster cell, for being received to the M financial asset according to risk according to the risk income quota of the M financial asset
Beneficial classification is clustered to obtain N number of cluster, N < M.
6. financial asset analytical equipment according to claim 5, which is characterized in that the cluster cell is specifically used for:
S1, the risk income quota of N number of financial asset is randomly choosed from the M financial asset respectively as in N number of cluster
Center value；
S2, a financial asset is selected from the surplus assets of the M financial asset, calculate separately selected financial asset
Risk income quota and N number of cluster central value Euclidean distance；
S3, selected financial asset is included into the smallest cluster of Euclidean distance and updates the central value of corresponding cluster, wherein is described right
Answer the average value of Euclidean distance of the central value of cluster between the risk income quota of all financial assets in the corresponding cluster；
S4, S2 is repeated until criterion function is restrained.
7. financial asset analytical equipment according to claim 5, which is characterized in that the financial asset analytical equipment also wraps
Display unit is included, for showing the average value of the risk income quota of all financial assets in each cluster.
8. according to the described in any item financial asset analytical equipments of claim 5-7, which is characterized in that the risk income quota
It is withdrawn including average return, volatitle revenue dynamic rate, Sharpe Ratio, maximum, maximum is withdrawn in period, normotopia ratio or negative position ratio extremely
One item missing.
9. a kind of computer readable storage medium for storing one or more programs, which is characterized in that one or more of journeys
Sequence includes instruction, and it is according to any one of claims 1-4 that described instruction when executed by a computer executes the computer
Financial asset analysis method.
10. a kind of computer program product comprising instruction, which is characterized in that when described instruction is run on computers, make
It obtains the computer and executes financial asset analysis method according to any one of claims 1-4.
11. a kind of financial asset analytical equipment characterized by comprising processor and memory, memory is for storing journey
Sequence, processor calls the program of memory storage, to execute financial asset analysis side according to any one of claims 1-4
Method.
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| CN201811238185.4A CN109447814A (en) | 2018-10-23 | 2018-10-23 | Financial asset analysis method and device |
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| CN201811238185.4A CN109447814A (en) | 2018-10-23 | 2018-10-23 | Financial asset analysis method and device |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111754317A (en) * | 2020-06-16 | 2020-10-09 | 曹明洲 | Financial investment data evaluation method and system |
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2018
- 2018-10-23 CN CN201811238185.4A patent/CN109447814A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111754317A (en) * | 2020-06-16 | 2020-10-09 | 曹明洲 | Financial investment data evaluation method and system |
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Address after: 100031 West Chang'an Avenue, Xicheng District, Xicheng District, Beijing Applicant after: State Grid Corporation of China Applicant after: State Grid Agel Ecommerce Ltd Applicant after: Guowang Xiongan Finance Technology Group Co., Ltd. Address before: 100031 West Chang'an Avenue, Xicheng District, Xicheng District, Beijing Applicant before: State Grid Corporation of China Applicant before: State Grid Agel Ecommerce Ltd Applicant before: Guowang Xiongan Finance Technology Co., Ltd. |
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