CN112884577A - Stock behavior science and technology application method and system - Google Patents
Stock behavior science and technology application method and system Download PDFInfo
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- CN112884577A CN112884577A CN202110150680.5A CN202110150680A CN112884577A CN 112884577 A CN112884577 A CN 112884577A CN 202110150680 A CN202110150680 A CN 202110150680A CN 112884577 A CN112884577 A CN 112884577A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Abstract
The invention provides a stock behavior science and technology application method and a stock behavior science and technology application system. The method comprises the following steps: acquiring a behavior factor from stock trading data; determining a main flow behavior factor from the obtained behavior factors; performing mainstream behavior factor portrait according to the determined mainstream behavior factor; and performing current stock trading risk assessment according to the mainstream behavior factor portrait. The stock behavior science and technology application method and system provided by the invention can discover possible risks by means of a big data model.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a stock behavior science and technology application method and system.
Background
The existing stock market analysis is gradually changed into the past, the traditional and classical analysis methods and indexes can only be used by knowledge and experience respectively, and the expected effect and accuracy are not high due to the subjective influence of human rationality and irrational nature. The appearance of big data changes the existing traditional market analysis mode, historical data modeling collection is carried out through multidimensional factors, and an algorithm is deduced through deep simulation, so that the investment risk of a client is reduced by assisting the client through automatic learning and model optimization of Artificial Intelligence (AI), and the investment value is improved. The big data analysis has: the method has the characteristics of high mass, diversity, complexity, high speed and the like, and can provide stronger decision-making power for investors in key signal capture, insight discovery and timeliness. Behavioral science techniques are defined in stock: each investment transaction is equal to a behavior factor, and the behavior factor is not random and is a behavior awareness result of the tendency. There are no profits and losses, only positive and negative consumption. And researching the operation rule of the behavior factors, establishing a big data model, and finding and controlling the possible risks.
Disclosure of Invention
The invention aims to provide a stock behavior science and technology application method and system, which can find possible risks by means of a big data model.
In order to solve the technical problems, the invention provides a method for applying stock behavior science and technology, which comprises the following steps: acquiring a behavior factor from stock trading data; determining a main flow behavior factor from the obtained behavior factors; performing mainstream behavior factor portrait according to the determined mainstream behavior factor; and performing current stock trading risk assessment according to the mainstream behavior factor portrait.
In some embodiments, the behavior factor is obtained from stock trading data, including: acquiring a current day behavior factor from current day stock transaction data; or obtaining the periodic behavior factor from the historical stock trading data.
In some embodiments, the mainstream behavior factor representation obtained from the current day behavior factor and the periodic behavior factor is used for risk assessment of the stock exchange risk of the current day.
In some embodiments, further comprising: after the behavior factors are obtained from the stock transaction data, the behavior factors are polished and cleaned before the main flow behavior factors are determined from the obtained behavior factors.
In some embodiments, the polishing and cleaning of the daily activity factor involves the daily activity factor comprising: capital factor, amplitude, buying and selling power, up and down attack times, up and down breakthrough times, up and down penetration force, up and down fluctuation speed, crossing density, strength factor and average price factor.
In some embodiments, the polishing and cleaning of the periodic behavior factor involves the periodic behavior factor comprising: trend factors, energy factors, pressure factors, support factors, time factors, space factors, index factors, active factors, popularity factors and basic surface factors.
In some embodiments, determining the mainstream behavior factor from the obtained behavior factors comprises: and determining the main flow behavior factor from the behavior factors by utilizing the established main flow and scattered family classifiers.
In some embodiments, the mainstream and casual classifier is classified according to the following factors: consciousness, action, reaction and result running track.
In some embodiments, the stock exchange risk assessment comprises: the current stage position risk coefficient, the capital risk coefficient, the human confidence risk coefficient, the comprehensive index risk coefficient, the trending risk coefficient, the policy risk coefficient and the peripheral market risk coefficient.
In addition, the invention also provides a stock behavior science and technology application system, and the device comprises: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the stock behavior science and technology application method according to the foregoing.
After adopting such design, the invention has at least the following advantages:
the method and the system perform mainstream behavior factor portrayal aiming at the behavior factors through the technology of big data analysis and data portrayal, perform stock market risk assessment by using the portrayal, and accurately assess the potential risk in stock trading.
Drawings
The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
FIG. 1 is a behavioral science and technology law operational diagram;
FIG. 2 is a schematic illustration of the steps of a behavioral science technology stock system application;
FIG. 3 is a schematic diagram of a behavioral science technology stock system application;
FIG. 4 is a behavioral science core technology operation diagram;
FIG. 5 is a flow chart of a stock behavior science and technology application method;
FIG. 6 is a flow chart of a stock behavior science and technology application method;
fig. 7 is a block diagram of a stock behavioral science and technology application system.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Aiming at the defects of the prior art, the invention provides a stock behavior scientific technology application and an algorithm model, which are used for solving at least one problem in the prior art.
Capital is devotive, and as market prices fluctuate, "behavioral awareness" arises, so a series of "processes" come. Referring to fig. 1, the process of applying the method for stock behavior science and technology is cyclic.
Referring to fig. 2, the application method of the stock behavior science and technology comprises the following six steps: 1. a trajectory; 2. converting; 3. circulating; 4. modeling; 5. risk; 6. and (5) controlling.
Referring to fig. 3, the method for applying the scientific and technical method of stock behavior images various factors of stock trading by means of big data analysis and evaluates potential risks by using the images.
Fig. 4 shows a specific operation of the core technology of behavioral science. Referring to fig. 4, these operations include: and constructing risk behavior factors through the daily behavior factors and the N-day behavior factors, and then performing big data modeling and risk control by using the risk behavior factors.
The stock behavior science and technology application and algorithm model provided by the invention comprises the following steps:
and obtaining 9.30 minutes of first transaction of opening the stock on the same day of the preset mark and confirming the first transaction as the behavior factor until 3.00 hours of closing the stock in the afternoon.
When all the stock trades of the day standard are exchanged, a behavior factor database is established.
When all data of the stock of the daily standard are cleaned and classified, two classifiers are established, wherein one type is a scattered factor and the other type is a main factor.
And (3) cleaning and polishing data of all the behavior factors, including: capital factors, amplitude, buying and selling power, up and down attack times, up and down breakthrough times, up and down penetration force, up and down fluctuation speed, transaction density, strength and weakness factors, average price factors and the like.
And filtering out the stray factors, confirming the mainstream behavior factors, and modeling the mainstream behavior factors to form an image.
The above are day behavior factors.
Historical mainstream behavior factor data of the industry in a preset time period is obtained, and the mainstream behavior factors are divided into a change factor data model and a cycle data model.
Cleaning and polishing the change factor data of the main flow behavior factor in the preset time, comprising the following steps: trend factors, energy factors, pressure factors, support factors, time factors, space factors, index factors, activity factors, popularity factors, ground plane factors, and the like.
The method for cleaning the circulation factor data of the main flow behavior factor in the preset time comprises the following steps: the next stage, the start of the stage, and the end of the stage.
Confirming four elements of the main flow behavior factor: modeling and representing consciousness, action, reaction and result running track.
And confirming the main flow behavior factors to evaluate and model the risk generated by the stock price of the current index.
Confirming that the large disk factor evaluates and models the risk generated by the current mainstream behavior factor.
The above is the N day cycle behavior factor.
Modeling the data of the risk factors of the large disk:
and acquiring historical stock data of all stocks in a preset time period.
Aiming at the risk evaluation of the current stage large disk factor, the content is as follows: the current stage position risk coefficient, the capital risk coefficient, the human confidence risk coefficient, the comprehensive index risk coefficient, the trending risk coefficient, the policy risk coefficient and the peripheral market risk coefficient.
Modeling of control behavior factors:
aiming at user risk capability test, a fund management system, risk defense combination and daily monitoring.
Fig. 5 shows a flow of a stock behavior science and technology application method. Referring to fig. 5, the method for applying the stock behavior science and technology includes:
s10, opening the stock on the same day by 9.30 minutes, confirming the action factor of the first trade, and closing the stock in the afternoon by 3.00.
S20, when the stock of the daily standard trades all the action factor database is established.
S30, data cleaning: capital factor, amplitude, buying and selling strength, times of getting up and down, times of getting through and getting down, force of getting through and getting through, speed of getting up and down, transaction density, strong and weak factor and average price factor.
S40, eliminating the stray factors, confirming the mainstream behavior factors, and modeling the mainstream behavior factors to form images.
And S50, dividing the historical mainstream behavior factor data in a preset time period into a change factor data model and a cycle period data model.
S60, cleaning the change factor data of the main flow behavior factor within the preset time, including: trend factors, energy factors, pressure factors, support factors, time factors, space factors, index factors, active factors, popularity factors, basic surface factors and the like.
S70, confirming the four elements of the mainstream behavior factor: consciousness, action, reaction, result track modeling and portraying.
And S80, confirming that the mainstream behavior factors evaluate and model the risk generated by the stock price of the current index.
And S90, confirming that the large disk factor carries out evaluation modeling on the risk generated by the current mainstream behavior factor.
S100, carrying out risk evaluation aiming at the current stage of the large disk factor, wherein the content is as follows: the current stage position risk coefficient, the capital risk coefficient, the human confidence risk coefficient, the comprehensive index risk coefficient, the trending risk coefficient, the policy risk coefficient and the peripheral market risk coefficient.
S110, aiming at user risk capability test, a fund management system, risk defense combination and daily monitoring.
Fig. 6 shows a flow of a stock behavior science and technology application method provided by another embodiment. Referring to fig. 6, the application method of the stock behavior science and technology includes the following steps: acquiring a behavior factor; cleaning and polishing all the behavior factor data; confirming the main flow behavior factor; modeling and portraying the mainstream behavior factor data in the N-day period; performing a risk assessment evaluation, which specifically comprises: performing risk assessment and evaluation on the stock at the current stage and performing risk assessment and evaluation on the stock at the current stage; the target stock controls the risk.
Fig. 7 shows the structure of the stock behavioral science and technology application system. Referring to fig. 7, for example, the stock behavior science and technology application system 700 may be used to act as a risk assessment host in a stock trading system. As described herein, the stock behavior science and technology application system 700 may be used to implement an assessment function of potential risks in a stock trading system. The stock behavior science application system 700 may be implemented in a single node, or the functionality of the stock behavior science application system 700 may be implemented in multiple nodes in a network. Those skilled in the art will appreciate that the term stock behavior science and technology application system includes devices in a broad sense, and that the stock behavior science and technology application system 700 shown in fig. 7 is only one example thereof. The inclusion of the stock behavior science and technology application system 700 is for clarity of presentation and is not intended to limit the application of the present invention to a particular stock behavior science and technology application system embodiment or a class of stock behavior science and technology application system embodiments. At least some of the features/methods described herein may be implemented in a network device or component, such as the stock behavioral science and technology application system 700. For example, the features/methods of the present invention may be implemented in hardware, firmware, and/or software running installed on hardware. The stock behavior science and technology application system 700 may be any device that processes, stores, and/or forwards data frames over a network, such as a server, a client, a data source, and the like. As shown in fig. 7, the stock behavior science and technology application system 700 may include a transceiver (Tx/Rx)710, which may be a transmitter, a receiver, or a combination thereof. Tx/Rx 710 may be coupled to a plurality of ports 750 (e.g., an uplink interface and/or a downlink interface) for transmitting and/or receiving frames from other nodes. Processor 730 may be coupled to Tx/Rx 710 to process frames and/or determine to which nodes to send frames. Processor 730 may include one or more multi-core processors and/or memory devices 732, which may serve as data stores, buffers, and the like. Processor 730 may be implemented as a general-purpose processor or may be part of one or more Application Specific Integrated Circuits (ASICs) and/or Digital Signal Processors (DSPs).
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.
Claims (10)
1. A method for applying a stock behavior science and technology is characterized by comprising the following steps:
acquiring a behavior factor from stock trading data;
determining a main flow behavior factor from the obtained behavior factors;
performing mainstream behavior factor portrait according to the determined mainstream behavior factor;
and performing current stock trading risk assessment according to the mainstream behavior factor portrait.
2. The method of claim 1, wherein the step of obtaining the behavior factor from the stock trading data comprises:
acquiring a current day behavior factor from current day stock transaction data; or
And acquiring a periodic behavior factor from historical stock trading data.
3. The method of claim 2, wherein the mainstream behavioral factor images obtained from the daily behavioral factors and the periodic behavioral factors are used for risk assessment of stock exchange risk in the same day.
4. The method for applying a stock behavioral science and technology according to claim 1, further comprising:
after the behavior factors are obtained from the stock transaction data, the behavior factors are polished and cleaned before the main flow behavior factors are determined from the obtained behavior factors.
5. The method for applying the stock behavior science and technology according to claim 2 or 4, wherein the polishing and cleaning of the daily behavior factors involve the following steps: capital factor, amplitude, buying and selling power, up and down attack times, up and down breakthrough times, up and down penetration force, up and down fluctuation speed, crossing density, strength factor and average price factor.
6. The method as claimed in claim 2 or 4, wherein the grinding and cleaning of the periodic behavior factors includes: trend factors, energy factors, pressure factors, support factors, time factors, space factors, index factors, active factors, popularity factors and basic surface factors.
7. The method for applying the stock behavior science and technology according to claim 1, wherein the step of determining the mainstream behavior factor from the acquired behavior factors comprises the steps of:
and determining the main flow behavior factor from the behavior factors by utilizing the established main flow and scattered family classifiers.
8. The method of claim 7, wherein the mainstream and outlier classifiers are classified according to the following factors: consciousness, action, reaction and result running track.
9. The method of applying stock behavioral science and technology according to claim 1, wherein the stock trading risk assessment includes: the current stage position risk coefficient, the capital risk coefficient, the human confidence risk coefficient, the comprehensive index risk coefficient, the trending risk coefficient, the policy risk coefficient and the peripheral market risk coefficient.
10. A stock behavioral science and technology application system, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the stock behavioral science and technology application method of any one of claims 1 to 9.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110150680.5A CN112884577A (en) | 2021-02-03 | 2021-02-03 | Stock behavior science and technology application method and system |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110150680.5A CN112884577A (en) | 2021-02-03 | 2021-02-03 | Stock behavior science and technology application method and system |
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| CN112884577A true CN112884577A (en) | 2021-06-01 |
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| CN202110150680.5A Pending CN112884577A (en) | 2021-02-03 | 2021-02-03 | Stock behavior science and technology application method and system |
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Application publication date: 20210601 |