CN120407920A - Stock screening method and system - Google Patents

Stock screening method and system

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Publication number
CN120407920A
CN120407920A CN202510480370.8A CN202510480370A CN120407920A CN 120407920 A CN120407920 A CN 120407920A CN 202510480370 A CN202510480370 A CN 202510480370A CN 120407920 A CN120407920 A CN 120407920A
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stock
user
information
stocks
level
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张得一
张清风
王晓利
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Beijing Liangxue Weiye Technology Development Co ltd
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Beijing Liangxue Weiye Technology Development Co ltd
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Priority to CN202510480370.8A priority Critical patent/CN120407920A/en
Publication of CN120407920A publication Critical patent/CN120407920A/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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

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  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
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  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
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  • General Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The application relates to the technical field of stock recommendation, in particular to a stock screening method and system. The method comprises the steps of collecting a plurality of user information, classifying user grades based on the plurality of user information, collecting stock information, calculating the recommendation grade of each stock based on the stock information, screening a plurality of alternative stocks by combining the user grade of the user and the recommendation grade of the stock, finally calculating expected benefits of each alternative stock, judging whether the expected benefits of the alternative stocks meet expected targets of the clients, and screening out alternative stock tickets meeting the expected targets of the clients as recommended stocks.

Description

Stock screening method and system
Technical Field
The invention relates to the technical field of stock recommendation, in particular to a stock screening method and system.
Background
A stock is a certificate issued by the equity company limited to prove that the equity is held by the stakeholder, indicating that the holder of the stock has ownership of a portion of the capital of the equity company. Since stocks contain economic benefits and can be transferred to market circulation, stocks are also a valuable document.
The Chinese patent with publication number CN118134640A discloses an intelligent stock recommendation method, wherein a DIKW knowledge graph is combined with stock recommendation, a user DIKWP model is built based on basic information of a user before the stock DIKW model is built, stocks can be screened according to stock constraint conditions obtained by the user DIKWP model, the calculated amount is reduced, and the recommendation efficiency and accuracy are improved, but related conditions of the user are not fused in the prior art, so that the recommended stocks are difficult to meet actual conditions and demands of the user.
Disclosure of Invention
The invention aims at solving the problems in the background technology and provides a stock screening method and system.
The technical scheme of the invention is as follows:
in one aspect, the present application provides a stock screening method, including:
collecting a plurality of user information, and classifying user grades based on the plurality of user information;
collecting stock information and calculating a recommended level of each stock based on the stock information;
screening a plurality of alternative stocks by combining the user grade of the user and the recommended grade of the stock;
And calculating expected benefits of each alternative stock, judging whether the expected benefits of the alternative stocks meet the expected targets of the clients, and screening out the alternative stock tickets meeting the expected targets of the clients as recommended stocks.
Preferably, collecting a plurality of user information and classifying user grades based on the plurality of user information includes:
creating a user information table;
Collecting user information of a plurality of users, and putting all the collected user information into a user information table.
Preferably, collecting a plurality of user information and classifying user grades based on the plurality of user information, further comprising:
Selecting user information of a user from the user information table;
Calculating a user score corresponding to the user through a formula 1 based on the user information;
Wherein P is a user score, M i is the ith user information, N i is a weight corresponding to the ith user information, and c is the total number of user information;
setting user grades and user grading ranges corresponding to the user grades;
Setting the user grade of the user according to the user grading range of the user grading corresponding to the user;
and returning the user information of one user selected from the user information table until all the users in the user information table are selected, and obtaining the user grade of each user.
Preferably, collecting stock information and calculating a recommended rank for each stock based on the stock information includes:
Creating a stock information table;
And collecting a plurality of stock information, and putting all the collected stock information into a stock information table.
Preferably, collecting stock information and calculating a recommended rank for each stock based on the stock information, further comprises:
selecting one stock information from the stock information list;
Collecting user intent, and adjusting the weight of each stock information based on the user intent;
calculating a stock score of the stock through formula 2 in combination with the adjusted weights;
Wherein Q is a stock score, M i is the i-th stock information, N i is a weight corresponding to the i-th stock information, and k is the total number of stock information;
setting a recommendation level and a stock scoring range corresponding to each recommendation level;
setting the stock grade of the stock according to the stock scoring range of the stock corresponding to the stock scoring;
and returning to select one stock and acquiring the stock information of the stock until all the stocks are selected to be completed, and obtaining the stock grade of each stock.
Preferably, the selecting a plurality of candidate stocks by combining the user grade of the user and the recommended grade of the stock includes:
obtaining a user grade;
selecting a recommendation level corresponding to the user level, and recording the selected recommendation level as an alternative level;
All the stocks in the alternative grades are ranked from high to low according to the stock scores, the top N stocks are selected from the alternative grades, and the selected stocks are marked as alternative stocks.
Preferably, calculating expected returns of each candidate stock, judging whether the expected returns of the candidate stocks meet the expected targets of the clients, screening out candidate stock tickets meeting the expected targets of the clients as recommended stocks, including:
Acquiring all the alternative stocks and the rising and falling trend of each alternative stock in a setting period, wherein the setting period can be a period set by a user, such as a month, a quarter or a year;
Calculating the maximum expected loss of each alternative stock by combining the intention funds of the clients and the rising and falling trend of the alternative stock;
judging whether the maximum expected loss of each alternative stock is greater than or equal to the maximum accepted loss of the client in turn, and if the maximum expected loss of the alternative stock is greater than the maximum accepted loss of the client, rejecting the alternative stock;
the reserved candidate stock is recorded as a recommended stock.
Preferably, the user information includes user funds, user occupation and user age, and the stock information includes industries in which stocks are located, financial states of stock companies and trade information of stocks.
On the other hand, the application also provides a stock screening system, which comprises an information collection component control component, wherein the information collection component is used for collecting user information and stock information, the stock screening method according to any one of the above-mentioned control component is used for transmitting the user information and stock information collected by the information collection component into the control component, and the control component is used for calculating recommended stocks suitable for users by combining the user information and the stock information.
Preferably, the information collecting component comprises a client module and a stock module, wherein the client module collects user information, and the stock module collects stock information.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial technical effects:
The method comprises the steps of collecting a plurality of user information, classifying user grades based on the plurality of user information, collecting stock information, calculating the recommendation grade of each stock based on the stock information, screening a plurality of alternative stocks by combining the user grade of the user and the recommendation grade of the stock, finally calculating expected benefits of each alternative stock, judging whether the expected benefits of the alternative stocks meet expected targets of the clients, and screening out alternative stock tickets meeting the expected targets of the clients as recommended stocks.
Drawings
FIG. 1 is a schematic flow chart of a stock screening method according to the present invention;
FIG. 2 is a schematic diagram of a stock screening system according to the present invention;
Reference numeral 100, an information collection component, 101, a client module, 102, a stock module;
200. and a control assembly.
Detailed Description
In a first embodiment, as shown in fig. 1, the stock screening method provided by the present invention includes:
S100, collecting a plurality of user information, and classifying user grades based on the plurality of user information;
S200, collecting stock information, and calculating the recommended grade of each stock based on the stock information;
s300, screening out a plurality of alternative stocks by combining the user grade of the user and the recommendation grade of the stocks;
s400, calculating expected benefits of each alternative stock, judging whether the expected benefits of the alternative stocks meet expected targets of clients, and screening out the alternative stock tickets meeting the expected targets of the clients as recommended stocks.
According to the application, the user information is collected, the user grades are divided based on the user information, then the stock information is collected, the recommended grade of each stock is calculated based on the stock information, a plurality of alternative stocks are screened out by combining the user grades of the users and the recommended grades of the stocks, finally the expected benefits of each alternative stock are calculated, whether the expected benefits of the alternative stocks accord with the expected targets of the clients or not is judged, and the alternative stock tickets meeting the expected targets of the clients are screened out as the recommended stocks.
In an alternative embodiment, the 100 includes:
S110, creating a user information table;
s120, collecting user information of a plurality of users, and putting all collected user information into a user information table.
It should be noted that, for new users who just enter the stock market, there is a great difference between the user information corresponding to each new user, and just because the difference between different new users causes each new user to have a difference to the stock that wants to select, by collecting the user information of multiple users and establishing the user grades for different users according to different user information, it is convenient to recommend proper stocks according to the user recommendation.
In an alternative embodiment, the step S100 further includes:
s130, selecting user information of a user from a user information table;
s140, calculating a user score corresponding to the user through a formula 1 based on the user information;
Wherein P is a user score, M i is the ith user information, N i is a weight corresponding to the ith user information, and c is the total number of user information;
Specifically, the weight corresponding to each piece of user information can be adjusted according to different real-time practical emphasis, for example, the weight corresponding to the invested funds can be improved if the invested funds are paid, but in one calculation, the weight standard adopted for all users should be kept consistent;
s150, setting user grades and user grading ranges corresponding to the user grades;
s160, setting the user grade of the user according to the user grading range of the user grading corresponding to the user;
s170, returning to the step S130 until all users in the user information table are selected to be completed, and obtaining the user grade of each user.
It should be noted that, the amount of money that can be put into stock market and the risk bearing capability are different for each user, so that suitable stocks need to be recommended for different users in combination with their user information, and in order to better recommend stocks to users, the user grade of the user is calculated by formula 1 in combination with the user information, so as to ensure that the personal condition of the user can be fully considered.
If there are now 3 new users who have just entered the stock market, they are respectively noted as user a, user B and user C, and the user information of each user is shown in the user information table of table-1.
TABLE-1
User' s User occupation Annual income/ten thousand of users Plan to invest in capital/ten thousand ...
User A Doctor (doctor) 30 30 ...
User B Teacher's teacher 20 10 ...
User C Individual enterprise manager 100 200 ...
... ... ... ... ...
It can be seen that user a and user B are higher in job stability than user C, so that although user C is higher in annual revenue, user C is lower in job stability than user a and user B, so that if the criterion "job" is simply taken as the only term in equation 1, the risk resistance of user a and user B is higher than user C.
However, if the job stability, annual income and planned investment are taken into consideration comprehensively, and the corresponding weights are set to 0.3, 0.4 and 0.3, respectively, the user scores corresponding to the user a, the user B and the user C are 39, 26 and 106 (stable job is recorded as 50, unstable job is recorded as 20) after calculation by the formula 1.
Under the conditions of the classified user grades and each user grade range as in the table-2 user grade table, the user grade of the user A is 1 grade, the user grade of the user B is 1 grade, and the user grade of the user C is 2 grade.
TABLE-2
In an alternative embodiment, the S200 includes:
s210, creating a stock information table;
s220, collecting a plurality of stock information, and putting all the collected stock information into a stock information table.
It should be noted that, because the industries to which the stocks belong are different, and the companies corresponding to each stock are different, different stocks have different attractions and suitable degrees for different users, and the stock most suitable for the user condition is conveniently screened out by collecting a plurality of stock information.
In an alternative embodiment, the step S200 further includes:
S230, selecting one stock information from the stock information table;
s240, collecting user intention, and adjusting the weight of each stock information based on the user intention;
s250, calculating the stock score of the stock through a formula 2 in combination with the adjusted weight;
Wherein Q is a stock score, M i is the i-th stock information, N i is a weight corresponding to the i-th stock information, and k is the total number of stock information;
s260, setting recommendation levels and stock scoring ranges corresponding to each recommendation level;
s270, setting the stock grade of the stock according to the stock scoring range of the stock corresponding to the stock scoring;
S280, returning to the step S230 until all the stocks are selected to be completed, and obtaining the stock grade of each stock.
It should be noted that, by combining the stock information to calculate the stock score corresponding to each stock, and dividing all the stocks into different grades according to the stock scores, the recommended stocks are recommended according to the stock grades and the user grades, so as to ensure the rationality of the recommended stocks.
In an alternative embodiment, the S300 includes:
s310, obtaining a user grade;
S320, screening a recommendation level corresponding to the user level, and marking the selected recommendation level as an alternative level;
s330, all the stocks in the alternative grades are ranked from high to low according to the stock scores, the top N stocks are selected from the alternative grades, and the selected stocks are marked as alternative stocks.
It should be noted that, since the stock level and the user level are calculated by considering the stock information and the user information, the stock level may reflect the related information of the stock, and the user level may reflect the related information of the user, so that matching may be performed directly through the stock level and the user level, and the rationality of matching may be ensured.
In an alternative embodiment, the step S400 includes:
s410, acquiring the rising and falling trend of all the alternative stocks in a setting period;
In particular, the setting period may be a period set by the user himself, for example, a month, a quarter, or a year;
S420, calculating the maximum expected loss of each alternative stock by combining the intention funds of the clients and the rising and falling trend of the alternative stock;
alternatively, calculating the VaR of the alternative stock by equation 3 based on the history modeling;
var=e (ω) - ω * formula 3;
Where E (ω) is the average daily revenue and ω * is the lowest revenue level with confidence a;
S430, judging whether the maximum expected loss of each alternative stock is larger than or equal to the maximum accepted loss of the client in sequence, and if the maximum expected loss of the alternative stock is larger than the maximum accepted loss of the client, rejecting the alternative stock;
and S440, recording the reserved alternative stock as recommended stock.
It should be noted that, by calculating the maximum expected loss of an alternative stock by the VaR, the VaR is generally referred to as "risk value" or "risk value", which refers to the maximum possible loss of a certain financial asset (or portfolio) for a certain period of time in the future, at a certain confidence level.
In an alternative embodiment, the user information includes user funds, user occupation, and user age, and the stock information includes industries in which stocks are located, financial status of stock companies, and trading information of stocks.
It should be noted that, after the user information and the stock information are collected, a corresponding score needs to be assigned to each piece of user information and stock information, so as to calculate the user score and the stock score, and for different types of stocks, for example, the stocks of the AI chip industry should have a score higher than that of the civil industry.
In assigning scores, different scores may be assigned according to different emphasis, but consistency of scores needs to be maintained during calculation.
As shown in fig. 2, the present application further provides a stock screening system, which includes an information collecting component and a control component, wherein the information collecting component collects user information and stock information, the control component executes the stock screening method according to any one of the first embodiment, the user information and stock information collected by the information collecting component are transmitted to the control component, and the control component combines the user information and the stock information to calculate a recommended stock suitable for the user.
It should be noted that, after the information collected by the information collecting component is transmitted to the control component, the control component needs to calculate the user grade and the stock grade by combining the user information and the stock information respectively, and then recommends the stock of the corresponding grade based on the user grade.
In an alternative embodiment, the information collection component includes a client module through which user information is collected and a stock module through which stock information is collected.
It should be noted that, the client module and the stock module are both in communication connection with the control component, so as to facilitate information transmission.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.

Claims (10)

1.一种股票筛选方法，其特征在于，包括：1. A stock screening method, comprising: 采集多个用户信息，并基于多个用户信息划分用户等级；Collecting multiple user information and dividing user levels based on the multiple user information; 采集股票信息，并基于股票信息计算每一个股票的推荐等级；Collect stock information and calculate the recommendation level of each stock based on the stock information; 结合用户的用户等级与股票的推荐等级筛选出多个备选股票；Combine the user's user level and the stock's recommendation level to screen out multiple candidate stocks; 计算每一个备选股票的期望收益，判断备选股票的期望收益是否符合客户的期望目标，筛选出符合客户的期望目标的备选股票作为推荐股票。Calculate the expected return of each alternative stock, determine whether the expected return of the alternative stock meets the customer's expected goals, and screen out the alternative stocks that meet the customer's expected goals as recommended stocks. 2.根据权利要求1所述的一种股票筛选方法，其特征在于，采集多个用户信息，并基于多个用户信息划分用户等级，包括：2. A stock screening method according to claim 1, characterized in that collecting multiple user information and dividing the user levels based on the multiple user information comprises: 创建用户信息表；Create a user information table; 采集多个用户的用户信息，并将采集到的所有用户信息放入用户信息表。Collect user information of multiple users and put all collected user information into the user information table. 3.根据权利要求2所述的一种股票筛选方法，其特征在于，采集多个用户信息，并基于多个用户信息划分用户等级，还包括：3. A stock screening method according to claim 2, characterized in that the method further comprises: collecting information of multiple users and classifying users based on the information of the multiple users; 从用户信息表选取一个用户的用户信息；Select a user's user information from the user information table; 基于用户信息通过公式1计算该用户对应的用户评分；Calculate the user score corresponding to the user based on the user information using Formula 1; 其中，P是用户评分，Mi是第i个用户信息，Ni是第i个用户信息对应的权重，c是用户信息的总数量；Where P is the user rating, Mi is the i-th user information, Ni is the weight corresponding to the i-th user information, and c is the total number of user information; 设置用户等级及每一个用户等级对应的用户评分范围；Set user levels and the user rating range corresponding to each user level; 根据该用户对应的用户评分所处的用户评分范围设置该用户的用户等级；Setting the user level of the user according to the user rating range of the user's corresponding user rating; 返回从用户信息表选取一个用户的用户信息，直至用户信息表中的所有用户均被选取完成，得到每一个用户的用户等级。Return to select the user information of a user from the user information table until all users in the user information table are selected and the user level of each user is obtained. 4.根据权利要求3所述的一种股票筛选方法，其特征在于，采集股票信息，并基于股票信息计算每一个股票的推荐等级，包括：4. A stock screening method according to claim 3, characterized in that collecting stock information and calculating the recommendation level of each stock based on the stock information comprises: 创建股票信息表；Create a stock information table; 采集多个股票信息，并将采集到的所有股票信息均放入股票信息表。Collect multiple stock information and put all the collected stock information into the stock information table. 5.根据权利要求4所述的一种股票筛选方法，其特征在于，采集股票信息，并基于股票信息计算每一个股票的推荐等级，还包括：5. The stock screening method according to claim 4, wherein the step of collecting stock information and calculating the recommendation level of each stock based on the stock information further comprises: 从股票信息表中选取一个股票信息；Select a stock information from the stock information table; 采集用户意向，基于用户意向调节各个股票信息的权重；Collect user intentions and adjust the weight of each stock information based on user intentions; 结合调节后的权重通过公式2计算该股票的股票评分；The stock score of the stock is calculated using Formula 2 in combination with the adjusted weights; 其中，Q是股票评分，Mi是第i个股票信息，Ni是第i个股票信息对应的权重，k是股票信息的总数量；Where Q is the stock score, Mi is the i-th stock information, Ni is the weight corresponding to the i-th stock information, and k is the total number of stock information; 设置推荐等级及每一个推荐等级对应的股票评分范围；Set the recommended level and the stock score range corresponding to each recommended level; 根据该股票对应的股票评分所处的股票评分范围设置该股票的股票等级；Set the stock grade of the stock according to the stock score range of the stock; 返回选取一个股票并获取该股票的股票信息，直至所有的股票均被选取完成，得到每一个股票的股票等级。Return to select a stock and obtain the stock information of the stock until all stocks are selected and the stock level of each stock is obtained. 6.根据权利要求5所述的一种股票筛选方法，其特征在于，结合用户的用户等级与股票的推荐等级筛选出多个备选股票，包括：6. A stock screening method according to claim 5, characterized in that the method combines the user level of the user and the recommended level of the stock to screen out a plurality of candidate stocks, comprising: 获取用户等级；Get user level; 筛选与用户等级对应的推荐等级；将被选中的推荐等级记为备选等级；Filter the recommended level corresponding to the user level; record the selected recommended level as an alternative level; 将备选等级中的所有股票根据其股票评分的大小从高到低进行排序，并从备选等级中选取出位于前N个的股票；将被选取的股票记为备选股票。All stocks in the candidate level are sorted from high to low according to their stock scores, and the top N stocks are selected from the candidate level; the selected stocks are recorded as candidate stocks. 7.根据权利要求6所述的一种股票筛选方法，其特征在于，计算每一个备选股票的期望收益，判断备选股票的期望收益是否符合客户的期望目标，筛选出符合客户的期望目标的备选股票作为推荐股票，包括：7. A stock screening method according to claim 6, characterized in that the expected return of each candidate stock is calculated, and whether the expected return of the candidate stock meets the client's expected target is determined, and the candidate stocks that meet the client's expected target are selected as recommended stocks, comprising: 获取所有的备选股票及每一个备选股票的在设置周期内的涨跌趋势；结合客户的意向资金与备选股票的涨跌趋势计算每一个备选股票的最大预期损失；Obtain all candidate stocks and the rising and falling trends of each candidate stock within the set period; calculate the maximum expected loss of each candidate stock by combining the client's intended funds and the rising and falling trends of the candidate stocks; 依次判断每一个备选股票的最大预期损失是否大于或等于客户的最大接受损失，若备选股票的最大预期损失大于客户的最大接受损失，则剔除该备选股票；Determine in turn whether the maximum expected loss of each candidate stock is greater than or equal to the client's maximum acceptable loss. If the maximum expected loss of the candidate stock is greater than the client's maximum acceptable loss, eliminate the candidate stock. 将保留的备选股票记为推荐股票。The retained alternative stocks are recorded as recommended stocks. 8.根据权利要求7所述的一种股票筛选方法，其特征在于，所述用户信息包括用户资金、用户职业和用户年龄，所述股票信息包括股票所处的行业、股票公司的财务状态和股票的交易信息。8. A stock screening method according to claim 7, characterized in that the user information includes user funds, user occupation and user age, and the stock information includes the industry in which the stock is located, the financial status of the stock company and the trading information of the stock. 9.一种股票筛选系统，其特征在于，包括：9. A stock screening system, comprising: 信息收集组件，通过所述信息收集组件收集用户信息与股票信息；An information collection component, through which user information and stock information are collected; 控制组件，通过所述控制组件执行权利要求1至权利要求8中任一项所述的股票筛选方法，所述信息收集组件采集到的用户信息与股票信息传输至控制组件内，并通过所述控制组件结合用户信息与股票信息计算出适合用户的推荐股票。A control component, wherein the stock screening method according to any one of claims 1 to 8 is executed by the control component, the user information and stock information collected by the information collection component are transmitted to the control component, and the control component combines the user information and stock information to calculate recommended stocks suitable for the user. 10.根据权利要求8所述的一种股票筛选系统，其特征在于，所述信息收集组件包括客户模块和股票模块，通过所述客户模块收集用户信息，通过所述股票模块采集股票信息。10. A stock screening system according to claim 8, characterized in that the information collection component includes a customer module and a stock module, user information is collected through the customer module, and stock information is collected through the stock module.
CN202510480370.8A 2025-04-17 2025-04-17 Stock screening method and system Pending CN120407920A (en)

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