Risk early warning and transaction strategy processing method and device for financial products

Abstract

The invention provides a risk early warning and transaction strategy processing method and device for financial products, and relates to the field of big data technology and financial science and technology, and the method comprises the steps: obtaining first-class financial index data and second-class financial index data; performing normalization processing on the first type of financial index data, and constructing a first risk assessment model according to the first mapping relationship; extracting repayment information of each object according to the second type of financial index data, generating a repayment vector corresponding to the object, performing clustering analysis on the repayment vector, determining a clustering centroid of each clustering data cluster, determining a corresponding default vector according to the clustering centroid, and establishing a second mapping relationship between the second type of financial indexes and the default vector, constructing a second risk assessment model; analyzing the first type of financial indexes and the second type of financial indexes corresponding to the target financial product to obtain financial product risk information; and performing risk early warning on the target financial product according to the financial product risk information, and generating a transaction strategy corresponding to the target financial product.

Description

Technical Field

[0001] The present invention relates to big data technology and the field of financial technology, and in particular to a method and device for processing risk warning and transaction strategies of financial products. Background Art

[0003] At present, when faced with a large number of financial products, ordinary investors usually buy and sell financial products based on the historical data of financial product returns obtained manually, or verbally recommended by investment managers. This transaction processing method of financial products is highly subjective, time-consuming, and cannot effectively analyze the potential risks of financial products. In response to this, the existing technology proposes to use machine learning models for risk prediction, but the existing technology usually uses a single model for processing. When processing complex financial products (for example, a portfolio of multiple financial products), it is impossible to accurately analyze the risks of financial products, resulting in poor effectiveness of risk warning information for customers. In addition, financial products have the characteristics of multiple data dimensions. If data of different dimensions are input into a single model, the data processing efficiency is low, the risk warning effect is poor, and it is impossible to provide a trading strategy that meets the current trading environment.

[0004] In summary, there is an urgent need for a technical solution that can overcome the above-mentioned defects, accurately analyze and warn of the risks of financial products, and provide effective trading strategies. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention proposes a risk warning and transaction strategy processing method and device for financial products.

[0006] In a first aspect of an embodiment of the present invention, a risk warning and trading strategy processing method for a financial product is proposed, comprising:

[0007] Obtaining the first category financial indicator data and the second category financial indicator data;

[0008] Normalizing the first type of financial indicator data, determining market risk information corresponding to the first type of financial indicator data, establishing a first mapping relationship between the first type of financial indicator and the market risk information, and constructing a first risk assessment model based on the first mapping relationship;

[0009] Extracting repayment information of each object according to the second type of financial indicator data, generating a repayment vector corresponding to the object, performing cluster analysis on the repayment vector, determining the cluster centroid of each cluster data cluster, determining the corresponding default vector according to the cluster centroid, establishing a second mapping relationship between the second type of financial indicator and the default vector, and constructing a second risk assessment model according to the second mapping relationship;

[0010] For a target financial product, analyzing the first category financial indicator and the second category financial indicator corresponding to the target financial product, determining the market risk information corresponding to the first category financial indicator and the default risk information corresponding to the second category financial indicator according to the first risk assessment model and the second risk assessment model, and integrating the market risk information and the default risk information to obtain the financial product risk information; wherein the target financial product is a single financial product or an investment portfolio of financial products;

[0011] Based on the financial product risk information, a risk warning is issued for the target financial product, and a trading strategy corresponding to the target financial product is generated.

[0012] In a second aspect of an embodiment of the present invention, a risk warning and transaction strategy processing device for a financial product is provided, comprising:

[0013] A financial indicator data acquisition module, used to acquire the first type of financial indicator data and the second type of financial indicator data;

[0014] A first risk assessment model building module, used to normalize the first type of financial indicator data, determine the market risk information corresponding to the first type of financial indicator data, establish a first mapping relationship between the first type of financial indicator and the market risk information, and build a first risk assessment model according to the first mapping relationship;

[0015] a second risk assessment model construction module, configured to extract repayment information of each object according to the second type of financial indicator data, generate a repayment vector corresponding to the object, perform cluster analysis on the repayment vector, determine the cluster centroid of each cluster data cluster, determine the corresponding default vector according to the cluster centroid, establish a second mapping relationship between the second type of financial indicator and the default vector, and construct a second risk assessment model according to the second mapping relationship;

[0016] A financial product risk analysis module is used to analyze the first and second financial indicators corresponding to the target financial product, determine the market risk information corresponding to the first and second financial indicators corresponding to the second financial indicators according to the first and second risk assessment models, and integrate the market risk information and the default risk information to obtain the financial product risk information; wherein the target financial product is a single financial product or an investment portfolio of financial products;

[0017] The risk warning and trading strategy processing module is used to issue a risk warning to the target financial product based on the risk information of the financial product and generate a trading strategy corresponding to the target financial product.

[0018] In a third aspect of an embodiment of the present invention, a computer device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a risk warning and trading strategy processing method for financial products when executing the computer program.

[0019] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a risk warning and trading strategy processing method for a financial product is implemented.

[0020] In a fifth aspect of an embodiment of the present invention, a computer program product is proposed. The computer program product includes a computer program. When the computer program is executed by a processor, a risk warning and trading strategy processing method for a financial product is implemented.

[0021] The risk warning and trading strategy processing method and device for financial products proposed in the present invention can obtain first-category financial indicator data and second-category financial indicator data; normalize the first-category financial indicator data, determine the market risk information corresponding to the first-category financial indicator data, establish a first mapping relationship between the first-category financial indicator and the market risk information, and construct a first risk assessment model based on the first mapping relationship; extract the repayment information of each object based on the second-category financial indicator data, generate a repayment vector corresponding to the object, perform cluster analysis on the repayment vector, determine the cluster centroid of each cluster data cluster, determine the corresponding default vector based on the cluster centroid, establish a second mapping relationship between the second-category financial indicator and the default vector, and construct a second risk assessment model based on the second mapping relationship; for a target financial product, analyze the first-category financial indicator and the second-category financial indicator corresponding to the target financial product, and determine the market risk information corresponding to the first-category financial indicator and the default vector corresponding to the second-category financial indicator based on the first risk assessment model and the second risk assessment model. , integrate the market risk information and the default risk information to obtain the financial product risk information; wherein, the target financial product is a single financial product or an investment portfolio of financial products; according to the financial product risk information, a risk warning is issued for the target financial product, and a trading strategy corresponding to the target financial product is generated. The overall solution adopts multiple data processing models to analyze the financial product risk, and adopts different data processing methods according to the characteristics of multi-dimensional data to reasonably analyze the risk information; adopts a normalized processing method to mine the risk information of market data, and adopts a cluster analysis processing method to analyze the default risk information of the bond business, and then integrates the market risk information with the debt default risk information to determine the risk information. The risk information can effectively, accurately, comprehensively and comprehensively reflect the risk information of the financial product, and then provide timely and effective risk warnings to customers based on the risk information, and give trading strategies based on the current trading environment to assist customers in completing financial product transactions, improve customer transaction processing efficiency, and provide strong technical support for financial product transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 It is a flowchart of a risk warning and trading strategy processing method for financial products according to an embodiment of the present invention.

[0024] Figure 2It is a schematic diagram of the construction process of the first type of risk assessment model according to an embodiment of the present invention.

[0025] Figure 3 It is a schematic diagram of the construction process of the second type of risk assessment model according to an embodiment of the present invention.

[0026] Figure 4 It is a schematic diagram of a process of determining risk information of a financial product according to an embodiment of the present invention.

[0027] Figure 5 It is a schematic diagram of a process of determining risk information of an investment portfolio according to an embodiment of the present invention.

[0028] Figure 6 It is a schematic diagram of the architecture of a peripheral device according to an embodiment of the present invention.

[0029] Figure 7 It is a schematic diagram of a selling process based on selling decision according to a specific embodiment of the present invention.

[0030] Figure 8 It is a schematic diagram of a buying process based on buying decision according to a specific embodiment of the present invention.

[0031] Fig. 9 It is a schematic diagram of the architecture of a risk warning and trading strategy processing device for financial products according to an embodiment of the present invention.

[0032] Fig.10 It is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0034] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0035] According to the implementation of the present invention, a risk warning and trading strategy processing method and device for financial products are proposed, which involves big data technology and financial technology fields. The present invention evaluates the investment risk of financial products through multiple risk assessment models, effectively displays the risk through a risk warning device, and provides investment decisions to customers in a timely manner through an investment decision model.

[0036] The principle and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0037] Figure 1 FIG. 1 is a flow chart of a risk warning and trading strategy processing method for financial products according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0038] S101, obtaining first-category financial indicator data and second-category financial indicator data;

[0039] S102, normalizing the first type of financial indicator data, determining market risk information corresponding to the first type of financial indicator data, establishing a first mapping relationship between the first type of financial indicator and the market risk information, and constructing a first risk assessment model according to the first mapping relationship;

[0040] S103, extracting repayment information of each object according to the second type of financial indicator data, generating a repayment vector corresponding to the object, performing cluster analysis on the repayment vector, determining the cluster centroid of each cluster data cluster, determining the corresponding default vector according to the cluster centroid, establishing a second mapping relationship between the second type of financial indicator and the default vector, and constructing a second risk assessment model according to the second mapping relationship;

[0041] S104, for a target financial product, analyzing the first type of financial indicator and the second type of financial indicator corresponding to the target financial product, determining the market risk information corresponding to the first type of financial indicator and the default risk information corresponding to the second type of financial indicator according to the first risk assessment model and the second risk assessment model, and integrating the market risk information and the default risk information to obtain the financial product risk information; wherein the target financial product is a single financial product or an investment portfolio of financial products;

[0042] S105: Based on the financial product risk information, issue a risk warning for the target financial product and generate a trading strategy corresponding to the target financial product.

[0043] In actual application scenarios, the risk warning and trading strategy processing method for financial products proposed in the present invention has the following working principles: obtaining first-category financial indicator data and second-category financial indicator data; normalizing the first-category financial indicator data, determining the market risk information corresponding to the first-category financial indicator data, establishing a first mapping relationship between the first-category financial indicator and the market risk information, and constructing a first risk assessment model based on the first mapping relationship; extracting the repayment information of each object based on the second-category financial indicator data, generating a repayment vector corresponding to the object, clustering the repayment vector, determining the cluster centroid of each cluster data cluster, determining the corresponding default vector based on the cluster centroid, establishing a second mapping relationship between the second-category financial indicator and the default vector, and constructing a second risk assessment model based on the second mapping relationship; for a target financial product, analyzing the first-category financial indicator and the second-category financial indicator corresponding to the target financial product, determining the market risk information corresponding to the first-category financial indicator and the second-category financial indicator based on the first risk assessment model and the second risk assessment model. The default risk information corresponding to the indicator is obtained by integrating the market risk information and the default risk information to obtain the risk information of the financial product; wherein, the target financial product is a single financial product or an investment portfolio of financial products; according to the risk information of the financial product, a risk warning is issued for the target financial product, and a trading strategy corresponding to the target financial product is generated. The overall solution adopts multiple data processing models to analyze the risks of financial products, and adopts different data processing methods according to the characteristics of multi-dimensional data to reasonably analyze the risk information; the normalization processing method is used to mine the risk information of market data, and the cluster analysis processing method is used to analyze the default risk information of the bond business, and then the market risk information and the debt default risk information are integrated to determine the risk information. The risk information can effectively, accurately, comprehensively and comprehensively reflect the risk information of the financial product, and then provide timely and effective risk warnings to customers based on the risk information, and give trading strategies based on the current trading environment to assist customers in completing financial product transactions, improve customer transaction processing efficiency, and provide strong technical support for financial product transactions.

[0044] In order to explain more clearly the risk warning and trading strategy processing methods of the above financial products, each step is explained in detail below.

[0045] S101, obtaining first-category financial indicator data and second-category financial indicator data.

[0046] The first category of financial indicator data at least includes broad-based index data of multiple broad-based funds and industry index data of multiple industries;

[0047] The second category of financial indicator data at least includes transaction data of multiple bond businesses.

[0048] S102, normalizing the first-category financial indicator data, determining market risk information corresponding to the first-category financial indicator data, establishing a first mapping relationship between the first-category financial indicator and the market risk information, and constructing a first risk assessment model according to the first mapping relationship.

[0049] refer to Figure 2 , is a schematic diagram of the construction process of the first type of risk assessment model in one embodiment of the present invention. The first risk assessment model is a market risk assessment model, such as Figure 2 As shown, the detailed process is:

[0050] S201, based on the broad-based index data of multiple broad-based funds and the industry index data of multiple industries, normalize the broad-based index data and the industry index data, and calculate the normalized percentile data corresponding to each broad-based fund and industry respectively;

[0051] S202, based on the normalized percentile data corresponding to the broad-based funds and industries, construct a mapping relationship between the broad-based funds, industries and the corresponding normalized percentile data, and construct a market risk assessment model based on the mapping relationship; wherein the normalized percentile data at least includes price-to-earnings ratio percentile data and price-to-book ratio percentile data; the price-to-book ratio percentile data has a higher priority than the price-to-book ratio percentile data, and when the price-to-earnings ratio percentile data cannot be determined, the price-to-book ratio percentile data is used.

[0052] In a specific embodiment, the broad-based index data of multiple broad-based funds and the industry index data of multiple industries can be normalized, and the price-earnings ratio percentile data and price-to-book ratio percentile data corresponding to each broad-based fund or industry can be calculated respectively; the percentile data is used as the basis for judging market risk information. For a certain financial product or investment portfolio (investment portfolio is regarded as a combination of multiple single financial products), when it involves investing in broad-based funds or different industries, the mapping relationship constructed in the first risk assessment model can be used to determine the corresponding risk information. Based on this processing method, the market type (broad-based index and industry index) parameters can be uniformly processed, and the risk information corresponding to the first type of financial indicators can be integrated through a normalized rapid processing method.

[0053] S103, extracting the repayment information of each object according to the second type of financial indicator data, generating a repayment vector corresponding to the object, performing cluster analysis on the repayment vector, determining the cluster centroid of each cluster data cluster, determining the corresponding default vector according to the cluster centroid, establishing a second mapping relationship between the second type of financial indicator and the default vector, and constructing a second risk assessment model according to the second mapping relationship.

[0054] refer to Figure 3, is a schematic diagram of the construction process of the second type of risk assessment model in one embodiment of the present invention. The second risk assessment model is a default risk assessment model, such as Figure 3 As shown, the detailed process is:

[0055] S301, extracting features from repayment information of each object in the transaction data of the bond business, and generating a repayment vector corresponding to the object, wherein the object includes a defaulting object and a non-defaulting object, and the repayment information includes attribute information, historical repayment information, and credit information of the object;

[0056] S302, performing cluster analysis on the repayment vectors of the plurality of default objects to obtain a plurality of data clusters and a cluster centroid corresponding to each of the data clusters;

[0057] S303, sampling a plurality of the cluster centroids to generate at least one centroid pair;

[0058] S304, generating a plurality of default vectors based on the centroid distance between the first centroid and the second centroid in each of the centroid pairs;

[0059] S305, calculating the total number of the default vector and the repayment vectors corresponding to the multiple default objects, and the ratio of the total number of the repayment vectors corresponding to all the objects, and setting the default risk information corresponding to the default vector according to the ratio; establishing a mapping relationship between the second type of financial indicators and the default risk information corresponding to the default vector, and constructing a second risk assessment model based on the mapping relationship.

[0060] In fact, the default risk is determined by analyzing the repayment vector to determine the quantitative value of the probability of default in the bond business, and the quantitative value of the probability is used as the default risk information. For example, if the probability of default of a bond business is 0.7 after analysis, the default risk information is 0.7.

[0061] Furthermore, in order to improve the rationality and accuracy of the calculation of the default vector, for S304, based on the centroid distance between the first centroid and the second centroid in each of the centroid pairs, the specific process of generating multiple default vectors further includes:

[0062] For each of the centroid pairs, determine the number of team defaults of the centroid pair, where the number of team defaults is the sum of the numbers of repayment vectors in the data clusters corresponding to the first centroid and the second centroid in the centroid pair respectively; determine the new data generation ratio corresponding to the centroid pair based on the number of team defaults and the total amount of repayment vectors in the bond business transaction data; determine the number of team new data corresponding to the centroid pair based on the preset new data number and the new data generation ratio.

[0063] For each of the centroid pairs, determine the centroid distance between the first centroid and the second centroid; generate a new centroid distance for the number of new data for the team based on the centroid distance using a random function; and generate a default vector for the number of new data for the team based on the new centroid distance and the first centroid or the second centroid.

[0064] In actual application scenarios, if the calculation process is simplified, the current default status of the bond business can be directly used. For example, when the status of the bond business is non-default, the bond risk value is 0; when the status of the bond business is default, the bond risk value is 1.

[0065] The default risk information can be quantified by the above method, and the default probability value can be predicted by analyzing the current repayment information as the default risk information of the bond business. The default risk information analyzed based on this method is more reasonable and accurate. The default risk information (corresponding to the second type of financial indicators) obtained by the above method belongs to the same type of data as the risk information of the first type of financial indicators (both belong to normalized parameters between 0-1), and can be further integrated to calculate the comprehensive risk information of financial products.

[0066] S104, for the target financial product, analyze the first category financial indicators and the second category financial indicators corresponding to the target financial product, determine the market risk information corresponding to the first category financial indicators and the default risk information corresponding to the second category financial indicators according to the first risk assessment model and the second risk assessment model, and integrate the market risk information and the default risk information to obtain the financial product risk information.

[0067] like Figure 4 As shown in Figure 1, the specific process for determining the risk information of financial products is as follows:

[0068] S401, when the target financial product is a single financial product, analyze the first category financial indicators and the second category financial indicators corresponding to the single financial product, wherein the first category financial indicators at least include the broad-based fund information and the industry information of the single financial product; the second category financial indicators are bond business information.

[0069] In one embodiment, corresponding financial indicator data may be obtained for a single financial product to determine broad-based fund information, industry information, and bond business details of the single financial product.

[0070] S402: For the first and second financial indicators of a single financial product, the corresponding market risk information and default risk information are determined according to the first risk assessment model and the second risk assessment model respectively; and the financial product risk information is determined according to the ratio of the market risk information and the default risk information to the first and second financial indicators.

[0071] In one embodiment, for the broad-based fund information, industry details and bond business information of a single financial product, a market risk assessment model is used to determine the risk information corresponding to the broad-based fund and the risk information corresponding to the industry, and a default risk assessment model is used to determine the default risk information corresponding to the bond business.

[0072] The calculation method for risk information of a single financial product is as follows:

[0073]

[0074] Where R is the risk information of a single financial product; PE i is the risk information of the ith broad-based fund or industry; p i is the proportion of investment in the i-th broad-based fund or industry; m is the total number of investments in broad-based funds or industries; BR j is the risk information of the j-th bond business; q j is the proportion invested in the jth bond business; n is the total amount invested in bond business.

[0075] The relationship between the investment ratios is:

[0076] For a single financial product (i.e., a certain financial product invested by a customer), the above methods from S401 to S402 can be used. For a customer who has invested in multiple financial products to form a portfolio of financial products, the risk information of the portfolio of financial products can be calculated. Figure 5 , the specific method is:

[0077] S501, when the target financial product is an investment portfolio, obtain the financial product risk information of each financial product in the investment portfolio and the proportion of each financial product corresponding to the investment direction;

[0078] S502, determining the financial product risk information of the investment portfolio according to the financial product risk information of each financial product in the investment portfolio and the proportion of each financial product corresponding to the investment direction.

[0079] The calculation method of investment portfolio risk information is as follows:

[0080]

[0081] Where PR is the risk information of the investment portfolio; R i is the risk information of the ith financial product; s i is the investment proportion of the ith financial product; k is the total number of financial products invested.

[0082] The investment ratio relationship is:

[0083] S105: Based on the financial product risk information, issue a risk warning for the target financial product and generate a trading strategy corresponding to the target financial product.

[0084] 1. Trading strategy:

[0085] Trading strategies can be divided into trading strategies targeting a single financial product (i.e., a single financial product), investment portfolios (a combination of multiple financial products), and further include selling and buying trading strategies.

[0086] Specifically, the processing flow of the selling strategy is as follows:

[0087] For a single financial product, when the risk information of the single financial product is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio is determined based on the risk information of the single financial product and the selling risk threshold, and the trading strategy is displayed through the client;

[0088] For investment portfolios, when the risk information of the investment portfolio is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio is determined based on the risk information of the investment portfolio and the selling risk threshold, and the trading strategy is displayed through the client;

[0089] The selling risk threshold is calculated by taking the average value of the risk information of each financial product when it is sold for the first time.

[0090] Specifically, the processing flow of the buy strategy is:

[0091] For a single financial product, when the risk information of the single financial product is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount is determined based on the risk information of the single financial product, the allowed purchase amount of the single financial product and the buying risk threshold, and the trading strategy is displayed through the client;

[0092] For investment portfolios, when the risk information of the investment portfolio is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount is determined based on the risk information of the investment portfolio, the total amount allowed to be purchased by the investment portfolio and the buying risk threshold, and the trading strategy is displayed through the client;

[0093] The buying risk threshold is calculated by taking the average value of the risk information of each financial product when it is first bought.

[0094] The specific algorithm of the selling strategy is explained in detail below.

[0095] For a single financial product, when the risk information of a single financial product is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio in the trading strategy is:

[0096] SP=(RR 0 ) / (1-R 0 );

[0097] Where, SP is the selling ratio; R 0 is the selling risk threshold; R is the risk information of a single financial product;

[0098] For an investment portfolio, when the risk information of the investment portfolio is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio in the trading strategy is:

[0099] SP=(PR-R 0 ) / (1-R 0 )

[0100] Where, SP is the selling ratio; R 0 is the selling risk threshold; PR is the risk information of the investment portfolio;

[0101] Wherein, the method for determining the selling risk threshold is:

[0102] R 0 =AVERAGE(R 1 ,R 2 ,…,R i ,…,R n-1 ,R n );

[0103] In the formula, R 0 is the selling risk threshold; AVERAGE means the calculated average; R i is the risk information of the i-th financial product when it is sold for the first time, and n is the total number of financial products.

[0104] The specific algorithm of the buying strategy is explained in detail below.

[0105] For a single financial product, when the risk information of the single financial product is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount in the trading strategy is:

[0106] BA=A 单一金融产品 *(R 0 '-R) / R 0 ';

[0107] In the formula, BA is the purchase amount; A 单一金融产品 The amount of a single financial product that can be purchased; R 0 ' is the buying risk threshold; R is the risk information of a single financial product;

[0108] For an investment portfolio, when the risk information of the investment portfolio is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount in the trading strategy is:

[0109] BA=A 投资组合 *(R 0 '-PR) / R 0 ';

[0110] In the formula, BA is the purchase amount; A 投资组合 is the total amount of investment portfolio allowed to be purchased; R 0 ' is the buying risk threshold; PR is the risk information of the investment portfolio;

[0111] Wherein, the method for determining the buying risk threshold is:

[0112] R 0 =AVERAGE(R 1 ',R 2 ',…,R i ',…,R n-1 ',R n ');

[0113] In the formula, R 0 ' is the buying risk threshold; AVERAGE means calculating the average; R i ' is the risk information of the i-th financial product when it is first purchased, and n is the total number of financial products.

[0114] 2. Risk warning:

[0115] After obtaining risk information (a type of financial product risk information, investment portfolio risk information), a variety of risk warning methods can be used, such as reminding customers by adjusting the light color on electronic equipment terminals, and reminding customers by adjusting the light brightness and sensing temperature on peripheral devices. In actual application scenarios, one or a combination of these can be used.

[0116] Electronic equipment terminal:

[0117] The color pixel value is determined based on the risk information corresponding to a single financial product or investment portfolio, and the light color is adjusted through the color pixel value on the electronic device terminal to issue a risk warning.

[0118] R and PR are values ​​in the range of [0,1]. The fund portfolio risk warning device includes two parts: an electronic device terminal and a peripheral device. In the electronic device terminal (such as a mobile phone), the risk value is displayed in red. The RGB color pixel value display method of the red color is:

[0119] RGB=PR*255

[0120] RGB=R*255

[0121] Where RGB is the color pixel; PR is the risk information of the investment portfolio; and R is the risk information of a single financial product.

[0122] That is, the greater the risk information, the greater the RGB color pixel value and the darker the red.

[0123] Peripherals:

[0124] Configuring peripheral devices corresponding to the electronic equipment terminal;

[0125] The brightness value and the resistor position are determined according to the risk information corresponding to a single financial product or an investment portfolio, and the brightness of the indicator light is adjusted by the brightness value in the peripheral device, and risk warning is performed based on the heat generated by the resistor position.

[0126] To adjust the indicator light brightness:

[0127] LD=PR*MAX

[0128] LD=R*MAX

[0129] Where LD is the indicator brightness; MAX is the maximum brightness of the indicator; PR is the risk information of the investment portfolio; and R is the risk information of a single financial product.

[0130] The method for risk warning based on the heat generated by the resistor gear is:

[0131] Q=U 2 *T / R 电阻

[0132] In the formula, Q is heat energy; U is voltage value; R 电阻 is the resistance value; T is the time.

[0133] R 电阻 It can include multiple resistor gears, each corresponding to different risk information, that is, according to the risk information, it can be adjusted to a resistor gear to generate corresponding heat for risk warning.

[0134] 3. Financial product recommendations:

[0135] For all financial products, update financial product risk information according to the preset cycle;

[0136] Based on the financial product risk information, a certain number of financial products with the lowest risk values ​​are pushed to the customer.

[0137] For example, after updating the risk information of financial products, the risk values ​​are sorted from low to high, and the top 10 (i.e., the 10 with the lowest risk values) financial products are selected and pushed to customers for reference.

[0138] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0139] The risk warning and transaction strategy processing method for financial products proposed by the present invention is described below in conjunction with a specific embodiment.

[0140] The fund market includes the following fund types: stock type (including index type), hybrid type-stock-biased, hybrid type-debt-biased, hybrid type-flexible, hybrid type-balanced, bond type-long-term bond, bond type-medium-short-term bond, bond type-hybrid bond, bond type-convertible bond, money type, etc. Among them, stock type (including index type) and hybrid type (including equity investment) have market risk; hybrid type (including bond investment) and bond type have default risk; money type can be considered risk-free.

[0141] Based on the market risk and default risk of the above fund types, a market risk assessment model and a default risk assessment model are constructed respectively to assess market risk and default risk. The following is a detailed explanation of the five aspects: 1. Market risk assessment model, 2. Default risk assessment model, 3. Risk assessment, 4. Risk early warning device, and 5. Trading strategy.

[0142] 1. Market risk assessment model:

[0143] Based on the broad-based index data of multiple broad-based funds and the industry index data of multiple industries, the broad-based index data and the industry index data are normalized, and the normalized percentile data corresponding to each broad-based fund and industry are calculated respectively; based on the normalized percentile data corresponding to the broad-based funds and industries, a mapping relationship between the broad-based funds, industries and the corresponding normalized percentile data is constructed, and a market risk assessment model is constructed based on the mapping relationship; wherein the normalized percentile data at least includes the price-earnings ratio percentile data and the price-to-book ratio percentile data; the price-to-book ratio percentile data has a higher priority than the price-to-book ratio percentile data, and when the price-earnings ratio percentile data cannot be determined, the price-to-book ratio percentile data is used.

[0144] Market risk refers to equity market risk. Five broad-based indices, including SSE 50, CSI 300, ChiNext, CSI 500, and CSI 1000, and 31 Shenwan first-level industry indices, are selected as vertical indicators of the market risk assessment model, and price-earnings ratio PE, PE ten-year percentile, price-to-book ratio PB, and PB ten-year percentile are selected as horizontal indicators.

[0145] It is preferred to use the PE ten-year percentile as a risk measure for broad-based markets or industry markets (when the PE ten-year percentile cannot be calculated, use the PB ten-year percentile instead).

[0146] Price-to-earnings ratio (PE): the ratio of a company's market value to its earnings. Price-to-earnings ratio = stock price / earnings per share = total market value / company net profit. The lower the price-to-earnings ratio, the lower the stock price and the lower the market risk. The most commonly used price-to-earnings ratio is the rolling price-to-earnings ratio (PE-TTM), which is updated every quarter and has a stronger timeliness.

[0147]

[0148] Where, SV i is the market value of the i-th stock; SP i is the net profit of the i-th stock in the past 12 months; n is the total number of stocks.

[0149] PE 10-year percentile: The percentile of the current PE ratio in the PE ratio of the past ten years. For example, when the percentile is 10%, it means that the current price is cheaper than 90% of the time in the past ten years.

[0150] PE 十年百分位 =1-NUM i / NUM T

[0151] Where NUM i NUM is the number of PEs in the past ten years that are greater than the current PE; T The total number of PEs in the past ten years.

[0152] Price-to-book ratio PB: the ratio of a company's market price to its net assets, price-to-book ratio = stock price / net assets per share = total market value of stocks / net assets of the company. The lower the price-to-book ratio, the lower the stock price is relatively and the smaller the market risk is.

[0153]

[0154] Where, SV i is the market value of the i-th stock; SA i is the net assets of the i-th stock.

[0155] Price-to-book ratio PB ten-year percentile: The percentile of the current price-to-book ratio in the price-to-book ratios of the past ten years. When the percentile is 10%, it means that the current price is cheaper than 90% of the time in the past ten years.

[0156] PB ten-year percentile = 1-NUM i ' / NUM T '

[0157] Where NUM i' is the number of PBs in the past ten years that are greater than the current PB; NUM T ' is the total number of PB in the past ten years.

[0158] Taking April 4, 2023 as an example, in the broad-based market, the risk value of the CSI 300 is 48.21%, which is higher than the risk value of the ChiNext at 5.47%. In the industry market, the risk value of the computer industry is 79.23%, which is higher than the risk value of power equipment at 5.35%.

[0159] In actual application scenarios, there are usually corresponding PE and PB data for the broad base or industry that the fund product invests in. If there is no PE data, the PB data can be used; if there is no PE or PB data (extremely small probability), the risk value cannot be calculated, and the PE and PB data will be recalculated while waiting for the update of the financial product risk information.

[0160] As shown in Table 1, these are exemplary market risk assessment model indicators.

[0161] Table 1

[0162]

[0163]

[0164] 2. Default risk assessment model:

[0165] Feature extraction is performed on the repayment information of each object in the bond business transaction data to generate a repayment vector corresponding to the object, wherein the object includes a default object and a non-default object, and the repayment information includes the object's attribute information, historical repayment information, and credit information. Cluster analysis is performed on the repayment vectors of multiple default objects to obtain multiple data clusters and cluster centroids corresponding to each data cluster. Multiple cluster centroids are sampled to generate at least one centroid pair. Based on the centroid distance between the first centroid and the second centroid in each centroid pair, multiple default vectors are generated. The total number of default vectors and repayment vectors corresponding to multiple default objects is calculated, and the ratio of the total number of repayment vectors corresponding to multiple non-default objects is calculated, and the default risk information corresponding to the default vector is set according to the ratio; a mapping relationship between the second type of financial indicators and the default risk information corresponding to the default vector is established, and a second risk assessment model is constructed based on the mapping relationship.

[0166] The bond business transaction data includes a default data set and a non-default data set, where the default data set includes attribute information, historical repayment information, and credit information of multiple default objects and non-default objects. Among them, the attribute information includes basic information such as the bond issuance time. The historical repayment information includes the number of business defaults, the number of non-defaults, the default amount, and the non-default amount. Credit information includes credit records, credit scores, etc. The acquisition of this information has taken necessary confidentiality measures and has been authorized by financial institutions and customers.

[0167] The attribute information, historical repayment information and credit information of each object in the bond business transaction data are extracted to generate the repayment vector corresponding to each object. The repayment vector of the default object is clustered and analyzed. In the process of cluster analysis, kmeans (k-means clustering algorithm) can be used to obtain multiple data clusters and the cluster centroid corresponding to each data cluster.

[0168] When using kmeans for cluster analysis, first randomly select k points as cluster centroids. Calculate the distances from other vectors in the repayment vector to the cluster centroids, and use these vectors as the categories in the closest data clusters. Determine the average value of each category for the classified repayment vectors and solve for the new cluster centroids. Compare the new cluster centroids with the previous cluster centroids. If the cluster centroids change, iteratively calculate the distances from other vectors in the repayment vector to the cluster centroids again, and solve for the new cluster centroids until the cluster centroids no longer change, and the cluster centroids are solved.

[0169] The biggest risk of a bond is default. By analyzing the repayment vector, we can determine the quantitative value of the probability of default in a bond business, and use this quantitative value as default risk information. For example, if the probability of default in a bond business is 0.7 after analysis, the default risk information BR is 0.7.

[0170] 3. Risk assessment:

[0171] For a fund product, its risk value is:

[0172]

[0173] Where R is the risk information of financial products; PE i is the risk information of the ith broad-based fund or industry; p i is the proportion of investment in the i-th broad-based fund or industry; m is the total number of investments in broad-based funds or industries; BR j is the risk information of the j-th bond business; q j is the proportion invested in the jth bond business; n is the total amount invested in bond business.

[0174] The relationship between the investment ratios is:

[0175] For a fund product, when calculating the risk value R, when the equity is invested in one or more industries (non-broad-based), the market risk value PE adopts the industry risk value; when the equity is invested in a broad-based, the market risk value PE adopts the broad-based risk value.

[0176] For a portfolio, the portfolio risk value is:

[0177]

[0178] Where PR is the risk information of the investment portfolio; R i is the risk information of the ith financial product; s i is the investment proportion of the ith financial product; k is the total number of financial products invested.

[0179] The investment ratio relationship is:

[0180] 4. Risk warning device:

[0181] The risk warning device can provide risk warning for an investment portfolio or a certain financial product based on actual needs.

[0182] R and PR are values ​​in the range of [0,1]. The fund portfolio risk warning device includes two parts: an electronic device terminal and a peripheral device. In the electronic device terminal (such as a mobile phone), the risk value is displayed in red. The RGB color pixel value display method of the red color is:

[0183] RGB=PR*255

[0184] RGB=R*255

[0185] Where RGB is the color pixel; PR is the risk information of the investment portfolio; and R is the risk information of a single financial product.

[0186] The greater the risk information, the larger the RGB color pixel value and the darker the red.

[0187] In one embodiment, the present invention also proposes to use peripheral devices for risk warning. The architecture of the peripheral devices is as follows: Figure 6 As shown, the peripheral equipment includes a resistor element, an indicator light, a timer, an auxiliary device, a bottom plate, a housing (including a finger temperature sensing area), and a connecting wire (connected to the electronic equipment).

[0188] Resistor: The resistors include 10 resistors from R1 to R10 from large to small. The resistors can be adjusted according to the actual size of the peripheral device and other constraints.

[0189] Indicator light: Red bulb indicator light.

[0190] Timer: You can set the countdown timer to T, where T is 5 seconds, 10 seconds, etc.

[0191] Auxiliary device: This auxiliary device can convert the risk value PR of the fund portfolio into the brightness of the indicator light in the following way:

[0192] LD=PR*MAX

[0193] LD=R*MAX

[0194] Where LD is the indicator brightness; MAX is the maximum brightness of the indicator; PR is the risk information of the investment portfolio; and R is the risk information of a single financial product.

[0195] The larger the fund portfolio risk value PR (or the larger the risk value R of a single financial product), the brighter the indicator light will be.

[0196] Depending on the actual situation, a fund portfolio or a single financial product can be set as a risk warning target.

[0197] Taking the risk warning of fund investment portfolio as an example, the risk value PR of fund investment portfolio is converted into resistor level DW by the following method:

[0198]

[0199] According to the electric-to-heat conversion formula, the greater the fund portfolio risk value PR, the greater the thermal energy Q value.

[0200] In actual application scenarios, the gear position DW can be adjusted according to actual conditions and is not limited to the above setting method. For example, it can be divided into a gear with an interval of 0.2 or a gear with an interval of 0.05.

[0201] The method for risk warning based on the heat generated by the resistor gear is:

[0202] Q=U 2 *T / R 电阻

[0203] In the formula, Q is heat energy; U is voltage value; R 电阻 is the resistance value; T is the time.

[0204] Bottom plate pressure plate: a device used to fix the circuit, with good thermal insulation performance, so that the heat emitted by the resistor element is concentrated to the finger temperature sensing area.

[0205] Shell (including finger temperature sensing area): a device that encapsulates the circuit and has the functions of safety protection, improving thermal efficiency and beautifying decoration.

[0206] Connecting wire: used to connect electronic devices and transmit electricity and risk value data.

[0207] 5. Trading strategy:

[0208] Trading strategies are mainly used to provide analytical advice to clients on their investments and to assist them in selling or buying financial products.

[0209] Selling decision model, including single fund selling model and fund portfolio selling model.

[0210] Single fund selling model; if the risk value threshold is R 0 , when the risk value of a single fund R is greater than R 0 A selling reminder is triggered when the selling ratio is:

[0211] SP=(RR 0 ) / (1-R 0 );

[0212] Where, SP is the selling ratio; R 0 is the selling risk threshold; R is the risk information of a single financial product;

[0213] Fund portfolio selling model; if the risk value threshold is R 0 , then when the fund portfolio risk value PR is greater than R 0 A selling reminder is triggered when the selling ratio is:

[0214] SP=(PR-R 0 ) / (1-R 0 )

[0215] Where, SP is the selling ratio; R 0 is the selling risk threshold; PR is the risk information of the investment portfolio;

[0216] Wherein, the method for determining the selling risk threshold is:

[0217] R 0 =AVERAGE(R 1 ,R 2 ,…,R i ,…,R n-1 ,R n );

[0218] In the formula, R 0 is the selling risk threshold; AVERAGE means the calculated average; R i is the risk information of the i-th financial product when it is sold for the first time, and n is the total number of financial products.

[0219] refer to Figure 7, is a schematic diagram of a selling process based on a selling decision according to a specific embodiment of the present invention. Figure 7 As shown, the specific process is:

[0220] S701, identify the client's holdings of funds / fund portfolios.

[0221] S702, calculate the single fund risk value / fund portfolio risk value.

[0222] S703, calculating the selling risk threshold.

[0223] S704, determining whether a selling decision is triggered.

[0224] S705: If yes, execute the single fund selling / fund combination selling according to the selling decision during the trading hours.

[0225] S706: If no, no operation.

[0226] Buying decision models include single fund buying model, fund portfolio buying model and fund recommendation model.

[0227] Single fund purchase model: If the risk value threshold is R 0 'And the amount that can be purchased is A 第一金融产品 , when the single fund risk value R is less than R 0 ' triggers a buy reminder, the buy amount is:

[0228] BA=A 单一金融产品 *(R 0 '-R) / R 0 ';

[0229] In the formula, BA is the purchase amount; A 单一金融产品 The amount of a single financial product that can be purchased; R 0 ' is the buying risk threshold; R is the risk information of a single financial product;

[0230] Fund portfolio buying model: If the risk value threshold is R 0 'And the amount that can be purchased is A 投资组合 , when the fund portfolio risk value R is less than R 0 ' triggers a buy reminder, the buy amount is:

[0231] BA=A 投资组合 *(R 0 '-PR) / R 0 ';

[0232] In the formula, BA is the purchase amount; A 投资组合 is the total amount of investment portfolio allowed to be purchased; R 0 ' is the buying risk threshold; PR is the risk information of the investment portfolio;

[0233] Wherein, the method for determining the buying risk threshold is:

[0234] R 0 =AVERAGE(R 1 ',R 2 ',…,R i ',…,R n-1 ',R n ');

[0235] In the formula, R 0 ' is the buying risk threshold; AVERAGE means calculating the average; R i ' is the risk information of the i-th financial product when it is first purchased, and n is the total number of financial products.

[0236] refer to Figure 8 , is a schematic diagram of a buying process based on buying decision according to a specific embodiment of the present invention. Figure 8 As shown, the specific process is:

[0237] S801, identify the client's holdings of funds / fund portfolios.

[0238] S802, calculate the single fund risk value / fund portfolio risk value.

[0239] S803, calculating the buying risk threshold.

[0240] S804, determining whether a buy decision is triggered.

[0241] S805: If yes, execute single fund purchase / fund combination purchase according to the selling decision during the trading hours.

[0242] S806: If no, no operation.

[0243] In one embodiment, the present invention may also set up a fund recommendation model to regularly evaluate the risk values ​​of fund products in the entire market according to a set period, and push the top N funds with the lowest risk values ​​to all customers with fund holdings or transaction records.

[0244] After introducing the method of the exemplary embodiment of the present invention, next, refer to Fig. 9 A risk warning and transaction strategy processing device for financial products according to an exemplary embodiment of the present invention is introduced.

[0245] The implementation of the risk warning and trading strategy processing device for financial products can refer to the implementation of the above method, and the repeated parts will not be repeated. The term "module" or "unit" used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0246] Based on the same inventive concept, the present invention also proposes a risk warning and transaction strategy processing device for financial products, such as Fig. 9 As shown, the device comprises:

[0247] A financial indicator data acquisition module 910 is used to acquire first-category financial indicator data and second-category financial indicator data;

[0248] A first risk assessment model building module 920 is used to normalize the first type of financial indicator data, determine the market risk information corresponding to the first type of financial indicator data, establish a first mapping relationship between the first type of financial indicator and the market risk information, and build a first risk assessment model according to the first mapping relationship;

[0249] A second risk assessment model construction module 930 is used to extract the repayment information of each object according to the second type of financial indicator data, generate a repayment vector corresponding to the object, perform cluster analysis on the repayment vector, determine the cluster centroid of each cluster data cluster, determine the corresponding default vector according to the cluster centroid, establish a second mapping relationship between the second type of financial indicator and the default vector, and construct a second risk assessment model according to the second mapping relationship;

[0250] The financial product risk analysis module 940 is used to analyze the first type of financial indicators and the second type of financial indicators corresponding to the target financial product, determine the market risk information corresponding to the first type of financial indicators and the default risk information corresponding to the second type of financial indicators according to the first risk assessment model and the second risk assessment model, and integrate the market risk information and the default risk information to obtain the financial product risk information; wherein the target financial product is a single financial product or an investment portfolio of financial products;

[0251] The risk warning and trading strategy processing module 950 is used to issue a risk warning to the target financial product according to the financial product risk information, and generate a trading strategy corresponding to the target financial product.

[0252] In one embodiment, the first category of financial indicator data includes at least broad-based index data of multiple broad-based funds and industry index data of multiple industries; the second category of financial indicator data includes at least transaction data of multiple bond businesses.

[0253] In one embodiment, the first risk assessment model building module is specifically used to:

[0254] According to the broad-based index data of multiple broad-based funds and the industry index data of multiple industries, the broad-based index data and the industry index data are normalized, and the normalized percentile data corresponding to each broad-based fund and industry are calculated respectively;

[0255] According to the normalized percentile data corresponding to broad-based funds and industries, a mapping relationship between broad-based funds, industries and the corresponding normalized percentile data is constructed, and a market risk assessment model is constructed based on the mapping relationship; wherein, the normalized percentile data at least includes price-to-earnings ratio percentile data and price-to-book ratio percentile data; the price-to-book ratio percentile data has a higher priority than the price-to-book ratio percentile data, and when the price-to-earnings ratio percentile data cannot be determined, the price-to-book ratio percentile data is used.

[0256] In one embodiment, the second risk assessment model building module is specifically used to:

[0257] Extracting features from repayment information of each object in the transaction data of the bond business, and generating a repayment vector corresponding to the object, wherein the object includes a default object and a non-default object, and the repayment information includes attribute information, historical repayment information, and credit information of the object;

[0258] Performing cluster analysis on the repayment vectors of the plurality of default objects to obtain a plurality of data clusters and a cluster centroid corresponding to each of the data clusters;

[0259] Sampling a plurality of the cluster centroids to generate at least one centroid pair;

[0260] generating a plurality of default vectors based on a centroid distance between a first centroid and a second centroid in each of the centroid pairs;

[0261] Calculate the total number of repayment vectors corresponding to the default vector and multiple default objects, and the ratio of the total number of repayment vectors corresponding to all objects, and set the default risk information corresponding to the default vector according to the ratio; establish a mapping relationship between the second type of financial indicators and the default risk information corresponding to the default vector, and construct a second risk assessment model based on the mapping relationship.

[0262] In one embodiment, the financial product risk analysis module is specifically used to:

[0263] If the target financial product is a single financial product, the first and second financial indicators corresponding to the single financial product are analyzed, wherein the first financial indicators at least include the broad-based fund information and the industry information of the single financial product; the second financial indicators are the bond business information;

[0264] For the first and second financial indicators of a single financial product, the corresponding market risk information and default risk information are determined based on the first risk assessment model and the second risk assessment model respectively. The financial product risk information is determined based on the ratio of the market risk information and the default risk information to the first and second financial indicators.

[0265] In one embodiment, the financial product risk analysis module is specifically used to:

[0266] When the target financial product is an investment portfolio, obtain the financial product risk information of each financial product in the investment portfolio and the proportion of each financial product corresponding to the investment direction;

[0267] The financial product risk information of the investment portfolio is determined based on the financial product risk information of each financial product in the investment portfolio and the proportion of the corresponding investment direction of each financial product.

[0268] In one embodiment, the risk warning and trading strategy processing module is specifically used to:

[0269] For a single financial product, when the risk information of the single financial product is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio is determined based on the risk information of the single financial product and the selling risk threshold, and the trading strategy is displayed through the client;

[0270] For investment portfolios, when the risk information of the investment portfolio is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio is determined based on the risk information of the investment portfolio and the selling risk threshold, and the trading strategy is displayed through the client;

[0271] The selling risk threshold is calculated by taking the average value of the risk information of each financial product when it is sold for the first time.

[0272] In one embodiment, the risk warning and trading strategy processing module is specifically used to:

[0273] For a single financial product, when the risk information of the single financial product is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount is determined based on the risk information of the single financial product, the allowed purchase amount of the single financial product and the buying risk threshold, and the trading strategy is displayed through the client;

[0274] For investment portfolios, when the risk information of the investment portfolio is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount is determined based on the risk information of the investment portfolio, the total amount allowed to be purchased by the investment portfolio and the buying risk threshold, and the trading strategy is displayed through the client;

[0275] The buying risk threshold is calculated by taking the average value of the risk information of each financial product when it is first bought.

[0276] In one embodiment, the risk warning and trading strategy processing module is specifically used to:

[0277] The color pixel value is determined based on the risk information corresponding to a single financial product or investment portfolio, and the light color is adjusted through the color pixel value on the electronic device terminal to issue a risk warning.

[0278] In one embodiment, the risk warning and trading strategy processing module is specifically used to:

[0279] Configuring peripheral devices corresponding to the electronic equipment terminal;

[0280] The brightness value and the resistor position are determined according to the risk information corresponding to a single financial product or an investment portfolio, and the brightness of the indicator light is adjusted by the brightness value in the peripheral device, and risk warning is performed based on the heat generated by the resistor position.

[0281] In one embodiment, the risk warning and trading strategy processing module is specifically used to:

[0282] For all financial products, update financial product risk information according to the preset cycle;

[0283] Based on the financial product risk information, a certain number of financial products with the lowest risk values ​​are pushed to the customer.

[0284] In one embodiment, when the financial product risk analysis module calculates risk information, the single financial product risk information calculation method is:

[0285]

[0286] Where R is the risk information of a single financial product; PE i is the risk information of the ith broad-based fund or industry; p i is the proportion of investment in the i-th broad-based fund or industry; m is the total number of investments in broad-based funds or industries; BR j is the risk information of the j-th bond business; q j is the proportion invested in the jth bond business; n is the total amount invested in bond business.

[0287] For a portfolio, the portfolio risk value is:

[0288]

[0289] Where PR is the risk information of the investment portfolio; R i is the risk information of the ith financial product; s iis the investment proportion of the ith financial product; k is the total number of financial products invested.

[0290] In one embodiment, when the risk warning and trading strategy processing module determines the trading strategy, for a single financial product, when the risk information of the single financial product is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio in the trading strategy is:

[0291] SP=(RR 0 ) / (1-R 0 );

[0292] Where, SP is the selling ratio; R 0 is the selling risk threshold; R is the risk information of a single financial product;

[0293] For an investment portfolio, when the risk information of the investment portfolio is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio in the trading strategy is:

[0294] SP=(PR-R 0 ) / (1-R 0 )

[0295] Where, SP is the selling ratio; R 0 is the selling risk threshold; PR is the risk information of the investment portfolio;

[0296] Wherein, the method for determining the selling risk threshold is:

[0297] R 0 =AVERAGE(R 1 ,R 2 ,…,R i ,…,R n-1 ,R n );

[0298] In the formula, R 0 is the selling risk threshold; AVERAGE means the calculated average; R i is the risk information of the i-th financial product when it is sold for the first time, and n is the total number of financial products.

[0299] For a single financial product, when the risk information of the single financial product is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount in the trading strategy is:

[0300] BA=A 单一金融产品 *(R 0 '-R) / R 0 ';

[0301] In the formula, BA is the purchase amount; A 单一金融产品The amount of a single financial product that can be purchased; R 0 ' is the buying risk threshold; R is the risk information of a single financial product;

[0302] For an investment portfolio, when the risk information of the investment portfolio is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount in the trading strategy is:

[0303] BA=A 投资组合 *(R 0 '-PR) / R 0 ';

[0304] In the formula, BA is the purchase amount; A 投资组合 is the total amount of investment portfolio allowed to be purchased; R 0 ' is the buying risk threshold; PR is the risk information of the investment portfolio;

[0305] Wherein, the method for determining the buying risk threshold is:

[0306] R 0 =AVERAGE(R 1 ',R 2 ',…,R i ',…,R n-1 ',R n ');

[0307] In the formula, R 0 ' is the buying risk threshold; AVERAGE means calculating the average; R i ' is the risk information of the i-th financial product when it is first purchased, and n is the total number of financial products.

[0308] It should be noted that although several modules of the risk warning and trading strategy processing device for financial products are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules described above can be concretized in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules for concretization.

[0309] Based on the above invention concept, Fig.10 As shown, the present invention also proposes a computer device 1000, including a memory 1010, a processor 1020, and a computer program 1030 stored in the memory 1010 and executable on the processor 1020, wherein the processor 1020 implements the risk warning and trading strategy processing method for the aforementioned financial product when executing the computer program 1030.

[0310] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the risk warning and trading strategy processing method for the aforementioned financial product is implemented.

[0311] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program, and when the computer program is executed by a processor, a risk warning and trading strategy processing method for financial products is implemented.

[0312] The risk warning and trading strategy processing method and device for financial products proposed in the present invention can obtain first-category financial indicator data and second-category financial indicator data; normalize the first-category financial indicator data, determine the market risk information corresponding to the first-category financial indicator data, establish a first mapping relationship between the first-category financial indicator and the market risk information, and construct a first risk assessment model based on the first mapping relationship; extract the repayment information of each object based on the second-category financial indicator data, generate a repayment vector corresponding to the object, perform cluster analysis on the repayment vector, determine the cluster centroid of each cluster data cluster, determine the corresponding default vector based on the cluster centroid, establish a second mapping relationship between the second-category financial indicator and the default vector, and construct a second risk assessment model based on the second mapping relationship; for a target financial product, analyze the first-category financial indicator and the second-category financial indicator corresponding to the target financial product, and determine the market risk information corresponding to the first-category financial indicator and the default vector corresponding to the second-category financial indicator based on the first risk assessment model and the second risk assessment model. , integrate the market risk information and the default risk information to obtain the financial product risk information; wherein, the target financial product is a single financial product or an investment portfolio of financial products; according to the financial product risk information, a risk warning is issued for the target financial product, and a trading strategy corresponding to the target financial product is generated. The overall solution adopts multiple data processing models to analyze the financial product risk, and adopts different data processing methods according to the characteristics of multi-dimensional data to reasonably analyze the risk information; adopts a normalized processing method to mine the risk information of market data, and adopts a cluster analysis processing method to analyze the default risk information of the bond business, and then integrates the market risk information with the debt default risk information to determine the risk information. The risk information can effectively, accurately, comprehensively and comprehensively reflect the risk information of the financial product, and then provide timely and effective risk warnings to customers based on the risk information, and give trading strategies based on the current trading environment to assist customers in completing financial product transactions, improve customer transaction processing efficiency, and provide strong technical support for financial product transactions.

[0313] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0314] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0315] The present invention is described with reference to flowcharts and / or block diagrams of methods and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0316] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0317] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0318] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A risk warning and trading strategy processing method for financial products, characterized in that: include: Obtaining the first category financial indicator data and the second category financial indicator data; Normalizing the first type of financial indicator data, determining market risk information corresponding to the first type of financial indicator data, establishing a first mapping relationship between the first type of financial indicator and the market risk information, and constructing a first risk assessment model based on the first mapping relationship; Extracting repayment information of each object according to the second type of financial indicator data, generating a repayment vector corresponding to the object, performing cluster analysis on the repayment vector, determining the cluster centroid of each cluster data cluster, determining the corresponding default vector according to the cluster centroid, establishing a second mapping relationship between the second type of financial indicator and the default vector, and constructing a second risk assessment model according to the second mapping relationship; For a target financial product, analyzing the first category financial indicator and the second category financial indicator corresponding to the target financial product, determining the market risk information corresponding to the first category financial indicator and the default risk information corresponding to the second category financial indicator according to the first risk assessment model and the second risk assessment model, and integrating the market risk information and the default risk information to obtain the financial product risk information; wherein the target financial product is a single financial product or an investment portfolio of financial products; Based on the financial product risk information, a risk warning is issued for the target financial product, and a trading strategy corresponding to the target financial product is generated.

2. The method according to claim 1, characterized in that: The first category of financial indicator data includes at least the broad-based index data of multiple broad-based funds and the industry index data of multiple industries; the second category of financial indicator data includes at least the transaction data of multiple bond businesses.

3. The method according to claim 2, characterized in that Normalizing the first-category financial indicator data, determining market risk information corresponding to the first-category financial indicator data, establishing a first mapping relationship between the first-category financial indicator and the market risk information, and constructing a first risk assessment model according to the first mapping relationship, including: According to the broad-based index data of multiple broad-based funds and the industry index data of multiple industries, the broad-based index data and the industry index data are normalized, and the normalized percentile data corresponding to each broad-based fund and industry are calculated respectively; According to the normalized percentile data corresponding to broad-based funds and industries, a mapping relationship between broad-based funds, industries and the corresponding normalized percentile data is constructed, and a market risk assessment model is constructed based on the mapping relationship; wherein, the normalized percentile data at least includes price-to-earnings ratio percentile data and price-to-book ratio percentile data; the price-to-book ratio percentile data has a higher priority than the price-to-book ratio percentile data, and when the price-to-earnings ratio percentile data cannot be determined, the price-to-book ratio percentile data is used.

4. The method according to claim 2, characterized in that: Extracting repayment information of each object according to the second type of financial indicator data, generating a repayment vector corresponding to the object, performing cluster analysis on the repayment vector, determining the cluster centroid of each cluster data cluster, determining the corresponding default vector according to the cluster centroid, establishing a second mapping relationship between the second type of financial indicator and the default vector, and constructing a second risk assessment model according to the second mapping relationship, including: Extracting features from repayment information of each object in the transaction data of the bond business, and generating a repayment vector corresponding to the object, wherein the object includes a default object and a non-default object, and the repayment information includes attribute information, historical repayment information, and credit information of the object; Performing cluster analysis on the repayment vectors of the plurality of default objects to obtain a plurality of data clusters and a cluster centroid corresponding to each of the data clusters; Sampling a plurality of the cluster centroids to generate at least one centroid pair; generating a plurality of default vectors based on a centroid distance between a first centroid and a second centroid in each of the centroid pairs; Calculate the total number of repayment vectors corresponding to the default vector and multiple default objects, and the ratio of the total number of repayment vectors corresponding to all objects, and set the default risk information corresponding to the default vector according to the ratio; establish a mapping relationship between the second type of financial indicators and the default risk information corresponding to the default vector, and construct a second risk assessment model based on the mapping relationship.

5. The method according to claim 1, characterized in that For a target financial product, the first category financial indicator and the second category financial indicator corresponding to the target financial product are analyzed, and the market risk information corresponding to the first category financial indicator and the default risk information corresponding to the second category financial indicator are determined according to the first risk assessment model and the second risk assessment model, and the market risk information and the default risk information are integrated to obtain the financial product risk information, including: If the target financial product is a single financial product, the first and second financial indicators corresponding to the single financial product are analyzed, wherein the first financial indicators at least include the broad-based fund information and the industry information of the single financial product; the second financial indicators are the bond business information; For the first and second financial indicators of a single financial product, the corresponding market risk information and default risk information are determined based on the first risk assessment model and the second risk assessment model respectively. The financial product risk information is determined based on the market risk information, default risk information and the ratio of the corresponding investments of the first and second financial indicators.

6. The method according to claim 5, characterized in that For a target financial product, the first category financial indicator and the second category financial indicator corresponding to the target financial product are analyzed, and the market risk information corresponding to the first category financial indicator and the default risk information corresponding to the second category financial indicator are determined according to the first risk assessment model and the second risk assessment model, and the market risk information and the default risk information are integrated to obtain the financial product risk information, including: When the target financial product is an investment portfolio, obtain the financial product risk information of each financial product in the investment portfolio and the proportion of each financial product corresponding to the investment direction; The financial product risk information of the investment portfolio is determined based on the financial product risk information of each financial product in the investment portfolio and the proportion of the corresponding investment direction of each financial product.

7. The method according to claim 1, characterized in that Generate a trading strategy corresponding to the target financial product, including: For a single financial product, when the risk information of the single financial product is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio is determined based on the risk information of the single financial product and the selling risk threshold, and the trading strategy is displayed through the client; For investment portfolios, when the risk information of the investment portfolio is greater than the selling risk threshold, a selling reminder is triggered and a trading strategy is generated. The selling ratio is determined based on the risk information of the investment portfolio and the selling risk threshold, and the trading strategy is displayed through the client; The selling risk threshold is calculated by taking the average value of the risk information of each financial product when it is sold for the first time.

8. The method according to claim 1, characterized in that Generate a trading strategy corresponding to the target financial product, including: For a single financial product, when the risk information of the single financial product is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount is determined based on the risk information of the single financial product, the allowed purchase amount of the single financial product and the buying risk threshold, and the trading strategy is displayed through the client; For investment portfolios, when the risk information of the investment portfolio is less than the buying risk threshold, a buying reminder is triggered and a trading strategy is generated. The buying amount is determined based on the risk information of the investment portfolio, the total amount allowed to be purchased by the investment portfolio and the buying risk threshold, and the trading strategy is displayed through the client; The buying risk threshold is calculated by taking the average value of the risk information of each financial product when it is first bought.

9. The method according to claim 1, characterized in that: Based on the financial product risk information, a risk warning is issued for the target financial product, including: The color pixel value is determined based on the risk information corresponding to a single financial product or investment portfolio, and the light color is adjusted through the color pixel value on the electronic device terminal to issue a risk warning.

10. The method according to claim 9, characterized in that Providing risk warning for the target financial product based on the financial product risk information also includes: Configuring peripheral devices corresponding to the electronic equipment terminal; The brightness value and the resistor position are determined according to the risk information corresponding to a single financial product or an investment portfolio, and the brightness of the indicator light is adjusted by the brightness value in the peripheral device, and risk warning is performed based on the heat generated by the resistor position.

11. The method according to claim 1, characterized in that: The method further includes: For all financial products, update financial product risk information according to the preset cycle; Based on the financial product risk information, a certain number of financial products with the lowest risk values ​​are pushed to the customer.

12. A risk warning and transaction strategy processing device for financial products, characterized in that: include: A financial indicator data acquisition module, used to acquire the first type of financial indicator data and the second type of financial indicator data; A first risk assessment model building module, used to normalize the first type of financial indicator data, determine the market risk information corresponding to the first type of financial indicator data, establish a first mapping relationship between the first type of financial indicator and the market risk information, and build a first risk assessment model according to the first mapping relationship; a second risk assessment model construction module, configured to extract repayment information of each object according to the second type of financial indicator data, generate a repayment vector corresponding to the object, perform cluster analysis on the repayment vector, determine the cluster centroid of each cluster data cluster, determine the corresponding default vector according to the cluster centroid, establish a second mapping relationship between the second type of financial indicator and the default vector, and construct a second risk assessment model according to the second mapping relationship; A financial product risk analysis module is used to analyze the first and second financial indicators corresponding to the target financial product, determine the market risk information corresponding to the first and second financial indicators corresponding to the second financial indicators according to the first and second risk assessment models, and integrate the market risk information and the default risk information to obtain the financial product risk information; wherein the target financial product is a single financial product or an investment portfolio of financial products; The risk warning and trading strategy processing module is used to issue a risk warning to the target financial product based on the risk information of the financial product and generate a trading strategy corresponding to the target financial product.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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