Real-time security transaction risk assessment method

Abstract

The invention discloses a real-time security transaction risk assessment method, and relates to the technical field of transaction risk assessment, and the method comprises the steps: S1, data collection: collecting personal investor transaction instructions, features and transaction environment information from multiple channels, providing a comprehensive data basis for risk assessment, and covering transaction behaviors, backgrounds and external environment factors; according to the invention, through multi-channel data acquisition, personal transaction information, investor characteristics and transaction environment aspects are covered, a comprehensive data basis is laid for risk assessment, in the risk assessment process, risk indexes and levels in multiple aspects of transaction errors, knowledge and experience mismatching and transaction environment influence are respectively calculated, multiple factors are comprehensively considered, and the risk assessment accuracy is improved. The one-sidedness of single factor evaluation is avoided, and the operation risk of the investor can be measured more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction risk assessment, and in particular to a real-time securities transaction risk assessment method. Background Art

[0002] In today's complex and ever-changing securities trading market, individual investors have become important participants. With the continuous development and popularization of the financial market, more and more individuals are engaged in securities trading in an attempt to obtain investment returns. However, due to the limitations of their own conditions and market environment, individual investors face many risks in the trading process. Individual investors lack professional investment knowledge and experience, find it difficult to accurately grasp market changes, and are prone to making wrong investment decisions. At the same time, various operational errors in the trading process, trading system problems, and personal emotional fluctuations also bring significant operational risks to individual investors, which may lead to investment losses and even affect personal wealth status and financial stability; Although the existing securities trading risk assessment methods can analyze market risks, credit risks, etc. to a certain extent, there are obvious deficiencies in the assessment of personal operational risks. On the one hand, most traditional methods focus on macro-level risk assessment and fail to fully consider the unique characteristics and trading behavior details of individual investors. For example, when assessing personal operational risks, there is a lack of in-depth analysis of factors such as investors' personal trading habits (such as trading frequency, trading product preferences, etc.), knowledge level (including financial knowledge mastery, investment experience, etc.), and psychological state during trading. On the other hand, existing assessment methods are usually unable to monitor and assess risks in real time and have poor timeliness. The real-time operation of individual investors during the transaction process cannot be included in the risk assessment system in a timely manner, resulting in a lag in risk assessment results, making it difficult to provide investors with timely and effective risk warnings, and unable to meet the needs of individual investors for the timeliness and accuracy of risk assessment in actual transactions.

[0003] It is of great significance to evaluate the operational risks of individual securities transactions in real time. For individual investors, timely understanding of their own operational risk status can help them adjust their investment strategies in time during the trading process and avoid suffering major losses due to operational errors or unreasonable trading behaviors. For example, when investors realize that their trading frequency is too high and the trading error rate is rising, they can control the trading rhythm in time and reduce unnecessary transactions.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that securities trading risk assessment methods are difficult to assess the operational risks of individual investors in real time and comprehensively, resulting in individual investors being unable to obtain effective risk warnings in time to adjust their investment strategies, and to propose a real-time securities trading risk assessment method.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A real-time securities trading risk assessment method comprises the following steps: S1. Data collection: Collect individual investors’ trading instructions, characteristics, and trading environment information from multiple channels to provide a comprehensive data basis for risk assessment, covering trading behavior, background, and external environmental factors; S2. Data preprocessing: Clean, correct errors, standardize and encode the collected data to ensure data accuracy and standardization, make it suitable for subsequent risk assessment model calculations, and improve data quality and availability; S3. Risk assessment: Calculate the risk indicators and levels of transaction errors, mismatch of knowledge and experience, and impact of the trading environment, comprehensively measure the investor's operational risks, and comprehensively assess the risk status of multiple factors; S4. Comprehensive risk assessment and early warning: Calculate the comprehensive assessment score based on the risk assessment results, set thresholds accordingly for risk warnings and prompts, and help investors understand the risk level in a timely manner so that they can take appropriate measures.

[0007] Furthermore, the specific process of S3 is as follows: Transaction error risk assessment: Calculate the transaction error rate MER by the formula: MER = (number of erroneous transaction instructions / total number of transaction instructions) × 100%; erroneous transaction instructions include errors in buying and selling directions, errors in transaction quantity input, and prices deviating from a reasonable range; evaluate the transaction error risk level based on the MER value, when MER < 2% is low risk; 2% ≤ MER < 5% is medium risk; MER ≥ 5% is high risk; Knowledge and experience mismatch risk assessment: Define the investor's knowledge level score and conduct a comprehensive assessment based on investor characteristic data, including age, occupation, and education level; The age range is divided into different scoring intervals, specifically 2 points for 20-30 years old, 3 points for 31-40 years old, 4 points for 41-50 years old, 3 points for 51-60 years old, and 2 points for those over 60 years old; Regarding occupations, for practitioners in the financial industry, including bank wealth management managers and securities traders, the occupation score is recorded as 8 points. For practitioners in non-financial industries, the correlation between the occupation and the financial industry is analyzed, and the occupation is matched with several preset correlation intervals according to different correlations. The preset correlation intervals correspond to different occupation scores, and the occupation score is determined according to the correlation between the occupation and the financial industry; the occupation score is determined by the investor's occupation; Education level is measured by academic qualifications, and different academic qualifications are defined as educational levels. Specifically, doctoral education is scored as 10 points, master's education is scored as 8 points, bachelor's education is scored as 6 points, college education is scored as 4 points, and high school education and below is scored as 2 points; Then determine the weights of different characteristic data of investors. The weights of age score, occupation score and education level score are The obtained age score, occupation score and education level score are marked as nn, zy and jy respectively, and after normalization, they are entered into the following formula: The comprehensive analysis value ZXZ is obtained, and the comprehensive analysis value ZXZ is used as the standard for measuring the risk assessment of knowledge and experience mismatch; It is the influencing factor of investors' psychological pressure index.

[0008] Furthermore, the investor psychological pressure index influencing factor in S3 The analysis process is as follows: Obtain the operation data of investors during the trading process, specifically by recording the time interval from clicking the order button to confirming the order submission for each order placed by investors in the trading system; calculating the time interval between two consecutive transactions of investors; and counting the number of times investors modify submitted orders within a preset time period; Clean the collected data to remove erroneous data caused by system failure or network anomaly; then standardize the operation speed, transaction time interval and order modification frequency data to make the data at the same order of magnitude for subsequent calculations; Set corresponding weights for operation speed, transaction time interval and order modification frequency to reflect the relative importance of each factor on psychological stress, and set corresponding weights for operation speed, transaction time and transaction interval respectively. , and The sum is 1; by the formula: Calculate the psychological stress index , where , , They are the standardized operation speed value, transaction time interval value, and order modification frequency value; Then the psychological stress index Percentage is used to obtain the impact factor of investor psychological pressure index .

[0009] Furthermore, the specific operation steps of S4 are as follows: Comprehensive risk calculation: According to the risk assessment results of the three aspects in step S3, calculate the comprehensive operational risk score, normalize the transaction error rate MER, comprehensive analysis value ZXZ and shadow value HYP obtained by analysis, and substitute them into the following formula: To obtain the comprehensive operational risk score FXF, where They are the preset weight coefficients of transaction error rate MER, comprehensive analysis value ZXZ and shadow value HYP respectively; Then substitute the obtained comprehensive operational risk score FXF into the following formula: To get the comprehensive evaluation score ZPZ, where is the comprehensive risk dynamic adjustment coefficient, where are the standard deviation of market return and the standard deviation of investor return respectively. The calculation period is consistent with the risk assessment period, and the obtained comprehensive assessment score ZPZ is used as the standard for measuring comprehensive risk assessment; Risk warning and reminder: Set risk thresholds based on comprehensive assessment points ZPZ, including the first threshold and the second threshold ,and < , when ZPZ> Triggering a high-risk warning. <ZPZ≤ Triggering medium risk warning, ZPZ≤ It is a low risk warning; when an early warning or reminder is triggered, risk warning information will be pushed to investors through the trading terminal, including the current comprehensive risk level, the evaluation results of various risk factors and corresponding suggestions.

[0010] Furthermore, the specific operation steps of S1 are as follows: Personal transaction information collection: Obtain individual investors' transaction instruction information in real time from the securities broker's trading system interface, including transaction types, buying and selling directions, transaction quantities, transaction prices, and transaction times. These data reflect each transaction operation of the investor; at the same time, collect their account transaction history records, covering past transaction frequencies and transaction type distribution content, so as to analyze investors' trading habits; Investor characteristic data collection: Obtain basic information of individual investors, including age, occupation, and education level, through online questionnaires or in collaboration with securities brokers. This information is related to investors' risk tolerance and knowledge level. Investors' self-assessment of financial knowledge and records of financial training and learning courses they have participated in are collected regularly; Trading environment data collection: Real-time monitoring of the operating status information of the trading platform used by individual investors, including network delays and the response time of the trading software; these data reflect the potential impact of the trading system on investor operations; at the same time, obtain update information on the trading platform, the launch of new functions, and investor usage.

[0011] Furthermore, the specific operation steps of S2 are as follows: Data cleaning and organization: Clean transaction behavior data, check and delete duplicate transaction records; at the same time, correct erroneous data; Data quantification and coding: Standardize the continuous data of investors’ age and investment experience. Specifically, use the Z-score standardization method to standardize age to a value with a mean of 0 and a standard deviation of 1, which is used to unify the dimensions in subsequent calculations. Encode classified data, including occupational categories and order methods, and assign different integer codes to different occupational categories so that computers can process this information; encode trading directions and market activity levels.

[0012] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention, through multi-channel data collection, covers personal transaction information, investor characteristics and transaction environment, etc., laying a comprehensive data foundation for risk assessment. In the process of risk assessment, it calculates the risk indicators and levels of various aspects such as transaction errors, knowledge and experience mismatch, and transaction environment impact, and comprehensively considers multiple factors to avoid the one-sidedness of single factor assessment, and can more accurately measure investor operation risks. At the same time, it introduces investor psychological stress indicators to further improve the risk assessment system, which can more keenly capture the impact of investors' psychological state on risks during the transaction process, thereby achieving more comprehensive and accurate risk assessment; (2) The present invention can assess risks in real time and provide timely and effective warnings. During the transaction process, data is continuously collected and analyzed. Once the comprehensive assessment score exceeds the set threshold, risk warning information is immediately pushed to investors through the trading terminal. The content includes the current comprehensive risk level, the assessment results of various risk factors and corresponding suggestions. This enables investors to understand their own risk status in a timely manner and make quick adjustments in transactions; (3) The present invention is helpful in maintaining market stability. Financial institutions and regulatory authorities can use this method to monitor the risk status of individual investors in real time and discover potential risks in the market in advance. The widespread application of this invention can also promote the improvement of the risk management level of the securities trading industry, prompt market participants to pay more attention to operational risks, and guide investors to improve their own quality and trading capabilities, thereby optimizing the investment environment of the entire market, promoting the stability and prosperity of the financial market, and having positive significance for the sustainable development of the securities trading industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0014] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0017] like Figure 1 As shown, a real-time securities trading risk assessment method comprises the following steps: Data collection: collects individual investors’ trading instructions, characteristics, and trading environment information from multiple channels to provide a comprehensive data basis for risk assessment, covering trading behavior, background, and external environmental factors; Personal transaction information collection: Obtain individual investors' transaction instruction information in real time from the securities broker's trading system interface, including transaction types, buying and selling directions, transaction quantities, transaction prices, and transaction times. These data reflect each transaction operation of the investor; at the same time, collect their account transaction history records, covering past transaction frequencies and transaction type distribution content, so as to analyze investors' trading habits; Investor characteristic data collection: Obtain basic information of individual investors, including age, occupation, and education level, through online questionnaires or in collaboration with securities brokers. This information is related to investors' risk tolerance and knowledge level. Investors' self-assessment of financial knowledge and records of financial training and learning courses they have participated in are collected regularly; Trading environment data collection: Real-time monitoring of the operating status information of the trading platform used by individual investors, including network delays and the response time of the trading software; these data reflect the potential impact of the trading system on investor operations; at the same time, obtain update information on the trading platform, the launch of new features, and investors' use of them.

[0018] Data preprocessing: cleaning, error correction, standardization and coding of collected data to ensure data accuracy and standardization, making it suitable for subsequent risk assessment model calculations and improving data quality and availability; Data cleaning and organization: Clean transaction behavior data, check and delete duplicate transaction records, which may be caused by network failures or system errors; at the same time, correct erroneous data; Data quantification and coding: Standardize continuous data such as investors' age and investment experience. Specifically, use the Z-score standardization method to standardize age to a value with a mean of 0 and a standard deviation of 1, which is used to unify the dimensions in subsequent calculations. Encode categorical data, such as occupational category, order method, etc. Different occupational categories are assigned different integer codes so that computers can process this information. Similar coding is also performed for transaction direction (buy is 1, sell is -1) and market activity level (high, medium, and low are coded as 3, 2, and 1, respectively).

[0019] Risk assessment: calculate the risk indicators and levels of transaction errors, mismatch of knowledge and experience, and impact of the trading environment, comprehensively measure investors' operational risks, and comprehensively evaluate risk status based on multiple factors; Transaction error risk assessment: Calculate the transaction error rate (MER) using the formula: MER = (number of erroneous transaction instructions / total number of transaction instructions) × 100%; erroneous transaction instructions include errors in buying and selling directions, errors in transaction quantity input, and prices deviating from a reasonable range; evaluate the transaction error risk level based on the MER value: MER < 2% is low risk; 2% ≤ MER < 5% is medium risk; MER ≥ 5% is high risk; Knowledge and experience mismatch risk assessment: define the investor's knowledge level score (KLS), and conduct a comprehensive assessment through investor characteristic data, including: age, occupation, and education level; divide the age into different intervals for scoring, specifically 2 points for 20-30 years old, 3 points for 31-40 years old, 4 points for 41-50 years old, 3 points for 51-60 years old, and 2 points for those over 60 years old; this scoring standard takes into account the comprehensive influence of learning ability, knowledge update speed and experience of different age groups; young investors may have a better ability to learn new knowledge, but have relatively less experience; middle-aged investors may achieve a better balance between knowledge and experience; although elderly investors are experienced, they may learn new knowledge more slowly; determine the age score based on the investor's age; for occupation, for those in the financial industry who have For non-financial industry practitioners, including bank wealth management managers, securities traders, etc., the occupational score is recorded as 8 points. For practitioners in non-financial industries, the correlation between the occupation and the financial industry is analyzed, and the occupation is matched with a number of preset correlation intervals according to different correlations. The preset correlation intervals correspond to different occupational scores. The occupational score is determined according to the correlation between the occupation and the financial industry. The greater the correlation, the higher the occupational score; the occupational score is determined by the investor's occupation; the education level is measured by the academic qualifications, and specific education level scores are defined for different academic qualifications. Specifically: the education level score of doctoral degree is 10 points, the education level score of master's degree is 8 points, the education level score of bachelor's degree is 6 points, the education level score of college degree is 4 points, and the education level score of high school and below is 2 points; Then determine the weights of different characteristic data of investors. The weights of age score, occupation score and education level score are The obtained age score, occupation score and education level score are marked as nn, zy and jy respectively, and after normalization, they are put into the following formula: The comprehensive analysis value ZXZ is obtained, and the comprehensive analysis value ZXZ is used as the standard for measuring the risk assessment of knowledge and experience mismatch; is the impact factor of investor psychological pressure index, the impact factor of investor psychological pressure index The analysis process is as follows: Obtain the operation data of investors during the transaction process, specifically: record the time interval from clicking the order button to confirming the order submission in the trading system; calculate the time interval between two consecutive transactions (including buying and selling) of investors; count the number of times investors modify the submitted orders within a certain period of time (such as a trading day); clean the collected data to remove erroneous data caused by system failures or network anomalies. Then standardize the operation speed, transaction time interval and order modification frequency data to make them at the same order of magnitude to facilitate subsequent calculations; Set corresponding weights for operation speed, transaction time interval and order modification frequency to reflect the relative importance of each factor on psychological stress, and set corresponding weights for operation speed, transaction time and transaction interval respectively. , and The sum is 1; by the formula: Calculate the psychological stress index , where , , They are the standardized operation speed value, transaction time interval value and order modification frequency value; and the psychological stress index Percentage is used to obtain the impact factor of investor psychological pressure index ; Risk assessment of the impact of trading environment: Calculate the trading delay risk index through the formula: TDI = (average delay time of trading instructions / normal trading instruction processing time) × 100%; the average delay time of trading instructions is obtained by counting the time from the issuance to the execution of investors' trading instructions, and the normal trading instruction processing time is determined according to the market average level; at the same time, analyze the impact of market activity on trading, calculate the difference between the trading frequency of investors in high-activity markets and the trading frequency of users in low-activity markets, and record it as the cross-frequency difference. When the cross-frequency difference exceeds the threshold, it is considered that there is an impulsive trading risk caused by the market environment, and the corresponding risk level is increased; the high-activity market and the low-activity market are judged as market activity higher or lower than the preset standard respectively; Then, the obtained transaction delay risk index TDI and cross-frequency difference JLC are normalized and input into the following formula: To obtain the environmental shadow value HYP, and use this environmental shadow value as a risk assessment standard to measure the impact of the trading environment, where They are the preset weight coefficients of the transaction delay risk indicator TDI and the cross-frequency difference JLC, and OP is the correction factor of the cross-frequency difference JLC.

[0020] Comprehensive risk assessment and early warning: Calculate the comprehensive assessment score based on the risk assessment results, set the threshold accordingly for risk warning and reminder, and help investors understand the risk level in a timely manner so that they can take corresponding measures; Comprehensive risk calculation: According to the risk assessment results of the three aspects in the risk assessment step, the comprehensive operational risk score is calculated. The transaction error rate MER, comprehensive analysis value ZXZ and shadow value HYP obtained by analysis are normalized and substituted into the following formula: To obtain the comprehensive operational risk score FXF, where They are the preset weight coefficients of transaction error rate MER, comprehensive analysis value ZXZ and shadow value HYP respectively; Then substitute the obtained comprehensive operational risk score FXF into the following formula: To obtain the comprehensive evaluation score ZPZ, where is the comprehensive risk dynamic adjustment coefficient, where are the standard deviation of market return and the standard deviation of investor return respectively. The calculation period is consistent with the risk assessment period, and the obtained comprehensive assessment score ZPZ is used as the standard for measuring comprehensive risk assessment; Risk warning and reminder: Set risk thresholds based on comprehensive assessment points ZPZ, including the first threshold and the second threshold ,and < , when ZPZ> Triggering a high-risk warning. <ZPZ≤ Triggering medium risk warning, ZPZ≤ It is a low risk warning; when an early warning or reminder is triggered, risk warning information will be pushed to investors through the trading terminal, including the current comprehensive risk level, the evaluation results of various risk factors and corresponding suggestions.

[0021] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A real-time securities trading risk assessment method, characterized in that: The following steps are involved: S1. Data collection: Collect individual investors’ trading instructions, characteristics, and trading environment information from multiple channels to provide a comprehensive data basis for risk assessment, covering trading behavior, background, and external environmental factors; S2. Data preprocessing: Clean, correct errors, standardize and encode the collected data to ensure data accuracy and standardization, make it suitable for subsequent risk assessment model calculations, and improve data quality and availability; S3. Risk assessment: Calculate the risk indicators and levels of transaction errors, mismatch of knowledge and experience, and impact of the trading environment, comprehensively measure the investor's operational risks, and comprehensively assess the risk status of multiple factors; S4. Comprehensive risk assessment and early warning: Calculate the comprehensive assessment score based on the risk assessment results, set thresholds accordingly for risk warnings and prompts, and help investors understand the risk level in a timely manner so that they can take appropriate measures.

2. A real-time securities trading risk assessment method according to claim 1, characterized in that: The specific process of S3 is as follows: Transaction error risk assessment: Calculate the transaction error rate MER by the formula: MER = (number of erroneous transaction instructions / total number of transaction instructions) × 100%; erroneous transaction instructions include errors in buying and selling directions, errors in transaction quantity input, and prices deviating from a reasonable range; evaluate the transaction error risk level based on the MER value, when MER < 2% is low risk; 2% ≤ MER < 5% is medium risk; MER ≥ 5% is high risk; Knowledge and experience mismatch risk assessment: Define the investor's knowledge level score and conduct a comprehensive assessment based on investor characteristic data, including age, occupation, and education level; The age range is divided into different scoring intervals, specifically 2 points for 20-30 years old, 3 points for 31-40 years old, 4 points for 41-50 years old, 3 points for 51-60 years old, and 2 points for those over 60 years old; Regarding occupations, for practitioners in the financial industry, including bank wealth management managers and securities traders, the occupation score is recorded as 8 points. For practitioners in non-financial industries, the relevance between the occupation and the financial industry is analyzed, and the occupation is matched with several preset relevance intervals according to different relevances. The preset several relevance intervals correspond to different occupation scores, and the occupation score is determined according to the relevance between the occupation and the financial industry; the occupation score is determined by the investor's occupation; Education level is measured by academic qualifications, and different academic qualifications are defined as educational levels. Specifically, doctoral education is scored as 10 points, master's education is scored as 8 points, bachelor's education is scored as 6 points, college education is scored as 4 points, and high school education and below is scored as 2 points; Then determine the weights of different characteristic data of investors. The weights of age score, occupation score and education level score are The obtained age score, occupation score and education level score are marked as nn, zy and jy respectively, and after normalization, they are entered into the following formula: The comprehensive analysis value ZXZ is obtained, and the comprehensive analysis value ZXZ is used as the standard for measuring the risk assessment of knowledge and experience mismatch; It is the influencing factor of investors' psychological pressure index.

3. A real-time securities trading risk assessment method according to claim 1, characterized in that: The influencing factors of investor psychological pressure index in S3 The analysis process is as follows: Obtain the operation data of investors during the transaction process, specifically by recording the time interval from clicking the order button to confirming the order submission for each order operation of investors in the trading system; calculate the time interval between two consecutive transactions of investors; Count the number of times investors modify submitted orders within a preset time period; Clean the collected data to remove erroneous data caused by system failure or network anomaly; then standardize the operation speed, transaction time interval and order modification frequency data to make the data at the same order of magnitude for subsequent calculations; Set corresponding weights for operation speed, transaction time interval and order modification frequency to reflect the relative importance of each factor on psychological stress, and set corresponding weights for operation speed, transaction time and transaction interval respectively. , and The sum is 1; by the formula: Calculate the psychological stress index , where , , They are the standardized operation speed value, transaction time interval value, and order modification frequency value; Then the psychological stress index Percentage is used to obtain the impact factor of investor psychological pressure index .

4. A real-time securities trading risk assessment method according to claim 1, characterized in that: The specific operation steps of S4 are as follows: Comprehensive risk calculation: According to the risk assessment results of the three aspects in step S3, calculate the comprehensive operational risk score, normalize the transaction error rate MER, comprehensive analysis value ZXZ and shadow value HYP obtained by analysis, and substitute them into the following formula: To obtain the comprehensive operational risk score FXF, where They are the preset weight coefficients of transaction error rate MER, comprehensive analysis value ZXZ and shadow value HYP respectively; Then substitute the obtained comprehensive operational risk score FXF into the following formula: To obtain the comprehensive evaluation score ZPZ, where is the comprehensive risk dynamic adjustment coefficient, where are the standard deviation of market return and the standard deviation of investor return respectively. The calculation period is consistent with the risk assessment period, and the obtained comprehensive assessment score ZPZ is used as the standard for measuring comprehensive risk assessment; Risk warning and reminder: Set risk thresholds based on comprehensive assessment points ZPZ, including the first threshold and the second threshold ,and < , when ZPZ> Triggering a high-risk warning. <ZPZ≤ Triggering medium risk warning, ZPZ≤ It is a low risk reminder; When an early warning or reminder is triggered, risk warning information will be pushed to investors through the trading terminal, including the current comprehensive risk level, the assessment results of various risk factors and corresponding recommendations.

5. A real-time securities trading risk assessment method according to claim 1, characterized in that: The specific operation steps of S1 are as follows: Personal transaction information collection: Obtain individual investors' transaction instruction information in real time from the securities broker's trading system interface, including transaction types, buying and selling directions, transaction quantities, transaction prices, and transaction times. These data reflect each transaction operation of the investor; at the same time, collect their account transaction history records, covering past transaction frequencies and transaction type distribution content, so as to analyze investors' trading habits; Investor characteristic data collection: Obtain basic information of individual investors, including age, occupation, and education level, through online questionnaires or in collaboration with securities brokers. This information is related to investors' risk tolerance and knowledge level. Investors' self-assessment of financial knowledge and records of financial training and learning courses they have participated in are collected regularly; Trading environment data collection: Real-time monitoring of the operating status information of the trading platform used by individual investors, including network delays and the response time of the trading software; these data reflect the potential impact of the trading system on investor operations; at the same time, obtain update information on the trading platform, the launch of new functions, and investor usage.

6. A real-time securities trading risk assessment method according to claim 1, characterized in that: The specific operation steps of S2 are as follows: Data cleaning and organization: Clean transaction behavior data, check and delete duplicate transaction records; at the same time, correct erroneous data; Data quantification and coding: Standardize the continuous data of investors’ age and investment experience. Specifically, use the Z-score standardization method to standardize age to a value with a mean of 0 and a standard deviation of 1, which is used to unify the dimensions in subsequent calculations. Encode classified data, including occupational categories and order methods, and assign different integer codes to different occupational categories so that computers can process this information; encode trading directions and market activity levels.

Images