Investment optimization system based on artificial intelligence investment research

Abstract

The invention discloses an investment optimization system based on artificial intelligence investment research, and relates to the technical field of financial investment, and the system comprises the following components: a data collection module which is responsible for collecting data related to an investment portfolio, including historical transaction data, market trend data and company financial data, and carrying out the preprocessing and storage, the risk analysis module is used for analyzing the collected data, evaluating the risk level of the investment portfolio and constructing a risk measurement prediction model, and the module can identify potential risk factors such as market fluctuation and industry risks and give corresponding risk scores and probability distribution, so that the risk level of the investment portfolio is evaluated; according to the invention, rapid processing and accurate analysis of mass financial data are realized through an unsupervised learning algorithm of the data collection module, related information is efficiently captured and integrated from a plurality of data sources, the efficiency and accuracy of investment decision making are greatly improved, investors can better grasp market opportunities, the investment risk is reduced, and the economic benefit is improved. And higher investment return is realized.

Description

Technical Field

[0001] The present invention relates to the field of financial investment technology, and in particular to an investment optimization system based on artificial intelligence investment research. Background Art

[0002] With the increasing complexity of financial markets and the rapid development of information technology, traditional investment decision-making methods can no longer meet the diversified needs of investors. Traditional investment decisions mainly rely on people's subjective judgment and experience accumulation, which is not only inefficient but also easily affected by human factors, resulting in low decision-making accuracy. At the same time, with the globalization trend of financial markets, the market environment faced by investors is becoming increasingly complex and changeable, requiring more comprehensive and accurate data analysis and decision-making support.

[0003] Therefore, how to build an investment optimization system that can provide investors with efficient and accurate decision-making support has important practical application value. An ideal artificial intelligence investment optimization system should have the following characteristics: collect data related to the investment portfolio, and automatically generate customized investment strategies based on the personalized needs of investors, monitor the risk status of the investment portfolio in real time, adjust investment strategies in time according to market changes, improve the pertinence and effectiveness of investment decisions, and realize intelligent processing of investment decisions.

[0004] However, although the existing investment optimization systems on the market have improved the efficiency of investment decisions to a certain extent, they still have many shortcomings. For example, they lack sufficient data processing capabilities, intelligent decision-making capabilities and personalized service capabilities, and cannot make full use of big data resources and market changes to adjust investment strategies in a timely manner, and cannot meet the diverse needs of different investors. Therefore, developing an investment optimization system that can comprehensively realize the above-mentioned characteristics using artificial intelligence technology is of great significance to improving the accuracy and efficiency of investment decisions. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide an investment optimization system based on artificial intelligence investment research. It can realize intelligent investment decision-making and personalized investment strategies by constructing a risk measurement prediction model and an objective function optimization algorithm, and provide investors with an efficient, accurate and personalized investment optimization solution. The system has broad application prospects and market potential, and can bring better investment returns and risk control effects to investors.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an investment optimization system based on artificial intelligence investment research, characterized in that the system includes the following components:

[0007] Data collection module: responsible for collecting data related to the investment portfolio, including historical transaction data, market trend data, and company financial data, and pre-processing and storing them;

[0008] Risk analysis module: Analyzes the collected data, evaluates the risk level of the investment portfolio and builds a risk measurement prediction model. This module can identify potential risk factors, such as market fluctuations and industry risks, and give corresponding risk scores and probability distributions;

[0009] Optimization decision module: Based on the risk analysis results, the optimization algorithm is used to adjust and optimize the investment portfolio. This module can consider factors such as investors' risk preferences and investment goals to generate optimized investment decision plans to reduce risks and improve investment returns;

[0010] Risk control module: monitors the risk status of the investment portfolio in real time, and adjusts investment strategies in a timely manner according to market changes to keep risks within a controllable range. This module can set a risk threshold. Once the threshold is exceeded, the system will automatically trigger the risk control mechanism and take corresponding risk control measures.

[0011] Furthermore, the specific steps of the data collection module are:

[0012] (1) Data source determination: Determine the data sources that need to be collected, including financial databases, market data providers, news reports, and social media platforms;

[0013] (2) Data crawling: using web crawler technology to crawl relevant financial data from certain data sources;

[0014] (3) Data cleaning: Use unsupervised learning algorithms to clean the collected raw data containing noise, duplication, or irrelevant information, remove outliers, fill in missing values, and perform standardization;

[0015] (4) Data integration: Integrate the cleaned data into a specific format or structure to facilitate subsequent analysis and processing;

[0016] (5) Data storage: Store the integrated data in an appropriate database or data warehouse for subsequent data analysis and optimization.

[0017] Furthermore, the risk analysis module includes the following specific steps:

[0018] (1) Data preprocessing: preprocess the collected portfolio-related data to eliminate outliers and ensure data quality and consistency;

[0019] (2) Feature extraction: Extract risk-related features from preprocessed data. These features may include historical returns, volatility, market trend indicators, and financial indicators.

[0020] (3) Risk modeling: using risk-related features to construct risk measurement prediction models, these models can predict the future risk level of the investment portfolio based on the extracted features;

[0021] (4) Risk assessment: The model outputs a quantitative assessment of the risk of the investment portfolio and generates a risk score and probability distribution, which helps investors understand the potential risks of the investment portfolio and make corresponding investment decisions;

[0022] (5) Risk reporting and visualization: Risk assessment results are presented to investors in the form of reports to help them better understand and monitor the risk status of their investment portfolios.

[0023] Furthermore, the optimization decision module includes the following specific steps:

[0024] (1) Input investor needs: Investors input their personalized needs through the interface, such as risk preference, investment objectives, and investment period;

[0025] (2) Integrate risk analysis results: The system integrates the output of the risk analysis module, including the risk score and probability distribution of the investment portfolio;

[0026] (3) Select optimization algorithm: Based on the needs of investors and the results of risk analysis, use the optimization algorithm of maximizing (or minimizing) the objective function in linear programming;

[0027] (4) Constructing an optimization model: Constructing an investment optimization model based on investor demand, risk analysis results, and the optimization algorithm for maximizing (or minimizing) the objective function;

[0028] (5) Solving optimization problems: Solving the optimization combination through iterative calculation and obtaining the optimal investment portfolio solution;

[0029] (6) Output optimization results: Output the optimized investment portfolio plan to investors, including recommended asset allocation, buying and selling points.

[0030] Furthermore, the risk control module includes the following specific steps:

[0031] (1) Real-time monitoring of risk status: by collecting real-time market data and information on portfolio performance, the risk status of the portfolio is continuously tracked;

[0032] (2) Risk assessment and analysis: Use machine learning algorithms and techniques to process and analyze the collected data to assess the current risk level of the investment portfolio and identify potential risk factors;

[0033] (3) Setting risk thresholds: Based on investors’ risk preferences and investment objectives, set reasonable risk thresholds as the criteria for triggering risk control mechanisms;

[0034] (4) Formulate risk control strategies: Once the risk exceeds the set threshold, the risk control module will be automatically triggered to generate corresponding risk control measures based on pre-set strategies and algorithms;

[0035] (5) Execute risk control measures: The system automatically adjusts the configuration of the investment portfolio based on the generated risk control measures to reduce the risk level.

[0036] Furthermore, the data collection module uses the K-means clustering algorithm in the unsupervised learning algorithm to help identify and process outliers in the data cleaning and integration stage, because outliers are usually distributed differently from most data points, and are thus assigned to separate clusters or locations away from other clusters. The operation method and formula are as follows: K points are selected as the initial cluster centers (also called centroids), and for each point in the data set, the Euclidean distance is used to calculate its distance to each cluster center, namely: x and y are two data points, n is the number of features, calculate the sum of the squares of the differences in each dimension, then take the square root and assign it to the closest cluster. For each cluster, calculate the average of all points in the cluster and set the average as the new cluster center: C k is the kth cluster, c k is the cluster center of the cluster, |C k | is cluster C k The number of midpoints. This formula calculates the sum of all points in the cluster and then divides it by the number of points to get the average value as the new cluster center. By iteratively updating the cluster center and redistributing data points until the cluster center no longer changes significantly or reaches the preset maximum number of iterations, the K-means clustering algorithm is used for data cleaning and integration to improve the quality and effectiveness of the data and provide stronger support for subsequent model training and decision analysis.

[0037] Furthermore, the risk analysis module constructs a variance-covariance model through a risk measurement prediction model to measure the overall risk of the investment portfolio, and evaluates the risk by calculating the expected rate of return, variance, and covariance between different assets of assets and asset portfolios. Its operation mode and formula are as follows: The square of the average deviation between the rate of return of a single asset and its expected value is measured by variance: p i is each return observation x iThe probability of occurrence, μ is the expected rate of return, and covariance is used to measure the degree to which the returns of two assets change together. The covariance is calculated through expectation, emphasizing the expected value of the product of the deviations of the returns of two assets from their respective expected values: Cov(X,Y)=E[(XE[X])(YE[Y])], E[X] and E[Y] are the expected values ​​of X and Y respectively. The correlation coefficient is used to measure the degree of linear correlation between the changes in the returns of two assets, and the correlation coefficient is calculated through covariance and standard deviation: Var(X)Var(Y) are the variances of X and Y respectively. This formula emphasizes that the correlation coefficient is the ratio of the covariance to the product of the standard deviations of the two assets, so its value range is [-1, 1]. In risk modeling, the variance-covariance model calculates the variance of each asset in the portfolio and the covariance between them to obtain the overall risk of the portfolio, which helps investors understand the volatility of the portfolio and the mutual influence between different assets, so as to make more informed investment decisions.

[0038] Furthermore, the optimization decision module defines a linear objective function according to the investor's goals and preferences through the maximization (or minimization) objective function optimization algorithm in linear programming, and obtains the expected return of the investment portfolio that is maximized or the risk of the investment portfolio that is minimized. The formula is: maximization objective function z = c T x, minimize the objective function z = -c T x, vector x and vector c are decision variables and objective function coefficients, c T represents the transpose of vector c, c T x represents the dot product of vector c and x, A series of linear constraints are set, including the upper and lower limits of the investment proportion of each asset and the risk level of the entire investment portfolio. Then, a linear programming algorithm is used to solve the optimization problem and find the asset group that meets all constraints and optimizes the objective function. This optimal asset portfolio is the output result of the optimization decision module, which will recommend investors to allocate various assets in specific proportions to generate investment decision plans that meet investor needs and risk tolerance.

[0039] Furthermore, the optimization decision module uses the simplex method in linear programming to iteratively calculate and solve the optimal solution of the linear maximization (minimization) objective function that satisfies a series of linear constraints, and continuously adjusts the value of the decision variable in an iterative manner so that the objective function gradually approaches the optimal solution. In each iteration, the simplex method will select an exit variable and an entry variable according to the current solution, and then determine the next iteration direction by calculation. Its operation mode and formula are as follows: Select an initial feasible solution x (0) , calculate the initial objective function value z (0) =c Tx (0) , in each iteration, according to the current solution x (k) and the gradient c of the objective function, select a leaving variable x1 and an entering variable x e , determine the iteration direction, calculate the step length along that direction, and update the value of the decision variable x according to the calculated step length (k+1) =x (k) +λd, λ is the step size, d is the iteration direction, calculate the new objective function value z (k+1) =c T x (k+1) , determine whether the termination conditions are met, such as the improvement of the objective function value is less than a preset threshold, or the preset maximum number of iterations is reached. If the termination conditions are met, stop the iteration and output the current solution as the optimal solution. Otherwise, return to continue the iteration to obtain the optimal investment portfolio plan and build an investment optimization model.

[0040] Furthermore, the risk control module performs risk assessment through the logistic regression algorithm in the machine learning algorithm. This algorithm helps the system to more accurately identify potential risks and formulate corresponding risk control strategies by mining and analyzing a large amount of data. The formula is: P(y=1|x) represents the probability that the output variable y is 1 given the input variable x. w is the weight vector, x is the input variable vector, b is the bias term, and e is the base of the natural logarithm. When setting the risk threshold, y=1 is interpreted as the occurrence of a risk event, that is, the portfolio loss exceeds a certain threshold, while y=0 is interpreted as the risk event not occurring. By training the logistic regression algorithm, we can learn the optimal values ​​of the weight vector w and the bias term b, so that the investment optimization model can accurately predict the probability of occurrence of risk events. This can help us understand the predictive ability of the model and adjust the model parameters accordingly to automatically adjust the configuration of the investment portfolio to reduce the risk level.

[0041] Compared with the existing technology, this investment optimization system based on artificial intelligence investment research has the following advantages:

[0042] Beneficial effects:

[0043] 1. The present invention realizes rapid processing and accurate analysis of massive financial data through the unsupervised learning algorithm of the data collection module, efficiently captures and integrates relevant information from multiple data sources, and the risk analysis module can accurately evaluate the risk level of the investment portfolio and build a risk measurement prediction model, which greatly improves the efficiency and accuracy of investment decisions, helps investors better grasp market opportunities, reduce investment risks, and achieve higher investment returns.

[0044] 2. The present invention integrates personalized information of investors' risk preferences and investment goals through an optimization decision-making module, and generates customized investment strategies based on this, so that each investor can obtain an investment plan that meets their own characteristics and needs, thereby optimizing the service experience of investors. In addition, the risk control module can also monitor and adjust the risk status of the investment portfolio in real time according to market changes and the personalized needs of investors, ensuring that investment risks are always controlled within an acceptable range. This personalized investment service experience helps to enhance investors' satisfaction and trust, and further improve the market competitiveness of the system.

[0045] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 This is an operation diagram of the investment optimization system process based on artificial intelligence investment research.

[0048] Figure 2 A flowchart of an investment optimization system based on artificial intelligence investment research. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0050] Example 1

[0051] An investment optimization system based on artificial intelligence investment research, the system includes the following components:

[0052] Data collection module: responsible for collecting data related to the investment portfolio, including historical transaction data, market trend data, and company financial data, and pre-processing and storing them;

[0053] Risk analysis module: Analyzes the collected data, evaluates the risk level of the investment portfolio and builds a risk measurement prediction model. This module can identify potential risk factors, such as market fluctuations and industry risks, and give corresponding risk scores and probability distributions;

[0054] Optimization decision module: Based on the risk analysis results, the optimization algorithm is used to adjust and optimize the investment portfolio. This module can consider factors such as investors' risk preferences and investment goals to generate optimized investment decision plans to reduce risks and improve investment returns;

[0055] Risk control module: monitors the risk status of the investment portfolio in real time, and adjusts investment strategies in a timely manner according to market changes to keep risks within a controllable range. This module can set a risk threshold. Once the threshold is exceeded, the system will automatically trigger the risk control mechanism and take corresponding risk control measures.

[0056] The risk analysis module constructs a variance-covariance model through a risk measurement prediction model to measure the overall risk of the investment portfolio. It evaluates risk by calculating the expected rate of return, variance, and covariance between different assets of assets and asset portfolios. Its operation mode and formula are as follows: First, the square of the average deviation between the rate of return of a single asset and its expected value is measured by variance. The calculation formula is: p i (x i -μ) 2 , p i is each return observation x i The probability of occurrence, μ is the expected rate of return, and covariance is used to measure the degree to which the returns of the two assets change together. Then, covariance is calculated through expectation, emphasizing the expected value of the product of the deviations of the returns of the two assets from their respective expected values. The calculation formula is: Cov(X,Y)=E[(XE[X])(YE[Y])], E[X] and E[Y] are the expected values ​​of X and Y respectively. Finally, the correlation coefficient is used to measure the degree of linear correlation between the changes in the returns of the two assets, and the correlation coefficient is calculated through covariance and standard deviation: Var(X)Var(Y) are the variances of X and Y respectively. In addition, this formula emphasizes that the correlation coefficient is the ratio of the covariance to the product of the standard deviations of the two assets, so its value range is [-1, 1]. In risk modeling, the variance-covariance model calculates the variance of each asset in the portfolio and the covariance between them to obtain the overall risk of the portfolio, which helps investors understand the volatility of the portfolio and the mutual influence between different assets, so as to make more informed investment decisions.

[0057] The risk analysis module measures the overall risk of the investment portfolio by constructing a variance-covariance model, achieving accurate risk assessment, helping investors to more clearly understand the risk status of the investment portfolio and how to minimize risk and maximize returns. It also monitors risk status in real time to ensure investment security, providing investors with accurate, efficient and personalized investment decision support, improving investment results and reducing investment risks.

[0058] Example 2

[0059] The system consists of the following components:

[0060] Data collection module: responsible for collecting data related to the investment portfolio, including historical transaction data, market trend data, and company financial data, and pre-processing and storing them;

[0061] Risk analysis module: Analyzes the collected data, evaluates the risk level of the investment portfolio and builds a risk measurement prediction model. This module can identify potential risk factors, such as market fluctuations and industry risks, and give corresponding risk scores and probability distributions;

[0062] Optimization decision module: Based on the risk analysis results, the optimization algorithm is used to adjust and optimize the investment portfolio. This module can consider factors such as investors' risk preferences and investment goals to generate optimized investment decision plans to reduce risks and improve investment returns;

[0063] Risk control module: monitors the risk status of the investment portfolio in real time, and adjusts investment strategies in a timely manner according to market changes to keep risks within a controllable range. This module can set a risk threshold. Once the threshold is exceeded, the system will automatically trigger the risk control mechanism and take corresponding risk control measures.

[0064] The optimization decision module uses the maximization (or minimization) objective function optimization algorithm in linear programming and the simplex method for iterative calculation. For example, first, according to the investor's goals and preferences, a linear objective function is defined to obtain the expected return of the portfolio that is maximized or the risk of the portfolio that is minimized. The formula is: maximization objective function z = c T x, minimize the objective function z = -c T x, vector x and vector c are decision variables and objective function coefficients, c T represents the transpose of vector c, c T x represents the dot product of vector c and x, Then, a series of linear constraints are set, including the upper and lower limits of the investment proportion of each asset and the risk level of the entire investment portfolio. The simplex method in the linear programming algorithm is then used to iteratively calculate the optimal solution of the linear maximization (minimization) objective function that satisfies a series of linear constraints to solve this optimization problem. Finally, the asset group that satisfies all constraints and optimizes the objective function is found. This optimal asset portfolio is the output result of the optimization decision module, which will recommend investors to allocate various assets in specific proportions to generate investment decision plans that meet investor needs and risk tolerance.

[0065] The optimization decision module provides investors with scientific, efficient and reasonable investment decision support through the optimization algorithm of maximizing (or minimizing) the objective function in linear programming and the iterative calculation of the simplex method. It can not only ensure the accuracy of decisions and improve decision-making efficiency, but also optimize asset allocation, reduce investment risks, and ultimately maximize investment returns. These benefits make the optimization decision module an indispensable and important part of the investment optimization system.

[0066] Example 3

[0067] The steps of the system based on Example 1 and Example 2 are as follows:

[0068] (1) Data collection and preprocessing

[0069] Determine the data source and capture relevant financial data;

[0070] Apply unsupervised learning algorithms to clean data, remove duplicate and irrelevant information, fill missing values, and standardize data;

[0071] Integrate the cleaned data and store it in a unified format or structure to facilitate subsequent analysis.

[0072] (2) Risk analysis and modeling

[0073] Preprocess the collected portfolio data to remove noise and outliers;

[0074] Extract risk-related features such as historical returns, volatility, market trend indicators, and financial indicators;

[0075] Use the extracted features to build a risk measurement prediction model to predict the future risk level of the investment portfolio;

[0076] Risk assessment is carried out through model output, and the risk assessment results are presented to investors in the form of a report.

[0077] (3) Optimizing decision making

[0078] Investors input personalized requirements through the interface, such as risk preference, investment objectives and investment period;

[0079] Utilize optimization algorithms based on investor demand and risk analysis results;

[0080] Construct investment optimization models that combine investor needs, risk analysis results, and optimization algorithms;

[0081] Solve the optimization problem through iterative calculation and obtain the optimal investment portfolio solution;

[0082] Output optimization results to investors.

[0083] (4) Risk control and adjustment

[0084] Monitor the risk status of the investment portfolio in real time, collect real-time market data and portfolio performance information;

[0085] Analyze the collected data using machine learning algorithms to assess current risk levels and identify potential risk factors;

[0086] Set risk thresholds based on investors’ risk appetite and investment objectives;

[0087] Once the risk exceeds the set threshold, the risk control mechanism is automatically triggered and corresponding risk control measures are generated;

[0088] The system automatically adjusts the portfolio configuration, reduces the risk level and provides feedback to investors.

[0089] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An investment optimization system based on artificial intelligence investment research, characterized in that: The system consists of the following components: Data collection module: responsible for collecting data related to the investment portfolio, including historical transaction data, market trend data, and company financial data, and pre-processing and storing them; Risk analysis module: Analyzes the collected data, evaluates the risk level of the investment portfolio and builds a risk measurement prediction model. This module can identify potential risk factors, such as market fluctuations and industry risks, and give corresponding risk scores and probability distributions; Optimization decision module: Based on the risk analysis results, the optimization algorithm is used to adjust and optimize the investment portfolio. This module can consider factors such as investors' risk preferences and investment goals to generate optimized investment decision plans to reduce risks and improve investment returns; Risk control module: monitors the risk status of the investment portfolio in real time, and adjusts investment strategies in a timely manner according to market changes to keep risks within a controllable range. This module can set a risk threshold. Once the threshold is exceeded, the system will automatically trigger the risk control mechanism and take corresponding risk control measures.

2. The investment optimization system based on artificial intelligence investment research according to claim 1 is characterized in that: The specific steps of the data collection module are as follows: (1) Data source determination: Determine the data sources that need to be collected, including financial databases, market data providers, news reports, and social media platforms; (2) Data crawling: using web crawler technology to crawl relevant financial data from certain data sources; (3) Data cleaning: Use unsupervised learning algorithms to clean the collected raw data containing noise, duplication, or irrelevant information, remove outliers, fill in missing values, and perform standardization; (4) Data integration: Integrate the cleaned data into a specific format or structure to facilitate subsequent analysis and processing; (5) Data storage: Store the integrated data in an appropriate database or data warehouse for subsequent data analysis and optimization.

3. The investment optimization system based on artificial intelligence investment research according to claim 1 is characterized in that: The specific steps of the risk analysis module are as follows: (1) Data preprocessing: preprocess the collected portfolio-related data to eliminate outliers and ensure data quality and consistency; (2) Feature extraction: Extract risk-related features from preprocessed data. These features may include historical returns, volatility, market trend indicators, and financial indicators. (3) Risk modeling: using risk-related features to construct risk measurement prediction models, these models can predict the future risk level of the investment portfolio based on the extracted features; (4) Risk assessment: The model outputs a quantitative assessment of the risk of the investment portfolio and generates a risk score and probability distribution, which helps investors understand the potential risks of the investment portfolio and make corresponding investment decisions; (5) Risk reporting and visualization: Risk assessment results are presented to investors in the form of reports to help them better understand and monitor the risk status of their investment portfolios.

4. The investment optimization system based on artificial intelligence investment research according to claim 1 is characterized in that: The optimization decision module has the following specific steps: (1) Input investor needs: Investors input their personalized needs through the interface, such as risk preference, investment objectives, and investment period; (2) Integrate risk analysis results: The system integrates the output of the risk analysis module, including the risk score and probability distribution of the investment portfolio; (3) Select optimization algorithm: Based on the needs of investors and the results of risk analysis, use the optimization algorithm of maximizing (or minimizing) the objective function in linear programming; (4) Constructing an optimization model: Constructing an investment optimization model based on investor demand, risk analysis results, and the optimization algorithm for maximizing (or minimizing) the objective function; (5) Solving optimization problems: Solving the optimization combination through iterative calculation and obtaining the optimal investment portfolio solution; (6) Output optimization results: Output the optimized investment portfolio plan to investors, including recommended asset allocation, buying and selling points.

5. The investment optimization system based on artificial intelligence investment research according to claim 1 is characterized in that: The specific steps of the risk control module are as follows: (1) Real-time monitoring of risk status: by collecting real-time market data and information on portfolio performance, the risk status of the portfolio is continuously tracked; (2) Risk assessment and analysis: Use machine learning algorithms and techniques to process and analyze the collected data, assess the current risk level of the investment portfolio, and identify potential risk factors; (3) Setting risk thresholds: Based on investors’ risk preferences and investment objectives, set reasonable risk thresholds as the criteria for triggering risk control mechanisms; (4) Formulate risk control strategies: Once the risk exceeds the set threshold, the risk control module will be automatically triggered to generate corresponding risk control measures based on pre-set strategies and algorithms; (5) Execute risk control measures: The system automatically adjusts the configuration of the investment portfolio based on the generated risk control measures to reduce the risk level.

6. The investment optimization system based on artificial intelligence investment research according to claim 2 is characterized in that: The data collection module uses the K-means clustering algorithm in the unsupervised learning algorithm to help identify and process outliers in the data cleaning and integration stage, because outliers are usually distributed differently from most data points, so they are assigned to separate clusters or locations far away from other clusters. The operation method and formula are as follows: K points are selected as the initial cluster centers (also called centroids). For each point in the data set, the Euclidean distance is used to calculate its distance to each cluster center, that is: x and y are two data points, n is the number of features, calculate the sum of the squares of the differences in each dimension, then take the square root and assign it to the closest cluster. For each cluster, calculate the average of all points in the cluster and set the average as the new cluster center: C k is the kth cluster, c k is the cluster center of the cluster, |C k | is cluster C k The number of midpoints. This formula calculates the sum of all points in the cluster and then divides it by the number of points to get the average value as the new cluster center. By iteratively updating the cluster center and redistributing data points until the cluster center no longer changes significantly or reaches the preset maximum number of iterations, the K-means clustering algorithm is used for data cleaning and integration to improve the quality and effectiveness of the data and provide stronger support for subsequent model training and decision analysis.

7. The investment optimization system based on artificial intelligence investment research according to claim 3 is characterized in that: The risk analysis module constructs a variance-covariance model through a risk measurement prediction model to measure the overall risk of the investment portfolio, and evaluates the risk by calculating the expected rate of return, variance, and covariance between different assets of assets and asset portfolios. Its operation mode and formula are as follows: The square of the average deviation between the rate of return of a single asset and its expected value is measured by variance: p i is each return observation x i The probability of occurrence, μ is the expected rate of return, and covariance is used to measure the degree to which the returns of two assets change together. The covariance is calculated through expectation, emphasizing the expected value of the product of the deviations of the returns of two assets from their respective expected values: Cov(X,Y)=E[(XE[X])(YE[Y])], E[X] and E[Y] are the expected values ​​of X and Y respectively. The correlation coefficient is used to measure the degree of linear correlation between the changes in the returns of two assets, and the correlation coefficient is calculated through covariance and standard deviation: Var(X)Var(Y) are the variances of X and Y respectively. This formula emphasizes that the correlation coefficient is the ratio of the covariance to the product of the standard deviations of the two assets, so its value range is [-1, 1]. In risk modeling, the variance-covariance model calculates the variance of each asset in the portfolio and the covariance between them to obtain the overall risk of the portfolio, which helps investors understand the volatility of the portfolio and the mutual influence between different assets, so as to make more informed investment decisions.

8. The investment optimization system based on artificial intelligence investment research according to claim 4 is characterized in that: The optimization decision module defines a linear objective function according to the investor's goals and preferences through the maximization (or minimization) objective function optimization algorithm in linear programming to obtain the expected return of the investment portfolio that is maximized or the risk of the investment portfolio that is minimized. The formula is: maximization objective function z = c T x, minimize the objective function z = -c T x, vector x and vector c are decision variables and objective function coefficients, c T represents the transpose of vector c, c T x represents the dot product of vector c and x, A series of linear constraints are set, including the upper and lower limits of the investment proportion of each asset and the risk level of the entire investment portfolio. Then, a linear programming algorithm is used to solve the optimization problem and find the asset group that meets all constraints and optimizes the objective function. This optimal asset portfolio is the output result of the optimization decision module, which will recommend investors to allocate various assets in specific proportions to generate investment decision plans that meet investor needs and risk tolerance.

9. The investment optimization system based on artificial intelligence investment research according to claim 4 is characterized in that: The optimization decision module uses the simplex method in linear programming to iteratively calculate and solve the optimal solution of the linear maximization (minimization) objective function that satisfies a series of linear constraints. The value of the decision variable is continuously adjusted in an iterative manner so that the objective function gradually approaches the optimal solution. In each iteration, the simplex method will select an exit variable and an entry variable according to the current solution, and then determine the next iteration direction by calculation. Its operation mode and formula are as follows: Select an initial feasible solution x (0) , calculate the initial objective function value z (0) =c T x (0) , in each iteration, according to the current solution x (k) and the gradient c of the objective function, select a leaving variable x1 and an entering variable x e , determine the iteration direction, calculate the step length along that direction, and update the value of the decision variable x according to the calculated step length (k+1) =x (k) +λd, λ is the step size, d is the iteration direction, calculate the new objective function value z (k+1) =c T x (k+1) , determine whether the termination conditions are met, such as the improvement of the objective function value is less than a preset threshold, or the preset maximum number of iterations is reached. If the termination conditions are met, stop the iteration and output the current solution as the optimal solution. Otherwise, return to continue the iteration to obtain the optimal investment portfolio plan and build an investment optimization model.

10. The investment optimization system based on artificial intelligence investment research according to claim 5, characterized in that: The risk control module performs risk assessment through the logistic regression algorithm in the machine learning algorithm. This algorithm helps the system to more accurately identify potential risks and formulate corresponding risk control strategies by mining and analyzing a large amount of data. The formula is: P(y=1|x) represents the probability that the output variable y is 1 given the input variable x. w is the weight vector, x is the input variable vector, b is the bias term, and e is the base of the natural logarithm. When setting the risk threshold, y=1 is interpreted as the occurrence of a risk event, that is, the portfolio loss exceeds a certain threshold, while y=0 is interpreted as the risk event not occurring. By training the logistic regression algorithm, we can learn the optimal values ​​of the weight vector w and the bias term b, so that the investment optimization model can accurately predict the probability of occurrence of risk events. This can help us understand the predictive ability of the model and adjust the model parameters accordingly to automatically adjust the configuration of the investment portfolio to reduce the risk level.

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