Industrial plate rotation configuration and income risk assessment method based on big data

Abstract

The invention relates to the technical field of risk assessment, and discloses an industry plate rotation configuration and income risk assessment method based on big data. Comprising the following modules and components: a multi-source data hierarchical acquisition and standardization processing module, a multi-dimensional factor modeling and trend prediction module, a plate rotation signal generation and intelligent configuration module, a dynamic risk monitoring and precise hedging module, and an income risk assessment and dynamic iterative optimization module. The multi-source data hierarchical acquisition and standardization processing module comprises a multi-source data interface terminal, a data acquisition server cluster, an IoT time synchronizer, an intelligent data cleaning tool and a data standardization subsystem; the multi-dimensional factor modeling and trend prediction module comprises a factor calculation server, an entropy weight TOPSIS weight calculation terminal, an LSTM trend prediction model training platform and a trend visualization terminal; the invention aims to solve the problem that a data level generally faces fragmentation dilemma and lacks a unified standardized processing system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of risk assessment, and in particular to an industry sector rotation configuration and yield risk assessment method based on big data. BACKGROUND

[0002] The industry sector rotation configuration of big data is based on big data technology, integrates macroeconomic indicators, industry fundamental data, market fund flow, public opinion dynamics and other multi-source heterogeneous information, constructs a unified analysis base through data cleaning and standardization processing, and then uses quantitative modeling to mine industry cycle rules and strong and weak differentiation signals to dynamically adjust the configuration weight of different industry sectors, so as to balance the yield and risk of asset allocation while adapting to the market environment. The prior art has significant limitations in data basis and modeling logic. At the data level, there is a fragmentation dilemma. Macro, meso and micro data are scattered in different information islands, lack a unified standardized processing system, resulting in data distortion or lag, and even systematic misjudgment due to data chimney effect. SUMMARY

[0003] The purpose of the present application is to provide an industry sector rotation configuration and yield risk assessment method based on big data to solve the problem of the prior art in the background art, which has significant limitations in data basis and modeling logic, and the data level generally faces a fragmentation dilemma. Macro, meso and micro data are scattered in different information islands, lack a unified standardized processing system, resulting in data distortion or lag, and even systematic misjudgment due to data chimney effect.

[0004] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: An industry sector rotation configuration and yield risk assessment method based on big data, comprising the following modules and components: Multi-source data hierarchical acquisition and standardization processing module, multi-dimensional factor modeling and trend prediction module, sector rotation signal generation and intelligent configuration module, dynamic risk monitoring and precise hedging module, yield risk assessment and dynamic iteration optimization module; The multi-source data hierarchical acquisition and standardization processing module comprises a multi-source data interface terminal, a data acquisition server cluster, an IoT time synchronizer, an intelligent data cleaning tool and a data standardization subsystem. The multi-dimensional factor modeling and trend prediction module comprises a factor calculation server, an entropy weight TOPSIS weight calculation terminal, an LSTM trend prediction model training platform and a trend visualization terminal. The sector rotation signal generation and intelligent configuration module comprises a rotation signal trigger engine, a mean-variance optimization configuration tool, a configuration execution terminal and a position monitoring instrument panel. The dynamic risk monitoring and accurate hedging module comprises a real-time market information receiving server, a risk index calculation engine, a risk early warning terminal and a hedging tool management system. The income risk assessment and dynamic iteration optimization module comprises an income risk assessment subsystem, a historical case library server, a model parameter iteration tool, a factor updating and verification terminal.

[0005] As a further improvement of the technical solution: the intelligent data cleaning tool comprises: outlier rejection, differential missing completion, noise filtering, the outlier rejection adopts 3 Principle to automatically identify abnormal data, the differential missing completion adopts the industry mean and trend correction filling method, the formula is: , wherein is the mean of other industries in the same plate, k is the trend correction coefficient, the value range is 0.3-0.7, and is adjusted adaptively according to the industry volatility, the noise filtering filters short-term noise in market information data through a 5-day window moving average method, the data standardization subsystem comprises: numerical standardization and industry adaptation optimization, the numerical standardization adopts Z-score normalization to eliminate the dimension influence, the formula is: , wherein is the original factor value, is the mean of the factor in the past 3 years, is the standard deviation of the factor in the past 3 years, the industry adaptation optimization is a special industry, such as a cycle industry, and a seasonal correction term is added, the formula is: , wherein is the seasonal correction coefficient, the value is 0.1-0.3, is a seasonal identification variable, 1 for peak season, -1 for off-season, and 0 for flat season.

[0006] As a further improvement of the technical solution: the entropy weight TOPSIS weight calculation terminal comprises: factor standardization processing, entropy weight value calculation, TOPSIS comprehensive score calculation and weight fine-tuning function, the factor standardization processing adopts the output result of the above-mentioned data standardization subsystem, the entropy weight value calculation first calculates the information entropy of the jth factor: , wherein m is the number of plates, is the standardized value of the jth factor of the ith plate, if =0, then =0, to avoid the meaninglessness of logarithm; the second step calculates the weight of the jth factor: , wherein n is the number of effective factors, and the total weight ; The TOPSIS comprehensive score calculation: determines the optimal solution and the worst solution :

[0007] Calculate the Euclidean distance of each plate with the optimal and worst solution: ; ; Calculate the comprehensive score: , the score range is 0-1, the closer to 1 represents the basic face of the plate is better; The LSTM trend prediction model training platform includes: time window dynamic adjustment, model adaptive training, the time window optimization adaptively selects the time window according to the volatility of the plate, formula: , wherein is the volatility of the plate in the past 30 days.

[0008] As a further improvement scheme of the technical solution: the mean-variance optimization configuration tool includes: basic constraint setting, dynamic constraint adaptation, optimal position solving, configuration result output, the objective function of the optimal position solving is to maximize the expected return of the portfolio, and the constraint condition is that the portfolio variance is minimum, the mathematical model: objective function: , wherein is the position ratio of the i-th plate, Ri is the predicted annualized return of the i-th plate, and the constraint condition is: , is the return covariance of plate i and j, is the upper limit of the portfolio volatility, is the upper and lower limit of the single plate position.

[0009] As a further improvement scheme of the technical solution: the risk indicator calculation engine includes: core risk indicator calculation, risk attribution analysis, the core risk indicator calculation is calculated by parameter method, formula: , wherein is the average daily return of the portfolio in the past 60 days, is the standard deviation of the portfolio in the past 60 days, representing the maximum possible loss of the day under 95% confidence, the risk attribution analysis adopts the marginal risk contribution method, and the contribution of a single plate to the portfolio VaR value is calculated, formula: , wherein is the marginal risk contribution of the i-th plate, is the portfolio return.

[0010] As a further improvement scheme of the technical solution: the optimization objective function in the model parameter iteration tool is: , wherein is the risk-free rate.

[0011] An industry plate rotation configuration and yield risk evaluation method based on big data further includes the following steps: Phase one: preparation stage; Phase two: daily operation stage; Phase three: special scene coping stage; Phase four: iterative optimization stage.

[0012] As a further improved scheme of the technical solution: the preliminary preparation stage includes: hardware and software deployment operation and preliminary test and verification, the hardware and software deployment operation includes: hardware device deployment, software and data interface docking, parameter and threshold initialization, the daily operation stage includes: daily operation process and weekly auxiliary operation, the daily operation process includes: factor modeling and trend prediction, rotation signal generation and configuration execution, intraday real-time risk monitoring, the special scene coping stage includes: market violent fluctuation scene, data anomaly scene, model early warning scene, the iterative optimization stage includes: monthly optimization operation, quarterly optimization operation, annual optimization operation.

[0013] Compared with the prior art, the beneficial effects of the present application are: 1、The present application converts macro, meso and micro data scattered in different platforms into a unified and accurate analysis base through multi-source data integration and standardization processing, avoiding analysis deviation caused by heterogeneous data; in the decision-making link, the objective allocation of factor weight is realized relying on the entropy weight TOPSIS method, and the trend prediction of the dynamic adaptation of the plate fluctuation characteristics is combined with the LSTM model, replacing the traditional subjective judgment relying on experience, so that the plate strength ranking and rotation signal have more quantitative basis; in the risk control link, the high-risk source is accurately located and the graded early warning is triggered through real-time calculation of the combined VaR value and the plate marginal risk contribution, and the risk response is upgraded from post-loss stop to pre-control in cooperation with the targeted hedging scheme, which greatly reduces the loss caused by sudden risk of the portfolio.

[0014] 2、The model parameter iterative mechanism of the present application with the target of maximizing the Sharpe ratio can continuously optimize the factor system and the model structure according to the market style change, avoid the decline of the adaptability of the traditional static model, ensure that the market law is always followed in long-term application, and the response strategies for special scenes such as market violent fluctuation, data anomaly and model early warning can quickly adjust the constraint conditions, switch the data sources or optimize the model, so as to ensure that the configuration process is not interrupted and the risk is not out of control.

[0015] The above description is only a summary of the technical solution of the present application, in order to more clearly understand the technical means of the present application, and the content of the specification can be implemented, the following preferred embodiments of the present application are described in detail with the help of the drawings. The specific embodiments of the present application are given in detail by the following examples and their drawings. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application, the schematic embodiments of the present application and their description are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings: Fig. 1 A system block diagram of an industry sector rotation configuration and yield risk assessment method based on big data; Fig. 2 A flowchart of an industry sector rotation configuration and yield risk assessment method based on big data. DETAILED DESCRIPTION

[0017] The principles and features of the present application are described below in conjunction with the accompanying drawings, and the examples are used only to explain the present application and are not intended to limit the scope of the present application. In the following paragraphs, the present application is described in more detail with reference to the accompanying drawings. It should be noted that the drawings are very simplified and use non-precise proportions, only to facilitate, clearly assist in explaining the purpose of the embodiments of the present application.

[0018] Please refer to Figs. 1-2 In the embodiments of the present application, an industry sector rotation configuration and yield risk assessment method based on big data includes the following modules and components: Multi-source data hierarchical acquisition and standardized processing module, multi-dimensional factor modeling and trend prediction module, sector rotation signal generation and intelligent configuration module, dynamic risk monitoring and precise hedging module, yield risk assessment and dynamic iterative optimization module; The multi-source data hierarchical acquisition and standardized processing module includes: a multi-source data interface terminal, a data acquisition server cluster, an IoT time synchronizer, an intelligent data cleaning tool, and a data standardization subsystem; The multi-dimensional factor modeling and trend prediction module includes: a factor calculation server, an entropy weight TOPSIS weight calculation terminal, an LSTM trend prediction model training platform, and a trend visualization terminal; The sector rotation signal generation and intelligent configuration module includes: a rotation signal trigger engine, a mean-variance optimization configuration tool, a configuration execution terminal, and a position monitoring dashboard; The dynamic risk monitoring and precise hedging module includes: a real-time market data receiving server, a risk indicator calculation engine, a risk warning terminal, and a hedging tool management system; The yield risk assessment and dynamic iterative optimization module includes: a yield risk assessment subsystem, a historical case library server, a model parameter iteration tool, a factor updating and verification terminal; Specifically, the multi-source data hierarchical acquisition and standardized processing module: core positioning: build a unified data base of macro, meso and micro, solve the pain points of data fragmentation and low quality; the functions of the subordinate components are: Multi-source data interface terminal: interface with multiple data sources such as the National Bureau of Statistics, Wind, and stock market data, synchronize macro (GDP, CPI), meso (industry capacity), and micro (individual stock financial) data, achieve full-dimensional data coverage; Data collection server cluster: 8 high-performance cloud servers form a distributed architecture, parallel data collection (daily collection time ≤30 minutes), avoid single server lag; IoT time synchronizer: based on NTP protocol to calibrate all device timestamps (error ≤1 second), prevent time series data misplacement (such as monthly macro data and weekly industry data mismatch); Intelligent data cleaning tool: eliminate outliers, complete missing values, filter noise, output accurate data; Data standardization subsystem: eliminate dimensional differences, adapt to different industry characteristics, provide fair input for subsequent modeling; Multi-dimensional factor modeling and trend prediction module: Core positioning: quantify factor influence, accurately predict sector trends, solve the pain points of subjective traditional rotation factor weight and experiential trend prediction; Sub-component role: Factor calculation server: GPU cluster supports parallel calculation of 4 categories of factors (sentiment, valuation, liquidity, and profitability), and tests factor effectiveness (|r|≥0.3 is effective); Entropy weight TOPSIS weight calculation terminal: objectively assigns factor weights, generates sector comprehensive scores, and avoids subjective weight bias; LSTM trend prediction model training platform: dynamically adjusts the time window, trains the model to predict the future 30-day sector price change, and improves prediction accuracy; Trend visualization terminal: display factor scores and prediction results through heat maps and line charts, facilitate quick understanding of sector strength; Sector rotation signal generation and intelligent configuration module: Core positioning: generate accurate rotation signals, optimize position allocation, solve the pain points of signal ambiguity and subjective position; Sub-component role: Rotation signal trigger engine: automatically trigger signals according to trend intensity thresholds (≥0.7 strong rise, ≤0.3 strong fall), mark priority; Mean-variance optimization configuration tool: find the optimal balance between returns and risks, output single-sector position (5%-20%); Configuration execution terminal: support automation (interface with broker API) and manual execution, cater to different user needs; Position monitoring dashboard: real-time display of actual position and recommended position difference, warning when deviation ≥5%, avoid excessive concentration of risks; Dynamic risk monitoring and precise hedging module: Core positioning: Real-time risk prevention and control, quick hedging loss, solve the pain points of risk lag, blind hedging; Subordinate component function: Real-time market data receiving server: Access Level-2 high-frequency market data (delay ≤0.1 seconds), provide real-time data for risk calculation; Risk index calculation engine: Quantitative VaR value, maximum drawdown, attribute risk source, and clarify high-risk sectors; Risk warning terminal: Multi-channel (sound and light alarm, APP push) graded warning, ensure timely risk awareness; Hedging tool management system: Maintain stock index futures / ETF options pool, recommend targeted hedging schemes (such as reducing high-risk sector hedging); Profit and risk assessment and dynamic iteration optimization module: Core positioning: Quantitative allocation effect, continuous model optimization, solve the pain points of unquantifiable effect and rigid model; Subordinate component function: Profit and risk assessment subsystem: Calculate annualized return, Sharpe ratio (target ≥1.5), compare with benchmark index, and generate evaluation report; Historical case library server: Store market environment, allocation scheme, and effect case in the past 10 years, support retrieval and reference; Model parameter iteration tool: Optimize LSTM parameters and factor weights with the goal of maximizing Sharpe ratio; Factor updating and verification terminal: Add effective factors and eliminate ineffective factors to maintain the timeliness of the factor library.

[0019] The intelligent data cleaning tool includes: outlier removal, differential missing completion, and noise filtering. The outlier removal uses the 3 sigma principle to automatically identify abnormal data, the differential missing completion uses the industry mean and trend correction filling method, the formula is: , where is the mean of other industries in the same sector, and k is the trend correction coefficient, with a value range of 0.3-0.7, which is adjusted adaptively according to industry volatility. The noise filtering filters short-term noise in market data through a 5-day window moving average method. The data standardization subsystem includes: numerical standardization and industry adaptation optimization. The numerical standardization uses Z-score normalization to eliminate dimension influence, the formula is: , where is the original factor value, is the mean of the factor in the past 3 years, is the standard deviation of the factor in the past 3 years. The industry adaptation optimization adds a seasonal correction term for special industries such as cyclical industries, the formula is: , where is the seasonal correction coefficient, with a value range of 0.1-0.3, is the seasonal identification variable, with 1 for peak season, -1 for off-season, and 0 for flat season. In detail, the core function of the intelligent data cleaning tool is to handle abnormal, missing, and noise problems in raw data and output clean data. The outlier removal method uses the 3 sigma principle, that is, if the data ( Anomalies are marked as outliers and are removed after manual review. The role is to filter extreme data (such as individual stock daily price change ≥10% without announcement) to avoid abnormal values interfering with factor calculation. Differentiated missing data completion: Different strategies are used for different missing lengths. Long missing data (>3 days) uses industry mean and trend correction filling method, , formula annotation: : The completed value of missing data; : Factor mean of other industries in the same plate (reflecting industry commonality); : Trend correction coefficient (0.3-0.7, high volatility industry takes large value, such as technology =0.7, low volatility takes small value, such as utilities =0.3); : Factor data 1 period before the missing time; : Factor data 3 periods before the missing time; Reflects the trend of the plate itself; formula effect: avoids distortion caused by traditional single mean filling ignoring plate trend, reduces filling error by more than 40%; Noise filtering: 5-day window moving average method, i.e. daily factor data , filter short-term fluctuations in market data (such as sudden increase in individual stock daily turnover rate), retain long-term trend, and improve data stability; Data standardization subsystem: core role: eliminate factor dimension differences (such as turnover rate% and ROE%), while adapt to industry characteristics, ensure factor comparability, effect: convert different magnitude factors to the same scale, avoid high value factor (such as trading volume) weight problem; Industry adaptation optimization: add seasonal correction term for special industries (such as cyclical industries) to adapt to seasonal fluctuations in cyclical industries (such as steel demand rising in the peak season), avoid ignoring industry characteristics after standardization, and improve factor modeling correlation by 25%.

[0020] Entropy TOPSIS weight calculation terminal includes: factor standardization processing, entropy weight calculation, TOPSIS comprehensive score calculation, weight fine-tuning function, factor standardization processing uses the output results of the above data standardization subsystem, entropy weight calculation first step calculates the information entropy of the jth factor: , where m is the number of plates, is the standardized value of the jth factor of the ith plate, if =0, then =0, avoid meaningless logarithm; second step calculates the weight of the jth factor: , where n is the number of effective factors, and the weight sum ; TOPSIS comprehensive score calculation: determine the optimal solution and the worst solution :

[0021] Calculate the Euclidean distance of each plate and the optimal and worst solutions: ; ; Calculate the comprehensive score: , the score range is 0-1, the closer to 1 represents the better basic of the plate; The LSTM trend prediction model training platform includes: time window dynamic adjustment, model adaptive training, time window optimization, and adaptive selection of time window according to the volatility of the plate, formula: , where is the volatility of the plate in the past 30 days; Specifically, the entropy weight TOPSIS weight calculation terminal: core function: objective allocation of factor weight, calculation of plate comprehensive score, solving the problem of subjective weight deviation; The output of the data standardization subsystem is used for factor standardization processing , to ensure uniform input of factors and avoid errors caused by repeated standardization, and to maintain data consistency; Entropy weight calculation: first step: calculate the information entropy of the jth factor, second step: calculate the weight of the jth factor, objectively allocate the weight through the information entropy of the factor, avoid analysts set weight, such as the information entropy of the prosperity factor is small, the weight is automatically increased; TOPSIS comprehensive score calculation: first step: determine the optimal solution and the worst solution , second step: calculate the Euclidean distance of each plate and the optimal and worst solutions, third step: calculate the comprehensive score, accurately distinguish the subtle differences between plates and improve the sorting accuracy; Weight fine-tuning function: support analysts to manually fine-tune (amplitude ≤ ±0.05) on the basis of objective weight, such as new energy plate that is optimistic about policy support, can adjust the prosperity factor weight from 0.35 to 0.4, balance objectivity and flexibility; LSTM trend prediction model training platform dynamically adapts to the volatility characteristics of the plate, improves the accuracy of trend prediction, and solves the problem of poor adaptability of fixed window prediction; Model adaptive training: use 3-layer LSTM hidden layer (neuron number 128 / 64 / 32), activation function ReLU, optimizer Adam (learning rate 0.001), input factor comprehensive score, plate amplitude, output future 30-day amplitude; function: train the model through historical data, real-time adapt to market style (such as growth, value switching), prediction accuracy ≥75%.

[0022] The mean-variance optimization configuration tool includes: basic constraint setting, dynamic constraint adaptation, optimal position solving, and configuration result output. The objective function of the optimal position solving is to maximize the portfolio expected return, and the constraint condition is to minimize the portfolio variance. The mathematical model is: Wherein is the position ratio of the i-th plate, Ri is the predicted annualized return of the i-th plate, and the constraint condition is: ; is the return covariance of plate i and j, is the upper limit of the portfolio volatility, is the upper and lower limits of the single plate position; Specifically, the mean-variance optimization configuration tool: under the preset constraints, find the position combination that maximizes the return and minimizes the risk, solve the problem of subjective and arbitrary risk out of control in traditional configuration; Basic constraint setting: content: preset single plate position (5%-20%), total portfolio position (50%-100%), portfolio annualized volatility (≤15%); Effect: set the risk bottom line to avoid excessive concentration of single plate (such as single plate position ≤20%) or high portfolio volatility (such as annualized ≤15%); Dynamic constraint adaptation: content: adjust the constraints according to market environment, such as bull market (Shanghai and Shenzhen 300 near 60 days up ≥10%) when the single plate position upper limit is raised to 25% and the volatility constraint is relaxed to 18%; Bear market (Shanghai and Shenzhen 300 near 60 days down ≥10%) when the single plate upper limit is reduced to 15% and the volatility constraint is tightened to 12%; Effect: adapt to market changes to capture returns in bull markets and control risks in bear markets; Optimal position solving: : portfolio expected annualized return; : position ratio of the i-th plate (such as consumer plate =18%); : predicted annualized return of the i-th plate; : portfolio variance; : return covariance of plate i and j; : upper limit of portfolio volatility; : upper and lower limits of single plate position; Solve the optimal position through the mathematical model to avoid risk concentration caused by experience-based position increase and find the balance point between return and risk; The configuration result output generates a list of plate names, recommended positions, and configuration reasons.

[0023] The risk indicator calculation engine includes: core risk indicator calculation, risk attribution analysis, and core risk indicator calculation using parameter method. The formula is: Wherein is the portfolio average daily return in the past 60 days, To combine the standard deviation of nearly 60 days, representing the maximum possible loss on the day of 95% confidence, the risk attribution analysis uses the marginal risk contribution method to calculate the contribution of individual sectors to the portfolio VaR value, the formula is: Wherein is the marginal risk contribution of the i-th sector, is the portfolio return; Specifically, the risk indicator calculation engine: quantifies portfolio risk, locates risk sources, solves the problem of knowing only the risk size but not knowing where the risk is; Quantify the risk bottom line to provide basis for setting early warning thresholds; Risk attribution analysis uses the marginal risk contribution method to calculate the contribution of individual sectors to the portfolio VaR, : Position ratio of the i-th sector; : Covariance of the i-th sector and the portfolio (positive covariance represents that the volatility of the sector exacerbates the risk of the portfolio); σp: Portfolio standard deviation; Quickly locate high-risk sectors such as cyclical sectors = 45%, then prefer to hedge (reduce or sell stock index futures) cyclical sectors, and the hedging efficiency is improved by more than 50%.

[0024] The optimization objective function in the model parameter iteration tool is: Wherein is the risk-free rate; Specifically, the model parameter iteration tool: when the model prediction accuracy decreases or the portfolio return is not good, optimize the model parameters to solve the problem of model rigidity and poor adaptability; SR: Sharpe ratio (target ≥ 1.5, the higher the better represents the higher excess return per unit risk); : Annualized return of the portfolio; : Risk-free rate; : Annualized volatility of the portfolio; Iteration logic: when the portfolio SR is less than 1.0 for two consecutive months or the LSTM prediction accuracy is less than 65%, automatically start iteration: optimize parameter range: LSTM time window (60-90 days), hidden layer neuron number (64-256), factor weight (±0.05); Optimization tool: use the Hyperopt framework to maximize SR as the target to search for the optimal parameter combination.

[0025] An industry sector rotation configuration and yield risk assessment method based on big data further includes the following steps: Phase I: Preparation phase; Phase II: Daily operation phase; Phase III: Special scenario response phase; Phase IV: Iterative optimization phase; The preparation stage includes: hardware and software deployment operations and preliminary testing and verification, hardware and software deployment operations include: hardware device deployment, software and data interface docking, parameter and threshold initialization, daily operation stage includes: daily operation process and weekly auxiliary operation, daily operation process includes: factor modeling and trend prediction, rotation signal generation and configuration execution, intraday real-time risk monitoring, special scene response stage includes: market volatile scenario, data anomaly scenario, model early warning scenario, iterative optimization stage includes: monthly optimization operation, quarterly optimization operation, annual optimization operation; Specifically, section one: preparation stage: core content: complete hardware deployment, software docking and parameter initialization, lay the foundation for daily operation; subordinate content: hardware and software deployment: deploy server cluster, GPU, market terminal, dock data source API, install core software (data cleaning tool, LSTM platform); preliminary testing and verification: simulate data processing and modeling for 3 consecutive days, check data integrity (missing rate ≤0.5%), model accuracy (prediction accuracy ≥75%), and ensure normal operation of the system; Stage two: daily operation stage: core content: perform high-frequency operations daily and weekly to capture short-term rotation opportunities; subordinate content: daily operation process: data cleaning after market close, factor modeling, signal generation, configuration execution, intraday real-time risk monitoring; weekly auxiliary operation: backup data to historical case library, review factor effectiveness (mark for removal factors); Stage three: special scene response stage: core content: respond to extreme market conditions to avoid significant losses; subordinate content: market volatile (market up / down ≥5%): tighten risk thresholds, reduce positions in high-volatility sectors, and start hedging; data anomaly (missing rate ≥5%): switch to backup data source, reduce LSTM window, and manually review data; model early warning (prediction accuracy <65%): retrain LSTM model, adjust factor weights, and reduce portfolio position; Stage four: iterative optimization stage: core content: regularly optimize models and factors to ensure long-term effectiveness; subordinate content: monthly optimization: calculate the portfolio Sharpe ratio, and fine-tune model parameters (such as LSTM learning rate); quarterly optimization: update factor library (add new effective factors, remove ineffective factors), and calibrate constraints; annual optimization: fully retrain the LSTM model, review historical cases, and upgrade hardware and software; Preparation stage: hardware and software deployment operations: hardware device deployment: build data collection cluster, GPU cluster, and market server, and install monitoring terminal; software and data interface docking: dock multiple data source APIs, install core software and connect data interfaces; parameter and threshold initialization: set data cleaning parameters (such as outlier rules), model parameters (such as LSTM initial window), and risk thresholds (such as VaR=5%); Daily operation stage: Daily operation process: data cleaning, factor modeling, signal generation, configuration execution, intraday risk control; Weekly auxiliary operation: data backup (this week's data archiving), factor effectiveness review (Spearman test); Special scene response stage: Market volatile fluctuation scenario: adjust position constraint, reduce position in high volatility plate, start hedging; Data anomaly scenario: switch to backup data source, adjust modeling parameters, manual review; Model early warning scenario: retrain model, adjust factor weight, reduce position; Iterative optimization stage: Monthly optimization operation: calculate yield risk indicators, fine-tune model parameters; Quarterly optimization operation: update factor library, calibrate constraint conditions; Annual optimization operation: retrain model, review cases, upgrade software and hardware The use method and working principle of the application are: Use method: first enter the early preparation stage, complete the deployment and networking of hardware devices, installation of software tools and docking of multi-source data interfaces, at the same time, initialize data cleaning rules, model parameters and risk thresholds, verify data integrity and model accuracy through continuous multi-day simulation test, and ensure that the system can stably run; Then enter the daily operation stage, after the daily closing, carry out data collection and standardization processing, multi-dimensional factor modeling and plate trend prediction in turn, trigger the rotation signal according to the trend strength, determine the optimal position of each plate and execute the configuration combined with the mean-variance optimization tool, and monitor the combination risk index in the intraday, and respond to the early warning in time; When encountering special scenes such as market violent fluctuation, data anomaly or model early warning, start the corresponding response strategy, such as tightening risk constraint, switching to backup data source or retraining model; Finally, enter the iterative optimization stage according to the monthly, quarterly and annual, fine-tune the model parameters based on the yield risk evaluation results, update the factor library, and review the historical cases to continuously improve the method adaptability.

[0026] Working principle: first, the multi-source data hierarchical acquisition and standardization processing module obtains macro, meso and micro data from multiple channels, removes abnormal values, fills in missing values and standardizes to form a unified data base; second, the multi-dimensional factor modeling and trend prediction module objectively calculates the factor weight and the comprehensive score of the plate by using the entropy weight TOPSIS method, combines the LSTM model to dynamically adjust the time window according to the volatility of the plate, and accurately predicts the future trend of the plate; then, the plate rotation signal generation and intelligent configuration module triggers the buy and sell signals according to the trend intensity, solves the optimal position under the constraints of yield and risk through mean-variance optimization and executes; at the same time, the dynamic risk monitoring and accurate hedging module calculates the portfolio VaR value and the marginal risk contribution of each plate in real time, triggers the hierarchical early warning and recommends the targeted hedging scheme; finally, the yield risk evaluation and dynamic iterative optimization module quantifies the portfolio yield risk index, iterates the model parameters and factor system to maximize the Sharpe ratio, forms a complete working link that continuously adapts to market changes, and ensures the scientific nature and risk controllability of the rotation configuration.

[0027] The above is only a preferred embodiment of the present application, and does not limit the present application in any form; any person skilled in the art can easily implement the present application according to the description and the above; however, any equivalent changes, modifications and evolutions made by those skilled in the art within the scope of the technical solutions of the present application, using the above disclosed technical content, are equivalent embodiments of the present application; at the same time, any equivalent changes, modifications and evolutions of the above embodiments according to the essence of the present application are still within the protection scope of the technical solutions of the present application.

Claims

1. A method for industry sector rotation allocation and return / risk assessment based on big data, characterized in that, Includes the following modules and components: The module includes: multi-source data hierarchical acquisition and standardized processing module, multi-dimensional factor modeling and trend prediction module, sector rotation signal generation and intelligent configuration module, dynamic risk monitoring and precise hedging module, and return and risk assessment and dynamic iterative optimization module. The multi-source data hierarchical acquisition and standardization processing module includes: a multi-source data interface terminal, a data acquisition server cluster, an IoT time synchronizer, an intelligent data cleaning tool, and a data standardization subsystem. The multi-dimensional factor modeling and trend prediction module includes: a factor calculation server, an entropy weight TOPSIS weight calculation terminal, an LSTM trend prediction model training platform, and a trend visualization terminal. The sector rotation signal generation and intelligent configuration module includes: a rotation signal triggering engine, a mean-variance optimization configuration tool, a configuration execution terminal, and a position monitoring dashboard; The dynamic risk monitoring and precise hedging module includes: a real-time market data receiving server, a risk indicator calculation engine, a risk warning terminal, and a hedging tool management system; The return and risk assessment and dynamic iterative optimization module includes: a return and risk assessment subsystem, a historical case library server, a model parameter iteration tool, and a factor update and verification terminal.

2. The method for industry sector rotation allocation and return / risk assessment based on big data as described in claim 1, characterized in that, The intelligent data cleaning tool includes: outlier removal, differential missing value completion, and noise filtering. The outlier removal employs a 3D algorithm. The principle is to automatically identify abnormal data. The differential missing data completion uses an industry mean and trend correction method, with the following formula: ,in The average value is the same as other industries in the same sector. k is a trend correction coefficient, ranging from 0.3 to 0.7, adaptively adjusted according to industry volatility. Noise filtering uses a 5-day moving average method to filter short-term noise in the market data. The data standardization subsystem includes numerical standardization and industry adaptation optimization. Numerical standardization uses Z-score normalization to eliminate the influence of dimensions. The formula is: ,in These are the original factor values. This is the average of the factor over the past 3 years. The standard deviation of this factor over the past 3 years is used. The industry adaptation optimization is applied to specific industries, such as cyclical industries, by adding a seasonality correction term. The formula is as follows: ,in This is a seasonal correction factor, ranging from 0.1 to 0.

3. The variable is used to indicate the season, with 1 for peak season, -1 for off-season, and 0 for shoulder season.

3. The method for industry sector rotation allocation and return / risk assessment based on big data as described in claim 1, characterized in that, The TOPSIS entropy weight calculation terminal includes: factor standardization processing, entropy weight calculation, TOPSIS comprehensive score calculation, and weight fine-tuning functions. The factor standardization processing uses the output results of the aforementioned data standardization subsystem. The first step of the entropy weight calculation is to calculate the information entropy of the j-th factor. Where m is the number of plates. Let j be the standardized value of the j-th factor in the i-th sector. =0, then =0, to avoid the logarithm being meaningless; the second step is to calculate the weight of the j-th factor: Where n is the number of effective factors and the total weights ; The TOPSIS comprehensive score calculation: Determine the optimal solution. With the worst solution :

4. Calculate the Euclidean distance between each plate and the optimal and worst solutions: ; Calculate the overall score: The score ranges from 0 to 1, with the closer to 1 indicating a better fundamental outlook for the sector. The LSTM trend prediction model training platform includes: Dynamic adjustment of the time series window and adaptive model training are employed. The time series window optimization adaptively selects the time series window based on sector volatility, as shown in the formula: ,in This represents the sector's volatility over the past 30 days.

5. The method for industry sector rotation allocation and return / risk assessment based on big data as described in claim 1, characterized in that, The mean-variance optimization configuration tool includes: basic constraint setting, dynamic constraint adaptation, optimal position calculation, and configuration result output. The objective function for optimal position calculation is to maximize the expected return of the portfolio, with the constraint condition being to minimize the portfolio variance. The mathematical model is as follows: Objective function: ,in Let be the position ratio of the i-th sector, and Ri be the predicted annualized return of the i-th sector. Constraints: , Let be the covariance of returns for sectors i and j. This represents the upper limit of portfolio volatility. These represent the upper and lower limits of a single sector's position.

6. The method for industry sector rotation allocation and return / risk assessment based on big data as described in claim 1, characterized in that, The risk indicator calculation engine includes: core risk indicator calculation and risk attribution analysis. The core risk indicator calculation uses a parametric method, with the following formula: ,in This represents the portfolio's average daily return over the past 60 days. The portfolio's 60-day standard deviation represents the maximum possible daily loss at a 95% confidence level. The risk attribution analysis uses the marginal risk contribution method to calculate the contribution of individual sectors to the portfolio's VaR value. The formula is: ,in Contribution to the marginal risk of the i-th segment. For portfolio returns.

7. The method for industry sector rotation allocation and return / risk assessment based on big data as described in claim 1, characterized in that, The objective function is optimized within the model parameter iteration tool. ,in This is the risk-free interest rate.

8. A method for industry sector rotation allocation and return / risk assessment based on big data, employing any one of the module components within the industry sector rotation allocation and return / risk assessment method based on big data according to claims 1-6, characterized in that... It also includes the following steps: Phase 1: Preliminary Preparation Phase; Phase Two: Routine Operation Phase; Phase Three: Special Scenario Response Phase; Phase Four: Iterative Optimization Phase.

9. The method for industry sector rotation allocation and return / risk assessment based on big data as described in claim 7, characterized in that, The preliminary preparation phase includes: hardware and software deployment operations and preliminary testing and verification. The hardware and software deployment operations include: hardware device deployment, software and data interface docking, and parameter and threshold initialization. The daily operation phase includes: daily operation procedures and weekly auxiliary operations. The daily operation procedures include: factor modeling and trend prediction, rotation signal generation and configuration execution, and real-time risk monitoring during trading. The special scenario response phase includes: scenarios of severe market fluctuations, data anomalies, and model early warning scenarios. The iterative optimization phase includes: monthly optimization operations, quarterly optimization operations, and annual optimization operations.

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