CN111915443A - System and method for realizing intelligent investment product combination analysis and calculation processing - Google Patents
System and method for realizing intelligent investment product combination analysis and calculation processing Download PDFInfo
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Abstract
The invention relates to a system for realizing the analysis, calculation and processing of intelligent investment product combinations, which comprises a target risk data acquisition and storage module, a relational database and a data analysis and calculation module, wherein the target risk data acquisition and storage module is used for acquiring basic data of investment target groups and storing the basic data in the relational database; the investment product data analysis and calculation module is used for collecting basic data of the investment product; the investment large-class ratio analysis and calculation module is used for calculating the feasible investment product large-class ratio; the specific product selection analysis calculation module is used for selecting specific products from the investment large-class pool; and the investment portfolio profit return measurement and simulation analysis and calculation module is used for calculating the historical profit fluctuation of the investment product portfolio and analyzing and calculating the future profit distribution and fluctuation. The invention also relates to a method for realizing the analysis and calculation processing of the intelligent investment product combination. The system and the method for realizing the analysis, calculation and processing of the intelligent investment product portfolio overcome the defect that common investors are not clear about how to carry out the position matching, and are convenient for the common investors to carry out investment portfolio investment or investment operation.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to the field of investment portfolio buying and selling, and particularly relates to a system and a method for realizing intelligent analysis and calculation processing of investment product portfolio.
Background
The combined investment is a circulation transfer activity which is carried out by an investment group by taking a plurality of specific investment products as trading targets and self-assuming risk and income.
In the traditional investment buying and selling, independent buying and selling are generally carried out by investor buying and selling operations aiming at each investment product, no system is used for assisting investors to carry out systematic risk-income analysis, the total risk cognition of the investors on each held investment can have blindness, the investors can have blindness on the chip distribution of each held investment, the investors have difficulty in selecting specific investment of an investment combination, and the investors do not have a proper fixed-investment tool and can have complicated operation when holding the combination for a long time.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a system and a method for realizing the analysis, calculation and processing of the intelligent investment product combination, which have the advantages of low gap, simple and convenient operation and wider application range.
In order to achieve the above object, the system and method for realizing intelligent investment product portfolio analysis and calculation processing of the present invention are as follows:
the system for realizing the analysis, calculation and processing of the intelligent investment product combination is mainly characterized by comprising the following components:
the target risk data acquisition and storage module is used for acquiring basic data of the investment target population and storing the basic data in the relational database;
the investment product data analysis and calculation module is connected with the target risk data acquisition and storage module and is used for acquiring basic data of investment products and calculating products and product class derivative analysis data through a large data batch processing technology;
the investment large-class ratio analyzing and calculating module is connected with the investment product data analyzing and calculating module and is used for calculating feasible investment product large-class ratio by adopting a distributed numerical calculation engine and combining a mathematical model;
the specific product selection analysis calculation module is connected with the investment large-class ratio analysis calculation module and is used for selecting specific products from the investment large-class pool by adopting a large data batch processing technology and combining a specific recommendation algorithm;
the return measuring and simulation analysis and calculation module of the investment portfolio is connected with the specific product selection analysis and calculation module and is used for calculating the historical return fluctuation of the investment portfolio and analyzing and calculating the future return distribution and fluctuation;
the investment portfolio fixed investment analysis and calculation module is connected with the investment portfolio income return measurement and simulation analysis and calculation module and is used for calculating a fixed investment time point and a fixed investment amount in real time by combining with fixed investment configuration;
and the investment portfolio profit analysis and calculation module is connected with the investment portfolio fixed-investment analysis and calculation module and is used for collecting, summarizing, storing and managing the logs of all the modules.
Preferably, the system further comprises a portfolio profit analysis and calculation module connected with the log management module for analyzing the overall use condition of the system.
Preferably, the investment product data analysis and calculation module acquires basic information such as codes, names, historical net values, types and the like of investment products, and calculates the daily average profitability and daily average profitability fluctuation of the investment products according to basic classification and summarization of the investment products.
Preferably, the investment product data analysis and calculation module calculates analysis data of the products by traversing the product set, then classifies the products according to product classes, summarizes all data of the product classes, calculates analysis data of the product classes by traversing the product classes, processes product raw data of large data magnitude by adopting a distributed batch processing framework, and performs transverse expansion.
Preferably, the investment products selected by the specific product selection analysis and calculation module, the fixed investment amount and the fixed investment method are fixed investment periodically and according to the method.
The method for realizing the analysis, calculation and processing of the intelligent investment product combination based on the system is mainly characterized by comprising the following steps of:
(1) the investment is given and taken to obtain the risk attribute of the investment group, and the reference data of the proportion of the large-class investment products is calculated for the investment group;
(2) determining the proportion of large-class investment products by an investment group, and providing a selection method of specific investment products for the investment group;
(3) determining a specific investment portfolio by an investment group, and carrying out retest and simulation on the investment portfolio for a client;
(4) the investment group confirms a specific investment portfolio and selects to place an order or decide the investment portfolio once;
(5) and checking the overall income of the investment portfolio after the starting time point of the order placement or the fixed investment after the order placement or the fixed investment of the investment group, and determining whether to continue holding the portfolio.
Preferably, the step (1) specifically comprises the following steps:
(1.1) classifying the investment into large categories according to different risk attributes by using the large category proportion of the investment products;
(1.2) carrying out average daily rate of return and average daily rate of return fluctuation calculation on the large category of investment products;
(1.3) calculating the large-class variance of the investment products by using a covariance matrix;
(1.4) calculating expected income and risk bearing capacity, average daily income rate and average daily income rate covariance matrix of the large investment classes, and calculating the mixture ratio of different large investment class products.
Preferably, the step (2) specifically comprises the following steps:
(2.1) scoring the products according to the related information of the products, and ranking companies to which the products belong in the industry;
and (2.2) grading and ranking the products according to the relevant information of the products, and preferentially selecting the products according to the grading and ranking for investment.
Preferably, the step (3) specifically includes the following steps:
(3.1) according to the historical net value of the specific investment products in the selected investment portfolio, measuring the net value curve of the portfolio in the historical time period, and transversely comparing the net value curve with the large plate index such as Shanghai depth 300;
and (3.2) calculating a possible future income interval and a possible confidence interval by using Monte Carlo according to the historical daily average income rate and daily income rate fluctuation of specific investments in the selected investment portfolio.
Preferably, the step (4) specifically includes the following steps:
(4.1) directly placing an order once according to the specific investment in the selected investment portfolio;
and (4.2) carrying out fixed investment according to the specific investment in the selected investment portfolio, the selected fixed investment period, the selected fixed investment method and the selected fixed investment amount.
The system and the method for realizing the analysis, calculation and processing of the intelligent investment product portfolio meet the requirements of providing a full-flow investment portfolio operation tool set and a method set for the analysis and calculation of the large investment portfolio investment category proportion, the selection of the specific investment of the investment portfolio, the retest and simulation analysis of the specific investment portfolio, the ordering and fixed investment of the specific investment portfolio and the monitoring of the specific investment portfolio in a production environment, overcome the problems that a common investor does not know how to carry out the matching of a taken position, does not know the risk-income characteristics of the investment of the taken position and is inconvenient to carry out the long-term investment and monitoring of the investment portfolio, and facilitate the common investor to carry out the investment portfolio investment or the related operation of the.
Drawings
FIG. 1 is a schematic diagram of a system for implementing intelligent investment product portfolio analysis and calculation processing in accordance with the present invention.
FIG. 2 is a deployment diagram of the system for implementing intelligent investment portfolio analysis calculation processing of the present invention.
FIG. 3 is a timing diagram of a method of implementing the intelligent investment portfolio analysis calculation process of the present invention.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
The system for realizing the analysis, calculation and processing of the intelligent investment product combination comprises the following components:
the target risk data acquisition and storage module is used for acquiring basic data of the investment target population and storing the basic data in the relational database;
the investment product data analysis and calculation module is connected with the target risk data acquisition and storage module and is used for acquiring basic data of investment products and calculating products and product class derivative analysis data through a large data batch processing technology;
the investment large-class ratio analyzing and calculating module is connected with the investment product data analyzing and calculating module and is used for calculating feasible investment product large-class ratio by adopting a distributed numerical calculation engine and combining a mathematical model;
the specific product selection analysis calculation module is connected with the investment large-class ratio analysis calculation module and is used for selecting specific products from the investment large-class pool by adopting a large data batch processing technology and combining a specific recommendation algorithm;
the return measuring and simulation analysis and calculation module of the investment portfolio is connected with the specific product selection analysis and calculation module and is used for calculating the historical return fluctuation of the investment portfolio and analyzing and calculating the future return distribution and fluctuation;
the investment portfolio fixed investment analysis and calculation module is connected with the investment portfolio income return measurement and simulation analysis and calculation module and is used for calculating a fixed investment time point and a fixed investment amount in real time by combining with fixed investment configuration;
and the investment portfolio profit analysis and calculation module is connected with the investment portfolio fixed-investment analysis and calculation module and is used for collecting, summarizing, storing and managing the logs of all the modules.
Preferably, the system further comprises a portfolio profit analysis and calculation module connected with the log management module for analyzing the overall use condition of the system.
Preferably, the investment product data analysis and calculation module acquires basic information such as codes, names, historical net values, types and the like of investment products, and calculates the daily average profitability and daily average profitability fluctuation of the investment products according to basic classification and summarization of the investment products.
Preferably, the investment product data analysis and calculation module calculates analysis data of the products by traversing the product set, then classifies the products according to product classes, summarizes all data of the product classes, calculates analysis data of the product classes by traversing the product classes, processes product raw data of large data magnitude by adopting a distributed batch processing framework, and performs transverse expansion.
Preferably, the investment products selected by the specific product selection analysis and calculation module, the fixed investment amount and the fixed investment method are fixed investment periodically and according to the method.
The method for realizing the analysis, calculation and processing of the intelligent investment product combination based on the system comprises the following steps:
(1) the investment is given and taken to obtain the risk attribute of the investment group, and the reference data of the proportion of the large-class investment products is calculated for the investment group;
(1.1) classifying the investment into large categories according to different risk attributes by using the large category proportion of the investment products;
(1.2) carrying out average daily rate of return and average daily rate of return fluctuation calculation on the large category of investment products;
(1.3) calculating the large-class variance of the investment products by using a covariance matrix;
(1.4) calculating expected income and risk bearing capacity, average daily income rate and average daily income rate covariance matrix of the large investment class, and calculating the ratio of different large investment class products;
(2) determining the proportion of large-class investment products by an investment group, and providing a selection method of specific investment products for the investment group;
(2.1) scoring the products according to the related information of the products, and ranking companies to which the products belong in the industry;
(2.2) grading and ranking the products according to the related information of the products, and preferentially selecting the products according to the grading and ranking for investment
(3) Determining a specific investment portfolio by an investment group, and carrying out retest and simulation on the investment portfolio for a client;
(3.1) reviewing the net worth of the portfolio over the historical time period, yij, according to the historical net worth of the particular investment product in the selected portfolio, and comparing the net worth of the portfolio with the Shanghai depth 30qqy index;
(3.2) calculating a possible future income interval and a possible confidence interval by using Monte Carlo according to the historical daily average income rate and daily income rate fluctuation of specific investment in the selected investment portfolio;
(4) the investment group confirms a specific investment portfolio and selects to place an order or decide the investment portfolio once;
(4.1) directly placing an order once according to the specific investment in the selected investment portfolio;
(4.2) carrying out fixed investment according to the specific investment in the selected investment portfolio, the selected fixed investment period, the selected fixed investment method and the selected fixed investment amount;
(5) and checking the overall income of the investment portfolio after the starting time point of the order placement or the fixed investment after the order placement or the fixed investment of the investment group, and determining whether to continue holding the portfolio.
The method for realizing the analysis, calculation and processing of the intelligent investment product portfolio based on the system comprises the following steps of a target risk data acquisition and storage module, an investment product data analysis and calculation module, an investment major proportion analysis and calculation module, a specific product selection analysis and calculation module, an investment portfolio return test and simulation analysis and calculation module, an investment portfolio fixed investment analysis and calculation module and an investment portfolio return analysis and calculation module.
The system comprises:
target risk data acquisition and storage module: collecting data such as risk preference and risk bearing capacity of investment crowd by adopting modes such as questionnaire and the like and storing the data in a relational database;
the investment product data analysis and calculation module: the method comprises the steps of collecting classification and net value data of investment products, calculating daily average profitability and daily average profitability fluctuation of the investment products, large daily average profitability and large daily average profitability fluctuation of the investment products, storing the same, and supporting large-data mass product data operation by mainly adopting a Spark large-data batch processing technology in the calculation process.
The investment major proportion analysis and calculation module: and the large-scale proportion of the investment products matched with the client financing requirements on the premise of meeting the client risk attributes is calculated by adopting numerical calculation tools such as Matlab, Scilab and the like in combination with a numerical analysis model on the basis of the client risk attributes, the client financing requirements and the large-scale risk attributes of the products.
The specific product selection analysis calculation module: and the data analysis and calculation module is connected with the investment product, selects specific investment products from the investment pool of the investment product category, comprehensively scores the specific investment products mainly by using Spark big data batch processing technology, and selects the specific investment products according to the scoring sorting result.
Return testing and simulation analysis calculation module of investment portfolio: and the investment product data analysis and calculation module is used for measuring the income trend of the investment portfolio within a specified time according to the specific investment proportion and comparing the income trend with the related index. The method predicts the future income distribution interval and probability under the conditions of appointed investment period, fixed investment period and fixed investment amount through a Monte Carlo numerical calculation method, and the module mainly adopts Storm distributed real-time calculation and simultaneously carries out simulation sampling calculation of large-batch independent random events.
Investment portfolio decision analysis and calculation module: and (3) carrying out fixed investment of specific investment according to the finally determined investment proportion, fixed investment period, fixed investment amount and fixed investment method, wherein the fixed investment time point and the fixed investment amount are mainly calculated, the calculated amount is small, and if more investment crowds exist, the calculation capability is provided by adopting distributed service.
The investment portfolio income display module: and summarizing and analyzing the real performance data of the investment portfolio after the investment portfolio is purchased and displaying the data, and mainly adopting Spark batch data processing.
The system also comprises a log management module which is connected with the target risk data acquisition and storage module, the investment large-class ratio analysis and calculation module, the specific product selection analysis and calculation module, the investment portfolio profit return measurement and simulation analysis and calculation module, the investment portfolio investment setting analysis and calculation module and the investment portfolio investment setting analysis and calculation module, and logs of all the modules are acquired, summarized, stored and managed.
The system also comprises a log data analysis module which is connected with the log management module and used for analyzing the overall use condition of the system.
The target risk data acquisition and storage module mainly has the functions of acquiring and evaluating the risk bearing capacity of an investment group according to national laws and regulations, preventing the total risk of an investment portfolio from exceeding the risk bearing capacity of the investment group, and storing acquired data in a relational database.
The investment product data analysis and calculation module mainly has the functions of acquiring basic information such as codes, names, historical net values and types of investment products, and calculating product category indexes such as daily average earning rate and daily average earning rate fluctuation of the investment products, overall average daily average earning rate and overall average daily average earning rate fluctuation of investment categories according to basic classification and collection of the investment products, wherein the investment categories are mainly divided into currency products, bond products, stock products and the like. The integral Map-Reduce calculation process of the module comprises the steps of reading daily product data of a product in a specified time span, conducting Shuffle summarization on the data according to the product, traversing analysis data of the product in a product set, classifying the product according to the product category, summarizing all data of the product category, traversing analysis data of the product category in a product category set, conducting Map-Reduce processing on the analysis data of the product category by adopting a distributed batch processing frame Spark, and conducting transverse expansion conveniently.
The method and the system for analyzing and calculating the intelligent investment product combination solve the product large-class matching which possibly meets the requirements of investment groups by taking the income demand and the risk bearing capacity of investment crowds, and the average profitability and the profitability variance of the large class of the investment products as input variables according to a Markov's mean-variance combination model, wherein the large-class matching of the investment products is an effective analysis result in a historical time range based on historical data, only reference is made in the future time, accurate prediction cannot be carried out, and in addition, the overall risk of the large class of the investment cannot completely represent the risk of specific investment under the large class. The module needs a professional numerical analysis and calculation tool to perform optimal solution, and calculation engines of the numerical analysis tools such as Matlab and Scilab are embedded into distributed services, so that the parallel calculation capability is improved, and meanwhile, complex numerical calculation can be solved.
The specific product selection analysis calculation module needs to select specific investment from a large-class pool of investment products after the large-class proportion analysis is completed, the selection method is mainly based on comprehensive scoring, scored component data comprise product asset scale, product rate, recent rate of return ranking of products in the product large class, ranking of companies to which the products belong in the industry and the like, and the comprehensive scoring is calculated and stored in the investment product data analysis calculation module by Spark batch processing.
The return measurement and simulation analysis and calculation module of the investment portfolio is mainly used for measuring the return variation curve of the net value of the investment portfolio in a specified historical time period based on the specific investment ratio and the historical net value fluctuation of specific investment and transversely comparing the return variation curve with the large scale indexes such as Shanghai depth 300 and the like, the return measurement of the investment portfolio is visually displayed for customers in a net value variation curve mode, the possible return range and the confidence interval of future investment portfolio are calculated in a Monte Carlo mode based on the average value of the return rate and the return variation of the investment portfolio in the historical time period, the fixed investment amount and the like, the return measurement calculation in the module is objective data, Monte Carlo simulation is that simulation data is only used as reference, and accurate prediction cannot be carried out. The Monte Carlo simulation needs a large amount of random sampling calculation, independent random sampling can be distributed to a plurality of processes of a plurality of machines by adopting a distributed real-time calculation framework Storm to accelerate the calculation speed, the parallel calculation is carried out in a mode of a plurality of threads, and the simulation result is obtained by fast reading.
The investment portfolio decision module mainly decides investment according to the investment product, the decision investment amount and the decision investment method selected by the specific product selection analysis calculation module according to the period and the method. The fixed-throwing period can be selected to be fixed according to days, weeks and months, and the fixed-throwing method can be selected to be fixed according to fixed rated throwing, stock ratio comparison and the like.
And the investment portfolio income analysis display module summarizes and analyzes the performance data of the investment portfolio after the investment portfolio is purchased by the investment group.
A method for performing analysis calculations on an investment product portfolio comprising the steps of:
(1) calculating the reference data of the ratio of the large-class investment products for the investment group after the investment group is given and taken to obtain the risk attribute of the investment group;
(2) after determining the proportion of the large-class investment products, the investment group provides a selection method of specific investment products for the investment group;
(3) after the investment group determines a specific investment portfolio, return test and simulation are carried out on the investment portfolio for the client to refer to;
(4) after the investment group confirms a specific investment portfolio, single ordering or fixed investment according to the investment portfolio can be selected;
(5) after the investment group places an order or makes a fixed investment, the overall income of the investment portfolio after the time point of the order or the fixed investment is started can be checked, and whether the portfolio is kept or not can be determined.
The step (1) further comprises the following steps:
(1.1) selecting the major proportion of investment products, namely classifying the investment products into major classes according to different risk attributes, such as a currency investment class, a bond investment class, a stock investment class and the like;
(1.2) carrying out average daily rate of return and average daily rate of return fluctuation calculation on the large category of investment products, wherein samples are all invested under the large category during calculation so as to reduce large-category risk estimation deviation brought by a few investment abnormal values, and carrying out data point supplementation by linear interpolation when net investment values are missing.
(1.3) different investment classes may have mutual influence, and a covariance matrix is adopted when the variance of the investment product classes needs to be calculated.
(1.4) calculating the large-class ratio of the investment products by adopting a mean-variance combination model of Markoviz, calculating an expected income and risk bearing capacity of the client as parameters, an average daily income rate and an average daily income rate covariance matrix of the investment products, and calculating the ratio of the different investment products as parameters under the constraint condition that the upper limit and the lower limit of the large-class ratio of the different investment products corresponding to investment groups with different risk levels are defined.
The step (2) specifically comprises the following steps:
and (2.1) scoring the products according to the related information of the products, wherein the scored component data comprises the asset scale of the products, the product rate, the recent income rate ranking of the products in the product category and the ranking of companies to which the products belong in the industry.
And (2.2) grading and ranking the products according to the relevant information of the products, and preferentially selecting the products according to the grading and ranking for investment.
The step (3) specifically comprises the following steps:
(3.1) measuring the net-value curve of the selected investment portfolio over the historical time period according to the historical net-value of the specific investment products in the selected investment portfolio, and transversely comparing the net-value curve with the large-scale index such as Shanghai depth 300.
And (3.2) calculating a possible future income interval and a possible confidence interval by using Monte Carlo according to the historical daily average income rate and daily income rate fluctuation of specific investments in the selected investment portfolio.
The step (4) specifically comprises the following branches:
(4.1) placing an order in a single instance directly according to the specific investment in the selected portfolio.
And (4.2) carrying out fixed investment according to the specific investment in the selected investment portfolio, the selected fixed investment period, the selected fixed investment method and the selected fixed investment amount.
The invention aims to overcome the defects of the prior art and provide a tool/method set for providing investors with investment portfolio proportion calculation, investment portfolio specific investment selection, investment portfolio retest, simulation, investment portfolio fixed investment and investment portfolio monitoring which meet the requirements of the investors on risk bearing capacity and income. The investor can conveniently obtain the investment large-class investment allocation proportion meeting the self risk attribute and the expected earning rate by using the tool/method set, can conveniently select specific investment to complete the investment large-class allocation, can conveniently analyze the historical objective performance of specific investment portfolio and analyze the possible future earning-fluctuation distribution, can conveniently make order placing and fixed investment of the investment portfolio, can conveniently monitor the earning condition of the investment portfolio, and forms a complete closed loop for buying and selling the guest intelligent investment portfolio. The system is provided to investors primarily as an investment advisor exhibition tool or as intelligent patronage.
In the embodiment of the invention, the method and the system for analyzing and calculating the intelligent investment product combination are as follows:
the method and the system for analyzing and calculating the intelligent investment product combination are characterized in that the system comprises the following components:
target risk data acquisition and storage module: risk preference and risk tolerance data of the client are collected by means of questionnaires and the like and stored in a relational database in a data storage layer in fig. 2.
The investment product data analysis and calculation module: and (4) collecting classification and net value data of the investment products, calculating daily average profitability and daily average profitability fluctuation of the large investment product classes, calculating average daily average profitability and average daily average profitability fluctuation of the large investment product classes, and storing the average daily average profitability and average daily average profitability fluctuation. And (3) carrying out data calculation by adopting a Spark big data batch processing technology in a data calculation layer in the figure 2.
The investment major proportion analysis and calculation module: and the investment product data analysis and calculation module is connected with the target risk data acquisition and storage module and the investment product data analysis and calculation module, and calculates the large-class proportion of the investment products matched with the appeal of the target investment group on the premise of meeting the risk attribute of the target investment group by adopting a mathematical model based on the target risk attribute, the large-class risk attribute of the products and the large-class risk data of the products. The calculations were performed using a numerical calculation engine in the data calculation layer of fig. 2.
The specific product selection analysis calculation module: and the investment product data analysis and calculation module is used for selecting specific investment products from the investment pools of the large categories of investment products, scoring and calculating the specific investment products, and calculating data by adopting a Spark big data batch processing technology of a data calculation layer in the figure 2.
Return testing and simulation analysis calculation module of investment portfolio: and the data analysis and calculation module is connected with the investment product, and the income trend of the investment portfolio within a specified time is measured back according to the proportion of specific investment and is compared with the related index. And predicting the future income distribution interval and probability under the conditions of appointed investment period, fixed investment period and fixed investment amount by a numerical calculation method. The Storm in the data computation layer in fig. 2 is used for distributed real-time computation.
Investment portfolio decision analysis and calculation module: the fixed investment is made according to the finally determined investment proportion, fixed investment period, fixed investment amount and fixed investment method, and the module is relatively simple in calculation and adopts SpringBoot in a data calculation layer in the figure 2 for calculation.
The investment portfolio income display module: and (4) carrying out fixed investment on the specific investment according to the finally determined investment proportion, fixed investment period, fixed investment amount and fixed investment method.
The system also comprises a log management module which is connected with the client data acquisition module, the large-investment type proportion analysis and calculation module, the large-investment type specific investment recommendation module, the investment portfolio return measurement and simulation module and the investment portfolio fixed-investment calculation module and is used for acquiring, summarizing, storing and managing logs of all modules.
The system also comprises a data analysis module which is connected with the log management module.
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
The target risk data acquisition and storage module acquires investment group data in a questionnaire mode according to related national laws and regulations, the data include but are not limited to financial conditions of the investment group, investment experience of the investment group, investment preference of the investment group, risk bearing capacity of the investment group and other items, and the investment group is classified in a grading mode after the data are acquired. The risk characteristics of the investment group are fully considered in other modules, and the module data is stored in a relational database.
The investment product data analysis and calculation module carries out product related data calculation and comprises the following steps (1):
(1.1) collecting basic data: the collected data includes, but is not limited to, information such as a code of the investment product, a name of the investment product, a net historical value, a type of the investment product, a risk of the investment product, a size of the investment product, a rate of the investment product, a company to which the investment product belongs, a custodian to which the investment product belongs, a rating of the investment product, and the like.
(1.2) completing investment product data: the calculation is needed to include but not limited to net completion when a single investment product lacks a net post date, profitability of a single investment product on two adjacent trading days, average daily profitability and average daily profitability fluctuation of a single investment in a specified time frame, and average daily profitability fluctuation of a large investment class in a specified time frame. And the net value compensation adopts a linear interpolation mode, for example, the net value of the trading day A is Va, the net value of the trading day C is Vc, the trading day B is positioned between the trading day A and the trading day C but has no net value data, and the net value linear interpolation of the trading day B is (Vc-Va)/(Tc-Ta) x (Tb-Ta) + Va. Where Tc-Ta is the natural daily difference between transaction day C and transaction day A, and Tb-Ta is the natural daily difference between transaction day B and transaction day A.
(1.3) calculating the daily yield of a single investment product: and the daily gain rate P of the single investment product does not consider the purchase and redemption rates of the investment product, the net values of two adjacent trading days of the single investment product are Va and Vb respectively, and then the daily gain rate of the investment is (Vb-Va)/(Tb-Ta)/Va multiplied by 100, wherein Tb-Ta is the natural daily difference between the trading day B and the trading day A. The average value of all daily yields of a single investment in a specified time range is the average value E (P) of the daily yields of the investment, and the mean square error of all the daily yields of a single investment in the specified time range is the daily yield fluctuation (P) of the investment.
(1.4) investment major classification: the investment products are classified mainly according to the risk attributes of the investment products, for example, the investment products are classified according to currency investment products and stock investment products, the yield rate of the currency investment products is relatively fixed but small, the yield rate of the stock investment products is greatly changed, excess income can be obtained, and corresponding risks are also borne.
(1.5) calculating the average daily rate of return and the average daily rate of return of the investment class: average daily profitability of all investments under the investment major category is averaged across investment products to be the daily profitability average E (CPBP) of the investment major category, average daily profitability fluctuation of all investments under the investment major category across investment products to be the daily yield fluctuation (CPBP) of the investment major category, but the simple calculation method cannot reflect the incidence relation among the investment major categories, different investment major categories are processed to be independent behaviors, and different investment major categories have mutual influence, so that in the invention, the daily profitability of each investment under the investment major category is averaged by date to obtain the daily trading major category daily profitability average, and then the daily profitability average E (CPBD) of the investment major category over a specified time span is calculated, E (CPBP) is strictly equal to E (CPBD) mathematically, and 3 investment major categories, major category A, major category B and major category C are assumed, the daily average profitability of each major class is obtained according to the above calculation, and if there are 100 dates within the calculated time period, there are three daily profitability time series or arrays, PA ═ a1, a2, a3, a4, a5... a99, a100], PB ═ B1, B2, B3, B4, b5... B99, B100], PC ═ C1, C2, C3, C4, c5... C99, C100, and the three arrays are used to calculate daily profitability covariance matrices of major class a, major class B, and major class C, which may indicate the profitability fluctuation of the major classes themselves and the correlation between the two major classes, but the method may have a problem, and the calculated major class fluctuation rate (CPBD) may be smaller than (CPBP), so there may be a deviation in this part of the calculation, and the secondary investment portfolio calculation may be verified subsequently.
The investment large-class ratio analysis and calculation module comprises the following steps of (2):
(2.1) setting constraint conditions: and calculating the large-investment-class proportion according to the average yield of the large investment classes and the covariance matrix of the large investment classes, so that the overall yield and the variance of the large-investment-class proportion meet the financial demand and the risk bearing capacity of the client. And calculating a Markov's mean-variance model quadratic optimization problem with constraint conditions by adopting an effective leading edge calculation method. The constraint conditions are the upper and lower limits of different proportions of clients with different risk bearing capacities to the large class of investment products, for example, the investor B can bear less risk than the investor A, the proportion of stock type investment which can be held by the investor B is smaller than that of the investor A, the proportion of currency type investment which can be held by the investor B is larger than that of the investor A, the calculated combination proportion can be more scientific by taking the upper and lower limits of different groups as the constraint conditions, and the upper and lower limits of the holding proportion of different groups are determined according to national relevant laws and regulations and scientific calculation. The part can adopt a calculation engine library of numerical analysis calculation tools Matlab, Scilab and the like, and is embedded in an application program to finish high-efficiency complex calculation.
(2.2) setting input conditions: the investment major proportion is input into the maximum loss bearing capacity of the investor, the risk aversion coefficient of the investor, the risk type of the investor, and the average profitability and the profitability covariance matrix of the investment major.
(2.3) checking output parameters: the output of the large investment class proportion is the large investment class proportion, the expected yield of the combination and the fluctuation rate of the combination, which can ensure that the investor can obtain the maximum profit under the precondition of meeting the risk of the investor. And comparing the calculated and output combined expected profitability with the financing expected profitability of the investor, and returning an error if the expected profitability of the investor cannot be achieved. Taking Matlab as an example: the following functions are mainly used:
[PortRisk，PortReturn，PortWts]＝frontcon(ExpReturn，ExpCovariance，AssetBounds)
wherein the parameters of the average yield rate of the ExpReturn product broad class, the yield rate covariance matrix of the ExpCovariance product broad class and the proportion limit of the assetBounds product broad class. Expected return on PortReturn product portfolio. The references portresk, PortReturn, and PortWts represent the total expected profitability fluctuation of the product major portfolio, the total expected profitability of the product major portfolio, and the mix ratio of each product major portfolio.
The specific product selection analysis calculation module is connected with the product data acquisition module, and the specific product selection from the investment pool mainly comprises the following step (3).
(3.1) product comprehensive scoring, wherein the scored component data comprises but is not limited to the product asset size, the product rate, the recent profitability ranking of the product in the product category, and the ranking of the company to which the product belongs in the industry.
(3.2) product scoring and sorting: and sorting according to the comprehensive scores of the products, and selecting specific products according to the ranking.
And (3.3) iteratively adjusting the coefficients of the scores of all the products of the product comprehensive scoring method according to the needs.
And the return measuring and simulation analysis and calculation module of the investment portfolio is connected with the product data acquisition and calculation module, measures the return trend of the investment portfolio within a specified time period according to the proportion of specific investment and compares the return trend with the related index. And predicting the future income distribution interval and probability under the condition that the client specifies the investment period, the fixed investment period and the fixed investment amount by a numerical calculation method. Because the large investment class proportion analysis and calculation module is used for calculating the large investment class, the rate of return and the fluctuation rate of the large investment class cannot completely represent the rate of return and the fluctuation rate of a specific investment combination, secondary calculation is carried out according to historical objective data of the specific investment when the specific investment combination is selected, and the secondary calculation content comprises objective historical return test and Monte Carlo simulation calculation for prediction. Mainly comprises the following branches (4)
(4.1) the backlog calculation is to objectively show the net-investment variation curve of the selected specific investment portfolio in the historical time period, so that the profitability and the volatility of the investment portfolio can be visually perceived, and the net-investment variation curve can be compared with the grand plate index of the Shanghai depth 300 and the Zhongzhen 50 in the same coordinate reference system (the abscissa of the coordinate system is time and the ordinate of the coordinate system is profitability) in the transverse direction, and mainly comprises the following step (5).
(5.1) selecting a time origin, and calculating a position holding ratio: and (3) selecting a time origin by backtesting calculation, calculating the position holding ratio of the investment portfolio according to the amount ratio of the investment portfolio at the time origin, normalizing the amount of the time origin to 1, calculating the total amount of the position holding investment portfolio at the subsequent time point according to the position holding ratio and the net value of the investment portfolio, and normalizing the amount of the position holding investment portfolio to the yield relative to the time origin according to the amount of the time origin. Assuming that the total investment amount is 1, the investment A investment proportion is Ra, the net value is Va0, the investment B investment proportion is Rb, the net value is Vb0, the investment C investment proportion is Rc, and the net value is Vc0, the initial investment A taken position is 1 xRa/Va 0, the investment B taken position is 1 xRb/Vb 0, and the investment C taken position is 1 xRc/Vc 0.
(5.2) calculating the net position taken at a specific time point: assuming that net investment values become Va1, Vb1 and Vc1 at a certain time point, the normalized net investment value (amount) at the time point is (1 × Ra × Va1/Va0+1 × Rb × Vb1/Vb0+1 × Rc × Vc1/Vc 0).
(5.3) drawing a net value curve: and (3) drawing the net value of the investment portfolio at each time point under a coordinate reference system to form a net value change curve, and meanwhile, normalizing the large plate index curves of the Shanghai depth 300, the Zhongzhen 50 and the like under the coordinate reference system according to time to serve as reference curves.
(4.2) the Monte Carlo simulation calculation is based on probability statistics, calculates the variation curve of net value (or sum) of investment portfolio along with time after the orders (or the fixed investment) are placed in a plurality of investment portfolios with the same investment ratio through a large number of random sampling calculations, and statistically analyzes the net value mean value and the net value fluctuation confidence interval after the orders (or the fixed investment) are placed in the plurality of investment portfolios, and mainly comprises the following step (5).
(5.1) selecting a time origin, and calculating a position holding ratio: taking two investments as an example, the monte carlo calculation assumes that the daily average rate of return of the investment a obeys the random distribution X, the daily average rate of return of the investment B serves the random distribution Y, and assumes that the distribution X and the distribution Y are independent and uncorrelated, at the time T equal to 0, the investment share of the investment a is a, the net value is Va0, the investment share of the investment B is B, and the net value is Vb0, so that the total amount of the investment portfolio is a × Va0+ B × Vb0 at the time T equal to 0.
(5.2) calculating the net value curve of the single simulation investment portfolio: the net investment value at the time T-1 is obtained by random sampling, Va 1-Va 0+ X, Vb 1-Vb 0+ Y, wherein X and Y are random sampling samples of X and Y, the total amount of T-1 time is a multiplied by Va1+ multiplied by Vb1, and the process can be analogized to more time points, and a net value change curve of single simulation is drawn.
(5.3) calculating the income distribution and the confidence probability of the multiple simulation investment portfolio: after multiple sampling, the confidence probability of the possible distribution interval and distribution of the investment portfolio in the future can be described, but Monte Carlo simulation is only used as reference and cannot completely and accurately predict the future, and in addition, the investment in the investment portfolio should calculate a covariance matrix.
The fund throwing combination fixed-throwing analysis and calculation module mainly comprises the following branches (6)
(6.1) directly ordering: and inputting the total investment amount, converting the system into an investment procurement order with corresponding amount according to the selected specific investment and the investment ratio, and issuing an investment transaction system.
(6.2) rating and timing and dosing: inputting a fixed investment period and a fixed investment amount. The system regularly performs rated investment ordering according to the selected specific investment and the investment proportion and the designated fixed investment period and fixed investment amount.
(6.3) periodically and strategy deciding: inputting a fixed investment period, a fixed investment strategy and a fixed investment reference amount. The system regularly places policy orders according to the selected specific investment and the investment ratio and the appointed fixed investment period and fixed investment reference amount. The strategy mainly adopts a uniform line/net value comparison algorithm, and the specific steps are as follows (7)
(7.1) calculating the average net value of the specified product over a fixed length of time in the past;
(7.2) calculating the ratio of the current net value to the average net value of the specified product;
and (7.3) acquiring the investment proportion corresponding to the ratio according to the ratio of the current net value to the average net value of the specified product, and determining the product of the investment reference amount and the investment proportion as the final investment amount, wherein the investment proportion is acquired by iterative calculation through a big data calculation technology.
The investment portfolio profit display module mainly comprises the following steps (8)
(8.1) calculating the accumulated investment amount of the appointed investment combination;
(8.2) calculating the accumulated income of the appointed investment portfolio;
(8.3) calculating a total asset transition curve of the specified investment portfolio;
the system also comprises a log management module which mainly adopts ElasticSearch, Kafka and Logstash to collect and store data.
The system also comprises a data analysis module which is connected with the log management module and mainly uses Kibana to analyze the logs and analyze the service condition of each module and the funnel model of the client.
The return measurement and simulation calculation module for the investment portfolio income can perform return measurement and simulation of the fixed investment plan, and is different from the steps (4) and (5) in that share change caused by fixed investment needs to be superposed for investment taking position at each calculation moment.
The system and the method for realizing the analysis, calculation and processing of the intelligent investment product portfolio meet the requirements of providing a full-flow investment portfolio operation tool set and a method set for the analysis and calculation of the large investment portfolio investment category proportion, the selection of the specific investment of the investment portfolio, the retest and simulation analysis of the specific investment portfolio, the ordering and fixed investment of the specific investment portfolio and the monitoring of the specific investment portfolio in a production environment, overcome the problems that a common investor does not know how to carry out the matching of a taken position, does not know the risk-income characteristics of the investment of the taken position and is inconvenient to carry out the long-term investment and monitoring of the investment portfolio, and facilitate the common investor to carry out the investment portfolio investment or the related operation of the.
In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (10)
1. A system for implementing intelligent investment product portfolio analysis computing processes, said system comprising:
the target risk data acquisition and storage module is used for acquiring basic data of the investment target population and storing the basic data in the relational database;
the investment product data analysis and calculation module is connected with the target risk data acquisition and storage module and is used for acquiring basic data of investment products and calculating products and product class derivative analysis data through a large data batch processing technology;
the investment large-class ratio analyzing and calculating module is connected with the investment product data analyzing and calculating module and is used for calculating feasible investment product large-class ratio by adopting a distributed numerical calculation engine and combining a mathematical model;
the specific product selection analysis calculation module is connected with the investment large-class ratio analysis calculation module and is used for selecting specific products from the investment large-class pool by adopting a large data batch processing technology and combining a specific recommendation algorithm;
the return measuring and simulation analysis and calculation module of the investment portfolio is connected with the specific product selection analysis and calculation module and is used for calculating the historical return fluctuation of the investment portfolio and analyzing and calculating the future return distribution and fluctuation;
the investment portfolio fixed investment analysis and calculation module is connected with the investment portfolio income return measurement and simulation analysis and calculation module and is used for calculating a fixed investment time point and a fixed investment amount in real time by combining with fixed investment configuration;
and the investment portfolio profit analysis and calculation module is connected with the investment portfolio fixed-investment analysis and calculation module and is used for collecting, summarizing, storing and managing the logs of all the modules.
2. The system for realizing intelligent analysis, calculation and processing of investment product portfolio according to claim 1, wherein the system further comprises a portfolio profit analysis and calculation module connected with the log management module for analyzing the overall usage of the system.
3. The system for realizing intelligent analysis, calculation and processing of investment product portfolio according to claim 1, wherein the investment product data analysis and calculation module obtains the basic information of investment product such as code, name, historical net worth, type, etc., and calculates the average daily profitability and the average daily profitability fluctuation of the investment product according to the basic classification and collection of the investment product.
4. The system according to claim 1, wherein the investment product data analysis and calculation module traverses the analysis data of the product set calculation products, then classifies the products according to product categories, summarizes all data of the product categories, traverses the analysis data of the product categories, processes the raw data of the products with big data magnitude by using a distributed batch processing framework, and performs horizontal expansion.
5. The system for realizing intelligent analysis and calculation of investment product portfolio according to claim 1, wherein the analysis and calculation module selects investment products, the amount of money to be invested, the method of investment to be invested is determined according to the period and the method to be invested.
6. A method for implementing intelligent investment product portfolio analysis calculation processing based on the system of claim 1, the method comprising the steps of:
(1) the investment is given and taken to obtain the risk attribute of the investment group, and the reference data of the proportion of the large-class investment products is calculated for the investment group;
(2) determining the proportion of large-class investment products by an investment group, and providing a selection method of specific investment products for the investment group;
(3) determining a specific investment portfolio by an investment group, and carrying out retest and simulation on the investment portfolio for a client;
(4) the investment group confirms a specific investment portfolio and selects to place an order or decide the investment portfolio once;
(5) and checking the overall income of the investment portfolio after the starting time point of the order placement or the fixed investment after the order placement or the fixed investment of the investment group, and determining whether to continue holding the portfolio.
7. The method for realizing intelligent investment product portfolio analysis and calculation processing according to claim 6, wherein the step (1) comprises the following steps:
(1.1) classifying the investment into large categories according to different risk attributes by using the large category proportion of the investment products;
(1.2) carrying out average daily rate of return and average daily rate of return fluctuation calculation on the large category of investment products;
(1.3) calculating the large-class variance of the investment products by using a covariance matrix;
and (1.4) calculating expected income and risk bearing capacity, average daily income rate and average daily income rate covariance matrix of the large investment classes, and calculating the ratio of different large investment class products.
8. The method for realizing intelligent investment product portfolio analysis and calculation processing according to claim 6, wherein the step (2) comprises the following steps:
(2.1) scoring the products according to the related information of the products, and ranking companies to which the products belong in the industry;
and (2.2) grading and ranking the products according to the relevant information of the products, and preferentially selecting the products according to the grading and ranking for investment.
9. The method for realizing intelligent investment product portfolio analysis and calculation processing according to claim 6, wherein the step (3) comprises the following steps:
(3.1) according to the historical net value of the specific investment products in the selected investment portfolio, measuring the net value curve of the portfolio in the historical time period, and transversely comparing the net value curve with the large plate index such as Shanghai depth 300;
and (3.2) calculating a possible future income interval and a possible confidence interval by using Monte Carlo according to the historical daily average income rate and daily income rate fluctuation of specific investments in the selected investment portfolio.
10. The method for realizing intelligent investment product portfolio analysis and calculation processing according to claim 6, wherein the step (4) comprises the following steps:
(4.1) directly placing an order once according to the specific investment in the selected investment portfolio;
and (4.2) carrying out fixed investment according to the specific investment in the selected investment portfolio, the selected fixed investment period, the selected fixed investment method and the selected fixed investment amount.
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