CN120031665A - A method and system for recommending carbon financial product investment portfolios - Google Patents
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Abstract
本申请公开了一种碳金融产品投资组合推荐方法及系统，该方法包括：服务器响应于接收到的客户端发送的用于对碳金融产品集合中各碳金融产品进行投资推荐的投资组合推荐请求，确定多个待推荐的候选推荐组合，投资组合推荐请求中包括用户画像信息、推荐种类信息以及用于推荐方式信息；基于用户画像信息、推荐种类信息以及获取到的碳金融产品历史数据，调用量子旋转门更新策略计算得到目标投资比例组合，目标投资比例组合为碳金融产品集合中各碳金融产品对应的投资比例的组合；结合多个待推荐的候选推荐组合与目标投资比例组合确定目标推荐组合；按照推荐方式信息中指示的推荐方式将目标推荐组合作为推荐信息发送至客户端显示。
The present application discloses a method and system for recommending a carbon financial product investment portfolio, the method comprising: a server responds to a received investment portfolio recommendation request sent by a client for recommending investment in each carbon financial product in a carbon financial product set, determines a plurality of candidate recommendation combinations to be recommended, the investment portfolio recommendation request comprising user portrait information, recommendation category information and recommendation method information; based on the user portrait information, recommendation category information and acquired historical data of carbon financial products, calls a quantum revolving door update strategy to calculate a target investment ratio combination, the target investment ratio combination being a combination of investment ratios corresponding to each carbon financial product in the carbon financial product set; determines a target recommendation combination by combining a plurality of candidate recommendation combinations to be recommended with the target investment ratio combination; and sends the target recommendation combination as recommendation information to the client for display according to the recommendation method indicated in the recommendation method information.
Description
Technical Field
The application relates to the field of information recommendation, in particular to a carbon financial product investment combination recommendation method and system.
Background
In the prior art, when information recommendation related to carbon financial product investment portfolios is performed, expected benefits are generally calculated by adopting a mean-variance model, and the carbon financial product investment portfolio recommendation is performed for users based on the classical asset allocation strategy by balancing between maximizing expected benefits and minimizing investment portfolio variances. Such methods assume that the yield is subject to a normal distribution and rely on historical data to estimate the expected yield and covariance matrix.
However, if the yield distribution deviates or the covariance matrix is estimated inaccurately, uncertainty and extreme fluctuation scenes in the carbon financial market cannot be effectively treated, the investment combination is recommended to the user by adopting the method, the expected yield of the recommendation combination is easily influenced by fluctuation, the stability of the recommendation is difficult to ensure, the stability of the product investment combination recommended to the client is easily caused to be insufficient, and robustness is lacking, and particularly, the existing method does not consider objective factors such as self characteristics of industries where the user is located, current conditions of industries, historical preferences of the user and the like in the recommendation, so that the recommendation matching accuracy is low, and the customer experience is poor.
In addition, through manual calculation and recommendation, the problem of low recommendation efficiency and accuracy of recommending carbon financial product investment combinations to users exists, and further satisfaction of the users is possibly reduced, and experience of recommended services is poor.
Disclosure of Invention
Aiming at least one defect or improvement requirement of the prior art, the invention provides a carbon financial product investment combination recommending method and device, which solve the problems of low satisfaction degree of recommending users on recommended products, low recommending efficiency, low recommending stability and low recommending precision in the existing carbon financial product investment combination recommending method, improve the robustness of investment combination, improve user experience and improve recommending efficiency and recommending precision.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a carbon financial product investment portfolio recommendation method, comprising the steps of determining a plurality of candidate recommendation combinations to be recommended in response to a received investment portfolio recommendation request sent by a client for performing investment recommendation on each carbon financial product in a carbon financial product set, wherein the investment portfolio recommendation request includes user portrait information associated with the client, recommendation type information for determining the plurality of candidate recommendation combinations to be recommended, and recommendation mode information for determining a recommendation mode, and calling a quantum rotation door update strategy to calculate a target investment portfolio based on the user portrait information, the recommendation type information, and acquired carbon financial product history data, wherein the target investment portfolio is a combination of investment scales corresponding to each carbon financial product in the carbon financial product set, determining a target recommendation combination by combining the plurality of candidate recommendation combinations to be recommended with the target investment portfolio, and sending the target recommendation combination as recommendation information to the client according to a recommendation mode indicated in the recommendation mode information.
According to a second aspect of the invention, a carbon financial product investment portfolio recommendation system is provided, and is applied to the method, the recommendation system comprises a client and a server, wherein the client is used for sending an investment portfolio recommendation request for conducting investment recommendation on each carbon financial product in a carbon financial product set to the server, displaying received recommendation information and conducting recommendation of the carbon financial product investment portfolio, the recommendation information comprises target recommendation combinations, the server is used for determining a plurality of candidate recommendation combinations to be recommended in response to the received investment portfolio recommendation request, a quantum rotation gate updating strategy is called to calculate and obtain target investment proportion combinations based on the user image information, the recommendation category information and acquired carbon financial product historical data, the target investment proportion combinations are combinations of investment proportions corresponding to each carbon financial product in the carbon financial product set, the target recommendation combinations are determined according to the target recommendation combinations indicated in the recommendation mode information, and the target recommendation combinations are sent to the client as recommendation mode information.
According to a third aspect of the invention, there is further provided a carbon financial product investment portfolio recommendation device, which comprises a response unit, a determining unit and a recommendation unit, wherein the response unit is used for determining a plurality of candidate recommendation combinations to be recommended in response to a received investment portfolio recommendation request sent by a client and used for conducting investment recommendation on each carbon financial product in a carbon financial product set, the investment portfolio recommendation request comprises user portrait information associated with the client, recommendation type information used for determining the plurality of candidate recommendation combinations to be recommended and recommendation mode information used for determining a recommendation mode, the invoking unit is used for invoking a quantum rotation door update strategy to calculate and obtain a target investment portfolio based on the user portrait information, the recommendation type information and acquired carbon financial product history data, the target investment portfolio is a combination of investment proportions corresponding to each carbon financial product in the carbon financial product set, the determining unit is used for combining the plurality of candidate recommendation combinations to be recommended with the target investment portfolio to determine the target recommendation combination, and the recommendation mode information is used for displaying the target combination as recommendation mode information to the client.
According to a fourth aspect of the present invention there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the above-described carbon financial product portfolio recommendation method when run.
According to a fifth aspect of the present invention, there is also provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the carbon financial product portfolio recommendation method described above by the computer program.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
(1) The invention provides a carbon financial product investment portfolio recommendation method, which is used for carrying out uncertainty modeling, introducing a scene generation technology and an interval type uncertainty model, wherein all possible price and yield fluctuation are limited in a predefined interval in the interval type uncertainty, so as to ensure that an optimization model can cover the potential market fluctuation range. The uncertainty of the carbon financial market can be effectively described, and the investment decision is optimized under the worst market situation, so that the risk resistance of the investment portfolio in an extreme fluctuation scene is remarkably improved, even in the impact caused by severe fluctuation of the market price or policy adjustment, the yield loss and risk level of the investment portfolio can be kept within a controllable range, the robustness of investment portfolio recommendation is improved, and the user satisfaction is improved;
(2) By taking the characteristics of the industry where the user is located, the current situation of the industry, the historical preference of the user and other objective factors into consideration, the accuracy of recommending and matching to the user is improved by collecting user portrait data, namely, collecting various enterprise characteristic information, describing key attributes such as historical behaviors, market positions, policy sensitivity and the like of the enterprise from multiple dimensions and determining target recommendation combinations by combining quantum turnstile updating strategies;
(3) The quantum revolving door updating strategy is adopted, and user portrait data and carbon financial product historical data are combined to generate more robust investment combinations, and target recommendation combinations recommended to clients are determined, so that the solving efficiency is remarkably improved, the method is suitable for processing large-scale investment combination recommendation problems containing various carbon financial products, and the recommendation efficiency and user satisfaction are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an alternative carbon financial product portfolio recommendation method provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative carbon financial product portfolio recommendation device in accordance with an embodiment of the present application;
fig. 3 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The terms first, second, third and the like in the description and in the claims and in the above drawings, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
According to one aspect of an embodiment of the present application, a carbon financial product portfolio recommendation method is provided. The carbon financial product portfolio recommendation method provided by the embodiment of the application is described below with reference to fig. 1.
FIG. 1 is a schematic flow chart of an alternative carbon financial product portfolio recommendation method according to an embodiment of the present application, as shown in FIG. 1, the flow chart of the method may include the following steps:
S102, a server responds to a received investment combination recommendation request sent by a client for carrying out investment recommendation on each carbon financial product in a carbon financial product set, and a plurality of candidate recommendation combinations to be recommended are determined, wherein the investment combination recommendation request comprises user portrait information associated with the client, recommendation type information for determining the candidate recommendation combinations to be recommended and recommendation mode information for determining a recommendation mode;
S104, based on the user portrait information, the recommended category information and the acquired carbon financial product history data, invoking a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
s106, determining a target recommendation combination by combining the candidate recommendation combinations to be recommended and the target investment proportion combination;
s108, sending the target recommendation combination to the client for display as recommendation information according to the recommendation mode indicated in the recommendation mode information.
The carbon financial product investment combination recommending method provided by the application can be suitable for a scene of recommending product combinations for clients in the carbon financial market investment field, and optionally, the carbon financial product investment combination recommending method can be applied to an application environment, but is not limited to the following, and clients can be respectively connected with a background server for recommending the combinations through a network.
The related art has significant drawbacks in dealing with carbon financial market portfolio optimization problems, including insufficient processing power for uncertainty and extreme scenarios, failure to consider specific attributes of the carbon financial market, and limitations in balance of revenue and risk and robust control. These deficiencies lead to the fact that the existing methods may face higher risks in practical application, and cannot fully meet the needs of carbon financial market investors, so that recommendation accuracy is low, and experience of users is poor.
The embodiment of the application provides a carbon financial product investment portfolio recommendation method which is more scientific, robust and suitable for the characteristics of a carbon financial market, optionally, an investment target can be established first, uncertainty modeling is carried out based on the investment target, after all possible price and yield fluctuation are limited in a predefined interval so as to ensure that an optimization model can cover a potential market fluctuation range, the robust optimization model construction is carried out, and a quantum heuristic algorithm is adopted for model solving so as to determine the final investment proportion of each carbon financial product, and the optimal investment strategy recommended to a client is realized, thereby improving the user satisfaction.
Illustratively, common carbon financial products include carbon allotment, carbon credit, carbon futures, carbon options. The investment range can be expressed as: , wherein, Is a collection of carbon financial products,Represent the firstAnd (5) a product. The server may determine a plurality of candidate recommendation combinations to be recommended based on recommendation type information in the investment portfolio recommendation request in response to a received investment portfolio recommendation request sent by the client for performing investment recommendation on each carbon financial product in the carbon financial product set, where the recommendation type information may be a personalized investment range of a carbon financial product preset by a user to be recommended at the client, for example, the candidate recommendation combinations may be (carbon quota, carbon credit, carbon futures), (carbon quota, carbon credit), or other combinations, which are not limited herein.
Further, to ensure stability of the recommendation, the relevant carbon financial product history data needs to be considered, in particular, at timeCarbon financial productMarket price of (2)Transaction amount。
Carbon financial productThe yield of (2) is expressed as:
;
Carbon financial product The price volatility of (c) is expressed as:
;
investment goals established based on the above data are expressed as: , wherein, Representing the risk preference of the investor.
Representing the total revenue expectations, expressed as:
;
Wherein, Representing a carbon-to-carbon financial productIs a proportion of investment.
Representing portfolio risk, expressed as:
;
Wherein, Representing carbon financial productsAndIs a covariance of (c).
Optionally, based on user portrait information, recommendation type information and acquired carbon financial product history data contained in the investment portfolio recommendation request, a quantum turnstile update strategy is called to calculate to obtain a target investment proportion combination, a plurality of candidate recommendation combinations to be recommended and the target investment proportion combination are combined to determine a target recommendation combination, and the target recommendation combination is sent to the client to be displayed as recommendation information according to a recommendation mode indicated in recommendation mode information.
The recommendation method may be various, for example, a target recommendation combination is displayed in a fan-shaped pattern, a target recommendation combination is displayed in a bar-shaped pattern, a target recommendation combination is displayed in a collection, or other recommendation methods, which are not limited herein.
According to the method, a plurality of candidate recommendation combinations to be recommended are determined through a server in response to a received investment combination recommendation request sent by a client for conducting investment recommendation on each carbon financial product in a carbon financial product set, the investment combination recommendation request comprises user portrait information, recommendation type information and recommendation mode information, a quantum turnstile update strategy is called to calculate and obtain a target investment ratio combination based on the user portrait information, the recommendation type information and acquired carbon financial product historical data, the target investment ratio combination is a combination of investment ratios corresponding to each carbon financial product in the carbon financial product set, the target recommendation combination is determined by combining the candidate recommendation combinations to be recommended with the target investment ratio combination, the target recommendation combination is sent to the client to be displayed according to a recommendation mode indicated in the recommendation mode information, the problems that recommendation users experience on recommended products is low, recommendation efficiency and recommendation accuracy are low in an existing carbon financial product investment combination recommendation method are solved, robustness of the investment combination is improved, and user experience is improved.
In an exemplary embodiment, the user portrait information comprises collected first portrait data, second portrait data and third portrait data of a user to be recommended, wherein the first portrait data is enterprise scale data of the user to be recommended, the second portrait data is historical investment data of the user to be recommended, and the third portrait data is policy responsiveness data of the user to be recommended;
S11, calculating the user characteristic coefficient of the user to be recommended according to the first portrait data, the second portrait data and the third portrait data.
In this embodiment, in order to ensure accuracy and high efficiency of recommendation, user portrait data may be combined. User profile data is a collection of enterprise feature information that describes key attributes of an enterprise, such as historical behavior, market status, and policy sensitivity, from multiple dimensions. Alternatively, the first image data (enterprise-scale data A), the second image data (historical investment data B), and the third image data (policy responsiveness data C) may be included. And further calculating user characteristic coefficients of the users to be recommended according to the enterprise scale data, the historical investment data and the policy responsiveness data, wherein the user characteristic coefficients respectively comprise a first user characteristic coefficient, a second user characteristic coefficient and a third user characteristic coefficient.
In particular, enterprise size is an important indicator of the ability of an enterprise to invest in a carbon financial market. Larger-scale enterprises generally have stronger capital practices and wider resource allocation capabilities and can afford higher investment risks. For example, annual financial statements of an enterprise may be collectedCarbon quota allocation dataTotal amount of assetsTo scale the enterprise.
Further, the enterprise-scale data a may be expressed as:
;
it should be noted that the number of the substrates, Representation of variablesAnd carrying out normalization processing, wherein the normalization formula is as follows:
;
And The minimum and maximum values of the variable respectively,Is a weight coefficient, reflects the relative importance of each index to the enterprise scale, meets the following requirementsAnd calculating by using an entropy weight method. The larger the enterprise scale is, the wider the investment selection range is, and the influence of the scale on the benefits and risks is needed to be reflected when the investment weight is distributed.
Similarly, historical investment data B may be used to describe the investment behavior history of an enterprise in a carbon financial product, reflecting its investment preferences and decision patterns, calculated in conjunction with the following data.
Investment frequency ratio-ratio of investment frequency of enterprises to different carbon financial products (such as carbon quota, carbon futures, carbon credit, etc.) in the past period of time:
;
Wherein, Representing a carbon-to-carbon financial productRatio of investment frequency of (2)
Investment amount ratio: historical investment amount ratio of enterprise to each product:
;
The historical investment data B may be expressed as:
;
Wherein, AndWeights of frequency and amount respectively, satisfy. And calculating by using an entropy weight method. The historical preferences of different enterprises influence the selection of future investment portfolios, and the individuation and the accuracy of decisions can be improved by integrating the preferences into an optimization model.
Further, the policy responsiveness data C is a quantitative description of the policy pressure and responsiveness of the business's business in the carbon market. Modeling in combination with the following factors:
Industry carbon emission intensity, namely carbon emission amount Icarbon per unit yield value, can be expressed by average data of industries where enterprises are located.
Industry policy applicability the industry is subject to a policy strength, such as carbon trade participation requirements or carbon emission reduction target ratio, indicated at Ppolicy.
The policy responsiveness data C is defined as:
;
Wherein, AndThe weight of carbon emission intensity and policy intensity, respectively.
After the enterprise-scale data a, the historical investment data B, and the policy responsiveness data C are obtained, the first user characteristic coefficient, the second user characteristic coefficient, and the third user characteristic coefficient may be determined based on the above data, respectively, and adjustment coefficients in subsequent quantum computation are specifically described in subsequent computation steps.
In an exemplary embodiment, before the invoking the quantum turnstile update strategy calculates the target investment scale combination, the method further comprises:
S21, respectively constructing uncertainty sets of price and yield of the carbon financial products by using the acquired carbon financial product historical data based on uncertainty modeling, and respectively obtaining a first set and a second set;
S22, the first set is represented as,
;
Wherein, Is the upper and lower bounds of the price of the carbon financial product determined based on the quantile of the historical data of the carbon financial product,Is the time ofTime-carbon financial productIs a price of (2);
S23, the second set is denoted as, ;
Wherein, Is the time ofTime-carbon financial productIs a rate of return (i.e., the aforementioned rate of returnIn a simplified expression) of (c) a),For a lower bound of carbon financial product price determined based on the quantile of the carbon financial product history data,For an upper bound of carbon financial product price determined based on the quantile of the carbon financial product history data,Is the time of-1 Time carbon financial productIs a price of (3).
It will be appreciated that uncertainty modeling is an important basis for robust optimization in the carbon financial market. Uncertainty in the carbon market is mainly due to price fluctuations, market liquidity changes, policy interventions, and other factors. These uncertainties can directly affect the price and return of carbon financial products, and thus investment decisions. To efficiently describe and capture these uncertainties, a scene generation technique and an interval-type uncertainty model are optionally introduced.
In embodiments of the present application, the price of a carbon financial product may be modeled with a random process, for example, assuming a time-dependent change in the price. Based on a geometric Brownian motion model (GBM), assuming that the price of a product is subject to lognormal distribution, the dynamic change formula is as follows:
;
Wherein, Is the time ofTime-carbon financial productIs a price of (a) to (b),Is the drift rate, which represents the long-term average gain,Is the price fluctuation rate of the price, and the price fluctuation rate is the price fluctuation rate,Is a random variable subject to a standard normal distribution, representing random fluctuations in the market.
By discretization, the equation for the simulated price path is:
;
Wherein, Is the step of the time that is required,N (0, 1) is an independent normally distributed random variable. Using the monte carlo simulation approach, N possible price paths can be generated, each path representing one potential market scenario:
;
Wherein, Represent the firstTime in individual scenesIs a price of (3). To ensure that the scene covers the range of possible fluctuations of the market, an uncertainty set is built based on historical data statistical analysis. Assuming at confidence levelThe fluctuation range of the price can be defined as interval type uncertainty, namely, the first set is:
;
Wherein, Is a price upper and lower bound determined based on the quantiles of the historical data of the carbon financial product. Similarly, the profitability of a carbon financial product can also be modeled as interval uncertainty, profitabilityI.e. the second set is:
;
in interval-type uncertainty, all possible price and return fluctuations are defined within a predefined interval to ensure that the optimization model can cover the range of potential market fluctuations.
In an exemplary embodiment, after the utilizing the obtained carbon financial product history data to construct uncertainty sets of price and yield of the carbon financial product based on uncertainty modeling, respectively, the method further includes, after obtaining the first set and the second set, respectively:
S31, constructing a target robust optimization model by combining the first set and the second set, obtaining an objective function corresponding to the target robust optimization model as, ;
Wherein, Is a risk preference coefficient and is used to determine,Is a general expectation of benefit and,Is a risk of investing in the portfolio,For the investment ratio corresponding to each carbon financial product in the carbon financial product collection,For the first set and the second set,Is the price of the carbon financial product at t.
Illustratively, after the uncertainty modeling is completed, a robust optimization model may be built based on the uncertainty set. The optimization objective is to keep the portfolio in the worst case still at a high yield while controlling risk. Set the recommended combination proportion asWhereinIs a carbon financial productThe investment ratio of (2) meets the following constraints:
;
the optimization objective can be expressed as a set of uncertainties A weighted function within (i.e., the aforementioned first and second sets) that maximizes revenue and minimizes risk. First, the total revenue is expected to be:
;
Wherein, Is a carbon financial productIs a desired rate of return for (a). The risk of a portfolio is expressed in terms of variance of the returns:
;
Wherein, Is a carbon financial productAndIs defined as:
;
The objective function of the objective robust optimization model is:
;
Wherein, Is a risk preference factor that controls the trade-off between revenue and risk. Under interval type uncertainty, both the revenue expectations and the variance calculations need to be solved in the worst case. For this purpose, the model can be converted into an equivalent deterministic optimization problem and the optimal portfolio proportions can be solved using linear or quadratic programming methods。
By means of the model, the optimal investment strategy can be determined in an uncertain environment of a carbon market, potential risks are effectively controlled, and robustness of decision making of recommendation combination recommended to a client is improved.
In an exemplary embodiment, the invoking the quantum turnstile update strategy to calculate the target investment scale portfolio comprises:
s41, defining the quantum bit state of each carbon financial product in the carbon financial product set;
S42, obtaining the investment proportion of each carbon financial product in the carbon financial product set through quantum state measurement of the quantum bit state;
S43, summarizing the investment proportions of all the carbon financial products in the carbon financial product collection to form an investment proportion combination.
In an embodiment of the application, in the quantum heuristic algorithm, each investment ratioThe qubit states of a single carbon financial product may be represented by a qubit code, specifically defined as:
;
Wherein, 、Respectively representing the probability and the amplitude of the quantum state, and meeting the normalization condition:
;
Obtaining the investment ratio of each carbon financial product in the set of carbon financial products by measuring the qubit state in a quantum state, expressed as:
;
The complete investment portfolio is represented by n qubits, i.e., the investment scale portfolio can be represented as:
;
According to the method and the device, complete investment combinations expressed by n quantum bits are obtained, namely, the investment proportions of all carbon financial products in the carbon financial product set are summarized to form the investment proportion combination, so that target recommendation combinations at subsequent determination positions are facilitated, and an optimal investment strategy recommended to a client is realized.
In one exemplary embodiment, after the summarizing the investment scales for all of the carbon financial products in the collection of carbon financial products forms an investment scale combination, the method further comprises:
S51, updating the quantum bit state of each carbon financial product based on the quantum revolving door strategy;
S52, determining an adaptability function according to the objective function corresponding to the obtained objective robust optimization model;
s53, calculating a rotation angle in the quantum revolving door strategy based on the rotation step length, the user portrait information and the fitness function, wherein the rotation angle is dynamically adjusted;
And S54, when the change of the fitness function is smaller than a threshold value, determining an investment proportion combination corresponding to the current quantum bit state as a target investment proportion combination, and determining the target recommended combination according to the investment combination for carbon financial product investment according to the target investment proportion combination.
In the embodiment of the application, a quantum turnstile updating strategy is realized to determine a final target recommendation combination.
Specifically, to gradually optimize the recommended combination, the quantum heuristic updates the qubit state through the quantum rotation gate. The quantum revolving door is defined as follows:
;
Wherein, In order for the angle of rotation to be a function of,In the form of quantum turnstiles for the desired benefits of investments based on recommended portfolios.
The update formula of the quantum rotation gate acting on the state of the quantum bit is as follows:
;
Wherein, The probability of a qubit state for a single carbon financial product for the t-th round,The amplitude of the qubit state for a single carbon financial product for the t-th round,Probability of qubit state for a single carbon financial product at round t +1,Amplitude, rotation angle of qubit state of t+1 th round single carbon financial productDynamically adjusted to speed up convergence and global searching.
Further, the calculation formula of the rotation angle is as follows:
;
Wherein, For the rotation step (learning rate),For the current optimal fitness value,As the fitness value of the current portfolio,For the first image data (enterprise-scale data) in the user portrait information,For the first user characteristic coefficient,For the second portrayal data (historical investment data) in the user portrayal information,For the second user characteristic coefficient,For the third image data (policy responsiveness data) in the user portrait information,And is a third user characteristic coefficient.
It should be noted that, the first user characteristic coefficient, the second user characteristic coefficient and the third user characteristic coefficient are used as adjustment coefficients of weights of the user portrait data in the quantum optimization algorithm in rotation angle calculation, and may be determined by combining the user portrait data with a conventional method for determining adjustment coefficients, which is not described herein.
Further, the fitness function may be determined from an objective function corresponding to the obtained objective robust optimization model, expressed as,
;
Wherein, Is a risk preference coefficient and is used to determine,Is a general expectation of benefit and,Is a risk of investment portfolios.
Finally, convergence determination is performed, and iteration is terminated when the following conditions are satisfied:
;
Here the number of the elements is the number, Is a convergence threshold, and when the fitness function variation is less than the threshold, the algorithm considers that the optimal portfolio has been reached and stops iterating.AndThe fitness values of the t+1st and t th rounds are respectively shown.
That is, when the fitness function variation is less than the convergence thresholdAnd when the investment proportion combination corresponding to the current qubit state is determined as a target investment proportion combination, determining the investment combination for carbon financial product investment according to the target investment proportion combination as a target recommended combination, and feeding back the target recommended combination to the client for combination recommendation.
According to the method and the device, expected benefits and risk variances are comprehensively considered in an optimization target, model parameters are dynamically adjusted, and more flexible balance between benefit optimization and risk control can be achieved. Compared with the traditional method, the optimization result can better adapt to the change of market conditions, and particularly shows better benefit-risk balance effect in the high-fluctuation market.
According to another aspect of the embodiment of the present application, there is also provided a carbon financial product portfolio recommendation system for implementing the above-mentioned carbon financial product portfolio recommendation method, the recommendation system including a client and a server;
The client is used for sending an investment portfolio recommendation request for recommending investment to each carbon financial product in the carbon financial product set to the server, displaying the received recommendation information and recommending the carbon financial product investment portfolio, wherein the recommendation information comprises the target recommendation combination;
The server is used for responding to the received investment portfolio recommendation request and determining a plurality of candidate recommendation combinations to be recommended;
based on the user portrait information, the recommended type information and the acquired carbon financial product history data, invoking a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
determining a target recommendation combination by combining the candidate recommendation combinations to be recommended and the target investment scale combination;
and sending the target recommendation combination to the client for display as recommendation information according to the recommendation mode indicated in the recommendation mode information.
According to another aspect of the embodiment of the present application, there is also provided a carbon financial product portfolio recommendation apparatus for implementing the above-mentioned carbon financial product portfolio recommendation method. FIG. 2 is a schematic diagram of an alternative carbon financial product portfolio recommendation device, in accordance with an embodiment of the present application, as shown in FIG. 2, which may include:
A response unit 202, configured to determine a plurality of candidate recommendation combinations to be recommended in response to a received investment combination recommendation request sent by a client for performing investment recommendation on each carbon financial product in a carbon financial product set, where the investment combination recommendation request includes user portrait information associated with the client, recommendation type information for determining the plurality of candidate recommendation combinations to be recommended, and recommendation mode information for determining a recommendation mode;
A calling unit 204, configured to call a quantum turnstile update policy to calculate a target investment proportion combination based on the user portrait information, the recommended category information and the acquired carbon financial product history data, where the target investment proportion combination is a combination of investment proportions corresponding to each carbon financial product in the carbon financial product set;
a determining unit 206, configured to combine the candidate recommendation combinations to be recommended with the target investment scale combination to determine a target recommendation combination;
And a recommending unit 208, configured to send the target recommendation combination as recommendation information to the client for display according to the recommendation mode indicated in the recommendation mode information.
It should be noted that, the response unit 202 in this embodiment may be used to perform the above-mentioned step S102, the calling unit 204 in this embodiment may be used to perform the above-mentioned step S104, the determining unit 206 in this embodiment may be used to perform the above-mentioned step S106, and the recommending unit 208 in this embodiment may be used to perform the above-mentioned step S108.
According to the method, a plurality of candidate recommendation combinations to be recommended are determined through a server in response to a received investment combination recommendation request sent by a client for conducting investment recommendation on each carbon financial product in a carbon financial product set, the investment combination recommendation request comprises user portrait information, recommendation type information and recommendation mode information, a quantum turnstile updating strategy is called to calculate to obtain a target investment ratio combination based on the user portrait information, the recommendation type information and acquired carbon financial product historical data, the target investment ratio combination is a combination of investment ratios corresponding to each carbon financial product in the carbon financial product set, the target recommendation combination is determined by combining the candidate recommendation combinations to be recommended with the target investment ratio combination, the target recommendation combination is sent to the client to be displayed according to a recommendation mode indicated in the recommendation mode information, the problems that recommendation users have low experience feeling on recommended products and low recommendation efficiency and accuracy in an existing carbon financial product investment combination recommendation method are solved, the robustness of the investment combination is improved, and simultaneously the recommendation efficiency and the recommendation accuracy are improved.
It should be noted that, the examples and the scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to what is disclosed in the above embodiments, and it should be noted that, the above modules may be implemented by software or hardware as a part of an apparatus, where the hardware environment includes a network environment.
According to yet another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in the present embodiment, the storage medium described above may be used to execute the program code of the carbon financial product portfolio recommendation method of any one of the above-described embodiments of the present application.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of:
s1, a server responds to a received investment combination recommendation request sent by a client for carrying out investment recommendation on each carbon financial product in a carbon financial product set, and a plurality of candidate recommendation combinations to be recommended are determined, wherein the investment combination recommendation request comprises user portrait information associated with the client, recommendation type information for determining the candidate recommendation combinations to be recommended and recommendation mode information for determining a recommendation mode;
S2, based on the user portrait information, the recommended type information and the acquired carbon financial product history data, invoking a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
s3, determining a target recommendation combination by combining the candidate recommendation combinations to be recommended and the target investment proportion combination;
And S4, sending the target recommendation combination to the client for display as recommendation information according to the recommendation mode indicated in the recommendation mode information.
Alternatively, specific examples in the present embodiment may refer to examples described in the above embodiments, which are not described in detail in the present embodiment.
The computer-readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
According to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the above-mentioned carbon financial product portfolio recommendation method, which may be a server, a terminal, or a combination thereof.
Fig. 3 is a schematic diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 3, including a processor 302, a communication interface 304, a memory 306, and a communication bus 308, wherein the processor 302, the communication interface 304, and the memory 306 communicate with each other via the communication bus 308, wherein,
A memory 306 for storing a computer program;
The processor 302 is configured to execute the computer program stored in the memory 306, and implement the following steps:
s1, a server responds to a received investment combination recommendation request sent by a client for carrying out investment recommendation on each carbon financial product in a carbon financial product set, and a plurality of candidate recommendation combinations to be recommended are determined, wherein the investment combination recommendation request comprises user portrait information associated with the client, recommendation type information for determining the candidate recommendation combinations to be recommended and recommendation mode information for determining a recommendation mode;
S2, based on the user portrait information, the recommended type information and the acquired carbon financial product history data, invoking a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
s3, determining a target recommendation combination by combining the candidate recommendation combinations to be recommended and the target investment proportion combination;
And S4, sending the target recommendation combination to the client for display as recommendation information according to the recommendation mode indicated in the recommendation mode information.
Alternatively, the communication bus may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus. The communication interface is used for communication between the electronic device and other equipment.
The memory may include RAM or may include non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
As an example, the memory 306 may include, but is not limited to, the response unit 202, the calling unit 204, the determining unit 206, and the recommending unit 208 in the carbon financial product portfolio recommending apparatus. In addition, other module units in the carbon financial product portfolio recommendation device may be included, but are not limited to, and are not described in detail in this example.
The processor may be a general-purpose processor, including but not limited to a CPU (Central Processing Unit ), NP (Network Processor, network processor), DSP (DIGITAL SIGNAL Processing unit), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field-Programmable gate array) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. The Memory includes a U disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, etc. which can store the program codes.
Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable Memory, where the Memory may include a flash disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, etc.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A carbon financial product portfolio recommendation method, comprising:
The method comprises the steps that a server responds to a received investment combination recommendation request sent by a client for conducting investment recommendation on each carbon financial product in a carbon financial product set, and a plurality of candidate recommendation combinations to be recommended are determined, wherein the investment combination recommendation request comprises user portrait information associated with the client, recommendation type information for determining the candidate recommendation combinations to be recommended and recommendation mode information for determining a recommendation mode;
based on the user portrait information, the recommended type information and the acquired carbon financial product history data, invoking a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
determining a target recommendation combination by combining the candidate recommendation combinations to be recommended and the target investment scale combination;
and sending the target recommendation combination to the client for display as recommendation information according to the recommendation mode indicated in the recommendation mode information.
2. The carbon financial product portfolio recommendation method of claim 1, wherein the user portrayal information comprises first portrayal data, second portrayal data and third portrayal data of a user to be recommended, wherein the first portrayal data is enterprise scale data of the user to be recommended, the second portrayal data is historical investment data of the user to be recommended, and the third portrayal data is policy responsiveness data of the user to be recommended;
and calculating the user characteristic coefficient of the user to be recommended according to the first portrait data, the second portrait data and the third portrait data.
3. The carbon financial product portfolio recommendation method of claim 1, wherein prior to said invoking the quantum turnstile update strategy to calculate a target investment portfolio, the method further comprises:
Respectively constructing uncertainty sets of price and yield of the carbon financial products by using the obtained carbon financial product historical data based on uncertainty modeling to respectively obtain a first set and a second set;
the first set is represented as a set of images,
;
Wherein, Is the upper and lower bounds of the price of the carbon financial product determined based on the quantile of the historical data of the carbon financial product,Is the time ofTime-carbon financial productIs a price of (2);
the second set is represented as a set of sets, ;
Wherein, Is the time ofTime-carbon financial productIs added to the rate of return of (c) to,For a lower bound of carbon financial product prices determined based on the carbon financial product history data quantile,For an upper bound of carbon financial product prices determined based on the carbon financial product history data quantile,Is the time of-1 Time carbon financial productIs a price of (3).
4. The carbon financial product portfolio recommendation method of claim 3, wherein after said utilizing the acquired carbon financial product history data to construct respective sets of uncertainty of price and rate of return of the carbon financial product based on uncertainty modeling, respectively, the method further comprises:
Combining the first set and the second set to construct a target robust optimization model, obtaining an objective function corresponding to the target robust optimization model as, ;
Wherein, Is a risk preference coefficient and is used to determine,Is a general expectation of benefit and,Is a risk of investing in the portfolio,For the investment ratio corresponding to each carbon financial product in the carbon financial product collection,For the first set and the second set,Is the price of the carbon financial product at t.
5. The carbon financial product portfolio recommendation method of claim 4, further comprising, after said deriving an objective function corresponding to the objective robust optimization model:
defining a qubit state for each carbon financial product in the set of carbon financial products;
The investment proportion of each carbon financial product in the carbon financial product set is obtained through quantum state measurement of the quantum bit state;
summarizing the investment proportions of all carbon financial products in the collection of carbon financial products to form an investment proportion combination.
6. The carbon financial product portfolio recommendation method of claim 5, wherein after said summarizing the investment proportions of all carbon financial products in the collection of carbon financial products to form an investment proportion combination, the method further comprises:
updating the qubit state of each carbon financial product based on the quantum turnstile strategy;
determining an adaptability function according to an objective function corresponding to the obtained objective robust optimization model;
Calculating a rotation angle in the quantum rotation door strategy based on the rotation step length, the user portrait information and the fitness function, wherein the rotation angle is dynamically adjusted;
And when the change of the fitness function is smaller than a threshold value, determining an investment proportion combination corresponding to the current qubit state as a target investment proportion combination, and determining the target recommended combination according to the investment combination for carbon financial product investment according to the target investment proportion combination.
7. A carbon financial product portfolio recommendation system applied to the method of any one of claims 1 to 6, wherein the recommendation system comprises a client and a server;
The client is used for sending an investment portfolio recommendation request for recommending investment to each carbon financial product in the carbon financial product set to the server, displaying the received recommendation information and recommending the carbon financial product investment portfolio, wherein the recommendation information comprises the target recommendation combination;
The server is used for responding to the received investment portfolio recommendation request and determining a plurality of candidate recommendation combinations to be recommended;
based on the user portrait information, the recommended type information and the acquired carbon financial product history data, invoking a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
determining a target recommendation combination by combining the candidate recommendation combinations to be recommended and the target investment scale combination;
and sending the target recommendation combination to the client for display as recommendation information according to the recommendation mode indicated in the recommendation mode information.
8. A carbon financial product portfolio recommendation device, comprising:
The response unit is used for responding to a received investment combination recommendation request sent by a client for carrying out investment recommendation on each carbon financial product in the carbon financial product set, and determining a plurality of candidate recommendation combinations to be recommended, wherein the investment combination recommendation request comprises user portrait information associated with the client, recommendation type information for determining the plurality of candidate recommendation combinations to be recommended and recommendation mode information for determining a recommendation mode;
The calling unit is used for calling a quantum turnstile updating strategy to calculate and obtain a target investment proportion combination based on the user portrait information, the recommended category information and the acquired carbon financial product history data, wherein the target investment proportion combination is a combination of investment proportions corresponding to all carbon financial products in the carbon financial product set;
A determining unit for determining a target recommendation combination by combining the plurality of candidate recommendation combinations to be recommended and the target investment scale combination;
and the recommending unit is used for sending the target recommending combination to the client for displaying as recommending information according to the recommending mode indicated in the recommending mode information.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program when run performs the method of any one of claims 1 to 6.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method according to any of claims 1 to 6 by means of the computer program.
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