WO2019019346A1 - Procédé et appareil d'acquisition de stratégie d'attribution d'actifs, dispositif informatique et support d'informations - Google Patents

Procédé et appareil d'acquisition de stratégie d'attribution d'actifs, dispositif informatique et support d'informations Download PDF

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WO2019019346A1
WO2019019346A1 PCT/CN2017/103954 CN2017103954W WO2019019346A1 WO 2019019346 A1 WO2019019346 A1 WO 2019019346A1 CN 2017103954 W CN2017103954 W CN 2017103954W WO 2019019346 A1 WO2019019346 A1 WO 2019019346A1
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management product
wealth management
historical
trend
price
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PCT/CN2017/103954
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English (en)
Chinese (zh)
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窦宏辰
马文利
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上海壹账通金融科技有限公司
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Publication of WO2019019346A1 publication Critical patent/WO2019019346A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present application relates to the field of computer technologies, and in particular, to an asset allocation policy acquisition method, apparatus, computer device, and storage medium.
  • the inventors of the present application have found that the prior art has the following problems: the user's financial management experience is limited, and the accuracy of the user's judgment of the rise and fall of the wealth management product is low, and Due to the large number of wealth management products and the large amount of information on wealth management products, it takes a lot of time for users to view one by one on the terminal, which wastes computer network resources.
  • an asset configuration policy acquisition method is provided.
  • An asset allocation policy acquisition method includes:
  • An asset allocation strategy acquisition device includes:
  • a level obtaining unit configured to acquire a wealth management product grade of the target wealth management product according to current attribute information of the target wealth management product and a preset wealth management product level model, wherein the wealth management product level model is based on historical attribute information of the first wealth management product and the Pre-training the model of the wealth management product corresponding to the historical attribute information;
  • a trend tendency obtaining unit configured to acquire a trend tendency of the target wealth management product according to a current indicator state of the target wealth management product and a preset wealth management product trend model, wherein the wealth management product trend model is based on the second wealth management product at a historical moment
  • the state of the historical indicator and the historical trend corresponding to the historical moment are pre-trained by the model
  • the policy obtaining unit is configured to acquire an asset allocation policy according to the wealth management product level of the target wealth management product and the trend tendency of the target wealth management product.
  • a computer apparatus comprising a memory and a processor, the memory storing computer readable instructions, the computer readable instructions being executed by the processor such that the processor performs the following steps:
  • the wealth management product grade is pre-trained by the model
  • the trend of the target wealth management product tends to be pre-trained according to the historical indicator state of the second wealth management product at the historical moment and the historical trend corresponding to the historical moment;
  • One or more non-transitory readable storage mediums storing computer readable instructions, when executed by one or more processors, cause the one or more processors to perform the following steps:
  • the wealth management product grade is pre-trained by the model
  • FIG. 1 is an implementation environment diagram of an asset configuration policy acquisition method provided in an embodiment
  • FIG. 2 is a block diagram showing the internal structure of a computer device in an embodiment
  • FIG. 3 is a flowchart of an asset allocation policy acquisition method in an embodiment
  • FIG. 5 is a flowchart of an asset configuration policy acquisition method in an embodiment
  • FIG. 6 is a structural block diagram of an asset allocation policy acquisition apparatus in an embodiment
  • FIG. 7 is a structural block diagram of an asset allocation policy acquisition apparatus in an embodiment
  • FIG. 8 is a structural block diagram of an asset configuration policy obtaining apparatus in an embodiment.
  • first may be used to describe various elements, and the elements are not limited by these terms unless specifically stated. These terms are only used to distinguish one element from another.
  • the first preset threshold may be referred to as a second preset threshold without departing from the scope of the present application, and similarly, the second preset threshold may be referred to as a first preset threshold.
  • the present application clearly indicates that the above first and second are indicative of the order, for example, herein, the first, nth, and tth include the limitation of the order.
  • FIG. 1 is an implementation environment diagram of an asset configuration policy acquisition method provided in an embodiment. As shown in FIG. 1 , the implementation environment includes a market feature acquisition device 110, a computer device 120, and a transaction device 130.
  • the market feature device 110 obtains the current wealth management product market of the target wealth management product, and inputs the result to the computer device 120.
  • the computer device 120 according to the current wealth management product market, the current attribute information of the target wealth management product and the wealth management product level model.
  • Obtain the wealth management product grade of the target wealth management product obtain the trend tendency of the target wealth management product according to the current indicator status of the target wealth management product and the wealth management product trend model, and then obtain the asset allocation strategy according to the wealth management product grade of the target wealth management product and the trend tendency, when obtained
  • the computer device 120 can output the asset configuration policy to the client for viewing by the user. You can also send transactions directly according to the asset allocation policy.
  • the transaction device 130 is requested to conduct a transaction.
  • the user may be prompted to purchase the wealth management product, and the transaction device may be sent a transaction request to purchase the wealth management product, and the transaction volume may be determined according to the user preset. It can also be judged according to the specific grade of the wealth management product and the intensity of the trend tendency.
  • the above-mentioned computer device 120 may be an independent physical server or terminal, or may be a server cluster composed of multiple physical servers, and may be a cloud server that provides basic cloud computing services such as a cloud server, a cloud database, a cloud storage, and a CDN.
  • Wealth management products can be precious metals such as silver and gold, as well as oil, stocks and futures.
  • FIG. 2 is an internal structural diagram of a computer device in one embodiment
  • the computer device is connected to a processor, a non-volatile storage medium, an internal memory, and a network interface through a system connection bus.
  • the non-volatile storage medium of the computer device can store an operating system and computer readable instructions that, when executed, can cause the processor to perform an asset configuration policy acquisition method.
  • the processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device.
  • the internal memory can store computer readable instructions that, when executed by the processor, cause the processor to perform an asset configuration policy acquisition method.
  • the network interface of the computer device is used for network communication, such as sending an asset configuration policy. It will be understood by those skilled in the art that the structure shown in FIG.
  • FIG. 2 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation of the computer device to which the solution of the present application is applied.
  • the specific computer device may It includes more or fewer components than those shown in the figures, or some components are combined, or have different component arrangements.
  • an asset configuration policy acquisition method is provided.
  • the asset configuration policy acquisition method may be applied to the foregoing computer device 120, and specifically includes the following steps:
  • Step S302 Acquire a wealth management product grade of the target wealth management product according to the current attribute information of the target wealth management product and the preset wealth management product level model.
  • the property information of wealth management products is the essential characteristics or characteristics of wealth management products, such as the price-earnings ratio, market rate, current ratio and information ratio of wealth management products.
  • the current attribute information refers to the attribute information of the target wealth management product at the current time
  • the historical attribute information refers to the genus of the first wealth management product at the historical moment before the current time.
  • the number of attribute information of the current time and historical time wealth management products can be set according to actual needs.
  • the attribute information of the current time and historical time wealth management products may be two price-to-earning ratios and a market current rate. It can also be four price-to-earnings ratios, market rates, current ratios, and information ratios.
  • the wealth management product level corresponding to the historical attribute information is the level of the first wealth management product under the historical attribute information.
  • the level of wealth management products can be quantified by pre-set levels such as excellent, medium and poor, or can be quantified by specific scores, as long as the wealth management products can be classified.
  • the preset wealth management product level model is obtained by pre-training according to the historical attribute information of the first wealth management product and the wealth management product level corresponding to the historical attribute information. After training the model, the current attribute information of the target wealth management product is input to the model. In the trained wealth management product level model, the wealth management product grade corresponding to the target wealth management product can be obtained.
  • Step S304 Obtain a trend tendency of the target wealth management product according to the current indicator status of the target wealth management product and the preset wealth management product trend model.
  • the indicator status refers to one or a combination of the status of each indicator of the wealth management product and the relationship between the indicators.
  • it may be a size relationship between moving averages of wealth management products, whether DEA (Difference Exponential Average) is positive or negative, DIF (Difference, disparity) and DEA (Difference Exponential Average, smoothing)
  • the relationship between the indicators can be divided into multiple, for example, the index 1 is greater than the index 2, the index 1 is equal to the index 2, the index 1 is smaller than the index 2, the index 1 is equal to the index 2, and the value of the index 1 is decreased.
  • the 5-day moving average is greater than the 10-day moving average
  • the DIF is higher than DEA from low to lower, and the distance between the stock pressure line, the support line, and the stock average is narrowed.
  • the current indicator status refers to the indicator status of the target wealth management product at the current moment.
  • the historical indicator status refers to the status of the indicator of the second wealth management product at the historical moment before the current time. It can be understood that the time can refer to a time point or a period of time.
  • the indicator status of the current time and the number of indicator status of the historical time can be set according to the actual situation.
  • the historical trend corresponding to the historical moment refers to the trend of the second wealth management product relative to the historical moment in the next period of history, for example, a historical moment is 2016. On December 15th, the corresponding historical trend can be the ups and downs after 3 days, that is, on December 18, 2016, compared with December 15, the price of wealth management products is rising, falling or oscillating.
  • Trends tend to refer to the ups and downs of wealth management products, which can be rise, fall and shock, or the probability of a rising or falling trend, for example, the probability of rising is 80%.
  • Concussion refers to the unstable price of wealth management products, which is high when rising.
  • the preset wealth management product trend model is based on the historical indicator status of the second wealth management product at the historical moment and the historical trend corresponding to the historical moment. The model is pre-trained after the model is trained, and the current indicator status of the target wealth management product is input into the training. In the good wealth management product trend model, the trend tendency of the target wealth management products can be obtained.
  • Step S306 obtaining an asset allocation strategy according to the wealth management product grade of the target wealth management product and the trend tendency of the target wealth management product.
  • An asset allocation strategy is a proposal to buy or sell wealth management products and can include one or more wealth management products. For example, for a wealth management product held by a user, if it is judged that the level of the wealth management product is low, and the trend of the wealth management product tends to fall, it may be recommended to sell the wealth management product. For the wealth management products that the user does not hold, if the user's stock selection conditions are met, and the level is high and the trend is rising, the user may be sent an asset allocation strategy for buying the wealth management product. Of course, for the wealth management products that the user already holds, and the level is high and the trend is rising, the user may be advised to continue to buy.
  • the asset allocation strategy may also include a wealth management product transaction volume.
  • the trading volume of wealth management products can be determined according to the trend tendency of the target wealth management products and/or the level of wealth management products. For example, set the correspondence between the trend tendency and the transaction volume, and the correspondence between the wealth management product level and the wealth management product transaction volume. The higher the trend is, the higher the probability of a rise, the greater the volume of transactions bought, and the higher the level of wealth management products, the greater the volume of transactions bought.
  • the corresponding wealth management product transaction volume may be obtained according to the correspondence between the trend tendency and the transaction volume and/or the correspondence between the wealth management product level and the wealth management product transaction volume.
  • the quantity of the first wealth management product, the second wealth management product and the target wealth management product may be one or more, and the first wealth management product, the second wealth management product and the target wealth management product may be completely different or identical. , or partially the same.
  • the method for obtaining the target wealth management product is not limited in the embodiment of the present application.
  • the method may be acquired according to the type of risk that the user can bear, or may be randomly obtained. It is also possible to select an industry wealth management product as a target wealth management product according to the user's choice of an industry.
  • steps S302 and S304 may be performed simultaneously or sequentially.
  • the execution order of S302 and S304 may be set according to actual needs, and the asset policy configuration apparatus receives and executes steps S302 and S304 according to the execution order.
  • the trend tendency of the target wealth management product may be first obtained, and when the trend tends to rise, the level of the target wealth management product may be acquired.
  • the level of the target wealth management product is first obtained, and when the level is higher than the preset level, it is the trend of the target wealth management product.
  • the step S304 is to obtain a trend tendency of the target wealth management product according to the current indicator status of the target wealth management product and the preset wealth management product trend model, including: when the wealth management product level of the target wealth management product is greater than or equal to the preset level.
  • the trend tendency of the target wealth management product is obtained.
  • Step S306 The step of acquiring an asset allocation strategy according to the level of the wealth management product and the trend tendency includes: when the wealth management product grade of the target wealth management product is greater than or equal to the preset level, and the trend of the target wealth management product tends to rise, obtaining the target financial product for purchase Asset allocation strategy.
  • the wealth management product grade Take the wealth management product grade as the excellent, medium and bad grades. If the preset grade is medium, when the wealth management product grade obtained according to the wealth management product grade model is medium or excellent, the current indicator status of the target wealth management product is input to In the preset wealth management product trend model, the trend tendency of obtaining the target wealth management product is obtained, and if the trend tends to rise, the asset allocation strategy for buying the target wealth management product is obtained. If the level of the wealth management product of the target wealth management product is poor, the trend of the trend of the wealth management product is not predicted.
  • the above-mentioned asset allocation strategy acquisition method is based on the historical property information of the first wealth management product and the wealth management product level corresponding to the historical property information, and the wealth management product trend model is based on the history of the second wealth management product.
  • the status of the indicator and the historical trend corresponding to the historical moment are pre-trained by the model. Since the attribute information of the wealth management product and the status of the indicator often contain the rank of the wealth management product and the law of the subsequent rise of the wealth management product, the historical wealth management product attribute information is utilized. And the indicator status information for model training can accurately find out the law of these data.
  • the preset wealth management product level model predicts the wealth management product level of the wealth management product, predicts the trend tendency of the wealth management product according to the current indicator state of the target wealth management product and the preset wealth management product trend model, and then according to the wealth management product grade of the target wealth management product and the wealth management product.
  • the trend tends to obtain the corresponding asset allocation strategy, which saves the time for users to view a large amount of information, and the acquired asset allocation strategy has a high accuracy rate.
  • the asset policy configuration method may further include the following steps:
  • the historical price time series is a sequence of historical time and the price of the preset time after the historical time are sorted in order of time development.
  • the preset time can be set according to actual requirements.
  • the preset time after the historical moment includes the historical moment.
  • the time interval between each historical price is the same. For example, it can be a daily closing price of 7 consecutive days.
  • the historical time is May 1, 2016, and the preset time is 7 days.
  • the historical price time series is a sequence of prices for each day from May 1 to May 8, for a total of 8 prices.
  • Price historical moment is P 0, i.e. initial price P 0, the final price of P n
  • the historical price series can be expressed as ⁇ P 0, P 1, P 2, ...... P n ⁇ .
  • S404 Calculate a trend judgment parameter according to a historical price time series, where the trend judgment parameter includes a price standard score, or the trend judgment parameter includes a price standard score and a change utility coefficient.
  • the price standard score is the difference between the n+1th price in the historical price time series minus the historical price time series average value divided by the historical price time series standard deviation
  • the change utility coefficient is the tth price in the historical price series
  • the absolute value of the difference from the first price is then divided by the sum of the absolute values of the adjacent price differences between the first price and the tth price, t is equal to n or n+1, and n is a positive integer.
  • the n+1th price refers to the final price in the historical price series
  • the first price refers to the initial price in the historical price series, which is expressed by a formula
  • the formula of the price standard score A can be as shown in the formula (1).
  • the formula for changing the utility coefficient B can be as shown in formula (2).
  • P is the historical price series
  • P n is the n+1th price, that is, the final price in the preset time
  • P 0 is the first price, that is, the price at the historical time is the starting price
  • P i+1 For the i+2th price in the historical price series, P i is the i+1th price in the historical price series, where i is greater than or equal to 0 and less than or equal to n-1.
  • E(P) is the mean of the historical price series
  • ⁇ (P) is the standard deviation of the historical price series.
  • Equation 2 can be expressed as formula (3), which is known from Equation 3.
  • B is less than or equal to 1 and greater than or equal to 0. If it is equal to 1, it indicates that the wealth management product has been rising or falling all the time. When there are other values, there is fluctuation, so the fluctuation of the wealth management products can be judged according to the size of B.
  • the historical trend corresponding to the historical moment refers to the trend of the second wealth management product relative to the historical moment in the next period of history.
  • the trend judgment parameter includes the price standard score
  • the historical trend corresponding to the historical moment is increased when the price standard score is greater than the preset threshold, and the historical trend corresponding to the historical moment is decreased when the threshold is less than the preset threshold.
  • the trend judgment parameter includes the price standard score and the change utility coefficient
  • the historical trend can be determined by combining the price standard score and the change utility coefficient.
  • the step of obtaining the historical trend corresponding to the historical moment includes: when the price standard score is greater than the first preset threshold, and the change utility coefficient is greater than the second preset threshold, the historical trend corresponding to the historical moment is increased; when the absolute value of the price standard is When the value is less than the first preset threshold, and the change utility coefficient is less than the second preset threshold, the historical trend corresponding to the historical moment is obtained as an oscillation; when the price standard score is less than the first preset threshold, and the change utility coefficient is greater than the second pre- When the threshold is set, the historical trend corresponding to the historical moment is decreased; wherein the first preset threshold and the second preset threshold are greater than 0 and less than or equal to 1.
  • the first preset threshold is 0.1 and the second preset threshold is 0.4
  • the price standard score of a second wealth management product is 0.5 and the change utility coefficient is 0.6
  • the historical trend is increased.
  • the acquired trend sample model predicts good results.
  • the historical indicators of the second wealth management product in the historical moment and the historical trend corresponding to the historical moment constitute the training data, and the model training is performed according to the training data, and the trend model of the wealth management product is obtained.
  • the number of the second wealth management products may be one or more, and the number of the training data may be specifically obtained according to actual conditions, for example, several hundred or tens of thousands.
  • Each training data includes the historical indicator status of a second wealth management product at a historical moment and the corresponding historical trend.
  • the historical indicator status can be one or more. For example, according to the historical time at 12:55 on June 16, 2016, the historical indicator status may include a 5-day moving average greater than a 10-day moving average, a DEA value greater than 0, and a DEA value from a small value. Going up above the DIF value, etc., the corresponding historical trend is rising.
  • the machine learning is performed according to the training data, and the model parameters obtained by the machine learning training are obtained, and the wealth management product trend model is obtained.
  • the model of machine learning can be Support Vector Machine (SVM) classifier model, Artificial Neural Network (ANN) classifier model, Logistic Regression (LR) classifier model and hidden Markov.
  • SVM Support Vector Machine
  • ANN Artificial Neural Network
  • LR Logistic Regression
  • Various models for classification such as the model (Hidden Markov Model, HMM).
  • the kernel function used can be set according to actual requirements.
  • the support vector machine can be used for supervised machine learning, and the kernel function can adopt a polynomial function.
  • the asset policy configuration method may further include the following steps:
  • the number of the first wealth management products may be one or more.
  • the number of the first training samples in the first training sample set may be specifically obtained according to actual conditions, for example, several hundred or tens of thousands.
  • Each first training sample includes a plurality of historical attribute information and a corresponding wealth management product level.
  • the historical attribute information may include a price-to-earnings ratio of 5%, a market rate of 10%, a turnover rate of 20%, etc.
  • the corresponding wealth management product grade is a blue-chip stock.
  • the level of wealth management products can be marked by humans, or by obtaining data of wealth management products, and then obtained according to relevant quantitative formulas.
  • the level of the first wealth management product can be judged by the Sotino ratio, which is the unit income that can be obtained for each unit of downward fluctuation. The larger the value, the risk of the same decline. In the case of the situation, you can get more excess returns or returns on the benchmark.
  • Sotino ratio is the unit income that can be obtained for each unit of downward fluctuation. The larger the value, the risk of the same decline. In the case of the situation, you can get more excess returns or returns on the benchmark.
  • it may be set as a non-preferred stock when the Sotino ratio is greater than the third preset threshold, and is a good stock, and less than a certain fourth preset threshold.
  • the third preset threshold and the fourth preset threshold may be set according to specific requirements.
  • S504 Perform model training according to the first training sample set to obtain a wealth management product level model.
  • the model of machine learning can be Support Vector Machine (SVM) classifier model, Artificial Neural Network (ANN) classifier model, Logistic Regression (LR) classifier model and hidden Markov.
  • SVM Support Vector Machine
  • ANN Artificial Neural Network
  • LR Logistic Regression
  • Various models for classification such as the model (Hidden Markov Model, HMM).
  • the kernel function used can be set according to actual requirements.
  • the support vector machine can be used for supervised machine learning, and the kernel function can adopt a polynomial function.
  • an asset configuration policy obtaining device is provided.
  • the asset configuration policy obtaining device may be integrated into the computer device 120, and may include a level obtaining unit 602 and a trend tendency obtaining unit. 604 and a policy acquisition unit 606.
  • a level obtaining unit 602 configured to use current attribute information of the target wealth management product and preset financial management
  • the product level model obtains the wealth management product grade of the target wealth management product.
  • the preset wealth management product level model is obtained by pre-training the model according to the historical attribute information of the first wealth management product and the wealth management product level corresponding to the historical attribute information.
  • the trend tendency obtaining unit 604 is configured to obtain a trend tendency of the target wealth management product according to the current indicator state of the target wealth management product and the preset wealth management product trend model.
  • the preset wealth management product trend model is obtained by pre-training the model according to the historical indicator status of the second wealth management product at the historical moment and the historical trend corresponding to the historical moment.
  • the policy obtaining unit 606 is configured to obtain an asset allocation policy according to the wealth management product level of the target wealth management product and the trend tendency of the target wealth management product.
  • the trend tendency obtaining unit 604 is configured to: when the wealth management product level of the target wealth management product is greater than or equal to the preset level, obtain the target wealth management product according to the current indicator state of the target wealth management product and the preset wealth management product trend model. The trend is trending.
  • the policy obtaining unit 606 is configured to: when the wealth management product level of the target wealth management product is greater than or equal to the preset level, and the trend of the target wealth management product is inclined to increase, obtain an asset allocation strategy for buying the target wealth management product.
  • the wealth management product level model is obtained by pre-training the model according to the historical attribute information of the first wealth management product and the wealth management product level corresponding to the historical attribute information, and the wealth management product trend model is based on the history of the second wealth management product.
  • the status of the indicator and the historical trend corresponding to the historical moment are pre-trained by the model.
  • the attribute information and indicator status of the wealth management product often contain the level of the wealth management product and the follow-up trend of the wealth management product, so the historical wealth management product attribute information is used.
  • Model status information for model training can accurately find out the laws of these data.
  • the wealth management product level of the wealth management product can be predicted according to the current attribute information of the target wealth management product and the preset wealth management product level model, according to the current indicator status of the target wealth management product and the preset wealth management product trend.
  • the model predicts the trend tendency of wealth management products, and then obtains the corresponding asset allocation strategy according to the wealth management product grade of the target wealth management product and the trend tendency of the wealth management product, which saves the time for the user to view a large amount of information, and has high accuracy.
  • the asset policy configuration apparatus may further include a price time series acquisition unit 702, a trend determination parameter calculation unit 704, a history trend acquisition unit 706, and a second Model training unit 708:
  • the price time series obtaining unit 702 is configured to obtain a historical price time series of the preset time of the second wealth management product after the historical time, and the historical price time series includes n+1 prices.
  • the trend judgment parameter calculation unit 704 is configured to acquire a trend judgment parameter of the historical price time series, the trend judgment parameter includes a price standard score, or the trend judgment parameter includes a price standard score and a change utility coefficient.
  • the historical trend obtaining unit 706 is configured to obtain a historical trend according to the trend determining parameter and the preset determining rule.
  • the second model training unit 708 is configured to compose the training data of the historical indicator state of the second wealth management product at the historical moment and the historical trend corresponding to the historical moment, and perform model training according to the training data to obtain a wealth management product trend model.
  • the asset policy configuration apparatus may further include a first sample set acquisition unit 802 and a first model training unit 804:
  • the first sample set obtaining unit 802 is configured to acquire a first training sample set that is composed of a plurality of first training samples, where the first training sample includes multiple historical attribute information of the first wealth management product at a historical moment and a historical attribute corresponding to Financial product grade;
  • the first model training unit 804 is configured to perform model training according to the first training sample set to obtain a wealth management product level model.
  • the network interface may be an Ethernet card or a wireless network card.
  • the above modules may be embedded in the hardware in the processor or in the memory in the server, or may be stored in the memory in the server, so that the processor calls the corresponding operations of the above modules.
  • the processor can be a central processing unit (CPU), a microprocessor, a microcontroller, or the like.
  • the asset configuration policy obtaining apparatus may be implemented in the form of a computer readable instruction executable on a computer device as shown in FIG. 2, a nonvolatile of the computer device
  • the storage medium may store various program modules constituting the asset configuration policy acquisition means, such as the level acquisition unit 602, the trend tendency acquisition unit 604, and the policy acquisition in FIG. Unit 606.
  • Computer program readable instructions are included in each program module for causing a computer device to perform the steps in the asset configuration policy acquisition method of various embodiments of the present application described in this specification.
  • the computer device may acquire the wealth management product level of the target wealth management product according to the current attribute information of the target wealth management product and the preset wealth management product level model, as shown in FIG.
  • the trend tendency acquisition unit 604 by the trend tendency acquisition unit 604 according to the target.
  • the current indicator status of the wealth management product and the preset wealth management product trend model obtain the trend tendency of the target wealth management product
  • the strategy acquisition unit 606 obtains the asset allocation strategy according to the wealth management product level of the target wealth management product and the trend tendency of the target wealth management product.
  • a computer device is proposed.
  • the internal structure of the computer device may correspond to the structure shown in FIG. 2, that is, the computer device may be a server or a terminal, and the computer device includes a memory, a processor, and Computer readable instructions stored on the memory and operable on the processor, the processor executing the computer readable instructions to: obtain the target wealth management product according to the current attribute information of the target wealth management product and the preset wealth management product level model
  • the wealth management product level and the wealth management product level model are pre-trained according to the historical attribute information of the first wealth management product and the wealth management product level corresponding to the historical attribute information; the target is obtained according to the current indicator state of the target wealth management product and the preset wealth management product trend model.
  • the trend of wealth management products tends to be based on the historical indicators of the second wealth management products in the historical moments and the historical trends corresponding to historical moments.
  • the model is pre-trained according to the wealth management products of the target wealth management products and the target wealth management products. Trends tend to acquire asset allocation strategies.
  • the processor when executing the computer readable instructions, further performs the steps of: obtaining a historical price time series of the second financial product at a preset time after the historical time, the historical price time series including n+1 prices;
  • the price time series calculation obtains the trend judgment parameter, the trend judgment parameter includes the price standard score, or the trend judgment parameter includes the price standard score and the change utility coefficient;
  • the historical trend corresponding to the historical moment is obtained according to the trend judgment parameter;
  • the second wealth management product is in the historical moment
  • the historical indicator state and the historical trend corresponding to the historical moment constitute the training data, and the model training is performed according to the training data to obtain the wealth management product trend model;
  • the price standard score includes the n+1th price minus the historical price in the historical price time series
  • the difference between the time series mean values is divided by the standard deviation of the historical price time series
  • the change utility coefficient is the difference between the tth price and the first price in the historical price series.
  • the absolute value is then divided by the sum of the absolute values of the
  • the historical trend corresponding to the historical moment is obtained according to the trend determining parameter, including: when the price standard score is greater than the first preset threshold, and the changing utility coefficient is greater than the second preset threshold, obtaining a historical trend corresponding to the historical moment When the absolute value of the price standard score is less than the first preset threshold, and the utility coefficient is less than the second preset threshold, the historical trend corresponding to the historical moment is obtained as an oscillation; when the price standard score is less than the first preset threshold, When the utility coefficient is greater than the second preset threshold, the historical trend corresponding to the historical time is decreased; wherein the first preset threshold and the second preset threshold are greater than 0 and less than or equal to 1.
  • the trend tendency of the target wealth management product is obtained according to the current indicator status of the target wealth management product and the preset wealth management product trend model, including: when the wealth management product level of the target wealth management product is greater than or equal to the preset level, according to the target
  • the current indicator status of the wealth management product and the preset wealth management product trend model obtain the trend tendency of the target wealth management product
  • the steps of obtaining the asset allocation strategy according to the wealth management product level and the trend tendency include: when the wealth management product level of the target wealth management product is greater than or equal to the preset Level, and the trend of the target wealth management products tends to increase, and obtain the asset allocation strategy of buying the target wealth management products.
  • the asset allocation strategy is obtained according to the level of the wealth management product of the target wealth management product and the trend of the target wealth management product, and further includes: determining the target wealth management product according to the trend tendency of the target wealth management product and/or the wealth management product grade of the target wealth management product. Corresponding transaction volume.
  • a storage medium storing computer readable instructions that, when executed by one or more processors, cause one or more processors to perform the steps of:
  • the current attribute information and the preset wealth management product level model obtain the wealth management product grade of the target wealth management product, and the wealth management product level model is pre-trained according to the historical attribute information of the first wealth management product and the wealth management product level corresponding to the historical attribute information;
  • the current indicator status of the target wealth management product and the preset wealth management product trend model obtain the trend tendency of the target wealth management product.
  • the wealth management product trend model performs model pre-training according to the historical indicator status of the second wealth management product at the historical moment and the historical trend corresponding to the historical moment. Get; according to the level of wealth management products of the target wealth management products and Trends in targeted wealth management products tend to capture asset allocation strategies.
  • the processor when executing the computer readable instructions, further performs the steps of: obtaining a historical price time series of the second financial product at a preset time after the historical time, the historical price time series including n+1 prices;
  • the price time series calculation obtains the trend judgment parameter, the trend judgment parameter includes the price standard score, or the trend judgment parameter includes the price standard score and the change utility coefficient;
  • the historical trend corresponding to the historical moment is obtained according to the trend judgment parameter;
  • the second wealth management product is in the historical moment
  • the historical indicator state and the historical trend corresponding to the historical moment constitute the training data, and the model training is performed according to the training data to obtain the wealth management product trend model;
  • the price standard score includes the n+1th price minus the historical price in the historical price time series
  • the difference between the time series averages is divided by the standard deviation of the historical price time series
  • the change utility coefficient is the absolute value of the difference between the tth price and the first price in the historical price series divided by the first price to the tth
  • the historical trend corresponding to the historical moment is obtained according to the trend determining parameter, including: when the price standard score is greater than the first preset threshold, and the changing utility coefficient is greater than the second preset threshold, obtaining a historical trend corresponding to the historical moment When the absolute value of the price standard score is less than the first preset threshold, and the utility coefficient is less than the second preset threshold, the historical trend corresponding to the historical moment is obtained as an oscillation; when the price standard score is less than the first preset threshold, When the utility coefficient is greater than the second preset threshold, the historical trend corresponding to the historical time is decreased; wherein the first preset threshold and the second preset threshold are greater than 0 and less than or equal to 1.
  • the trend tendency of the target wealth management product is obtained according to the current indicator status of the target wealth management product and the preset wealth management product trend model, including: when the wealth management product level of the target wealth management product is greater than or equal to the preset level, according to the target
  • the current indicator status of the wealth management product and the preset wealth management product trend model obtain the trend tendency of the target wealth management product
  • the steps of obtaining the asset allocation strategy according to the wealth management product level and the trend tendency include: when the wealth management product level of the target wealth management product is greater than or equal to the preset Level, and the trend of the target wealth management products tends to increase, and obtain the asset allocation strategy of buying the target wealth management products.
  • the asset allocation strategy is obtained according to the level of the wealth management product of the target wealth management product and the trend of the target wealth management product, and further includes: according to the trend tendency and/or target of the target wealth management product.
  • the wealth management product level of the wealth management product determines the transaction volume corresponding to the target wealth management product.
  • the storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, or a read-only memory (ROM).

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Abstract

L'invention concerne un procédé d'acquisition de stratégie d'attribution d'actifs, comprenant les étapes qui consistent : à acquérir une notation de produit financier d'un produit financier cible en fonction d'informations d'attributs courantes du produit financier cible et d'un modèle de notation de produit financier prédéfini, le modèle de notation de produit financier étant obtenu par réalisation d'un entraînement préalable de modèle selon des informations d'attributs historiques d'un premier produit financier et une notation de produit financier correspondant aux informations d'attributs historiques ; à acquérir une tendance du produit financier cible conformément à un état d'indicateur courant du produit financier cible et à un modèle de tendance de produit financier prédéfini, le modèle de tendance de produit financier étant obtenu par réalisation d'un entraînement préalable de modèle en fonction d'un état d'indicateur historique d'un second produit financier à un moment historique et d'une tendance historique correspondant au moment historique ; et à acquérir une stratégie d'attribution d'actifs selon la notation de produit financier du produit financier cible et la tendance du produit financier cible.
PCT/CN2017/103954 2017-07-25 2017-09-28 Procédé et appareil d'acquisition de stratégie d'attribution d'actifs, dispositif informatique et support d'informations WO2019019346A1 (fr)

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