CN111091466A - Method and device for generating asset investment suggestion information and readable storage medium - Google Patents
Method and device for generating asset investment suggestion information and readable storage medium Download PDFInfo
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- CN111091466A CN111091466A CN201911169498.3A CN201911169498A CN111091466A CN 111091466 A CN111091466 A CN 111091466A CN 201911169498 A CN201911169498 A CN 201911169498A CN 111091466 A CN111091466 A CN 111091466A
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Abstract
The invention discloses a method for generating asset investment suggestion information, which comprises the following steps: determining a prediction time period of assets to be invested and a time length of the prediction time period, wherein the assets to be invested are large-class assets; obtaining corresponding logarithm difference data of the assets to be invested before a target investment period starts according to the time length, and determining a merging sequence corresponding to the logarithm difference data in each preset observation period; determining a corresponding predicted value of the assets to be invested on the logarithmic difference data according to the merging sequence; and when the predicted value is larger than a preset threshold value, generating prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period. The invention also discloses a device for generating the asset investment suggestion information and a readable storage medium. The invention provides the investment suggestion of the financing product with reliable and stable income for the user.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for generating asset investment suggestion information and a readable storage medium.
Background
With the improvement of living standard of people, mobile funds on hand are more and more abundant, and people prefer to invest the funds in financial products with higher income, such as stocks, funds, bonds and the like, compared with the method of depositing the funds in banks.
In the prior art, when people invest in a financial product, the people invest funds by looking up the risk and the return description of the financial product and based on the own investment experience, the risk and the return of the financial product. Therefore, the investment mode of the user is determined by the investment experience of the user and the expected income of the financial product, and the investment mode is poor in reliability and cannot bring stable and reliable investment income for the user.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method and a device for generating asset investment suggestion information and a readable storage medium, and aims to solve the problem that the capital investment mode in the prior art is poor in reliability.
In order to achieve the above object, the present invention provides a method for generating asset investment advice information, which comprises the following steps:
determining a prediction time period of assets to be invested and a time length of the prediction time period, wherein the assets to be invested are large-class assets;
obtaining corresponding logarithm difference data of the assets to be invested before a target investment period starts according to the time length, and determining a merging sequence corresponding to the logarithm difference data in each preset observation period;
determining a corresponding predicted value of the assets to be invested on the logarithmic difference data according to the merging sequence;
and when the predicted value is larger than a preset threshold value, generating prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period.
In one embodiment, the step of generating a prompt suggesting a binned purchase of the asset to be invested during the predicted time period comprises:
and generating prompt information for recommending that the fund is split to be invested in the forecast time period at preset time intervals.
In one embodiment, the number of split funds and the number of investments of the asset to be invested in the prediction time period are equal to the ratio of the time length to the preset time length, and one investment amount in the prediction time period corresponds to one split fund.
In an embodiment, the step of determining the corresponding predicted value of the asset to be invested on the logarithmic difference data according to the merging sequence comprises:
determining the output length of the merging sequence, and modifying the output length by a preset length to obtain an extrapolation merging sequence;
determining a linear regression coefficient according to the logarithmic difference data and the merging sequence;
and determining a predicted value corresponding to the asset to be invested according to the extrapolation merging sequence and the linear regression coefficient.
In an embodiment, the step of determining the predicted value corresponding to the asset to be invested according to the extrapolation and combination sequence and the linear regression coefficient includes:
determining a factor predicted value according to the extrapolation merging sequence;
and inputting a linear regression model according to the factor predicted value and the linear regression coefficient to obtain a predicted value corresponding to the asset to be invested.
In an embodiment, the step of determining a merging sequence corresponding to the logarithmic difference data in each preset observation period includes:
determining a filtering sequence corresponding to each type of assets in each preset observation period according to the sequence data of the same proportion, wherein each type of assets belongs to the same large class of assets;
merging the filtering sequences corresponding to the assets to obtain a merged sequence of each preset observation period,
in an embodiment, the step of determining, according to the sequence data of the same-proportion, a filtering sequence corresponding to each type of assets in each preset observation period includes:
carrying out Fourier transform on the logarithmic difference data to obtain corresponding frequency domain data;
determining a group of filter coefficients according to each preset observation period, and obtaining a middle sequence according to the filter coefficients and the frequency domain data;
and carrying out inverse Fourier transform on the intermediate sequence to obtain a filtering sequence of the asset in each preset observation period.
In order to achieve the above object, the present invention further provides an apparatus for generating asset investment advice information, the apparatus comprising: a memory, a processor and a program for generating asset investment advice information stored on the memory and operable on the processor, the program for generating asset investment advice information when executed by the processor implementing the steps of the method for generating asset investment advice information as described above.
To achieve the above object, the present invention also provides a readable storage medium having stored thereon a program for generating asset investment advice information, which when executed by a processor, implements the steps of the method for generating asset investment advice information as described above.
The device determines a prediction time period of assets to be invested and a time length of the prediction time period, so as to obtain corresponding logarithmic difference data of the assets to be invested before a target investment period starts according to the time length, determine a merging sequence corresponding to the logarithmic difference data in each preset observation period, determine a corresponding prediction value of the assets on the logarithmic difference data according to the merging sequence, and generate prompt information for recommending the assets to be invested to be purchased in different bins in the prediction time period when the prediction value is larger than a preset threshold value. The device can predict the assets to be invested according to the logarithmic difference data of the assets to be invested to obtain a predicted value, if the predicted value is larger than a preset threshold value, the held income of the assets to be invested after the prediction time period is positive, and therefore the user is advised to buy the assets to be invested in different bins in the prediction time period, and investment advice of financial products with reliable and stable income is provided for the user.
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Fig. 1 is a schematic hardware configuration diagram of an asset investment advice information generation apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a method for generating asset investment advice information in accordance with the present invention;
fig. 3 is a schematic diagram illustrating a detailed flow of determining the merged sequence corresponding to the logarithmic difference data in each preset observation period in step S10 in fig. 2;
FIG. 4 is a detailed flowchart of step S20 in FIG. 2;
fig. 5 is a flowchart illustrating a second embodiment of the method for generating asset investment advice information according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: determining a prediction time period of assets to be invested and a time length of the prediction time period, wherein the assets to be invested are large-class assets; obtaining corresponding logarithm difference data of the assets to be invested before a target investment period starts according to the time length, and determining a merging sequence corresponding to the logarithm difference data in each preset observation period; determining a corresponding predicted value of the assets to be invested on the logarithmic difference data according to the merging sequence; and when the predicted value is larger than a preset threshold value, generating prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period.
The device can predict the assets to be invested according to the logarithmic difference data of the assets to be invested to obtain a predicted value, if the predicted value is larger than a preset threshold value, the held income of the assets to be invested after the prediction time period is positive, and therefore the user is advised to buy the assets to be invested in different bins in the prediction time period, and investment advice of financial products with reliable and stable income is provided for the user.
As shown in fig. 1, fig. 1 is a schematic diagram of a hardware configuration of an asset investment advice information generation apparatus according to an embodiment of the present invention.
As shown in fig. 1, the asset investment advice information generating means may include: a processor 1001, such as a CPU, a communication bus 1002, and a memory 1003. Wherein a communication bus 1002 is used to enable connective communication between these components. The memory 1003 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). The memory 1003 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the means for generating asset investment advice information illustrated in fig. 1 does not constitute a limitation on the means for generating asset investment advice information and may include more or fewer components than illustrated, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1003, which is a kind of computer storage medium, may include therein an operating system and a generation program of asset investment advice information.
In the apparatus shown in fig. 1, the processor 1001 may be configured to call a generation program of asset investment advice information stored in the memory 1003, and perform the following operations:
determining a prediction time period of assets to be invested and a time length of the prediction time period, wherein the assets to be invested are large-class assets;
obtaining corresponding logarithm difference data of the assets to be invested before a target investment period starts according to the time length, and determining a merging sequence corresponding to the logarithm difference data in each preset observation period;
determining a corresponding predicted value of the assets to be invested on the logarithmic difference data according to the merging sequence;
and when the predicted value is larger than a preset threshold value, generating prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period.
In one embodiment, the processor 1001 may call the generation processing program of the asset investment advice information stored in the memory 1003, and further perform the following operations:
and generating prompt information for recommending that the fund is split to be invested in the forecast time period at preset time intervals.
In one embodiment, the processor 1001 may call the generation processing program of the asset investment advice information stored in the memory 1003, and further perform the following operations:
the number of split funds and the investment times of the assets to be invested in the prediction time period are equal to the ratio of the time length to the preset time length, and one investment amount in the prediction time period corresponds to one split fund.
In one embodiment, the processor 1001 may call the generation processing program of the asset investment advice information stored in the memory 1003, and further perform the following operations:
determining the output length of the merging sequence, and modifying the output length by a preset length to obtain an extrapolation merging sequence;
determining a linear regression coefficient according to the logarithmic difference data and the merging sequence;
and determining a predicted value corresponding to the asset to be invested according to the extrapolation merging sequence and the linear regression coefficient.
In one embodiment, the processor 1001 may call the generation processing program of the asset investment advice information stored in the memory 1003, and further perform the following operations:
determining a factor predicted value according to the extrapolation merging sequence;
and inputting a linear regression model according to the factor predicted value and the linear regression coefficient to obtain a predicted value corresponding to the asset to be invested.
In one embodiment, the processor 1001 may call the generation processing program of the asset investment advice information stored in the memory 1003, and further perform the following operations:
determining a filtering sequence corresponding to each type of assets in each preset observation period according to the sequence data of the same proportion, wherein each type of assets belongs to the same large class of assets;
merging the filtering sequences corresponding to the assets to obtain a merged sequence of each preset observation period,
in one embodiment, the processor 1001 may call the generation processing program of the asset investment advice information stored in the memory 1003, and further perform the following operations:
carrying out Fourier transform on the logarithmic difference data to obtain corresponding frequency domain data;
determining a group of filter coefficients according to each preset observation period, and obtaining a middle sequence according to the filter coefficients and the frequency domain data;
and carrying out inverse Fourier transform on the intermediate sequence to obtain a filtering sequence of the asset in each preset observation period.
Based on the hardware construction, the invention provides various embodiments of the asset investment advice information generation method.
Referring to fig. 2, a first embodiment of the present invention provides a method for generating asset investment advice information, the method including:
step S10, determining a prediction time period of assets to be invested and the time length of the prediction time period, wherein the assets to be invested are large-class assets;
in the present embodiment, the execution subject is a device for generating the asset investment advice information, and for convenience of description, the device is hereinafter referred to as the device for generating the asset investment advice information. The device can be regarded as a server side, and the device can be loaded in the client side through the APP form, so that the client side is in communication connection with the device based on the APP.
The user may input a predicted time period to the device, the predicted time period being a time period in which the user wants to invest in the asset. The apparatus may determine a length of time to which the predicted time period corresponds. Assets to be invested refer to a broad class of assets, e.g., commodities, stocks, etc. It should be noted that the time length of the investment plan of the user for the large-scale assets may be greater than or equal to the time length of the prediction time period, for example, the user plans to invest in the commodity for 5 years, but may predict the income of the commodity in the next year, the time corresponding to 5 years is the investment time period of the investment plan, and the predicted next year is the prediction time period, and the preset time period may generally be 1 month to 12 months, preferably 12 months.
Step S20, obtaining corresponding logarithm difference data of the assets to be invested before the start of a target investment period according to the time length, and determining a merging sequence corresponding to the logarithm difference data in each preset observation period;
in this embodiment, the assets to be invested may be capital, bond, stock, commodity, and the like. The user can open the APP loaded on the client, wherein the APP is an investment program of the financial product; the user can select financial products needing investment based on the APP, for example, the user wants to invest in the A stocks and the B bonds, the user can select the A stocks and the B bonds on the selection interface of the assets based on the APP, the APP sends the A stocks and the B bonds selected by the user to the device, and the device takes the A stocks and the B bonds as assets to be invested.
After the time length is determined, the device determines the logarithmic difference data of the assets to be invested before the target investment period begins according to the time length. The target investment period is generally an investment period corresponding to a future time, that is, data generated before the target investment period starts needs to be used as log difference data. The data included in the log difference data are monthly difference data. Each monthly difference data is calculated in a set period, the set period can be 1 month, 6 months, 12 months and the like, and the time length of the set period is the same as the time length corresponding to the prediction time period. For example, if the asset to be invested is a stock, and the set period is 6 months, the ratio or the logarithmic value of the ratio of the stock closing price at the end of the month 9 and the month 3 and the month end of the 2019 is taken as the logarithmic difference data of the month 9 and the 2019 as the observation time, that is, the target investment period is the month 9 and the 2019, the logarithmic difference data is taken as the target investment period
Calculated according to the following formula:
After determining the log difference data, the apparatus determines a merging sequence corresponding to the log difference data in each preset observation period, specifically, referring to fig. 3, that is, determining the merging sequence corresponding to the log difference data in each preset observation period in step S20 includes:
step S21, determining a filtering sequence corresponding to the assets to be invested in each preset observation period according to the logarithmic difference data;
the sequence of asset data over a continuous period of time has a time dimension, e.g., stock price data is a sequence of values over a continuous period of time, so that the resulting log differential data of the asset can be viewed as a time sequence. The time sequence can be analogized to a time domain signal generated by the motion of the economic financial asset, and the time domain signal has a Fourier series expression mode, so that the logarithm difference data can be subjected to Fourier transform to obtain corresponding frequency domain data, and the frequency domain data is analyzed and processed.
The assets have an economic period in a unified financial economic system, the economic period is a preset observation period, the comparation data of the assets show regular changes in the preset observation period, the preset observation period is 42 months, 100 months and 200 months, and the preset observation period is arranged in the device. In the frequency domain, each predetermined observation period corresponds to a target frequency signal, which is stable and continuous, and other unstable or unsustainable frequency signals can be regarded as noise. Therefore, the device carries out frequency domain filtering on the logarithmic difference data of the assets so as to reserve the target frequency signal and reduce the interference of noise, and the reserved logarithmic difference data of the target frequency signal is frequency domain data.
Due to the fence effect in the Fourier transform, when the filtering sequence of each type of assets is solved, zero padding is firstly carried out on the logarithmic difference data, and the Fourier transform is carried out on the logarithmic difference data after zero padding to obtain the corresponding frequency domain data. To be provided with
Representing logarithmic difference data of assets of type i to
And frequency domain data corresponding to the logarithmic difference data representing the i-th type assets.
As shown in the following equations (1), (2) and (3), a set of filter coefficients gauss is determined according to each preset observation periodwinAnd according to each set of filter coefficients gausswinAnd frequency domain data wavefftObtaining a set of intermediate sequences
Where nfft is the length of homonymous data after zero padding, gaussindexIs a number series of 1 to nfft, centerfrequencyRepresenting the center frequency, i.e. the frequency, gauss, corresponding to the periodic factor to be extractedalphaAre parameters that affect the bandwidth of the gaussian filter. These parameters may preferably be set as: 4096 for nfft, corresponding 42, 100 or 200 months for period, gaussalphaAnd 10 is taken.
It is to be noted that the following equation (4) pair
Carrying out conjugate symmetry operation:
then, the intermediate sequence of each preset observation period in the ith type assets
Performing inverse Fourier transform to obtain a filtering sequence of the i-th asset in each preset observation period
Obtaining a formula (5) through the transformation of the formula, wherein the transformation process is as follows: performing inverse Fourier transform on each group of intermediate sequences to obtain a group of second intermediate sequences; intercepting data points from the second intermediate sequence according to the preset sequence length LEN to obtain a filtering sequence of each type of assets in each preset observation period
Wherein the preset sequence length LEN is equal to the sum of the log difference length L1 and the extrapolation length L2.
Where real (Z) is the real part of Z.
And step S22, merging the filtering sequences corresponding to the assets to obtain a merged sequence of each preset observation period.
After determining the filtering sequences, the apparatus combines the filtering sequences, thereby obtaining a combined sequence. In particular, in the face of the same global economic financial environment, assets are driven by the same economic cycle to exhibit extremely relevant behaviors. Therefore, the assets of other types can affect the income of the assets to be invested, the filtering sequences of the assets to be invested and the assets of other types under the same preset observation period need to be merged, namely similar common economic period change characteristics of the assets to be invested and the filtering sequences of the assets of other types are merged, and the merged sequences can reflect uniform system-level periodic motion in the market so as to be used for better fitting the price logarithm difference data of various assets in subsequent processing.
When the merging mode is the same type of asset merging mode, the synthesis of the merging sequence of the preset observation period can refer to the following flow:
a, obtaining a first filtering matrix according to filtering sequences of various assets;
in this embodiment, the assets can be stocks, bonds and commodities, and the filtering sequences of the stocks, bonds and commodities are
Each filtering sequence is taken as a vector, and three vectors are combined to form a filtering matrix M1, wherein M1 is the first filtering matrix.
B, performing Hilbert transform on the first filter matrix to obtain a corresponding second filter matrix;
in this step, as shown in the following formula, a software platform library function hibert is called to perform hilbert transform on M1 to obtain a second filter matrix M2:
M2＝hilbert(M1)
and step C, performing iterative calculation of combining weights according to the second filter matrix.
Specifically, a merge weight vector with a preset observation period value of period is initialized first
The length N is a vector of all 1 s, where N is the number of vectors in the matrix M2, i.e., the number of categories of the assets, and the value is 3 when the assets only contain stocks, bonds, and commodities.
Combining the weight vectors according to the following equations (6), (7) and (8)
The iterative calculation of (2):
weightm＝mean(weight)(7)
wherein the content of the first and second substances,
representing the combined weight vector after k-th iteration calculation, M represents matrix M and N multiplication, (M)′Is the conjugate transpose of M, diag (W) is the diagonal matrix containing W on the main diagonal, conj (M) is the complex conjugate of M, M.
When the iterative computation times reach a preset iterative times threshold value dcnt, merging weight convergence is carried out to obtain a final merging weight vector
In this embodiment, dcnt may be taken as 100.
Because the filtering sequences of various assets can be regarded as time domain signals, the influence of the period of the whole economic and financial system is similar to the signal propagation principle, most of the filtering sequences are interfered by strong noise, and the signal-to-noise ratio is usually not high, the combining weight iterative computation method provided by the step obtains the optimal weight of the combining sequences of various assets by reducing phase difference estimation errors, so that the signal-to-noise ratio and the stability of the combining sequences of the filtering sequences of various assets are effectively improved.
And D, obtaining a merging sequence according to the second filter matrix and the merging weight vector.
A synthetic sequence X with period as a preset observation period obtained according to the following formula (9)periodIs a vector, i.e.
abs (W) is the complex magnitude of each element of W (each element of W is a complex number), and sum (W) is the sum of the elements of W.
Step S30, determining the corresponding predicted value of the assets to be invested on the logarithmic difference data according to the merging sequence;
after determining the merging sequence, the apparatus may determine a predicted value corresponding to the to-be-invested asset on the log difference data according to the merging sequence, specifically refer to fig. 4, that is, step S20 includes:
step S31, determining the output length of the merging sequence, and modifying the output length by a preset length to obtain an extrapolation merging sequence;
in this embodiment, after acquiring the merge sequence, the apparatus determines the output length of the merge sequence, and further modifies the output length by a preset length, thereby obtaining an extrapolation merge sequence. For example, the output length of the 42-month combined sequence is 120, and the first extrapolation filtering period is an extrapolation length L2, which is the output length of the combined sequence 121, and the extrapolation length L2 can be set according to actual needs, and is not limited to 1.
Step S32, determining a linear regression coefficient according to each logarithm difference data and the merging sequence;
the device stores a linear regression model, can take the logarithmic difference data corresponding to various assets as dependent variables, combines sequences as independent variables, and inputs the independent variables into the linear regression model so as to obtain linear regression coefficients. Specifically, the linear regression model has a corresponding formula, that is, a linear regression formula, where the linear regression formula is:
wherein, X42、X100、X200Merging sequences corresponding to preset observation periods of 42 months, 100 months and 200 months, respectively, b1Is an intercept term, b2、b3、b4Is a coefficient of a linear regression,
is logarithmic difference data. The linear regression model obtains an estimated value of a linear regression coefficient by adopting a least square estimation algorithm according to a linear regression formula.
And step S33, performing fitting prediction on the logarithm difference data according to the extrapolation merging sequence and the linear regression coefficient to obtain a predicted value.
After the linear regression coefficient is determined, the device can perform fitting prediction on logarithmic difference data of the assets to be invested according to the extrapolation merging sequence and the linear regression coefficient to obtain a predicted value. Specifically, the device obtains a factor predicted value by using the extrapolation merging sequence as an independent variable, and inputs the factor predicted value and the linear regression coefficient into the linear regression model to obtain a predicted value
And step S40, when the predicted value is larger than a preset threshold value, generating prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period.
After the device determines the predicted value, it needs to judge whether the predicted value is greater than a preset threshold, and the preset threshold may be zero. When the predicted value is larger than the preset threshold value, the held income of the assets to be invested after the investment time period is positive.
The benefits of the assets for the warehouse-sharing purchase are stable and the risks are small. Therefore, the device generates prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period, and the prompt information is sent to the terminal corresponding to the user, so that the user invests the assets to be invested according to the prompt information. The warehouse-dividing purchase of the assets to be invested in the prediction time period means that the funds are divided into the number of shares corresponding to the time length corresponding to the prediction time period, for example, the funds are divided into 12 shares if the time length is 12 months. Each fund corresponds to a corresponding purchasing time period in the prediction time period, and each purchasing time period forms the prediction time period.
In the technical scheme provided by this embodiment, the device determines a prediction time period of the asset to be invested and a time length of the prediction time period, so as to obtain logarithmic difference data corresponding to the asset to be invested in a target investment period according to the time length, determine a merging sequence corresponding to the logarithmic difference data in each preset observation period, determine a corresponding prediction value of the asset on the logarithmic difference data according to the merging sequence, and generate a prompt message suggesting that the asset to be invested is purchased in a bin in the prediction time period when the prediction value is greater than a preset threshold value. The device can predict the assets to be invested according to the logarithmic difference data of the assets to be invested to obtain a predicted value, if the predicted value is larger than a preset threshold value, the held income of the assets to be invested after the prediction time period is positive, and therefore the user is advised to buy the assets to be invested in different bins in the prediction time period, and investment advice of financial products with reliable and stable income is provided for the user.
Referring to fig. 5, fig. 5 is a second embodiment of the method for generating the asset investment advice information according to the present invention, wherein the step S40 includes:
and step S41, generating prompt information for recommending that the fund is split to be invested in the forecast time period at preset time intervals.
In this embodiment, when the forecast value is greater than the preset threshold, the held benefit of the asset to be invested after the forecast time period is positive. Therefore, if the assets to be invested need to be purchased, the assets to be invested need to be purchased from the current period, namely, the user can purchase the assets to be invested in different bins within the investment time period from the current period.
Specifically, the investment of the assets to be invested can be carried out at preset intervals in the investment time period. The preset duration may be one month. For example, if the time length of the prediction time period is 6 months, the fund is firstly split into 6 shares, and each month of the prediction time period corresponds to one share of the fund. The number of split funds and the investment times of the assets to be invested in the prediction time period are equal to the ratio of the time length to the preset time length, and the amount of one investment in the prediction time period corresponds to one split fund.
It should be noted that the share of the split assets may be the same, that is, the investment amount of each warehouse investment is the same. Through actual measurement, the same fund of the assets to be invested is purchased every 1 month, the risk is low, and the income is stable.
In the technical scheme provided by this embodiment, when it is determined that the predicted value is greater than the preset threshold, the device generates a prompt message suggesting that the fund splitting is performed at intervals of a preset duration for investment of assets to be invested in the prediction time period, so as to provide investment suggestion information with a relatively high cost performance for the user.
In an embodiment, the merging sequence may also be synthesized according to the SUMPLE algorithm. Specifically, each log difference data may be represented as:
in the formula, k is a time variable,
is the data of the ith log-differential data at time k,
is noise. The resultant weight coefficient is expressed as:
where K is the correlation time interval ncorThe time variable in units, i.e. the number of iterations in the synthesis,
is an ideal weight value, and the weight value is,
is the weight estimation error caused by noise, the synthesized sequence can be represented as:
wherein, is taking complex conjugation, and L is the total number of logarithmic difference data. If the output of the synthesized sequence is represented in the form:
then, the signal and noise terms are respectively:
weight coefficient of K +1 time in SUMPLE algorithm
From the Kth
Recursion is carried out to obtain:
in the formula RK+1For normalizing the coefficients, it is possible to prevent the magnitude of the weights from becoming unstable due to continuous accumulation, and it is ensured that the sum of squares of the weight coefficients of the respective logarithmic difference data is equal to the number of logarithmic difference data, i.e., the number of logarithmic difference data
The weight of the above formula
Can also utilize CkThe rewrite is:
in this embodiment, when synthesizing each log difference data by using the SUMPLE algorithm, the weight coefficient of each log difference data is converged by iteration of a preset number of times, that is, the iteration number of the weight coefficient convergence is determined in advance through experiments and stored as the preset number of times, and when synthesizing each log difference data by using the SUMPLE algorithm, when the iteration number reaches the preset number of times, the iteration is stopped, and a synthesized merging sequence is output. The financial data motion rule of the system level represented by the combined sequence is more stable and reliable, and the predictability is stronger. Meanwhile, the SUMPLE algorithm is suitable for synthesizing data with low signal-to-noise ratio, and when the SUMPLE algorithm is used for synthesizing each log difference data, the optimal weight of each log difference data can be calculated, so that the signal-to-noise ratio of the synthesized merged sequence is higher.
In this embodiment, when synthesizing each log difference data by using the SUMPLE algorithm, iteration may be performed in an integral iteration manner or a rolling iteration manner, so that the weight coefficient of each log difference data is converged. When the synthesis is carried out in an integral iteration mode, sampling is carried out once, then the integral data is used for carrying out iteration updating on the integral data, and the relevant time interval n is carried out at the momentcorThe time length corresponding to the whole data is obtained; when the synthesis is carried out in a rolling iteration mode, a sampling window is rolled forward, multiple times of sampling are carried out, the weight coefficient obtained at the last moment is updated by using the sequence obtained by sampling at the next moment, and at the moment, the relevant time interval n iscorThe preset time period may be set according to actual conditions, for example, the preset time period may be set to 4 months (or 120 days). In the actual synthesis, because the data length is limited, when rolling iteration is used, the number of iterations is limited by the total duration of the sampled data, so that the number of iterations is less due to insufficient data length, thereby affecting the convergence of the weight coefficient, and the number of iterations in the overall iteration mode is not limited by the length of the logarithmic difference data. Therefore, in this embodiment, preferably, when synthesizing each log difference data by using the SUMPLE algorithm, an overall iteration manner is selected for synthesis, so that it is possible to ensure that the weight coefficients of each log difference data are converged.
In one embodiment, the user can send a prediction instruction to the terminal, wherein the prediction instruction refers to an instruction for performing income prediction on the asset to be invested. The user can input the prediction time period based on the APP interface, and the device loading the APP generates the prediction instruction according to the prediction time period. Certainly, the user does not need to input the prediction time period to the APP interface, that is, the user defaults to predict a set several months in the future by taking the current time as the observation time. It is understood that the prediction instruction may or may not include the prediction time period.
When the prediction instruction contains the prediction time period, the steps S10 to S40 are performed.
When the prediction instruction does not contain a prediction time period, the device directly acquires the logarithm difference data of the assets to be invested, the time length of the logarithm difference data is preset, namely a set period, the device determines the merging sequence of the logarithm difference data corresponding to each preset observation period, so that the step S30 is executed to obtain a predicted value, and when the predicted value is greater than a preset threshold value, the preset time length corresponding to the logarithm difference data is determined; and determining a prediction time period according to a preset time length, wherein the preset time length is equal to the time length of a default prediction time period, so that prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period is generated.
For example, the preset time length corresponding to the logarithmic subtraction data is 12 months, and if the predicted value is greater than the preset threshold value, it is determined that the held benefit of the asset to be invested after 12 months is positive.
Therefore, after the predicted value is larger than the preset threshold value, the device predicts the time period corresponding to the preset time length. For example, if the preset time length is 12 months and the current time is 2019, year 3, the predicted time period is 2019, year 3 to 2020, year 3. It should be noted that the preset time length can be set by the user at the terminal. For example, if the user wants to know the profit of the stock after 12 months, the preset time period may be set to 12 months, and if the user wants to know the profit of the stock after 6 months, the preset time period may be set to 6 months.
In addition, the predicted value is smaller than the preset threshold value, and the holding income of the assets to be invested after the time length corresponding to the logarithmic subtraction data can be judged to be negative, for example, the current time is 2019 and 3 months, the preset time length is 12 months, and the holding income after 2020 and 3 months is negative, so that prompt information which does not suggest purchasing the assets to be invested in 2019 and 3 months 2020 and 3 months can be generated.
In the technical scheme provided by the embodiment, the device receives the prediction instruction so as to judge whether the user sets the prediction time period, so that different measures are adopted for predicting the income of the asset to be invested according to different judgment results, and an accurate investment suggestion is provided for the user.
The present invention also provides an apparatus for generating asset investment advice information, the apparatus comprising: a memory, a processor and a program for generating asset investment advice information stored on the memory and operable on the processor, the program for generating asset investment advice information implementing the steps of the method for generating asset investment advice information as described in the above embodiments when executed by the processor.
The present invention also provides a readable storage medium having stored thereon a program for generating asset investment advice information, which when executed by a processor, implements the steps of the method for generating asset investment advice information as described in the above embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A method for generating asset investment advice information, the method comprising the steps of:
determining a prediction time period of assets to be invested and a time length of the prediction time period, wherein the assets to be invested are large-class assets;
obtaining corresponding logarithm difference data of the assets to be invested before a target investment period starts according to the time length, and determining a merging sequence corresponding to the logarithm difference data in each preset observation period;
determining a corresponding predicted value of the assets to be invested on the logarithmic difference data according to the merging sequence;
and when the predicted value is larger than a preset threshold value, generating prompt information for suggesting that the assets to be invested are purchased in different bins in the prediction time period.
2. The method for generating asset investment advice information according to claim 1, wherein the step of generating advice information that advises the binned purchase of the asset to be invested during the forecasted time period comprises:
and generating prompt information for recommending that the fund is split to be invested in the forecast time period at preset time intervals.
3. The method for generating asset investment advice information according to claim 2, wherein the number of split shares of capital and the number of investments of the asset to be invested in the prediction time period is equal to the ratio of the time length to the preset time length, and one investment amount in the prediction time period corresponds to one split fund.
4. The method for generating asset investment advice information according to claim 1, wherein the step of determining the corresponding predicted value of the asset to be invested on the logarithmic subtraction data according to the merging sequence comprises:
determining the output length of the merging sequence, and modifying the output length by a preset length to obtain an extrapolation merging sequence;
determining a linear regression coefficient according to the logarithmic difference data and the merging sequence;
and determining a predicted value corresponding to the asset to be invested according to the extrapolation merging sequence and the linear regression coefficient.
5. The method for generating the asset investment advice information according to claim 4, wherein the step of determining the corresponding forecast value of the asset to be invested according to the extrapolated merge sequence and the linear regression coefficient comprises:
determining a factor predicted value according to the extrapolation merging sequence;
and inputting a linear regression model according to the factor predicted value and the linear regression coefficient to obtain a predicted value corresponding to the asset to be invested.
6. The method for generating asset investment advice information according to claim 1, wherein the step of determining the merging sequence corresponding to the logarithmic difference data at each preset observation period comprises:
determining a filtering sequence corresponding to each type of assets in each preset observation period according to the sequence data of the same proportion, wherein each type of assets belongs to the same large class of assets;
and combining the filtering sequences corresponding to the assets to obtain a combined sequence of each preset observation period.
7. The method for generating the asset investment advice information according to claim 6, wherein the step of determining the filtering sequences corresponding to the assets in the preset observation periods according to the sequence data of the same proportion comprises:
carrying out Fourier transform on the logarithmic difference data to obtain corresponding frequency domain data;
determining a group of filter coefficients according to each preset observation period, and obtaining a middle sequence according to the filter coefficients and the frequency domain data;
and carrying out inverse Fourier transform on the intermediate sequence to obtain a filtering sequence of the asset in each preset observation period.
8. An apparatus for generating asset investment advice information, the apparatus comprising: a memory, a processor and a program for generating asset investment advice information stored on the memory and operable on the processor, the program for generating asset investment advice information when executed by the processor implementing the steps of the method for generating asset investment advice information according to any one of claims 1 to 7.
9. A readable storage medium, characterized in that the readable storage medium has stored thereon a program for generating asset investment advice information, which when executed by a processor implements the steps of the method for generating asset investment advice information according to any one of claims 1 to 7.
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| WO2021022977A1 (en) * | 2019-08-06 | 2021-02-11 | 华泰证券股份有限公司 | Method for predictng asset price trends, server and computer readable storage medium |
| WO2021103572A1 (en) * | 2019-11-25 | 2021-06-03 | 华泰证券股份有限公司 | Method and apparatus for generating asset investment suggestion infromation, and readable storage medium |
| CN113656470A (en) * | 2020-05-12 | 2021-11-16 | 北京市天元网络技术股份有限公司 | Asset purchase prompting method and device, electronic equipment and storage medium |
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| CA2756770A1 (en) * | 2010-11-04 | 2012-05-04 | Investpic, Llc | Method and system for analyzing investment information |
| CN108446984A (en) * | 2018-03-20 | 2018-08-24 | 张家林 | A kind of investment data management method and device |
| CN109859052B (en) * | 2019-01-16 | 2024-02-23 | 平安科技（深圳）有限公司 | Intelligent recommendation method and device for investment strategy, storage medium and server |
| CN110458352A (en) * | 2019-08-06 | 2019-11-15 | 华泰证券股份有限公司 | Predict method, server and the computer readable storage medium of assets price tendency |
| CN111091466A (en) * | 2019-11-25 | 2020-05-01 | 华泰证券股份有限公司 | Method and device for generating asset investment suggestion information and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2021022977A1 (en) * | 2019-08-06 | 2021-02-11 | 华泰证券股份有限公司 | Method for predictng asset price trends, server and computer readable storage medium |
| WO2021103572A1 (en) * | 2019-11-25 | 2021-06-03 | 华泰证券股份有限公司 | Method and apparatus for generating asset investment suggestion infromation, and readable storage medium |
| CN113656470A (en) * | 2020-05-12 | 2021-11-16 | 北京市天元网络技术股份有限公司 | Asset purchase prompting method and device, electronic equipment and storage medium |
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