CN108648121A - Generation of simulating data method and device and electronic equipment - Google Patents
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Abstract
A kind of Generation of simulating data method and device of this specification embodiment offer and electronic equipment, the method includes：Obtain the sequence of real resources；Wherein, the sequence of the real resources is the chronologically-based stock number really acquired；According to the sequence of the real resources, the achievement data of at least one real resources is calculated；According to the statistical distribution type of the achievement data, corresponding statistical distribution pattern is matched；Stochastic model is built according to the sequence of the real resources and based on the matched statistical distribution pattern of institute；The sequence for the random sources for meeting the statistical distribution type is generated based on the stochastic model.
Description
Technical field
This specification embodiment be related to Internet technical field more particularly to a kind of Generation of simulating data method and device and
Electronic equipment.
Background technology
With the arrival of Internet era, internet is widely answered in the daily study of people, work and life
With.For internet product, the operation of any one user can all lead to the variation of Internet resources.It is confusing seeming
In resource, the objective law of some is concealed, the resource discovering of variation can be relied on hidden for experienced operation personnel
The risk or problem of Tibetan.For example, determining different risk class according to the different fluctuation frequency of resource.In order to cultivate it is more have through
The operation personnel tested generally requires to give operation personnel training.In general, analog platform simulation may be used very in the mode of training
The variation of real Internet resources.
Analog platform needs to generate the emulation data for simulating true Internet resources variation, such as generates a preset time
The change procedure of resource in section.In order to which the simulation carried out repeatedly to complicated showing resource environment usually may be used in the related art
The random sources at each moment are generated in a manner of using random algorithm.However, since random algorithm is not with greatly true
Qualitative, the random sources of generation change often and do not meet some existing objective laws when real resources variation.In this way, causing
Result of training is undesirable.
Invention content
A kind of Generation of simulating data method and device and electronic equipment that this specification embodiment provides：
According to this specification embodiment in a first aspect, provide a kind of Generation of simulating data method, the method includes：
Obtain the sequence of real resources；Wherein, the sequence of the real resources be really acquire it is chronologically-based
Stock number；
According to the sequence of the real resources, the achievement data of at least one real resources is calculated；
According to the statistical distribution type of the achievement data, corresponding statistical distribution pattern is matched；
Stochastic model is built according to the sequence of the real resources and based on the matched statistical distribution pattern of institute；
The sequence for the random sources for meeting the statistical distribution type is generated based on the stochastic model.
According to the second aspect of this specification embodiment, a kind of Generation of simulating data device is provided, described device includes：
Acquiring unit obtains the sequence of real resources；Wherein, the sequence of the real resources be really acquire based on when
Between sequence stock number；
Computing unit calculates the achievement data of at least one real resources according to the sequence of the real resources；
Matching unit matches corresponding statistical distribution pattern according to the statistical distribution type of the achievement data；
Construction unit builds random mould according to the sequence of the real resources and based on the matched statistical distribution pattern of institute
Type；
Generation unit generates the sequence for the random sources for meeting the statistical distribution type based on the stochastic model.
According to the third aspect of this specification embodiment, a kind of electronic equipment is provided, including：
Processor；
Memory for storing processor-executable instruction；
Wherein, the processor is configured as any of the above-described Generation of simulating data method.
This specification embodiment, provides a kind of Generation of simulating data scheme, and the program is based on body when real resources change
The statistical distribution type revealed, to build the corresponding stochastic model of statistical distribution type；So that the random sources generated at random
Height is similar in statistical distribution type to real resources, but not exactly the same with real resources sequence.In this way, based in this way
The analog platform of the random sources operation of generation carrys out training personnel, and start-up can be made not operated to actual services
In the case of, as far as possible result of training is improved the case where change in resources in experience actual services.On the other hand, due to using
Stochastic model so that the sequence of the random sources generated every time is not fully identical, embodies change in resources in actual services
Uncertainty is conducive to start-up and accumulates experience, and cultivates the susceptibility to risk.
Description of the drawings
Fig. 1 is the flow chart for the Generation of simulating data method that one embodiment of this specification provides；
Fig. 2 is the schematic diagram for the achievement data curve that one embodiment of this specification provides；
Fig. 3 is the configuration diagram for the analogue system that one embodiment of this specification provides；
Fig. 4 is the hardware structure diagram for the Generation of simulating data device that one embodiment of this specification provides；
Fig. 5 is the module diagram for the Generation of simulating data device that one embodiment of this specification provides.
Specific implementation mode
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with this specification.On the contrary, they are only and such as institute
The example of the consistent device and method of some aspects be described in detail in attached claims, this specification.
It is the purpose only merely for description specific embodiment in the term that this specification uses, is not intended to be limiting this explanation
Book.The "an" of used singulative, " described " and "the" are also intended to packet in this specification and in the appended claims
Most forms are included, unless context clearly shows that other meanings.It is also understood that term "and/or" used herein is
Refer to and include one or more associated list items purposes any or all may combine.
It will be appreciated that though various information may be described using term first, second, third, etc. in this specification, but
These information should not necessarily be limited by these terms.These terms are only used for same type of information being distinguished from each other out.For example, not taking off
In the case of this specification range, the first information can also be referred to as the second information, and similarly, the second information can also be claimed
For the first information.Depending on context, word as used in this " if " can be construed to " ... when " or
" when ... " or " in response to determination ".
As previously mentioned, with the arrival of Internet era, internet obtains in the daily study of people, work and life
It is widely applied.For internet product, the operation of any one user can all lead to the variation of Internet resources.It is seeing
Like in confusing resource, the objective law of some is concealed, the money of variation can be relied on for experienced operation personnel
Feed Discovery hiding risk or problem.For example, determining different risk class according to the different fluctuation frequency of resource.In order to cultivate
More experienced operation personnel, generally require to give operation personnel training.It is put down in general, simulation may be used in the mode of training
Platform simulates the variation of true Internet resources.
Analog platform needs to generate the emulation data for simulating true Internet resources variation, such as generates a preset time
The change procedure of resource in section.In order to improve the effect of training as possible, usually require that the change procedure generated every time is endless as possible
It is exactly the same.In the related art, the mode that random algorithm usually may be used generates the random sources at each moment.However,
Since there is random algorithm greatly uncertainty, the random sources variation of generation often and when not meeting real resources variation to deposit
Some objective laws.In this way, causing result of training undesirable.
By taking financial scenario as an example, for new hand, since the financial markets such as stock, futures, fund are there are greater risk,
It can first select by simulation softward without using true fund, experience the investment impression in true financial market, it is ripe
Financial market market variation is known, avoids investment risk, increases investment return.Currently, being related to the simulation softward of financial market market
There are mainly two types of (such as financial simulations), one is true financial market market are used completely, only produces specific finance
The information such as the name of product conceal；Another kind is to generate financial market market using random algorithm.For the first, due to adopting completely
With true financial market market, the market conditions for simulating generation each time are the same, and are lacked variation, can not be generated unlimited number
The complementary random financial market market repeated of amount, user just lose interest in after may trying out several times.For second, due to
The chance mechanism of random algorithm is too simple, for example, completely random generate financial market market, therefore so that the finance of generation
Market conditions change often and do not meet some existing objective laws when true financial market market variation.The financial market
Market are a kind of sequences of the daily price of target, by taking this financial market of stock market as an example, the true financial market row
Feelings can refer to the sequence of the daily price in one or more stock exchange, such as Shanghai one week sequence of security composite index
For { 2990,2993,3000,2995,2994 }.
A kind of embodiment of Generation of simulating data method of this specification can be introduced in conjunction with example shown in FIG. 1 below, it should
Method can apply the server (hereinafter referred to as server) in Generation of simulating data, and this method may comprise steps of：
Step 110：Obtain the sequence of real resources；Wherein, the sequence of the real resources be really acquire based on when
Between sequence stock number.
Server can actively or passively obtain the sequence of real resources.The sequence of the real resources can consider
It is the set of the stock number of different moments in certain time, and these stock numbers are arranged according to the sequencing of time.
For example, resource situation of change is as follows in some period：
When 1, stock number A；
When 2, stock number B；
When 3, stock number C；
When 4, stock number D；
Then, the sequence of the period corresponding real resources is { A, B, C, D }；Wherein, earliest at the time of A, at the time of B time
It, at the time of C third, at the time of D the latest.
Step 120：According to the sequence of the real resources, the achievement data of at least one real resources is calculated.
In one embodiment, each achievement data can be corresponding with a kind of index algorithm；That is system achievement data and finger
Marking algorithm has one-to-one relationship.Therefore, for the achievement data of needs, using corresponding index algorithm, so that it may with root
The achievement data is calculated according to the sequence of real resources.
It illustrating, it is assumed that the sequence of real resources is { A, B, C, D }, needs the achievement data X for calculating real resources, that
The corresponding index algorithm Y of the achievement data X can be based on, each real resources in sequence are calculated respectively, from
And the sequence of corresponding achievement data is obtained, such as real resources A is based on index algorithm Y, and achievement data A ' is calculated；It is similar
, the achievement data B ' of real resources B；The achievement data D ' of the achievement data C ' of real resources C, real resources D therefore refer to
It can be { A ', B ', C ', D ' } to mark data X.
In one embodiment, the sequence of the real resources includes the daily price sequence of true financial market market target
Row；
The achievement data includes financial indicator data.
In one embodiment, the financial indicator data include：
At least one of the daily earning rates of financial market market, daily earning rate distribution, stability bandwidth.
Wherein, daily earning rate can refer to the daily amount of increase and amount of decrease of financial market market price；Usually as a percentage,
Such as A daily earning rates are+0.3%, i.e. the amount of increase of A days financial market market price is 0.3%；B daily earning rates are -0.3%, i.e.,
The drop range of B days financial market market prices is 0.3%.
Stability bandwidth can refer to the index for weighing the fluctuation of financial market market price amount of increase and amount of decrease.
In the embodiment having, the financial indicator data can also withdraw including maximum, the degree of bias, kurtosis etc..
Wherein, maximum withdraw can refer to amplitude peak that financial market market price drops from peak.
The degree of bias can refer to the measurement in financial market market price distribution direction and degree.
Kurtosis can be the kurtosis of value financial market market stochastic price variable probability distribution.
As previously mentioned, for each financial indicator data, it can be corresponding with a kind of index algorithm, pass through corresponding finger
Mark algorithm, so that it may to calculate financial indicator data.For example, passing through daily earning rate algorithm, so that it may with according to true financial market row
The daily price series of feelings target calculate daily earning rate sequence.
Step 130：According to the statistical distribution type of the achievement data, corresponding statistical distribution pattern is matched.
Server analyzes the statistical distribution type of real resources according to the achievement data of real resources, and matches corresponding
Statistical distribution pattern.
In one embodiment, each statistical distribution type can be corresponded to there are one statistical distribution pattern.That is statistical distribution
Type has one-to-one relationship with statistical distribution pattern.
In one embodiment, the statistical distribution type according to the achievement data, matches corresponding statistical distribution mould
Type, including any one of the following manners：
When the statistical distribution type of the achievement data is normal distribution, corresponding normal distribution model is matched；
When the statistical distribution type of the achievement data is standardized normal distribution, corresponding standardized normal distribution mould is matched
Type；
When the statistical distribution type of the achievement data is logarithm normal distribution, corresponding logarithm normal distribution mould is matched
Type；
When the statistical distribution type of the achievement data is that t is distributed, corresponding t distributed models are matched；
When the statistical distribution type of the achievement data is that x^2 is distributed, corresponding x^2 distributed models are matched；
When the statistical distribution type of the achievement data is that F is distributed, corresponding F distributed models are matched.
It, can be according to day after the daily earning rate for calculating each day below by taking daily earning rate this achievement data as an example
Earning rate draws out a daily earning rate curve, it is assumed that as shown in Figure 2；Since the statistical distribution type of daily earning rate meets logarithm
Normal distribution, therefore corresponding logarithm normal distribution model can be matched to.
Step 140：Stochastic model is built according to the sequence of the real resources and based on the matched statistical distribution pattern of institute.
Server builds a stochastic model according to the sequences of the real resources and based on the matched statistical distribution pattern of institute.
In one embodiment, the stochastic model is also set with the distribution trend of random sources to be generated.
Wherein, the distribution trend includes at least one of overall distribution, long-term trend, short-term trend.
Wherein, long-term trend can refer to the trend feature of financial market market change in long term；
Short-term trend can refer to the trend feature of financial market market short term variations.
In one embodiment, the method further includes：
When distribution trend is overall distribution, according to resource growth rate in the unit interval of the real resources, with unit
The statistical distribution of resource growth rate amplitude fitting setting in time.
By taking resource growth rate in the unit interval is daily earning rate as an example, when distribution trend is overall distribution, server can
According to the daily earning rate characteristic distributions of real resources, to select the preset statistical distribution of daily earning rate amplitude fitting.It is real one
It applies in example, the preset statistical distribution may include logarithm normal distribution.Certainly, statistical distribution here is to pre-set
, it other than logarithm normal distribution, can also be adjusted at any time according to actual needs, for example, it can be set to for hypergeometry point
Cloth, laplacian distribution etc..
In one embodiment, the method further includes：
Distribution trend be long-term trend when, according to the growth rate of the real resources be distributed, calculate factor I and
Factor Ⅱ；
The statistical distribution that the factor Ⅱ is obeyed to setting using the valuation that is averaged for a long time as mean value, sets the length of factor I
The fluctuation distributed constant of phase growth factor and factor Ⅱ.
Wherein, the preset statistical distribution may include logarithm normal distribution.Certainly, statistical distribution here is advance
It is arranged, other than logarithm normal distribution, can also be adjusted at any time according to actual needs, for example, it can be set to is standard
Normal distribution.
Wherein, factor I indicates longer term resource increment；Factor Ⅱ indicates Current resource amount and longer term resource increment
Ratio；
In financial scenario, the factor I may include potential profit, and factor Ⅱ may include PE multiples.
Wherein, potential profit can refer to the potential value basis of target.
PE multiples can refer to assessment level of the financial market market price based on potential profit.
By the embodiment, the long-term growth coefficient of the factor I of the growth rate profile set based on real resources and
The fluctuation distributed constant of two-factor can make the variation of the long-term trend of the random sources sequence of stochastic model generation close to very
The variation of real resource long-term trend.
In one embodiment, the method further includes：
Distribution trend be short-term trend when, according to the increase and decrease amplitude of the real resources and close on the time be weighted it is flat
, current short-term trend direction is determined.
By the embodiment, server is weighted averagely according to the increase and decrease amplitude of the real resources with the time is closed on,
It can determine current short-term trend direction so that the variation of the short-term trend for the random sources sequence that stochastic model generates is close to very
The variation of real resource short-term trend.
In practical applications, real resources variation, which exists, persistently changes to a direction or persistently changes round about
The case where.Such case can be referred to as trend reversion.In order to enable can also to have trend anti-for the scheme that provides of this specification
Turn, in one embodiment, the average weighted weighted value increases at any time within the default duration to be failed with exponential form.
Wherein, the default duration and the half-life period of the decline are determined by the fluctuation frequency of real resources.
The default duration can be the index of duration before the reversion of measurement trend；
Half-life period can be the index that measurement trend reversion probability is promoted.
By the embodiment, weighted value increases at any time within the default duration to be failed with exponential form until gradually consuming
To the greatest extent so that the sequence for the random sources that stochastic model generates, when short-term trend changes, reversion probability gradually rises；I.e. random money
The case where trend reversion can occur in the sequence in source.
Step 150：The sequence for the random sources for meeting the statistical distribution type is generated based on the stochastic model.
By above-described embodiment, the statistical distribution type embodied when being changed based on real resources, to build the statistical
The corresponding stochastic model of cloth type；So that the random sources generated at random with real resources the height phase in statistical distribution type
Seemingly, but it is not exactly the same with real resources sequence.In this way, being instructed based on the analog platform of the random sources operation generated in this way
Practice start-up, start-up can be made in the case where not operated to actual services, experiences provided in actual services as far as possible
The case where source changes, improves result of training.On the other hand, due to using stochastic model so that the random sources generated every time
Sequence it is not fully identical, embody the uncertainty of change in resources in actual services, be conducive to start-up and accumulate experience,
Cultivate the susceptibility to risk.
For financial scenario, by above-described embodiment, the statistical that system goes out when being changed based on true financial market market
Cloth type, to build the corresponding stochastic model of statistical distribution type；So that the random financial market market generated at random with it is true
Real financial market market height in statistical distribution type is similar, but not exactly the same with financial market market.In this way, being based on
The simulation softward of the random financial market market operation generated in this way, can make user without using true fund the case where
Under, the investment impression in true financial market is experienced, and since the random financial market market generated every time are not fully identical,
Also the risk that user faces in true financial market has been fully demonstrated, has been conducive to that user is helped to be familiar with financial market market variation,
User is promoted to the susceptibility of risk, increases investment experiences, investment return of user etc..
This specification can be related to one or more systems.Such as shown in figure 3, a kind of system of this specification framework
It may include analogue system 310.The analogue system 310 may include that real resources analysis module 311 and random sources are imitative
True module 312.The real resources analysis module 311 can be used for the sequence according to real resources, calculate at least one true
The achievement data of real resource.As shown in figure 3, after real resources are input to the analogue system 310, it first can be by described true
Real resource analysis module 311 is handled；And then the real resources analysis module 311 handling result can be passed to it is described with
Machine resource emulation module 312.The random sources emulation module 312 can be distributed based on the achievement data of real resources, structure
Stochastic model, and generate the sequence of random sources；Wherein, long-term trend, short-term trend can be added in the stochastic model
And/or trend inverts the relevant factor.In another embodiment, the analogue system 310 can also include authentication module 313
With monitoring module 314.
Wherein, preset algorithm such as Monte Carlo Analysis algorithm may be used to building random mould in the authentication module 313
The sequence of a large amount of random sources trained during type carries out verification analysis, according to the achievement data of random sources and true money
The difference of the achievement data in source is modified stochastic model parameter, until the index for the random sources that stochastic model trains
The achievement data of data and real resources is almost the same.In simple terms, the authentication module 313 can be in structure stochastic model
Run in the process, can be used for being modified stochastic model parameter so that stochastic model generate random sources variation with
Real resources variation is almost the same, also and true while being different to the sequence of the random sources generated every time in guarantee
The variation of real resource is consistent on statistical nature.The Monte Carlo Analysis algorithm be it is a kind of using largely generate it is random because
Son carries out the statistic algorithm of risk verification, can be generally used for statistical analysis stochastic model risk, random to improve and correct
Model.
Wherein, the monitoring module 314 can be to the sequence of the random sources generated in the real application process of stochastic model
It is monitored analysis, the sequence filter of the achievement data of random sources and the achievement data of real resources to differ greatly is fallen,
To ensure that the stability (not generating the random sources sequence to differ greatly with real resources) of random sources, independence are (every
The sequence of the random sources of secondary generation is different), (it is special in statistics that random sources change the variation with real resources to consistency
It is consistent in sign).
It for authentication module 313 shown in Fig. 3, is illustrated in conjunction with next specific embodiment, in above-mentioned Fig. 1 institutes
On the basis of showing embodiment, the method can also include：
According to the sequence of the random sources, the achievement data of at least one random sources is calculated；
Calculate the difference value of the achievement data of the random sources and the achievement data of the real resources；
When the difference value is more than threshold value, the stochastic model is modified, until the index of the random sources
Data and the difference value of the achievement data of the real resources are less than threshold value.
Wherein, the difference value of the achievement data for calculating the random sources and the achievement data of the real resources,
Specifically include following at least one：
The first：Calculate the mean value of the mean value of the achievement data of the random sources and the achievement data of the real resources
Between difference.
Calculate the mean value of the achievement data of the random sources；
Calculate the mean value of the achievement data of the real resources；
Calculate the difference of the two mean values.
It illustrates, it is assumed that the achievement data { A, B, C, D } of random sources then calculates the equal of the achievement data of random sources
Value A=(A+B+C+D)/4；
The achievement data { E, F, G, H } of real resources then calculates the mean value B=(E+F+G+ of the achievement data of real resources
H)/4；
The difference for calculating mean value A and B is | A-B |.Positive value in order to obtain takes absolute value to difference.The difference is to count
Obtained difference value.
In this kind of mode, mean value (being referred to as average value, Average) is a kind of finger of reflection data central tendency
Mark.Difference between the mean value of the achievement data of random sources and the mean value of the achievement data of real resources can reflect random money
Whole difference degree between source and real resources, difference is bigger, illustrates that the difference between random sources and real resources is bigger, poor
It is worth smaller, illustrates that the difference between random sources and real resources is smaller.
Second：Calculate the difference of the variance of the achievement data of the random sources and the achievement data of the real resources
Value.
Calculate the variance of the achievement data of the random sources；
Calculate the variance of the achievement data of the real resources；
Calculate the difference of the two variances.
In one embodiment, the calculation formula of variance (Variance) is：
Wherein, x1,x2,x3,...,xnFor achievement data；M is x1,x2,x3,...,xnMean value.
It illustrates, it is assumed that the achievement data { A, B, C, D } of random sources calculates random sources based on above-mentioned formula of variance
The variance of achievement data be assumed to be A；
The achievement data { E, F, G, H } of real resources calculates the achievement data of real resources based on above-mentioned formula of variance
Variance is assumed to be B；
The difference for calculating mean value A and B is | A-B |.Positive value in order to obtain takes absolute value to difference.The difference is to count
Obtained difference value.
In this kind of mode, variance is a kind of index indicating data stability, and variance is smaller, indicates that this group of data are more steady
Fixed, variance is bigger, indicates that this group of data are more unstable.In turn, the finger of the variance and real resources of the achievement data of random sources
Whole difference degree between random sources and real resources can also be reflected by marking the difference between the variance of data, and difference is bigger,
Illustrate that the difference between random sources and real resources is bigger, difference is smaller, illustrates the difference between random sources and real resources
It is different smaller.
The third：Calculate the quartile of the achievement data of the random sources and the achievement data of the real resources
The difference of quartile.
Calculate the quartile of the achievement data of the random sources；
Calculate the quartile of the achievement data of the real resources；
Calculate the difference of the two quartiles.
In one embodiment, quartile (Quartile) is value by the ascending arrangement of all data and is divided into four etc.
Point, using the data in three cut-point positions as quartile.
In general, first quartile (Q1), also known as " smaller quartile ", equal to all numerical value in the sample by it is small to
25%th number after longer spread；The as position of Q1=(n+1) × 0.25；N is the number of data；
Second quartile (Q2), also known as " median " are equal in the sample after all ascending arrangements of numerical value the
50% number；The as position of Q2=(n+1) × 0.5；
Third quartile (Q3), also known as " larger quartile " are equal to all ascending arrangements of numerical value in the sample
75%th number afterwards；The position of Q3=(n+1) × 0.75.
It illustrates, it is assumed that be A1, A2, A3, A4, A5, A6, A7 after the ascending sequence of achievement data；It can then obtain
Quartile：Q1=A2；Q2=A4；Q3=A6.
In this kind of mode, the quartile of the quartile of the achievement data of random sources and the achievement data of real resources
Between difference can also reflect between random sources and real resources whole difference degree, difference is bigger, illustrates random sources
Difference between real resources is bigger, and difference is smaller, illustrates that the difference between random sources and real resources is smaller.
By the embodiment, during preset algorithm such as Monte Carlo Analysis algorithm may be used to structure stochastic model
The sequence of a large amount of random sources trained carries out verification analysis, according to the index of the achievement data of random sources and real resources
The difference of data is modified stochastic model parameter, until random sources that stochastic model trains achievement data with it is true
The achievement data of real resource is almost the same.To while ensureing that the sequence of the random sources generated every time is different from, go back
It is consistent with the entire change of real resources.
It for monitoring module 314 shown in Fig. 3, is illustrated in conjunction with next specific embodiment, in above-mentioned Fig. 1 institutes
On the basis of showing embodiment, the method can also include：
The sequence of the random sources is monitored with the presence or absence of abnormal.
Specifically, can calculate the achievement data of at least one random sources according to the sequence of the random sources；
Calculate the difference value of the achievement data of the random sources and the achievement data of the real resources；
When the difference value is more than threshold value, it is abnormal to determine that the sequence of the random sources exists.
Wherein, the sequence according to the random sources calculates the achievement data of at least one random sources, and
The achievement data for calculating the random sources is identical as a upper embodiment as the difference value of the achievement data of the real resources, tool
Body can refer to a upper embodiment, no longer be repeated herein.
After the sequence for determining the random sources is there are exception, illustrate the variations of the random sources that this is generated with it is true
The variation of resource is larger, without design requirement is met, can filter out the sequence of this group of random sources.
By the embodiment, the sequences of the random sources to being generated in the real application process of stochastic model is monitored point
Analysis, the sequence filter of the achievement data of random sources and the achievement data of real resources to differ greatly is fallen, to ensure that
The stability (not generating the random sources sequence to differ greatly with real resources) of random sources, independence (generate every time with
The sequence of machine resource is different), (variation that random sources change with real resources keeps one to consistency on statistical nature
It causes).
Corresponding with aforementioned Generation of simulating data embodiment of the method, this specification additionally provides Generation of simulating data device
Embodiment.Described device embodiment can also be realized by software realization by way of hardware or software and hardware combining.
As the device on a logical meaning, deposited non-volatile by the processor of equipment where it for implemented in software
Corresponding computer business program instruction reads what operation in memory was formed in reservoir.For hardware view, such as Fig. 4 institutes
Show, is a kind of hardware structure diagram of equipment where this specification Generation of simulating data device, in addition to processor shown in Fig. 4, net
Except network interface, memory and nonvolatile memory, the equipment in embodiment where device is given birth to generally according to the emulation data
At actual functional capability, it can also include other hardware, this is repeated no more.
Fig. 5 is referred to, for the module map for the Generation of simulating data device that one embodiment of this specification provides, described device pair
The embodiment illustrated in fig. 1, described device has been answered to include：
Acquiring unit 410 obtains the sequence of real resources；Wherein, the sequence of the real resources is the base really acquired
In the stock number of time sequencing；
Computing unit 420 calculates the achievement data of at least one real resources according to the sequence of the real resources；
Matching unit 430 matches corresponding statistical distribution pattern according to the statistical distribution type of the achievement data；
Construction unit 440 is built random according to the sequence of the real resources and based on the matched statistical distribution pattern of institute
Model；
Generation unit 450 generates the sequence for the random sources for meeting the statistical distribution type based on the stochastic model.
In a kind of optional embodiment：
Any one of described matching unit 430, including following subelement：
First coupling subelement, when the statistical distribution type of the achievement data is normal distribution, matching is corresponding just
State distributed model；
Second coupling subelement, when the statistical distribution type of the achievement data is standardized normal distribution, matching corresponds to
Standardized normal distribution model；
Third coupling subelement, when the statistical distribution type of the achievement data is logarithm normal distribution, matching corresponds to
Logarithm normal distribution model；
4th coupling subelement matches corresponding t distributions when the statistical distribution type of the achievement data is that t is distributed
Model；
5th coupling subelement matches corresponding x^2 when the statistical distribution type of the achievement data is that x^2 is distributed
Distributed model；
6th coupling subelement matches corresponding F distributions when the statistical distribution type of the achievement data is that F is distributed
Model.
In a kind of optional embodiment：
The stochastic model is also set with the distribution trend of random sources to be generated.
In a kind of optional embodiment：
The distribution trend includes at least one of overall distribution, long-term trend, short-term trend.
In a kind of optional embodiment：
Described device further includes：
Overall distribution subelement, when distribution trend is overall distribution, the unit interval according to the real resources is domestic-investment
Source growth rate, with the statistical distribution of resource growth rate amplitude fitting setting in the unit interval.
In a kind of optional embodiment：
Described device further includes：
Computation subunit is distributed according to the growth rate of the real resources, calculates when distribution trend is long-term trend
Factor I and factor Ⅱ；
Subelement is set, the factor Ⅱ is obeyed to the statistical distribution of setting, setting using the valuation that is averaged for a long time as mean value
The long-term growth coefficient of factor I and the fluctuation distributed constant of factor Ⅱ；
Wherein, factor I indicates longer term resource increment；Factor Ⅱ indicates Current resource amount and longer term resource increment
Ratio.
In a kind of optional embodiment：
The statistical distribution of the setting includes logarithm normal distribution.
In a kind of optional embodiment：
Described device further includes：
Determination subelement, when distribution trend is short-term trend, when according to the increase and decrease amplitude of the real resources and closing on
Between be weighted average, determine current short-term trend direction.
In a kind of optional embodiment：
The average weighted weighted value increases at any time within the default duration to be failed with exponential form.
In a kind of optional embodiment：
The default duration and the half-life period of the decline are determined by the fluctuation frequency of real resources.
In a kind of optional embodiment：
Described device further includes：
First computation subunit calculates the index number of at least one random sources according to the sequence of the random sources
According to；
Second computation subunit calculates the difference of the achievement data of the random sources and the achievement data of the real resources
Different value；
Revise subelemen is modified the stochastic model when the difference value is more than threshold value, until described random
The achievement data of resource and the difference value of the achievement data of the real resources are less than threshold value.
In a kind of optional embodiment：
Described device further includes：
Monitoring unit monitors the sequence of the random sources with the presence or absence of abnormal.
In a kind of optional embodiment：
The monitoring unit, specifically includes：
First computation subunit calculates the index number of at least one random sources according to the sequence of the random sources
According to；
Second computation subunit calculates the difference of the achievement data of the random sources and the achievement data of the real resources
Different value；
It is abnormal to determine that the sequence of the random sources exists when the difference value is more than threshold value for abnormal determination subelement.
In a kind of optional embodiment：
Second computation subunit, specifically includes following at least one：
Mean value computation subelement calculates the mean value of the achievement data of the random sources and the index number of the real resources
According to mean value between difference；
Variance computation subunit calculates the variance of the achievement data of the random sources and the index number of the real resources
According to variance between difference；
Quartile computation subunit calculates the quartile of the achievement data of the random sources and the real resources
Difference between the quartile of achievement data.
In a kind of optional embodiment：
The sequence of the real resources includes the daily price series of true financial market market target；
The achievement data includes financial indicator data.
In a kind of optional embodiment：
The financial indicator data include：
At least one of the daily earning rates of financial market market, daily earning rate distribution, stability bandwidth.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.A kind of typically to realize that equipment is computer, the concrete form of computer can
To be personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play
In device, navigation equipment, E-mail receiver/send equipment, game console, tablet computer, wearable device or these equipment
The combination of arbitrary several equipment.
The function of each unit and the realization process of effect specifically refer to and correspond to step in the above method in above-mentioned apparatus
Realization process, details are not described herein.
For device embodiments, since it corresponds essentially to embodiment of the method, so related place is referring to method reality
Apply the part explanation of example.The apparatus embodiments described above are merely exemplary, wherein described be used as separating component
The unit of explanation may or may not be physically separated, and the component shown as unit can be or can also
It is not physical unit, you can be located at a place, or may be distributed over multiple network units.It can be according to actual
It needs that some or all of module therein is selected to realize the purpose of this specification scheme.Those of ordinary skill in the art are not
In the case of making the creative labor, you can to understand and implement.
Figure 5 above describes inner function module and the structural representation of Generation of simulating data device, substantial execution
Main body can be a kind of electronic equipment, including：
Processor；
Memory for storing processor-executable instruction；
Wherein, the processor is configured as：
Obtain the sequence of real resources；Wherein, the sequence of the real resources be really acquire it is chronologically-based
Stock number；
According to the sequence of the real resources, the achievement data of at least one real resources is calculated；
According to the statistical distribution type of the achievement data, corresponding statistical distribution pattern is matched；
Stochastic model is built according to the sequence of the real resources and based on the matched statistical distribution pattern of institute；
The sequence for the random sources for meeting the statistical distribution type is generated based on the stochastic model.
Optionally, the statistical distribution type according to the achievement data matches corresponding statistical distribution pattern, including
Any one of the following manners：
When the statistical distribution type of the achievement data is normal distribution, corresponding normal distribution model is matched；
When the statistical distribution type of the achievement data is standardized normal distribution, corresponding standardized normal distribution mould is matched
Type；
When the statistical distribution type of the achievement data is logarithm normal distribution, corresponding logarithm normal distribution mould is matched
Type；
When the statistical distribution type of the achievement data is that t is distributed, corresponding t distributed models are matched；
When the statistical distribution type of the achievement data is that x^2 is distributed, corresponding x^2 distributed models are matched；
When the statistical distribution type of the achievement data is that F is distributed, corresponding F distributed models are matched.
Optionally, the stochastic model is also set with the distribution trend of random sources to be generated.
Optionally, the distribution trend includes at least one of overall distribution, long-term trend, short-term trend.
Optionally, further include：
When distribution trend is overall distribution, according to resource growth rate in the unit interval of the real resources, with unit
The statistical distribution of resource growth rate amplitude fitting setting in time.
Optionally, further include：
Distribution trend be long-term trend when, according to the growth rate of the real resources be distributed, calculate factor I and
Factor Ⅱ；
The statistical distribution that the factor Ⅱ is obeyed to setting using the valuation that is averaged for a long time as mean value, sets the length of factor I
The fluctuation distributed constant of phase growth factor and factor Ⅱ；
Wherein, factor I indicates longer term resource increment；Factor Ⅱ indicates Current resource amount and longer term resource increment
Ratio.
Optionally, the statistical distribution of the setting includes logarithm normal distribution.
Optionally, further include：
Distribution trend be short-term trend when, according to the increase and decrease amplitude of the real resources and close on the time be weighted it is flat
, current short-term trend direction is determined.
Optionally, the average weighted weighted value is increased within the default duration and is failed with exponential form at any time.
Optionally, the default duration and the half-life period of the decline are determined by the fluctuation frequency of real resources.
Optionally, further include：
According to the sequence of the random sources, the achievement data of at least one random sources is calculated；
Calculate the difference value of the achievement data of the random sources and the achievement data of the real resources；
When the difference value is more than threshold value, the stochastic model is modified, until the index of the random sources
Data and the difference value of the achievement data of the real resources are less than threshold value.
Optionally, further include：
The sequence of the random sources is monitored with the presence or absence of abnormal.
Optionally, the sequence of the monitoring random sources is specifically included with the presence or absence of exception：
According to the sequence of the random sources, the achievement data of at least one random sources is calculated；
Calculate the difference value of the achievement data of the random sources and the achievement data of the real resources；
When the difference value is more than threshold value, it is abnormal to determine that the sequence of the random sources exists.
Optionally, the difference of the achievement data for calculating the random sources and the achievement data of the real resources
Value, specifically includes following at least one：
It calculates between the mean value of the achievement data of the random sources and the mean value of the achievement data of the real resources
Difference；
It calculates between the variance of the achievement data of the random sources and the variance of the achievement data of the real resources
Difference；
Calculate the quartile of the quartile of the achievement data of the random sources and the achievement data of the real resources
Difference between number.
Optionally, the sequence of the real resources includes the daily price series of true financial market market target；
The achievement data includes financial indicator data.
Optionally, the financial indicator data include：
At least one of the daily earning rates of financial market market, daily earning rate distribution, stability bandwidth.
In the embodiment of above-mentioned electronic equipment, it should be appreciated that the processor can be central processing unit (English：
Central Processing Unit, referred to as：CPU), it can also be other general processors, digital signal processor (English：
Digital Signal Processor, referred to as：DSP), application-specific integrated circuit (English：Application Specific
Integrated Circuit, referred to as：ASIC) etc..General processor can be microprocessor or the processor can also be
Any conventional processor etc., and memory above-mentioned can be read-only memory (English：Read-only memory, abbreviation：
ROM), random access memory (English：Random access memory, referred to as：RAM), flash memory, hard disk or solid
State hard disk.The step of method in conjunction with disclosed in the embodiment of the present invention, can be embodied directly in hardware processor and execute completion, or
Hardware and software module combination in person's processor execute completion.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.It is set especially for electronics
For standby embodiment, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to method reality
Apply the part explanation of example.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to this specification
Other embodiments.This specification is intended to cover any variations, uses, or adaptations of this specification, these modifications,
Purposes or adaptive change follow the general principle of this specification and include that this specification is undocumented in the art
Common knowledge or conventional techniques.The description and examples are only to be considered as illustrative, the true scope of this specification and
Spirit is indicated by the following claims.
It should be understood that this specification is not limited to the precision architecture for being described above and being shown in the accompanying drawings,
And various modifications and changes may be made without departing from the scope thereof.The range of this specification is only limited by the attached claims
System.
Claims (18)
1. a kind of Generation of simulating data method, the method includes：
Obtain the sequence of real resources；Wherein, the sequence of the real resources is the chronologically-based resource really acquired
Amount；
According to the sequence of the real resources, the achievement data of at least one real resources is calculated；
According to the statistical distribution type of the achievement data, corresponding statistical distribution pattern is matched；
Stochastic model is built according to the sequence of the real resources and based on the matched statistical distribution pattern of institute；
The sequence for the random sources for meeting the statistical distribution type is generated based on the stochastic model.
2. according to the method described in claim 1, the statistical distribution type according to the achievement data, matches corresponding system
Count distributed model, including any one of the following manners：
When the statistical distribution type of the achievement data is normal distribution, corresponding normal distribution model is matched；
When the statistical distribution type of the achievement data is standardized normal distribution, corresponding standardized normal distribution model is matched；
When the statistical distribution type of the achievement data is logarithm normal distribution, corresponding logarithm normal distribution model is matched；
When the statistical distribution type of the achievement data is that t is distributed, corresponding t distributed models are matched；
When the statistical distribution type of the achievement data is that x^2 is distributed, corresponding x^2 distributed models are matched；
When the statistical distribution type of the achievement data is that F is distributed, corresponding F distributed models are matched.
3. according to the method described in claim 1, the stochastic model is also set with the distribution trend of random sources to be generated.
4. according to the method described in claim 3, the distribution trend includes in overall distribution, long-term trend, short-term trend
It is at least one.
5. according to the method described in claim 4, the method further includes：
When distribution trend is overall distribution, according to resource growth rate in the unit interval of the real resources, with the unit interval
The statistical distribution of interior resource growth rate amplitude fitting setting.
6. according to the method described in claim 4, the method further includes：
When distribution trend is long-term trend, it is distributed according to the growth rate of the real resources, calculates factor I and second
The factor；
The statistical distribution that the factor Ⅱ is obeyed to setting using the valuation that is averaged for a long time as mean value, sets the long-term increasing of factor I
The fluctuation distributed constant of long coefficient and factor Ⅱ；
Wherein, factor I indicates longer term resource increment；Factor Ⅱ indicates the ratio of Current resource amount and longer term resource increment.
7. the statistical distribution of method according to claim 5 or 6, the setting includes logarithm normal distribution.
8. according to the method described in claim 4, the method further includes：
When distribution trend is short-term trend, it is weighted averagely according to the increase and decrease amplitude of the real resources with the time is closed on,
Determine current short-term trend direction.
9. according to the method described in claim 8, the average weighted weighted value increase at any time within the default duration with
Exponential form fails.
10. according to the method described in claim 9, the wave of the default duration and the half-life period of the decline by real resources
The dynamic frequency determines.
11. according to the method described in claim 1, the method further includes：
According to the sequence of the random sources, the achievement data of at least one random sources is calculated；
Calculate the difference value of the achievement data of the random sources and the achievement data of the real resources；
When the difference value is more than threshold value, the stochastic model is modified, until the achievement data of the random sources
It is less than threshold value with the difference value of the achievement data of the real resources.
12. according to the method described in claim 1, the method further includes：
The sequence of the random sources is monitored with the presence or absence of abnormal.
13. according to the method for claim 12, the sequence of the monitoring random sources is specific to wrap with the presence or absence of exception
It includes：
According to the sequence of the random sources, the achievement data of at least one random sources is calculated；
Calculate the difference value of the achievement data of the random sources and the achievement data of the real resources；
When the difference value is more than threshold value, it is abnormal to determine that the sequence of the random sources exists.
14. the method according to claim 11 or 13, the achievement data for calculating the random sources with it is described true
The difference value of the achievement data of resource specifically includes following at least one：
Calculate the difference between the mean value of the achievement data of the random sources and the mean value of the achievement data of the real resources；
Calculate the difference between the variance of the achievement data of the random sources and the variance of the achievement data of the real resources；
Calculate the quartile of the achievement data of the random sources and the achievement data of the real resources quartile it
Between difference.
15. according to the method described in claim 1, the sequence of the real resources includes the every of true financial market market target
Day price series；
The achievement data includes financial indicator data.
16. according to the method for claim 15, the financial indicator data include：
At least one of the daily earning rates of financial market market, daily earning rate distribution, stability bandwidth.
17. a kind of Generation of simulating data device, described device include：
Acquiring unit obtains the sequence of real resources；Wherein, the sequence of the real resources be really acquire it is suitable based on the time
The stock number of sequence；
Computing unit calculates the achievement data of at least one real resources according to the sequence of the real resources；
Matching unit matches corresponding statistical distribution pattern according to the statistical distribution type of the achievement data；
Construction unit builds stochastic model according to the sequence of the real resources and based on the matched statistical distribution pattern of institute；
Generation unit generates the sequence for the random sources for meeting the statistical distribution type based on the stochastic model.
18. a kind of electronic equipment, including：
Processor；
Memory for storing processor-executable instruction；
Wherein, the processor is configured as the method described in any one of the claims 1-16.
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