CN116757873A - Carbon market effectiveness evaluation method, device and medium based on entropy calculation - Google Patents
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Abstract
The application relates to a carbon market effectiveness evaluation method, a device, a computer device and a computer readable storage medium based on entropy calculation, wherein the carbon market effectiveness evaluation method based on entropy calculation comprises the following steps: acquiring historical trading prices and trading volumes of all target carbon markets in a target time period, and constructing corresponding price sequences and trading volume sequences according to a time sequence; calculating sample entropy, arrangement entropy and flow ratio of each target carbon market based on the price sequence and the volume sequence; based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets, a composite score for each of the target carbon markets is determined to evaluate the effectiveness of each of the target carbon markets. The method realizes the accurate and quantitative assessment of the effectiveness of each carbon target market.
Description
Technical Field
The present application relates to the technical field of carbon markets, and in particular, to a method, an apparatus, a computer device, and a computer readable storage medium for evaluating the effectiveness of a carbon market based on entropy calculation.
Background
At present, the method for evaluating the effectiveness of the carbon market mainly comprises the following steps: 1) Evaluating the effectiveness of the carbon market based on a constructed index system of market fluidity, trade conditions and the like; 2) Evaluating the effectiveness of the carbon market by using an effective market theory and adopting a run length test, a variance ratio test and other test methods; 3) Considering the carbon market as nonlinear dynamics, the carbon market effectiveness is evaluated based on fractal features.
The existing evaluation method based on the index system generally converts basic data such as carbon price, transaction amount and the like into indexes such as market concentration, liquidity, price difference stability and the like by adopting a metering economy tool, and essentially describes the characteristics of market transaction results, lacks explanatory indexes, and is used for connecting the market results with the performance of market functions.
The existing evaluation method based on the test is usually only aimed at price signals, and whether the market belongs to weak effectiveness, semi-strong effectiveness or strong effectiveness is judged through the test of the overall distribution, randomness and the like of the market price, and the result is incomparable and cannot be used for evaluating market conditions among different carbon markets and in different time periods.
The existing fractal-based evaluation method generally adopts complex methods such as multi-fractal trend fluctuation analysis and the like, time sequence data are required to be segmented, fitted and trended, and parameter selection greatly influences evaluation results, so that parting scale index distortion is caused, and effectiveness index errors are larger.
Aiming at the problem that the validity of each carbon market cannot be accurately and quantitatively evaluated in the related art, no effective solution is proposed at present.
Disclosure of Invention
Based on this, it is necessary to provide a carbon market effectiveness evaluation method, apparatus, computer device, and computer-readable storage medium based on entropy value calculation, in view of the above-described technical problems.
In a first aspect, an embodiment of the present application provides a method for evaluating effectiveness of a carbon market based on entropy calculation, the method including:
acquiring historical trading prices and trading volumes of all target carbon markets in a target time period, and constructing corresponding price sequences and trading volume sequences according to a time sequence;
based on the price sequences of all target carbon markets, establishing a sample entropy calculation model based on a price difference sequence, and calculating the sample entropy of each target carbon market;
based on the volume sequence of each target carbon market, establishing an arrangement entropy calculation model based on the volume sequence, and calculating the arrangement entropy of each target carbon market;
calculating a flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market;
based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets, a composite score for each of the target carbon markets is determined to evaluate the effectiveness of each of the target carbon markets.
In one embodiment, the establishing a sample entropy calculation model based on the price differential sequence based on the price sequence of each target carbon market, and calculating the sample entropy of each target carbon market includes:
performing differential processing on the price sequences of the target carbon markets to obtain price differential sequences;
calculating a first sequence similarity probability for the price differential sequence at a first preset dimension within a given similarity tolerance, an
Calculating a second sequence similarity probability of the price differential sequence added by one to the first preset dimension within the given similarity tolerance range;
and obtaining sample entropy of each target carbon market based on the first sequence similarity probability and the second sequence similarity probability of each target carbon market.
In one embodiment, the establishing a permutation entropy calculation model based on the volume sequence of each target carbon market based on the volume sequence, and calculating the permutation entropy of each target carbon market includes:
based on a second preset dimension and time delay, performing phase space reconstruction on the intersection sequences of each target carbon market to obtain a plurality of intersection sub-sequences;
rearranging the formation sub-sequences according to ascending order;
reconstructing each rearranged traffic sub-sequence into a sequence number sequence according to the sequence number of the ascending order, and calculating the occurrence probability of each sequence number sequence;
calculating the probability of zero component sequences in the plurality of intersection sub-sequences;
and obtaining the permutation entropy of each target carbon market based on the probability of the sequence number sequence of each target carbon market and the probability of the zero component sequence.
In one embodiment, the calculating the flow ratio for each target carbon market based on the price sequence and the volume sequence for each target carbon market comprises:
obtaining a volatility sequence based on the price sequence for each of the target carbon markets;
based on the traffic sequence, obtaining a normalized traffic differential sequence;
a flow ratio for each of the target carbon markets is obtained based on an average of the ratio of the volatility sequence to the contribution differentiation sequence for each of the target carbon markets.
In one embodiment, the determining the composite score for each of the target carbon markets based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets comprises:
constructing a standardized index matrix based on the sample entropy, the permutation entropy and the flow ratio of each of the target carbon markets;
and inputting the standardized index matrix into an entropy weight model to obtain the efficiency index of each target carbon market.
In one embodiment, the inputting the standardized index matrix into the entropy weight model, and obtaining the efficiency index of each target carbon market includes:
inputting the standardized index matrix into an entropy weight model, obtaining sample entropy weight, permutation entropy weight and flow ratio weight, and constructing an index weight matrix based on the sample entropy weight, the permutation entropy weight and the flow ratio weight;
and calculating the efficiency index of each target carbon market based on the index weight matrix and the standardized index matrix.
In one embodiment, the entropy weight model is as follows:
d j ＝1-e j
wherein A represents the number of target carbon markets, i represents the number of target carbon markets, j represents the number of indexes, the indexes are sample entropy, permutation entropy and flow ratio,values representing the ith row and jth column of the normalized index matrix, p ij Index value specific gravity, e, representing the ith target carbon market under the jth index j Represents the index entropy value, d j Represents redundancy of information entropy, W represents weight, < ->Represents a standardized index matrix, W T And R represents an efficiency index for an index weight matrix.
In a second aspect, an embodiment of the present application further provides a carbon market effectiveness evaluation device based on entropy calculation, where the device includes:
the acquisition module acquires the historical trading prices and the trading volume of each target carbon market in a target time period, and constructs a corresponding price sequence and a corresponding trading volume sequence according to a time sequence;
the first calculation module is used for establishing a sample entropy calculation model based on a price difference sequence based on the price sequence of each target carbon market, and calculating the sample entropy of each target carbon market;
the second calculation module is used for establishing an arrangement entropy calculation model based on the volume sequence of each target carbon market and calculating the arrangement entropy of each target carbon market;
a third calculation module for calculating a flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market;
a determination evaluation module determines a composite score for each of the target carbon markets based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets to evaluate the effectiveness of each of the target carbon markets.
In a third aspect, an embodiment of the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in the first aspect above.
The method, the device, the computer equipment and the computer readable storage medium for evaluating the effectiveness of the carbon market based on the entropy calculation are used for acquiring the historical trading prices and the trading volume of each target carbon market in a target time period and constructing a corresponding price sequence and a corresponding trading volume sequence according to a time sequence; based on the price sequences of all target carbon markets, establishing a sample entropy calculation model based on a price difference sequence, and calculating the sample entropy of each target carbon market; based on the volume sequence of each target carbon market, establishing an arrangement entropy calculation model based on the volume sequence, and calculating the arrangement entropy of each target carbon market; calculating a flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market; based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets, a composite score for each of the target carbon markets is determined to evaluate the effectiveness of each of the target carbon markets. The problem that the validity of each carbon market cannot be accurately and quantitatively evaluated is solved, and the validity of each carbon market is accurately and quantitatively evaluated.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic illustration of an application environment of a carbon market effectiveness assessment method based on entropy calculation in one embodiment;
FIG. 2 is a flow diagram of a carbon market effectiveness evaluation method based on entropy calculation in one embodiment;
FIG. 3 is a flow chart illustrating steps performed S202 in one embodiment;
FIG. 4 is a flow chart illustrating steps performed in step S203 in one embodiment;
FIG. 5 is a flow chart illustrating steps performed S204 in one embodiment;
FIG. 6 is a flow chart illustrating steps performed in step S205 in one embodiment;
FIG. 7 is a flow chart illustrating steps performed S602 in one embodiment;
FIG. 8 is a block diagram of a carbon market effectiveness evaluation device based on entropy calculation in one embodiment;
FIG. 9 is a schematic diagram of a computer device architecture in one embodiment.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The method embodiments provided in the present embodiment may be executed in a terminal, a computer, or similar computing device. For example, the method is run on a terminal, and fig. 1 is a block diagram of a hardware structure of the terminal of the carbon market effectiveness evaluation method based on entropy calculation of the present embodiment. As shown in fig. 1, the terminal may include one or more (only one is shown in fig. 1) processors 102 and a memory 104 for storing data, wherein the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, or the like. The terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the terminal. For example, the terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to the carbon market effectiveness evaluation method based on entropy calculation in the present embodiment, and the processor 102 performs various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above-described method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. The network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (NIC) that may be connected to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The embodiment of the application provides a carbon market effectiveness evaluation method based on entropy calculation, which is shown in fig. 2 and comprises the following steps:
step S201, obtaining historical trading prices and trading volumes of all target carbon markets in a target time period, and constructing corresponding price sequences and trading volume sequences according to a time sequence;
specifically, in an example embodiment, the historical price and corresponding volume of each target carbon market in a period of time may be obtained in a unit of time of day, and arranged into a corresponding price sequence and volume sequence according to the chronological order.
Step S202, based on the price sequences of all target carbon markets, a sample entropy calculation model based on price difference sequences is established, and the sample entropy of each target carbon market is calculated;
specifically, a sample entropy calculation model based on a price difference sequence is established based on the price sequence of each target carbon market, and the sample entropy of each target carbon market is calculated based on the sample entropy calculation model based on the price difference sequence.
The sample entropy is an improvement on an approximate entropy algorithm and is also a widely applied entropy characteristic value calculation method at present. Sample entropy is also a measure of the complexity of a time series, with smaller sample entropy, smaller time series complexity and higher self-similarity. Sample entropy has two advantages: calculating the sample entropy without depending on the data length; sample entropy has better consistency.
Step S203, based on the volume sequence of each target carbon market, establishing an arrangement entropy calculation model based on the volume sequence, and calculating the arrangement entropy of each target carbon market;
specifically, based on the volume sequence of each target carbon market, an arrangement entropy calculation model based on the volume sequence is established, and the arrangement entropy of each target carbon market is calculated based on the arrangement entropy calculation model of the volume sequence.
Permutation entropy is also an index for measuring the complexity of a time sequence, and is different from sample entropy in that it introduces the idea of permutation when calculating the complexity between reconstructed sub-sequences. The permutation entropy is used as an index for measuring the complexity degree of the time sequence, and the more regular the time sequence is, the smaller the permutation entropy corresponding to the permutation entropy is; the more complex the time series, the greater its corresponding permutation entropy.
Step S204, calculating the flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market;
step S205, determining a composite score of each target carbon market based on the sample entropy, the arrangement entropy, and the flow ratio of each target carbon market to evaluate effectiveness of each target carbon market.
The carbon market effectiveness evaluation method based on entropy calculation is low in data demand and strong in expansibility, can be operated only by acquiring carbon market trading price and trading volume data in a target period, and can be applied to multi-period evaluation analysis. Calculating sample entropy, arrangement entropy and flow ratio of each target carbon market according to the trading price and the trading volume data of each target carbon market, wherein the sample entropy represents the market estimation efficiency, namely market price reflection; the permutation entropy characterizes the information efficiency, namely the degree of timely reflecting the information value of the market price and the trading volume; the flow ratio characterizes the efficiency of operation, i.e., the mechanism and ability of the liquidity, quick and low cost trade provided by the market. Based on the sample entropy, the arrangement entropy and the flow ratio of each target carbon market, a comprehensive score of each target carbon market is calculated to evaluate the effectiveness of each target carbon market, and the higher the comprehensive score is, the better the effectiveness of the carbon market is, so that the effectiveness of each carbon market is accurately and quantitatively evaluated. Therefore, the embodiment also has the following beneficial effects:
1) The market efficiency index calculation based on the microscopic transaction data can be realized, and the method has the advantages of simplicity and easiness in implementation for the dependence of macroscopic factors on external conditions. Quantitative evaluation results can be obtained, the effectiveness of different carbon markets in different time phases can be effectively compared and analyzed, and evidence basis is provided for carbon market policy establishment and dynamic perfection.
2) The sample entropy, the permutation entropy and the flow ratio of each target carbon market represent market estimation efficiency, information efficiency and operation efficiency, and can comprehensively reflect basic functions of market value discovery, production and consumption decision guiding and resource allocation. The basic microscopic transaction data can be converted into a quantifiable, contrasting and interpretable index system through a market estimation efficiency index based on sample entropy, a market information efficiency index based on permutation entropy and a market operation efficiency index based on flow ratio. Therefore, the obtained comprehensive score of each target carbon market has strong interpretation, and the comprehensive score result can be comprehensively analyzed in aspects of transaction data, important event analysis, market mechanism and the like, so that prospective guidance can be provided for carbon market mechanism construction and policy formulation.
3) The method has strong comparability, can be used for independently comparing single indexes of a plurality of carbon markets, can be used for comprehensively comparing the effectiveness of the plurality of carbon markets, has pertinence and applicability, and can meet the evaluation target requirements of all stakeholders.
In one embodiment, as shown in fig. 3, the establishing a sample entropy calculation model based on a price difference sequence based on the price sequence of each target carbon market, and calculating the sample entropy of each target carbon market includes the following steps:
step S301, carrying out differential processing on the price sequences of all the target carbon markets to obtain price differential sequences;
specifically, taking a price sequence of one target carbon market as an example, for a price sequence of length NDifferential processing is performed to form a price differential sequence { x (n) }. Wherein:
step S302, calculating a first sequence similarity probability of the price differential sequence in a first preset dimension within a given similarity tolerance range, and calculating a second sequence similarity probability of the price differential sequence added by the first preset dimension within the given similarity tolerance range;
specifically, a phase space is constructed according to a first preset dimension m, namely, a group of vector sequences with dimension m are formed according to the sequence number, wherein each vector sequence is represented by a formula (2):
X m (i)＝{x(i),…,x(i+m-1)},1≤i≤N-m+1 (2)
the chebyshev distance between each vector sequence, namely the maximum difference value of the corresponding elements of the two groups of vectors, is calculated as follows:
D i [X m (i),X m (j)]＝max k＝0,…,m-1 |x(i+k)-x(j+k)| , 1<j<N-m,j≠i (3)
wherein D is i [X m (i),X m (j)]Representing a vector sequence X m (i) And vector sequence X m (j) Chebyshev distance between.
Given a similarity tolerance range r, counting the number of times that the Chebyshev distance between each vector sequence and the rest vectors is smaller than r, and recording as C i (r) calculating vector similarity probabilities:
in this embodiment, the given similarity tolerance range r takes 0.15 standard deviation, which represents the similarity probability that the sequences match m points under the similarity tolerance r.
Calculating the sum of the similarity probabilities of all vector sequences:
further, the first preset dimension m is increased to m+1, step S302 is repeated, and C is calculated m+1 (r)。
Step S303, obtaining a sample entropy of each target carbon market based on the first sequence similarity probability and the second sequence similarity probability of each target carbon market.
Specifically, according to each of the target carbon marketsAnd C m+1 (r) calculating sample entropy SE of each target carbon market price difference sequence, namely obtaining sample entropy of each target carbon market, wherein the calculation mode is as follows:
where a larger SE (m, r, N) represents a higher complexity of the change in price of the exchange, a lower self-similarity, a price change that is difficult to predict, and a more efficient market price signal.
In one embodiment, as shown in fig. 4, the establishing an permutation entropy calculation model based on the volume sequence of each target carbon market, and calculating the permutation entropy of each target carbon market includes:
step S401, carrying out phase space reconstruction on the volume sequence of each target carbon market based on a second preset dimension and time delay to obtain a plurality of volume sub-sequences;
specifically, the transaction sequence { Y (N) } = [ Y (1), Y (2), … Y (N) with length N]Constructing a phase space Y according to the second preset dimension m and the time sequence delay t m The specific value of the second preset dimension in this embodiment may also be represented by other values, which is not limited in this embodiment.
Y m (i)＝[y(i),y(i+t),…,y(i+(m-1)t)],1≤i≤N-m+1 (7)
Step S402, rearranging each intersection sub-sequence according to ascending order;
each reconstructed subsequence Y m (i) Rearranged in ascending order of magnitude.
Step S403, reconstructing each rearranged traffic sub-sequence into a sequence number sequence according to the sequence number of the ascending order, and calculating the occurrence probability of each sequence number sequence;
each rearranged productReconstructing the alternating sub-sequences into sequence numbers according to the sequence numbers of ascending order to obtain column indexes of the positions of each element in the vector, and forming a group of sequence numbers consisting of index sequence numbers
Traversing sequence of sequence numbersCounting the number of occurrences of the sequence S (j), divided by the total number of occurrences of all sequence numbers as the probability B (j) of occurrence of the sequence S (j):
wherein K is the number of components, namely the number of the formed traffic sub-sequences, K=N-m+1,representing the sequence of statistical sequence numbers->Equal to the number of sequences S (j).
For vectors in m dimensions, the possible arrangement is m-! The standard permutation number matrix is defined as m-! Matrix S of x m:
S＝[s(1),(2),…,s(！)]
step S404, calculating the probability of zero component sequence occurrence in the plurality of the traffic sub-sequences;
specifically, the zero-component sequence represents a subsequence in which the traffic is unchanged and no information is contained in m sampling points. Statistics of zero component sequence occurrence probability B null ：
Step S405, obtaining permutation entropy of each target carbon market based on the probability of occurrence of the sequence number sequence of each target carbon market and the probability of occurrence of the zero-component sequence.
Based on the probability B (j) of each sequence number sequence occurrence and the zero component sequence occurrence probability B null Calculating permutation entropy PE of the traffic sequence to obtain permutation entropy of each target carbon market:
wherein 1/ln (m|) represents normalization of permutation entropy values, 1-B null The larger PE (m, N) represents the more patterns of change of the transaction amount, the more uniform the transaction frequency and patterns, and the more effective the market-volume signal.
According to the market information efficiency index based on permutation entropy, the non-zero sequence detection and permutation entropy calculation mode is added to correct, comprehensive calculation of information content and information entropy of the transaction amount sequence can be achieved, and analysis influence of uniformity degree of market transaction amount distribution and diversity degree and uniformity degree of transaction amount change modes on the information efficiency index can be intuitively embodied. The response degree and speed of the main body to the market information can be described through the information efficiency index based on the permutation entropy, and the processing capacity and the exchange capacity of the market to the information are embodied.
In one embodiment, as shown in fig. 5, the calculating the flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market includes the steps of:
step S501, obtaining a fluctuation rate sequence based on the price sequence of each target carbon market;
specifically, based on price sequenceConstructing a fluctuation rate sequence { p (n) }, wherein the fluctuation rate is the absolute value of the price relative change of the front sampling point and the back sampling point, and the calculation formula is as follows:
step S502, obtaining a normalized traffic differential sequence based on the traffic sequence;
specifically, based on the traffic { y (n) } sequence, a traffic difference sequence { v (n) } is calculated and normalized:
step S503, obtaining a flow ratio of each target carbon market based on an average value of the ratio of the fluctuation ratio sequence to the component differential sequence of each target carbon market.
Specifically, based on the fluctuation rate sequence { p (n) } and the success rate difference sequence { v (n) }, the ratio of the average unit fluctuation rate change to the required success rate is calculated to obtain the flow rate L Ami :
In one embodiment, as shown in fig. 6, the determining the composite score for each of the target carbon markets based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets includes the steps of:
step S601, constructing a standardized index matrix based on the sample entropy, the arrangement entropy and the flow ratio of each target carbon market;
constructing an index matrix H by column standards from the sample entropy, the permutation entropy and the flow ratio obtained by each target carbon marketForming a standardized index matrix
Where a represents the target carbon market number, i represents the target carbon market index i=1, …, a, j represents the index number j=1, 2,3.
Step S602, inputting the standardized index matrix into an entropy weight model to obtain efficiency indexes of each target carbon market.
In one embodiment, as shown in fig. 7, the step of inputting the standardized index matrix into an entropy weight model to obtain the efficiency index of each target carbon market includes the following steps:
step S701, inputting the standardized index matrix into an entropy weight model, obtaining a sample entropy weight, an arrangement entropy weight and a flow ratio weight, and constructing an index weight matrix based on the sample entropy weight, the arrangement entropy weight and the flow ratio weight;
specifically, a matrix with an index weight matrix of 3×1 is constructed based on the sample entropy weight, the permutation entropy weight, and the flow ratio weight.
Step S702, calculating an efficiency index of each target carbon market based on the index weight matrix and the standardized index matrix.
In one embodiment, the entropy weight model is as follows:
d j ＝1-e j
wherein A represents the number of target carbon markets, i represents the number of target carbon markets, j represents the number of indexes, the indexes are sample entropy, permutation entropy and flow ratio,values representing the ith row and jth column of the normalized index matrix, p ij Index value specific gravity, e, representing the ith target carbon market under the jth index j Represents the index entropy value, d j Represents the redundancy of the information entropy, W represents the weight,represents a standardized index matrix, W T And R represents an efficiency index for an index weight matrix.
The embodiment of the application also provides a carbon market effectiveness evaluation device based on entropy calculation, as shown in fig. 8, the device comprises:
the obtaining module 810 obtains the historical trading prices and the trading volume of each target carbon market in the target time period, and constructs a corresponding price sequence and a corresponding trading volume sequence according to the time sequence;
a first calculation module 820, configured to establish a sample entropy calculation model based on a price difference sequence based on the price sequence of each target carbon market, and calculate a sample entropy of each target carbon market;
a second calculation module 830, configured to establish an permutation entropy calculation model based on the volume sequence of each target carbon market, and calculate permutation entropy of each target carbon market;
a third calculation module 840 for calculating a flow ratio for each target carbon market based on the price sequence and the volume sequence for each target carbon market;
a determination evaluation module 850 determines a composite score for each of the target carbon markets based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets to evaluate the effectiveness of each of the target carbon markets.
In one embodiment, the first computing module 820 is further to:
performing differential processing on the price sequences of the target carbon markets to obtain price differential sequences;
calculating a first sequence similarity probability for the price differential sequence at a first preset dimension within a given similarity tolerance, an
Calculating a second sequence similarity probability of the price differential sequence added by one to the first preset dimension within the given similarity tolerance range;
and obtaining sample entropy of each target carbon market based on the first sequence similarity probability and the second sequence similarity probability of each target carbon market.
In one embodiment, the second computing module 830 is further configured to:
based on a second preset dimension and time delay, performing phase space reconstruction on the intersection sequences of each target carbon market to obtain a plurality of intersection sub-sequences;
rearranging the formation sub-sequences according to ascending order;
reconstructing each rearranged traffic sub-sequence into a sequence number sequence according to the sequence number of the ascending order, and calculating the occurrence probability of each sequence number sequence;
calculating the probability of zero component sequences in the plurality of intersection sub-sequences;
and obtaining the permutation entropy of each target carbon market based on the probability of the sequence number sequence of each target carbon market and the probability of the zero component sequence.
In one embodiment, the third calculation module 840 is further configured to:
obtaining a volatility sequence based on the price sequence for each of the target carbon markets;
based on the traffic sequence, obtaining a normalized traffic differential sequence;
a flow ratio for each of the target carbon markets is obtained based on an average of the ratio of the volatility sequence to the contribution differentiation sequence for each of the target carbon markets.
In one embodiment, the determination evaluation module 850 is further configured to:
constructing a standardized index matrix based on the sample entropy, the permutation entropy and the flow ratio of each of the target carbon markets;
and inputting the standardized index matrix into an entropy weight model to obtain the efficiency index of each target carbon market.
In one embodiment, the determination evaluation module 850 is further configured to:
inputting the standardized index matrix into an entropy weight model, obtaining sample entropy weight, permutation entropy weight and flow ratio weight, and constructing an index weight matrix based on the sample entropy weight, the permutation entropy weight and the flow ratio weight;
and calculating the efficiency index of each target carbon market based on the index weight matrix and the standardized index matrix.
In one embodiment, the entropy weight model is as follows:
d j ＝1-e j
wherein A represents the number of target carbon markets, i represents the number of target carbon markets, j represents the number of indexes, the indexes are sample entropy, permutation entropy and flow ratio,values representing the ith row and jth column of the normalized index matrix, p ij Index value specific gravity, e, representing the ith target carbon market under the jth index j Represents the index entropy value, d j Represents the redundancy of the information entropy, W represents the weight,represents a standardized index matrix, W T And R represents an efficiency index for an index weight matrix.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a method for evaluating the effectiveness of a carbon market based on entropy calculations. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of any of the carbon market effectiveness evaluation method embodiments described above based on entropy value calculations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for evaluating the effectiveness of a carbon market based on entropy calculation, the method comprising:
acquiring historical trading prices and trading volumes of all target carbon markets in a target time period, and constructing corresponding price sequences and trading volume sequences according to a time sequence;
based on the price sequences of all target carbon markets, establishing a sample entropy calculation model based on a price difference sequence, and calculating the sample entropy of each target carbon market;
based on the volume sequence of each target carbon market, establishing an arrangement entropy calculation model based on the volume sequence, and calculating the arrangement entropy of each target carbon market;
calculating a flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market;
based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets, a composite score for each of the target carbon markets is determined to evaluate the effectiveness of each of the target carbon markets.
2. The method of claim 1, wherein the establishing a sample entropy calculation model based on a price differential sequence based on the price sequence for each target carbon market, calculating the sample entropy for each target carbon market comprises:
performing differential processing on the price sequences of the target carbon markets to obtain price differential sequences;
calculating a first sequence similarity probability for the price differential sequence at a first preset dimension within a given similarity tolerance, an
Calculating a second sequence similarity probability of the price differential sequence added by one to the first preset dimension within the given similarity tolerance range;
and obtaining sample entropy of each target carbon market based on the first sequence similarity probability and the second sequence similarity probability of each target carbon market.
3. The method of claim 1, wherein the establishing a permutation entropy calculation model based on the volume sequence for each target carbon market, calculating permutation entropy for each target carbon market comprises:
based on a second preset dimension and time delay, performing phase space reconstruction on the intersection sequences of each target carbon market to obtain a plurality of intersection sub-sequences;
rearranging the formation sub-sequences according to ascending order;
reconstructing each rearranged traffic sub-sequence into a sequence number sequence according to the sequence number of the ascending order, and calculating the occurrence probability of each sequence number sequence;
calculating the probability of zero component sequences in the plurality of intersection sub-sequences;
and obtaining the permutation entropy of each target carbon market based on the probability of the sequence number sequence of each target carbon market and the probability of the zero component sequence.
4. The method of claim 1, wherein the calculating a flow ratio for each target carbon market based on the price sequence and the volume sequence for each target carbon market comprises:
obtaining a volatility sequence based on the price sequence for each of the target carbon markets;
based on the traffic sequence, obtaining a normalized traffic differential sequence;
a flow ratio for each of the target carbon markets is obtained based on an average of the ratio of the volatility sequence to the contribution differentiation sequence for each of the target carbon markets.
5. The method of claim 1, wherein the determining a composite score for each of the target carbon markets based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets comprises:
constructing a standardized index matrix based on the sample entropy, the permutation entropy and the flow ratio of each of the target carbon markets;
and inputting the standardized index matrix into an entropy weight model to obtain the efficiency index of each target carbon market.
6. The method of claim 5, wherein inputting the normalized index matrix into an entropy weight model to obtain efficiency indexes for each target carbon market comprises:
inputting the standardized index matrix into an entropy weight model, obtaining sample entropy weight, permutation entropy weight and flow ratio weight, and constructing an index weight matrix based on the sample entropy weight, the permutation entropy weight and the flow ratio weight;
and calculating the efficiency index of each target carbon market based on the index weight matrix and the standardized index matrix.
7. The method of claim 6, wherein the entropy weight model is as follows:
d j ＝1-e j
wherein A represents the number of target carbon markets, i represents the number of target carbon markets, j represents the number of indexes, the indexes are sample entropy, permutation entropy and flow ratio,values representing the ith row and jth column of the normalized index matrix, p ij Index value specific gravity, e, representing the ith target carbon market under the jth index j Represents the index entropy value, d j Represents redundancy of information entropy, W represents weight, < ->Represents a standardized index matrix, W T And R represents an efficiency index for an index weight matrix.
8. A carbon market effectiveness evaluation device based on entropy calculation, the device comprising:
the acquisition module acquires the historical trading prices and the trading volume of each target carbon market in a target time period, and constructs a corresponding price sequence and a corresponding trading volume sequence according to a time sequence;
the first calculation module is used for establishing a sample entropy calculation model based on a price difference sequence based on the price sequence of each target carbon market, and calculating the sample entropy of each target carbon market;
the second calculation module is used for establishing an arrangement entropy calculation model based on the volume sequence of each target carbon market and calculating the arrangement entropy of each target carbon market;
a third calculation module for calculating a flow ratio of each target carbon market based on the price sequence and the volume sequence of each target carbon market;
a determination evaluation module determines a composite score for each of the target carbon markets based on the sample entropy, the permutation entropy, and the flow ratio of each of the target carbon markets to evaluate the effectiveness of each of the target carbon markets.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method of any one of claims 1 to 7.
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