CN114240653A - Noble metal pairing transaction method, device, equipment and readable storage medium - Google Patents
Noble metal pairing transaction method, device, equipment and readable storage medium Download PDFInfo
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- CN114240653A CN114240653A CN202111483906.XA CN202111483906A CN114240653A CN 114240653 A CN114240653 A CN 114240653A CN 202111483906 A CN202111483906 A CN 202111483906A CN 114240653 A CN114240653 A CN 114240653A
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Abstract
The invention relates to the field of software automation, in particular to a precious metal pairing transaction method, a device, equipment and a readable storage medium, wherein the method comprises the steps of obtaining first information, wherein the first information comprises historical transaction market data of precious metals; inputting the first information into a first model to obtain initial price characteristic information of the precious metal; standardizing and clustering the initial price characteristic information of the precious metals to obtain contract clustering information; then, checking whether the contract clustering information has a model of harmony or not; the average liquidity of the combination clusters is scored, then the score is sent to a trader, and the trader can pair the precious metals.
Description
Technical Field
The invention relates to the field of software automation, in particular to a precious metal pairing transaction method, a precious metal pairing transaction device, precious metal pairing transaction equipment and a readable storage medium.
Background
The precious metal deadline arbitrage is a common multi-space strategy for quantitative investment in financial markets, belongs to a typical scene of pairing trading, and can utilize the stationarity of price difference change of a futures market and a spot market to perform multi-space operation to obtain profits. The method is very important for obtaining the pairing target to be traded in the pairing transaction, and determines the effectiveness of the pairing transaction. However, the contracts of precious metal futures and spot markets are numerous, the selection range of manual targets has limitations, historical data characteristic information cannot be fully considered, indexes such as the coordination and the like are not considered in place, effective and multidimensional quantitative indexes are lacked for screening standards, the manual pairing selection efficiency is low, and the manual selection has operation risks, so that a method for fully extracting the characteristics of the historical data, quantitatively screening the indexes, improving the selection efficiency and effectiveness of the paired targets and providing an automatic auxiliary decision scheme of the paired targets for traders is needed.
Disclosure of Invention
The invention aims to provide a precious metal pairing transaction method, a precious metal pairing transaction device, equipment and a readable storage medium, so as to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in one aspect, the present application provides a precious metal pairing transaction method, the method comprising: acquiring first information, wherein the first information comprises historical transaction market data of precious metals; inputting the first information into a first model to obtain second information, wherein the first model is a data preprocessing model, and the second information is the initial price characteristic information of the precious metal; inputting the second information into a second model to obtain third information, wherein the second model is a model for standardizing and clustering the second information, and the third information is contract cluster clustering information; inputting the third information into a third model to obtain fourth information, wherein the third model is a model for checking whether the third information has the consistency, and the fourth information is contract cluster clustering information with the consistency; inputting the fourth information into a fourth model to obtain fifth information, wherein the fourth model is a calculation model of the score value of the average fluidity of the contract cluster, and the fifth information is the score value of the average fluidity of the contract cluster; and sending the fourth information to communication equipment of a trader, and enabling the trader to pair the precious metals according to the fourth information.
Optionally, the inputting the first information into the first model to obtain the second information includes:
deleting abnormal quotation data of the empty price, the zero price and the negative price in the first information to obtain filtered historical trading quotation data;
processing the start price, the maximum price, the minimum price and the end price of the filtered historical trading market data, respectively calculating the start price, the maximum price, the minimum price and the end price of the trading in each five-minute time period and the start price, the maximum price, the minimum price and the end price of the trading in each ten-minute time period, and calculating the Tick level price of the trading;
and taking the Tick grade price of the transaction, the starting price, the highest price, the lowest price and the ending price of the transaction in each five-minute time period and the starting price, the highest price, the lowest price and the ending price of the transaction in each ten-minute time period as the initial price characteristic information of the precious metal.
Optionally, the inputting the second information into the second model to obtain third information includes:
carrying out cubic spline interpolation processing on the initial price characteristic information of the precious metal to obtain price characteristic information of a unified price sequence dimension;
carrying out normalization processing on the price characteristic information of the unified price sequence dimension, and taking the price characteristic information after the normalization processing as a standard characteristic value;
calculating the standard characteristic value by adopting an SVD algorithm to obtain a characteristic vector after dimension reduction;
and processing the feature vectors subjected to the dimension reduction by adopting a DBSCAN clustering algorithm to obtain a plurality of feature clusters, and acquiring corresponding contract cluster clustering information according to the feature clusters.
Optionally, the inputting the fourth information into a fourth model to obtain fifth information includes:
processing the contract cluster clustering information with the coordination to obtain price continuity information of a contract, trade average price difference information of the contract and daily average volume information of the contract in the contract cluster clustering information;
calculating liquidity index of the price continuity information of the contract, liquidity index of the trade average difference information of the contract and liquidity index of the day average volume information of the contract according to the price continuity information of the contract, the trade average difference information of the contract and the day average volume information of the contract;
and carrying out weighted average calculation on the liquidity index of the price continuity information of the contract, the liquidity index of the trade average difference information of the contract and the liquidity index of the day average transaction amount information of the contract to obtain the score value of the average liquidity of the contract cluster.
In a second aspect, embodiments of the present application provide a precious metal pairing transaction apparatus, the apparatus including:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring first information which comprises historical transaction market data of precious metals;
the first processing unit is used for inputting the first information into a first model to obtain second information, wherein the first model is a data preprocessing model, and the second information is the initial price characteristic information of the precious metal;
the second processing unit is used for inputting the second information into a second model to obtain third information, the second model is a model for standardizing and clustering the second information, and the third information is contract cluster clustering information;
a third processing unit, configured to input the third information into a third model to obtain fourth information, where the third model is a model for checking whether the third information has a coordination property, and the fourth information is contract cluster clustering information having a coordination property;
a fourth processing unit, configured to input the fourth information into a fourth model to obtain fifth information, where the fourth model is a calculation model of a score value of average fluidity of the contract cluster, and the fifth information is a score value of average fluidity of the contract cluster;
and the first sending unit is used for sending the fourth information to communication equipment of a trader, so that the trader pairs the precious metals according to the fourth information.
Optionally, the apparatus comprises:
the first processing subunit is used for deleting the abnormal market data of the empty price, the zero price and the negative price in the first information to obtain filtered historical trading market data;
the second processing subunit is used for processing the start price, the highest price, the lowest price and the end price of the filtered historical trading market data, respectively calculating the start price, the highest price, the lowest price and the end price of the trading in each five-minute time period and the start price, the highest price, the lowest price and the end price of the trading in each ten-minute time period, and calculating the Tick level price of the trading;
and the third processing subunit is used for taking the Tick grade price of the transaction, the starting price, the highest price, the lowest price and the ending price of the transaction in each five-minute time period and the starting price, the highest price, the lowest price and the ending price of the transaction in each ten-minute time period as the initial price characteristic information of the precious metal.
Optionally, the apparatus comprises:
the fourth processing subunit is used for carrying out cubic spline interpolation processing on the initial price characteristic information of the precious metal to obtain price characteristic information of a unified price sequence dimension;
the fifth processing subunit is configured to perform normalization processing on the price characteristic information of the unified price sequence dimension, and use the price characteristic information after the normalization processing as a standard characteristic value;
the sixth processing subunit is configured to calculate the standard feature value by using an SVD algorithm to obtain a feature vector after the dimension reduction;
and the seventh processing subunit is configured to process the feature vector after the dimension reduction by using a DBSCAN clustering algorithm to obtain a plurality of feature clusters, and obtain corresponding contract cluster clustering information according to the feature clusters.
Optionally, the apparatus comprises:
the eighth processing subunit is configured to process the contract cluster clustering information with the coordination property, so as to obtain price continuity information of a contract, trade average price difference information of the contract, and daily average volume information of the contract in the contract cluster clustering information;
a ninth processing subunit, configured to calculate a liquidity index of the price continuity information of the contract, a liquidity index of the trade average difference information of the contract, and a liquidity index of the day average volume information of the contract according to the price continuity information of the contract, the trade average difference information of the contract, and the day average volume information of the contract;
and the tenth processing subunit is configured to perform weighted average calculation on the liquidity index of the price continuity information of the contract, the liquidity index of the trade average difference information of the contract, and the liquidity index of the daily average volume of the contract to obtain a score value of the average liquidity of the contract cluster.
In a third aspect, embodiments of the present application provide a precious metal paired transaction device, which includes a memory and a processor. The memory is used for storing a computer program; the processor is used for realizing the steps of the precious metal pairing transaction method when executing the computer program.
In a fourth aspect, the present application provides a readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the precious metal pairing transaction method.
The invention has the beneficial effects that:
1. the invention selects the matched targets through a machine, has wide target selection range, fully extracts the characteristics of the historical data by using an automatic method, quantificationally screens indexes, improves the selection efficiency and effectiveness of the matched targets, and provides an automatic matched target auxiliary decision scheme for traders.
2. The invention selects and considers the full-amount noble metal contract to ensure the pairing to obtain optimality; and the historical data is fully extracted, so that key information such as price and the like is lost as little as possible, and the invention also provides reasonable automatic screening quantitative indexes from multiple dimensions.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a noble metal pairing transaction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a noble metal pairing transaction device according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a precious metal paired transaction device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
As shown in fig. 1, the present embodiment provides a precious metal pairing transaction method, which includes step S1, step S2, step S3, step S4 and step S5.
Step S1, acquiring first information, wherein the first information comprises historical trading market data of precious metals;
step S2, inputting the first information into a first model to obtain second information, wherein the first model is a data preprocessing model, and the second information is the initial price characteristic information of the precious metal;
step S3, inputting the second information into a second model to obtain third information, wherein the second model is a model for standardizing and clustering the second information, and the third information is contract cluster clustering information;
step S4, inputting the third information into a third model to obtain fourth information, wherein the third model is a model for checking whether the third information has the consistency, and the fourth information is contract cluster clustering information with the consistency;
step S5, inputting the fourth information into a fourth model to obtain fifth information, wherein the fourth model is a calculation model of the score value of the average fluidity of the contract cluster, and the fifth information is the score value of the average fluidity of the contract cluster;
and step S6, sending the fourth information to communication equipment of a trader, and enabling the trader to pair the precious metals according to the fourth information.
The method selects the matched targets through the machine, has wide target selection range, fully extracts the characteristics of the historical data by using an automatic method, quantifies and screens indexes, improves the selection efficiency and the effectiveness of the matched targets, provides an automatic auxiliary decision scheme of the matched targets for traders, processes abnormal market values by using a method that a data preprocessing and characteristic extraction module receives historical market data from an internal trading platform and an external trading platform, and extracts a multi-frequency price time sequence to serve as initial price characteristics for output. And then performing characteristic transformation on the output data.
The contract cluster information is obtained by clustering and standardizing the historical data information of the precious metals, then the contract clusters obtained by clustering are received by the input pairing selection module according to each different contract cluster, and the coordination relation check is carried out on all futures spot-goods pairings in the contracts to preliminarily obtain candidate pairings; and on the other hand, three market liquidity indexes are set, target historical data are obtained according to the requirement of the candidate pairs, liquidity scores are calculated and ranked, and the obtained liquidity scores serve as final decision support for the traders to carry out pairing selection. And finally, after the final pairing is determined through manual intervention of a trader, the final pairing is sent to a downstream module for pairing trading.
In a specific embodiment of the present disclosure, the step S2 includes steps S21, S22 and S23.
Step S21, deleting the abnormal market data of the empty price, the zero price and the negative price in the first information to obtain filtered historical trading market data;
step S22, processing the start price, the highest price, the lowest price and the end price of the filtered historical trading market data, respectively calculating the start price, the highest price, the lowest price and the end price of the trading in each five-minute time period and the start price, the highest price, the lowest price and the end price of the trading in each ten-minute time period, and calculating the Tick level price of the trading;
step S23, taking the Tick grade price of the transaction, the starting price, the highest price, the lowest price and the ending price of the transaction in each five-minute time period and the starting price, the highest price, the lowest price and the ending price of the transaction in each ten-minute time period as the initial price characteristic information of the precious metal.
The understandable data preprocessing and feature extraction module receives historical market data, performs abnormal value processing on the market data, extracts and calculates tick-level prices, the starting price, the highest price, the lowest price and the ending price of the transaction in each five-minute time period and the starting price, the highest price, the lowest price and the ending price of the transaction in each ten-minute time period respectively aiming at each contract, and obtains and outputs 9 price time series features;
in a specific embodiment of the present disclosure, the step S3 includes a step S31, a step S32, a step S33, and a step S34.
Step S31, carrying out cubic spline interpolation processing on the initial price characteristic information of the precious metal to obtain price characteristic information of a uniform price sequence dimension;
step S32, carrying out normalization processing on the price characteristic information of the unified price sequence dimension, and taking the price characteristic information after the normalization processing as a standard characteristic value;
step S33, calculating the standard characteristic value by adopting an SVD algorithm to obtain a characteristic vector after dimension reduction;
and step S34, processing the feature vectors after dimensionality reduction by adopting a DBSCAN clustering algorithm to obtain a plurality of feature clusters, and acquiring corresponding contract cluster clustering information according to the feature clusters.
It can be understood that in the above steps, cubic spline interpolation is used to perform interpolation operation on the price features of 3 frequencies respectively, and sampling date, time and frequency are set for resampling, so that 9 price time sequence dimensions are unified, and the price vectors after 9 price sequences are cascaded and normalized are used as standard feature values; secondly, reducing the dimension of a feature matrix formed by the standardized feature vectors by using a Singular Value Decomposition (SVD) method, selecting a singular vector corresponding to a singular value with the first K large as a dimension reduction result to obtain a final feature value of each target product, and reducing the dimension by using the SVD (SVD), wherein the high latitude and the large data volume of the feature of the original time sequence are considered, and the high efficiency of dimension reduction calculation can be ensured by using the SVD (SVD) for dimension reduction; and finally, clustering by using an unsupervised clustering (DBSCAN) method to obtain a plurality of contract clusters and outputting the contract clusters to the next module.
It can be understood that the interpolation and resampling operations can unify sampling time and frequency, ensure that the feature dimensions are consistent, match feature sampling points of different contracts, reduce the dimensions of data to retain typical features, remove redundant features, ensure the representativeness and specificity of contract features, and ensure the high-dimensional price feature dimension reduction calculation efficiency by adopting an SVD algorithm.
In a specific embodiment of the present disclosure, the step S5 includes steps S51, S52 and S53.
Step S51, processing the contract cluster information with the coordination to obtain the price continuity information of the contract, the trade average price difference information of the contract and the daily average volume information of the contract in the contract cluster information;
step S52, calculating liquidity index of the price continuity information of the contract, liquidity index of the trade average difference information of the contract and liquidity index of the day average volume information of the contract according to the price continuity information of the contract, the trade average difference information of the contract and the day average volume information of the contract;
and step S53, carrying out weighted average calculation on the liquidity index of the price continuity information of the contract, the liquidity index of the trade average difference information of the contract and the liquidity index of the daily average volume information of the contract to obtain the score value of the average liquidity of the contract cluster.
It can be understood that the price continuity information in this step is obtained by calculating the mean price difference of tick level for the purchase price and the sale price respectively, averaging the two prices, and making the difference with the minimum fluctuation interval of the market price. Wherein, the smaller the difference value is, the higher the price continuity is, and the higher the market liquidity is; taking the reciprocal of the difference as a price continuity index to ensure that the index is positively correlated with the fluidity, and calculating the average trading difference by calculating the difference between the optimal buying price and the optimal selling price at the tick level and calculating the average value; wherein the lower the trade mean spread, the higher the liquidity; and the average price difference reciprocal is taken to represent the index, so that the positive correlation between the index and the fluidity is ensured.
It can be understood that the higher the market liquidity of the contract in this step, the larger the selection range of the quantitative trading method is, the easier the risk management is, so that the trade needs to be performed by selecting the pairing target with higher liquidity, and the price continuity, the trade average spread and the daily average volume are three typical indexes for showing the market liquidity.
In a specific embodiment of the present disclosure, the step S4 includes steps S41 and S42.
Step S41, traversing all possible precious metals for pairing the contract cluster clustering information to obtain at least one pairing information;
and step S42, checking the coordination of each pairing information by adopting an EG two-step coordination check method to obtain a contract which has a coordination relation with the contract clustering information.
It can be understood that the two price time series of the pairing transaction in the present invention must have the consistency, so the consistency of each of the pairing information is checked by using the EG two-step consistency check method to perform the consistency check of the candidate pairing.
In a specific embodiment of the present disclosure, the step S6 includes a step S61, a step S62, a step S63, and a step S64.
Step S61, acquiring a first threshold value, wherein the first threshold value is a qualified value of average fluidity of a contract cluster;
step S62, comparing the first threshold value with the score value of the average fluidity of the contract clusters with the first threshold value, and marking the score value of the average fluidity of the contract clusters larger than the first threshold value;
s63, sorting the marked average value of the average fluidity of the contract clusters, sorting the average value of the average fluidity according to a sorting mode that the average value is sorted from high to low, and counting the average value in a contract cluster average fluidity table;
and step S64, sending a first command, wherein the first command is a command for a trader to select a paired precious metal contract according to the contract cluster average liquidity table.
It can be understood that in the step, three liquidity indexes are respectively calculated for left and right contracts based on historical data by acquiring price market data and historical deal data of corresponding contracts, an average value of contract index values is taken as a candidate pairing liquidity index, the three index values are respectively normalized and weighted and summed to obtain a liquidity final score of the candidate pairing, and then the candidate pairing is ranked and output based on the liquidity score to be used as a reference pairing target for assisting a trader in determining the pairing contract.
Example 2
As shown in fig. 2, the present embodiment provides a precious metal pairing transaction apparatus, which includes a first obtaining unit 701, a first processing unit 702, a second processing unit 703, a third processing unit 704, a fourth processing unit 705, and a first sending unit 706.
A first obtaining unit 701, configured to obtain first information, where the first information includes historical transaction market data of precious metals;
a first processing unit 702, configured to input the first information into a first model to obtain second information, where the first model is a data preprocessing model, and the second information is initial price characteristic information of the precious metal;
a second processing unit 703, configured to input the second information into a second model to obtain third information, where the second model is a model for normalizing and clustering the second information, and the third information is contract cluster clustering information;
a third processing unit 704, configured to input the third information into a third model to obtain fourth information, where the third model is a model for checking whether the third information has a coordination property, and the fourth information is contract cluster clustering information having a coordination property;
a fourth processing unit 705, configured to input the fourth information into a fourth model to obtain fifth information, where the fourth model is a calculation model of a score value of average fluidity of the contract cluster, and the fifth information is a score value of average fluidity of the contract cluster;
a first sending unit 706, configured to send the fourth information to a communication device of a trader, so that the trader pairs the precious metals according to the fourth information.
In a specific embodiment of the present disclosure, the first processing unit 702 includes a first processing subunit 7021, a second processing subunit 7022, and a third processing subunit 7023.
A first processing subunit 7021, configured to delete the abnormal quotation data with null price, zero price, and negative price in the first information,
obtaining filtered historical transaction market data;
a second processing subunit 7022, configured to process the start price, the maximum price, the minimum price, and the end price of the filtered historical trading market data, calculate the start price, the maximum price, the minimum price, and the end price of the trade in each fifth-minute time period, and the start price, the maximum price, the minimum price, and the end price of the trade in each tenth-minute time period, respectively, and calculate a Tick-level price of the trade;
a third processing subunit 7023, configured to use the Tick-level price of the transaction, the start price, the maximum price, the minimum price, and the end price of the transaction in each five-minute time period, and the start price, the maximum price, the minimum price, and the end price of the transaction in each ten-minute time period as the initial price characteristic information of the precious metal.
In a specific embodiment of the present disclosure, the second processing unit 703 includes a fourth processing subunit 7031, a fifth processing subunit 7032, a sixth processing subunit 7033, and a seventh processing subunit 7034.
A fourth processing subunit 7031, configured to perform cubic spline interpolation processing on the initial price characteristic information of the precious metal to obtain price characteristic information of a uniform price sequence dimension;
a fifth processing subunit 7032, configured to perform normalization processing on the price characteristic information of the uniform price sequence dimensions, and use the price characteristic information after the normalization processing as a standard characteristic value;
a sixth processing subunit 7033, configured to calculate the standard feature value by using an SVD algorithm, to obtain a feature vector after the dimension reduction;
a seventh processing subunit 7034, configured to process the feature vector after the dimension reduction by using a DBSCAN clustering algorithm to obtain a plurality of feature clusters, and obtain, according to the feature clusters, contract cluster clustering information corresponding to the feature clusters.
In a specific embodiment of the present disclosure, the fourth processing unit 705 includes an eighth processing subunit 7051, a ninth processing subunit 7052, and a tenth processing subunit 7053.
An eighth processing subunit 7051, configured to process the contract cluster clustering information with the coordination, to obtain price continuity information of a contract, trade average spread information of the contract, and daily average volume information of the contract in the contract cluster information;
a ninth processing sub-unit 7052, configured to calculate a liquidity index of the price continuity information of the contract, a liquidity index of the trade average difference information of the contract, and a liquidity index of the day average volume information of the contract according to the price continuity information of the contract, the trade average difference information of the contract, and the day average volume information of the contract;
a tenth processing subunit 7053, configured to perform weighted average calculation on the liquidity index of the price continuity information of the contract, the liquidity index of the trade average difference information of the contract, and the liquidity index of the daily average transaction amount information of the contract, to obtain a score of average liquidity of the contract cluster.
In a specific embodiment of the present disclosure, the third processing unit 704 includes an eleventh processing subunit 7041 and a twelfth processing subunit 7042.
An eleventh processing subunit 7041, configured to pair all possible precious metals traversed by the contract cluster clustering information to obtain at least one piece of pairing information;
a twelfth processing sub-unit 7042, configured to use an EG two-step coordination check method to check the coordination of each piece of pairing information, so as to obtain a contract having a coordination relationship with the contract clustering information.
In a specific embodiment of the present disclosure, the first sending unit 706 includes a first obtaining sub-unit 7061, a thirteenth processing sub-unit 7062, a fourteenth processing sub-unit 7063, and a first sending sub-unit 7064.
A first obtaining subunit 7061, configured to obtain a first threshold, where the first threshold is a qualified value of average fluidity of a contract cluster;
a thirteenth processing subunit 7062, configured to compare the first threshold value and the score value of the average fluidity of the contract clusters with the first threshold value, and mark the score value of the average fluidity of the contract clusters that is greater than the first threshold value;
a fourteenth processing subunit 7063, configured to sort the marked average value of the average fluidity of the contract clusters, sort the average value of the average fluidity according to a sorting manner that the average value is from high to low, and count the sorted average value in a contract cluster average fluidity table;
a first sending subunit 7064, configured to send a first command, where the first command is a command for a trader to select a paired precious metal contract according to the contract cluster average liquidity table.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3
Corresponding to the above method embodiment, the disclosed embodiment also provides a precious metal paired transaction device, and a precious metal paired transaction device described below and a precious metal paired transaction method described above can be correspondingly referred to each other.
Fig. 3 is a block diagram illustrating a precious metal paired transaction device 800 according to an example embodiment. As shown in fig. 3, the precious metal paired transaction device 800 may include: a processor 801, a memory 802. The precious metal paired transaction device 800 may also include one or more of a multimedia component 803, an input/output (I/O) interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the precious metal paired transaction device 800, so as to complete all or part of the steps of the precious metal paired transaction method. The memory 802 is used to store various types of data to support operation at the precious metal paired transaction device 800, such data may include, for example, instructions for any application or method operating on the precious metal paired transaction device 800, as well as application-related data, such as contact data, messaging, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the precious metal paired transaction device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 805 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the precious metal paired transaction Device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing one of the precious metal paired transaction methods described above.
In another exemplary embodiment, a computer readable storage medium comprising program instructions that when executed by a processor implement the steps of the precious metal pairing transaction method described above is also provided. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the precious metal paired transaction device 800 to perform the precious metal paired transaction method described above.
Example 4
Corresponding to the above method embodiment, the disclosed embodiment also provides a readable storage medium, and a readable storage medium described below and a precious metal pairing transaction method described above can be correspondingly referred to each other.
A readable storage medium, on which a computer program is stored, which, when executed by a processor, carries out the steps of the precious metal pairing transaction method of the above-described method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A precious metal pairing transaction method, comprising:
acquiring first information, wherein the first information comprises historical transaction market data of precious metals;
inputting the first information into a first model to obtain second information, wherein the first model is a data preprocessing model, and the second information is the initial price characteristic information of the precious metal;
inputting the second information into a second model to obtain third information, wherein the second model is a model for standardizing and clustering the second information, and the third information is contract cluster clustering information;
inputting the third information into a third model to obtain fourth information, wherein the third model is a model for checking whether the third information has the consistency, and the fourth information is contract cluster clustering information with the consistency;
inputting the fourth information into a fourth model to obtain fifth information, wherein the fourth model is a calculation model of the score value of the average fluidity of the contract cluster, and the fifth information is the score value of the average fluidity of the contract cluster;
and sending the fourth information to communication equipment of a trader, and enabling the trader to pair the precious metals according to the fourth information.
2. The precious metal pairing transaction method of claim 1, wherein the entering the first information into a first model results in second information comprising:
deleting abnormal quotation data of the empty price, the zero price and the negative price in the first information to obtain filtered historical trading quotation data;
processing the start price, the maximum price, the minimum price and the end price of the filtered historical trading market data, respectively calculating the start price, the maximum price, the minimum price and the end price of the trading in each five-minute time period and the start price, the maximum price, the minimum price and the end price of the trading in each ten-minute time period, and calculating the Tick level price of the trading;
and taking the Tick grade price of the transaction, the starting price, the highest price, the lowest price and the ending price of the transaction in each five-minute time period and the starting price, the highest price, the lowest price and the ending price of the transaction in each ten-minute time period as the initial price characteristic information of the precious metal.
3. The precious metal pairing transaction method of claim 1, wherein the entering of the second information into a second model results in third information comprising:
carrying out cubic spline interpolation processing on the initial price characteristic information of the precious metal to obtain price characteristic information of a unified price sequence dimension;
carrying out normalization processing on the price characteristic information of the unified price sequence dimension, and taking the price characteristic information after the normalization processing as a standard characteristic value;
calculating the standard characteristic value by adopting an SVD algorithm to obtain a characteristic vector after dimension reduction;
and processing the feature vectors subjected to the dimension reduction by adopting a DBSCAN clustering algorithm to obtain a plurality of feature clusters, and acquiring corresponding contract cluster clustering information according to the feature clusters.
4. The precious metal pairing transaction method of claim 1, wherein the entering of the fourth information into a fourth model results in fifth information comprising:
processing the contract cluster clustering information with the coordination to obtain price continuity information of a contract, trade average price difference information of the contract and daily average volume information of the contract in the contract cluster clustering information;
calculating liquidity index of the price continuity information of the contract, liquidity index of the trade average difference information of the contract and liquidity index of the day average volume information of the contract according to the price continuity information of the contract, the trade average difference information of the contract and the day average volume information of the contract;
and carrying out weighted average calculation on the liquidity index of the price continuity information of the contract, the liquidity index of the trade average difference information of the contract and the liquidity index of the day average transaction amount information of the contract to obtain the score value of the average liquidity of the contract cluster.
5. A precious metal paired transaction device, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring first information which comprises historical transaction market data of precious metals;
the first processing unit is used for inputting the first information into a first model to obtain second information, wherein the first model is a data preprocessing model, and the second information is the initial price characteristic information of the precious metal;
the second processing unit is used for inputting the second information into a second model to obtain third information, the second model is a model for standardizing and clustering the second information, and the third information is contract cluster clustering information;
a third processing unit, configured to input the third information into a third model to obtain fourth information, where the third model is a model for checking whether the third information has a coordination property, and the fourth information is contract cluster clustering information having a coordination property;
a fourth processing unit, configured to input the fourth information into a fourth model to obtain fifth information, where the fourth model is a calculation model of a score value of average fluidity of the contract cluster, and the fifth information is a score value of average fluidity of the contract cluster;
and the first sending unit is used for sending the fourth information to communication equipment of a trader, so that the trader pairs the precious metals according to the fourth information.
6. The precious metal paired transaction device of claim 5, wherein the device comprises:
the first processing subunit is used for deleting the abnormal market data of the empty price, the zero price and the negative price in the first information to obtain filtered historical trading market data;
the second processing subunit is used for processing the start price, the highest price, the lowest price and the end price of the filtered historical trading market data, respectively calculating the start price, the highest price, the lowest price and the end price of the trading in each five-minute time period and the start price, the highest price, the lowest price and the end price of the trading in each ten-minute time period, and calculating the Tick level price of the trading;
and the third processing subunit is used for taking the Tick grade price of the transaction, the starting price, the highest price, the lowest price and the ending price of the transaction in each five-minute time period and the starting price, the highest price, the lowest price and the ending price of the transaction in each ten-minute time period as the initial price characteristic information of the precious metal.
7. The precious metal paired transaction device of claim 5, wherein the device comprises:
the fourth processing subunit is used for carrying out cubic spline interpolation processing on the initial price characteristic information of the precious metal to obtain price characteristic information of a unified price sequence dimension;
the fifth processing subunit is configured to perform normalization processing on the price characteristic information of the unified price sequence dimension, and use the price characteristic information after the normalization processing as a standard characteristic value;
the sixth processing subunit is configured to calculate the standard feature value by using an SVD algorithm to obtain a feature vector after the dimension reduction;
and the seventh processing subunit is configured to process the feature vector after the dimension reduction by using a DBSCAN clustering algorithm to obtain a plurality of feature clusters, and obtain corresponding contract cluster clustering information according to the feature clusters.
8. The precious metal paired transaction device of claim 5, wherein the device comprises:
the eighth processing subunit is configured to process the contract cluster clustering information with the coordination property, so as to obtain price continuity information of a contract, trade average price difference information of the contract, and daily average volume information of the contract in the contract cluster clustering information;
a ninth processing subunit, configured to calculate a liquidity index of the price continuity information of the contract, a liquidity index of the trade average difference information of the contract, and a liquidity index of the day average volume information of the contract according to the price continuity information of the contract, the trade average difference information of the contract, and the day average volume information of the contract;
and the tenth processing subunit is configured to perform weighted average calculation on the liquidity index of the price continuity information of the contract, the liquidity index of the trade average difference information of the contract, and the liquidity index of the daily average volume of the contract to obtain a score value of the average liquidity of the contract cluster.
9. A precious metal paired transaction device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the precious metal pairing transaction method according to any one of claims 1 to 4 when executing the computer program.
10. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when executed by a processor, carries out the steps of the noble metal pairing transaction method according to any one of claims 1 to 4.
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