Data Processing Method, Apparatus, Computer Device, and Storage Medium

Abstract

The present disclosure provides a data processing method, apparatus, computer device, and storage medium. The trading object corresponds to a first data set and a second data set. The method includes: comparing the order of each hanging order price and the order quantity of each first data record in the first data set with the order of each price group and the trading quantity of each second data record in the second data set; if the sorting of the order quantity of the target first data record is consistent with the sorting of the trading quantity of the target second data record, and the values of the order quantity of the target first data record and the trading quantity of the target second data record match, then determining the trader in the target second data record as the target trader; combining the information of the target first data record with the information of the target second data record to obtain the behavior data of the target trader.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technologies, and in particular, to a data processing method, apparatus, computer device, and storage medium. Background Art

[0002] Existing trading platforms can enable multiple traders to trade various trading objects. There are two roles for traders: sellers and buyers. Sellers issue sell orders for a certain trading object through the trading platform. The sell order data includes trading object information, trading quantity, asking price, etc. After the sell order is submitted to the trading platform, it waits for a transaction. Buyers issue buy orders for a certain trading object through the trading platform. The buy order data includes trading object information, trading quantity, asking price, etc. After the buy order is submitted to the trading platform, it waits for a transaction.

[0003] The trading platform is connected to a data analysis platform. The trading platform will provide the trader queue data of each trading object to the data analysis platform irregularly. This data includes trading object information and trader queue information, etc. The trader queue information includes m groups, and each group includes at least one trader information. The trader queue information is generated by the trading platform based on n orders within a certain time range in a set price order (such as the highest price, etc.). Different groups can distinguish different prices, but the trading platform does not provide specific price information in this data.

[0004] In related technologies, the data analysis platform can only use the trader queue data for trader behavior analysis. Due to the incomplete information in the trader queue data, it is difficult to meet the information needs of users, and it also makes it difficult for the data analysis platform to effectively utilize the data, resulting in a waste of data processing resources. Summary of the Invention

[0005] To overcome the problems existing in related technologies, the present disclosure provides a data processing method, apparatus, computer device, and storage medium.

[0006] According to a first aspect of an embodiment of the present specification, a data processing method is provided. The trading object corresponds to a first data set and a second data set;

[0007] Each first data record in the first data set records the various asking prices of the trading object and the order quantity of each asking price; each first data is obtained based on the target asking price carried in the currently received order data of the trading object and the information recorded in the currently latest first data in the first data set when receiving the order data of the trading object;

[0008] Each second data record in the second dataset includes various price groups of the trader and the traders and trading volumes in each price group. Each second data is obtained after receiving the trader queue data of the trading object;

[0009] The method includes:

[0010] Compare the order and order quantity of each limit order price in each first data record in the first dataset with the order and trading volume of each price group in each second data record in the second dataset;

[0011] If the sorting of the order quantity of the target first data record is consistent with the sorting of the trading volume of the target second data record, and the values of the order quantity of the target first data record and the trading volume of the target second data record match, then determine the trader in the target second data record as the target trader;

[0012] Combine the information of the target first data record with the information of the target second data record to obtain the behavior data of the target trader.

[0013] According to the second aspect of the embodiments of the present specification, a data processing platform for a trading object is provided. The data processing platform is connected to a trading platform, and the trading object corresponds to a first dataset and a second dataset;

[0014] Each first data record in the first dataset includes various limit order prices of the trading object and the order quantity at each limit order price; each first data is obtained based on the target limit order price carried in the currently received limit order data and the information recorded in the currently latest first data in the first dataset when receiving the limit order data of the trading object sent by the trading platform;

[0015] Each second data record in the second dataset includes the traders and trading volumes in each price group. Each second data is obtained after receiving the trader queue data of the trading object sent by the trading platform;

[0016] The data processing platform is configured to:

[0017] Compare the order and order quantity of each limit order price in each first data record in the first dataset with the order and trading volume of each price group in each second data record in the second dataset;

[0018] If the sorting of the order quantity of the target first data record is consistent with the sorting of the trading volume of the target second data record, and the values of the order quantity of the target first data record and the trading volume of the target second data record match, then determine the trader in the target second data record as the target trader;

[0019] Combine the information of the target first data record with the information of the target second data record to obtain the behavior data of the target trader.

[0020] According to the third aspect of the embodiments of the present specification, there is provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the method embodiment described in the foregoing first aspect are implemented.

[0021] According to the fourth aspect of the embodiments of the present specification, there is provided a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method embodiment described in the foregoing first aspect are implemented.

[0022] The technical solutions provided by the embodiments of the present specification may include the following beneficial effects:

[0023] In the embodiments of the present specification, when analyzing the behavior data of traders, the order placement data and the trader queue data are creatively combined for analysis. The two pieces of data are reconstructed to obtain a first data set and a second data set, and the two are matched. When the target first data and the target second data can be matched, the information of the target first data record is combined with the information of the target second data record to obtain the behavior data of the target trader, meeting the user's data acquisition requirements. Moreover, through the above data processing solution, effective data can be obtained without wasting processing resources.

[0024] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present specification, and are used together with the specification to explain the principles of the present specification.

[0026] Figure 1 is a schematic diagram of a trading scenario shown according to an exemplary embodiment of the present specification.

[0027] Figure 2A is a flowchart of a data processing method shown according to an exemplary embodiment of the present specification.

[0028] Figure 2B is a schematic diagram of data processing shown according to an exemplary embodiment of the present specification.

[0029] Figure 2C is a schematic diagram of a first data set and a second data set shown according to an exemplary embodiment of the present specification.

[0030] Figure 2D It is a schematic diagram of another data processing method shown in this specification according to an exemplary embodiment.

[0031] Figure 3 It is a schematic diagram of a data processing platform shown in this specification according to an exemplary embodiment.

[0032] Figure 4 It is a block diagram of a computer device where a data processing device is located shown in this specification according to an exemplary embodiment. Detailed implementation manners

[0033] Here, the exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with this specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0034] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the", and "said" used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0035] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0036] Such as Figure 1As shown, it is a schematic diagram of a trading scenario illustrated in this specification according to an exemplary embodiment, which shows a trading platform and multiple traders. Each trader can conduct transactions on various trading objects in this trading platform. There are two roles for the traders: the buyer and the seller. Among them, the seller posts a sell order through the trading platform. The sell order includes the name, quantity, and price of the trading object. After the sell order is submitted to the trading platform, it waits for a transaction. The buyer posts a buy order through the trading platform. The buy order includes the name, quantity, and price of the trading object. After the buy order is submitted to the trading platform, it waits for a transaction.

[0037] In the related art, the data analysis platform can only use the trader queue data for trader behavior analysis. Due to the incomplete information in the trader queue data, it is difficult to meet the information needs of users, and it also makes it difficult for the data analysis platform to effectively utilize the data, resulting in a waste of data processing resources.

[0038] In this embodiment, on the basis of the trader queue data, the order data is creatively combined for data analysis. The trading platform provides the order data of each order to the data analysis platform in real time, but the trading platform does not provide trader information in the order data. The trading platform also provides the trader queue data of each trading object to the data analysis platform irregularly. This data includes trading object information and trader queue information, etc. The trader queue information includes m groups, and each group includes at least one trader information. The trader queue information is generated by the trading platform based on the n orders with the highest prices within a certain time range. Different groups can distinguish different prices, but the trading platform does not provide specific price information in this data.

[0039] For the data analysis platform, the information in the order data and the trader queue data is not comprehensive. Moreover, the order data is provided in real time according to the trader's order, but the trader queue data is provided irregularly. This brings great difficulties to the data analysis platform in processing the order data and the trader queue data, and it is difficult to extract effective information.

[0040] Specifically, the trading platform provides the following two types of trading data to the data analysis platform:

[0041] (1) The first type of source data

[0042] The first type of source data refers to the data of each order (add_order; in actual applications, it may also include other types of data, such as information on the deletion or modification of orders, etc. This embodiment takes adding an order as an example), that is, the order data of the trader for the trading object. Among them, the order data includes information such as the order type, trading object identifier, order price, order quantity, etc., but does not carry trader information. Therefore, the data analysis platform cannot obtain the trader information of the trader who initiated this order.

[0043] The first type of source data is updated in real-time streaming. That is, when the trading platform receives a limit order request from a trader, it will send a piece of limit order data to the data analysis platform.

[0044] (2) The second type of source data

[0045] The second type of source data refers to trader queue data, which is data used to represent the limit order information of each trader within a certain time range for the trading object. The trader queue data includes: limit order type, trading object identifier, and trader queue information. The trader queue information includes: multiple groups, with each group including at least one trader identifier. The limit order prices of the trader identifiers in each group are the same, and the limit order prices of the trader identifiers in different groups are different.

[0046] Moreover, due to reasons such as message size limitations, the number of traders in the trader queue is limited and does not exceed N.

[0047] The second type of source data is not updated in real-time streaming, but rather the changes over a period of time are sent uniformly.

[0048] For the data analysis platform, the information in the limit order data and the trader queue data is incomplete. The limit order data is provided in real-time according to the trader's limit order, but the trader queue data is provided irregularly. This brings great difficulties to the data analysis platform in processing the limit order data and the trader queue data, making it difficult to extract effective information and also causing a waste of storage space due to the storage of a large amount of invalid data in the data analysis platform.

[0049] Based on this, the embodiments of this specification provide a data processing solution. When analyzing trader behavior data, it creatively combines the limit order data and the trader queue data for analysis, reconstructs these two pieces of data to obtain a first data set and a second data set, matches the two, and when the target first data and the target second data can be matched, combines the information recorded in the target first data with the information recorded in the target second data to obtain the behavior data of the target trader, meeting the user's data acquisition requirements, and through the above data processing solution, effective data can be obtained without causing waste of storage space.

[0050] In this embodiment, the trading object corresponds to a first data set and a second data set; each first data record in the first data set records each order price of the trading object and the order quantity of each order price; each piece of first data is obtained based on the target order price carried in the currently received order data and the information recorded in the currently latest first data in the first data set when the order data of the trading object is received; each second data record in the second data set records each price group of the trader and the trader and trading quantity of each price group, and each piece of second data is obtained after receiving the trader queue data of the trading object.

[0051] As Figure 2A and Figure 2B shown, it is a flowchart of a data processing method shown in this specification according to an exemplary embodiment, including the following steps:

[0052] In step 202, compare the order of each order price and the order quantity recorded in each first data record in the first data set with the order of each price group and the trading quantity recorded in each second data record in the second data set.

[0053] In step 204, if the sorting of the order quantity of the target first data record is consistent with the sorting of the trading quantity of the target second data record, and the values of the order quantity of the target first data record and the trading quantity of the target second data record match, then determine the trader in the target second data record as the target trader.

[0054] In step 206, combine the information of the target first data record with the information of the target second data record to obtain the behavior data of the target trader.

[0055] Combined Figure 2B For illustration, the solution of this embodiment can be applied to a data analysis platform for processing the data sent by a trading platform to obtain trader behavior data and output it to the user. Exemplarily, the data analysis platform can run on a computer device or can be implemented by a device cluster including multiple computer devices.

[0056] For a data analysis platform, the data provided by the trading platform is a mixed data stream. That is, the trading platform provides the order data of each order in real time to the data analysis platform, but the trading platform does not provide trader information in the order data. The trading platform also provides the trader queue data of each trading object to the data analysis platform irregularly. This data includes trading object information, trader queue information, etc. The trader queue information includes m groups, and each group includes at least one trader information. The trader queue information is generated by the trading platform based on the n orders with the highest price within a certain time range. Different groups can distinguish different prices, but the trading platform does not provide specific price information in this data.

[0057] Exemplarily, the trading platform may include trading platforms for various trading objects, and the trading objects may also include various types, such as stocks or ETFs (Exchange Traded Funds), etc.

[0058] The order data received from the trading platform can carry various information, such as message type, trading object identifier, order serial number, order price, order quantity, order direction, or order type, etc.

[0059] The trader queue data received from the trading platform can carry various information, such as message type, trading object identifier, number of entries, order direction, entries, and trader queue information. Among them, the trader queue information includes m groups (also called price levels), and each group includes at least one trader information. The trader queue information is generated by the trading platform based on the n orders in the set price order within a certain time range. Different groups can distinguish different prices. The set price order here can include the highest price or the lowest price, etc. For example, for buyers, it can be the highest, and for sellers, it can be the lowest.

[0060] The order data sent by the trading platform is updated in real-time stream, while the trader queue data is updated irregularly. In addition, since there is no time synchronization mechanism between the order data and the trader queue data, it is impossible to determine which order data corresponds to which trader queue data, and their order relationship is not fixed. For the order data and the trader queue data at the same moment, in terms of the order of data, it is possible that the order data comes first, or the trader queue data comes first. Therefore, in this embodiment, a first data set and a second data set are designed to reconstruct the information required for the order data and the trader queue data, and store the information for subsequent matching.

[0061] In this embodiment, whenever a pending order data is received, the pending order data is reconstructed. This embodiment refers to it as the first data, and the combination of each piece of the first data is called the first data set. Similarly, whenever a trader queue data is received, the trader queue data is reconstructed. This embodiment refers to it as the second data, and the combination of each piece of the second data is called the second data set.

[0062] Among them, each trading object corresponds to a first data set and a second data set. The first data set and the second data set can be stored in various ways, such as storage spaces like memory or local disk. In some examples, the first data set is stored in memory using a queue structure; and / or, the second data set is stored in memory using a queue structure. Based on this, caching the first data set or the second data set in memory can achieve fast access to the first data set or the second data set and can achieve real-time data processing.

[0063] In this embodiment, the information recorded in the first data includes each pending order price of the trading object and the pending order quantity at each pending order price; the information recorded in the second data includes: traders and the number of traders under each price group.

[0064] As an example, the following is an example of a piece of the first data: 204800:15500(7) 204600:31800(9) 204400:34600(7) 204200:25200(6) 204000:7100(11)

[0070] Among them, the value before the colon represents the pending order price, the value after the colon represents the trading quantity, and the value in the parentheses represents the pending order quantity. For example, in "204800:15500(7)" in the first data, "204800" represents the pending order price, "15500" represents the traded quantity of the trading object, and "7" represents the pending order quantity. The above-shown piece of the first data includes 5 pending order prices and their corresponding trading quantities and pending order quantities. In this embodiment, the first data also additionally includes the traded quantity of the trading object, which can be flexibly configured according to needs in practical applications.

[0071] In some examples, the first data is obtained in the following manner:

[0072] If the target pending order price is recorded in the currently latest first data in the first data set, obtain the information recorded in the currently latest first data, and add one to the pending order quantity corresponding to the target pending order price to obtain the first data;

[0073] If the target order price is not recorded in the currently latest first data in the first dataset, obtain the information recorded in the currently latest first data, and add the target order price and record the order quantity corresponding to the target order price as one to obtain the first data.

[0074] As mentioned above, when a trader's order is generated, the trading platform sends an order data. Therefore, the "204800:15500(7)" in the above first data is obtained by counting 7 order data.

[0075] For example, for the data analysis platform:

[0076] ① At 9:31:00, an order data is received, including: trading object A, price 204.8, and the traded quantity of trading object A is 500;

[0077] ② At 9:31:03, an order data is received, including: trading object A, price 204.8, and the trading quantity of trading object A is 400;

[0078] ③ At 9:31:08, an order data is received, including: trading object B, price 235.0, and the trading quantity of trading object B is 300;

[0079] ④ At 9:31:11, an order data is received, including: trading object A, price 204.6, and the trading quantity of trading object A is 300;

[0080] ……

[0081] Trading object A corresponds to a first dataset order book. According to each source data for statistics, each order information in the above order book is obtained. For example, in "204800:15500(7)" in the first data, "15500" is obtained by counting 7 order data such as the above ① and ②.

[0082] That is, when adding a new first data, the information recorded in the new first data is obtained by adding the order quantity corresponding to the order price in the target order data to the information recorded in the previous first data.

[0083] For example, if another order data is received later, and the information carried by the order data includes "trading object A, price 204.6, trading quantity 200", then the added first data is obtained by adding the trading quantity and increasing the order quantity in the trading price corresponding to "204600" in the above first data. The new first data example is as follows: 204800:15500(7) 204600:32000(10) 204400:34600(7) 204200:25200(6) 204000:7100(11)

[0089] It can be seen that in the new first data, "204600:32000(10)" has changed compared with the previous first data, while other information remains unchanged.

[0090] In practical applications, the types of order data include sell-type order data and buy-type order data. Therefore, the first data in the first dataset can also include the order type to distinguish between sell type and buy type. In practical applications, as mentioned above, the order type may include addition or deletion, etc. Corresponding first data can be generated based on the order type of the order data. For example, for deletion-type order data, the corresponding negative number is added, and the same applies to other types of order data.

[0091] As an example, the following is an example of a second data:

[0092] 0:S1|S2|……S7(7)

[0093] 1:S9|S10|……S16(9)

[0094] 2:S17|S18|……S23(7)

[0095] 3:S24|S25|……S29(6)

[0096] 4:S30|S2|……S40(11)

[0097] 0 to 5 before the colon represent 6 groups; taking the group with the identifier "1" as an example, after the colon, "S1|S2|……" are the identifiers of each trader, and the identifiers of each trader are distinguished by the symbol "|". For example, "S1" represents the identifier of a certain trader, and the value in the parentheses represents the trading quantity of the trader under this price group. The "trading quantity of the trader under the price group" in the second data here represents the number of times the trader proposes a transaction under this price group, which is a different concept from the "trading quantity of the trading object" in the first data.

[0098] In this embodiment, if trader queue data of a trading object is received, second data is added to the second dataset corresponding to the trading object according to the trader queue data; the information recorded in the second data includes: trader information and trader quantity under each price group.

[0099] In practical applications, the information recorded in the first data can be organized in various ways as needed, and other information can also be carried as needed. This embodiment does not limit this. Similarly, the information recorded in the second data can be organized in various ways as needed, and other information can also be carried as needed. This embodiment does not limit this.

[0100] Therefore, based on each piece of first data in the first data set and each piece of second data in the second data set, the first data set and the second data set can be matched to determine whether there are matching target first data and target second data. The matching in this embodiment means that the sorting of the order quantity of the limit orders recorded in the target first data is consistent with the sorting of the trading quantity recorded in the target second data, and the numerical values of the order quantity of the limit orders recorded in the target first data and the trading quantity recorded in the target second data match.

[0101] In some examples, the limit order prices in the first data are sorted in descending order of the set price, and the price groups in the second data are sorted in descending order of the set price.

[0102] The sorting of the order quantity of the limit orders recorded in the target first data is consistent with the sorting of the trading quantity recorded in the target second data, and the numerical values of the order quantity of the limit orders recorded in the target first data and the trading quantity recorded in the target second data match, including:

[0103] The numerical values of the trading quantities of the first k - 1 price groups in the target second data are the same as the numerical values of the order quantities of the first k - 1 limit order prices in the target first data, and the numerical value of the trading quantity of the kth price group in the target second data is less than or equal to the numerical value of the order quantity of the (k - 1)th limit order price in the target first data; k is the number of price groups in the target second data.

[0104] For example, the following table shows two matching target first data and target second data:

[0105]

[0106] Among them, the left - hand target first data includes 5 trading prices, and each trading price corresponds to an order quantity; the right - hand target second data includes 5 groups "0 to 5", and the groups are sorted in descending order of the trading price, and each group corresponds to the number of traders. Among them, each symbol in "S1 to S40" represents a trader identifier. From the above table, it can be seen that the order quantities corresponding to the 5 trading prices match the number of traders corresponding to the 5 groups.

[0107] In some other examples, the trader queue information in the trader queue data may be incomplete. For example, due to reasons such as message size limitations, the trading platform can carry at most 40 trader information in one piece of trader queue data. A piece of trader queue data is generated based on the 40 highest-priced pending orders; while the pending order data is generated for each pending order. Therefore, it is possible that the number of trading prices in the first data is more than the number of groups in the second data. For example:

[0108]

[0109] Among them, the target second data includes the number of traders in 4 groups, the first data includes the number of pending orders corresponding to 5 trading prices, and the information carried in the second data can match the information of the number of pending orders corresponding to the first 4 trading prices in the first data.

[0110] After obtaining the matching target first data and target second data, the traders in the target second data record can be determined as target traders; for example, S1 to S40 in the above example are target traders. Further, combining the information recorded in the target first data with the information recorded in the target second data, the behavior data of the target traders can be obtained. The target first data records each pending order price, while the target second data records the target traders in each price group. By combining the two, the pending order price in the target first data corresponds to the price group of the same order in the target second data. For example, 204800 in the target first data in the above example corresponds to the 0th gear in the target second data, and so on. Therefore, the trading price of each target trader can be obtained.

[0111] In some other examples, if the sorting of the number of pending orders recorded in the target first data is consistent with the sorting of the number of transactions recorded in the target second data, and the numerical values of the number of pending orders recorded in the target first data and the number of transactions recorded in the target second data match, then determining the traders in the target second data record as target traders includes:

[0112] If the sorting of the number of pending orders recorded in the target first data is consistent with the sorting of the number of transactions recorded in the target second data, and the numerical values of the number of pending orders recorded in the target first data and the number of transactions recorded in the target second data match, and the time difference between the acquisition time of the target first data and the acquisition time of the target second data meets the preset time difference condition, then the traders in the target second data record are determined as target traders.

[0113] Among them, the preset time difference condition includes:

[0114] In the case where the acquisition time of the target second data is earlier than the acquisition time of the target first data, the time difference is less than or equal to the duration of the acquisition period of the target second data; or,

[0115] In the case where the acquisition time of the target second data is later than the acquisition time of the target first data, the time difference is less than or equal to the duration of the acquisition period of the target first data.

[0116] The matching operation in this embodiment can also be judged in combination with the time difference. Although there may be a time difference between the order placement data corresponding to the matched target first data and the trader queue data corresponding to the target second data when both are sent from the trading platform to the data processing platform, in practical applications, if the time difference between the two is very large, there may be a problem with data reception, and the reliability of the data may be difficult to guarantee. For example, there are two cases where the acquisition time of the target second data is earlier than or later than the acquisition time of the target first data. Based on this, this embodiment sets a time difference condition based on the acquisition time of the target first data and the acquisition time of the target second data.

[0117] As an example, for the preset time difference condition, in the case where the acquisition time of the target second data is earlier than the acquisition time of the target first data, the time difference is less than or equal to the duration of the acquisition period of the target second data; wherein, the duration of the acquisition period of the target second data may include the interval time between the acquisition time of the target second data and the acquisition time of the previous second data, etc. This situation is considered because there is a certain time interval between the arrival of each second data. If the acquisition time of the target second data is earlier than the acquisition time of the target first data, theoretically, no new second data should arrive between obtaining the target second data and obtaining the target first data. If one or more new second data arrive after obtaining the target second data and then the target first data is obtained, there is a long time interval between the target second data and the target first data. Therefore, there may be a situation where data reception is incorrect, and the data reliability may be relatively low. Similarly, in the case where the acquisition time of the target second data is later than the acquisition time of the target first data, the time difference is less than or equal to the duration of the acquisition period of the target first data.

[0118] In practical applications, there can be various other implementation methods for the preset time difference condition, which are not limited in this embodiment. For example, the preset time difference condition further includes any of the following: the time difference is less than or equal to a preset duration threshold, etc. The preset time threshold can be a value set according to experience or determined based on the duration of the acquisition period of the second data, etc. For example, the acquisition time differences between historical first data items can be statistically analyzed, or the acquisition time differences between historical second data items can be statistically analyzed, and the preset time threshold can be determined based on the statistical information. The specific value can be determined according to actual needs.

[0119] In some examples, the step of comparing the order of the various limit order prices and the order quantities of each first data record in the first dataset with the order of the various price groups and the transaction quantities of each second data record in the second dataset is triggered each time the first data is acquired, and / or is triggered each time the second data is acquired. That is to say, the triggering of the above matching operation can be executed each time new first data is acquired and each time new second data is acquired, so as to be able to determine the trader behavior data in real time during the trading process. Of course, in other examples, any rule can also be set according to needs to trigger the execution of the matching operation, which is not limited in this embodiment.

[0120] Therefore, the target first data and the target second data that are matched carry the trader identifier and the trading price of the trader. Therefore, based on the target first data and the target second data in this embodiment, the trader in the target second data record can be determined as the target trader, and the information in the target first data record is combined with the information in the target second data record to obtain the behavior data of the target trader and output it to the user to determine the trader behavior data. Optionally, the behavior data of the target trader can also be output to the user.

[0121] In some examples, the information of the first data is reconstructed based on the limit order data, and the information of the second data is reconstructed based on the trader queue data. The information carried in the two datasets is limited. In order to provide the user with more information in the trader behavior data, in this embodiment, obtaining the behavior data of the target trader includes: obtaining each limit order data corresponding to the target first data, and obtaining the trader queue data corresponding to the target second data; based on the information carried in the obtained each limit order data and the information carried in the obtained target trader queue data, obtaining the behavior data of the target trader. Based on this, through the matched target first data and target second data, this embodiment further obtains other information carried in the corresponding each limit order data and trader queue data, and the determined trader behavior data can carry more information to be provided to the user, making the information of the provided data more comprehensive.

[0122] In some examples, the trader behavior data includes at least one of the following pieces of information: the trader information of each trader, the order price of each order placed by each trader, or the traded quantity of the trading object in each order placed by each trader. Through this information, comprehensive trader behavior information can be provided to the user. In practical applications, other information may also be included as needed, and this embodiment does not limit this.

[0123] In some examples, as time goes by, more and more data will be stored in the first data set and the second data set. Based on this, in the method of this embodiment, after the step of obtaining the behavior data of the target trader, it may further include: deleting the target first data and the first data before it in the first data set. Therefore, invalid data can be deleted in a timely manner to prevent the ineffective occupation of the storage space of the computer device.

[0124] In some examples, the method may further include: deleting the target second data and the second data before it in the second data set. Therefore, invalid data can be deleted in a timely manner to prevent the ineffective occupation of the storage space of the computer device.

[0125] As Figure 2C shown, it is a schematic diagram of the first data set and the second data set. The first data set on the left includes 4 pieces of first data, namely DataA1 to DataA4. The generation time of each piece of first data is as shown in the figure. DataA4 is the latest generated, and DataA1 is the earliest generated; the second data set includes 2 pieces of second data, namely DataB1 and DataB2. The generation time of DataB2 is later than that of DataB2. Assuming that DataA3 matches DataB2, therefore, DataA1 to DataA3 can be deleted. Similarly, DataB1 and DataB2 can be deleted.

[0126] As Figure 2D shown, it is a schematic diagram of data processing shown in this specification according to an exemplary embodiment. In a trading cycle (for example, each trading day can be a trading cycle), after the trading platform starts trading, the method of this embodiment can start to perform data processing on the trading objects of the trading platform. The time axis in the figure represents the chronological order from bottom to top. The order flow represents each piece of order data, and the first data set includes each piece of first data; the seat flow represents each piece of trader queue data, and the second data set includes each piece of second data.

[0127] The first order 1 is received, and the first data A1 is generated using this order 1. Similarly, the first data A2 is generated through order 2, and the first data A3 is generated through order 3. For the convenience of illustration, orders 4 to 39 are not shown. The first data A40 is generated through order 40, and the first data A41 is generated through order 41. Starting from the first data A2, each piece of the first data is accumulated based on the information recorded in the previous piece of the first data. The order prices in the first data are sorted in a set order, such as sorted from high to low by price, etc.

[0128] For example, the order price carried by order 1 is 204.2, and the trading quantity is 200. Therefore, the corresponding first data A1 generated is "204200:200(1)"; the number "1" in the parentheses indicates that the order quantity at this order price is 1.

[0129] The order price carried by order 2 is 204.0, and the trading quantity is 300. Therefore, based on A1, and accumulating according to the information carried by order 2, the generated A2 is:

[0130] "204200:200(1)

[0131] 204000:300(1)".

[0132] The order price carried by order 3 is 204.2, and the trading quantity is 300. Therefore, based on A2, and accumulating according to the information carried by order 3, the generated A3 is as shown in the figure. Among them, there are already two orders for the order price 204200, and the number "2" in the parentheses indicates that the order quantity is 2.

[0133] Optionally, after generating a piece of the first data according to the received order data each time, a judgment operation can be triggered, that is, each piece of the first data in the first data set is respectively matched with each piece of the second data in the second data set to determine whether there are matching target first data and target second data in the first data set and the second data set. For the first data A1 to the first data A41, since the second data set is empty, the matching is not successful.

[0134] After order 41, the first trader queue data 1 is received. The trader queue data 1 carries the trader information under each group. According to the trader information in each group of the trader queue data 1, the first piece of the second data B1 is generated. The second data records the trader information and the number of traders under each price group. For example, B1 records 5 price groups, and each price group is sorted in a set order, such as sorted from high to low by price, etc. Among them, S1 to S40 respectively represent different trader information, and the number in the parentheses in each price group indicates the number of traders in that group.

[0135] After generating a second piece of data each time based on the received trader queue data, a judgment operation can be triggered. Through the judgment operation, it is determined that B1 matches A40. The matching of the two means that the number of traders in 5 groups in B1 matches the number of orders at the first 5 order prices in A40.

[0136] Based on the matched B1 and A40, trader behavior data can be determined and output to the user. For example, the traders in B1 are determined as target traders; the information recorded in B1 is combined with the information recorded in A40 to obtain the behavior data of the target traders. Optionally, the first data of A40 and each piece of first data before it in the first dataset can also be deleted, and B1 in the second dataset can be deleted to reduce the amount of cached data.

[0137] After that, trader queue data 2 is received, and second data B2 is generated; no matching first data is found when the judgment operation is triggered. Among them, the trading platform continuously updates the trader queue data according to the order data. The trader queue data carries the trader queue information from the start time of a trading cycle to the current time.

[0138] As shown in the figure, during the time period from B1 to B2, due to the continuous generation of order data, the information of the 40 transactions with the highest prices carried in B2 is different from that in B1. The information carried in B2 has been updated to 6 groups. In the group with the highest price, the number in the parentheses is 1, indicating that the trader S5 with the highest price has traded 1 time; the same applies to others. Compared with B1, B2 has updated two higher prices. The group with the highest price recorded in B1 is in the second price group in B2, and the trading volume has also increased. Among them, the quantity "5" in the fifth price group in B2 has become less than the quantity in the third price group in B1. As mentioned before, this is due to the restriction of the trader information carried in the trader queue data by the trading platform, which only provides the information of the first 40 transactions.

[0139] After that, order 51 is received, and first data A51 is generated. The judgment operation is triggered, and it is determined that A51 matches B2. Among them, the matching of A51 and B2 means that the trading volume in the first 5 price groups in B2 matches the number of orders at the first 5 order prices in A51, and the trading volume in the last price group in B2 is less than the number of orders at the 6th order price in A51.

[0140] By matching B2 with A51, the trader behavior data can be determined and output to the user. For example, the trader in B2 is determined as the target trader; the information recorded in B2 is combined with the information recorded in A51 to obtain the behavior data of the target trader. Optionally, the first data of A51 and each previous piece of the first data in the first dataset can also be deleted, and B2 in the second dataset can be deleted to reduce the amount of cached data.

[0141] In some examples, there are multiple trading objects on the trading platform. Since each trading object corresponds to a first dataset and a second dataset, and the user has a need to obtain the trading behavior data of multiple trading objects, when the data analysis platform applies the solution of this embodiment, in the face of the data of multiple trading objects, it is necessary to solve the problem of how to process it efficiently. Based on this, in some examples, at least one process runs on the data analysis platform of this embodiment, and the number of processes can be determined based on the number of trading objects. Each of the processes corresponds to at least one trading object, and each process is used to execute the method to output the trader behavior data of the corresponding trading objects; that is, one process processes the data of one or more trading objects per trading object, and each process executes the method of this embodiment in parallel, so as to achieve parallel processing of the data of multiple trading objects. Exemplarily, according to the set allocation rule, multiple trading objects can be allocated to multiple processes. For example, it can be allocated in various ways such as according to the identifier of the trading object. This embodiment does not limit this.

[0142] In some examples, the trading time of the trading platform is fixed. In this embodiment, multiple processes can be created regularly according to the trading time of the trading platform. Among them, the time to create the process can be before the start time of the transaction, and each process can end at the end time of the transaction. To facilitate the management of each process, the computing resources occupied by each process can be the same, so that the data analysis platform can create each process uniformly. The number of trading objects corresponding to each process can be the same or different, and can be configured according to needs in actual applications.

[0143] In some examples, considering that some trading objects may be traded frequently, so order placement data and trader queue data will be generated frequently. Therefore, the process of this trading object needs to process data frequently. Based on this, in this embodiment, the number of trading objects corresponding to each process is determined based on the trading activity information of each trading object. The number of trading objects corresponding to the process is negatively correlated with the trading activity of the trading object. The trading activity information is determined based on the historical order placement data volume of the trading object. That is, if a trading object is traded frequently, the number of trading objects corresponding to this process will be less, so that the computing resources of the process can handle the data processing of the trading object.

[0144] In practical applications, the historical trading activity information can be determined based on the historical order placement data volume of the trading object. For example, it can be determined based on the average historical order placement data volume per unit of the trading object within a set historical time range. The set historical time range can be various custom time ranges such as one day, one week, or one month, and this embodiment does not limit this.

[0145] In other examples, the trading activity information of each trading object can also be predicted through a machine learning model. The machine learning model can be trained using the historical order placement data of the trading object. Optionally, there can be one machine learning model corresponding to one trading object, or one machine learning model corresponding to multiple trading objects, etc. The output target of the machine learning model can be to predict the activity information of the trading object at a future set time. The activity information can include information representing the order placement data volume. The future set time can be custom times such as one day, two days, or one week, and this embodiment does not limit this.

[0146] Corresponding to the embodiments of the foregoing data processing method, this specification also provides embodiments of a data processing platform, a device, and a computer device to which they are applied.

[0147] As Figure 3 shown, according to an exemplary embodiment of this specification, a data processing platform 31 for a trading object is further provided. The data processing platform is connected to a trading platform 32, and the trading object corresponds to a first data set and a second data set;

[0148] Each first data record in the first data set includes the respective order placement prices of the trading object and the order placement quantity at each order placement price; each piece of first data is obtained based on the target order placement price carried by the currently received order placement data of the trading object and the information recorded in the currently latest first data in the first data set when receiving the order placement data of the trading object sent by the trading platform;

[0149] Each second data record in the second data set includes the traders and trading quantities of each price group. Each piece of second data is obtained after receiving the trader queue data of the trading object sent by the trading platform;

[0150] The data processing platform 31 is used for:

[0151] Comparing the order of the respective order placement prices and the order placement quantities recorded in each piece of first data in the first data set with the order of the respective price groups and the trading quantities recorded in each piece of second data in the second data set;

[0152] If the sorting of the order quantity of the target first data record is consistent with the sorting of the transaction quantity of the target second data record, and the numerical values of the order quantity of the target first data record and the transaction quantity of the target second data record match, then determine the trader in the target second data record as the target trader;

[0153] Combine the information of the target first data record with the information of the target second data record to obtain the behavioral data of the target trader.

[0154] In some examples, the first data is obtained in the following manner:

[0155] If the target order price is recorded in the currently latest first data in the first data set, obtain the information of the currently latest first data record, and add one to the order quantity corresponding to the target order price to obtain the first data;

[0156] If the target order price is not recorded in the currently latest first data in the first data set, obtain the information of the currently latest first data record, and add the target order price and record the order quantity corresponding to the target order price as one to obtain the first data.

[0157] In some examples, after the step of obtaining the behavioral data of the target trader, the method further includes:

[0158] Delete the target first data and the first data before it in the first data set; and / or,

[0159] Delete the target second data and the second data before it in the second data set.

[0160] In some examples, the step of comparing the order of each order price and the order quantity of each first data record in the first data set with the order of each price group and the transaction quantity of each second data record in the second data set is triggered each time the first data is obtained, and / or is triggered each time the second data is obtained.

[0161] In some examples, each order price in the first data is sorted in the order of the set price from high to low, and each price group in the second data is sorted in the order of the set price from high to low;

[0162] The transaction quantity of the first k - 1 price groups in the target second data is the same as the order quantity of the first k - 1 order prices in the target first data, and the number of traders in the kth price group in the target second data is less than or equal to the order quantity of the (k - 1)th order price in the target first data; k is the number of price groups in the target second data.

[0163] In some examples, if the sorting of the order quantity of the target first data record is consistent with the sorting of the transaction quantity of the target second data record, and the numerical values of the order quantity of the target first data record and the transaction quantity of the target second data record match, then determining the trader in the target second data record as the target trader includes:

[0164] If the sorting of the order quantity of the target first data record is consistent with the sorting of the transaction quantity of the target second data record, and the numerical values of the order quantity of the target first data record and the transaction quantity of the target second data record match, and the time difference between the acquisition time of the target first data and the acquisition time of the target second data meets the preset time difference condition, then determining the trader in the target second data record as the target trader.

[0165] In some examples, the preset time difference condition includes any one of the following:

[0166] The time difference is less than or equal to a preset duration threshold; or,

[0167] The time difference is less than or equal to the duration of the acquisition period of the target second data

[0168] In some examples, there are multiple trading objects, the method is applied to a data analysis platform, the data analysis platform runs at least one process, each process corresponds to at least one trading object, and the process is used to execute the method to output the trader behavior data of each trading object corresponding to the process.

[0169] In some examples, the number of trading objects corresponding to each process is determined based on the trading activity information of each trading object, the number of trading objects corresponding to the process is negatively correlated with the trading activity of the trading object, and the trading activity information is determined based on the historical order data volume of the trading object.

[0170] In some examples, obtaining the behavior data of the target trader includes:

[0171] Obtaining each order data corresponding to the target first data, and obtaining the trader queue data corresponding to the target second data;

[0172] Based on the information carried in the obtained order data and the information carried in the obtained target trader queue data, obtaining the behavior data of the target trader.

[0173] In some examples, the trader behavior data includes at least one of the following information:

[0174] The trader information of each trader, the order price of each order placed by each trader, or the quantity of the trading object being traded in each order placed by each trader.

[0175] Corresponding to the embodiments of the foregoing data processing method, this specification also provides embodiments of a data processing apparatus and a computer device to which it is applied.

[0176] The embodiments of the data processing apparatus in this specification can be applied to a computer device, such as a server or a terminal device. The apparatus embodiments can be implemented by software, or by hardware, or by a combination of software and hardware. Taking software implementation as an example, as a logically meaningful apparatus, it is formed by the processor of the data processing where it is located reading the corresponding computer program instructions in the non-volatile memory into the memory for operation. From the hardware level, as Figure 4 shown, it is a hardware structure diagram of the computer device where the data processing apparatus in this specification is located. In addition to Figure 4 the processor 410, memory 430, network interface 420, and non-volatile memory 440 shown, the computer device where the data processing apparatus 431 is located in the embodiments usually includes other hardware according to the actual functions of the computer device, which will not be elaborated here. Among them, the data processing apparatus may include multiple modules for implementing the steps of the foregoing data processing method embodiments.

[0177] The implementation processes of the functions and roles of each module in the above data processing apparatus are specifically described in detail in the implementation processes of the corresponding steps in the above data processing method, and will not be elaborated here.

[0178] Correspondingly, this specification embodiment also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the foregoing data processing method embodiments.

[0179] Correspondingly, this specification embodiment also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the data processing method embodiments.

[0180] Correspondingly, this specification embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the data processing method embodiments.

[0181] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the descriptions of the method embodiments. The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this specification. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0182] The above embodiments can be applied to one or more electronic devices. The electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. The hardware of the electronic device includes, but is not limited to, a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0183] The electronic device can be any electronic product that can interact with users. For example, a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an Internet Protocol Television (IPTV), a smart wearable device, etc.

[0184] The electronic device may further include a network device and / or a user device. Among them, the network device includes, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing (Cloud Computing).

[0185] The network where the electronic device is located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0186] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0187] The step division of the above various methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as the same logical relationship is included, it is within the protection scope of this patent. Making insignificant modifications to the algorithm or process or introducing insignificant designs, but without changing the core design of the algorithm and process, are within the protection scope of this application.

[0188] Among them, the description of "specific examples" or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of this specification. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0189] Those skilled in the art will readily conceive of other implementations of this specification after considering the specification and practicing the invention herein. This specification is intended to cover any variations, uses, or adaptations of this specification, which follow the general principles of this specification and include the common general knowledge or conventional technical means in the technical field not claimed in this application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of this specification are pointed out by the following claims.

[0190] It should be understood that this specification is not limited to the exact structures described above and shown in the figures, and various modifications and changes can be made without departing from its scope. The scope of this specification is only limited by the appended claims.

[0191] The above are only the preferred embodiments of this specification and are not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification shall be included within the protection scope of this specification.

Claims

1. A data processing method for a trading object, where the trading object corresponds to a first data set and a second data set; Each first data record in the first dataset includes the various order prices of the trading object and the order quantity for each order price; Each piece of first data is obtained based on the target order price carried in the currently received order data and the information recorded in the currently latest first data in the first data set when the order data of the trading object is received; Each piece of second data in the second data set records each price group of traders and the traders and trading volumes of each price group, and each piece of second data is obtained after receiving the trader queue data of the trading object; The method includes: Comparing the order and order volume of each order price recorded in each piece of first data in the first data set with the order and trading volume of each price group recorded in each piece of second data in the second data set; If the sorting of the order volume recorded in the target first data is consistent with the sorting of the trading volume recorded in the target second data, and the values of the order volume recorded in the target first data and the trading volume recorded in the target second data match, then determine the trader in the target second data as the target trader; Combining the information in the target first data record with the information in the target second data record to obtain the behavior data of the target trader.

2. The method according to claim 1, wherein the first data is obtained in the following manner: If the target order price is recorded in the currently latest first data in the first data set, obtain the information recorded in the currently latest first data, and add one to the order volume corresponding to the target order price to obtain the first data; If the target order price is not recorded in the currently latest first data in the first data set, obtain the information recorded in the currently latest first data, and add the target order price and record the order volume corresponding to the target order price as one to obtain the first data.

3. The method according to claim 1, after the step of obtaining the behavior data of the target trader, the method further includes: Deleting the target first data and the first data before it in the first data set; And / or, Deleting the target second data and the second data before it in the second data set.

4. The method according to claim 1, the step of comparing the order and order volume of each order price recorded in each piece of first data in the first data set with the order and trading volume of each price group recorded in each piece of second data in the second data set is triggered after each acquisition of the first data, and / or is triggered after each acquisition of the second data.

5. The method according to claim 1, the order prices in the first data are sorted in the order of the set price from high to low, and the price groups in the second data are sorted in the order of the set price from high to low; The sorting of the order volume recorded in the target first data is consistent with the sorting of the trading volume recorded in the target second data, and the values of the order volume recorded in the target first data and the trading volume recorded in the target second data match, including: The numerical value of the trading volume of the first k - 1 price groups in the target second data is the same as the numerical value of the order volume of the first k - 1 order prices in the target first data, and the numerical value of the trading volume of the kth price group in the target second data is less than or equal to the numerical value of the order volume of the (k - 1)th order price in the target first data; k is the number of price groups in the target second data.

6. The method according to claim 1 or 5, wherein if the sorting of the order volume recorded in the target first data is consistent with the sorting of the trading volume recorded in the target second data, and the numerical values of the order volume recorded in the target first data and the trading volume recorded in the target second data match, then determining the trader in the target second data record as the target trader includes: If the sorting of the order volume recorded in the target first data is consistent with the sorting of the trading volume recorded in the target second data, and the numerical values of the order volume recorded in the target first data and the trading volume recorded in the target second data match, and the time difference between the acquisition time of the target first data and the acquisition time of the target second data satisfies the preset time difference condition, then determining the trader in the target second data record as the target trader; Wherein, the preset time difference condition includes: In the case where the acquisition time of the target second data is earlier than the acquisition time of the target first data, the time difference is less than or equal to the duration of the acquisition period of the target second data; or, In the case where the acquisition time of the target second data is later than the acquisition time of the target first data, the time difference is less than or equal to the duration of the acquisition period of the target first data.

7. The method according to claim 1, wherein there are multiple trading objects, and the method is applied to a data analysis platform, the data analysis platform runs at least one process, each process corresponds to at least one trading object, and the process is used to execute the method to output the trader behavior data of each trading object corresponding to the process.

8. The method according to claim 7, wherein the number of trading objects corresponding to each process is determined based on the trading activity information of each trading object, the number of trading objects corresponding to the process is negatively correlated with the trading activity of the trading object, and the trading activity information is determined based on the historical order data volume of the trading object.

9. The method according to claim 1, wherein obtaining the behavior data of the target trader includes: Obtaining each order data corresponding to the target first data, and obtaining the trader queue data corresponding to the target second data; Based on the information carried in the obtained each order data and the information carried in the obtained target trader queue data, obtaining the behavior data of the target trader.

10. The method according to claim 9, wherein the trader behavior data includes at least one of the following information: The trader information of each trader, the order price of each order of each trader, or the traded quantity of the trading object in each order of each trader.

11. A data processing platform for trading objects, the data processing platform being connected to a trading platform, the trading objects corresponding to a first data set and a second data set; Each first data record in the first dataset includes the various limit order prices of the trading object and the quantity of limit orders at each limit order price; Each piece of first data is obtained based on the target order price carried by the currently received order data of the trading object and the information recorded in the currently latest first data in the first data set when receiving the order data of the trading object sent by the trading platform; Each piece of second data in the second data set records the traders and trading volumes of each price group, and each piece of second data is obtained after receiving the trader queue data of the trading object sent by the trading platform; The data processing platform is configured to: Compare the order and order volume of each order price recorded in each piece of first data in the first data set with the order and trading volume of each price group recorded in each piece of second data in the second data set; If the sorting of the order volume of the target first data record is consistent with the sorting of the trading volume of the target second data record, and the values of the order volume of the target first data record and the trading volume of the target second data record match, then determine the trader in the target second data record as the target trader; Combine the information of the target first data record with the information of the target second data record to obtain the behavior data of the target trader.

12. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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