Transaction data processing method and device based on market point prediction, equipment and medium

Abstract

The application relates to the technical field of big data, and provides a transaction data processing method and device based on market point prediction, equipment and a medium, which can construct an algorithm thread independent of a transaction thread to avoid the influence of algorithm processing on normal transactions; in the algorithm thread, a benchmark market is processed by using a market point prediction strategy to obtain multiple candidate market points, and a factor value of each candidate market point is calculated, the factor value can be predicted and calculated in advance for subsequent rapid calling; when a new market is received, a target market point and a target factor value are selected in the algorithm thread based on a market point matching strategy, and transaction decision is made in the transaction thread according to the target factor value, so that transaction decision can be quickly made according to the factor value calculated in advance, and transaction delay is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and in particular to a method, apparatus, equipment and medium for processing transaction data based on market price prediction. Background Technology

[0002] Existing technologies, such as options trading systems, typically suffer from high latency.

[0003] For example, in options trading, when new market information is received, the Greek letter of the option needs to be calculated, and then an order should be placed based on the Greek letter. If the Greek letter algorithm is not well implemented, it may cause a delay at the microsecond level. In the process of calculating factors, the data cache of the CPU (Central Processing Unit) will be filled with the data of the factors. Different factors have different data, which will cause the cache pool to switch frequently, increasing the fluctuation of system latency.

[0004] In view of the above problems, it is necessary to provide a transaction data processing method that reduces latency. Summary of the Invention

[0005] In view of the above, it is necessary to provide a method, apparatus, equipment and medium for processing trading data based on market price prediction, in order to solve the problem of high latency in the process of trading data processing.

[0006] A trading data processing method based on market price prediction, the method comprising:

[0007] Construct an algorithm thread independent of the transaction thread;

[0008] After each market data processing is completed, the currently processed market data is determined as the baseline market data.

[0009] The algorithm thread is started to preload the strategy file, and the market point prediction strategy and market point matching strategy are determined according to the strategy file.

[0010] In the algorithm thread, the benchmark market data is processed using the market data prediction strategy to obtain multiple candidate market data points, and the factor value of each candidate market data point is calculated.

[0011] When new market data is received, in the algorithm thread, a target market data point is selected from the plurality of candidate market data points based on the market data point matching strategy, and the factor value corresponding to the target market data point is determined as the target factor value.

[0012] In the trading thread, trading decisions are made based on the target factor value.

[0013] A trading data processing device based on market price prediction, the trading data processing device based on market price prediction includes:

[0014] Construction units are used to build algorithm threads that are independent of transaction threads;

[0015] The determination unit is used to determine the currently processed market data as the baseline market data after each market data processing is completed;

[0016] The determining unit is further configured to start the algorithm thread to preload the strategy file, and determine the market point prediction strategy and the market point matching strategy according to the strategy file;

[0017] The processing unit is used in the algorithm thread to process the benchmark market data using the market data point prediction strategy to obtain multiple candidate market data points, and to calculate the factor value of each candidate market data point.

[0018] The selection unit is used, when receiving new market data, to select a target market data point from the plurality of candidate market data points in the algorithm thread based on the market data point matching strategy, and to determine the factor value corresponding to the target market data point as the target factor value.

[0019] A decision-making unit is used to make trading decisions based on the target factor value in the trading thread.

[0020] A computer device, the computer device comprising:

[0021] A memory for storing at least one instruction; and a processor for executing the instructions stored in the memory to implement the transaction data processing method based on market price prediction.

[0022] A computer-readable storage medium storing at least one instruction, which is executed by a processor in a computer device to implement the transaction data processing method based on market price prediction.

[0023] As can be seen from the above technical solutions, the present invention can construct an algorithm thread independent of the trading thread to avoid the algorithm processing from affecting normal trading. In the algorithm thread, the benchmark market is processed using a market point prediction strategy to obtain multiple candidate market points, and the factor value of each candidate market point is calculated. The factor value can be predicted and calculated in advance for subsequent quick invocation. When new market data is received, the target market point and target factor value are selected in the algorithm thread based on the market point matching strategy, and the trading thread makes a trading decision based on the target factor value. This enables quick trading decisions based on the pre-calculated factor value, effectively reducing trading latency. Attached Figure Description

[0024] Figure 1This is a flowchart of a preferred embodiment of the transaction data processing method based on market point prediction of the present invention.

[0025] Figure 2 This is a functional block diagram of a preferred embodiment of the transaction data processing device based on market point prediction of the present invention.

[0026] Figure 3 This is a schematic diagram of the structure of a computer device that implements a preferred embodiment of the transaction data processing method based on market price prediction according to the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the transaction data processing method based on market price prediction according to the present invention. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0029] The transaction data processing method based on market point prediction is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0030] The computer device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), game console, interactive network television (IPTV), smart wearable device, etc.

[0031] The computer equipment may also include network equipment and / or user equipment. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0032] The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0033] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0034] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0035] The network in which the computer device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0036] S10, constructs an algorithm thread independent of the transaction thread.

[0037] In this embodiment, the trading thread refers to the thread directly used for making trading decisions, and has relatively high latency requirements.

[0038] In this embodiment, the algorithm thread is a thread specifically constructed for calculating factors. The number of algorithm threads can be configured according to the number of factors.

[0039] S11: After each market data processing step is completed, the currently processed market data is determined as the baseline market data.

[0040] In this embodiment, after each market data processing is completed, the next round of market data point prediction, factor value calculation, and other operations are initiated so that they can be used before the next round of market data arrives, thereby effectively reducing the system's waiting time.

[0041] S12, the algorithm thread is started to preload the strategy file, and the market point prediction strategy and market point matching strategy are determined according to the strategy file.

[0042] In this embodiment, the strategy file is used to indicate the currently adapted market point prediction strategy and market point matching strategy.

[0043] The policy file can be in the form of a configuration file for machine recognition.

[0044] S13, in the algorithm thread, the benchmark market is processed using the market point prediction strategy to obtain multiple candidate market points, and the factor value of each candidate market point is calculated.

[0045] In this embodiment, the market point prediction strategy may include interval strategy, key point strategy, multi-dimensional prediction strategy, etc.

[0046] In this embodiment, the step of processing the baseline market data using the market data prediction strategy to obtain multiple candidate market data points includes:

[0047] When the market point prediction strategy is an interval strategy, the price difference is configured;

[0048] Based on the benchmark market data, a candidate market data point is determined for each price difference, resulting in the plurality of candidate market data points.

[0049] The interval strategy described herein is applicable to all market conditions.

[0050] This can involve analyzing historical data and configuring the price difference based on the analysis results; alternatively, the optimal value can be selected as the price difference based on a large number of trials.

[0051] In this embodiment, the step of processing the baseline market data using the market data prediction strategy to obtain multiple candidate market data points further includes:

[0052] When the market point prediction strategy is a key point strategy, the transaction type is determined according to the market point prediction strategy;

[0053] Based on the transaction type, obtain multiple key points from the benchmark market data;

[0054] The multiple key points are identified as the multiple candidate market data points;

[0055] The key point strategy is applicable to market conditions using a phased algorithm.

[0056] For example, in grid trading, each market point where an order is placed can be designated as a candidate market point.

[0057] In this embodiment, the step of processing the baseline market data using the market data prediction strategy to obtain multiple candidate market data points further includes:

[0058] When the market point prediction strategy is a multi-dimensional prediction strategy, the market data of each contract in the benchmark market is obtained.

[0059] The interval strategy described above is used to obtain multiple market data points corresponding to each contract.

[0060] Construct a multi-dimensional array based on multiple market data points corresponding to each contract;

[0061] The elements of the multidimensional array are determined as the multiple candidate market data points;

[0062] The multidimensional prediction strategy is applicable to market conditions that include multiple contract prices.

[0063] For example, for options market data, the Greek letter of the option needs to represent both the underlying contract price and the option contract price, thus requiring the maintenance of market data points in two dimensions. If the contract price dimension determines N market data points, and the option contract price dimension determines N market data points, then the final constructed multidimensional array is a 2*N dimensional array, where each market data point in this multidimensional array is a candidate market data point.

[0064] Through the above embodiments, multiple candidate market points can be predicted in advance according to the corresponding market point prediction strategy, so that they can be directly called when making subsequent trading decisions, thereby reducing system latency.

[0065] In this embodiment, calculating the factor value for each candidate market data point includes:

[0066] The corresponding factor algorithm is called to calculate the factor value for each candidate market point.

[0067] The factor values ​​may include, but are not limited to, option Greek letter risk values, skewness, and peak values.

[0068] The above embodiments enable the factor values ​​of each candidate market data point to be calculated in advance for direct use later.

[0069] In this embodiment, after calculating the factor value of each candidate market point, the method further includes:

[0070] Store the factor value of each candidate market point in the specified storage space.

[0071] The above embodiments enable timely storage of calculated factor values, thereby preventing data loss.

[0072] S14, when new market data is received, in the algorithm thread, a target market data point is selected from the plurality of candidate market data points based on the market data point matching strategy, and the factor value corresponding to the target market data point is determined as the target factor value.

[0073] In this embodiment, the market data point matching strategy may include: similarity matching strategy, price range matching strategy, specified algorithm matching strategy, etc.

[0074] In this embodiment, selecting the target market point from the plurality of candidate market points based on the market point matching strategy includes:

[0075] When the market data point matching strategy is a similarity matching strategy, the similarity between the price of each candidate market data point and the price of the new market data is calculated.

[0076] The candidate market data point with the highest similarity among the multiple candidate market data points is selected as the target market data point.

[0077] In the above embodiments, the target market price point is selected based on the principle of closest price.

[0078] In this embodiment, the step of selecting the target market point from the plurality of candidate market points based on the market point matching strategy further includes:

[0079] When the market point matching strategy is a price range matching strategy, the target price range is determined according to the price range to which the new market price belongs.

[0080] The candidate market data point corresponding to the target price level is determined as the target market data point.

[0081] In the above embodiment, the target market point is selected based on the principle of the closest price level.

[0082] In this embodiment, the step of selecting the target market point from the plurality of candidate market points based on the market point matching strategy further includes:

[0083] When the market data point matching strategy is a specified algorithm matching strategy, the target algorithm is determined according to the market data point matching strategy.

[0084] The target market point is selected from the plurality of candidate market points according to the target algorithm.

[0085] For example, for the surface arbitrage trading strategy of options, you can first select the corresponding target market point based on the latest market conditions, obtain the Greek letter factor value corresponding to the target market point, and then calculate the factor values ​​such as kurtosis and stiffness.

[0086] In the above embodiments, the target market point is selected according to the specific requirements of the algorithm.

[0087] Through the above embodiments, the most suitable target market point can be selected according to the corresponding market point matching strategy.

[0088] S15, In the trading thread, a trading decision is made based on the target factor value.

[0089] In this embodiment, a decision algorithm can be invoked to make a trading decision based on the target factor value.

[0090] The decision algorithm refers to an algorithm that determines whether to trade based on factor values ​​calculated by a factor algorithm.

[0091] For example, the decision algorithm may include: when the Delta value in a certain combination of Greek letters is greater than a specified value, then a Delta balancing hedging transaction is performed.

[0092] The above embodiments enable rapid trading decisions based on pre-calculated factor values, eliminating the need to wait for factor calculations when new market conditions arrive, thereby effectively reducing trading latency.

[0093] In this embodiment, after making a trading decision based on the target factor value, the method further includes:

[0094] Remove the factor value of each candidate market point from the storage space.

[0095] The above embodiments enable timely release of storage space, further ensuring the system's operational performance.

[0096] This embodiment is suitable for scenarios where latency requirements are very stringent.

[0097] For example, this embodiment can be applied to an FPGA (Field Programmable Gate Array) quantitative platform, where the algorithm thread is configured in the software and the trading thread is configured in the FPGA hardware. The computing power of the algorithm thread is used to improve the latency of the trading thread, thereby reducing trading latency and improving system performance.

[0098] As can be seen from the above technical solutions, the present invention can construct an algorithm thread independent of the trading thread to avoid the algorithm processing from affecting normal trading. In the algorithm thread, the benchmark market is processed using a market point prediction strategy to obtain multiple candidate market points, and the factor value of each candidate market point is calculated. The factor value can be predicted and calculated in advance for subsequent quick invocation. When new market data is received, the target market point and target factor value are selected in the algorithm thread based on the market point matching strategy, and the trading thread makes a trading decision based on the target factor value. This enables quick trading decisions based on the pre-calculated factor value, effectively reducing trading latency.

[0099] like Figure 2The diagram shown is a functional block diagram of a preferred embodiment of the trading data processing device based on market price prediction of the present invention. The trading data processing device 11 based on market price prediction includes a construction unit 110, a determination unit 111, a processing unit 112, a selection unit 113, and a decision-making unit 114. The module / unit referred to in this invention refers to a series of computer program segments that can be executed by a processor and perform a fixed function, and are stored in memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0100] The construction unit 110 is used to construct an algorithm thread that is independent of the transaction thread;

[0101] The determining unit 111 is used to determine the currently processed market data as the benchmark market data after each market data processing is completed.

[0102] The determining unit 111 is further configured to start the algorithm thread to preload the strategy file, and determine the market point prediction strategy and the market point matching strategy according to the strategy file;

[0103] The processing unit 112 is used in the algorithm thread to process the benchmark market data using the market data point prediction strategy to obtain multiple candidate market data points, and to calculate the factor value of each candidate market data point.

[0104] The selection unit 113 is used to select a target market point from the plurality of candidate market points in the algorithm thread based on the market point matching strategy when receiving new market data, and to determine the factor value corresponding to the target market point as the target factor value.

[0105] The decision unit 114 is used to make trading decisions based on the target factor value in the trading thread.

[0106] As can be seen from the above technical solutions, the present invention can construct an algorithm thread independent of the trading thread to avoid the algorithm processing from affecting normal trading. In the algorithm thread, the benchmark market is processed using a market point prediction strategy to obtain multiple candidate market points, and the factor value of each candidate market point is calculated. The factor value can be predicted and calculated in advance for subsequent quick invocation. When new market data is received, the target market point and target factor value are selected in the algorithm thread based on the market point matching strategy, and the trading thread makes a trading decision based on the target factor value. This enables quick trading decisions based on the pre-calculated factor value, effectively reducing trading latency.

[0107] like Figure 3 The diagram shown is a schematic representation of the computer device used in a preferred embodiment of the present invention for processing transaction data based on market price prediction.

[0108] The computer device 1 may include a memory 12, a processor 13, and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and capable of running on the processor 13, such as a transaction data processing program based on market price prediction.

[0109] Those skilled in the art will understand that the schematic diagram is merely an example of computer device 1 and does not constitute a limitation on computer device 1. Computer device 1 can be either a bus topology or a star topology. Computer device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, computer device 1 may also include input / output devices, network access devices, etc.

[0110] It should be noted that the computer device 1 described is merely an example. Other existing or future electronic products that are adaptable to this invention should also be included within the scope of protection of this invention and are incorporated herein by reference.

[0111] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a portable hard drive of the computer device 1. In other embodiments, the memory 12 can be an external storage device of the computer device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 1. Furthermore, the memory 12 can include both internal and external storage units of the computer device 1. The memory 12 can be used not only to store application software and various types of data installed on the computer device 1, such as the code of a trading data processing program based on market price prediction, but also to temporarily store data that has been output or will be output.

[0112] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the computer device 1, connecting various components of the computer device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing a trading data processing program based on market price prediction) and calls data stored in the memory 12 to perform various functions and process data for the computer device 1.

[0113] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes these applications to implement the steps in the above embodiments of the trading data processing method based on market price prediction, for example... Figure 1 The steps are shown.

[0114] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a construction unit 110, a determination unit 111, a processing unit 112, a selection unit 113, and a decision-making unit 114.

[0115] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor to execute portions of the transaction data processing method based on market price prediction described in various embodiments of the present invention.

[0116] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0117] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0118] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0119] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0120] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 3 The bus is represented by only one straight line, but this does not mean that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0121] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0122] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish a communication connection between the computer device 1 and other computer devices.

[0123] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the computer device 1 and to display a visual user interface.

[0124] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0125] It will be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0126] Combination Figure 1 The memory 12 in the computer device 1 stores multiple instructions to implement a trading data processing method based on market price prediction, and the processor 13 can execute the multiple instructions to achieve the following:

[0127] Construct an algorithm thread independent of the transaction thread;

[0128] After each market data processing is completed, the currently processed market data is determined as the baseline market data.

[0129] The algorithm thread is started to preload the strategy file, and the market point prediction strategy and market point matching strategy are determined according to the strategy file.

[0130] In the algorithm thread, the benchmark market data is processed using the market data prediction strategy to obtain multiple candidate market data points, and the factor value of each candidate market data point is calculated.

[0131] When new market data is received, in the algorithm thread, a target market data point is selected from the plurality of candidate market data points based on the market data point matching strategy, and the factor value corresponding to the target market data point is determined as the target factor value.

[0132] In the trading thread, trading decisions are made based on the target factor value.

[0133] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0134] It should be noted that all data involved in this case was legally obtained. Software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0135] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0136] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0137] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0139] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0140] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0141] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in this invention can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for processing trading data based on market price prediction, characterized in that, The trading data processing method based on market point prediction includes: Construct an algorithm thread independent of the transaction thread; After each market data processing is completed, the currently processed market data is determined as the baseline market data. The algorithm thread is started to preload the strategy file, and the market point prediction strategy and market point matching strategy are determined according to the strategy file. In the algorithm thread, the benchmark market data is processed using the market data prediction strategy to obtain multiple candidate market data points, and the factor value of each candidate market data point is calculated. When new market data is received, in the algorithm thread, a target market data point is selected from the plurality of candidate market data points based on the market data point matching strategy, and the factor value corresponding to the target market data point is determined as the target factor value. In the trading thread, trading decisions are made based on the target factor value.

2. The transaction data processing method based on market point prediction as described in claim 1, characterized in that, The process of using the market point prediction strategy to process the baseline market data to obtain multiple candidate market points includes: When the market point prediction strategy is an interval strategy, the price difference is configured; Based on the benchmark market data, a candidate market data point is determined for each price difference, resulting in the plurality of candidate market data points. The interval strategy described herein is applicable to all market conditions.

3. The transaction data processing method based on market point prediction as described in claim 1, characterized in that, The step of processing the baseline market data using the market data prediction strategy to obtain multiple candidate market data points also includes: When the market point prediction strategy is a key point strategy, the transaction type is determined according to the market point prediction strategy; Based on the transaction type, obtain multiple key points from the benchmark market data; The multiple key points are identified as the multiple candidate market data points; The key point strategy is applicable to market conditions using a phased algorithm.

4. The transaction data processing method based on market point prediction as described in claim 2, characterized in that, The step of processing the baseline market data using the market data prediction strategy to obtain multiple candidate market data points also includes: When the market point prediction strategy is a multi-dimensional prediction strategy, the market data of each contract in the benchmark market is obtained. The interval strategy described above is used to obtain multiple market data points corresponding to each contract. Construct a multi-dimensional array based on multiple market data points corresponding to each contract; The elements of the multidimensional array are determined as the multiple candidate market data points; The multidimensional prediction strategy is applicable to market conditions that include multiple contract prices.

5. The transaction data processing method based on market point prediction as described in claim 1, characterized in that, The selection of the target market point from the plurality of candidate market points based on the market point matching strategy includes: When the market data point matching strategy is a similarity matching strategy, the similarity between the price of each candidate market data point and the price of the new market data is calculated. The candidate market data point with the highest similarity among the multiple candidate market data points is selected as the target market data point.

6. The transaction data processing method based on market point prediction as described in claim 1, characterized in that, The step of selecting the target market point from the plurality of candidate market points based on the market point matching strategy further includes: When the market point matching strategy is a price range matching strategy, the target price range is determined according to the price range to which the new market price belongs. The candidate market data point corresponding to the target price level is determined as the target market data point.

7. The transaction data processing method based on market point prediction as described in claim 1, characterized in that, The step of selecting the target market point from the plurality of candidate market points based on the market point matching strategy further includes: When the market data point matching strategy is a specified algorithm matching strategy, the target algorithm is determined according to the market data point matching strategy. The target market point is selected from the plurality of candidate market points according to the target algorithm.

8. A transaction data processing device based on market price prediction, characterized in that, The transaction data processing device based on market point prediction includes: Construction units are used to build algorithm threads that are independent of transaction threads; The determination unit is used to determine the currently processed market data as the baseline market data after each market data processing is completed; The determining unit is further configured to start the algorithm thread to preload the strategy file, and determine the market point prediction strategy and the market point matching strategy according to the strategy file; The processing unit is used in the algorithm thread to process the benchmark market data using the market data point prediction strategy to obtain multiple candidate market data points, and to calculate the factor value of each candidate market data point. The selection unit is used, when receiving new market data, to select a target market data point from the plurality of candidate market data points in the algorithm thread based on the market data point matching strategy, and to determine the factor value corresponding to the target market data point as the target factor value. A decision-making unit is used to make trading decisions based on the target factor value in the trading thread.

9. A computer device, characterized in that, The computer device includes: A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the transaction data processing method based on market point prediction as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the transaction data processing method based on market point prediction as described in any one of claims 1 to 7.

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