WO2022001131A1

WO2022001131A1 - Method and apparatus for reconstructing market quotations based on quote-by-quote data, and device

Info

Publication number: WO2022001131A1
Application number: PCT/CN2021/076925
Authority: WO
Inventors: 陈尧; 厉剑; 郝锐; 郭雷; 阚宏伟
Original assignee: 浪潮电子信息产业股份有限公司
Priority date: 2020-06-29
Filing date: 2021-02-19
Publication date: 2022-01-06
Also published as: CN111861743A

Abstract

A method and apparatus for reconstructing market quotations based on quote-by-quote data, a device and a readable storage medium can generate a quotation skip list and an order hash table, wherein the quotation skip list comprises multiple ticks divided by order price, each tick comprising an order price and an order quantity, and the order hash table comprises multiple order data items, the order data items comprising unfilled quantity, quotation tick pointer and order number as a key. The present application uses a skip list to store the price-volume relationships of quotations, uses a linear structure of the first layer of the skip list to improve the performance of market quotation output, and uses the order hash table to accelerate tick deduction after quote-by-quote matching. Thus, the performance of reconstructing market quotations based on quote-by-quote data is improved, and delay of a back-end trading system in receiving quotations is reduced.

Description

A method, device and equipment for reconstructing market conditions based on transaction-by-transaction data

This application claims the priority of the Chinese patent application submitted to the China Patent Office on June 29, 2020, the application number is 202010609099.0, and the invention name is "a method, device and equipment for reconstructing market conditions based on transaction-by-transaction data", The entire contents of which are incorporated herein by reference.

technical field

The present application relates to the field of computer technology, and in particular, to a method, apparatus, device, and readable storage medium for reconstructing market conditions based on transaction-by-transaction data.

Background technique

In recent years, with the rapid development of my country's securities market, related trading technologies have also advanced by leaps and bounds. In order to better serve the market, both the Shanghai Stock Exchange and the Shenzhen Stock Exchange have significantly upgraded their trading systems. In terms of market information release, Level-2 quotations (a fee-based quotation service provided by Shanghai and Shenzhen Stock Exchanges) including ten grades of quotations and transaction-by-transaction data have been successively launched. At present, the level-2 transaction-by-transaction data of the Shenzhen Stock Exchange includes transaction-by-transaction and transaction-by-transaction, which enables the recipient of the Shenzhen Stock Exchange's Level-2 quotation to reconstruct the market quotation by itself using the transaction-by-transaction data and transaction rules to match the transaction. Compared with the ten-speed snapshot market that is released every three seconds by the exchange, the self-reconstructed market market can maintain all the levels consistent with the exchange's matching machine at any time. This market situation can enable users to have a more in-depth and detailed understanding of the entire market. understanding.

The core idea of self-reconstructing market conditions based on tick-by-tick data is to maintain the volume-price relationship of the market by itself. In the current situation of more than 10,000 securities in the Shenzhen Stock Exchange, receiving the rapidly generated transaction-by-transaction data and maintaining the volume-price relationship of the order book by itself requires extremely high performance for saving the data structure of the order book and order and updating the algorithm. . When the market is very active, underperforming methods and programs can collapse under the onslaught of tick-by-tick data.

In the process of maintaining the volume-price relationship of the market, it is necessary to frequently perform a large number of addition, deletion, modification, query, and traversal operations on the data structure that saves the market, but most data structures can only optimize some of the above operations. aspect. In the open source implementation of common matching engines, the data structures used to save the market include linked lists, balanced trees, hash tables, and so on. If you use a linked list to save the market, it is good for adding, deleting, traversing, but not good for query; if you use a balanced tree to save the market, it is good for querying, but not conducive to adding, deleting, and traversing; if you use a hash table to save the market , which is beneficial to query, add, and delete, but cannot directly traverse the market in the order of price priority when generating quotations.

It can be seen that in the process of reconstructing market conditions based on data one by one, multiple types of data operations need to be performed frequently, and a single data structure cannot meet the current performance requirements.

SUMMARY OF THE INVENTION

The purpose of this application is to provide a method, device, device and readable storage medium for reconstructing market conditions based on transaction-by-transaction data, so as to solve the need to frequently perform multiple types of For data operations, a single data structure cannot meet the current performance requirements. Its specific plan is as follows:

In a first aspect, the present application provides a method for reconstructing market conditions based on transaction-by-transaction data, including:

For the target securities, a market jump table and an order hash table are generated, wherein the market jump table includes a plurality of tiers divided according to the order price, the tiers include the order price and the order quantity, and the order hash table Including a plurality of order data items, the key of the order data item is the order number of the order, and the value of the order data item includes the unfilled quantity and the handicap gear pointer;

receive tick-by-tick data;

When the trade-by-trade data is trade-by-trade order data, update the market jump table according to the trade-by trade-off data, and add the order number corresponding to the trade-by-trade entrustment data in the order hash table 's order data item;

When the transaction-by-transaction data is transaction-by-transaction data, determine the corresponding target order data item in the order hash table according to the order number in the transaction-by-transaction data; update the unexecuted order data item in the target order data item Quantity; according to the handicap gear pointer of the target order data item, determine the corresponding target gear in the handicap jump table, and update the order quantity in the target gear;

Traverse the updated handicap jump table according to the order of the order price, and generate the market data of the target security.

Preferably, for the target securities, generating a market jump table and an order hash table, including:

For the target securities, a market jump table, an order hash table and a gear hash table are generated, wherein the gear hash table includes a plurality of gear data items, the key of the gear data item is the commission price, and the The value of the above gear data item is the handicap gear pointer;

Correspondingly, when the transaction-by-transaction data is the transaction-by-transmission commission data, updating the handicap jump table according to the transaction-by-transmission commission data includes:

When the transaction-by-transaction data is the transaction-by-transmission entrustment data, the corresponding target gear data item in the gear hash table is determined according to the entrusted price in the transaction-by-transmission data; according to the disk of the target gear data item The handicap gear pointer is used to determine the corresponding target gear in the handicap jump table, and update the commission quantity of the target gear.

Preferably, determining the corresponding target gear data item in the gear hash table according to the entrusted price in the one-by-one entrustment data includes:

Judging whether there is the commission price of the one-by-one commission data in the gear hash table;

If there is, determine the target gear data item corresponding to the order price of the order-by-order order data;

If it does not exist, add a gear corresponding to the order price of the one-by-one order data in the handicap jump table, and add a gear corresponding to the order price of the one-by-one order data in the gear hash table the gear data item.

Preferably, after updating the unfilled quantity in the target order data item, the method further includes:

If the updated unexecuted quantity is 0, the target order data item is deleted.

Preferably, after determining the corresponding target gear in the handicap jump table according to the market gear pointer of the target order data item, and updating the order quantity in the target gear, the method further includes:

If the updated order quantity is 0, the target gear is deleted.

Preferably, the updated handicap jump table is traversed in the order of the entrusted prices to generate the market data of the target securities, including:

According to a preset period, the updated handicap jump table is traversed in the order of the order price, and a preset number of stalls are selected as the market data of the target securities.

Preferably, the handicap jump table includes a buy handicap jump table and a sell handicap jump table, and the updated handicap jump table is traversed in the order of the order price to generate the market data of the target securities ,include:

Determine whether the order price of the buy order jumping table is lower than the order price of the selling order jumping table;

If so, traverse the updated handicap jump table in the order of the order price to generate the market data of the target security.

In a second aspect, the present application provides a device for reconstructing market conditions based on transaction-by-transaction data, including:

Data structure generation module: used to generate a handicap jump table and an order hash table for the target securities, wherein the handicap jump table includes multiple tiers divided according to the entrusted price, and the tiers include the entrusted price and the order quantity , the order hash table includes a plurality of order data items, the key of the order data item is the entrusted order number, and the value of the order data item includes the unfilled quantity and the handicap gear pointer;

One-by-one data receiving module: used to receive one-by-one data;

The first data update module: when the transaction-by-transaction data is the transaction-by-transmission commission data, update the handicap jump table according to the transaction-by-transmission commission data, and add and the transaction-by-transaction data in the order hash table. The order data item corresponding to the order number of the order data of the order data;

The second data update module: when the transaction-by-transaction data is transaction-by-transaction data, according to the order number in the transaction-by-transaction data, determine the corresponding target order data item in the order hash table; update The unsettled quantity in the target order data item; determine the corresponding target gear in the handicap jump table according to the handicap gear pointer of the target order data item, and update the order quantity in the target gear;

Market quotation generation module: used to traverse the updated handicap jump table in the order of the order price, and generate the market quotation data of the target securities.

In a third aspect, the present application provides a device for reconstructing market conditions based on transaction-by-transaction data, including:

Memory: used to store computer programs;

Processor: for executing the computer program to implement the steps of the method for reconstructing market conditions based on tick-by-tick data as described above.

In a fourth aspect, the present application provides a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the computer program is used to realize the above-mentioned market reconstruction based on tick-by-tick data Quotes method steps.

A method for reconstructing market conditions based on transaction-by-transaction data provided by the present application includes: for a target security, generating a market jump table and an order hash table, wherein the market jump table includes a plurality of gears divided according to the entrusted price , each level includes the order price and order quantity, the order hash table includes multiple order data items, the key of the order data item is the order number of the order, and the value of the order data item includes the unfilled quantity and the handicap gear pointer; receive Tick-by-tick data; when the tick-by-tick data is the order-by-order data, update the market jump table according to the order-by-order data, and add the order data item corresponding to the order number of the order-by-order data in the order hash table; When the transaction data is transaction data, determine the corresponding target order data item in the order hash table according to the order number in the transaction data; update the unexecuted quantity in the target order data item; according to the target order data item The handicap gear pointer determines the corresponding target gear in the handicap jump table, and updates the order quantity in the target gear; traverses the updated handicap jump table according to the order of the order price to generate the market data of the target securities.

It can be seen that this method uses the skip table to save the volume-price relationship of the market, uses the linear structure of the first layer of the skip table to improve the performance of market output, and uses the order hash table to speed up the speed of positioning and deducting gears after receiving each transaction. Therefore, the performance of reconstructing market quotations based on transaction-by-transaction data can be improved, and the delay in receiving quotations by the back-end trading system can be reduced.

In addition, the present application also provides an apparatus, device and readable storage medium for reconstructing market conditions based on transaction-by-transaction data.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application or the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only For some embodiments of the present application, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is a realization flow chart of Embodiment 1 of a method for reconstructing market conditions based on transaction-by-transaction data provided by this application;

Fig. 2 is the schematic diagram of skip table result provided by this application;

Fig. 3 is a realization flow chart of Embodiment 2 of a method for reconstructing market conditions based on transaction-by-transaction data provided by the present application;

Fig. 4 is the realization flow chart of a kind of method application embodiment based on data reconstruction of market conditions provided by this application;

FIG. 5 is a functional block diagram of an embodiment of an apparatus for reconstructing market conditions based on transaction-by-transaction data provided by the present application.

detailed description

In order to make those skilled in the art better understand the solution of the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

At present, in the process of reconstructing market conditions based on the data one by one, various data operations need to be performed frequently on the market data. Therefore, when the number of securities is large, the data structure for saving the market and the performance of the update algorithm are extremely demanding. .

In response to this problem, the present application provides a method, device, device and readable storage medium for reconstructing market conditions based on transaction-by-transaction data, generating a market jump table to save the market data, and generating an order hash table to facilitate order update, It supports rapid addition, deletion, modification, and checking of market and order data. Based on the above data structure, it realizes rapid reconstruction of market conditions.

The first embodiment of a method for reconstructing market conditions based on transaction-by-transaction data provided by the present application will be introduced below. Referring to FIG. 1, the first embodiment includes:

S101. For the target securities, generate a handicap skip table and an order hash table;

Wherein the handicap jump table includes a plurality of gears divided according to the order price, the gears include the order price and the order quantity, and the order hash table includes a plurality of order data items. In the scenario of market restructuring, in addition to saving the market, it is also necessary to save each order that has not been fully filled. In specific implementation, this embodiment uses an order hash table, the key of the order hash table is the order number, and the value includes the unsettled quantity and the skip table node pointer of the corresponding gear.

S102. Receive data one by one;

S103. When the transaction-by-transaction data is the transaction-by-transmission data, update the handicap jump table according to the transaction-by-transmission data, and add the order number corresponding to the transaction-by-transmission data in the order hash table. the corresponding order data item;

Specifically, after receiving the order-by-order data, the corresponding stall is searched from the market jump table according to the order price in the order-by-order data, and the order quantity of the stall is updated according to the quantity in the order-by-order data. This method of using the handicap jump table to query the corresponding gear has a time complexity of logarithmic time. In addition, an order data item corresponding to the order number of the order-by-order order data needs to be added to the order hash table.

S104. When the transaction-by-transaction data is transaction-by-transaction data, determine a corresponding target order data item in the order hash table according to the order number in the transaction-by-transaction data; update the target order data item in the target order data item. The unfilled quantity; determine the corresponding target gear in the market skip table according to the market gear pointer of the target order data item, and update the order quantity in the target gear;

S105 , traverse the updated handicap jump table according to the order of the order price, and generate market data of the target security.

In the market data, the buyer's market consists of multiple gears with prices ranging from high to the bottom, and the seller's market consists of multiple gears with prices ranging from low to high. Each gear is the commission price and the corresponding number of orders.

In the scenario of market reconstruction, after receiving the transaction data one by one, it is necessary to find the corresponding order data item and update the number of unfilled transactions. Specifically, for the transaction data in the transaction message, the unexecuted quantity of the corresponding order data item and the order quantity of the stall are updated according to the order number and transaction quantity of the buyer or seller; for the order cancellation data in the transaction message , it will be updated accordingly according to the order number and order cancellation quantity of the buyer or seller.

It is understandable that if the updated untraded quantity is zero, the corresponding order data item should also be deleted. Similarly, if the updated order quantity is zero, the corresponding gear will be deleted.

When sending market quotes, it is necessary to traverse the market in both directions of buying and selling. Specifically, after each transaction is received and the market is updated, if there is no cross relationship between the prices of the market, the market can be sent to the outside world.

This embodiment provides a method for reconstructing market conditions based on transaction-by-transaction data, using a skip table to save the market, as shown in Figure 2, the skip table is a probabilistic multi-level indexed linked list, which can maintain the On the basis of the linear structure, adding multiple levels of indexes improves the query efficiency, so the efficiency of skipping table query, adding, and deleting is equivalent to a balanced tree in probability, and adding and deleting operations do not need to maintain subtree balance. Moreover, since the first layer of the skip table is a linear structure, it is more suitable for the traversal operation when sending market quotations than the balanced tree, and can achieve logarithmic time complexity.

The second embodiment of a method for reconstructing market conditions based on transaction-by-transaction data provided by the present application will be described in detail below.

The embodiment has described the process of updating the market according to the order-by-order data and the transaction data, wherein the time complexity of the process of searching for the corresponding gear position in the market jump table according to the order-by-order data is logarithmic time. The embodiment further optimizes the process to reduce query time. Specifically, in this embodiment, a gear hash table is set, and the corresponding relationship between the commission price and the handicap node pointer is stored in the gear hash table, thereby reducing the query time from logarithmic time to unit time.

Referring to Figure 3, the second embodiment specifically includes:

S301. For the target securities, generate a handicap skip table, an order hash table and a gear hash table;

Wherein the handicap jump table includes a plurality of gears divided according to the order price, and the gears include the order price and the order quantity; the order hash table includes a plurality of order data items, and the keys of the order data items are: The order number of the order, the value of the order data item includes the untraded quantity and the handicap gear pointer; the gear hash table includes a plurality of gear data items, the key of the gear data item is the commission price, and the The value of the above gear data item is the market gear pointer.

S302. Receive data one by one;

S303, when the transaction-by-transmission data is transaction-by-transmission entrustment data, determine a corresponding target gear data item in the gear hash table according to the commission price in the transaction-by-transmission data; according to the target gear data item the handicap gear pointer, determine the corresponding target gear in the handicap jump table, update the order quantity of the target gear; add the order number corresponding to the order-by-order order data in the order hash table the corresponding order data item;

In actual securities trading, traders quote the price of independent tranches. Due to the transaction and cancellation of orders, the number of tranches is rarely deducted to zero, and most operations are still aimed at modifying the quantity of existing tranches. In view of this operational feature, this embodiment uses a gear hash table to store the commission price and the skip table node pointer corresponding to the gear, and reduces the query time from the logarithmic time complexity of the skip table to the completion in unit time.

After receiving the entrusted data one by one, the corresponding gear data item is searched according to the entrusted price. If there is a corresponding gear data item, the corresponding gear is determined according to the handicap gear pointer of the gear data item. Update the number of orders on the order, and add the order data item corresponding to the order number of the order-by-order data in the order hash table; if there is no corresponding gear data item, you need to add a gear data item, and add a gear to record the order price and quantity, and also need to add an order data item to record the order number, unfilled quantity and the corresponding gear.

Specifically, it is judged whether there is the commission price of the one-by-one commission data in the gear hash table; if there is, the target gear data item corresponding to the commission price of the one-by-bit commission data is determined, and in the An order data item corresponding to the order number of the order-by-order data is added to the order hash table, and an order data item corresponding to the order number of the order-by-order data is added to the order hash table; if it does not exist, Then, add the stall corresponding to the commission price of the one-by-one order data in the handicap jump table, and add the stall data corresponding to the commission price of the one-by-one order data in the stall hash table. item, and an order data item corresponding to the order number of the order-by-order order data is added to the order hash table.

S304, when the transaction-by-transaction data is transaction-by-transaction data, determine the corresponding target order data item in the order hash table according to the order number in the transaction-by-transaction data; update the target order data item in the The unfilled quantity; determine the corresponding target gear in the market skip table according to the market gear pointer of the target order data item, and update the order quantity in the target gear;

It can be understood that, if the updated unexecuted quantity is 0, the target order data item is deleted. Similarly, if the updated order quantity is 0, the target gear is deleted.

S305, determine whether there is a cross relationship in the updated handicap jump table, if not, enter S306;

Specifically, it is determined whether the entrusted price of the buy order jumping table is lower than the order price of the selling order jumping table, if it is lower, it is determined that there is no cross relationship, otherwise it is determined that there is a cross relationship.

S306 , traverse the updated handicap jump table in the order of the order price, and generate market data of the target security.

When sending quotations, if the back-end trading system cannot process the continuously outputted market quotations containing all the gears, it can output only a preset number of gears each time, for example, output the same ten gears as the Level-2 market snapshot, You can also add a timer to output market conditions at regular intervals, or use a combination of the above two methods.

Specifically, when the above two methods are adopted at the same time, S306 specifically includes: according to a preset period, traversing the updated handicap jump table in the order of the order price, and selecting a preset number of stalls as the target securities market data.

It can be seen that a method for reconstructing market conditions based on transaction-by-transaction data provided by this embodiment utilizes a data structure in which a skip table and two hash tables cooperate with each other to jointly complete the process of reconstructing market conditions based on transaction-by-transaction data. Specifically, the skip table is used to save the market, the linear structure of the first layer of the skip table is used to improve the performance of market output, and the logarithmic time complexity that is consistent with the balance tree is achieved in probability when adding and deleting operations are required; Use the gear hash table to speed up the speed of locating the existing gear after receiving one-by-one orders; use the order hash table to speed up the speed of positioning the deducted gear after receiving the one-by-one transaction. Therefore, this embodiment can improve the market quotation reconstruction performance and reduce the delay in receiving quotations by the back-end trading system.

Based on the above embodiments, the method for reconstructing market conditions based on transaction-by-transaction data of the present application will be described below by taking a specific application as an example.

The data structure used to reconstruct the market quotation of a single security is shown in Table 1, including the two-way trading table, gear hash table, and order hash table. Each node of the handicap table is used to store The price and quantity of a stall. The top layer uses a security hash table to store the correspondence between security codes and six data structure instances.

Table 1

Based on the above data structure, the process of rapidly reconstructing market conditions based on transaction-by-transaction data in this embodiment is shown in Figure 4, including:

The first step is to create a corresponding data structure for all securities traded on that day according to the securities information file before the market opening, and write the securities code and corresponding data structure into the securities hash table.

The second step is to receive the data one by one, and use the security hash table to query the corresponding data structure according to the security code.

The third step is to judge the data type of each transaction. If it is a transaction-by-transmission message and is not a market order, use the order price as the key value to find the gear hash table. If there is a corresponding gear, the order quantity will be accumulated to the order quantity of the skip table node of the corresponding gear; if there is no corresponding gear, a new skip table node will be created to record the price and quantity of the order, and the skip table node will be inserted into the skip table node. At the same time, the record of the price and the jump table node pointer of the corresponding gear is added to the gear hash table. Finally, record the order number and the skip table node pointer of the corresponding gear in the order hash table.

The fourth step, if it is a transaction message one by one, when the transaction type is transaction, use the buy and sell order numbers as the key to query the order hash table, and the buy and sell orders are deducted from the transaction quantity, and then based on the order. The value of the table is searched for the corresponding gear, and the transaction quantity is deducted from the corresponding gear. When the transaction type is cancel order, use the buy order number or sell order number as the key to query the order hash table, deduct the transaction quantity for the buy or sell order, and then find the corresponding gear according to the value of the order hash table, The transaction quantity is deducted from the corresponding gear. When the quantity of the corresponding order or stall is zero after deduction, the corresponding order and stall are deleted.

The fifth step is to judge whether the price of buying one tranche is lower than the selling price of one tranche, if so, send the quotation, if not, wait for further transaction messages.

The sixth step, go back to the second step, until the end of the all-day trading time.

The following describes an apparatus for reconstructing market conditions based on transaction-by-transaction data provided by the embodiments of the present application. The methods of market conditions can refer to each other.

As shown in Figure 5, the device for reconstructing market conditions based on transaction-by-transaction data in this embodiment includes:

Data structure generation module 501: used to generate a market jump table and an order hash table for the target securities, wherein the market jump table includes a plurality of tiers divided according to the order price, and the notch includes the order price and the order price Quantity, the order hash table includes a plurality of order data items, the key of the order data item is the order number, and the value of the order data item includes the unfilled quantity and the handicap gear pointer;

Pen-by-transaction data receiving module 502: used for receiving pen-by-transaction data;

The first data update module 503 is configured to update the handicap jump table according to the transaction-by-transmission data when the transaction-by-transaction data is the transaction-by-transmission data, and add the same value as the transaction-by-transaction data in the order hash table. The order data item corresponding to the order order number of the order data;

The second data updating module 504 is used for determining the corresponding target order data item in the order hash table according to the order number in the transaction data when the transaction data is transaction transaction data; Update the unsettled quantity in the target order data item; determine the corresponding target gear in the handicap jump table according to the market gear pointer of the target order data item, and update the order quantity in the target gear;

Market quotation generation module 505 : used for traversing the updated handicap jump table according to the order of the order price, and generating the market quotation data of the target securities.

The apparatus for reconstructing market quotations based on transaction-by-transaction data in this embodiment is used to implement the aforementioned method for reconstructing market quotations based on transaction-by-transaction data. Therefore, the specific implementation of the device can be found in the previous section Reconstructing market quotations based on transaction-by-transaction data. The embodiment part of the method, for example, the data structure generation module 501, the transaction-by-transaction data receiving module 502, the first data update module 503, the second data update module 504, and the market quotation generation module 505 are respectively used to realize the above-mentioned transaction-by-transaction based data. Steps S101, S102, S103, S104, S105 in the method for data reconstruction of market conditions. Therefore, reference may be made to the descriptions of the corresponding partial embodiments for specific implementations thereof, which will not be described herein again.

In addition, since the apparatus for reconstructing market quotations based on transaction-by-transaction data in this embodiment is used to implement the aforementioned method for reconstructing market quotations based on transaction-by-transaction data, its function corresponds to that of the above method, and will not be repeated here.

In addition, the present application also provides a device for reconstructing market conditions based on transaction-by-transaction data, including:

Memory: used to store computer programs;

Finally, the present application provides a readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, is used to realize the above-mentioned reconstruction of market conditions based on tick-by-tick data steps of the method.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments may be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

The steps of a method or algorithm described in conjunction with the embodiments disclosed herein may be directly implemented in hardware, a software module executed by a processor, or a combination of the two. A software module can be placed in random access memory (RAM), internal memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other in the technical field. in any other known form of storage medium.

The solutions provided by this application have been introduced in detail above, and specific examples are used to illustrate the principles and implementations of this application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of this application; , for those of ordinary skill in the art, according to the idea of the application, there will be changes in the specific embodiments and application scope. To sum up, the content of this specification should not be construed as a limitation to the application.

Claims

A method for reconstructing market conditions based on transaction-by-transaction data, comprising:

For the target securities, a market jump table and an order hash table are generated, wherein the market jump table includes a plurality of tiers divided according to the order price, the tiers include the order price and the order quantity, and the order hash table Including a plurality of order data items, the key of the order data item is the order number of the order, and the value of the order data item includes the unfilled quantity and the handicap gear pointer;

receive tick-by-tick data;

When the trade-by-trade data is trade-by-trade order data, update the market jump table according to the trade-by trade-off data, and add the order number corresponding to the trade-by-trade entrustment data in the order hash table 's order data item;

When the transaction-by-transaction data is transaction-by-transaction data, determine the corresponding target order data item in the order hash table according to the order number in the transaction-by-transaction data; update the unexecuted order data item in the target order data item Quantity; according to the handicap gear pointer of the target order data item, determine the corresponding target gear in the handicap jump table, and update the order quantity in the target gear;

Traverse the updated handicap jump table according to the order of the order price, and generate the market data of the target security.
The method of claim 1, wherein generating a market skip table and an order hash table for the target securities, comprising:

For the target securities, a market jump table, an order hash table and a gear hash table are generated, wherein the gear hash table includes a plurality of gear data items, the key of the gear data item is the commission price, and the The value of the above gear data item is the handicap gear pointer;

Correspondingly, when the transaction-by-transaction data is the transaction-by-transmission commission data, updating the handicap jump table according to the transaction-by-transmission commission data includes:

When the transaction-by-transaction data is the transaction-by-transmission entrustment data, the corresponding target gear data item in the gear hash table is determined according to the entrusted price in the transaction-by-transmission data; according to the disk of the target gear data item The handicap gear pointer is used to determine the corresponding target gear in the handicap jump table, and update the commission quantity of the target gear.
The method according to claim 2, wherein the determining the corresponding target gear data item in the gear hash table according to the entrusted price in the one-by-one entrustment data comprises:

Judging whether there is the commission price of the one-by-one commission data in the gear hash table;

If there is, determine the target gear data item corresponding to the order price of the order-by-order order data;

If it does not exist, add a gear corresponding to the order price of the one-by-one order data in the handicap jump table, and add a gear corresponding to the order price of the one-by-one order data in the gear hash table the gear data item.
The method of claim 1, further comprising: after updating the unfilled quantity in the target order data item:

If the updated unexecuted quantity is 0, the target order data item is deleted.
The method according to claim 1, characterized in that, after determining the corresponding target gear in the market jumping table according to the market gear pointer of the target order data item, and updating the order quantity in the target gear ,Also includes:

If the updated order quantity is 0, the target gear is deleted.
The method according to any one of claims 1 to 5, wherein the step of traversing the updated handicap table according to the order of the entrusted prices, to generate the market data of the target securities, includes:

According to a preset period, the updated handicap jump table is traversed in the order of the order price, and a preset number of stalls are selected as the market data of the target securities.
The method according to claim 6, wherein the handicap jump table includes a buy handicap table and a sell handicap table, and the updated handicap jump table is traversed according to the order of the order price , to generate the market data of the target securities, including:

Determine whether the order price of the buy order jumping table is lower than the order price of the selling order jumping table;

If so, traverse the updated handicap jump table in the order of the order price, and generate the market data of the target security.
A device for reconstructing market conditions based on transaction-by-transaction data, characterized in that it includes:

Data structure generation module: used to generate a handicap jump table and an order hash table for the target securities, wherein the handicap jump table includes multiple tiers divided according to the entrusted price, and the tiers include the entrusted price and the order quantity , the order hash table includes a plurality of order data items, the key of the order data item is the order number, and the value of the order data item includes the unfilled quantity and the handicap gear pointer;

One-by-one data receiving module: used to receive one-by-one data;

The first data update module: when the transaction-by-transaction data is the transaction-by-transmission commission data, update the handicap jump table according to the transaction-by-transmission commission data, and add and the transaction-by-transaction data in the order hash table. The order data item corresponding to the order number of the order data of the order data;

The second data update module: when the transaction-by-transaction data is transaction-by-transaction data, according to the order number in the transaction-by-transaction data, determine the corresponding target order data item in the order hash table; update The unsettled quantity in the target order data item; determine the corresponding target gear in the handicap jump table according to the handicap gear pointer of the target order data item, and update the order quantity in the target gear;

Market quotation generation module: used to traverse the updated handicap jump table in the order of the size of the entrusted price, and generate the market quotation data of the target securities.
A device for reconstructing market conditions based on transaction-by-transaction data, characterized in that it includes:

Memory: used to store computer programs;

Processor: used to execute the computer program to implement the steps of the method for reconstructing market conditions based on transaction-by-transaction data according to any one of claims 1-7.
A readable storage medium, characterized in that, a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the computer program is used to implement the stroke-by-stroke-based method according to any one of claims 1-7. The steps of a method for data reconstruction of market conditions.