CN111415256A - Multi-account stock trading control method and device - Google Patents
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Abstract
The invention relates to a control method of multi-account stock trading, which comprises the steps of establishing a trading position management layer through a position controller, obtaining a stock list of each account and the stock trading volume of each account at each time point; simulating each strategy weight in the pool according to the bin controller and the trading algorithm, combining market data to obtain a trading signal, and entering stocks to be traded and stock trading volume at the current time point into a trading platform; the trading platform obtains the trading volume of each account according to the number of the trading shares and the trading volume of each account in the shares and sends a trading instruction to each account; and judging whether the transaction is successful or not, and finishing the transaction. According to the invention, by establishing a multi-account and multi-batch self-adaptive ordering strategy, the transaction impact cost can be effectively reduced, and the investment risk of each account is reduced.
Description
Technical Field
The invention relates to the technical field of stock analysis, in particular to a multi-account stock transaction control method and device.
Background
The user can do the trading operation of stock buying and selling through the stock trading system. The user can send a trading request to the stock trading system, the trading request indicates the trading volume of the stock, and the stock trading system completes the trading operation of the corresponding trading volume on the stock market trading platform according to the indicated trading volume. However, when all the stocks are put into a stock trading platform at one time, the trading impact cost is often large, particularly when the stocks are traded by multiple accounts, the trading time period and the stocks of each account are different, and the trading impact cost is continuously increased. The stock trading system generally divides the trading volume in the entrusting into a plurality of small trading volumes, and puts the trading volumes into a stock trading platform for a plurality of times at different trading times to reduce the impact cost, but the method is often suitable for the situation of a single account. Therefore, a method for controlling stock exchange of multiple accounts is needed to reduce the execution cost of stock exchange and further improve the satisfaction of the user's experience in using the stock exchange system.
Disclosure of Invention
To overcome at least some of the above problems in the prior art, the present invention provides a system and method for data exchange based on a front-end processor.
In a first aspect, the invention discloses a control method for multi-account stock trading, which comprises the following steps:
s1, establishing a trading position management layer through a position controller, and acquiring a stock list of each account and the stock trading volume of each account at each time point;
s2, simulating each strategy weight in the pool according to the bin controller and the trading algorithm, combining the market data to obtain a trading signal, and entering the stock and the stock trading volume to be traded at the current time point into a trading platform;
s3, the trading platform obtains the trading volume of each account according to the trading stock number and the trading volume of each account in the stock and sends trading instructions to each account;
s4 judges if the trade is successful, the trade is finished, if the trade is not successful, the execution continues to S1-S4.
In a second aspect, the present invention also discloses a stock trading control system, including: the system comprises a bin controller, a trading algorithm simulation pool and a trading platform;
the bin controller is used for establishing a trading bin management layer, and acquiring a stock list of each account and the stock trading volume of each account at each time point;
the trading algorithm simulation pool is used for obtaining a trading signal according to each strategy weight in the bin controller and the trading algorithm simulation pool and by combining market data, and entering stocks to be traded and stock trading volume at the current time point into a trading platform;
and the trading platform is used for obtaining the trading volume of each account according to the number of the trading shares and the trading volume of each account in the shares, sending a trading instruction to each account, judging whether the trading is successful or not and finishing the trading.
In a third aspect, the present invention also discloses an electronic device, which is characterized by including: a memory, a processor, and a computer program, the computer program being stored in the memory, the processor operating the computer program to perform the method of controlling a multi-account stock exchange according to any one of claims 1 to 6.
In a fourth aspect, the present invention also discloses a readable storage medium, wherein the readable storage medium is stored with a computer program, and the computer program is used for implementing the control method of multi-account stock trading according to any one of claims 1 to 6 when being executed by a processor.
The invention has the beneficial effects that:
the invention can effectively reduce the transaction impact cost and simultaneously reduce the investment risk of each account by establishing a multi-account and multi-batch self-adaptive ordering strategy, realizes the apportionment of the transaction cost of a plurality of stock accounts, reduces the total transaction cost, establishes an algorithm transaction pool, sets a plurality of strategy algorithms, and realizes the dynamic tracking of the market by dynamically adjusting the algorithm weight.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of a method for controlling a stock exchange transaction with multiple accounts according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for calculating weights of policies in a simulation pool of transaction algorithms according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating an exploded transaction instruction for a transaction platform according to an embodiment of the invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Before discussing exemplary embodiments in greater detail, it should be noted that the specific structural and functional details disclosed herein of some exemplary embodiments are merely representative and are provided for purposes of describing exemplary embodiments of the present invention. The present invention may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.
Example one
Referring to fig. 1, fig. 1 shows a flowchart of an embodiment of the present invention, which can effectively reduce transaction impact cost and simultaneously reduce investment risk of each account by establishing a multi-account, multi-batch adaptive ordering policy.
S1 establishing trading position management layer by position controller to obtain stock list of each account and stock trading volume of each account at each time point
According to the trading instructions of all M accounts, the trading volume required by each stock is summarized in real time, and the method comprises the following steps:
wherein the content of the first and second substances,
for the total amount of transactions that stock n needs to complete on this transaction day d,
and allocating the amount of the transaction to be completed under the account m for the stock n in the initial state. Then, the trading weight of each stock is obtained according to the ratio of the individual stock trading volume to the total trading volume, and the trading weight comprises the following steps:
wherein the content of the first and second substances,
is the weight of the stock occupied by stock n in account m in the initial state.
S2 simulating each strategy weight in the pool according to the bin controller and the trading algorithm, combining the market data to obtain a trading signal, and entering the stock and stock trading volume to be traded at the current time point into the trading platform
S201, constructing strategies in an algorithm simulation pool, wherein the selection of each strategy algorithm is as follows:
1. IS policy
And executing a drop (IS) algorithm trading strategy and carrying out relevant empirical analysis in the stock market A. The execution drop (IS) IS the difference between the actual transaction amount and the target deal amount determined by the decision before the transaction, and then the fixed transaction cost generated in the transaction IS subtracted. IS strategies attempt to minimize execution drop under certain risk control conditions, and the objective function IS usually represented by a weighted sum of transaction costs (mainly market impact costs) and opportunity costs. The opportunity cost IS related to the fluctuation of the transaction cost, and the fluctuation of the transaction cost IS increased along with the prolonging of the time according to the random walk theory, so that generally, the IS strategy has a large order quantity in the initial stage of the market opening of the transaction day and IS a typical early-stage mass transaction strategy. Compared with the traditional algorithm trading strategy, the impact cost in the IS strategy becomes more transparent, and the advantage can reduce the uncertainty of the impact cost of orders with larger number of parents and singles when trading. Meanwhile, the IS strategy can produce better performance when the investors have better disk surface prediction capability, which IS another advantage of the IS strategy.
2. SOR strategy
The SOR is a policy of issuing a single path preferably, investors can buy and sell securities from market makers and trade in exchanges through direct channels, part of investors can participate in dark pool trading outside exchanges, quotes and trading volume obtained by different trading ways are different, and the SOR is to seek the optimal price on the premise of ensuring the trading volume by analyzing trial trading data of different channels.
3. Staring at the dish strategy (PEG)
Staring a stock plan strategy (PEG) issues a trading instruction at every moment according to the current situation of market stock plan and only lowers the price, and the staring stock plan strategy (PEG) is generally carried out according to the following steps: sending out a certain number of price limit trading instructions according to the highest buying price at the moment when buying and the lowest selling price at the moment when selling, and waiting for a result; if the trading instruction is not finished and the market trading price is gradually far away from the instruction issued by the user, canceling the instruction and executing the first step again according to the existing market condition; if all the price limit transactions instruct the transaction to be completed, repeating the step 1 until all the planned transactions are completed or the deadline of the transaction execution is reached; when the demand of instruction execution is urgent, the trading instruction can be issued at the intermediate price of buying and selling in the market instead of the existing disk mouth, so that the instruction can be executed as early as possible.
4. Deep learning strategy
And (3) a neural network model is constructed in advance, and the price rising and falling trend of each transaction time interval in one transaction day is predicted. The neural network model is generated by using historical trading data in a training mode, the historical trading data comprises price rise and fall label data and stock price characteristic parameters, and the stock price characteristic parameters can influence the price rise and fall of the target stock in a trading time interval, so that the constructed neural network model also has the processing capacity of the stock price characteristic parameters, and the price rise and fall trend of the target stock can be accurately predicted by analyzing the target stock on the stock price characteristic parameters when the input target stock to be analyzed is predicted. It should be noted that the neural network model has good nonlinear expression and feature learning ability, and is widely applied in both supervised and unsupervised scenarios. The neural network is applied to price rise and fall prediction in stock trading days, meanwhile, the prediction result is deeply analyzed, and the advantage of stock price prediction based on the neural network is objectively evaluated.
S202, calculating the weight w of each strategy algorithm in the transaction algorithm simulation poolkAnd dynamically correcting the weighted value according to the market condition
As shown in fig. 3, the method for calculating the weight of each policy in the transaction algorithm simulation pool includes the following steps:
1. establishing a simulated transaction pool, assigning initial weights to each algorithm according to the historical retrieval similarity, and combining the weights w according to the currently known algorithm strategy for each transaction day dk；
2. Tracking simulation algorithm trading pool simulation results in the tray;
3. comparing the simulation result with the difference of the real disk structure, and judging whether to adjust the retest simulation weight;
4. adjusting the retest simulation weight to perform retest simulation, wherein each strategy weight in the retest construction algorithm strategy combination pool comprises: target trading volume per stock
Determined by the signal of the single algorithm strategy k, there are:
wherein V is the total transaction amount to be completed on the current transaction day d, and the transaction amount of each stock is calculated according to the formula, so that a corresponding algorithm strategy k virtual disk combination vector can be obtained:
calculating each strategy in the strategy pool according to a formula to obtain K virtual disk combination vectors P1,P2,…PK。
5. Judging the simulation result of the retest, and judging whether to adjust the weight of each algorithm
6. Performing tracking analysis by algorithm combination after daily tracking adjustment of weight
7. Judging whether to adjust the real disk algorithm strategy pool weight
8. Adjusting the weight of the real-disk algorithm strategy pool, outputting the weight of each algorithm of the transaction algorithm simulation pool, and counting the actual yield r of the day after the transaction of the transaction day is completedrealAnd simultaneously performing performance return measurement on the K virtual disk combinations obtained according to the S2034 to obtain K profitability r1,r2,…rK。
This patent depends on rrealAnd r1,r2,…rKError and gain conditions of (3), and combined gain attenuation possibly occurring in the weight in the process of back measurement, and dynamically correcting the weight value wkAnd making d equal to d +1, returning to the step 1 again, and entering the iterative operation of the next transaction day.
S203, calculating the real-time order-placing total amount of the single period according to the bin controller and the weight of each strategy in the trading algorithm simulation pool.
According to the situation of signal and position control, a certain stock ordering period T occurs, wherein T is 1,2, … and T. An account having a need to trade the stock will trade the stock. The total amount of transactions n needs to complete in the period t is
Comprises the following steps:
where K is the total number of algorithm strategies used, wkIs the weight of the algorithm policy k,
the signal score of stock n is obtained by standardized scoring of the index value obtained by real-time market calculation for strategy k in period t, and the strategy is characterized in thatAre of known quantity and are
The larger the score, the stronger the signal that indicates policy k is for stock n.
S204, dividing the transaction amount tasks of actually executing transaction accounts
Note the book
The target trading volume of account m to stock n in the next period t is as follows:
wherein the content of the first and second substances,
the amount of transactions, p, that stock n needs to complete in the period of placing an order tm,nThe weight of the stock occupied by the stock n in the account m in the last ordering period t-1.
In the actual transaction environment, due to problems of network delay, signal failure and the like, the situation that the order is not given in time or removed often exists, so that the actual transaction cannot necessarily complete all target transaction amounts. In addition, in order to reduce the transaction impact cost, the single transaction amount can not be larger than a certain limit value vLim. The actual trading volume of the account m to the stock n is
Comprises the following steps:
meanwhile, the stock weights in the accounts in the single period t are updated, and the method comprises the following steps:
then can be based on the weight
And submitting the ordering instruction.
S3 trading platform obtains trading volume of each account according to the number of trading shares and the trading volume of each account in the shares and sends trading command to each account
As shown in fig. 3, the process involved is as follows:
acquiring a transaction instruction;
selecting different security traders according to the account, and sending a trading instruction;
and judging whether the transaction is successful, if so, ending the transaction, and if not, canceling the transaction and summarizing a canceling instruction.
S4 judging whether the transaction is successful, completing the transaction
If the transaction is successful, the transaction is completed;
if the transaction is not completed, let t be t +1, enter the next ordering cycle, and loop to step S1 until all ordering instructions on the current transaction date are completed. After the transaction of the current transaction day is completed, the actual yield r of the current day is countedreal。
Examples performed according to S1-S4 are shown in the following table
TABLE 1 list of policy weights in simulation pool for trading algorithm
Example two
The second aspect of the invention also provides a control device for multi-account stock trading, which is used for implementing the method. Specifically, the control device comprises a bin controller, a trading algorithm simulation pool and a trading platform.
The bin controller is used for establishing a trading bin management layer, and acquiring a stock list of each account and the stock trading volume of each account at each time point;
the trading algorithm simulation pool is used for obtaining a trading signal according to each strategy weight in the bin controller and the trading algorithm simulation pool and by combining market data, and entering stocks to be traded and stock trading volume at the current time point into a trading platform;
and the trading platform is used for obtaining the trading volume of each account according to the number of the trading stocks and the trading volume of each account in the stocks, sending a trading instruction to each account, judging whether the trading is successful or not, finishing the trading if the trading is successful, and continuously returning to the bin controller and the trading analog algorithm pool for operation if the trading is not successful.
EXAMPLE III
An electronic device provided in an embodiment of the present invention includes: a processor, memory and computer program; wherein
A memory for storing the computer program, which may also be a flash memory (flash). The computer program is, for example, an application program, a functional module, or the like that realizes the above method.
A processor for executing the computer program stored in the memory to implement the steps of the above method. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory may be separate or integrated with the processor.
When the memory is a device separate from the processor, the electronic device may further include:
and the bus is used for connecting the memory and the processor.
The electronic device may be embodied in the form of a computer terminal, a server, a computer system with a display screen, or the like.
The present invention also provides a readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the methods provided by the various embodiments described above.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the electronic device, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A control method for multi-account stock trading, the control method comprising:
s1, establishing a trading position management layer through a position controller, and acquiring a stock list of each account and the stock trading volume of each account at each time point;
s2, simulating each strategy weight in the pool according to the bin controller and the trading algorithm, combining the market data to obtain a trading signal, and entering the stock and the stock trading volume to be traded at the current time point into a trading platform;
s3, the trading platform obtains the trading volume of each account according to the trading stock number and the trading volume of each account in the stock and sends trading instructions to each account;
s4 judges if the trade is successful, the trade is finished, if the trade is not successful, the execution continues to S1-S4.
2. The control method according to claim 1, wherein the S1 includes:
according to the trading instructions of all M accounts, the trading volume required by each stock is summarized in real time, and the method comprises the following steps:
wherein the content of the first and second substances,
for the total amount of transactions that stock n needs to complete on this transaction day d,
and allocating the amount of the transaction to be completed under the account m for the stock n in the initial state. Then, the trading weight of each stock is obtained according to the ratio of the individual stock trading volume to the total trading volume, and the trading weight comprises the following steps:
wherein the content of the first and second substances,
is the weight of the stock occupied by stock n in account m in the initial state.
3. The control method according to claim 1 or 2, wherein the S2 further includes:
s201, constructing each strategy algorithm in an algorithm simulation pool;
s202, calculating weights of all strategy algorithms in a trading algorithm simulation pool, and dynamically correcting the weight values;
s203, calculating the real-time order placement total amount of the single period according to the weights of all strategies in the bin controller and the trading algorithm simulation pool;
s204, dividing each account transaction amount task.
4. The control method according to claim 3, wherein the S202 includes:
step 1, establishing a simulation trading pool, and assigning initial weights to each algorithm according to historical return similarity;
step 2, tracking the simulation algorithm trading pool simulation result in the tray;
step 3, comparing the simulation result with the difference of the real disk structure, and judging whether to adjust the retest simulation weight;
step 4, adjusting the retest simulation weight to perform retest simulation;
step 5, judging the retest simulation result, and judging whether to adjust the weight of each algorithm;
step 6, tracking and adjusting the weight every day, then combining the algorithms, and performing tracking analysis;
step 7, judging whether to adjust the real disk algorithm strategy pool weight;
and 8, adjusting the weight of the real-disk algorithm strategy pool, and outputting the weight of each algorithm of the transaction algorithm simulation pool.
5. The control method of claim 4, wherein adjusting the backtesting simulation weights for the backtesting simulation comprises:
target trading volume per stock
Determined by the signal of the single algorithm strategy k, there are:
wherein V is the total transaction amount required to be completed on the transaction day d of the day.
Calculating the transaction amount of each stock according to a formula, and obtaining a corresponding algorithm strategy k virtual disk combination vector:
calculating each strategy in the strategy pool according to a formula to obtain K virtual disk combination vectors P1,P2,…PK。
6. The control method according to claim 5, wherein the S203 includes:
according to the situation of signal and position control, a certain stock ordering period T occurs, wherein T is 1,2, … and T. An account having a need to trade the stock will trade the stock. The total amount of transactions n needs to complete in the period t is
Comprises the following steps:
where K is the total number of algorithm strategies used, wkIs the weight of the algorithm policy k,
the signal score for policy k versus stock n over period t.
7. A control device for multi-account stock trading is characterized by comprising a bin controller, a trading algorithm simulation pool and a trading platform;
the bin controller is used for establishing a trading bin management layer, and acquiring a stock list of each account and the stock trading volume of each account at each time point;
the trading algorithm simulation pool is used for obtaining a trading signal according to each strategy weight in the bin controller and the trading algorithm simulation pool and by combining market data, and entering stocks to be traded and stock trading volume at the current time point into a trading platform;
and the trading platform is used for obtaining the trading volume of each account according to the number of the trading shares and the trading volume of each account in the shares, sending a trading instruction to each account, judging whether the trading is successful or not and finishing the trading.
8. The control device of claim 7, wherein the transaction algorithm simulation pool comprises: the system comprises an account transaction amount task dividing module, a strategy construction module in an algorithm simulation pool and each strategy weight calculation module in the transaction algorithm simulation pool.
9. An electronic device, comprising: a memory, a processor, and a computer program, the computer program being stored in the memory, the processor operating the computer program to perform the method of controlling a multi-account stock exchange according to any one of claims 1 to 6.
10. A readable storage medium in which a computer program is stored, the computer program being adapted to implement a method of controlling a multi-account stock exchange according to any one of claims 1 to 6 when executed by a processor.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112102084A (en) * | 2020-08-31 | 2020-12-18 | 东莞市龙兴基石智能科技有限公司 | Multi-account automatic transaction method, transaction system, equipment and storage medium |
| CN113656435A (en) * | 2021-08-20 | 2021-11-16 | 北京神州新桥科技有限公司 | Transaction data query method, electronic device and storage medium |
| TWI754419B (en) * | 2020-10-16 | 2022-02-01 | 國立臺北商業大學 | System and method for automatic allocation of deposit funds for multi-account stock subscription subscription |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112102084A (en) * | 2020-08-31 | 2020-12-18 | 东莞市龙兴基石智能科技有限公司 | Multi-account automatic transaction method, transaction system, equipment and storage medium |
| TWI754419B (en) * | 2020-10-16 | 2022-02-01 | 國立臺北商業大學 | System and method for automatic allocation of deposit funds for multi-account stock subscription subscription |
| CN113656435A (en) * | 2021-08-20 | 2021-11-16 | 北京神州新桥科技有限公司 | Transaction data query method, electronic device and storage medium |
| CN113656435B (en) * | 2021-08-20 | 2023-09-01 | 北京神州新桥科技有限公司 | Transaction data query method, electronic device and storage medium |
| CN116739789A (en) * | 2023-08-16 | 2023-09-12 | 中信证券股份有限公司 | Virtual article return information sending method and device, electronic equipment and medium |
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