CN113807966A - Stock exchange optimization management system and method based on big data - Google Patents
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Abstract
The invention discloses a stock trading optimization management system and method based on big data. The invention comprises the following steps: the information input unit is used for inputting user information and transaction order information by a user; the market server is used for acquiring the market of the exchange in real time through an external network and forwarding market data to the trader operation unit; the trader operation unit is used for the trader to carry out the operation panel cluster of the stock short-term trade; the trading server is used for receiving a trading instruction and trading securities according to the instruction; and the transaction decision unit is used for accessing the pre-stored data information through the file identifier and carrying out corresponding operation according to the control instruction. The stock trading method receives the stock trading request sent by the user through the stock data server, and the trading decision unit calls the information in the trader operation unit to be compared with the current trading order information in the trading process to complete the stock trading, so that the utilization rate of the stock information and the management of the stock trading data are improved, and the risk of investors is reduced.
Description
Technical Field
The invention belongs to the technical field of stock trading, and particularly relates to a stock trading optimization management system and method based on big data.
Background
The stock market in China is trial run from 1989, and after more than 30 years of development, the stock market undergoes important leaps from none to some, and from few to many. Initially, due to the lack of experience related to stock development, a little exploration has advanced, and until now the stock market has developed into an essential part of the economic development of china. The stock market in China has potential problems while making great progress. The price of a stock fluctuates greatly due to various factors, such as: more interior transactions appear in the stock market, the strength is slightly insufficient in the execution of the rules of the market withdrawal, the related laws and regulations in the stock market cannot be made in time, and the like, so that a part of stockholders suffer great loss. Moreover, the stock market in China also has the characteristic of policy market, and the stock price of each stock market in China can be influenced by the relevant policy of the government.
With the continuous development of the stock market, a large amount of stock data is generated. However, the value of the historical data is often ignored by people or it is difficult to deeply dig out the real value useful for people in the process of analyzing the historical data, and the utilization rate of the information is low. And the stock market is always 'cloudy and sunny indefinite', the price of the stock fluctuates greatly, and the price change of the stock is closely related to the vital interests of each stock investor.
Therefore, the advantages brought by the big data technology are fully utilized, the neural network algorithm is combined, historical trading data of the stocks are analyzed, rules hidden in the big data of the stocks are effectively mined as far as possible, and price trends of the stocks are found out.
Disclosure of Invention
The invention aims to provide a stock exchange optimization management system and a method based on big data, which receive a stock exchange request sent by a user through a stock data server, write the stock exchange request into an information input unit according to an intelligent contract and a trigger condition, and call information in a trader operation unit to be compared with current exchange order information in the exchange process by a trade decision unit to finish stock exchange, thereby solving the problems of difficult stock data management and higher investor risk in the prior art.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a stock exchange optimization management system based on big data, which is used for executing the process of stock exchange of a user to CSDCC and trust bank through an economic trader and a stock exchange, and comprises an information input unit, a market server, a trader operation unit, a stock management server, an exchange server and an exchange decision unit;
the information input unit is used for inputting user information and transaction order information by a user;
the market server is used for acquiring the market of the exchange in real time through an external network and forwarding market data to the trader operation unit;
the trader operation unit is used for the trader to carry out the operation panel cluster of the stock short-term trade, connect in parallel by different independent trader operation panels;
the stock management server stores the types and positions of the held tickets; the stock management server is used for selecting the types and the quantities of stocks which are subjected to short-term trading on the same day from the ticket holding information database and sending the stocks to the trader operation desk cluster for performing the trading on the same day;
the trading server is used for receiving a trading instruction, carrying out security trading according to the trading instruction and feeding back a trading result;
and the transaction decision unit is used for acquiring a file identifier corresponding to the stock data access request according to the transaction market ID corresponding to the stock data request, accessing the prestored data information through the file identifier and performing corresponding operation according to the control instruction.
As a preferred technical solution, the trade order information is stored in a stock data server; the stock data server is used for receiving a stock transaction request sent by a user; the stock trading request comprises a stock data request comprising a stock code, a trading market ID, stock data and a control instruction, and the stock data request is sent to a corresponding stock management server according to the trading market ID.
As a preferred technical scheme, the trade order information sent by the user is converted into an intelligent contract and a trigger condition, the intelligent contract and the trigger condition are written into the information input unit, and the trade order information is sent to the economic trader; the economic trader receives order information, writes the order information into the trader operation unit, sends trading order information to the stock exchange when receiving an intelligent contract execution instruction of the trading server, and returns inconsistent trading information when receiving a trading failure instruction of the trading server; the stock exchange receives order information, writes the order information into the trader operation unit, sends trade order information to the CSDCC and the trust bank when receiving the execution intelligent contract instruction of the trade server, and returns inconsistent trade information when receiving the trade failure instruction of the trade server.
As a preferred technical scheme, the trading server receives trading order information, writes the trading order information into a trader operation unit, transfers the securities ownership according to contract content when receiving an intelligent contract execution instruction of a trading decision unit, and returns trading information inconsistency information when receiving a trading failure instruction of the trading decision unit; the trusted bank receives the transaction details, writes the transaction details into the operation unit of a trader, settles and moves cash according to contract contents when receiving the instruction of executing the intelligent contract of the transaction decision unit, and returns inconsistent information of transaction information when receiving the instruction of transaction failure of the transaction decision unit; the broker-trader receives the information of transferring the ownership of the securities, settling the accounts and moving the cash and writes the information into the trader operation unit.
As a preferred technical scheme, the transaction decision unit calls information in the trader operation unit to be compared with current transaction order information in the transaction process, and judges whether the transaction order information meets the triggering condition of the intelligent contract, if the buyer account stored by the stock management server is equal to the buyer account agreed in the intelligent contract; the purchase price stored by the stock management server is equal to the purchase price agreed in the intelligent contract; the buying quantity of the stock management server is equal to the buying quantity agreed in the intelligent contract; and the trading time stored by the stock management server is equal to the trading time appointed in the intelligent contract, if so, an instruction for executing the intelligent contract is sent, and otherwise, a trading failure instruction is sent.
As a preferred technical scheme, the trader operation unit performs wind control monitoring on the operation process of each trading operation console, and performs risk monitoring on the trading information set of the data filter screen; in an initial state, a trader logs in a risk control device, and performs initial setting on wind control rules in the process of vote and transaction according to policy regulations and market transaction wind control rules, wherein the initial setting comprises trigger event rule setting, interval setting of a limit threshold value and initial value setting of the limit threshold value; the transaction data analysis server also comprises a wind control analysis unit; the data acquisition device periodically transmits the data related to the wind control information to the transaction data analysis server; and optimizing the limit threshold of the existing wind control rule by the stock management server according to the actual short-term transaction data statistical condition and the historical transaction data statistical condition to generate a new wind control rule, and sending the new wind control rule to the risk control device to realize wind control optimization of the vote and transaction process.
The invention relates to a stock exchange optimization management method based on big data, which comprises the following steps:
step S1: a user inputs user information and transaction order information into the information input unit;
step S2: a stock data server receives a stock transaction request sent by a user;
step S3: the stock data server writes information into the unit according to the intelligent contract and the triggering condition;
step S4: when executing the intelligent contract instruction, the trading server sends trading order information to the stock exchange;
step S5: when executing the intelligent contract instruction, the transaction server sends transaction order information to the CSDCC and the trust bank;
step S6: the trading decision unit transfers the security ownership according to the contract content when executing the intelligent contract instruction;
step S7: the trusted bank receives the transaction details, writes the transaction details into the operation unit of the trader, and settles and moves cash according to the contract content when receiving the instruction of executing the intelligent contract of the transaction decision unit;
step S8: the trading decision unit calls information in the trader operation unit to be compared with the current trading order information in the trading process, and if the comparison is successful, the stock trading is completed, and the stock trading is stored and recorded;
step S9: and the trader operation unit carries out wind control monitoring on the operation process of each trading operation platform.
As a preferable technical solution, in step S1, the information entry unit includes a user account management module for managing user account information and a transaction order management module for managing transaction order information.
As a preferable technical solution, in step S9, the wind control monitoring is processed by a wind control rule limiting threshold, the wind control rule limiting threshold is optimized according to a big data analysis manner of the transaction history, a proper neural network model is selected, the limiting threshold of a specific compliance wind control rule and corresponding stock earning data are input as training samples, the operation is performed, and the neural network model is continuously perfected through machine training and learning, so as to obtain an optimal solution of the limiting threshold of the compliance wind control rule.
As a preferred technical solution, the neural network model is expressed as:
Rj＝αj+βfRM+εj；
in the formula, RjExpressed as the profitability, R, of the asset in jMExpressed as the profitability of the market, αjExpressed as intercept term, εjExpressed as an error term.
The invention has the following beneficial effects:
the stock exchange system receives a stock exchange request sent by a user through the stock data server, writes the stock exchange request into the information input unit according to the intelligent contract converted by the stock data server and the triggering condition, and calls information in the trader operation unit to be compared with current exchange order information by the exchange decision unit in the exchange process to complete stock exchange, thereby improving the utilization rate of stock information and the management of stock exchange data and reducing the risk of investors.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flow chart of a stock exchange optimization management method based on big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to a stock exchange optimization management system based on big data, which is used for executing the process of stock exchange of a user to CSDCC and trust bank through an economic trader and a stock exchange, and is characterized by comprising an information input unit, a market server, a trader operation unit, a stock management server, an exchange server and an exchange decision unit;
the information input unit is used for inputting user information and transaction order information by a user;
the market server is used for acquiring the market of the exchange in real time through an external network and transmitting market data to the trader operation unit;
the trader operation unit is used for the trader to carry out the operation panel cluster of the stock short-term transaction, and different independent trader operation panels are connected in parallel;
the stock management server stores the type and the position of the held ticket; the stock management server is used for selecting the types and the quantities of stocks which are subjected to short-term transaction on the current day from the ticket holding information database and sending the stocks to the trader operation desk cluster for performing the current day transaction;
the trading server is used for receiving a trading instruction, carrying out security trading according to the trading instruction and feeding back a trading result;
and the transaction decision unit is used for acquiring a file identifier corresponding to the stock data access request according to the transaction market ID corresponding to the stock data request, accessing the pre-stored data information through the file identifier and performing corresponding operation according to the control instruction.
The transaction order information is stored in the stock data server; the stock data server is used for receiving a stock trading request sent by a user; the stock trading request comprises a stock data request comprising a stock code, a trading market ID, stock data and a control instruction, and the stock data request is sent to the corresponding stock management server according to the trading market ID.
The method comprises the steps that transaction order information sent by a user is converted into an intelligent contract and trigger conditions, the intelligent contract and the trigger conditions are written into an information input unit, and the transaction order information is sent to an economic trader; the economic trader receives the order information, writes the order information into the trader operation unit, sends trading order information to the stock exchange when receiving the execution intelligent contract instruction of the trading server, and returns inconsistent trading information when receiving the trading failure instruction of the trading server; the security exchange receives order information, writes the order information into the trader operation unit, sends trading order information to the CSDCC and the trust bank when receiving the intelligent contract execution instruction of the trading server, and returns inconsistent trading information when receiving the trading failure instruction of the trading server.
The trading server receives trading order information, writes the trading order information into the trader operation unit, transfers the securities ownership according to the contract content when receiving the intelligent contract execution instruction of the trading decision unit, and returns inconsistent trading information when receiving the trading failure instruction of the trading decision unit; the trusted bank receives the transaction details, writes the transaction details into the trader operation unit, settles and moves cash according to contract contents when receiving the intelligent contract execution instruction of the transaction decision unit, and returns inconsistent transaction information when receiving the transaction failure instruction of the transaction decision unit; the broker-trader receives the information of transferring the ownership of the securities, settling the accounts and moving the cash and writes the information into the trader operation unit.
The trading decision unit calls information in the trader operation unit to be compared with current trading order information in the trading process, whether the trading order information meets the triggering condition of the intelligent contract or not is judged, and if the buyer account stored by the stock management server is equal to the buyer account appointed in the intelligent contract; the purchase price stored by the stock management server is equal to the purchase price agreed in the intelligent contract; the buying quantity of the stock management server is equal to the buying quantity agreed in the intelligent contract; and the transaction time stored by the stock management server is equal to the transaction time appointed in the intelligent contract, if so, an instruction for executing the intelligent contract is sent, and if not, a transaction failure instruction is sent.
The trader operation unit carries out wind control monitoring on the operation process of each trading operation console and carries out risk monitoring on the trading information set of the data filter screen; in an initial state, a trader logs in the risk control device, and performs initial setting on the wind control rules in the vote and trading process according to policy regulations and market trading wind control rules, wherein the initial setting comprises trigger event rule setting, interval setting of a limit threshold value and initial value setting of the limit threshold value; the transaction data analysis server also comprises a wind control analysis unit; the data acquisition device periodically transmits the related data of the wind control information to the transaction data analysis server; and optimizing the limit threshold of the existing wind control rule by the stock management server according to the actual short-term transaction data statistical condition and the historical transaction data statistical condition to generate a new wind control rule, and sending the new wind control rule to the risk control device to realize wind control optimization of the vote and the transaction process.
Referring to fig. 1, the present invention is a stock exchange optimization management method based on big data, including the following steps:
step S1: a user inputs user information and transaction order information into the information input unit;
step S2: a stock data server receives a stock transaction request sent by a user;
step S3: the stock data server writes information into the unit according to the intelligent contract and the triggering condition;
step S4: when executing the intelligent contract instruction, the trading server sends trading order information to the stock exchange;
step S5: when executing the intelligent contract instruction, the transaction server sends transaction order information to the CSDCC and the trust bank;
step S6: the trading decision unit transfers the security ownership according to the contract content when executing the intelligent contract instruction;
step S7: the trusted bank receives the transaction details, writes the transaction details into the operation unit of the trader, and settles and moves cash according to the contract content when receiving the instruction of executing the intelligent contract of the transaction decision unit;
step S8: the trading decision unit calls information in the trader operation unit to be compared with the current trading order information in the trading process, and if the comparison is successful, the stock trading is completed, and the stock trading is stored and recorded;
step S9: and the trader operation unit carries out wind control monitoring on the operation process of each trading operation platform.
In step S1, the information entry unit includes a user account management module for managing user account information and a trade order management module for managing trade order information.
In step S9, the wind control monitoring is processed by the wind control rule limiting threshold, the wind control rule limiting threshold is optimized according to the big data analysis mode of the transaction history, a proper neural network model is selected, the limiting threshold of the specific compliance wind control rule and the corresponding stock earnings data are input as training samples for operation, and the neural network model is continuously improved through machine training and learning, so as to obtain the optimal solution of the limiting threshold of the compliance wind control rule.
The neural network model is represented as:
Rj＝αj+βfRM+εj；
in the formula, RjExpressed as the profitability, R, of the asset in jMExpressed as the profitability of the market, αjExpressed as intercept term, εjIs shown asAn error term.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it can be understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above can be implemented by a program to instruct related hardware, and the corresponding program can be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. A stock exchange optimization management system based on big data is used for executing the process of stock exchange of a user to CSDCC and trust banks through an economic trader and a stock exchange and is characterized by comprising an information input unit, a market server, a trader operation unit, a stock management server, a trading server and a trading decision unit;
the information input unit is used for inputting user information and transaction order information by a user;
the market server is used for acquiring the market of the exchange in real time through an external network and forwarding market data to the trader operation unit;
the trader operation unit is used for the trader to carry out the operation panel cluster of the stock short-term trade, and the operation panels of different independent traders are connected in parallel;
the stock management server stores the types and positions of the held tickets; the stock management server is used for selecting the types and the quantities of stocks which are subjected to short-term trading on the same day from the ticket holding information database and sending the stocks to the trader operation desk cluster for performing the trading on the same day;
the trading server is used for receiving a trading instruction, carrying out security trading according to the trading instruction and feeding back a trading result;
and the trading decision unit is used for acquiring a file identifier corresponding to the stock data access request according to the trading market ID corresponding to the stock data access request, accessing the pre-stored data information through the file identifier and performing corresponding operation according to the control instruction.
2. A big data based stock exchange optimization management system as claimed in claim 1, wherein the trade order information is stored in stock data server; the stock data server is used for receiving a stock transaction request sent by a user; the stock trading request comprises a stock data request comprising a stock code, a trading market ID, stock data and a control instruction, and the stock data request is sent to a corresponding stock management server according to the trading market ID.
3. A stock exchange optimization management system based on big data as claimed in claim 1, wherein the trade order information sent by the user is converted into an intelligent contract and a trigger condition, written into the information entry unit, and sent to the economic trader; the economic trader receives order information, writes the order information into the trader operation unit, sends trading order information to the stock exchange when receiving an intelligent contract execution instruction of the trading server, and returns inconsistent trading information when receiving a trading failure instruction of the trading server; the stock exchange receives order information, writes the order information into the trader operation unit, sends trade order information to the CSDCC and the trust bank when receiving the execution intelligent contract instruction of the trade server, and returns inconsistent trade information when receiving the trade failure instruction of the trade server.
4. A stock exchange optimization management system based on big data as claimed in claim 1, wherein the exchange server receives exchange order information, writes it into the trader operation unit, transfers the bond ownership according to the contract content when receiving the intelligent contract execution instruction of the exchange decision unit, and returns the exchange information inconsistency information when receiving the exchange failure instruction of the exchange decision unit; the trusted bank receives the transaction details, writes the transaction details into the trader operation unit, settles and moves cash according to contract contents when receiving the intelligent contract execution instruction of the transaction decision unit, and returns inconsistent transaction information when receiving the transaction failure instruction of the transaction decision unit; the broker-trader receives the information of transferring the ownership of the securities, settling the accounts and moving the cash and writes the information into the trader operation unit.
5. The system and method for optimizing and managing stock exchange based on big data as claimed in claim 1, wherein the trading decision unit calls the information in the trader operation unit to compare with the current trading order information in the trading process, and judges whether the trading order information meets the triggering condition of the intelligent contract, if the buyer account stored in the stock management server is equal to the buyer account agreed in the intelligent contract; the purchase price stored by the stock management server is equal to the purchase price agreed in the intelligent contract; the buying quantity of the stock management server is equal to the buying quantity agreed in the intelligent contract; and the trading time stored by the stock management server is equal to the trading time appointed in the intelligent contract, if so, an instruction for executing the intelligent contract is sent, and otherwise, a trading failure instruction is sent.
6. A stock exchange optimization management system and method based on big data as claimed in claim 1, wherein the trader operation unit performs wind control monitoring on the operation process of each trading operation platform, and performs risk monitoring on the trading information set of the data filter screen; in an initial state, a trader logs in a risk control device, and performs initial setting on wind control rules in the process of vote and transaction according to policy regulations and market transaction wind control rules, wherein the initial setting comprises trigger event rule setting, interval setting of a limit threshold value and initial value setting of the limit threshold value; the transaction data analysis server also comprises a wind control analysis unit; the data acquisition device periodically sends the related data of the wind control information to the transaction data analysis server; and optimizing the limit threshold of the existing wind control rule by the stock management server according to the actual short-term transaction data statistical condition and the historical transaction data statistical condition to generate a new wind control rule, and sending the new wind control rule to the risk control device to realize wind control optimization of the vote and the transaction process.
7. A stock exchange optimization management method based on big data is characterized by comprising the following steps:
step S1: a user inputs user information and transaction order information into the information input unit;
step S2: a stock data server receives a stock transaction request sent by a user;
step S3: the stock data server writes the stock data into the information input unit according to the intelligent contract and the triggering condition;
step S4: the trading server sends trading order information to the security exchange when executing the intelligent contract instruction;
step S5: when executing the intelligent contract instruction, the transaction server sends transaction order information to the CSDCC and the trust bank;
step S6: the trading decision unit transfers the security ownership according to the contract content when executing the intelligent contract instruction;
step S7: the trusted bank receives the transaction details, writes the transaction details into the operation unit of the trader, and settles and moves cash according to contract contents when receiving the instruction of executing the intelligent contract from the transaction decision unit;
step S8: the trading decision unit calls information in the trader operation unit to be compared with the current trading order information in the trading process, and if the comparison is successful, the stock trading is completed, and the stock trading is stored and recorded;
step S9: and the trader operation unit carries out wind control monitoring on the operation process of each trading operation platform.
8. A method for optimized management of stock exchange transaction based on big data as claimed in claim 7, wherein in said step S1, the information entry unit includes a user account management module for managing user account information and a trade order management module for managing trade order information.
9. The stock exchange optimization management method based on big data as claimed in claim 7, wherein in step S9, the wind control monitoring is processed by wind control rule limiting threshold, the wind control rule limiting threshold optimization is performed according to big data analysis mode of the exchange history, a proper neural network model is selected, the limiting threshold of a specific compliance wind control rule and corresponding stock earning data are used as training samples to be input, operation is performed, and the neural network model is continuously perfected through machine training and learning, so as to obtain the optimal solution of the limiting threshold of the compliance wind control rule.
10. The method as claimed in claim 9, wherein the neural network model is expressed as:
Rj＝αj+βfRM+εj；
in the formula, RjExpressed as the profitability, R, of the asset in jMExpressed as the profitability of the market, αjExpressed as intercept terms,. epsilonjExpressed as an error term.
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2021
- 2021-09-18 CN CN202111097021.6A patent/CN113807966A/en active Pending
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| US20030233306A1 (en) * | 2002-06-12 | 2003-12-18 | Itg, Inc. | System and method for estimating and optimizing transaction costs |
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