CN114297247A - Quantitative transaction data intelligent analysis system based on neural network and big data technology - Google Patents
Quantitative transaction data intelligent analysis system based on neural network and big data technology Download PDFInfo
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- CN114297247A CN114297247A CN202111589903.4A CN202111589903A CN114297247A CN 114297247 A CN114297247 A CN 114297247A CN 202111589903 A CN202111589903 A CN 202111589903A CN 114297247 A CN114297247 A CN 114297247A
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Abstract
The invention discloses a quantitative trading data intelligent analysis system based on neural network and big data technology, the server is connected with a data storage module, a securities trading data screening module, a securities trading data acquisition module, a user investment strategy setting module, a quantitative trading generation module and a quantitative trading execution module, a neural network data model is integrated in the quantitative trading generation module, the securities trading data screening module screens real-time trading market data and historical market trading data through big data technology based on an investment strategy set by a user in the user investment strategy setting module to obtain related real-time trading market data and historical market trading data, and then the relevant real-time trading market data and the historical market trading data are acquired through the securities trading data acquisition module to improve the accuracy of quantitative trading analysis, and the error between the analysis and prediction result and the future actual stock market trend is reduced.
Description
Technical Field
The invention relates to the technical field of quantitative transaction analysis, in particular to an intelligent analysis system for quantitative transaction data based on a neural network and a big data technology.
Background
Quantitative trading is characterized in that an advanced mathematical model is used for replacing artificial subjective judgment, and various 'high-probability' events which can bring excess income are selected from huge historical data by utilizing a computer technology to make a strategy, so that the influence of mood fluctuation of investors is greatly reduced, and an irrational investment decision is avoided under the condition of extreme mania or pessimism of the market. Popular is AI intelligent trading, and quantitative trading is an important investment method for traders in the traditional stock trading secondary market and the foreign exchange trading market.
The accuracy of the existing quantitative trading data analysis system on quantitative trading analysis is poor, so that certain errors exist between the analysis and prediction result and the trend of future actual stock quotations, and property loss of users is easily caused.
Disclosure of Invention
The invention aims to provide a quantitative transaction data intelligent analysis system based on a neural network and a big data technology, and aims to solve the problem that the accuracy of quantitative transaction analysis is poor by the existing quantitative transaction data analysis system in the background technology, so that a certain error exists between an analysis and prediction result and the trend of future actual stock quotations, and property loss of users is easily caused.
In order to achieve the purpose, the invention provides the following technical scheme: a quantitative transaction data intelligent analysis system based on a neural network and big data technology comprises a server, a client, a data storage module, a data transmission module, a security transaction data screening module, a security transaction data acquisition module, a user investment strategy setting module, a quantitative transaction generation module and a quantitative transaction execution module, wherein the server is connected with the client through the data transmission module, and is connected with the data storage module, the security transaction data screening module, the security transaction data acquisition module, the user investment strategy setting module, the quantitative transaction generation module and the quantitative transaction execution module, and a neural network data model is integrated in the quantitative transaction generation module;
the server is used for responding to the service request and processing the service request;
the client is used for a user to log in the server;
the data transmission module is used for transmitting data between the server and the client;
the user investment strategy setting module is used for setting an investment strategy and an implementation period of the strategy by a user;
the security trading data screening module screens real-time trading market data and historical market trading data through a big data technology based on an investment strategy set by a user in the user investment strategy setting module to obtain the real-time trading market data and the historical market trading data related to the investment strategy in the user investment strategy setting module;
the security trading data acquisition module is used for acquiring real-time trading market data and historical market trading data screened by the security trading data screening module;
the data storage module is used for storing the real-time trading market data and the historical market trading data which are acquired by the security trading data acquisition module;
the quantitative transaction generating module generates a quantitative transaction scheme through real-time transaction market data and historical market transaction data in the neural network data model training data storage module;
the quantitative transaction execution module is used for executing the quantitative transaction scheme generated by the quantitative transaction generation module.
Preferably, the server is connected with a historical data retrieval module, and the historical data retrieval module is used for retrieving the historical quantized transaction scheme generated by the quantized transaction generation module.
Preferably, the server is connected with a user login module, a user of the client logs in the server through the user login module, and the user login module logs in through a mobile phone number and a password or a mobile phone number and a short message verification code.
Preferably, the neural network data model is trained by inputting real-time transaction market data and historical market transaction data to obtain output parameters, and a quantitative transaction scheme is generated.
Preferably, the server is connected with a user investment report module, and the user investment report module generates a current-day investment report of the corresponding stocks according to the current-day stocks and the trading situation of the user investment stocks.
Preferably, the client is a mobile phone or a computer.
Compared with the prior art, the invention has the beneficial effects that:
the security trading data screening module screens real-time trading market data and historical market trading data through a big data technology based on an investment strategy set by a user in the user investment strategy setting module to obtain related real-time trading market data and historical market trading data, and then the related real-time trading market data and historical market trading data are collected through the security trading data collecting module, so that the accuracy of quantitative trading analysis is improved, and the error between the analysis and prediction result and the future actual stock market trend is reduced.
Drawings
FIG. 1 is a logic diagram of the present invention.
In the figure: the system comprises a server 1, a data storage module 2, a client 3, a security transaction data screening module 4, a user login module 5, a user investment strategy setting module 6, a quantitative transaction generation module 7, a user investment report module 8, a security transaction data acquisition module 9, a historical data retrieval module 10, a data transmission module 11 and a quantitative transaction execution module 12.
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.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top/bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "sleeved/connected," "connected," and the like are to be construed broadly, e.g., "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example (b):
referring to fig. 1, the present invention provides a technical solution: a quantitative transaction data intelligent analysis system based on a neural network and big data technology comprises a server 1, a client 3, a data storage module 2, a data transmission module 11, a security transaction data screening module 4, a security transaction data acquisition module 9, a user investment strategy setting module 6, a quantitative transaction generation module 7 and a quantitative transaction execution module 12, wherein the server 1 is connected with the client 3 through the data transmission module 11, the server 1 is connected with the data storage module 2, the security transaction data screening module 4, the security transaction data acquisition module 9, the user investment strategy setting module 6, the quantitative transaction generation module 7 and the quantitative transaction execution module 12, and a neural network data model is integrated in the quantitative transaction generation module 7;
the server 1 is used for responding to the service request and processing the service request;
the client 3 is used for logging in the server 1 by a user;
the data transmission module 11 is configured to perform data transmission between the server 1 and the client 3, specifically, perform data transmission through the internet;
the user investment strategy setting module 6 is used for setting an investment strategy and an implementation period of the strategy by a user, the system provides a plurality of common basic strategies, the user can simply and directly select the investment strategy according to the requirement, the user can also select the investment strategy according to the requirement, and the investment strategy comprises the investment amount, the investment requirement, the risk bearing capacity and the like, and the user defines the investment strategy by himself;
the security trading data screening module 4 screens real-time trading market data and historical market trading data of a security trading market through a big data technology based on an investment strategy set by a user in the user investment strategy setting module 6 to obtain the real-time trading market data and the historical market trading data related to the investment strategy in the user investment strategy setting module 6;
the security trading data acquisition module 9 is used for acquiring real-time trading market data and historical market trading data screened by the security trading data screening module 4;
the data storage module 2 is used for storing the real-time trading market data and the historical market trading data acquired by the security trading data acquisition module 9;
the quantitative transaction generating module 7 generates a quantitative transaction scheme through the real-time transaction market data and the historical market transaction data in the neural network data model training data storage module 2;
the quantitative transaction executing module 12 is configured to execute the quantitative transaction scheme generated by the quantitative transaction generating module 7.
The server 1 is connected with a historical data retrieval module 10, and the historical data retrieval module 10 is used for retrieving the historical quantized transaction scheme generated by the quantized transaction generation module 7.
The server 1 is connected with a user login module 5, a user of the client 3 logs in the server 1 through the user login module 5, and the user login module 5 logs in through a mobile phone number and a password or a mobile phone number and a short message verification code.
The neural network data model is trained by inputting real-time transaction market data and historical market transaction data to obtain output parameters and generate a quantitative transaction scheme.
The server 1 is connected with a user investment report module 8, and the user investment report module 8 generates a current-day investment report of corresponding stocks according to current-day stocks and the trading situation of the user investment stocks.
The client 3 is a mobile phone or a computer, and is convenient for users to use.
While there have been shown and described the fundamental principles and essential features of the invention and advantages thereof, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof; the present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. The utility model provides a quantitative transaction data intelligence analytic system based on neural network and big data technology, includes server (1), client (3), data storage module (2), data transmission module (11), securities transaction data screening module (4), securities transaction data acquisition module (9), user investment strategy set up module (6), quantitative transaction generate module (7), quantitative transaction execution module (12), its characterized in that: the system comprises a server (1), a client (3) and a data storage module (2), a security transaction data screening module (4), a security transaction data acquisition module (9), a user investment strategy setting module (6), a quantitative transaction generation module (7) and a quantitative transaction execution module (12), wherein the server (1) is connected with the client (3) through a data transmission module (11), and a neural network data model is integrated in the quantitative transaction generation module (7);
the server (1) is used for responding to the service request and processing the service request;
the client (3) is used for logging in the server (1) by a user;
the data transmission module (11) is used for transmitting data between the server (1) and the client (3);
the user investment strategy setting module (6) is used for setting an investment strategy and an implementation period of the strategy by a user;
the security trading data screening module (4) screens real-time trading market data and historical market trading data through a big data technology based on an investment strategy set by a user in the user investment strategy setting module (6) to obtain the real-time trading market data and the historical market trading data related to the investment strategy in the user investment strategy setting module (6);
the security trading data acquisition module (9) is used for acquiring real-time trading market data and historical market trading data screened by the security trading data screening module (4);
the data storage module (2) is used for storing the real-time trading market data and the historical market trading data which are acquired by the security trading data acquisition module (9);
the quantitative transaction generating module (7) generates a quantitative transaction scheme through real-time transaction market data and historical market transaction data in the neural network data model training data storage module (2);
the quantitative transaction execution module (12) is used for executing the quantitative transaction scheme generated by the quantitative transaction generation module (7).
2. The intelligent analysis system for quantitative transaction data based on neural network and big data technology as claimed in claim 1, wherein: the server (1) is connected with a historical data retrieval module (10), and the historical data retrieval module (10) is used for retrieving the historical quantitative transaction scheme generated by the quantitative transaction generation module (7).
3. The intelligent analysis system for quantitative transaction data based on neural network and big data technology as claimed in claim 1, wherein: the server (1) is connected with a user login module (5), a user of the client (3) logs in the server (1) through the user login module (5), and the user login module (5) logs in through the mobile phone number and the password or the mobile phone number and the short message verification code.
4. The intelligent analysis system for quantitative transaction data based on neural network and big data technology as claimed in claim 1, wherein: the neural network data model is trained by inputting real-time transaction market data and historical market transaction data to obtain output parameters and generate a quantitative transaction scheme.
5. The intelligent analysis system for quantitative transaction data based on neural network and big data technology as claimed in claim 1, wherein: the server (1) is connected with a user investment report module (8), and the user investment report module (8) generates a current-day investment report of corresponding stocks according to current-day stocks and the trading situation of the user investment stocks.
6. The intelligent analysis system for quantitative transaction data based on neural network and big data technology as claimed in claim 1, wherein: the client (3) is a mobile phone or a computer.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111589903.4A CN114297247A (en) | 2021-12-23 | 2021-12-23 | Quantitative transaction data intelligent analysis system based on neural network and big data technology |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202111589903.4A CN114297247A (en) | 2021-12-23 | 2021-12-23 | Quantitative transaction data intelligent analysis system based on neural network and big data technology |
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| CN114297247A true CN114297247A (en) | 2022-04-08 |
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| CN202111589903.4A Withdrawn CN114297247A (en) | 2021-12-23 | 2021-12-23 | Quantitative transaction data intelligent analysis system based on neural network and big data technology |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117522037A (en) * | 2023-11-14 | 2024-02-06 | 苏州云智度科技服务有限公司 | Multi-client multi-program product intelligent perception model |
| CN117522037B (en) * | 2023-11-14 | 2024-06-11 | 苏州云智度科技服务有限公司 | Multi-client multi-program product intelligent perception model |
-
2021
- 2021-12-23 CN CN202111589903.4A patent/CN114297247A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117522037A (en) * | 2023-11-14 | 2024-02-06 | 苏州云智度科技服务有限公司 | Multi-client multi-program product intelligent perception model |
| CN117522037B (en) * | 2023-11-14 | 2024-06-11 | 苏州云智度科技服务有限公司 | Multi-client multi-program product intelligent perception model |
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