CN112991030A - Financial market fluctuation wind control method and system based on big data - Google Patents

Abstract

The invention discloses a market fluctuation wind control method based on big data in the financial field and the computer field, which comprises the following steps of S1 big data acquisition in the financial market; s2, storing the collected financial market data into a cloud database and unifying the formats of the data; s3, preprocessing the data in the cloud database and summarizing the data to generate a visual chart; s4, establishing a data index analysis model by using a mathematical algorithm to perform early warning according to the size of the fluctuation risk index; s5 sets up a processing scheme that maximally circumvents market fluctuations. According to the invention, risk analysis is carried out through the collected financial data input model, and a corresponding response scheme is made according to the fluctuation risk index, so that financial institutions and individuals can timely master the financial market risk condition, a dynamic informatization processing mode is provided for perfecting a financial market management system, and data support is provided by depending on a large data platform.

Description

Financial market fluctuation wind control method and system based on big data

Technical Field

The invention relates to the field of computer big data and the field of finance, and discloses a financial big data wind control method and system.

Background

On one hand, with the development of economy, the financial market is prosperous and prosperous, and various fund security investment insurance industry company organizations actively participate in the financial market, and on the other hand, with the development of computer technology, internet finance tightly combines the financial industry with big data technology, and the system has the characteristics of strong transparency, high efficiency, good collaboration, low intermediate cost and the like, and increasingly becomes the mainstream of the industry depending on high-tech technologies such as third-party payment, cloud computing, big data analysis and the like. However, financial market fluctuation big data are complicated and difficult to analyze, a market participant may suffer huge loss due to the fact that no market wind control provides early warning, the market participant cannot obtain relevant real-time data support in time, and cannot rapidly customize a scheme for dealing with fluctuation in real time.

Disclosure of Invention

Solves the technical problem

Aiming at the conditions that the existing financial market fluctuates greatly and data collection and processing are difficult, the invention aims to provide a financial market fluctuation wind control method and system based on big data, which have the advantages of being convenient to timely early warn financial market fluctuation based on dynamic data so as to improve processing schemes and the like, and solve the problems in the background art.

The technical scheme of the invention is as follows: a financial market fluctuation wind control method and system based on big data comprises a cloud database module, a preprocessing visualization module, a mathematical analysis model module and a coping scheme module.

And the cloud database module is used for regularly dividing each piece of collected big data information, and the division comprises formatting and uniformly dividing similar information into corresponding cloud databases.

The preprocessing visualization module preprocesses the uniform format data stored in the database according to the rules and visualizes the uniform format data through corresponding tools to generate a chart, so that a business person can understand and process market information more quickly. The big data visualization tool can provide real-time information, and the whole financial market can be evaluated more easily.

And the mathematical analysis model module establishes a mathematical model, inputs the obtained market data into the model, detects the over-exponential growth condition of the market developing into a model dangerous area, and carries out risk early warning. And grading the super-exponential growth condition, wherein the exponential growth divergence rate of 1-30% is the first grade, 31-70% is the second grade, and 71-100% is the third grade.

And the coping scheme center makes a decision according to the grade of the early warning signal given by the mathematical analysis model module and gives wind control schemes corresponding to different grades.

Preferably, the cloud database system adopts AWS or Oracle with high safety, high concurrency and low dynamic acquisition delay.

Preferably, the preprocessing visualization module preprocesses the information through analysis, statistics and routine habits of big data.

Preferably, the preprocessing visualization module adopts a third-party visualization tool Matplotlib or finebi.

Preferably, the mathematical analysis model module adopts a super exponential growth model based on a logarithmic coordinate axis.

The invention also provides a financial market fluctuation wind control system based on big data, which is characterized in that,

s1, acquiring financial market big data;

s2, storing the collected financial market data into a cloud database and unifying the formats of the data;

s3, preprocessing the data in the cloud database and summarizing the data to generate a visual chart;

s4, establishing a data index analysis model by using a mathematical algorithm to perform early warning according to the size of the fluctuation risk index;

and S5, making a processing scheme for avoiding the market fluctuation to the maximum extent.

Advantageous effects

Compared with the prior art, the invention has the beneficial effects that: according to the financial market fluctuation wind control system based on the big data, market data can be rapidly and clearly obtained, possible fluctuation of the market can be accurately early warned through a mathematical model, decision making efficiency of financial market participants is improved, and the financial market fluctuation wind control system based on the big data has the advantages of being safer, high in response speed and high in timeliness.

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 apparent that the following description is some drawings of the present invention.

FIG. 1 is a flow chart of the method of the present invention;

FIG. 2 is a block diagram of the system of the present invention;

FIG. 3 is a mathematical formula diagram of the model of 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.

As shown in fig. 2, the financial market fluctuation wind control system based on big data provided by the invention comprises a cloud database module, a preprocessing visualization module, a mathematical analysis model module and a coping scheme module;

the cloud database module is used for acquiring data from a stock market, a bond market and other financial product derivative markets by using a financial data terminal, acquiring the data, further comprising financial supervision interaction information, financial market auditing information and feedback data of market participants, and storing the data into the cloud database so as to dynamically call the data in real time.

The preprocessing visualization module classifies and distinguishes different information, and the preprocessing module preprocesses the information through analysis and statistics of big data and conventional habits. The advantages of the cloud database or the distributed set group are utilized to summarize and count the stored mass financial data, and a visual chart is generated by utilizing a third-party tool, and the method comprises the steps of carrying out statistical analysis on various transaction total amounts of the financial market, carrying out statistical analysis on the capital conditions of financial customers, the liquidity of capital, the market direction of capital operation and the like.

The mathematical analysis model module establishes an exponential growth divergence model based on logarithmic coordinates according to a super-exponential growth model algorithm, calculates whether various risks of the financial market enter a fluctuation danger area of super-exponential growth or not through the established model, and makes prediction and early warning. According to the index divergence rate, 1-30 percent is classified into one grade, 31-70 percent is classified into two grades, and 71-100 percent is classified into three grades.

And the coping scheme module is used for respectively making wind control schemes which are in line with financial customers and avoid financial market risk investment exposure according to the divergence grade given by the mathematical analysis model, namely the first grade, the second grade and the third grade.

As shown in the method flowchart of fig. 1, the method includes: s1, collecting big data of the dynamic financial market through financial data terminals such as Bloomberg, Wind and the like; s2, storing the collected financial market data into an Oracle cloud database and unifying the formats of the financial market data; s3, preprocessing the data in the cloud database by using Matplotlib and summarizing to generate a visual chart; s4, establishing a data index analysis model by using a mathematical algorithm to perform early warning according to the size of the fluctuation risk index; s5, making a processing scheme for avoiding the market fluctuation to the maximum extent according to the risk index given by the model.

FIG. 3 is a schematic diagram of a mathematical analysis model, which shows in detail the mathematical formula and the principle of an exponential growth model for predicting the risk of fluctuation, and an exponential growth model is constructed based on the principle, a part exceeding the exponential growth is called super-exponential growth, the financial market enters the super-exponential growth to represent that the financial market enters a fluctuation dangerous area, and the fluctuation dangerous degree is predicted by using three levels of exponential growth divergence rate intervals of 1% -30%, 31% -70%, and 71% -100%, and accordingly different levels of wind control schemes are given.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes 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. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (8)

1. A financial market fluctuation wind control system based on big data comprises a cloud database module, a preprocessing visualization module, a mathematical analysis model module and a coping scheme module;

the cloud database module is used for regularly dividing each piece of collected big data information, and the division comprises formatting and uniformly dividing similar information into corresponding cloud databases;

and the preprocessing visualization module is used for preprocessing the unified format data stored in the database according to the rules and visualizing the preprocessed unified format data through corresponding tools to generate a chart.

2. The wind control system according to claim 1, wherein the mathematical analysis model module establishes a mathematical model, inputs the obtained market data into the model, detects a super exponential growth condition when the market develops into a dangerous area of the model, and performs risk pre-warning. And grading the super-exponential growth condition, wherein the exponential growth divergence rate of 1-30% is the first grade, 31-70% is the second grade, and 71-100% is the third grade.

3. The wind control system according to claim 1, wherein the coping scheme center makes a decision according to the level of the early warning signal given by the mathematical analysis model module, and gives wind control schemes corresponding to different levels.

4. The wind control system according to claim 1, wherein the cloud database system employs an AWS or Oracle with high security, high concurrency, and low dynamic acquisition latency.

5. Wind control system according to claim 1, wherein the pre-processing visualization module pre-processes the information by analysis of big data, statistics and routine habits.

6. The wind control system of claim 1, wherein the pre-processing visualization module employs a third party visualization tool Matplotlib or finebi.

7. The wind control system of claim 1, wherein the mathematical analysis model module employs a super exponential growth model based on a logarithmic coordinate axis.

8. A financial market fluctuation wind control system based on big data is characterized in that,

s1, acquiring financial market big data;

s2, storing the collected financial market data into a cloud database and unifying the formats of the data;

s3, preprocessing the data in the cloud database and summarizing the data to generate a visual chart;

s4, establishing a data index analysis model by using a mathematical algorithm to perform early warning according to the size of the fluctuation risk index;

and S5, making a processing scheme for avoiding the market fluctuation to the maximum extent.

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