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Wavelet noise analysis-based risk prediction method, equipment and storage medium

A technology of risk prediction and wavelet noise, applied in the financial field, can solve the problems of unsatisfactory performance of non-stationary time series, avoid irrational investment decisions, improve the rate of return, and improve the user experience.

Inactive Publication Date: 2018-09-04
上海宽全智能科技有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0004] However, the above methods have a good effect on the stationary time series, but the performance is not satisfactory for the non-stationary time series.

Method used

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  • Wavelet noise analysis-based risk prediction method, equipment and storage medium
  • Wavelet noise analysis-based risk prediction method, equipment and storage medium
  • Wavelet noise analysis-based risk prediction method, equipment and storage medium

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Experimental program
Comparison scheme
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Embodiment 1

[0047] refer to figure 1 , shows a flow chart of a risk prediction method based on wavelet noise analysis in an embodiment of the present invention. The risk prediction method based on wavelet noise analysis in the present invention is suitable for execution in user equipment, and the method includes the following steps:

[0048] Step 101, acquiring the historical market data of the target, and decomposing the market data by using wavelet analysis to generate low-frequency data and high-frequency data.

[0049] In this embodiment, the target includes a single target, a combination of targets or a price index, and the price index includes an average stock price index and a stock price composite index. The analysis time zone is preset, and the historical market data of the above-mentioned target within the above-mentioned time zone is extracted. The market data includes any set of the target’s highest price, lowest price, opening price, closing price, average price, and price in...

Embodiment 2

[0068] The present invention also provides a risk prediction method based on wavelet noise analysis, which is suitable for execution in computing equipment, refer to image 3 , shows the flow chart of the risk prediction method based on wavelet noise analysis of the present invention, the method includes the following steps:

[0069] Step 201, acquiring the historical market data of the target, and decomposing the market data by using wavelet analysis to generate low-frequency data and high-frequency data.

[0070] In this embodiment, the target includes a single target, a combination of targets or a price index, and the price index includes an average stock price index and a stock price composite index. The analysis time zone is preset, and the historical market data of the above-mentioned target within the above-mentioned time zone is extracted. The market data includes any set of the target’s highest price, lowest price, opening price, closing price, average price, and pric...

Embodiment 3

[0084] The present invention also provides a risk prediction method based on wavelet noise analysis, which is suitable for execution in computing equipment, refer to Figure 4 , shows the flow chart of the risk prediction method based on wavelet noise analysis of the present invention, the method includes the following steps:

[0085] In step 301, the historical market data of the target is dynamically acquired in real time, and the market data is decomposed by wavelet analysis to generate low-frequency data and high-frequency data.

[0086] In this embodiment, the target includes a single target, a combination of targets or a price index, and the price index includes an average stock price index and a stock price composite index. The analysis time zone is preset, and the historical market data of the above-mentioned target within the above-mentioned time zone is extracted. The market data includes any set of the target’s highest price, lowest price, opening price, closing pri...

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Abstract

The invention provides a wavelet noise analysis-based risk prediction method, equipment and a storage medium. The method comprises the following steps of: obtaining history market data of an object, and decomposing the market data by adoption of wavelet analysis so as to generate low-frequency data and high-frequency data; carrying out reconstruction on the basis of the high-frequency data to generate a fluctuation curve; calculating relevance between the fluctuation curve and risk factors, and determining related risk factors of the object on the basis of sizes of the relevance; and predicting a market trend of the object on the basis of trends of the related risk factors. According to the method, high-frequency signals of history markets are extracted, comparison is carried out via fluctuation laws of the high-frequency signals and fluctuation laws of risk factors so as to find relevance, and future markets of objects are predicted through related risk factors, so that scientific data judging basis is provided for scientific and correct investment of vast investors, the irrational investment decisions of the users are greatly avoided, investment return rates of the users can be greatly improved, and furthermore, the using experience of the users is improved.

Description

technical field [0001] The present invention relates to the financial field, in particular, to a risk prediction method, device and storage medium based on wavelet noise analysis. Background technique [0002] With the establishment and continuous development of securities investment technical analysis theory, investors have theoretical support for the predictability of financial markets. Technical analysis itself pays little attention to the company's fundamentals, and mainly studies the specific performance of stocks in the market, that is, through the recording, investigation and analysis of the direction, range and trading volume of various securities price changes that have existed in the stock market in the past, And based on this, the future price change trend of the securities market is predicted, so that investors can make the final decision. [0003] The stock market is a rather complex system, and changes in stock prices are affected by various factors such as th...

Claims

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Application Information

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IPC IPC(8): G06Q40/06G06Q10/06
CPCG06Q40/06G06Q10/0635G06Q10/06393
Inventor 李贵
Owner 上海宽全智能科技有限公司
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