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Hedging transaction analysis method and device based on wavelet analysis, and storage medium

A technology of wavelet analysis and analysis method, applied in the field of data processing, can solve the problem that the performance of non-stationary time series is not satisfactory, and achieve the effect of accurate market forecasting

Inactive Publication Date: 2018-07-31
上海宽全智能科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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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  • Hedging transaction analysis method and device based on wavelet analysis, and storage medium
  • Hedging transaction analysis method and device based on wavelet analysis, and storage medium
  • Hedging transaction analysis method and device based on wavelet analysis, and storage medium

Examples

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Embodiment 1

[0056] The invention provides a hedging transaction analysis method based on wavelet analysis, which is suitable for execution in computing equipment. This embodiment is suitable for generating alpha trend graphs and / or beta shock graphs based on wavelet analysis of stock market data, and predicting and trading stock prices based on the alpha trend graphs and / or beta shock graphs.

[0057] refer to figure 1 , shows the flow chart of the hedging transaction analysis method based on wavelet analysis of the present invention, including the following steps:

[0058] Step 101, acquiring historical market data of one or more combined targets.

[0059] In this embodiment, the historical time interval for obtaining the market data is preset, and the historical market data of one or more combined targets within the time interval is obtained. The market data includes the highest price, the lowest price, the opening price, the closing price, the average Any group or multiple groups in ...

Embodiment 2

[0081] The present invention also provides a hedging transaction analysis based on wavelet analysis, which is suitable for execution in computing equipment, including the following steps, still refer to figure 1 , shows the flow chart of the hedging transaction analysis method based on wavelet analysis in this embodiment:

[0082] Step 101, acquiring historical market data of one or more combined targets.

[0083] In this embodiment, the extracted market data includes the highest price, the lowest price, the opening price, and the closing price of one or more groups of minute lines, hour lines, daily lines, weekly lines, monthly lines, quarterly lines, and semi-annual lines. One or more of line, year line, etc.

[0084] In a specific embodiment, the closing price data of nearly 160 days is preset and acquired by the user as the analysis object, and the closing price of the target stock on each trading day is extracted as the value at a known moment.

[0085] Because in pract...

Embodiment 3

[0094] This embodiment is another implementation based on the above-mentioned embodiment. In this embodiment, the α trend graph generated by the wavelet decomposition and reconstruction of the above-mentioned embodiment is used to predict the market price of the future cycle, and according to the Predict the market to trade.

[0095] In a specific embodiment, the α value of the next cycle or multiple cycles is predicted by the α trend curve obtained by the wavelet decomposition in the above embodiment for nearly 160 days, and the α value is extrapolated based on the fluctuation range of the β threshold value and the predicted value of α in each cycle The market data of one or more cycles in the future, including one or more groups of opening price, closing price, highest price, lowest price, and average price of one or more cycles in the future; the forecast method includes linear Extrapolation and nonlinear extrapolation methods. In this embodiment, the α trend graph and / or ...

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Abstract

The invention provides a hedging transaction analysis method and device based on wavelet analysis, and a storage medium. The method comprises the steps: obtaining the historical market data of one group or more groups of combined targets; carrying out the preprocessing of the market data through wavelet analysis, and generating an alpha tendency chart and / or beta oscillation chart based on the preprocessing data; carrying out the transaction based on the alpha tendency chart and / or beta oscillation chart. A signal is decomposed to different frequency channels based on the wavelet analysis theory. Because the number of the frequency components of the decomposed signals is smaller than the number of the frequency components of an original signal and the smoothing processing of the signal isperformed and then the decomposed signals are reconstructed, a non-steady time sequence becomes an approximately steady time sequence after processing. The alpha tendency chart and / or beta oscillationchart is generated based on the reconstructed data after smoothing, and the alpha tendency chart and / or beta oscillation chart are / is used for the prediction of transactions, thereby enabling the market prediction to be more accurate.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a wavelet analysis-based hedging transaction analysis method, device and storage medium. Background technique [0002] The stock market is the barometer and early warning device of economic development, and the correct prediction of the stock market is the premise of the country's macro-control and management, and also the basis for the correct investment of stockholders. The stock market is a rather complex system, and changes in stock prices are affected by various factors such as the economy, related industries, politics, and investor psychology, and the degree of influence, time range, and method of each factor are also different; and each factor in the stock market The interrelationships among these factors are intricate, the primary and secondary relationships are uncertain, and the quantitative relationship is difficult to extract and quantitatively analyze. Therefore, we ne...

Claims

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Application Information

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IPC IPC(8): G06Q40/04G06Q10/04G06K9/62
CPCG06Q10/04G06Q40/04G06F18/2134
Inventor 李贵
Owner 上海宽全智能科技有限公司
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