Stock prediction method based on optimized historical data back testing policy

A technology of historical data and forecasting methods, applied in forecasting, data processing applications, complex mathematical operations, etc., can solve problems such as no strong data support, no optimization, roughness, etc., to achieve scientific and real-time effects

Inactive Publication Date: 2016-11-23
安徽磐众信息科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The stock selection strategies of most software in the market are formulas derived from extensive experience without strong data su

Method used

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  • Stock prediction method based on optimized historical data back testing policy
  • Stock prediction method based on optimized historical data back testing policy
  • Stock prediction method based on optimized historical data back testing policy

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Embodiment Construction

[0024] Such as figure 1 As shown, data providers on the Internet such as wind information, Guotaian, Dongfang Tenpay, etc. provide recent stock market data and 20-year historical stock market data and upload them to the server together, and use the server to perform complex calculations on big data. The obtained stock selection strategy function is pushed to the smart terminal, and in the smart terminal, investors can choose different stock selection strategy functions according to their own needs.

[0025] Such as figure 2 As shown, the software structure includes stock market data, using Monte Carlo method to optimize stock selection strategies, stock selection strategy functions of different time intervals and user-defined functions. Firstly, using the stock market data, the parameter optimization of the stock selection strategy function established by the Monte Carlo method is carried out to obtain a more accurate stock selection strategy function for predicting the stoc...

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Abstract

The invention discloses a stock prediction method based on an optimized historical data back testing policy. The stock quotation data of 20 years since 1990s and the recent stock quotation data from data suppliers at a time interval are calculated on a server, and a stock selection policy function is obtained by using a Monte Carlo method while the probability of the buying/selling point is estimated; the probability is used as the predicated stock buying/selling point and the stock selection policy function is pushed to an intelligent terminal; and in the stock selection policy function, the investors can select the functions predicted by use of different time intervals. According to the invention, the stock historical data is subjected to back testing by the Monte Carlo method, the parameters of the stock selection policy function are optimized, and the stock buying/selling point can be predicted more accurately.

Description

technical field [0001] The invention relates to the field of stock data forecasting methods, in particular to a stock forecasting method based on an optimized historical data backtesting strategy. Background technique [0002] The stock market can increase direct financing and feed the real economy. As the stock market rises, the market value of listed companies expands, and the amount of refinancing increases accordingly, which is conducive to the development of the real economy and contributes to steady growth; the stock market can use market forces to shake the cycle Listed companies in the sex industry must adjust their structure and transfer methods through industrial upgrading, mergers and acquisitions, etc.; the stock market rises, the stock price of listed companies rises, the market value increases, and the value of assets increases. At the same time, it affects the value of pledged assets and equity of local government debt. , And through asset securitization, to r...

Claims

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Application Information

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IPC IPC(8): G06Q40/04G06Q10/04G06F17/18
CPCG06Q40/04G06F17/18G06Q10/04
Inventor 江寅姜晶熊孜
Owner 安徽磐众信息科技有限公司
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